0: print(\"INFO: Class \" + str(classes_count[i][0])","resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X and Y if","LabelEncoder from collections import Counter import numpy as np self.bins = 3 self.pd","self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np","balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas","on what type of target was sent self.X[\"classes\"] = self.Y_classes if type(tmp) ==","= list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point =","tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)]","target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target # If","concatenate and return as dataframe or numpy # Based on what type of","function adds classes to each sample and returns the class list as a","__init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter","= 0 self.np = np # This function adds classes to each sample","as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as","import Counter import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder","int: self.target = tmp else: self.target = target return self.Y_classes # This function","1: print(\"Class Distribution:\\n-------------------\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda","\" + str(classes_count[i-1][0]) + \" due to low number of samples\") classes_count[i][0] =","numpy, then convert it into pandas if type(target) == int: if target <","verbose=2): self.bins = bins tmp = target # If data is numpy, then","re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created,","\" has been merged into Class \" + str(classes_count[i-1][0]) + \" due to","more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count,","# Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ ==","in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target = \"target\"","merged into Class \" + str(classes_count[i-1][0]) + \" due to low number of","# Use qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target],","If data is numpy, then convert it into pandas if type(target) == int:","q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning.","print(\"INFO: Class \" + str(classes_count[i][0]) + \" has been merged into Class \"","function if type(self.Y_classes) == int: print(\"Error! Run fit method first!!\") return None #","if type(self.Y_classes) == int: print(\"Error! Run fit method first!!\") return None # Finally,","None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__","# If classes haven't yet been created, then run the \"fit\" function if","verbose > 0: print() # Perform label-encoding once again # Avoids class skipping","qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0)","the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been","sorted(classes_count, key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\": \"+str(count))","= X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if","mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)): if classes_count[i][1]","merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose >","X[:,target] target = \"target\" else: self.X = X.copy() # Use qcut if balanced","target return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX,","return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY):","classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again","or numpy # Based on what type of target was sent self.X[\"classes\"] =","bins tmp = target # If data is numpy, then convert it into","fit method first!!\") return None # Finally, perform the re-sampling resampled_data, _ =","Merge classes if number of neighbours is more than the number of samples","# Based on what type of target was sent self.X[\"classes\"] = self.Y_classes if","number of neighbours is more than the number of samples classes_count = list(map(list,","= LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target =","is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target],","self.X = X.copy() # Use qcut if balanced binning is required if balanced_binning:","if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else:","= target return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj,","trainX, trainY): # If classes haven't yet been created, then run the \"fit\"","def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created, then","of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding","samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0])","+ \" due to low number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose","of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x:","lambda x: x[0]) mid_point = len(classes_count) # Logic for merging for i in","if verbose > 0: print() # Perform label-encoding once again # Avoids class","self.target = 0 self.np = np # This function adds classes to each","numpy as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter","for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0]","classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\"INFO:","a dataframe/numpy array (as per input) # It also merges classes as and","to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number","= sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic for","= tmp else: self.target = target return self.Y_classes # This function performs the","fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target","from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np self.bins","= sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) #","Finally concatenate and return as dataframe or numpy # Based on what type","self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"]","target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i]","# Perform label-encoding once again # Avoids class skipping after merging le =","= self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is","le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than the number","class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print","of neighbours is more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items()))","int: if target < 0: target = X.shape[1]+target tmp = target self.X =","has been merged into Class \" + str(classes_count[i-1][0]) + \" due to low","balanced_binning=False, verbose=2): self.bins = bins tmp = target # If data is numpy,","x[0]) for class_, count in classes_count: print(str(class_)+\": \"+str(count)) print() # Finally concatenate and","self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): #","Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes","self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to","= np # This function adds classes to each sample and returns the","data is numpy, then convert it into pandas if type(target) == int: if","0: print(\"INFO: Class \" + str(classes_count[i][0]) + \" has been merged into Class","str(classes_count[i][0]) + \" has been merged into Class \" + str(classes_count[i-1][0]) + \"","str(classes_count[i-1][0]) + \" due to low number of samples\") classes_count[i][0] = classes_count[i-1][0] if","target # If data is numpy, then convert it into pandas if type(target)","'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X and Y","int: print(\"Error! Run fit method first!!\") return None # Finally, perform the re-sampling","else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges","low number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() #","0 self.target = 0 self.np = np # This function adds classes to","target was sent self.X[\"classes\"] = self.Y_classes if type(tmp) == int: self.target = tmp","= target # If data is numpy, then convert it into pandas if","samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once","= 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X =","X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else: self.X = X.copy() # Use","min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target # If data is","\"target\" else: self.X = X.copy() # Use qcut if balanced binning is required","pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy","tmp else: self.target = target return self.Y_classes # This function performs the re-sampling","classes_count[i-1][0] if verbose > 0: print(\"INFO: Class \" + str(classes_count[i][0]) + \" has","classes_count: print(str(class_)+\": \"+str(count)) print() # Finally concatenate and return as dataframe or numpy","class_, count in classes_count: print(str(class_)+\": \"+str(count)) print() # Finally concatenate and return as","as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins","> 0: print(\"INFO: Class \" + str(classes_count[i][0]) + \" has been merged into","import LabelEncoder from collections import Counter import numpy as np self.bins = 3","if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct","Counter import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder =","np # This function adds classes to each sample and returns the class","balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes","= self.Y_classes if type(tmp) == int: self.target = tmp else: self.target = target","resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\",","required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins)","created, then run the \"fit\" function if type(self.Y_classes) == int: print(\"Error! Run fit","each sample and returns the class list as a dataframe/numpy array (as per","trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the","self.X[\"classes\"] = self.Y_classes if type(tmp) == int: self.target = tmp else: self.target =","= self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X and Y if type(self.target)","def __init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import","class resampler: def __init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from","# This function adds classes to each sample and returns the class list","x[0]) mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)): if","# Merge classes if number of neighbours is more than the number of","if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values, resampled_data[\"target\"].values else: return resampled_data.drop(self.target, axis=1), resampled_data[self.target]","self.X[\"target\"] = X[:,target] target = \"target\" else: self.X = X.copy() # Use qcut","\" + str(classes_count[i][0]) + \" has been merged into Class \" + str(classes_count[i-1][0])","print(\"Error! Run fit method first!!\") return None # Finally, perform the re-sampling resampled_data,","# Return the correct X and Y if type(self.target) == int: return resampled_data.drop(\"target\",","function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't","correct X and Y if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values, resampled_data[\"target\"].values else:","number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform","type(target) == int: if target < 0: target = X.shape[1]+target tmp = target","then run the \"fit\" function if type(self.Y_classes) == int: print(\"Error! Run fit method","been merged into Class \" + str(classes_count[i-1][0]) + \" due to low number","+ \" has been merged into Class \" + str(classes_count[i-1][0]) + \" due","# Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) #","X.copy() # Use qcut if balanced binning is required if balanced_binning: self.Y_classes =","and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins =","self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes le","self.np = np # This function adds classes to each sample and returns","into Class \" + str(classes_count[i-1][0]) + \" due to low number of samples\")","= sorted(classes_count, key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\":","== int: print(\"Error! Run fit method first!!\") return None # Finally, perform the","classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point","label-encoding once again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes","input) # It also merges classes as and when required def fit(self, X,","Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count","classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic","required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp","sample and returns the class list as a dataframe/numpy array (as per input)","+ str(classes_count[i][0]) + \" has been merged into Class \" + str(classes_count[i-1][0]) +","= target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] =","type(self.Y_classes) == int: print(\"Error! Run fit method first!!\") return None # Finally, perform","it into pandas if type(target) == int: if target < 0: target =","binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge","merges classes as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False,","= X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else: self.X = X.copy() #","= len(classes_count) # Logic for merging for i in range(len(classes_count)): if classes_count[i][1] <","If classes haven't yet been created, then run the \"fit\" function if type(self.Y_classes)","the \"fit\" function if type(self.Y_classes) == int: print(\"Error! Run fit method first!!\") return","columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X and Y if type(self.target) == int:","= bins tmp = target # If data is numpy, then convert it","dataframe or numpy # Based on what type of target was sent self.X[\"classes\"]","import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import","== 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X and","returns the class list as a dataframe/numpy array (as per input) # It","self.target = tmp else: self.target = target return self.Y_classes # This function performs","Use qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins,","Perform label-encoding once again # Avoids class skipping after merging le = self.LabelEncoder()","_ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns)","if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else: self.X","= classes_count[i-1][0] if verbose > 0: print(\"INFO: Class \" + str(classes_count[i][0]) + \"","array (as per input) # It also merges classes as and when required","first!!\") return None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY)","x: x[0]) mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)):","in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose","= self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\"Class","\" due to low number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose >","to low number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print()","for class_, count in classes_count: print(str(class_)+\": \"+str(count)) print() # Finally concatenate and return","due to low number of samples\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0:","sent self.X[\"classes\"] = self.Y_classes if type(tmp) == int: self.target = tmp else: self.target","as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter =","once again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes =","self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs","what type of target was sent self.X[\"classes\"] = self.Y_classes if type(tmp) == int:","for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target","> 0: print() # Perform label-encoding once again # Avoids class skipping after","target < 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for","= classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again #","and return as dataframe or numpy # Based on what type of target","Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np = np","if type(tmp) == int: self.target = tmp else: self.target = target return self.Y_classes","# It also merges classes as and when required def fit(self, X, target,","dataframe/numpy array (as per input) # It also merges classes as and when","is numpy, then convert it into pandas if type(target) == int: if target","if number of neighbours is more than the number of samples classes_count =","perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data","This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes","the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data =","= 0 self.target = 0 self.np = np # This function adds classes","# Logic for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes","le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\") classes_count = list(map(list,","\"fit\" function if type(self.Y_classes) == int: print(\"Error! Run fit method first!!\") return None","than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key","sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic for merging","Return the correct X and Y if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values,","after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) #","of target was sent self.X[\"classes\"] = self.Y_classes if type(tmp) == int: self.target =","== int: if target < 0: target = X.shape[1]+target tmp = target self.X","tmp = target # If data is numpy, then convert it into pandas","0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in","ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if","classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\"INFO: Class \" + str(classes_count[i][0]) +","bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes le =","pandas if type(target) == int: if target < 0: target = X.shape[1]+target tmp","and Y if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values, resampled_data[\"target\"].values else: return resampled_data.drop(self.target,","Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy':","as dataframe or numpy # Based on what type of target was sent","It also merges classes as and when required def fit(self, X, target, bins=3,","= le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than the","i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else: self.X =","after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose","i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target =","key = lambda x: x[0]) mid_point = len(classes_count) # Logic for merging for","return None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if","le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours","if type(target) == int: if target < 0: target = X.shape[1]+target tmp =","classes as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2):","and returns the class list as a dataframe/numpy array (as per input) #","sampler_obj, trainX, trainY): # If classes haven't yet been created, then run the","number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda","0: print() # Perform label-encoding once again # Avoids class skipping after merging","into pandas if type(target) == int: if target < 0: target = X.shape[1]+target","= lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\": \"+str(count)) print() #","= le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\") classes_count =","if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) #","self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes","print() # Perform label-encoding once again # Avoids class skipping after merging le","= 0 self.Y_classes = 0 self.target = 0 self.np = np # This","self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more","per input) # It also merges classes as and when required def fit(self,","classes haven't yet been created, then run the \"fit\" function if type(self.Y_classes) ==","print(str(class_)+\": \"+str(count)) print() # Finally concatenate and return as dataframe or numpy #","classes if number of neighbours is more than the number of samples classes_count","min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\"INFO: Class \"","ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes)","target = \"target\" else: self.X = X.copy() # Use qcut if balanced binning","sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return","performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet","print() # Finally concatenate and return as dataframe or numpy # Based on","= self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes","sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np self.bins =","trainY): # If classes haven't yet been created, then run the \"fit\" function","the class list as a dataframe/numpy array (as per input) # It also","== int: self.target = tmp else: self.target = target return self.Y_classes # This","= X[:,target] target = \"target\" else: self.X = X.copy() # Use qcut if","been created, then run the \"fit\" function if type(self.Y_classes) == int: print(\"Error! Run","= pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes =","0 self.np = np # This function adds classes to each sample and","Pandas outputs ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes","return as dataframe or numpy # Based on what type of target was","X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target #","axis=1).columns) # Return the correct X and Y if type(self.target) == int: return","# Finally concatenate and return as dataframe or numpy # Based on what","X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target:","< min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\"INFO: Class","if verbose > 0: print(\"INFO: Class \" + str(classes_count[i][0]) + \" has been","key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\": \"+str(count)) print()","adds classes to each sample and returns the class list as a dataframe/numpy","again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes)","class list as a dataframe/numpy array (as per input) # It also merges","# Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\") classes_count = list(map(list, self.Counter(self.Y_classes).items()))","re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data,","Distribution:\\n-------------------\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0])","else: self.X = X.copy() # Use qcut if balanced binning is required if","also merges classes as and when required def fit(self, X, target, bins=3, min_n_samples=6,","X and Y if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values, resampled_data[\"target\"].values else: return","i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if","< 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i","outputs ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes =","def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp =","range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose >","> 1: print(\"Class Distribution:\\n-------------------\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key =","np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter","range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else:","Logic for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes ==","self.Y_classes if type(tmp) == int: self.target = tmp else: self.target = target return","list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count)","verbose > 0: print(\"INFO: Class \" + str(classes_count[i][0]) + \" has been merged","Class \" + str(classes_count[i][0]) + \" has been merged into Class \" +","import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder","le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1:","is more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count =","= \"target\" else: self.X = X.copy() # Use qcut if balanced binning is","run the \"fit\" function if type(self.Y_classes) == int: print(\"Error! Run fit method first!!\")","list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for class_, count","in classes_count: print(str(class_)+\": \"+str(count)) print() # Finally concatenate and return as dataframe or","as a dataframe/numpy array (as per input) # It also merges classes as","self.X[str(i)] = X[:,i] self.X[\"target\"] = X[:,target] target = \"target\" else: self.X = X.copy()","resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created, then run","target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]):","self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\"INFO: Class \" +","type(tmp) == int: self.target = tmp else: self.target = target return self.Y_classes #","type of target was sent self.X[\"classes\"] = self.Y_classes if type(tmp) == int: self.target","the correct X and Y if type(self.target) == int: return resampled_data.drop(\"target\", axis=1).values, resampled_data[\"target\"].values","the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key =","when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins","= Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np =","self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after","classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for","\"+str(count)) print() # Finally concatenate and return as dataframe or numpy # Based","self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\") classes_count","pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np","then convert it into pandas if type(target) == int: if target < 0:","self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than","self.X = 0 self.Y_classes = 0 self.target = 0 self.np = np #","self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\"Class Distribution:\\n-------------------\")","classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again # Avoids","type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\"classes\", axis=1).columns) # Return the correct X","self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) #","if target < 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame()","self.Y_classes = 0 self.target = 0 self.np = np # This function adds"],"string":"[\n \"from collections import Counter import numpy as np self.bins = 3 self.pd =\",\n \"self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X\",\n \"for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]]\",\n \"LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target = 0\",\n \"count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return as dataframe\",\n \"collections import Counter import numpy as np self.bins = 3 self.pd = pd\",\n \"list as a dataframe/numpy array (as per input) # It also merges classes\",\n \"classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of\",\n \"pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes = 0\",\n \"self.bins = bins tmp = target # If data is numpy, then convert\",\n \"self.target = target return self.Y_classes # This function performs the re-sampling def resample(self,\",\n \"Class \\\" + str(classes_count[i-1][0]) + \\\" due to low number of samples\\\") classes_count[i][0]\",\n \"was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target = tmp else:\",\n \"0 self.Y_classes = 0 self.target = 0 self.np = np # This function\",\n \"skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if\",\n \"if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0:\",\n \"verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key\",\n \"x: x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate\",\n \"lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally\",\n \"print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count =\",\n \"haven't yet been created, then run the \\\"fit\\\" function if type(self.Y_classes) == int:\",\n \"classes_count = sorted(classes_count, key = lambda x: x[0]) for class_, count in classes_count:\",\n \"to each sample and returns the class list as a dataframe/numpy array (as\",\n \"resampler: def __init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from collections\",\n \"classes to each sample and returns the class list as a dataframe/numpy array\",\n \"= lambda x: x[0]) mid_point = len(classes_count) # Logic for merging for i\",\n \"method first!!\\\") return None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX,\",\n \"print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x:\",\n \"= X.copy() # Use qcut if balanced binning is required if balanced_binning: self.Y_classes\",\n \"else: self.target = target return self.Y_classes # This function performs the re-sampling def\",\n \"self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target\",\n \"Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty\",\n \"self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and Y if type(self.target) ==\",\n \"neighbours is more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count\",\n \"= list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for class_,\",\n \"len(classes_count) # Logic for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples:\",\n \"self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target]\",\n \"This function adds classes to each sample and returns the class list as\",\n \"# This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If\",\n \"= self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges\",\n \"# Pandas outputs ranges after binning. Convert ranges to classes le = self.LabelEncoder()\",\n \"3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0\",\n \"Based on what type of target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp)\",\n \"merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] =\",\n \"convert it into pandas if type(target) == int: if target < 0: target\",\n \"if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count,\",\n \"numpy # Based on what type of target was sent self.X[\\\"classes\\\"] = self.Y_classes\",\n \"(as per input) # It also merges classes as and when required def\",\n \"Run fit method first!!\\\") return None # Finally, perform the re-sampling resampled_data, _\",\n \"Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values else: return resampled_data.drop(self.target, axis=1),\",\n \"+ str(classes_count[i-1][0]) + \\\" due to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0]\",\n \"self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for class_, count in\",\n \"yet been created, then run the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error!\",\n \"= self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] =\",\n \"precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert\",\n \"# If data is numpy, then convert it into pandas if type(target) ==\",\n \"bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target # If data\",\n \"binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes =\",\n \"== classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0])\",\n \"resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and Y if\",\n \"LabelEncoder from collections import Counter import numpy as np self.bins = 3 self.pd\",\n \"self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np\",\n \"balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas\",\n \"on what type of target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) ==\",\n \"= list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point =\",\n \"tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)]\",\n \"target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target # If\",\n \"concatenate and return as dataframe or numpy # Based on what type of\",\n \"function adds classes to each sample and returns the class list as a\",\n \"__init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter\",\n \"= 0 self.np = np # This function adds classes to each sample\",\n \"as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as\",\n \"import Counter import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder\",\n \"int: self.target = tmp else: self.target = target return self.Y_classes # This function\",\n \"1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda\",\n \"\\\" + str(classes_count[i-1][0]) + \\\" due to low number of samples\\\") classes_count[i][0] =\",\n \"numpy, then convert it into pandas if type(target) == int: if target <\",\n \"verbose=2): self.bins = bins tmp = target # If data is numpy, then\",\n \"re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created,\",\n \"\\\" has been merged into Class \\\" + str(classes_count[i-1][0]) + \\\" due to\",\n \"more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count,\",\n \"# Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ ==\",\n \"in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\"\",\n \"merged into Class \\\" + str(classes_count[i-1][0]) + \\\" due to low number of\",\n \"# Use qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target],\",\n \"If data is numpy, then convert it into pandas if type(target) == int:\",\n \"q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning.\",\n \"print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has been merged into Class \\\"\",\n \"function if type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\") return None #\",\n \"if type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\") return None # Finally,\",\n \"None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__\",\n \"# If classes haven't yet been created, then run the \\\"fit\\\" function if\",\n \"verbose > 0: print() # Perform label-encoding once again # Avoids class skipping\",\n \"qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0)\",\n \"the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been\",\n \"sorted(classes_count, key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count))\",\n \"= X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if\",\n \"mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)): if classes_count[i][1]\",\n \"merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose >\",\n \"X[:,target] target = \\\"target\\\" else: self.X = X.copy() # Use qcut if balanced\",\n \"target return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX,\",\n \"return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY):\",\n \"classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again\",\n \"or numpy # Based on what type of target was sent self.X[\\\"classes\\\"] =\",\n \"bins tmp = target # If data is numpy, then convert it into\",\n \"fit method first!!\\\") return None # Finally, perform the re-sampling resampled_data, _ =\",\n \"Merge classes if number of neighbours is more than the number of samples\",\n \"# Based on what type of target was sent self.X[\\\"classes\\\"] = self.Y_classes if\",\n \"number of neighbours is more than the number of samples classes_count = list(map(list,\",\n \"= LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes = 0 self.target =\",\n \"is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target],\",\n \"self.X = X.copy() # Use qcut if balanced binning is required if balanced_binning:\",\n \"if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else:\",\n \"= target return self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj,\",\n \"trainX, trainY): # If classes haven't yet been created, then run the \\\"fit\\\"\",\n \"def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created, then\",\n \"of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding\",\n \"samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0])\",\n \"+ \\\" due to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose\",\n \"of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x:\",\n \"lambda x: x[0]) mid_point = len(classes_count) # Logic for merging for i in\",\n \"if verbose > 0: print() # Perform label-encoding once again # Avoids class\",\n \"self.target = 0 self.np = np # This function adds classes to each\",\n \"numpy as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter\",\n \"for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0]\",\n \"classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO:\",\n \"a dataframe/numpy array (as per input) # It also merges classes as and\",\n \"to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number\",\n \"= sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic for\",\n \"= tmp else: self.target = target return self.Y_classes # This function performs the\",\n \"fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target\",\n \"from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np self.bins\",\n \"= sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) #\",\n \"Finally concatenate and return as dataframe or numpy # Based on what type\",\n \"self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"]\",\n \"target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i]\",\n \"# Perform label-encoding once again # Avoids class skipping after merging le =\",\n \"= self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is\",\n \"le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than the number\",\n \"class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print\",\n \"of neighbours is more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items()))\",\n \"int: if target < 0: target = X.shape[1]+target tmp = target self.X =\",\n \"has been merged into Class \\\" + str(classes_count[i-1][0]) + \\\" due to low\",\n \"balanced_binning=False, verbose=2): self.bins = bins tmp = target # If data is numpy,\",\n \"x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and\",\n \"self.Y_classes # This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): #\",\n \"Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes\",\n \"self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to\",\n \"= np # This function adds classes to each sample and returns the\",\n \"data is numpy, then convert it into pandas if type(target) == int: if\",\n \"0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has been merged into Class\",\n \"str(classes_count[i][0]) + \\\" has been merged into Class \\\" + str(classes_count[i-1][0]) + \\\"\",\n \"str(classes_count[i-1][0]) + \\\" due to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if\",\n \"target # If data is numpy, then convert it into pandas if type(target)\",\n \"'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and Y\",\n \"int: print(\\\"Error! Run fit method first!!\\\") return None # Finally, perform the re-sampling\",\n \"else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges\",\n \"low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() #\",\n \"0 self.target = 0 self.np = np # This function adds classes to\",\n \"target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target = tmp\",\n \"= target # If data is numpy, then convert it into pandas if\",\n \"samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once\",\n \"= 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X =\",\n \"X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X = X.copy() # Use\",\n \"min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target # If data is\",\n \"\\\"target\\\" else: self.X = X.copy() # Use qcut if balanced binning is required\",\n \"pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy\",\n \"tmp else: self.target = target return self.Y_classes # This function performs the re-sampling\",\n \"classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has\",\n \"classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return as dataframe or numpy\",\n \"class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return as\",\n \"as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins\",\n \"> 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has been merged into\",\n \"import LabelEncoder from collections import Counter import numpy as np self.bins = 3\",\n \"if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct\",\n \"Counter import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder =\",\n \"np # This function adds classes to each sample and returns the class\",\n \"balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes\",\n \"= self.Y_classes if type(tmp) == int: self.target = tmp else: self.target = target\",\n \"resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\",\",\n \"required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins)\",\n \"created, then run the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run fit\",\n \"each sample and returns the class list as a dataframe/numpy array (as per\",\n \"trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the\",\n \"self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target = tmp else: self.target =\",\n \"= self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and Y if type(self.target)\",\n \"def __init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import\",\n \"class resampler: def __init__(self): import pandas as pd from sklearn.preprocessing import LabelEncoder from\",\n \"# This function adds classes to each sample and returns the class list\",\n \"x[0]) mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)): if\",\n \"# Merge classes if number of neighbours is more than the number of\",\n \"if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values else: return resampled_data.drop(self.target, axis=1), resampled_data[self.target]\",\n \"self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X = X.copy() # Use qcut\",\n \"\\\" + str(classes_count[i][0]) + \\\" has been merged into Class \\\" + str(classes_count[i-1][0])\",\n \"print(\\\"Error! Run fit method first!!\\\") return None # Finally, perform the re-sampling resampled_data,\",\n \"# Return the correct X and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\",\",\n \"function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't\",\n \"correct X and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values else:\",\n \"number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print() # Perform\",\n \"type(target) == int: if target < 0: target = X.shape[1]+target tmp = target\",\n \"then run the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run fit method\",\n \"been merged into Class \\\" + str(classes_count[i-1][0]) + \\\" due to low number\",\n \"+ \\\" has been merged into Class \\\" + str(classes_count[i-1][0]) + \\\" due\",\n \"# Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) #\",\n \"X.copy() # Use qcut if balanced binning is required if balanced_binning: self.Y_classes =\",\n \"and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins =\",\n \"self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes le\",\n \"self.np = np # This function adds classes to each sample and returns\",\n \"into Class \\\" + str(classes_count[i-1][0]) + \\\" due to low number of samples\\\")\",\n \"= sorted(classes_count, key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\\\":\",\n \"== int: print(\\\"Error! Run fit method first!!\\\") return None # Finally, perform the\",\n \"classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point\",\n \"label-encoding once again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes\",\n \"input) # It also merges classes as and when required def fit(self, X,\",\n \"Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count\",\n \"classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic\",\n \"required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp\",\n \"sample and returns the class list as a dataframe/numpy array (as per input)\",\n \"+ str(classes_count[i][0]) + \\\" has been merged into Class \\\" + str(classes_count[i-1][0]) +\",\n \"= target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target: self.X[str(i)] =\",\n \"type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\") return None # Finally, perform\",\n \"it into pandas if type(target) == int: if target < 0: target =\",\n \"binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge\",\n \"merges classes as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False,\",\n \"= X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X = X.copy() #\",\n \"= len(classes_count) # Logic for merging for i in range(len(classes_count)): if classes_count[i][1] <\",\n \"If classes haven't yet been created, then run the \\\"fit\\\" function if type(self.Y_classes)\",\n \"the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\") return\",\n \"columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and Y if type(self.target) == int:\",\n \"= bins tmp = target # If data is numpy, then convert it\",\n \"dataframe or numpy # Based on what type of target was sent self.X[\\\"classes\\\"]\",\n \"import pandas as pd from sklearn.preprocessing import LabelEncoder from collections import Counter import\",\n \"== 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X and\",\n \"returns the class list as a dataframe/numpy array (as per input) # It\",\n \"self.target = tmp else: self.target = target return self.Y_classes # This function performs\",\n \"Use qcut if balanced binning is required if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins,\",\n \"Perform label-encoding once again # Avoids class skipping after merging le = self.LabelEncoder()\",\n \"_ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns)\",\n \"if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X\",\n \"= classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\"\",\n \"array (as per input) # It also merges classes as and when required\",\n \"first!!\\\") return None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY)\",\n \"x: x[0]) mid_point = len(classes_count) # Logic for merging for i in range(len(classes_count)):\",\n \"in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose\",\n \"= self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\\\"Class\",\n \"\\\" due to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose >\",\n \"to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0: print()\",\n \"for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return\",\n \"due to low number of samples\\\") classes_count[i][0] = classes_count[i-1][0] if verbose > 0:\",\n \"sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target = tmp else: self.target\",\n \"as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter =\",\n \"once again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes =\",\n \"self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs\",\n \"what type of target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int:\",\n \"for i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target\",\n \"> 0: print() # Perform label-encoding once again # Avoids class skipping after\",\n \"target < 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for\",\n \"= classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again #\",\n \"and return as dataframe or numpy # Based on what type of target\",\n \"Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np = np\",\n \"if type(tmp) == int: self.target = tmp else: self.target = target return self.Y_classes\",\n \"# It also merges classes as and when required def fit(self, X, target,\",\n \"dataframe/numpy array (as per input) # It also merges classes as and when\",\n \"is numpy, then convert it into pandas if type(target) == int: if target\",\n \"if number of neighbours is more than the number of samples classes_count =\",\n \"perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data\",\n \"This function performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes\",\n \"the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data =\",\n \"= 0 self.target = 0 self.np = np # This function adds classes\",\n \"# Logic for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes\",\n \"le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list,\",\n \"\\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\") return None\",\n \"than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key\",\n \"sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) # Logic for merging\",\n \"Return the correct X and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values,\",\n \"after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) #\",\n \"of target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target =\",\n \"== int: if target < 0: target = X.shape[1]+target tmp = target self.X\",\n \"tmp = target # If data is numpy, then convert it into pandas\",\n \"0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in\",\n \"ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if\",\n \"classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) +\",\n \"bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes le =\",\n \"pandas if type(target) == int: if target < 0: target = X.shape[1]+target tmp\",\n \"and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values else: return resampled_data.drop(self.target,\",\n \"Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy':\",\n \"as dataframe or numpy # Based on what type of target was sent\",\n \"It also merges classes as and when required def fit(self, X, target, bins=3,\",\n \"= le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than the\",\n \"i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X =\",\n \"after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose\",\n \"i in range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target =\",\n \"key = lambda x: x[0]) mid_point = len(classes_count) # Logic for merging for\",\n \"return None # Finally, perform the re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if\",\n \"le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours\",\n \"if type(target) == int: if target < 0: target = X.shape[1]+target tmp =\",\n \"classes as and when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2):\",\n \"and returns the class list as a dataframe/numpy array (as per input) #\",\n \"sampler_obj, trainX, trainY): # If classes haven't yet been created, then run the\",\n \"number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda\",\n \"0: print() # Perform label-encoding once again # Avoids class skipping after merging\",\n \"into pandas if type(target) == int: if target < 0: target = X.shape[1]+target\",\n \"= lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() #\",\n \"= le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count =\",\n \"if balanced_binning: self.Y_classes = self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) #\",\n \"self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes\",\n \"print() # Perform label-encoding once again # Avoids class skipping after merging le\",\n \"= 0 self.Y_classes = 0 self.target = 0 self.np = np # This\",\n \"self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more\",\n \"per input) # It also merges classes as and when required def fit(self,\",\n \"classes haven't yet been created, then run the \\\"fit\\\" function if type(self.Y_classes) ==\",\n \"print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return as dataframe or numpy #\",\n \"classes if number of neighbours is more than the number of samples classes_count\",\n \"min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\"\",\n \"ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes)\",\n \"target = \\\"target\\\" else: self.X = X.copy() # Use qcut if balanced binning\",\n \"sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return\",\n \"performs the re-sampling def resample(self, sampler_obj, trainX, trainY): # If classes haven't yet\",\n \"print() # Finally concatenate and return as dataframe or numpy # Based on\",\n \"= self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after binning. Convert ranges to classes\",\n \"sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np self.bins =\",\n \"trainY): # If classes haven't yet been created, then run the \\\"fit\\\" function\",\n \"the class list as a dataframe/numpy array (as per input) # It also\",\n \"== int: self.target = tmp else: self.target = target return self.Y_classes # This\",\n \"= X[:,target] target = \\\"target\\\" else: self.X = X.copy() # Use qcut if\",\n \"been created, then run the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run\",\n \"= pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X = 0 self.Y_classes =\",\n \"0 self.np = np # This function adds classes to each sample and\",\n \"Pandas outputs ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes\",\n \"return as dataframe or numpy # Based on what type of target was\",\n \"X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp = target #\",\n \"axis=1).columns) # Return the correct X and Y if type(self.target) == int: return\",\n \"# Finally concatenate and return as dataframe or numpy # Based on what\",\n \"X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]): if i!=target:\",\n \"< min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class\",\n \"if verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has been\",\n \"key = lambda x: x[0]) for class_, count in classes_count: print(str(class_)+\\\": \\\"+str(count)) print()\",\n \"adds classes to each sample and returns the class list as a dataframe/numpy\",\n \"again # Avoids class skipping after merging le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes)\",\n \"class list as a dataframe/numpy array (as per input) # It also merges\",\n \"# Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items()))\",\n \"re-sampling resampled_data, _ = sampler_obj.fit_resample(trainX, trainY) if type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data,\",\n \"Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0])\",\n \"else: self.X = X.copy() # Use qcut if balanced binning is required if\",\n \"also merges classes as and when required def fit(self, X, target, bins=3, min_n_samples=6,\",\n \"X and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values else: return\",\n \"i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if\",\n \"< 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i\",\n \"outputs ranges after binning. Convert ranges to classes le = self.LabelEncoder() self.Y_classes =\",\n \"def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins tmp =\",\n \"range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose >\",\n \"> 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key =\",\n \"np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter\",\n \"range(X.shape[1]): if i!=target: self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else:\",\n \"Logic for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes ==\",\n \"self.Y_classes if type(tmp) == int: self.target = tmp else: self.target = target return\",\n \"list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count)\",\n \"verbose > 0: print(\\\"INFO: Class \\\" + str(classes_count[i][0]) + \\\" has been merged\",\n \"Class \\\" + str(classes_count[i][0]) + \\\" has been merged into Class \\\" +\",\n \"import numpy as np self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder\",\n \"le = self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1:\",\n \"is more than the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count =\",\n \"= \\\"target\\\" else: self.X = X.copy() # Use qcut if balanced binning is\",\n \"run the \\\"fit\\\" function if type(self.Y_classes) == int: print(\\\"Error! Run fit method first!!\\\")\",\n \"list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for class_, count\",\n \"in classes_count: print(str(class_)+\\\": \\\"+str(count)) print() # Finally concatenate and return as dataframe or\",\n \"as a dataframe/numpy array (as per input) # It also merges classes as\",\n \"self.X[str(i)] = X[:,i] self.X[\\\"target\\\"] = X[:,target] target = \\\"target\\\" else: self.X = X.copy()\",\n \"resample(self, sampler_obj, trainX, trainY): # If classes haven't yet been created, then run\",\n \"target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame() for i in range(X.shape[1]):\",\n \"self.Y_classes[self.np.where(self.Y_classes == classes_count[i][0])[0]] = classes_count[i-1][0] if verbose > 0: print(\\\"INFO: Class \\\" +\",\n \"type(tmp) == int: self.target = tmp else: self.target = target return self.Y_classes #\",\n \"type of target was sent self.X[\\\"classes\\\"] = self.Y_classes if type(tmp) == int: self.target\",\n \"the correct X and Y if type(self.target) == int: return resampled_data.drop(\\\"target\\\", axis=1).values, resampled_data[\\\"target\\\"].values\",\n \"the number of samples classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key =\",\n \"when required def fit(self, X, target, bins=3, min_n_samples=6, balanced_binning=False, verbose=2): self.bins = bins\",\n \"= Counter self.X = 0 self.Y_classes = 0 self.target = 0 self.np =\",\n \"self.pd.qcut(self.X[target], q=self.bins, precision=0) else: self.Y_classes = self.pd.cut(self.X[target], bins=self.bins) # Pandas outputs ranges after\",\n \"classes_count = list(map(list, self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) for\",\n \"\\\"+str(count)) print() # Finally concatenate and return as dataframe or numpy # Based\",\n \"self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\") classes_count\",\n \"pd from sklearn.preprocessing import LabelEncoder from collections import Counter import numpy as np\",\n \"then convert it into pandas if type(target) == int: if target < 0:\",\n \"self.Y_classes = le.fit_transform(self.Y_classes) # Merge classes if number of neighbours is more than\",\n \"self.X = 0 self.Y_classes = 0 self.target = 0 self.np = np #\",\n \"self.LabelEncoder() self.Y_classes = le.fit_transform(self.Y_classes) # Pretty print if verbose > 1: print(\\\"Class Distribution:\\\\n-------------------\\\")\",\n \"classes_count[i-1][0] if verbose > 0: print() # Perform label-encoding once again # Avoids\",\n \"type(resampled_data).__module__ == 'numpy': resampled_data = self.pd.DataFrame(resampled_data, columns=self.X.drop(\\\"classes\\\", axis=1).columns) # Return the correct X\",\n \"self.Counter(self.Y_classes).items())) classes_count = sorted(classes_count, key = lambda x: x[0]) mid_point = len(classes_count) #\",\n \"if target < 0: target = X.shape[1]+target tmp = target self.X = self.pd.DataFrame()\",\n \"self.Y_classes = 0 self.target = 0 self.np = np # This function adds\"\n]","isConversational":false}}},{"rowIdx":4080,"cells":{"ngram":{"kind":"list like","value":["def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor,","wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc):","os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility","/ (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X,","pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) =","train_X, _, _ = _split_data(X, y) out = [] means = [] stds","train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y,","\" directory. (We're going to put a lot of data there, so we","mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std","urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor,","doesn't seem to work so we do it manually. # A few samples","= torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For some","= [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean =","+ '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise","batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X = X # X is of","torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15","y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y,","tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc =","= _split_data(X, y) out = [] means = [] stds = [] for","don't make it automatically - \" \"thus giving you the opportunity to make","make it automatically - \" \"thus giving you the opportunity to make it","foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc","'../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please create a","'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio, _","dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes', 'no', 'up',","= here / '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975,","torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes', 'no',","34975, \"batch_index is {}\".format(batch_index) audio_X = X # X is of shape (batch=34975,","_split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\","random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor,","in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi -","if they fix it audio = audio / 2 ** 15 # Normalization","directory, so \" \"that the data can be stored elsewhere if you wish.)\")","tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here /","= train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std +","= torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index =","os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please create a directory called 'data'","\"that the data can be stored elsewhere if you wish.)\") base_loc = base_base_loc","val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main():","it requires a gradient, so detach. train_X, _, _ = _split_data(X, y) out","shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in","train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) /","it manually. # A few samples are shorter than the full length; for","torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they fix it","base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not","exist. Please create a directory called 'data' in the 'experiments'\" \" directory. (We're","loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here /","'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename in","simplicity we discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index]","2).detach() # X is of shape (batch=34975, length=81, channels=40). For some crazy reason","# for forward compatbility if they fix it audio = audio / 2","test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X,","'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename","make it a symlink rather than a normal directory, so \" \"that the","(batch=34975, length=81, channels=40). For some crazy reason it requires a gradient, so detach.","/ 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y,","0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop',","channels=40). For some crazy reason it requires a gradient, so detach. train_X, _,","= base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc /","than the full length; for simplicity we discard them. if len(audio) != 16000:","def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X,","'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for","sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def","stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify)","RuntimeError(\"data directory does not exist. Please create a directory called 'data' in the","0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True,","means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1)","val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments' / 'data'","a symlink rather than a normal directory, so \" \"that the data can","'experiments'\" \" directory. (We're going to put a lot of data there, so","to make it a symlink rather than a normal directory, so \" \"that","length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81,","train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def","= sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1,","testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor =","train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return","if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds,","train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return","base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with","import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here","str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please","not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please create a directory called","base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands' X =","we don't make it automatically - \" \"thus giving you the opportunity to","[] means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)):","shorter than the full length; for simplicity we discard them. if len(audio) !=","_ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they","1 y_index += 1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X =","return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value,","= [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std =","+ '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc,","out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X,","train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify)","/ 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long)","= sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors):","= y_index batch_index += 1 y_index += 1 assert batch_index == 34975, \"batch_index","import os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import","test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ == '__main__':","'..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y","= here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc):","y_index += 1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X = X","= torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they fix","def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) +","(train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data()","torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For some crazy","tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify):","train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name,","X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X","the full length; for simplicity we discard them. if len(audio) != 16000: continue","audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index ==","import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test","os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f:","train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y,","/ 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X,","compatbility if they fix it audio = audio / 2 ** 15 #","opportunity to make it a symlink rather than a normal directory, so \"","pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here =","/ 2 ** 15 # Normalization argument doesn't seem to work so we","/ foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False,","(train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor","we discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] =","y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index == 34975,","return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X","foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False)","tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc","2 ** 15 # Normalization argument doesn't seem to work so we do","train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments' /","stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items():","Normalization argument doesn't seem to work so we do it manually. # A","X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index","y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X,","torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y,","dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y)","so we don't make it automatically - \" \"thus giving you the opportunity","assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X = X # X is","def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory","raise RuntimeError(\"data directory does not exist. Please create a directory called 'data' in","\"thus giving you the opportunity to make it a symlink rather than a","1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X,","'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0","the data can be stored elsewhere if you wish.)\") base_loc = base_base_loc +","few samples are shorter than the full length; for simplicity we discard them.","for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'):","'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means,","going to put a lot of data there, so we don't make it","\" \"that the data can be stored elsewhere if you wish.)\") base_loc =","test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download()","some crazy reason it requires a gradient, so detach. train_X, _, _ =","in the 'experiments'\" \" directory. (We're going to put a lot of data","giving you the opportunity to make it a symlink rather than a normal","for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean)","= _process_data() loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data'","import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent","return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc)","it audio = audio / 2 ** 15 # Normalization argument doesn't seem","here / '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000,","# A few samples are shorter than the full length; for simplicity we","discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index","test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y","not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X,","+ 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X,","so \" \"that the data can be stored elsewhere if you wish.)\") base_loc","with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..'","of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of","do it manually. # A few samples are shorter than the full length;","download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) =","'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index","/ 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index =","torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data')","symlink rather than a normal directory, so \" \"that the data can be","train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ == '__main__': main()","val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X, val_X,","test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..'","not exist. Please create a directory called 'data' in the 'experiments'\" \" directory.","loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',","be stored elsewhere if you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc =","Please create a directory called 'data' in the 'experiments'\" \" directory. (We're going","directory. (We're going to put a lot of data there, so we don't","stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X,","y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in","std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X","random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value","called 'data' in the 'experiments'\" \" directory. (We're going to put a lot","forward compatbility if they fix it audio = audio / 2 ** 15","val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here /","/ '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc,","train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__","X is of shape (batch=34975, length=81, channels=40). For some crazy reason it requires","audio = audio / 2 ** 15 # Normalization argument doesn't seem to","- \" \"thus giving you the opportunity to make it a symlink rather","elsewhere if you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc +","1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername","'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975,","is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is","_ = _split_data(X, y) out = [] means = [] stds = []","15 # Normalization argument doesn't seem to work so we do it manually.","create a directory called 'data' in the 'experiments'\" \" directory. (We're going to","/ 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y =","lot of data there, so we don't make it automatically - \" \"thus","\"batch_index is {}\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000,","in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward","automatically - \" \"thus giving you the opportunity to make it a symlink","= str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist.","_split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y,","here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify,","== 34975, \"batch_index is {}\".format(batch_index) audio_X = X # X is of shape","y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y)","1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X = X # X","(batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975,","= _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X,","{}\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000, channels=1) X","rather than a normal directory, so \" \"that the data can be stored","data can be stored elsewhere if you wish.)\") base_loc = base_base_loc + '/SpeechCommands'","_split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify,","os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X)","in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc =","/ filename, channels_first=False, normalization=False) # for forward compatbility if they fix it audio","is of shape (batch=34975, length=81, channels=40). For some crazy reason it requires a","'..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X,","<reponame>patrick-kidger/generalised_shapelets import os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio","val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def","stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std","train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5))","to put a lot of data there, so we don't make it automatically","main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X)","test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name)","train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir","train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y,","here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc)","val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ ==","than a normal directory, so \" \"that the data can be stored elsewhere","filename, channels_first=False, normalization=False) # for forward compatbility if they fix it audio =","channels_first=False, normalization=False) # for forward compatbility if they fix it audio = audio","'/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r')","_save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if","it automatically - \" \"thus giving you the opportunity to make it a","you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if","+= 1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X = X #","fix it audio = audio / 2 ** 15 # Normalization argument doesn't","the 'experiments'\" \" directory. (We're going to put a lot of data there,","# Normalization argument doesn't seem to work so we do it manually. #","of shape (batch=34975, length=81, channels=40). For some crazy reason it requires a gradient,","'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y,","there, so we don't make it automatically - \" \"thus giving you the","can be stored elsewhere if you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc","X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert","for simplicity we discard them. if len(audio) != 16000: continue X[batch_index] = audio","loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not","('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc","'/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc)","val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir /","means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' /","sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for","= _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X,","'data' in the 'experiments'\" \" directory. (We're going to put a lot of","/ '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please create","torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y,","- mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X","'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X,","samples are shorter than the full length; for simplicity we discard them. if","val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds,","train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\ val_audio_X, test_audio_X def main(): download() (train_X,","batch_index = 0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down',","test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X,","= audio / 2 ** 15 # Normalization argument doesn't seem to work","**tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def","for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download():","test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir,","requires a gradient, so detach. train_X, _, _ = _split_data(X, y) out =","val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc","to work so we do it manually. # A few samples are shorter","X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For","= train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X =","(std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y)","test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X,","manually. # A few samples are shorter than the full length; for simplicity","/ '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1)","= 0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left',","length=81, channels=40). For some crazy reason it requires a gradient, so detach. train_X,","[] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean()","[] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std()","so we do it manually. # A few samples are shorter than the","train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out,","as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' /","testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5,","val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y,","f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' / 'data'","/ 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y,","# X is of shape (batch=34975, length=81, channels=40). For some crazy reason it","channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40).","'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc /","in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here","not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data():","'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments'","audio_X = X # X is of shape (batch=34975, length=16000, channels=1) X =","y_index batch_index += 1 y_index += 1 assert batch_index == 34975, \"batch_index is","** 15 # Normalization argument doesn't seem to work so we do it","are shorter than the full length; for simplicity we discard them. if len(audio)","= base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if","normalization=False) # for forward compatbility if they fix it audio = audio /","= X # X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1,","directory does not exist. Please create a directory called 'data' in the 'experiments'\"","crazy reason it requires a gradient, so detach. train_X, _, _ = _split_data(X,","= [] means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1),","'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio, _ =","= base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc)","16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for","= audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index","Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std)","def _process_data(): base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands'","loc = base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc","torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split","_process_data(): base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands' X","= torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes',","y) out = [] means = [] stds = [] for Xi, train_Xi","base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return","+ '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc):","directory called 'data' in the 'experiments'\" \" directory. (We're going to put a","detach. train_X, _, _ = _split_data(X, y) out = [] means = []","_process_data() loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data' if","is {}\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000, channels=1)","os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request","_, _ = _split_data(X, y) out = [] means = [] stds =","import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor,","shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor,","len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1","for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) #","work so we do it manually. # A few samples are shorter than","the opportunity to make it a symlink rather than a normal directory, so","if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index +=","import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor,","you the opportunity to make it a symlink rather than a normal directory,","reason it requires a gradient, so detach. train_X, _, _ = _split_data(X, y)","str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if","# X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() #","means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean","full length; for simplicity we discard them. if len(audio) != 16000: continue X[batch_index]","batch_index += 1 y_index += 1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index)","does not exist. Please create a directory called 'data' in the 'experiments'\" \"","urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here","torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X,","f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' / 'data' /","os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X,","A few samples are shorter than the full length; for simplicity we discard","base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not","put a lot of data there, so we don't make it automatically -","normal directory, so \" \"that the data can be stored elsewhere if you","_split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y,","we do it manually. # A few samples are shorter than the full","length; for simplicity we discard them. if len(audio) != 16000: continue X[batch_index] =","\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means,","= pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify)","continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index += 1","train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor,","gradient, so detach. train_X, _, _ = _split_data(X, y) out = [] means","'off', 'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio,","for forward compatbility if they fix it audio = audio / 2 **","test_audio_X) = _process_data() loc = here / '..' / 'experiments' / 'data' /","For some crazy reason it requires a gradient, so detach. train_X, _, _","'.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data","stored elsewhere if you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc","them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index","split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor,","seem to work so we do it manually. # A few samples are","sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True,","tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc):","stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor","stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments'","os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc","train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi","val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means),","(We're going to put a lot of data there, so we don't make","a gradient, so detach. train_X, _, _ = _split_data(X, y) out = []","shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape","zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean)","# 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0,","os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc =","'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername","audio / 2 ** 15 # Normalization argument doesn't seem to work so","y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on',","train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here","so detach. train_X, _, _ = _split_data(X, y) out = [] means =","'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc):","base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename,","_save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt')","it a symlink rather than a normal directory, so \" \"that the data","download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does","if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as","= _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y =","import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): #","16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index +=","torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0","test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds),","out = [] means = [] stds = [] for Xi, train_Xi in","!= 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index","val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X,","mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X =","a directory called 'data' in the 'experiments'\" \" directory. (We're going to put","a normal directory, so \" \"that the data can be stored elsewhere if","+= 1 y_index += 1 assert batch_index == 34975, \"batch_index is {}\".format(batch_index) audio_X","= torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X =","filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for","X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X","a lot of data there, so we don't make it automatically - \"","= 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off',","of data there, so we don't make it automatically - \" \"thus giving","audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if","_split_data(X, y) out = [] means = [] stds = [] for Xi,","if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def","0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right',","X # X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach()","tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' /","data there, so we don't make it automatically - \" \"thus giving you","stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7,","they fix it audio = audio / 2 ** 15 # Normalization argument","shape (batch=34975, length=81, channels=40). For some crazy reason it requires a gradient, so","/ tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not","if not os.path.exists(base_base_loc): raise RuntimeError(\"data directory does not exist. Please create a directory","if you wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz'","\" \"thus giving you the opportunity to make it a symlink rather than","argument doesn't seem to work so we do it manually. # A few","test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc ="],"string":"[\n \"def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor,\",\n \"wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc):\",\n \"os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility\",\n \"/ (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X,\",\n \"pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) =\",\n \"train_X, _, _ = _split_data(X, y) out = [] means = [] stds\",\n \"train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y,\",\n \"\\\" directory. (We're going to put a lot of data there, so we\",\n \"mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std\",\n \"urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor,\",\n \"doesn't seem to work so we do it manually. # A few samples\",\n \"= torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For some\",\n \"= [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean =\",\n \"+ '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise\",\n \"batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X # X is of\",\n \"torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15\",\n \"y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y,\",\n \"tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc =\",\n \"= _split_data(X, y) out = [] means = [] stds = [] for\",\n \"don't make it automatically - \\\" \\\"thus giving you the opportunity to make\",\n \"make it automatically - \\\" \\\"thus giving you the opportunity to make it\",\n \"foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc\",\n \"'../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please create a\",\n \"'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio, _\",\n \"dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes', 'no', 'up',\",\n \"= here / '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975,\",\n \"torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes', 'no',\",\n \"34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X # X is of shape (batch=34975,\",\n \"_split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\\",\n \"random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor,\",\n \"in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi -\",\n \"if they fix it audio = audio / 2 ** 15 # Normalization\",\n \"directory, so \\\" \\\"that the data can be stored elsewhere if you wish.)\\\")\",\n \"tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here /\",\n \"= train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std +\",\n \"= torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index =\",\n \"os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please create a directory called 'data'\",\n \"\\\"that the data can be stored elsewhere if you wish.)\\\") base_loc = base_base_loc\",\n \"val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main():\",\n \"it requires a gradient, so detach. train_X, _, _ = _split_data(X, y) out\",\n \"shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in\",\n \"train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) /\",\n \"it manually. # A few samples are shorter than the full length; for\",\n \"torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they fix it\",\n \"base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not\",\n \"exist. Please create a directory called 'data' in the 'experiments'\\\" \\\" directory. (We're\",\n \"loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here /\",\n \"'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename in\",\n \"simplicity we discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index]\",\n \"2).detach() # X is of shape (batch=34975, length=81, channels=40). For some crazy reason\",\n \"# for forward compatbility if they fix it audio = audio / 2\",\n \"test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X,\",\n \"'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename\",\n \"make it a symlink rather than a normal directory, so \\\" \\\"that the\",\n \"(batch=34975, length=81, channels=40). For some crazy reason it requires a gradient, so detach.\",\n \"/ 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y,\",\n \"0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop',\",\n \"channels=40). For some crazy reason it requires a gradient, so detach. train_X, _,\",\n \"= base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc /\",\n \"than the full length; for simplicity we discard them. if len(audio) != 16000:\",\n \"def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X,\",\n \"'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername for\",\n \"sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def\",\n \"stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify)\",\n \"RuntimeError(\\\"data directory does not exist. Please create a directory called 'data' in the\",\n \"0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True,\",\n \"means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1)\",\n \"val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments' / 'data'\",\n \"a symlink rather than a normal directory, so \\\" \\\"that the data can\",\n \"'experiments'\\\" \\\" directory. (We're going to put a lot of data there, so\",\n \"to make it a symlink rather than a normal directory, so \\\" \\\"that\",\n \"length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81,\",\n \"train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def\",\n \"= sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1,\",\n \"testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor =\",\n \"train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return\",\n \"if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds,\",\n \"train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return\",\n \"base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with\",\n \"import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here\",\n \"str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please\",\n \"not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please create a directory called\",\n \"base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands' X =\",\n \"we don't make it automatically - \\\" \\\"thus giving you the opportunity to\",\n \"[] means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)):\",\n \"shorter than the full length; for simplicity we discard them. if len(audio) !=\",\n \"_ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they\",\n \"1 y_index += 1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X =\",\n \"return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value,\",\n \"= [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std =\",\n \"+ '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc,\",\n \"out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X,\",\n \"train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify)\",\n \"/ 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long)\",\n \"= sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors):\",\n \"= y_index batch_index += 1 y_index += 1 assert batch_index == 34975, \\\"batch_index\",\n \"import os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import\",\n \"test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ == '__main__':\",\n \"'..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y\",\n \"= here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc):\",\n \"y_index += 1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X\",\n \"= torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if they fix\",\n \"def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) +\",\n \"(train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data()\",\n \"torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For some crazy\",\n \"tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify):\",\n \"train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name,\",\n \"X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X\",\n \"the full length; for simplicity we discard them. if len(audio) != 16000: continue\",\n \"audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index ==\",\n \"import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test\",\n \"os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f:\",\n \"train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y,\",\n \"/ 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X,\",\n \"compatbility if they fix it audio = audio / 2 ** 15 #\",\n \"opportunity to make it a symlink rather than a normal directory, so \\\"\",\n \"pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here =\",\n \"/ 2 ** 15 # Normalization argument doesn't seem to work so we\",\n \"/ foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False,\",\n \"(train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor\",\n \"we discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] =\",\n \"y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index == 34975,\",\n \"return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X\",\n \"foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False)\",\n \"tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc\",\n \"2 ** 15 # Normalization argument doesn't seem to work so we do\",\n \"train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments' /\",\n \"stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items():\",\n \"Normalization argument doesn't seem to work so we do it manually. # A\",\n \"X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index\",\n \"y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X,\",\n \"torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y,\",\n \"dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y)\",\n \"so we don't make it automatically - \\\" \\\"thus giving you the opportunity\",\n \"assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X # X is\",\n \"def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory\",\n \"raise RuntimeError(\\\"data directory does not exist. Please create a directory called 'data' in\",\n \"\\\"thus giving you the opportunity to make it a symlink rather than a\",\n \"1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X,\",\n \"'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0\",\n \"the data can be stored elsewhere if you wish.)\\\") base_loc = base_base_loc +\",\n \"few samples are shorter than the full length; for simplicity we discard them.\",\n \"for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'):\",\n \"'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means,\",\n \"going to put a lot of data there, so we don't make it\",\n \"\\\" \\\"that the data can be stored elsewhere if you wish.)\\\") base_loc =\",\n \"test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download()\",\n \"some crazy reason it requires a gradient, so detach. train_X, _, _ =\",\n \"in the 'experiments'\\\" \\\" directory. (We're going to put a lot of data\",\n \"giving you the opportunity to make it a symlink rather than a normal\",\n \"for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean)\",\n \"= _process_data() loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data'\",\n \"import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent\",\n \"return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc)\",\n \"it audio = audio / 2 ** 15 # Normalization argument doesn't seem\",\n \"here / '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000,\",\n \"# A few samples are shorter than the full length; for simplicity we\",\n \"discard them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index\",\n \"test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y\",\n \"not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X,\",\n \"+ 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X,\",\n \"so \\\" \\\"that the data can be stored elsewhere if you wish.)\\\") base_loc\",\n \"with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..'\",\n \"of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of\",\n \"do it manually. # A few samples are shorter than the full length;\",\n \"download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) =\",\n \"'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index\",\n \"/ 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index =\",\n \"torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data')\",\n \"symlink rather than a normal directory, so \\\" \\\"that the data can be\",\n \"train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ == '__main__': main()\",\n \"val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X,\",\n \"test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..'\",\n \"not exist. Please create a directory called 'data' in the 'experiments'\\\" \\\" directory.\",\n \"loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',\",\n \"be stored elsewhere if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc =\",\n \"Please create a directory called 'data' in the 'experiments'\\\" \\\" directory. (We're going\",\n \"directory. (We're going to put a lot of data there, so we don't\",\n \"stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X,\",\n \"y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in\",\n \"std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X\",\n \"random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value\",\n \"called 'data' in the 'experiments'\\\" \\\" directory. (We're going to put a lot\",\n \"forward compatbility if they fix it audio = audio / 2 ** 15\",\n \"val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here /\",\n \"/ '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc,\",\n \"train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__\",\n \"X is of shape (batch=34975, length=81, channels=40). For some crazy reason it requires\",\n \"audio = audio / 2 ** 15 # Normalization argument doesn't seem to\",\n \"- \\\" \\\"thus giving you the opportunity to make it a symlink rather\",\n \"elsewhere if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc +\",\n \"1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername\",\n \"'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y = torch.empty(34975,\",\n \"is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is\",\n \"_ = _split_data(X, y) out = [] means = [] stds = []\",\n \"15 # Normalization argument doesn't seem to work so we do it manually.\",\n \"create a directory called 'data' in the 'experiments'\\\" \\\" directory. (We're going to\",\n \"/ 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1) y =\",\n \"lot of data there, so we don't make it automatically - \\\" \\\"thus\",\n \"\\\"batch_index is {}\\\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000,\",\n \"in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward\",\n \"automatically - \\\" \\\"thus giving you the opportunity to make it a symlink\",\n \"= str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist.\",\n \"_split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y,\",\n \"here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify,\",\n \"== 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X # X is of shape\",\n \"y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y)\",\n \"1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X # X\",\n \"(batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975,\",\n \"= _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X,\",\n \"{}\\\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000, channels=1) X\",\n \"rather than a normal directory, so \\\" \\\"that the data can be stored\",\n \"data can be stored elsewhere if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands'\",\n \"_split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify,\",\n \"os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X)\",\n \"in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc =\",\n \"/ filename, channels_first=False, normalization=False) # for forward compatbility if they fix it audio\",\n \"is of shape (batch=34975, length=81, channels=40). For some crazy reason it requires a\",\n \"'..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X,\",\n \"<reponame>patrick-kidger/generalised_shapelets import os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio\",\n \"val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def\",\n \"stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std\",\n \"train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5))\",\n \"to put a lot of data there, so we don't make it automatically\",\n \"main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X)\",\n \"test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name)\",\n \"train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir\",\n \"train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y,\",\n \"here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc)\",\n \"val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if __name__ ==\",\n \"than a normal directory, so \\\" \\\"that the data can be stored elsewhere\",\n \"filename, channels_first=False, normalization=False) # for forward compatbility if they fix it audio =\",\n \"channels_first=False, normalization=False) # for forward compatbility if they fix it audio = audio\",\n \"'/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r')\",\n \"_save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X, test_audio_X=test_audio_X) if\",\n \"it automatically - \\\" \\\"thus giving you the opportunity to make it a\",\n \"you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if\",\n \"+= 1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X = X #\",\n \"fix it audio = audio / 2 ** 15 # Normalization argument doesn't\",\n \"the 'experiments'\\\" \\\" directory. (We're going to put a lot of data there,\",\n \"# Normalization argument doesn't seem to work so we do it manually. #\",\n \"of shape (batch=34975, length=81, channels=40). For some crazy reason it requires a gradient,\",\n \"'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y,\",\n \"there, so we don't make it automatically - \\\" \\\"thus giving you the\",\n \"can be stored elsewhere if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc\",\n \"X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert\",\n \"for simplicity we discard them. if len(audio) != 16000: continue X[batch_index] = audio\",\n \"loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data' if not\",\n \"('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc\",\n \"'/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc)\",\n \"val_tensor, test_tensor def _save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir /\",\n \"means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' /\",\n \"sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir, **tensors): for\",\n \"= _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X, test_X,\",\n \"'data' in the 'experiments'\\\" \\\" directory. (We're going to put a lot of\",\n \"/ '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please create\",\n \"torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y,\",\n \"- mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X\",\n \"'experiments' / 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X,\",\n \"samples are shorter than the full length; for simplicity we discard them. if\",\n \"val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds,\",\n \"train_y, val_y, test_y, torch.stack(means), torch.stack(stds), train_audio_X, \\\\ val_audio_X, test_audio_X def main(): download() (train_X,\",\n \"batch_index = 0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down',\",\n \"test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X, val_X,\",\n \"= audio / 2 ** 15 # Normalization argument doesn't seem to work\",\n \"**tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def\",\n \"for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download():\",\n \"test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor def _save_data(dir,\",\n \"requires a gradient, so detach. train_X, _, _ = _split_data(X, y) out =\",\n \"val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc\",\n \"to work so we do it manually. # A few samples are shorter\",\n \"X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40). For\",\n \"= train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X =\",\n \"(std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y)\",\n \"test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means, stds, train_audio_X,\",\n \"manually. # A few samples are shorter than the full length; for simplicity\",\n \"/ '..' / 'experiments' / 'data' / 'SpeechCommands' X = torch.empty(34975, 16000, 1)\",\n \"= 0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left',\",\n \"length=81, channels=40). For some crazy reason it requires a gradient, so detach. train_X,\",\n \"[] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean()\",\n \"[] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std()\",\n \"so we do it manually. # A few samples are shorter than the\",\n \"train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean) / (std + 1e-5)) X = torch.stack(out,\",\n \"as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' /\",\n \"testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5,\",\n \"val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y,\",\n \"f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' / 'data'\",\n \"/ 'data' / 'speech_commands_data' if not os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y,\",\n \"# X is of shape (batch=34975, length=81, channels=40). For some crazy reason it\",\n \"channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape (batch=34975, length=81, channels=40).\",\n \"'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc /\",\n \"in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here\",\n \"not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data():\",\n \"'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments'\",\n \"audio_X = X # X is of shape (batch=34975, length=16000, channels=1) X =\",\n \"y_index batch_index += 1 y_index += 1 assert batch_index == 34975, \\\"batch_index is\",\n \"** 15 # Normalization argument doesn't seem to work so we do it\",\n \"are shorter than the full length; for simplicity we discard them. if len(audio)\",\n \"= base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if\",\n \"normalization=False) # for forward compatbility if they fix it audio = audio /\",\n \"= X # X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1,\",\n \"directory does not exist. Please create a directory called 'data' in the 'experiments'\\\"\",\n \"crazy reason it requires a gradient, so detach. train_X, _, _ = _split_data(X,\",\n \"= [] means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1),\",\n \"'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio, _ =\",\n \"= base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc)\",\n \"16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for\",\n \"= audio y[batch_index] = y_index batch_index += 1 y_index += 1 assert batch_index\",\n \"Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std)\",\n \"def _process_data(): base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands'\",\n \"loc = base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc\",\n \"torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split\",\n \"_process_data(): base_loc = here / '..' / 'experiments' / 'data' / 'SpeechCommands' X\",\n \"= torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0 for foldername in ('yes',\",\n \"y) out = [] means = [] stds = [] for Xi, train_Xi\",\n \"base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return\",\n \"+ '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc): return if not os.path.exists(base_loc):\",\n \"directory called 'data' in the 'experiments'\\\" \\\" directory. (We're going to put a\",\n \"detach. train_X, _, _ = _split_data(X, y) out = [] means = []\",\n \"_process_data() loc = here / '..' / 'experiments' / 'data' / 'speech_commands_data' if\",\n \"is {}\\\".format(batch_index) audio_X = X # X is of shape (batch=34975, length=16000, channels=1)\",\n \"os import pathlib import sklearn.model_selection import tarfile import torch import torchaudio import urllib.request\",\n \"_, _ = _split_data(X, y) out = [] means = [] stds =\",\n \"import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor,\",\n \"shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor,\",\n \"len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1\",\n \"for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) #\",\n \"work so we do it manually. # A few samples are shorter than\",\n \"the opportunity to make it a symlink rather than a normal directory, so\",\n \"if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index +=\",\n \"import tarfile import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor,\",\n \"you the opportunity to make it a symlink rather than a normal directory,\",\n \"reason it requires a gradient, so detach. train_X, _, _ = _split_data(X, y)\",\n \"str(dir / tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if\",\n \"# X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() #\",\n \"means = [] stds = [] for Xi, train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean\",\n \"full length; for simplicity we discard them. if len(audio) != 16000: continue X[batch_index]\",\n \"batch_index += 1 y_index += 1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index)\",\n \"does not exist. Please create a directory called 'data' in the 'experiments'\\\" \\\"\",\n \"urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here\",\n \"torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X,\",\n \"f.extractall(base_loc) def _process_data(): base_loc = here / '..' / 'experiments' / 'data' /\",\n \"os.path.exists(loc): os.mkdir(loc) _save_data(loc, train_X=train_X, val_X=val_X, test_X=test_X, train_y=train_y, val_y=val_y, test_y=test_y, means=means, stds=stds, train_audio_X=train_audio_X, val_audio_X=val_audio_X,\",\n \"A few samples are shorter than the full length; for simplicity we discard\",\n \"base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not\",\n \"put a lot of data there, so we don't make it automatically -\",\n \"normal directory, so \\\" \\\"that the data can be stored elsewhere if you\",\n \"_split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y,\",\n \"we do it manually. # A few samples are shorter than the full\",\n \"length; for simplicity we discard them. if len(audio) != 16000: continue X[batch_index] =\",\n \"\\\\ val_audio_X, test_audio_X def main(): download() (train_X, val_X, test_X, train_y, val_y, test_y, means,\",\n \"= pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify)\",\n \"continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index += 1\",\n \"train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor,\",\n \"gradient, so detach. train_X, _, _ = _split_data(X, y) out = [] means\",\n \"'off', 'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc): audio,\",\n \"for forward compatbility if they fix it audio = audio / 2 **\",\n \"test_audio_X) = _process_data() loc = here / '..' / 'experiments' / 'data' /\",\n \"For some crazy reason it requires a gradient, so detach. train_X, _, _\",\n \"'.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data\",\n \"stored elsewhere if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc\",\n \"them. if len(audio) != 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index\",\n \"split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor,\",\n \"seem to work so we do it manually. # A few samples are\",\n \"sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0, shuffle=True, stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True,\",\n \"tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc):\",\n \"stratify=stratify) val_tensor, test_tensor = sklearn.model_selection.train_test_split(testval_tensor, train_size=0.5, random_state=1, shuffle=True, stratify=testval_stratify) return train_tensor, val_tensor, test_tensor\",\n \"stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here / '..' / 'experiments'\",\n \"os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc\",\n \"train_Xi in zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi\",\n \"val_y, test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means),\",\n \"(We're going to put a lot of data there, so we don't make\",\n \"a gradient, so detach. train_X, _, _ = _split_data(X, y) out = []\",\n \"shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach() # X is of shape\",\n \"zip(X.unbind(dim=-1), train_X.unbind(dim=-1)): mean = train_Xi.mean() std = train_Xi.std() means.append(mean) stds.append(std) out.append((Xi - mean)\",\n \"# 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7, random_state=0,\",\n \"os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc =\",\n \"'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'): loc = base_loc / foldername\",\n \"audio / 2 ** 15 # Normalization argument doesn't seem to work so\",\n \"y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on',\",\n \"train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc = here\",\n \"so detach. train_X, _, _ = _split_data(X, y) out = [] means =\",\n \"'on', 'off', 'stop', 'go'): loc = base_loc / foldername for filename in os.listdir(loc):\",\n \"base_loc / foldername for filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename,\",\n \"_save_data(dir, **tensors): for tensor_name, tensor_value in tensors.items(): torch.save(tensor_value, str(dir / tensor_name) + '.pt')\",\n \"it a symlink rather than a normal directory, so \\\" \\\"that the data\",\n \"download(): base_base_loc = str(here / '../experiments/data') if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does\",\n \"if os.path.exists(loc): return if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as\",\n \"= _split_data(audio_X, y) train_X, val_X, test_X = _split_data(X, y) train_y, val_y, test_y =\",\n \"import torch import torchaudio import urllib.request here = pathlib.Path(__file__).resolve().parent def _split_data(tensor, stratify): #\",\n \"16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index +=\",\n \"torch.empty(34975, 16000, 1) y = torch.empty(34975, dtype=torch.long) batch_index = 0 y_index = 0\",\n \"test_y = _split_data(y, y) return train_X, val_X, test_X, train_y, val_y, test_y, torch.stack(means), torch.stack(stds),\",\n \"out = [] means = [] stds = [] for Xi, train_Xi in\",\n \"!= 16000: continue X[batch_index] = audio y[batch_index] = y_index batch_index += 1 y_index\",\n \"val_X, test_X = _split_data(X, y) train_y, val_y, test_y = _split_data(y, y) return train_X,\",\n \"mean) / (std + 1e-5)) X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X =\",\n \"a directory called 'data' in the 'experiments'\\\" \\\" directory. (We're going to put\",\n \"a normal directory, so \\\" \\\"that the data can be stored elsewhere if\",\n \"+= 1 y_index += 1 assert batch_index == 34975, \\\"batch_index is {}\\\".format(batch_index) audio_X\",\n \"= torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X =\",\n \"filename in os.listdir(loc): audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for\",\n \"X = torch.stack(out, dim=-1) train_audio_X, val_audio_X, test_audio_X = _split_data(audio_X, y) train_X, val_X, test_X\",\n \"a lot of data there, so we don't make it automatically - \\\"\",\n \"= 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off',\",\n \"of data there, so we don't make it automatically - \\\" \\\"thus giving\",\n \"audio, _ = torchaudio.load_wav(loc / filename, channels_first=False, normalization=False) # for forward compatbility if\",\n \"_split_data(X, y) out = [] means = [] stds = [] for Xi,\",\n \"if not os.path.exists(base_loc): os.mkdir(base_loc) urllib.request.urlretrieve('http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', loc) with tarfile.open(loc, 'r') as f: f.extractall(base_loc) def\",\n \"0 y_index = 0 for foldername in ('yes', 'no', 'up', 'down', 'left', 'right',\",\n \"X # X is of shape (batch=34975, length=16000, channels=1) X = torchaudio.transforms.MFCC(log_mels=True)(X.squeeze(-1)).transpose(1, 2).detach()\",\n \"tarfile.open(loc, 'r') as f: f.extractall(base_loc) def _process_data(): base_loc = here / '..' /\",\n \"data there, so we don't make it automatically - \\\" \\\"thus giving you\",\n \"stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor, stratify, train_size=0.7,\",\n \"they fix it audio = audio / 2 ** 15 # Normalization argument\",\n \"shape (batch=34975, length=81, channels=40). For some crazy reason it requires a gradient, so\",\n \"/ tensor_name) + '.pt') def download(): base_base_loc = str(here / '../experiments/data') if not\",\n \"if not os.path.exists(base_base_loc): raise RuntimeError(\\\"data directory does not exist. Please create a directory\",\n \"if you wish.)\\\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz'\",\n \"\\\" \\\"thus giving you the opportunity to make it a symlink rather than\",\n \"argument doesn't seem to work so we do it manually. # A few\",\n \"test_X, train_y, val_y, test_y, means, stds, train_audio_X, val_audio_X, test_audio_X) = _process_data() loc =\"\n]","isConversational":false}}},{"rowIdx":4081,"cells":{"ngram":{"kind":"list like","value":["datetime from typing import List from flask import Blueprint, jsonify, request, json from","'{\"status\": \"error\", \"msg\": \"categories number must be between 1 and 5\"}', 400 categories","= Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if","def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST'])","\"msg\": \"product deleted, also %d product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\":","'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"})","request, json from app.models.products import Product, Category, products_categories from app import db products_blueprint","exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET'])","categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data","try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'],","existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def","products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for","request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id'])","payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if","jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d product_categories relations deleted\" % (products_result,","check values of fields\"}' except KeyError as e: return '{\"status\": \"error\", \"msg\": \"field","= db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\",","True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'],","list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories","product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured","import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from app","json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if","if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if","@products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] })","else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for","request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or","int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return","products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d product_categories relations","for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return","cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def","payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in","db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\":","1 and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured']","delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result","product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])})","= json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) >","'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter(","in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id","-> List[Category]: \"\"\" Func to get existing categories objects or create new otherwise","[cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\":","json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit()","= True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit()","\"TypeError occured: check values of fields\"}' except KeyError as e: return '{\"status\": \"error\",","%d product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products","expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if","5\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured'","8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in","e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values of fields\"}' except KeyError","in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']),","get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def","datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\",","product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating':","\"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data)","payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories'])","number must be between 1 and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod","payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return","create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories'])","return '{\"status\": \"error\", \"msg\": \"categories number must be between 1 and 5\"}', 400","cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name","datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return","get existing categories objects or create new otherwise :param p: payload of request","if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat)","except TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values of","Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result ==","as e: return '{\"status\": \"error\", \"msg\": \"field %s have not been found, but","400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in","of request :return: list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat","receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return","payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in","Func to get existing categories objects or create new otherwise :param p: payload","@products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d","\"error\", \"msg\": \"TypeError occured: check values of fields\"}' except KeyError as e: return","= json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product:","categories objects or create new otherwise :param p: payload of request :return: list","if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])})","app.models.products import Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__)","data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) <",":return: list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']]","for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category =","data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result =","== int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted,","brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as","= request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter(","methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S'","> 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date'","@products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d","found, but is required\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured =","from flask import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories","fields\"}' except KeyError as e: return '{\"status\": \"error\", \"msg\": \"field %s have not","as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values of fields\"}' except","'{\"status\": \"error\", \"msg\": \"TypeError occured: check values of fields\"}' except KeyError as e:","p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id ==","cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\":","cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results':","Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict)","'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating'","\"error\", \"msg\": \"field %s have not been found, but is required\"}' % str(e),","\"msg\": \"field %s have not been found, but is required\"}' % str(e), 400","jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories relations deleted\" % products_categories_result}) else:","datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories =","new otherwise :param p: payload of request :return: list of categories \"\"\" recevied_categories:","payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in","products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) ->","5: return '{\"status\": \"error\", \"msg\": \"categories number must be between 1 and 5\"}',","in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product():","relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also","if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if","product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories]","methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product',","'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None),","('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\"status\": \"error\", \"msg\":","request :return: list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in","is required\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod)","= [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists:","products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\":","return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product():","%s have not been found, but is required\"}' % str(e), 400 if new_prod.rating","deleted, also %d product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\":","int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in","in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock'","return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product","\"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product found with given","if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product","== int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat","of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories =","in payload.keys()) else None)) except TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError","recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0]","}) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format =","e: return '{\"status\": \"error\", \"msg\": \"field %s have not been found, but is","TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values of fields\"}'","[cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in","create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories]","payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys():","in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in","create new otherwise :param p: payload of request :return: list of categories \"\"\"","= '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\"status\":","from app.models.products import Product, Category, products_categories from app import db products_blueprint = Blueprint('products',","return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d product_categories relations deleted\" %","\"field %s have not been found, but is required\"}' % str(e), 400 if","categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()]","in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name ==","values of fields\"}' except KeyError as e: return '{\"status\": \"error\", \"msg\": \"field %s","'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if","datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\":","methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S'","\"\"\" Func to get existing categories objects or create new otherwise :param p:","< 1 or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories number must","[db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit()","== cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return","dict) -> List[Category]: \"\"\" Func to get existing categories objects or create new","= create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else","app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\"","\"ok\", \"msg\": \"product deleted, also %d product_categories relations deleted\" % products_categories_result}) else: return","float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock':","or create new otherwise :param p: payload of request :return: list of categories","\"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product found with given id\"}',","product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys():","typing import List from flask import Blueprint, jsonify, request, json from app.models.products import","\"msg\": \"TypeError occured: check values of fields\"}' except KeyError as e: return '{\"status\":","json from app.models.products import Product, Category, products_categories from app import db products_blueprint =","'%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\"status\": \"error\",","return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories relations deleted\" % products_categories_result})","products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if","in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data)","\"warning\", \"msg\": \"%d products deleted, also %d product_categories relations deleted\" % (products_result, products_categories_result)})","to get existing categories objects or create new otherwise :param p: payload of","[p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8')","> 5: return '{\"status\": \"error\", \"msg\": \"categories number must be between 1 and","if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories","new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format)","flask import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from","found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p","in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product","None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError","but is required\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True","payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])})","\"categories number must be between 1 and 5\"}', 400 categories = create_or_get_categories(payload) try:","been found, but is required\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured","data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id","Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return","categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if","datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\"status\":","if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])})","= db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else:","('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys())","return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values of fields\"}' except KeyError as","from datetime import datetime from typing import List from flask import Blueprint, jsonify,","db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d","return '{\"status\": \"error\", \"msg\": \"field %s have not been found, but is required\"}'","have not been found, but is required\"}' % str(e), 400 if new_prod.rating >","recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for cat","== payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in","request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id","'{\"status\": \"error\", \"msg\": \"field %s have not been found, but is required\"}' %","List[Category]: \"\"\" Func to get existing categories objects or create new otherwise :param","= [Category(name=cat) for cat in p['categories']] categories = [] for cat in recevied_categories:","KeyError as e: return '{\"status\": \"error\", \"msg\": \"field %s have not been found,","objects or create new otherwise :param p: payload of request :return: list of","\"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload =","if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)})","@products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id","payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None))","= create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in","received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format =","cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\":","must be between 1 and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod =","required\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for","if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products',","also %d product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d","product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted,","8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in","for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload",":param p: payload of request :return: list of categories \"\"\" recevied_categories: List[Category] =","db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data =","in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'],","int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand':","categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"})","Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get existing categories","payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys():","new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories]","\"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product found with","update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product =","occured: check values of fields\"}' except KeyError as e: return '{\"status\": \"error\", \"msg\":","int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj","in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating >","= request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1","True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return","'%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys():","import Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def","cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories","'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if","with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p =","products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product","= '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in","if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in","len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories number","'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data =","product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys():","featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys())","None)) except TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check values","p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload =","payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']),","else None)) except TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured: check","payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for","datetime import datetime from typing import List from flask import Blueprint, jsonify, request,","1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories relations deleted\" %","categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized","product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\":","db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat)","jsonify, request, json from app.models.products import Product, Category, products_categories from app import db","str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in","return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8')","import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func","p: payload of request :return: list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat)","db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no","categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p","between 1 and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']),","p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0]","List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for cat in","json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5:","jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product found","[Category(name=cat) for cat in p['categories']] categories = [] for cat in recevied_categories: exists","in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else","in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj)","None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format)","def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False)","len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories number must be between 1","categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = []","be between 1 and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'],","import List from flask import Blueprint, jsonify, request, json from app.models.products import Product,","and 5\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if","= True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date':","datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name'","payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id ==","new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for","def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product","= product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if","in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\",","\"error\", \"msg\": \"categories number must be between 1 and 5\"}', 400 categories =","existing categories objects or create new otherwise :param p: payload of request :return:","product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date'","exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category)","% products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d product_categories","payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0:","bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured =","of fields\"}' except KeyError as e: return '{\"status\": \"error\", \"msg\": \"field %s have","id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result","for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys():","in p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name ==","given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data)","product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8')","datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories","%H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name':","\"msg\": \"no product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data","or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories number must be between","product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False)","rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in","except KeyError as e: return '{\"status\": \"error\", \"msg\": \"field %s have not been","datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date'","%H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\":","for cat in p['categories']] categories = [] for cat in recevied_categories: exists =","[db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product',","= Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if","db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to","if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj =","int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also","'{\"status\": \"error\", \"msg\": \"no product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def","def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if","return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in","for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\",","create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None),","payload.keys()) else None)) except TypeError as e: return '{\"status\": \"error\", \"msg\": \"TypeError occured:","return '{\"status\": \"error\", \"msg\": \"no product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE'])","= Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result","= request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id ==","if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured':","import datetime from typing import List from flask import Blueprint, jsonify, request, json","Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured'","else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d product_categories relations deleted\"","else: return '{\"status\": \"error\", \"msg\": \"no product found with given id\"}', 404 @products_blueprint.route('/delete_product',","<reponame>duch94/spark_crud_test from datetime import datetime from typing import List from flask import Blueprint,","\"\"\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for","__name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get existing categories objects","'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0]","def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get existing categories objects or","payload of request :return: list of categories \"\"\" recevied_categories: List[Category] = [Category(name=cat) for","items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e:","categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat","cat in p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name","products_result == 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories relations","methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id ==","Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from app import","jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data","= Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products():","'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if","Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p:","products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get","cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date':","categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\":","\"error\", \"msg\": \"no product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product():","from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]:","= json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False)","in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product updated\"}) else:","[] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category","jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload","db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat)","categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys()","> 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat","product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']})","from typing import List from flask import Blueprint, jsonify, request, json from app.models.products","== 1: return jsonify({\"status\": \"ok\", \"msg\": \"product deleted, also %d product_categories relations deleted\"","Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format","= Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get existing","if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories","if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\"status\": \"error\",","product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if","not been found, but is required\"}' % str(e), 400 if new_prod.rating > 8.0:","400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories]","product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys():","updated\"}) else: return '{\"status\": \"error\", \"msg\": \"no product found with given id\"}', 404","if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else","\"product deleted, also %d product_categories relations deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\",","in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories'","List from flask import Blueprint, jsonify, request, json from app.models.products import Product, Category,","404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result =","otherwise :param p: payload of request :return: list of categories \"\"\" recevied_categories: List[Category]","\"msg\": \"categories number must be between 1 and 5\"}', 400 categories = create_or_get_categories(payload)","else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except","% str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat","== cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({","Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date'","if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload)","else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'],","\"product received\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format","if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True","categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)})","'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating","== int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1:","1 or len(payload['categories']) > 5: return '{\"status\": \"error\", \"msg\": \"categories number must be","\"no product found with given id\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data =","create_or_get_categories(p: dict) -> List[Category]: \"\"\" Func to get existing categories objects or create","in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand'","deleted\" % products_categories_result}) else: return jsonify({\"status\": \"warning\", \"msg\": \"%d products deleted, also %d","for cat in categories] db.session.commit() return jsonify({\"status\": \"ok\", \"msg\": \"product received\"}) @products_blueprint.route('/update_product', methods=['PUT'])","product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys():"],"string":"[\n \"datetime from typing import List from flask import Blueprint, jsonify, request, json from\",\n \"'{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1 and 5\\\"}', 400 categories\",\n \"= Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if\",\n \"def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST'])\",\n \"\\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\":\",\n \"'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"})\",\n \"request, json from app.models.products import Product, Category, products_categories from app import db products_blueprint\",\n \"exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET'])\",\n \"categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data\",\n \"try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'],\",\n \"existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def\",\n \"products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for\",\n \"request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id'])\",\n \"payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if\",\n \"jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories relations deleted\\\" % (products_result,\",\n \"check values of fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field\",\n \"= db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\",\",\n \"True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'],\",\n \"list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories\",\n \"product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured\",\n \"import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from app\",\n \"json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if\",\n \"if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if\",\n \"@products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] })\",\n \"else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for\",\n \"request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or\",\n \"int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return\",\n \"products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories relations\",\n \"for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return\",\n \"cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def\",\n \"payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in\",\n \"db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\":\",\n \"1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured']\",\n \"delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result\",\n \"product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])})\",\n \"= json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) >\",\n \"'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter(\",\n \"in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id\",\n \"-> List[Category]: \\\"\\\"\\\" Func to get existing categories objects or create new otherwise\",\n \"[cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\":\",\n \"json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit()\",\n \"= True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit()\",\n \"\\\"TypeError occured: check values of fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\",\",\n \"%d product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products\",\n \"expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if\",\n \"5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured'\",\n \"8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in\",\n \"e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except KeyError\",\n \"in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']),\",\n \"get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def\",\n \"datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\",\",\n \"product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating':\",\n \"\\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data)\",\n \"payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories'])\",\n \"number must be between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod\",\n \"payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return\",\n \"create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories'])\",\n \"return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1 and 5\\\"}', 400\",\n \"cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name\",\n \"datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return\",\n \"get existing categories objects or create new otherwise :param p: payload of request\",\n \"if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat)\",\n \"except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of\",\n \"Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result ==\",\n \"as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been found, but\",\n \"400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in\",\n \"of request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat\",\n \"receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return\",\n \"payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in\",\n \"Func to get existing categories objects or create new otherwise :param p: payload\",\n \"@products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d\",\n \"\\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except KeyError as e: return\",\n \"= json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product:\",\n \"categories objects or create new otherwise :param p: payload of request :return: list\",\n \"if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])})\",\n \"app.models.products import Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__)\",\n \"data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) <\",\n \":return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']]\",\n \"for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category =\",\n \"data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result =\",\n \"== int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted,\",\n \"brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as\",\n \"= request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter(\",\n \"methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S'\",\n \"> 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date'\",\n \"@products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d\",\n \"found, but is required\\\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured =\",\n \"from flask import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories\",\n \"fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not\",\n \"as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except\",\n \"'{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except KeyError as e:\",\n \"p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id ==\",\n \"cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\":\",\n \"cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results':\",\n \"Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict)\",\n \"'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating'\",\n \"\\\"error\\\", \\\"msg\\\": \\\"field %s have not been found, but is required\\\"}' % str(e),\",\n \"\\\"msg\\\": \\\"field %s have not been found, but is required\\\"}' % str(e), 400\",\n \"jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\" % products_categories_result}) else:\",\n \"datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories =\",\n \"new otherwise :param p: payload of request :return: list of categories \\\"\\\"\\\" recevied_categories:\",\n \"payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in\",\n \"products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) ->\",\n \"5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1 and 5\\\"}',\",\n \"in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product():\",\n \"relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also\",\n \"if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if\",\n \"product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories]\",\n \"methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product',\",\n \"'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None),\",\n \"('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\":\",\n \"request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in\",\n \"is required\\\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod)\",\n \"= [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists:\",\n \"products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\":\",\n \"return jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product():\",\n \"%s have not been found, but is required\\\"}' % str(e), 400 if new_prod.rating\",\n \"deleted, also %d product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\":\",\n \"int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in\",\n \"in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock'\",\n \"return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product\",\n \"\\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given\",\n \"if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product\",\n \"== int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat\",\n \"of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories =\",\n \"in payload.keys()) else None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError\",\n \"recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0]\",\n \"}) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format =\",\n \"e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been found, but is\",\n \"TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}'\",\n \"[cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in\",\n \"create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories]\",\n \"payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys():\",\n \"in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in\",\n \"create new otherwise :param p: payload of request :return: list of categories \\\"\\\"\\\"\",\n \"= '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\\\"status\\\":\",\n \"from app.models.products import Product, Category, products_categories from app import db products_blueprint = Blueprint('products',\",\n \"return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories relations deleted\\\" %\",\n \"\\\"field %s have not been found, but is required\\\"}' % str(e), 400 if\",\n \"categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in Product.query.all()]\",\n \"in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name ==\",\n \"values of fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s\",\n \"'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if\",\n \"datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\":\",\n \"methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S'\",\n \"\\\"\\\"\\\" Func to get existing categories objects or create new otherwise :param p:\",\n \"< 1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must\",\n \"[db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit()\",\n \"== cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return\",\n \"dict) -> List[Category]: \\\"\\\"\\\" Func to get existing categories objects or create new\",\n \"= create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else\",\n \"app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\"\",\n \"\\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\" % products_categories_result}) else: return\",\n \"float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock':\",\n \"or create new otherwise :param p: payload of request :return: list of categories\",\n \"\\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}',\",\n \"product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys():\",\n \"typing import List from flask import Blueprint, jsonify, request, json from app.models.products import\",\n \"\\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except KeyError as e: return '{\\\"status\\\":\",\n \"json from app.models.products import Product, Category, products_categories from app import db products_blueprint =\",\n \"'%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\",\",\n \"return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\" % products_categories_result})\",\n \"products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if\",\n \"in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data)\",\n \"\\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories relations deleted\\\" % (products_result, products_categories_result)})\",\n \"to get existing categories objects or create new otherwise :param p: payload of\",\n \"[p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8')\",\n \"> 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1 and\",\n \"if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories\",\n \"new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format)\",\n \"flask import Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from\",\n \"found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p\",\n \"in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product\",\n \"None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError\",\n \"but is required\\\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True\",\n \"payload['name']}) if 'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])})\",\n \"\\\"categories number must be between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try:\",\n \"been found, but is required\\\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured\",\n \"data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id\",\n \"Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return\",\n \"categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if\",\n \"datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\\\"status\\\":\",\n \"if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])})\",\n \"= db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else:\",\n \"('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys())\",\n \"return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values of fields\\\"}' except KeyError as\",\n \"from datetime import datetime from typing import List from flask import Blueprint, jsonify,\",\n \"db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d\",\n \"return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been found, but is required\\\"}'\",\n \"have not been found, but is required\\\"}' % str(e), 400 if new_prod.rating >\",\n \"recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for cat\",\n \"== payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in\",\n \"request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id\",\n \"'{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been found, but is required\\\"}' %\",\n \"List[Category]: \\\"\\\"\\\" Func to get existing categories objects or create new otherwise :param\",\n \"= [Category(name=cat) for cat in p['categories']] categories = [] for cat in recevied_categories:\",\n \"KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been found,\",\n \"objects or create new otherwise :param p: payload of request :return: list of\",\n \"\\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload =\",\n \"if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)})\",\n \"@products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id\",\n \"payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None))\",\n \"= create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in\",\n \"received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format =\",\n \"cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\":\",\n \"must be between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod =\",\n \"required\\\"}' % str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for\",\n \"if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products',\",\n \"also %d product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d\",\n \"product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted,\",\n \"8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in\",\n \"for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload\",\n \":param p: payload of request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] =\",\n \"db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data =\",\n \"in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'],\",\n \"int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand':\",\n \"categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"})\",\n \"Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get existing categories\",\n \"payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys():\",\n \"new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories]\",\n \"\\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with\",\n \"update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product =\",\n \"occured: check values of fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\":\",\n \"int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj\",\n \"in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating >\",\n \"= request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1\",\n \"True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return\",\n \"'%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys():\",\n \"import Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def\",\n \"cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories\",\n \"'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if\",\n \"with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p =\",\n \"products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product\",\n \"= '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in\",\n \"if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in\",\n \"len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number\",\n \"'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data =\",\n \"product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys():\",\n \"featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys())\",\n \"None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check values\",\n \"p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload =\",\n \"payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']),\",\n \"else None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured: check\",\n \"payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for\",\n \"datetime import datetime from typing import List from flask import Blueprint, jsonify, request,\",\n \"1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\" %\",\n \"categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized\",\n \"product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\":\",\n \"db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat)\",\n \"jsonify, request, json from app.models.products import Product, Category, products_categories from app import db\",\n \"str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in\",\n \"return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8')\",\n \"import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func\",\n \"p: payload of request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat)\",\n \"db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no\",\n \"categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p\",\n \"between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']),\",\n \"p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0]\",\n \"List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for cat in\",\n \"json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5:\",\n \"jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found\",\n \"[Category(name=cat) for cat in p['categories']] categories = [] for cat in recevied_categories: exists\",\n \"in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else\",\n \"in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj)\",\n \"None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format)\",\n \"def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False)\",\n \"len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1\",\n \"categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = []\",\n \"be between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'],\",\n \"import List from flask import Blueprint, jsonify, request, json from app.models.products import Product,\",\n \"and 5\\\"}', 400 categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if\",\n \"= True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date':\",\n \"datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name'\",\n \"payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id ==\",\n \"new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for\",\n \"def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product\",\n \"= product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if\",\n \"in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\",\",\n \"\\\"error\\\", \\\"msg\\\": \\\"categories number must be between 1 and 5\\\"}', 400 categories =\",\n \"existing categories objects or create new otherwise :param p: payload of request :return:\",\n \"product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date'\",\n \"exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category = Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category)\",\n \"% products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories\",\n \"payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0:\",\n \"bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured =\",\n \"of fields\\\"}' except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have\",\n \"id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result\",\n \"for cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys():\",\n \"in p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name ==\",\n \"given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data)\",\n \"product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8')\",\n \"datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories\",\n \"%H:%M:%S' product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name':\",\n \"\\\"msg\\\": \\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data\",\n \"or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be between\",\n \"product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False)\",\n \"rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in\",\n \"except KeyError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"field %s have not been\",\n \"datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date'\",\n \"%H:%M:%S' if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\":\",\n \"for cat in p['categories']] categories = [] for cat in recevied_categories: exists =\",\n \"[db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product',\",\n \"= Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if\",\n \"db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to\",\n \"if 'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj =\",\n \"int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also\",\n \"'{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def\",\n \"def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' if\",\n \"return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({ 'results': [p.serialized for p in\",\n \"for cat in categories] [db.session.add(cat) for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\",\",\n \"create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None),\",\n \"payload.keys()) else None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"TypeError occured:\",\n \"return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE'])\",\n \"= Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result\",\n \"= request.get_data().decode('utf-8') payload = json.loads(data) datetime_format = '%Y-%m-%d %H:%M:%S' product = Product.query.filter(Product.id ==\",\n \"if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured' in payload.keys(): product.update({'featured':\",\n \"import datetime from typing import List from flask import Blueprint, jsonify, request, json\",\n \"Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']}) if 'featured'\",\n \"else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d product_categories relations deleted\\\"\",\n \"else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product',\",\n \"<reponame>duch94/spark_crud_test from datetime import datetime from typing import List from flask import Blueprint,\",\n \"\\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for cat in p['categories']] categories = [] for\",\n \"__name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get existing categories objects\",\n \"'categories' in payload.keys(): categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0]\",\n \"def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get existing categories objects or\",\n \"payload of request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category] = [Category(name=cat) for\",\n \"items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except TypeError as e:\",\n \"categories = create_or_get_categories(payload) db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat\",\n \"cat in p['categories']] categories = [] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name\",\n \"products_result == 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations\",\n \"methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result = Product.query.filter(Product.id ==\",\n \"Blueprint, jsonify, request, json from app.models.products import Product, Category, products_categories from app import\",\n \"jsonify({ 'results': [p.serialized for p in Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data\",\n \"= Category.query.filter(Category.name == cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products():\",\n \"'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if\",\n \"Product, Category, products_categories from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p:\",\n \"products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get\",\n \"cat in categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date':\",\n \"categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\":\",\n \"\\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product():\",\n \"from app import db products_blueprint = Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]:\",\n \"= json.loads(data) products_result = Product.query.filter(Product.id == int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False)\",\n \"in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)}) db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product updated\\\"}) else:\",\n \"[] for cat in recevied_categories: exists = db.session.query(db.exists().where(Category.name == cat.name)).all()[0][0] if exists: existing_category\",\n \"jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload\",\n \"db.session.query(products_categories).filter( products_categories.c.product_id == int(payload['id'])).delete(synchronize_session=False) product_obj = product.all()[0] [cat.products.append(product_obj) for cat in categories] [db.session.add(cat)\",\n \"categories = create_or_get_categories(payload) try: new_prod = Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys()\",\n \"> 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories] [db.session.add(cat) for cat\",\n \"product = Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': payload['name']})\",\n \"from typing import List from flask import Blueprint, jsonify, request, json from app.models.products\",\n \"== 1: return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product deleted, also %d product_categories relations deleted\\\"\",\n \"Product.query.all()] }) @products_blueprint.route('/create_product', methods=['POST']) def create_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format\",\n \"= Blueprint('products', __name__) def create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get existing\",\n \"if len(payload['categories']) < 1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories\",\n \"if ('receipt_date' in payload.keys()) else None)) except TypeError as e: return '{\\\"status\\\": \\\"error\\\",\",\n \"product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if\",\n \"not been found, but is required\\\"}' % str(e), 400 if new_prod.rating > 8.0:\",\n \"400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat in categories]\",\n \"product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True if 'items_in_stock' in payload.keys():\",\n \"updated\\\"}) else: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"no product found with given id\\\"}', 404\",\n \"if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else\",\n \"\\\"product deleted, also %d product_categories relations deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\",\",\n \"in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories'\",\n \"List from flask import Blueprint, jsonify, request, json from app.models.products import Product, Category,\",\n \"404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data = request.get_data().decode('utf-8') p = json.loads(data) products_result =\",\n \"otherwise :param p: payload of request :return: list of categories \\\"\\\"\\\" recevied_categories: List[Category]\",\n \"\\\"msg\\\": \\\"categories number must be between 1 and 5\\\"}', 400 categories = create_or_get_categories(payload)\",\n \"else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'], datetime_format) if ('receipt_date' in payload.keys()) else None)) except\",\n \"% str(e), 400 if new_prod.rating > 8.0: new_prod.featured = True [cat.products.append(new_prod) for cat\",\n \"== cat.name).all()[0] categories.append(existing_category) else: categories.append(cat) return categories @products_blueprint.route('/products', methods=['GET']) def get_products(): return jsonify({\",\n \"Product(name=payload['name'], rating=float(payload['rating']), featured=bool(payload['featured'] if 'featured' in payload.keys() else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date'\",\n \"if 'brand' in payload.keys(): product.update({'brand': int(payload['brand'])}) if 'categories' in payload.keys(): categories = create_or_get_categories(payload)\",\n \"else None), expiration_date=(datetime.strptime(payload['expiration_date'], datetime_format) if ('expiration_date' in payload.keys()) else None), brand_id=int(payload['brand_id']), items_in_stock=int(payload['items_in_stock']), receipt_date=(datetime.strptime(payload['receipt_date'],\",\n \"\\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT']) def update_product(): data = request.get_data().decode('utf-8') payload = json.loads(data) datetime_format\",\n \"if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating > 8.0: product.featured = True\",\n \"categories] [db.session.add(cat) for cat in categories] if 'expiration_date' in payload.keys(): product.update({'expiration_date': datetime.strptime(payload['expiration_date'], datetime_format)})\",\n \"'featured' in payload.keys(): product.update({'featured': bool(payload['featured'])}) if 'rating' in payload.keys(): product.update({'rating': float(payload['rating'])}) if product.rating\",\n \"== int(p['id'])).delete(synchronize_session=False) products_categories_result = db.session.query(products_categories).filter( products_categories.c.product_id == int(p['id'])).delete(synchronize_session=False) db.session.commit() if products_result == 1:\",\n \"1 or len(payload['categories']) > 5: return '{\\\"status\\\": \\\"error\\\", \\\"msg\\\": \\\"categories number must be\",\n \"\\\"no product found with given id\\\"}', 404 @products_blueprint.route('/delete_product', methods=['DELETE']) def delete_product(): data =\",\n \"create_or_get_categories(p: dict) -> List[Category]: \\\"\\\"\\\" Func to get existing categories objects or create\",\n \"in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys(): product.update({'receipt_date': datetime.strptime(payload['receipt_date'], datetime_format)}) if 'brand'\",\n \"deleted\\\" % products_categories_result}) else: return jsonify({\\\"status\\\": \\\"warning\\\", \\\"msg\\\": \\\"%d products deleted, also %d\",\n \"for cat in categories] db.session.commit() return jsonify({\\\"status\\\": \\\"ok\\\", \\\"msg\\\": \\\"product received\\\"}) @products_blueprint.route('/update_product', methods=['PUT'])\",\n \"product.featured = True if 'items_in_stock' in payload.keys(): product.update({'items_in_stock': int(payload['items_in_stock'])}) if 'receipt_date' in payload.keys():\"\n]","isConversational":false}}},{"rowIdx":4082,"cells":{"ngram":{"kind":"list like","value":["app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\")","BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\":","(ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app):","[False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return { \"status_code\": 200, \"content\":","request): url_hit[0] = True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler):","from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from","import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import","{}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator","\"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with","url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\":","def handler(url, request): url_hit[0] = True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", }","\"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], )","import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\":","util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})),","import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators","{ \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname =","from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})),","urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import","200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\")","\"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config) assert url_hit[0]","test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return {","URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator","* @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})),","validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": {","{\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator()","import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import","pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] =","@urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\",","(ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator =","\"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, },","httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from","(ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException):","HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\":","import pytest from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator","= ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, )","= BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\":","\"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def","{\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config)","url_hit[0] = True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator","True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator()","return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname","test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\":","ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})),","ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import *","@pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ],","util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\",","URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", },","\"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config","ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config)","BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request):","test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False]","util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger","def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return","= [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return { \"status_code\": 200,","from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from","handler(url, request): url_hit[0] = True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with","import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\":","BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\",","{ \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config) assert",") def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit","with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0]","[ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def","with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( {","def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit =","= True return { \"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator =","HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException","} with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext(","unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\", }, }, url_scheme_and_hostname=url_scheme_and_hostname,","(ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_KEY\": \"foo\"}})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\": {\"CONSUMER_SECRET\": \"foo\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config,","import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [","util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures","validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def","\"status_code\": 200, \"content\": \"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\",","= BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url,","from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \"unvalidated_config\", [ (ValidatorContext({})), (ValidatorContext({\"BITBUCKET_TRIGGER_CONFIG\":","url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True return { \"status_code\":","validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\") def handler(url, request): url_hit[0] = True","], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app):","from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize(","\"oauth_token=foo&oauth_token_secret=bar\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config =","from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext","= URLSchemeAndHostname(\"http\", \"localhost:5000\") unvalidated_config = ValidatorContext( { \"BITBUCKET_TRIGGER_CONFIG\": { \"CONSUMER_KEY\": \"foo\", \"CONSUMER_SECRET\": \"bar\",","pytest from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import"],"string":"[\n \"app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\")\",\n \"BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\":\",\n \"(ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app):\",\n \"[False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\":\",\n \"request): url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler):\",\n \"from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from\",\n \"import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import\",\n \"{}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator\",\n \"\\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with\",\n \"url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\":\",\n \"def handler(url, request): url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", }\",\n \"\\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], )\",\n \"import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\":\",\n \"util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})),\",\n \"import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators\",\n \"{ \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname =\",\n \"from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})),\",\n \"urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import\",\n \"200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\")\",\n \"\\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config) assert url_hit[0]\",\n \"test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return {\",\n \"URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator\",\n \"* @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})),\",\n \"validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": {\",\n \"{\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator()\",\n \"import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import\",\n \"pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] =\",\n \"@urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\",\",\n \"(ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator =\",\n \"\\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, },\",\n \"httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from\",\n \"(ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException):\",\n \"HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\":\",\n \"import pytest from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator\",\n \"= ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, )\",\n \"= BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\":\",\n \"\\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def\",\n \"{\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config)\",\n \"url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator\",\n \"True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator()\",\n \"return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname\",\n \"test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\":\",\n \"ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})),\",\n \"ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import *\",\n \"@pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ],\",\n \"util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\",\",\n \"URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", },\",\n \"\\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config\",\n \"ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config)\",\n \"BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request):\",\n \"test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False]\",\n \"util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger\",\n \"def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return\",\n \"= [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return { \\\"status_code\\\": 200,\",\n \"from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from\",\n \"handler(url, request): url_hit[0] = True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with\",\n \"import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\":\",\n \"BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\",\",\n \"{ \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config) assert\",\n \") def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit\",\n \"with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0]\",\n \"[ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def\",\n \"with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( {\",\n \"def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit =\",\n \"= True return { \\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator =\",\n \"HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException\",\n \"} with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext(\",\n \"unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\", }, }, url_scheme_and_hostname=url_scheme_and_hostname,\",\n \"(ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_KEY\\\": \\\"foo\\\"}})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\": {\\\"CONSUMER_SECRET\\\": \\\"foo\\\"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config,\",\n \"import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [\",\n \"util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures\",\n \"validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def\",\n \"\\\"status_code\\\": 200, \\\"content\\\": \\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\",\",\n \"= BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url,\",\n \"from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \\\"unvalidated_config\\\", [ (ValidatorContext({})), (ValidatorContext({\\\"BITBUCKET_TRIGGER_CONFIG\\\":\",\n \"url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True return { \\\"status_code\\\":\",\n \"validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\\\"bitbucket.org\\\") def handler(url, request): url_hit[0] = True\",\n \"], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app):\",\n \"from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize(\",\n \"\\\"oauth_token=foo&oauth_token_secret=bar\\\", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config =\",\n \"from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext\",\n \"= URLSchemeAndHostname(\\\"http\\\", \\\"localhost:5000\\\") unvalidated_config = ValidatorContext( { \\\"BITBUCKET_TRIGGER_CONFIG\\\": { \\\"CONSUMER_KEY\\\": \\\"foo\\\", \\\"CONSUMER_SECRET\\\": \\\"bar\\\",\",\n \"pytest from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import\"\n]","isConversational":false}}},{"rowIdx":4083,"cells":{"ngram":{"kind":"list like","value":["#plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3)","matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def","x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda","x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2","as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x):","Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x:","scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0","yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000","coding: utf-8 -*- \"\"\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan","k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20)","# P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude')","x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2","##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3) #plt.plot(d,kernel(xa,xb)**2)","return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions","for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x:","np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return","s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100)","ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def","Silvan \"\"\" import numpy as np import scipy import matplotlib.pyplot as plt k=1000","plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1)","x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N):","$R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N)","# # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval","def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True)","plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k","print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition","xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R):","#for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta","plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for","r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0]","# #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $","i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration","Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2)","x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2","@author: Silvan \"\"\" import numpy as np import scipy import matplotlib.pyplot as plt","beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0)","alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return","N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in","x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3)","k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in","-*- coding: utf-8 -*- \"\"\" Created on Fri Mar 15 16:51:16 2019 @author:","M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i","np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0","range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 #","K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N)","subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda","on Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\" import numpy as np","x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best')","#plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3) #plt.plot(d,kernel(xa,xb)**2) #plt.show()","#plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show()","np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$')","Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\" import numpy as np import","in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x}","15 16:51:16 2019 @author: Silvan \"\"\" import numpy as np import scipy import","Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show()","def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral","plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20","x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range","real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0]","# #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # #","utf-8 -*- \"\"\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\"","numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0","of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0]","#plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i","for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x:","eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i","r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag","calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for","imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0]","return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01","xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum","in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda","np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:])","#N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) #","L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N)","n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def","2019 @author: Silvan \"\"\" import numpy as np import scipy import matplotlib.pyplot as","integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0]","s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N):","import numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0","real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i","\"\"\" import numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0","kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations","n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb):","1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50","real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda","k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2)","np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x:","imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection","plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) #","#plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x))","$ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() #","P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration","return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty')","np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M)","16:51:16 2019 @author: Silvan \"\"\" import numpy as np import scipy import matplotlib.pyplot","# #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:]))","as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha))","# -*- coding: utf-8 -*- \"\"\" Created on Fri Mar 15 16:51:16 2019","Mar 15 16:51:16 2019 @author: Silvan \"\"\" import numpy as np import scipy","##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2","e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show()","<reponame>silkoch42/Geometric-Optics-from-QM # -*- coding: utf-8 -*- \"\"\" Created on Fri Mar 15 16:51:16","i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k","# print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20)","#P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1)","-*- \"\"\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\" import","plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return","#plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) #","K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$')","x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$')","i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta","range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik","#plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$')","np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best')","plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): #","import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta)","range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x:","#dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) #","imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x:","# #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i])","Created on Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\" import numpy as","import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha)","s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of","np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda","x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200)","n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for","def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number","#plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta x$') ##plt.axis([0,10,0,1.1])","s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200)","#Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda","plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) #","\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100)","\"\"\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \"\"\" import numpy","probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N)","#plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\int_{-\\Delta x}^{\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\Delta","i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0]"],"string":"[\n \"#plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3)\",\n \"matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def\",\n \"x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda\",\n \"x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2\",\n \"as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x):\",\n \"Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x:\",\n \"scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0\",\n \"yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000\",\n \"coding: utf-8 -*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan\",\n \"k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20)\",\n \"# P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude')\",\n \"x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2\",\n \"##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3) #plt.plot(d,kernel(xa,xb)**2)\",\n \"return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions\",\n \"for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x:\",\n \"np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return\",\n \"s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100)\",\n \"ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def\",\n \"Silvan \\\"\\\"\\\" import numpy as np import scipy import matplotlib.pyplot as plt k=1000\",\n \"plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1)\",\n \"x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N):\",\n \"$R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N)\",\n \"# # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval\",\n \"def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True)\",\n \"plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k\",\n \"print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition\",\n \"xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R):\",\n \"#for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta\",\n \"plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for\",\n \"r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0]\",\n \"# #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $\",\n \"i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration\",\n \"Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2)\",\n \"x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2\",\n \"@author: Silvan \\\"\\\"\\\" import numpy as np import scipy import matplotlib.pyplot as plt\",\n \"beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0)\",\n \"alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return\",\n \"N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in\",\n \"x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3)\",\n \"k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019 @author:\",\n \"M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i\",\n \"np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0\",\n \"range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 #\",\n \"K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N)\",\n \"subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda\",\n \"on Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as np\",\n \"x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best')\",\n \"#plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3) #plt.plot(d,kernel(xa,xb)**2) #plt.show()\",\n \"#plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show()\",\n \"np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$')\",\n \"Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as np import\",\n \"in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x}\",\n \"15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as np import scipy import\",\n \"Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show()\",\n \"def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral\",\n \"plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20\",\n \"x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range\",\n \"real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0]\",\n \"# #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # #\",\n \"utf-8 -*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\"\",\n \"numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0\",\n \"of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0]\",\n \"#plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i\",\n \"for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x:\",\n \"eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i\",\n \"r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag\",\n \"calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for\",\n \"imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0]\",\n \"return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01\",\n \"xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum\",\n \"in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda\",\n \"np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:])\",\n \"#N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) #\",\n \"L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N)\",\n \"n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def\",\n \"2019 @author: Silvan \\\"\\\"\\\" import numpy as np import scipy import matplotlib.pyplot as\",\n \"integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0]\",\n \"s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N):\",\n \"import numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0\",\n \"real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i\",\n \"\\\"\\\"\\\" import numpy as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0\",\n \"kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations\",\n \"n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb):\",\n \"1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for integral calculations eps=0.01 N=50\",\n \"real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda\",\n \"k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2)\",\n \"np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x:\",\n \"imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection\",\n \"plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) #\",\n \"#plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x))\",\n \"$ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() #\",\n \"P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration\",\n \"return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty')\",\n \"np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M)\",\n \"16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as np import scipy import matplotlib.pyplot\",\n \"# #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:]))\",\n \"as np import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha))\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019\",\n \"Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as np import scipy\",\n \"##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2\",\n \"e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show()\",\n \"<reponame>silkoch42/Geometric-Optics-from-QM # -*- coding: utf-8 -*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16\",\n \"i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k\",\n \"# print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20)\",\n \"#P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1)\",\n \"-*- \\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import\",\n \"plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta) def s(x): return\",\n \"#plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) #\",\n \"K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$')\",\n \"x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x: np.sin(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] imag[i]=imag[i-1]+i1+i2 return np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$')\",\n \"i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta\",\n \"range(N): # print(i+1) # P[i]=trans_amp(dx[i]) # # #plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik\",\n \"#plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$')\",\n \"np.sqrt(real**2+imag**2),x,real,imag K2,x,r,i=K(3) M=np.mean(K2[25:]) plt.plot(x,K2/M,label=r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best')\",\n \"plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): #\",\n \"import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha) yb=-1.0 xb=-yb*np.tan(beta)\",\n \"range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] i2=scipy.integrate.quad(lambda x:\",\n \"#dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i]) #\",\n \"imag=np.empty(N) real[0]=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] imag[0]=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x:\",\n \"# #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1) # P[i]=trans_amp(dx[i])\",\n \"Created on Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy as\",\n \"import scipy import matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=-ya*np.tan(alpha)\",\n \"s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of\",\n \"np.sin(k*s(x)),-x[0],x[0],epsrel=eps,limit=L)[0] for i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda\",\n \"x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200)\",\n \"n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number of subdivisions for\",\n \"def s(x): return n1*np.sqrt((xa-x)**2+ya**2)+n2*np.sqrt((xb-x)**2+yb**2) def kernel(xa,xb): return 1.0/np.sqrt(xa**2+1)**(3/2.0)+1.0/np.sqrt(xa**2+1)**(3/2.0) def K(R): L=1000 #Maximum Number\",\n \"#plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta x$') ##plt.axis([0,10,0,1.1])\",\n \"s(x)}dx|^2$') #plt.errorbar(x,K2/M,0.1*K2/M) plt.xlabel(r'Integration range $R$') plt.ylabel('Detection probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200)\",\n \"#Maximum Number of subdivisions for integral calculations eps=0.01 N=50 x,dx=np.linspace(0.01,R,N,retstep=True) real=np.empty(N) imag=np.empty(N) real[0]=scipy.integrate.quad(lambda\",\n \"plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) #\",\n \"\\\\Delta x$') ##plt.axis([0,10,0,1.1]) #plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100)\",\n \"\\\"\\\"\\\" Created on Fri Mar 15 16:51:16 2019 @author: Silvan \\\"\\\"\\\" import numpy\",\n \"probabilty') plt.legend(loc='best') plt.text(2.4,0.2,r'$k=1000$') #plt.text(1.1,0.5,r'$|\\\\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) plt.savefig('refraction_v3',dpi=200) plt.show() #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N)\",\n \"#plt.figure(1) #plt.plot(dx,P/np.mean(P[20:])) #plt.text(4.0,0.5,r'$|\\\\int_{-\\\\Delta x}^{\\\\Delta x} e^{ik s(x)}dx$|',fontsize=20) #plt.ylabel('Transition Amplitude') #plt.xlabel(r'Integration Interval $ \\\\Delta\",\n \"i in range(1,N): r1=scipy.integrate.quad(lambda x: np.cos(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0] r2=scipy.integrate.quad(lambda x: np.cos(k*s(x)),x[i],x[i]+dx,epsrel=eps,limit=L)[0] real[i]=real[i-1]+r1+r2 i1=scipy.integrate.quad(lambda x: np.sin(k*s(x)),-x[i]-dx,-x[i],epsrel=eps,limit=L)[0]\"\n]","isConversational":false}}},{"rowIdx":4084,"cells":{"ngram":{"kind":"list like","value":["import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear","readthedocs elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import settings from django.core.management.base","elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle","Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle command.\"\"\" e_s = Elasticsearch(settings.ES_HOSTS)","BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args,","from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class","import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle command.\"\"\"","Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle command.\"\"\" e_s","__future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch","<reponame>italia/readthedocs.org<gh_stars>10-100 \"\"\"Remove the readthedocs elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import","class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle command.\"\"\" e_s =","elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import settings from django.core.management.base import","django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand):","import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self,","\"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options): \"\"\"handle command.\"\"\" e_s = Elasticsearch(settings.ES_HOSTS) e_s.indices.delete(index='_all')","django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def","from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\"","from __future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from","\"\"\"Remove the readthedocs elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import settings","absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch","from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch index.\"\"\" def handle(self, *args, **options):","settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear elasticsearch","import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import","the readthedocs elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import settings from","index.\"\"\" from __future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand"],"string":"[\n \"import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear\",\n \"readthedocs elasticsearch index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import settings from django.core.management.base\",\n \"elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle\",\n \"Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle command.\\\"\\\"\\\" e_s = Elasticsearch(settings.ES_HOSTS)\",\n \"BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args,\",\n \"from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class\",\n \"import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle command.\\\"\\\"\\\"\",\n \"Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle command.\\\"\\\"\\\" e_s\",\n \"__future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch\",\n \"<reponame>italia/readthedocs.org<gh_stars>10-100 \\\"\\\"\\\"Remove the readthedocs elasticsearch index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import\",\n \"class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle command.\\\"\\\"\\\" e_s =\",\n \"elasticsearch index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import settings from django.core.management.base import\",\n \"django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand):\",\n \"import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self,\",\n \"\\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options): \\\"\\\"\\\"handle command.\\\"\\\"\\\" e_s = Elasticsearch(settings.ES_HOSTS) e_s.indices.delete(index='_all')\",\n \"django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def\",\n \"from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\"\",\n \"from __future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from\",\n \"\\\"\\\"\\\"Remove the readthedocs elasticsearch index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import settings\",\n \"absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch\",\n \"from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch index.\\\"\\\"\\\" def handle(self, *args, **options):\",\n \"settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \\\"\\\"\\\"Clear elasticsearch\",\n \"import absolute_import from django.conf import settings from django.core.management.base import BaseCommand from elasticsearch import\",\n \"the readthedocs elasticsearch index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import settings from\",\n \"index.\\\"\\\"\\\" from __future__ import absolute_import from django.conf import settings from django.core.management.base import BaseCommand\"\n]","isConversational":false}}},{"rowIdx":4085,"cells":{"ngram":{"kind":"list like","value":["= 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the","# Update the config dict as necessary # This is for convenience, to","data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make sure","nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing","unofficial reimplementation of \"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\" by","reimplementation of \"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\" by <NAME>,","and copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if","utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE","into the config-dict (e.g. inferring the number of classes # and size of","weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if","config['num_epochs'] * len(loader) # Train for specified number of epochs, although we mostly","- state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}]","log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics])","to add settings derived from the user-specified # configuration into the config-dict (e.g.","/ (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}]","we pass in enough data to complete # a full D iteration (regardless","total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log =","state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) #","timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in","# Prepare a fixed z & y to see individual sample evolution throghout","nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA for G","(quang duong da di / thoi gian da di) eta = ( total_iters","loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] )","if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name","print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) #","(config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G =","train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not","(regardless of number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps']","_ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch","training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config =","IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default,","BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D)","{'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from a","label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and","print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init':","eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}] Ep {}, \".format( curr_time_str, eta_str,","for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim':","specified number of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters)","config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang","convenience, to add settings derived from the user-specified # configuration into the config-dict","device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see individual sample","None) # Prepare data; the Discriminator's batch size is all that needs to","mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True)","folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True","# Note that at every loader iteration we pass in enough data to","Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark","%+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save","G and D are in training mode, just in case they got set","D are in training mode, just in case they got set to eval","that at every loader iteration we pass in enough data to complete #","as necessary # This is for convenience, to add settings derived from the","data in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter","accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'],","for High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096).","# if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA,","*[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) # Prepare state","derived from the user-specified # configuration into the config-dict (e.g. inferring the number","epoch state_dict['epoch'] += 1 def main(): # parse command line and run parser","init if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] =","% state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader)","Prepare state dict, which holds things like epoch # and itr # state_dict","y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time =","loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number","parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ == '__main__': main()","Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code is an","track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer =","2 def run(config): # Update the config dict as necessary # This is","y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start()","= datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}] Ep {}, \".format(","duong da di / thoi gian da di) eta = ( total_iters -","interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval","config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema,","weights and copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']):","in training mode, just in case they got set to eval # For","import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def","def main(): # parse command line and run parser = utils.prepare_parser() config =","range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label'] #","Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark","True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' %","in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label']","training mode, just in case they got set to eval # For D,","is an unofficial reimplementation of \"Large-Scale GAN Training for High Fidelity Natural Image","# By default, skip init if resuming training. if config['resume']: print('Skipping initialization for","pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr","is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']:","training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed","G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of","G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see","fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train = train_fns.create_train_fn(","decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema =","the dataset, passing in a pytorch object # for the activation specified as","= 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init","mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False)","di / thoi gian da di) eta = ( total_iters - state_dict['itr']) //","resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG","* config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'],","\"\"\" import datetime import time import torch import dataset import BigGAN import train_fns","The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code is","iteration we pass in enough data to complete # a full D iteration","and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'],","epoch # and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0,","config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G","config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if not","'save_num': 0, 'config': config} # If loading from a pre-trained model, load weights","in enough data to complete # a full D iteration (regardless of number","utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']: print('Skipping initialization","# For D, which typically doesn't have BN, this shouldn't matter much. G.train()","state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}]","tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr']","experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name)","# Increment the iteration counter state_dict['itr'] += 1 # Make sure G and","nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None","print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()])","else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D =","number of classes # and size of the images from the dataset, passing","# IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config dict","not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval()","function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning","D, which typically doesn't have BN, this shouldn't matter much. G.train() D.train() if","necessary # This is for convenience, to add settings derived from the user-specified","metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str",": %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) #","z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']):","with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema","as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation']","((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} /","evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_()","<NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime import time","import torch import dataset import BigGAN import train_fns import utils from common import","Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import","activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] =","'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start'])","training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config)","by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime import","config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config,","gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1))","else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number","for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y =","config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch']","D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders =","iteration (regardless of number of D steps and accumulations) D_batch_size = (config['batch_size'] *","load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if","fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']:","ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD","run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ == '__main__':","= state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x,","utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if","Let's go. \"\"\" import datetime import time import torch import dataset import BigGAN","BigGAN import train_fns import utils from common import * # IMG_SIZE = 64","common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2","'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) #","(config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'],","of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for","= BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If","GAN Training for High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME>","doesn't require dataloading. # Note that at every loader iteration we pass in",") # Prepare noise and randomly sampled label arrays # Allow for different","skip init if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init']","in a pytorch object # for the activation specified as a string) config['resolution']","config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root","IMG_SIZE * 2 def run(config): # Update the config dict as necessary #","= loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number of","pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] += 1 def","If loading from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G,","x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1","= 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config)","state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch","state_dict['itr'] += 1 # Make sure G and D are in training mode,","for key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as","'config': config} # If loading from a pre-trained model, load weights if config['resume']:","for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images')","= tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr =","from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict,","mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer","iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start()","and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime import time import torch","Code by <NAME> and <NAME> This code is an unofficial reimplementation of \"Large-Scale","config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] #","release Code by <NAME> and <NAME> This code is an unofficial reimplementation of","holds things like epoch # and itr # state_dict = {'itr': 0, 'epoch':","& y to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y(","config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device","the training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict,","at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G","Increment the iteration counter state_dict['itr'] += 1 # Make sure G and D","itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} #","else None) # Prepare data; the Discriminator's batch size is all that needs","(state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G,","By default, skip init if resuming training. if config['resume']: print('Skipping initialization for training","= ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S')","This is for convenience, to add settings derived from the user-specified # configuration","utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare","+= ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log)","config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_,","if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...')","Prepare data; the Discriminator's batch size is all that needs to be passed","see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'],","for specified number of epochs, although we mostly track G iterations. pbar =","= \"[{}] [{}] [{} / {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters,","utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema']","timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di","% config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong /","= IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By","G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x,","arrays # Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size'])","experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to","= True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) #","= nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']:","(state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G,","# Save weights and copies as configured at specified interval if not (state_dict['itr']","if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config)","Unofficial PyTorch release Code by <NAME> and <NAME> This code is an unofficial","specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']]","y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at","for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples']","D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA","fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders","enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] +=","0, 'save_num': 0, 'config': config} # If loading from a pre-trained model, load","config dict as necessary # This is for convenience, to add settings derived","settings derived from the user-specified # configuration into the config-dict (e.g. inferring the","the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation']","name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if","G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update()","# quang duong / (quang duong da di / thoi gian da di)","# parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config)","the number of classes # and size of the images from the dataset,","a fixed z & y to see individual sample evolution throghout training fixed_z,","all that needs to be passed # to the dataloader, as G doesn't","the config dict as necessary # This is for convenience, to add settings","load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays # Allow","G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples,","a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'],","is all that needs to be passed # to the dataloader, as G","thoi gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) /","0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from a pre-trained","much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics =","params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net","metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies","save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval","D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' %","datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}] Ep {}, \".format( curr_time_str,","config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed'])","every loader iteration we pass in enough data to complete # a full","utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if","<NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime import time import","= (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G","sure G and D are in training mode, just in case they got","{} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]]))","go. \"\"\" import datetime import time import torch import dataset import BigGAN import","if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if","config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader =","G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D","prepare it if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay']))","quang duong / (quang duong da di / thoi gian da di) eta","<NAME> This code is an unofficial reimplementation of \"Large-Scale GAN Training for High","a pytorch object # for the activation specified as a string) config['resolution'] =","noise and randomly sampled label arrays # Allow for different batch sizes in","y to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples,","= timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da","line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__","and <NAME> This code is an unofficial reimplementation of \"Large-Scale GAN Training for","num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) #","of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema)","# Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G, D,","utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y","{}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema","mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays # Allow for","are loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD, z_,","sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16'])","# and size of the images from the dataset, passing in a pytorch","y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if","config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's","num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the","[{}] [{} / {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log","fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are","default, skip init if resuming training. if config['resume']: print('Skipping initialization for training resumption...')","to the dataloader, as G doesn't require dataloading. # Note that at every","= {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from","# Make sure G and D are in training mode, just in case","for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' #","z & y to see individual sample evolution throghout training fixed_z, fixed_y =","enough data to complete # a full D iteration (regardless of number of","/ thoi gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr)","[{} / {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log +=","start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader):","print(log) # Save weights and copies as configured at specified interval if not","= utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded,","# for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] =","a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] =","counter at end of epoch state_dict['epoch'] += 1 def main(): # parse command","config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True,","state_dict['epoch'] += 1 def main(): # parse command line and run parser =","batch size is all that needs to be passed # to the dataloader,","data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make sure G","utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G,","from the dataset, passing in a pytorch object # for the activation specified","model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights']","of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for _","dataset import BigGAN import train_fns import utils from common import * # IMG_SIZE","it if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema","full D iteration (regardless of number of D steps and accumulations) D_batch_size =","G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z,","not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval()","\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and","True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema","throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_()","cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else","= time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for","0, 'config': config} # If loading from a pre-trained model, load weights if","config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16'])","<NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime import time import torch import","High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's","the iteration counter state_dict['itr'] += 1 # Make sure G and D are","import BigGAN import train_fns import utils from common import * # IMG_SIZE =","( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log","# print(log) # Save weights and copies as configured at specified interval if","print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p","dict, which holds things like epoch # and itr # state_dict = {'itr':","G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device)","complete # a full D iteration (regardless of number of D steps and","eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp(","D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr']","config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming","copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']:","G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer()","fixed z & y to see individual sample evolution throghout training fixed_z, fixed_y","resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config","= BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D =","Save weights and copies as configured at specified interval if not (state_dict['itr'] %","G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] %","for i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment the","= True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s'","at end of epoch state_dict['epoch'] += 1 def main(): # parse command line","'.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log)","% config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D,","state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for","= None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in","config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a","and D are in training mode, just in case they got set to","D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) #","to be passed # to the dataloader, as G doesn't require dataloading. #","config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip","state dict, which holds things like epoch # and itr # state_dict =","different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_,","needs to be passed # to the dataloader, as G doesn't require dataloading.","object # for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes']","True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else:","ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params","train_fns import utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2","to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config,","config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D)","and size of the images from the dataset, passing in a pytorch object","%d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] *","% (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights","D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else","pytorch object # for the activation specified as a string) config['resolution'] = IMG_SIZE","fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train =","config} # If loading from a pre-trained model, load weights if config['resume']: print('Loading","key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured","+= 1 def main(): # parse command line and run parser = utils.prepare_parser()","start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train","D iteration (regardless of number of D steps and accumulations) D_batch_size = (config['batch_size']","torch import dataset import BigGAN import train_fns import utils from common import *","{}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f'","Increment epoch counter at end of epoch state_dict['epoch'] += 1 def main(): #","duong / (quang duong da di / thoi gian da di) eta =","size of the images from the dataset, passing in a pytorch object #","# Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup","# configuration into the config-dict (e.g. inferring the number of classes # and","command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if","{}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) # Prepare","# Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if","If using EMA, prepare it if config['ema']: print('Preparing EMA for G with decay","= utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']: print('Skipping","and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config}","= mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i,","utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free","state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y","shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device)","For D, which typically doesn't have BN, this shouldn't matter much. G.train() D.train()","total_iters = config['num_epochs'] * len(loader) # Train for specified number of epochs, although","the config-dict (e.g. inferring the number of classes # and size of the","size is all that needs to be passed # to the dataloader, as","the user-specified # configuration into the config-dict (e.g. inferring the number of classes","which typically doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']:","weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights']","batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly","config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment","pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays","i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration","train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch","{}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s","RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for","for p in net.parameters()]) for net in [G, D]])) # Prepare state dict,","for net in [G, D]])) # Prepare state dict, which holds things like","# print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z,","string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']]","import dataset import BigGAN import train_fns import utils from common import * #","/ {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ',","print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0]","config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D","G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch","datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi gian","\"[{}] [{}] [{} / {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch)","end of epoch state_dict['epoch'] += 1 def main(): # parse command line and","(arXiv 1809.11096). Let's go. \"\"\" import datetime import time import torch import dataset","data; the Discriminator's batch size is all that needs to be passed #","individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device,","at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters =","y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S')","'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device)","specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to","fp16=config['G_fp16']) # Prepare a fixed z & y to see individual sample evolution","for convenience, to add settings derived from the user-specified # configuration into the","D) print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for","config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) #","in net.parameters()]) for net in [G, D]])) # Prepare state dict, which holds","# Prepare noise and randomly sampled label arrays # Allow for different batch","state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None)","epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs']","config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']:","are in training mode, just in case they got set to eval #","datetime import time import torch import dataset import BigGAN import train_fns import utils","which holds things like epoch # and itr # state_dict = {'itr': 0,","curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di /","1 # Make sure G and D are in training mode, just in","* 2 def run(config): # Update the config dict as necessary # This","experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare","counter state_dict['itr'] += 1 # Make sure G and D are in training","if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema =","y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z","= BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema,","# Increment epoch counter at end of epoch state_dict['epoch'] += 1 def main():","if config['ema'] else None) # Prepare data; the Discriminator's batch size is all","len(loader) # Train for specified number of epochs, although we mostly track G","number of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for","just in case they got set to eval # For D, which typically","1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if","metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured at specified","training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters","dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare","have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y","Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name']","doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x,","num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled","matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics","None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch size","got set to eval # For D, which typically doesn't have BN, this","D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) #","time import torch import dataset import BigGAN import train_fns import utils from common","= utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD =","necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name =","for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for","if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...')","in [G, D]])) # Prepare state dict, which holds things like epoch #","G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch size is","* # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config):","print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema","# Prepare state dict, which holds things like epoch # and itr #","z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter","(curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}] Ep","EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True,","None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {}","typically doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train()","train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() #","D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y)","case they got set to eval # For D, which typically doesn't have","num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to","z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed","that needs to be passed # to the dataloader, as G doesn't require","iteration counter state_dict['itr'] += 1 # Make sure G and D are in","= datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi","config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data;","def run(config): # Update the config dict as necessary # This is for","# and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config':","= max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'],","y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']):","z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time","x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time =","config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G)","state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading","da di / thoi gian da di) eta = ( total_iters - state_dict['itr'])","config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema =","= nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA for","prepare the training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema,","D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size,","(e.g. inferring the number of classes # and size of the images from","# a full D iteration (regardless of number of D steps and accumulations)","True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare","else None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch","fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of","* config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'],","BigGAN: The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code","data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise","fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end","# If using EMA, prepare it if config['ema']: print('Preparing EMA for G with","dict as necessary # This is for convenience, to add settings derived from","Prepare a fixed z & y to see individual sample evolution throghout training","if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device)","= train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at","for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1","None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G:","import datetime import time import torch import dataset import BigGAN import train_fns import","data to complete # a full D iteration (regardless of number of D","% experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G =","Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\" import datetime","'epoch': 0, 'save_num': 0, 'config': config} # If loading from a pre-trained model,","a full D iteration (regardless of number of D steps and accumulations) D_batch_size","parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config)","be passed # to the dataloader, as G doesn't require dataloading. # Note","# to the dataloader, as G doesn't require dataloading. # Note that at","of classes # and size of the images from the dataset, passing in","log = \"[{}] [{}] [{} / {}] Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'],","eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name,","= BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D: {}'.format(","images from the dataset, passing in a pytorch object # for the activation","label arrays # Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'],","epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in","Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \"\"\"","config['ema'] else None) # Prepare data; the Discriminator's batch size is all that","root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark =","speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name","Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples =","the Discriminator's batch size is all that needs to be passed # to","Training for High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv","G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G,","GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch'])","64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config","Prepare noise and randomly sampled label arrays # Allow for different batch sizes","experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G)","This code is an unofficial reimplementation of \"Large-Scale GAN Training for High Fidelity","dataset, passing in a pytorch object # for the activation specified as a","add settings derived from the user-specified # configuration into the config-dict (e.g. inferring","timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for","// ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{}","G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if","as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: #","train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp(","# This is for convenience, to add settings derived from the user-specified #","epoch counter at end of epoch state_dict['epoch'] += 1 def main(): # parse","ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter()","y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 #","BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y =","1 def main(): # parse command line and run parser = utils.prepare_parser() config","G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training","= IMG_SIZE * 2 def run(config): # Update the config dict as necessary","loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number of epochs,","fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: #","number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations'])","inferring the number of classes # and size of the images from the","eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \"[{}] [{}] [{} / {}] Ep {},","curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi gian da","= data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make","/ (quang duong da di / thoi gian da di) eta = (","G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema =","shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label","steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders(","pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name,","= dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) #","of number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] *","config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out']","pass in enough data to complete # a full D iteration (regardless of","require dataloading. # Note that at every loader iteration we pass in enough","config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function","state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader","max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device,","if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_,","total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key","passed # to the dataloader, as G doesn't require dataloading. # Note that","code is an unofficial reimplementation of \"Large-Scale GAN Training for High Fidelity Natural","user-specified # configuration into the config-dict (e.g. inferring the number of classes #","utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name']","import utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2 =","[G, D]])) # Prepare state dict, which holds things like epoch # and","Train for specified number of epochs, although we mostly track G iterations. pbar","main(): # parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args())","an unofficial reimplementation of \"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\"","# If loading from a pre-trained model, load weights if config['resume']: print('Loading weights...')","+= 1 # Make sure G and D are in training mode, just","epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'],","to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z,","BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement()","dataloading. # Note that at every loader iteration we pass in enough data","# IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): #","time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified","in case they got set to eval # For D, which typically doesn't","loading from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D,","G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G,","IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config dict as","IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update","pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']):","save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] += 1","and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ ==","<NAME> and <NAME> This code is an unofficial reimplementation of \"Large-Scale GAN Training","range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'],","from the user-specified # configuration into the config-dict (e.g. inferring the number of","= config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare","if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True","Note that at every loader iteration we pass in enough data to complete","Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go.","of the images from the dataset, passing in a pytorch object # for","config-dict (e.g. inferring the number of classes # and size of the images","= utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders","training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() #","eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key])","# Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples","'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema,","Make sure G and D are in training mode, just in case they","import time import torch import dataset import BigGAN import train_fns import utils from","the dataloader, as G doesn't require dataloading. # Note that at every loader","G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr']","* len(loader) # Train for specified number of epochs, although we mostly track","curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong","of epoch state_dict['epoch'] += 1 def main(): # parse command line and run","is for convenience, to add settings derived from the user-specified # configuration into","G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...'","D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment","= train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str =","EMA, prepare it if config['ema']: print('Preparing EMA for G with decay of {}'.format(","print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in","= utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z &","BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'],","train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training","print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y,","# Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed","config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema,","G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict,","although we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']):","if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else","Ep {}, \".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s :","of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders","import train_fns import utils from common import * # IMG_SIZE = 64 #","= (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'],","we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update()","config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it","Discriminator's batch size is all that needs to be passed # to the","if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') #","to complete # a full D iteration (regardless of number of D steps","PyTorch release Code by <NAME> and <NAME> This code is an unofficial reimplementation","of \"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\" by <NAME>, <NAME>,","= x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time","# Train for specified number of epochs, although we mostly track G iterations.","not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang","epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in","(key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights and","experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] +=","%s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G","config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see individual","device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train","classes # and size of the images from the dataset, passing in a","drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays #","dataloader, as G doesn't require dataloading. # Note that at every loader iteration","curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key,","like epoch # and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num':","sampled label arrays # Allow for different batch sizes in G G_batch_size =","', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) #","in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured at","mode, just in case they got set to eval # For D, which","in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y(","sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ =","run(config): # Update the config dict as necessary # This is for convenience,","BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using","and randomly sampled label arrays # Allow for different batch sizes in G","Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD,","p in net.parameters()]) for net in [G, D]])) # Prepare state dict, which","net.parameters()]) for net in [G, D]])) # Prepare state dict, which holds things","pbar.set_description(log) # print(log) # Save weights and copies as configured at specified interval","randomly sampled label arrays # Allow for different batch sizes in G G_batch_size","they got set to eval # For D, which typically doesn't have BN,","x, y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] %","free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment","(state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong","by <NAME> and <NAME> This code is an unofficial reimplementation of \"Large-Scale GAN","initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda'","loader iteration we pass in enough data to complete # a full D","% config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D,","1809.11096). Let's go. \"\"\" import datetime import time import torch import dataset import","fixed_y.sample_() # Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G,","things like epoch # and itr # state_dict = {'itr': 0, 'epoch': 0,","configuration into the config-dict (e.g. inferring the number of classes # and size","D]])) # Prepare state dict, which holds things like epoch # and itr","\"\"\" BigGAN: The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This","print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device =","G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D)","in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in","as G doesn't require dataloading. # Note that at every loader iteration we","the images from the dataset, passing in a pytorch object # for the","if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data; the","da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str","= utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training.","in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr']","di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str =","= config['num_epochs'] * len(loader) # Train for specified number of epochs, although we","device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary","= nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None,","in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in","eval # For D, which typically doesn't have BN, this shouldn't matter much.","state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching","from common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE *","using EMA, prepare it if config['ema']: print('Preparing EMA for G with decay of","G doesn't require dataloading. # Note that at every loader iteration we pass","mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data","config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None,","# state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If","GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D:","at every loader iteration we pass in enough data to complete # a","if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare","batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_","Update the config dict as necessary # This is for convenience, to add","to eval # For D, which typically doesn't have BN, this shouldn't matter","configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching","torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is","G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if","\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' %","passing in a pytorch object # for the activation specified as a string)","this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device),","# Prepare data; the Discriminator's batch size is all that needs to be","net in [G, D]])) # Prepare state dict, which holds things like epoch","loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD, z_, y_,","set to eval # For D, which typically doesn't have BN, this shouldn't"],"string":"[\n \"= 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the\",\n \"# Update the config dict as necessary # This is for convenience, to\",\n \"data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make sure\",\n \"nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing\",\n \"unofficial reimplementation of \\\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\\\" by\",\n \"reimplementation of \\\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\\\" by <NAME>,\",\n \"and copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if\",\n \"utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE\",\n \"into the config-dict (e.g. inferring the number of classes # and size of\",\n \"weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if\",\n \"config['num_epochs'] * len(loader) # Train for specified number of epochs, although we mostly\",\n \"- state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}]\",\n \"log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics])\",\n \"to add settings derived from the user-specified # configuration into the config-dict (e.g.\",\n \"/ (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}]\",\n \"we pass in enough data to complete # a full D iteration (regardless\",\n \"total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log =\",\n \"state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) #\",\n \"timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in\",\n \"# Prepare a fixed z & y to see individual sample evolution throghout\",\n \"nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA for G\",\n \"(quang duong da di / thoi gian da di) eta = ( total_iters\",\n \"loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] )\",\n \"if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name\",\n \"print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) #\",\n \"(config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G =\",\n \"train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not\",\n \"(regardless of number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps']\",\n \"_ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch\",\n \"training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config =\",\n \"IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default,\",\n \"BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D)\",\n \"{'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from a\",\n \"label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and\",\n \"print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init':\",\n \"eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}] Ep {}, \\\".format( curr_time_str, eta_str,\",\n \"for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim':\",\n \"specified number of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters)\",\n \"config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang\",\n \"convenience, to add settings derived from the user-specified # configuration into the config-dict\",\n \"device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see individual sample\",\n \"None) # Prepare data; the Discriminator's batch size is all that needs to\",\n \"mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True)\",\n \"folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True\",\n \"# Note that at every loader iteration we pass in enough data to\",\n \"Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark\",\n \"%+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save\",\n \"G and D are in training mode, just in case they got set\",\n \"D are in training mode, just in case they got set to eval\",\n \"that at every loader iteration we pass in enough data to complete #\",\n \"as necessary # This is for convenience, to add settings derived from the\",\n \"data in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter\",\n \"accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'],\",\n \"for High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096).\",\n \"# if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA,\",\n \"*[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) # Prepare state\",\n \"derived from the user-specified # configuration into the config-dict (e.g. inferring the number\",\n \"epoch state_dict['epoch'] += 1 def main(): # parse command line and run parser\",\n \"init if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] =\",\n \"% state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader)\",\n \"Prepare state dict, which holds things like epoch # and itr # state_dict\",\n \"y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time =\",\n \"loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number\",\n \"parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ == '__main__': main()\",\n \"Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code is an\",\n \"track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer =\",\n \"2 def run(config): # Update the config dict as necessary # This is\",\n \"y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start()\",\n \"= datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}] Ep {}, \\\".format(\",\n \"duong da di / thoi gian da di) eta = ( total_iters -\",\n \"interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval\",\n \"config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema,\",\n \"weights and copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']):\",\n \"in training mode, just in case they got set to eval # For\",\n \"import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def\",\n \"def main(): # parse command line and run parser = utils.prepare_parser() config =\",\n \"range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label'] #\",\n \"Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark\",\n \"True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' %\",\n \"in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label']\",\n \"training mode, just in case they got set to eval # For D,\",\n \"is an unofficial reimplementation of \\\"Large-Scale GAN Training for High Fidelity Natural Image\",\n \"# By default, skip init if resuming training. if config['resume']: print('Skipping initialization for\",\n \"pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr\",\n \"is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']:\",\n \"training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed\",\n \"G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of\",\n \"G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see\",\n \"fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train = train_fns.create_train_fn(\",\n \"decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema =\",\n \"the dataset, passing in a pytorch object # for the activation specified as\",\n \"= 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init\",\n \"mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False)\",\n \"di / thoi gian da di) eta = ( total_iters - state_dict['itr']) //\",\n \"resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG\",\n \"* config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'],\",\n \"\\\"\\\"\\\" import datetime import time import torch import dataset import BigGAN import train_fns\",\n \"The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code is\",\n \"iteration we pass in enough data to complete # a full D iteration\",\n \"and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'],\",\n \"epoch # and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0,\",\n \"config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G\",\n \"config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if not\",\n \"'save_num': 0, 'config': config} # If loading from a pre-trained model, load weights\",\n \"in enough data to complete # a full D iteration (regardless of number\",\n \"utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']: print('Skipping initialization\",\n \"# For D, which typically doesn't have BN, this shouldn't matter much. G.train()\",\n \"state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}]\",\n \"tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr']\",\n \"experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name)\",\n \"# Increment the iteration counter state_dict['itr'] += 1 # Make sure G and\",\n \"nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None\",\n \"print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()])\",\n \"else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D =\",\n \"number of classes # and size of the images from the dataset, passing\",\n \"# IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config dict\",\n \"not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval()\",\n \"function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning\",\n \"D, which typically doesn't have BN, this shouldn't matter much. G.train() D.train() if\",\n \"necessary # This is for convenience, to add settings derived from the user-specified\",\n \"metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str\",\n \": %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) #\",\n \"z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']):\",\n \"with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema\",\n \"as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation']\",\n \"((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} /\",\n \"evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_()\",\n \"<NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import time\",\n \"import torch import dataset import BigGAN import train_fns import utils from common import\",\n \"Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import\",\n \"activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] =\",\n \"'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start'])\",\n \"training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config)\",\n \"by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import\",\n \"config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config,\",\n \"gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1))\",\n \"else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number\",\n \"for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y =\",\n \"config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch']\",\n \"D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders =\",\n \"iteration (regardless of number of D steps and accumulations) D_batch_size = (config['batch_size'] *\",\n \"load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if\",\n \"fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']:\",\n \"ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD\",\n \"run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ == '__main__':\",\n \"= state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x,\",\n \"utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if\",\n \"Let's go. \\\"\\\"\\\" import datetime import time import torch import dataset import BigGAN\",\n \"BigGAN import train_fns import utils from common import * # IMG_SIZE = 64\",\n \"common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2\",\n \"'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) #\",\n \"(config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'],\",\n \"of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for\",\n \"= BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If\",\n \"GAN Training for High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME>\",\n \"doesn't require dataloading. # Note that at every loader iteration we pass in\",\n \") # Prepare noise and randomly sampled label arrays # Allow for different\",\n \"skip init if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init']\",\n \"in a pytorch object # for the activation specified as a string) config['resolution']\",\n \"config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root\",\n \"IMG_SIZE * 2 def run(config): # Update the config dict as necessary #\",\n \"= loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number of\",\n \"pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] += 1 def\",\n \"If loading from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G,\",\n \"x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1\",\n \"= 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config)\",\n \"state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch\",\n \"state_dict['itr'] += 1 # Make sure G and D are in training mode,\",\n \"for key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as\",\n \"'config': config} # If loading from a pre-trained model, load weights if config['resume']:\",\n \"for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images')\",\n \"= tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr =\",\n \"from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict,\",\n \"mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer\",\n \"iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start()\",\n \"and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import time import torch\",\n \"Code by <NAME> and <NAME> This code is an unofficial reimplementation of \\\"Large-Scale\",\n \"config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] #\",\n \"release Code by <NAME> and <NAME> This code is an unofficial reimplementation of\",\n \"holds things like epoch # and itr # state_dict = {'itr': 0, 'epoch':\",\n \"& y to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y(\",\n \"config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device\",\n \"the training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict,\",\n \"at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G\",\n \"Increment the iteration counter state_dict['itr'] += 1 # Make sure G and D\",\n \"itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} #\",\n \"else None) # Prepare data; the Discriminator's batch size is all that needs\",\n \"(state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G,\",\n \"By default, skip init if resuming training. if config['resume']: print('Skipping initialization for training\",\n \"= ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S')\",\n \"This is for convenience, to add settings derived from the user-specified # configuration\",\n \"utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare\",\n \"+= ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log)\",\n \"config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_,\",\n \"if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...')\",\n \"Prepare data; the Discriminator's batch size is all that needs to be passed\",\n \"see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'],\",\n \"for specified number of epochs, although we mostly track G iterations. pbar =\",\n \"= \\\"[{}] [{}] [{} / {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters,\",\n \"utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema']\",\n \"timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di\",\n \"% config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong /\",\n \"= IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By\",\n \"G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x,\",\n \"arrays # Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size'])\",\n \"experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to\",\n \"= True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) #\",\n \"= nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']:\",\n \"(state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G,\",\n \"# Save weights and copies as configured at specified interval if not (state_dict['itr']\",\n \"if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config)\",\n \"Unofficial PyTorch release Code by <NAME> and <NAME> This code is an unofficial\",\n \"specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']]\",\n \"y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at\",\n \"for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples']\",\n \"D = nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA\",\n \"fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders\",\n \"enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] +=\",\n \"0, 'save_num': 0, 'config': config} # If loading from a pre-trained model, load\",\n \"config dict as necessary # This is for convenience, to add settings derived\",\n \"settings derived from the user-specified # configuration into the config-dict (e.g. inferring the\",\n \"the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation']\",\n \"name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if\",\n \"G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update()\",\n \"# quang duong / (quang duong da di / thoi gian da di)\",\n \"# parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config)\",\n \"the number of classes # and size of the images from the dataset,\",\n \"a fixed z & y to see individual sample evolution throghout training fixed_z,\",\n \"all that needs to be passed # to the dataloader, as G doesn't\",\n \"the config dict as necessary # This is for convenience, to add settings\",\n \"load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays # Allow\",\n \"G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples,\",\n \"a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'],\",\n \"is all that needs to be passed # to the dataloader, as G\",\n \"thoi gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) /\",\n \"0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from a pre-trained\",\n \"much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics =\",\n \"params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net\",\n \"metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies\",\n \"save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval\",\n \"D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' %\",\n \"datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}] Ep {}, \\\".format( curr_time_str,\",\n \"config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed'])\",\n \"every loader iteration we pass in enough data to complete # a full\",\n \"utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if\",\n \"<NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import time import\",\n \"= (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G\",\n \"sure G and D are in training mode, just in case they got\",\n \"{} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]]))\",\n \"go. \\\"\\\"\\\" import datetime import time import torch import dataset import BigGAN import\",\n \"if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if\",\n \"config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader =\",\n \"G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D\",\n \"prepare it if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay']))\",\n \"quang duong / (quang duong da di / thoi gian da di) eta\",\n \"<NAME> This code is an unofficial reimplementation of \\\"Large-Scale GAN Training for High\",\n \"a pytorch object # for the activation specified as a string) config['resolution'] =\",\n \"noise and randomly sampled label arrays # Allow for different batch sizes in\",\n \"y to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples,\",\n \"= timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da\",\n \"line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__\",\n \"and <NAME> This code is an unofficial reimplementation of \\\"Large-Scale GAN Training for\",\n \"num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) #\",\n \"of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema)\",\n \"# Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G, D,\",\n \"utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y\",\n \"{}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema\",\n \"mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays # Allow for\",\n \"are loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD, z_,\",\n \"sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16'])\",\n \"# and size of the images from the dataset, passing in a pytorch\",\n \"y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if\",\n \"config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's\",\n \"num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the\",\n \"[{}] [{} / {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log\",\n \"fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are\",\n \"default, skip init if resuming training. if config['resume']: print('Skipping initialization for training resumption...')\",\n \"to the dataloader, as G doesn't require dataloading. # Note that at every\",\n \"= {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading from\",\n \"# Make sure G and D are in training mode, just in case\",\n \"for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda' #\",\n \"z & y to see individual sample evolution throghout training fixed_z, fixed_y =\",\n \"enough data to complete # a full D iteration (regardless of number of\",\n \"/ thoi gian da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr)\",\n \"[{} / {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log +=\",\n \"start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader):\",\n \"print(log) # Save weights and copies as configured at specified interval if not\",\n \"= utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded,\",\n \"# for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] =\",\n \"a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] =\",\n \"counter at end of epoch state_dict['epoch'] += 1 def main(): # parse command\",\n \"config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True,\",\n \"state_dict['epoch'] += 1 def main(): # parse command line and run parser =\",\n \"batch size is all that needs to be passed # to the dataloader,\",\n \"data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make sure G\",\n \"utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G,\",\n \"from the dataset, passing in a pytorch object # for the activation specified\",\n \"model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights']\",\n \"of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for _\",\n \"dataset import BigGAN import train_fns import utils from common import * # IMG_SIZE\",\n \"it if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema\",\n \"full D iteration (regardless of number of D steps and accumulations) D_batch_size =\",\n \"G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z,\",\n \"not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval()\",\n \"\\\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and\",\n \"True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema\",\n \"throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_()\",\n \"cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else\",\n \"= time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for\",\n \"0, 'config': config} # If loading from a pre-trained model, load weights if\",\n \"config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16'])\",\n \"<NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import time import torch import\",\n \"High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's\",\n \"the iteration counter state_dict['itr'] += 1 # Make sure G and D are\",\n \"import BigGAN import train_fns import utils from common import * # IMG_SIZE =\",\n \"( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log\",\n \"# print(log) # Save weights and copies as configured at specified interval if\",\n \"print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p\",\n \"dict, which holds things like epoch # and itr # state_dict = {'itr':\",\n \"G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device)\",\n \"complete # a full D iteration (regardless of number of D steps and\",\n \"eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp(\",\n \"D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr']\",\n \"config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming\",\n \"copies as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']:\",\n \"G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer()\",\n \"fixed z & y to see individual sample evolution throghout training fixed_z, fixed_y\",\n \"resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True config\",\n \"= BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D =\",\n \"Save weights and copies as configured at specified interval if not (state_dict['itr'] %\",\n \"G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] %\",\n \"for i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment the\",\n \"= True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s'\",\n \"at end of epoch state_dict['epoch'] += 1 def main(): # parse command line\",\n \"'.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log)\",\n \"% config['save_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D,\",\n \"state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for\",\n \"= None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in\",\n \"config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a\",\n \"and D are in training mode, just in case they got set to\",\n \"D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) #\",\n \"to be passed # to the dataloader, as G doesn't require dataloading. #\",\n \"config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip\",\n \"state dict, which holds things like epoch # and itr # state_dict =\",\n \"different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_,\",\n \"needs to be passed # to the dataloader, as G doesn't require dataloading.\",\n \"object # for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes']\",\n \"True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else:\",\n \"ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params\",\n \"train_fns import utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2\",\n \"to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config,\",\n \"config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G) print(D)\",\n \"and size of the images from the dataset, passing in a pytorch object\",\n \"%d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] *\",\n \"% (key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights\",\n \"D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else\",\n \"pytorch object # for the activation specified as a string) config['resolution'] = IMG_SIZE\",\n \"fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train =\",\n \"config} # If loading from a pre-trained model, load weights if config['resume']: print('Loading\",\n \"key in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured\",\n \"+= 1 def main(): # parse command line and run parser = utils.prepare_parser()\",\n \"start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train\",\n \"D iteration (regardless of number of D steps and accumulations) D_batch_size = (config['batch_size']\",\n \"torch import dataset import BigGAN import train_fns import utils from common import *\",\n \"{}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f'\",\n \"Increment epoch counter at end of epoch state_dict['epoch'] += 1 def main(): #\",\n \"duong / (quang duong da di / thoi gian da di) eta =\",\n \"size of the images from the dataset, passing in a pytorch object #\",\n \"# Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup\",\n \"# configuration into the config-dict (e.g. inferring the number of classes # and\",\n \"command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if\",\n \"{}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) # Prepare\",\n \"# Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if\",\n \"If using EMA, prepare it if config['ema']: print('Preparing EMA for G with decay\",\n \"= utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']: print('Skipping\",\n \"and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config}\",\n \"= mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i,\",\n \"utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free\",\n \"state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y\",\n \"shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device)\",\n \"For D, which typically doesn't have BN, this shouldn't matter much. G.train() D.train()\",\n \"total_iters = config['num_epochs'] * len(loader) # Train for specified number of epochs, although\",\n \"the config-dict (e.g. inferring the number of classes # and size of the\",\n \"size is all that needs to be passed # to the dataloader, as\",\n \"the user-specified # configuration into the config-dict (e.g. inferring the number of classes\",\n \"which typically doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']:\",\n \"weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights']\",\n \"batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly\",\n \"config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment\",\n \"pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays\",\n \"i, data in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration\",\n \"train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch\",\n \"{}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s\",\n \"RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for\",\n \"for p in net.parameters()]) for net in [G, D]])) # Prepare state dict,\",\n \"for net in [G, D]])) # Prepare state dict, which holds things like\",\n \"# print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z,\",\n \"string) config['resolution'] = IMG_SIZE config['n_classes'] = 1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']]\",\n \"import dataset import BigGAN import train_fns import utils from common import * #\",\n \"/ {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ',\",\n \"print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0]\",\n \"config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D\",\n \"G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch\",\n \"datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi gian\",\n \"\\\"[{}] [{}] [{} / {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch)\",\n \"end of epoch state_dict['epoch'] += 1 def main(): # parse command line and\",\n \"(arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime import time import torch import dataset\",\n \"data; the Discriminator's batch size is all that needs to be passed #\",\n \"individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device,\",\n \"at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters =\",\n \"y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S')\",\n \"'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device)\",\n \"specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to\",\n \"fp16=config['G_fp16']) # Prepare a fixed z & y to see individual sample evolution\",\n \"for convenience, to add settings derived from the user-specified # configuration into the\",\n \"D) print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for\",\n \"config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) #\",\n \"in net.parameters()]) for net in [G, D]])) # Prepare state dict, which holds\",\n \"# Prepare noise and randomly sampled label arrays # Allow for different batch\",\n \"state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None)\",\n \"epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters = config['num_epochs']\",\n \"config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training. if config['resume']:\",\n \"are in training mode, just in case they got set to eval #\",\n \"datetime import time import torch import dataset import BigGAN import train_fns import utils\",\n \"which holds things like epoch # and itr # state_dict = {'itr': 0,\",\n \"curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di /\",\n \"1 # Make sure G and D are in training mode, just in\",\n \"* 2 def run(config): # Update the config dict as necessary # This\",\n \"experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare\",\n \"counter state_dict['itr'] += 1 # Make sure G and D are in training\",\n \"if config['ema']: print('Preparing EMA for G with decay of {}'.format( config['ema_decay'])) G_ema =\",\n \"y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z\",\n \"= BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema,\",\n \"# Increment epoch counter at end of epoch state_dict['epoch'] += 1 def main():\",\n \"if config['ema'] else None) # Prepare data; the Discriminator's batch size is all\",\n \"len(loader) # Train for specified number of epochs, although we mostly track G\",\n \"number of epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for\",\n \"just in case they got set to eval # For D, which typically\",\n \"1 config['G_activation'] = utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if\",\n \"metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured at specified\",\n \"training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader = loaders[0] total_iters\",\n \"dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare\",\n \"have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y\",\n \"Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name']\",\n \"doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x,\",\n \"num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled\",\n \"matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics\",\n \"None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch size\",\n \"got set to eval # For D, which typically doesn't have BN, this\",\n \"D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]])) #\",\n \"time import torch import dataset import BigGAN import train_fns import utils from common\",\n \"= utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD =\",\n \"necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name =\",\n \"for _ in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for\",\n \"if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...')\",\n \"in [G, D]])) # Prepare state dict, which holds things like epoch #\",\n \"G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch size is\",\n \"* # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config):\",\n \"print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None, G_ema\",\n \"# Prepare state dict, which holds things like epoch # and itr #\",\n \"z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter\",\n \"(curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}] Ep\",\n \"EMA for G with decay of {}'.format( config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True,\",\n \"None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {}\",\n \"typically doesn't have BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train()\",\n \"train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() #\",\n \"D.train() if config['ema']: G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y)\",\n \"case they got set to eval # For D, which typically doesn't have\",\n \"num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to\",\n \"z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed\",\n \"that needs to be passed # to the dataloader, as G doesn't require\",\n \"iteration counter state_dict['itr'] += 1 # Make sure G and D are in\",\n \"= datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi\",\n \"config['load_weights'] if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data;\",\n \"def run(config): # Update the config dict as necessary # This is for\",\n \"# and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config':\",\n \"= max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'],\",\n \"y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']):\",\n \"z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time\",\n \"x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time =\",\n \"config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D) print(G)\",\n \"state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If loading\",\n \"da di / thoi gian da di) eta = ( total_iters - state_dict['itr'])\",\n \"config['ema_decay'])) G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema =\",\n \"= nn.DataParallel(D) # If using EMA, prepare it if config['ema']: print('Preparing EMA for\",\n \"prepare the training function train = train_fns.create_train_fn( G, D, GD, z_, y_, ema,\",\n \"D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size,\",\n \"(e.g. inferring the number of classes # and size of the images from\",\n \"# a full D iteration (regardless of number of D steps and accumulations)\",\n \"True config = utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare\",\n \"else None, G_ema if config['ema'] else None) # Prepare data; the Discriminator's batch\",\n \"fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of\",\n \"* config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'],\",\n \"BigGAN: The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code\",\n \"data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise\",\n \"fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end\",\n \"# If using EMA, prepare it if config['ema']: print('Preparing EMA for G with\",\n \"dict as necessary # This is for convenience, to add settings derived from\",\n \"Prepare a fixed z & y to see individual sample evolution throghout training\",\n \"if config['experiment_name'] else 'generative_dog_images') print('Experiment name is %s' % experiment_name) G = BigGAN.Generator(**config).to(device)\",\n \"= train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at\",\n \"for the activation specified as a string) config['resolution'] = IMG_SIZE config['n_classes'] = 1\",\n \"None, None GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G:\",\n \"import datetime import time import torch import dataset import BigGAN import train_fns import\",\n \"data to complete # a full D iteration (regardless of number of D\",\n \"% experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G =\",\n \"Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\" import datetime\",\n \"'epoch': 0, 'save_num': 0, 'config': config} # If loading from a pre-trained model,\",\n \"a full D iteration (regardless of number of D steps and accumulations) D_batch_size\",\n \"parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config)\",\n \"be passed # to the dataloader, as G doesn't require dataloading. # Note\",\n \"# to the dataloader, as G doesn't require dataloading. # Note that at\",\n \"of classes # and size of the images from the dataset, passing in\",\n \"log = \\\"[{}] [{}] [{} / {}] Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'],\",\n \"eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name,\",\n \"= BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D: {}'.format(\",\n \"images from the dataset, passing in a pytorch object # for the activation\",\n \"label arrays # Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'],\",\n \"epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in\",\n \"Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. \\\"\\\"\\\"\",\n \"config['ema'] else None) # Prepare data; the Discriminator's batch size is all that\",\n \"root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark =\",\n \"speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name\",\n \"Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples =\",\n \"the Discriminator's batch size is all that needs to be passed # to\",\n \"Training for High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv\",\n \"G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in [G,\",\n \"GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch'])\",\n \"64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config\",\n \"Prepare noise and randomly sampled label arrays # Allow for different batch sizes\",\n \"experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G)\",\n \"This code is an unofficial reimplementation of \\\"Large-Scale GAN Training for High Fidelity\",\n \"dataset, passing in a pytorch object # for the activation specified as a\",\n \"add settings derived from the user-specified # configuration into the config-dict (e.g. inferring\",\n \"timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for\",\n \"// ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{}\",\n \"G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if\",\n \"as configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: #\",\n \"train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp(\",\n \"# This is for convenience, to add settings derived from the user-specified #\",\n \"epoch counter at end of epoch state_dict['epoch'] += 1 def main(): # parse\",\n \"ema, state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter()\",\n \"y = data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 #\",\n \"BN, this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y =\",\n \"1 def main(): # parse command line and run parser = utils.prepare_parser() config\",\n \"G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training\",\n \"= IMG_SIZE * 2 def run(config): # Update the config dict as necessary\",\n \"loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified number of epochs,\",\n \"fixed_y, state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: #\",\n \"number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations'])\",\n \"inferring the number of classes # and size of the images from the\",\n \"eta_str = datetime.datetime.fromtimestamp( eta).strftime('%H:%M:%S') log = \\\"[{}] [{}] [{} / {}] Ep {},\",\n \"curr_time).strftime('%H:%M:%S') # quang duong / (quang duong da di / thoi gian da\",\n \"= data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make\",\n \"/ (quang duong da di / thoi gian da di) eta = (\",\n \"G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema =\",\n \"shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label\",\n \"steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders(\",\n \"pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name,\",\n \"= dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) #\",\n \"of number of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] *\",\n \"config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'], shuffle=config['shuffle'], pin_memory=config['pin_memory'], drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out']\",\n \"pass in enough data to complete # a full D iteration (regardless of\",\n \"require dataloading. # Note that at every loader iteration we pass in enough\",\n \"config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function\",\n \"state_dict, config) print('Beginning training at epoch %d...' % state_dict['epoch']) start_time = time.perf_counter() loader\",\n \"max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device,\",\n \"if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_,\",\n \"total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key]) for key\",\n \"passed # to the dataloader, as G doesn't require dataloading. # Note that\",\n \"code is an unofficial reimplementation of \\\"Large-Scale GAN Training for High Fidelity Natural\",\n \"user-specified # configuration into the config-dict (e.g. inferring the number of classes #\",\n \"utils.prepare_root(config) # Setup cudnn.benchmark for free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name']\",\n \"import utils from common import * # IMG_SIZE = 64 # IMG_SIZE_2 =\",\n \"[G, D]])) # Prepare state dict, which holds things like epoch # and\",\n \"Train for specified number of epochs, although we mostly track G iterations. pbar\",\n \"main(): # parse command line and run parser = utils.prepare_parser() config = vars(parser.parse_args())\",\n \"an unofficial reimplementation of \\\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\\\"\",\n \"# If loading from a pre-trained model, load weights if config['resume']: print('Loading weights...')\",\n \"+= 1 # Make sure G and D are in training mode, just\",\n \"epoch in range(state_dict['epoch'], config['num_epochs']): for i, data in enumerate(loader): x, y = data['img'],\",\n \"to see individual sample evolution throghout training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z,\",\n \"BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement()\",\n \"dataloading. # Note that at every loader iteration we pass in enough data\",\n \"# IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): #\",\n \"time.perf_counter() loader = loaders[0] total_iters = config['num_epochs'] * len(loader) # Train for specified\",\n \"in case they got set to eval # For D, which typically doesn't\",\n \"loading from a pre-trained model, load weights if config['resume']: print('Loading weights...') utils.load_weights(G, D,\",\n \"G_ema = BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G,\",\n \"IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the config dict as\",\n \"IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update\",\n \"pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']):\",\n \"save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] += 1\",\n \"and run parser = utils.prepare_parser() config = vars(parser.parse_args()) print(config) run(config) if __name__ ==\",\n \"<NAME> and <NAME> This code is an unofficial reimplementation of \\\"Large-Scale GAN Training\",\n \"range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'],\",\n \"from the user-specified # configuration into the config-dict (e.g. inferring the number of\",\n \"= config['num_fixed_samples'] z_, y_ = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare\",\n \"if resuming training. if config['resume']: print('Skipping initialization for training resumption...') config['skip_init'] = True\",\n \"Note that at every loader iteration we pass in enough data to complete\",\n \"Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go.\",\n \"of the images from the dataset, passing in a pytorch object # for\",\n \"config-dict (e.g. inferring the number of classes # and size of the images\",\n \"= utils.update_config_roots(config) device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders\",\n \"training fixed_z, fixed_y = utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() #\",\n \"eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key, metrics[key])\",\n \"# Allow for different batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples\",\n \"'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema,\",\n \"Make sure G and D are in training mode, just in case they\",\n \"import time import torch import dataset import BigGAN import train_fns import utils from\",\n \"the dataloader, as G doesn't require dataloading. # Note that at every loader\",\n \"G_ema.train() x, y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr']\",\n \"* len(loader) # Train for specified number of epochs, although we mostly track\",\n \"curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong / (quang duong\",\n \"of epoch state_dict['epoch'] += 1 def main(): # parse command line and run\",\n \"is for convenience, to add settings derived from the user-specified # configuration into\",\n \"G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training at epoch %d...'\",\n \"D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=True) pbar.update() # Increment\",\n \"= train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str =\",\n \"EMA, prepare it if config['ema']: print('Preparing EMA for G with decay of {}'.format(\",\n \"print(D) print('Number of params in G: {} D: {}'.format( *[sum([p.data.nelement() for p in\",\n \"= utils.prepare_z_y( num_samples, G.module.dim_z, config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z &\",\n \"BigGAN.Generator(**{**config, 'skip_init': True, 'no_optim': True}).to(device) G_ema = nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'],\",\n \"train = train_fns.create_train_fn( G, D, GD, z_, y_, ema, state_dict, config) print('Beginning training\",\n \"print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y,\",\n \"# Prepare root folders if necessary utils.prepare_root(config) # Setup cudnn.benchmark for free speed\",\n \"config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema,\",\n \"G to eval mode...') G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict,\",\n \"although we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']):\",\n \"if config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else\",\n \"Ep {}, \\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s :\",\n \"of D steps and accumulations) D_batch_size = (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders\",\n \"import train_fns import utils from common import * # IMG_SIZE = 64 #\",\n \"= (config['batch_size'] * config['num_D_steps'] * config['num_D_accumulations']) loaders = dataset.get_data_loaders( data_root=config['data_root'], label_root=config['label_root'], batch_size=D_batch_size, num_workers=config['num_workers'],\",\n \"we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in range(state_dict['itr']): pbar.update()\",\n \"config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it\",\n \"Discriminator's batch size is all that needs to be passed # to the\",\n \"if not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') #\",\n \"to complete # a full D iteration (regardless of number of D steps\",\n \"PyTorch release Code by <NAME> and <NAME> This code is an unofficial reimplementation\",\n \"of \\\"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,\\\" by <NAME>, <NAME>,\",\n \"= x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] % config['log_interval']): curr_time\",\n \"# Train for specified number of epochs, although we mostly track G iterations.\",\n \"not (state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang\",\n \"epochs, although we mostly track G iterations. pbar = tqdm(total=total_iters) for _ in\",\n \"(key, metrics[key]) for key in metrics]) pbar.set_description(log) # print(log) # Save weights and\",\n \"experiment_name, save_weight=True) pbar.update() # Increment epoch counter at end of epoch state_dict['epoch'] +=\",\n \"%s' % experiment_name) G = BigGAN.Generator(**config).to(device) D = BigGAN.Discriminator(**config).to(device) # if config['parallel']: G\",\n \"config['n_classes'], device=device, fp16=config['G_fp16']) # Prepare a fixed z & y to see individual\",\n \"device=device, fp16=config['G_fp16']) fixed_z.sample_() fixed_y.sample_() # Loaders are loaded, prepare the training function train\",\n \"classes # and size of the images from the dataset, passing in a\",\n \"drop_last=True, load_in_mem=config['load_in_mem'], mask_out=config['mask_out'] ) # Prepare noise and randomly sampled label arrays #\",\n \"dataloader, as G doesn't require dataloading. # Note that at every loader iteration\",\n \"curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' % (key,\",\n \"like epoch # and itr # state_dict = {'itr': 0, 'epoch': 0, 'save_num':\",\n \"sampled label arrays # Allow for different batch sizes in G G_batch_size =\",\n \"', '.join(['%s : %+4.3f' % (key, metrics[key]) for key in metrics]) pbar.set_description(log) #\",\n \"in metrics]) pbar.set_description(log) # print(log) # Save weights and copies as configured at\",\n \"mode, just in case they got set to eval # For D, which\",\n \"in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ = utils.prepare_z_y(\",\n \"sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_ =\",\n \"run(config): # Update the config dict as necessary # This is for convenience,\",\n \"BigGAN.Discriminator(**config).to(device) # if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using\",\n \"and randomly sampled label arrays # Allow for different batch sizes in G\",\n \"Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD,\",\n \"p in net.parameters()]) for net in [G, D]])) # Prepare state dict, which\",\n \"net.parameters()]) for net in [G, D]])) # Prepare state dict, which holds things\",\n \"pbar.set_description(log) # print(log) # Save weights and copies as configured at specified interval\",\n \"randomly sampled label arrays # Allow for different batch sizes in G G_batch_size\",\n \"they got set to eval # For D, which typically doesn't have BN,\",\n \"x, y = x.to(device), y.to(device) metrics = train(x, y) if not (state_dict['itr'] %\",\n \"free speed torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment\",\n \"(state_dict['itr'] % config['log_interval']): curr_time = timer.since_start() curr_time_str = datetime.datetime.fromtimestamp( curr_time).strftime('%H:%M:%S') # quang duong\",\n \"by <NAME> and <NAME> This code is an unofficial reimplementation of \\\"Large-Scale GAN\",\n \"initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device = 'cuda'\",\n \"loader iteration we pass in enough data to complete # a full D\",\n \"% config['sample_every']): if config['G_eval_mode']: # print('Switching G to eval mode...') G.eval() train_fns.save_and_sample(G, D,\",\n \"1809.11096). Let's go. \\\"\\\"\\\" import datetime import time import torch import dataset import\",\n \"fixed_y.sample_() # Loaders are loaded, prepare the training function train = train_fns.create_train_fn( G,\",\n \"things like epoch # and itr # state_dict = {'itr': 0, 'epoch': 0,\",\n \"configuration into the config-dict (e.g. inferring the number of classes # and size\",\n \"D]])) # Prepare state dict, which holds things like epoch # and itr\",\n \"\\\"\\\"\\\" BigGAN: The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This\",\n \"print('Skipping initialization for training resumption...') config['skip_init'] = True config = utils.update_config_roots(config) device =\",\n \"G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None, None GD = BigGAN.G_D(G, D)\",\n \"in range(state_dict['itr']): pbar.update() timer = mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in\",\n \"as G doesn't require dataloading. # Note that at every loader iteration we\",\n \"the images from the dataset, passing in a pytorch object # for the\",\n \"if config['load_weights'] else None, G_ema if config['ema'] else None) # Prepare data; the\",\n \"da di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str\",\n \"= utils.activation_dict[config['G_nl']] config['D_activation'] = utils.activation_dict[config['D_nl']] # By default, skip init if resuming training.\",\n \"in enumerate(loader): x, y = data['img'], data['label'] # Increment the iteration counter state_dict['itr']\",\n \"di) eta = ( total_iters - state_dict['itr']) // ((state_dict['itr']-start_itr) / (curr_time+1)) eta_str =\",\n \"= config['num_epochs'] * len(loader) # Train for specified number of epochs, although we\",\n \"device = 'cuda' # Seed RNG utils.seed_rng(config['seed']) # Prepare root folders if necessary\",\n \"= nn.DataParallel(G_ema) ema = utils.ema(G, G_ema, config['ema_decay'], config['ema_start']) else: G_ema, ema = None,\",\n \"in G: {} D: {}'.format( *[sum([p.data.nelement() for p in net.parameters()]) for net in\",\n \"eval # For D, which typically doesn't have BN, this shouldn't matter much.\",\n \"state_dict, config, experiment_name, save_weight=False) if not (state_dict['itr'] % config['save_every']): if config['G_eval_mode']: # print('Switching\",\n \"from common import * # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE *\",\n \"using EMA, prepare it if config['ema']: print('Preparing EMA for G with decay of\",\n \"G doesn't require dataloading. # Note that at every loader iteration we pass\",\n \"mmcv.Timer() timer.start() start_itr = state_dict['itr'] for epoch in range(state_dict['epoch'], config['num_epochs']): for i, data\",\n \"config['resume']: print('Loading weights...') utils.load_weights(G, D, state_dict, config['weights_root'], experiment_name, config['load_weights'] if config['load_weights'] else None,\",\n \"# state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'config': config} # If\",\n \"GD = BigGAN.G_D(G, D) print(G) print(D) print('Number of params in G: {} D:\",\n \"at every loader iteration we pass in enough data to complete # a\",\n \"if config['parallel']: G = nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare\",\n \"batch sizes in G G_batch_size = max(config['G_batch_size'], config['batch_size']) num_samples = config['num_fixed_samples'] z_, y_\",\n \"Update the config dict as necessary # This is for convenience, to add\",\n \"to eval # For D, which typically doesn't have BN, this shouldn't matter\",\n \"configured at specified interval if not (state_dict['itr'] % config['sample_every']): if config['G_eval_mode']: # print('Switching\",\n \"torch.backends.cudnn.benchmark = True experiment_name = (config['experiment_name'] if config['experiment_name'] else 'generative_dog_images') print('Experiment name is\",\n \"G.eval() train_fns.save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name, save_weight=False) if\",\n \"\\\".format( curr_time_str, eta_str, state_dict['itr'], total_iters, epoch) log += ', '.join(['%s : %+4.3f' %\",\n \"passing in a pytorch object # for the activation specified as a string)\",\n \"this shouldn't matter much. G.train() D.train() if config['ema']: G_ema.train() x, y = x.to(device),\",\n \"# Prepare data; the Discriminator's batch size is all that needs to be\",\n \"net in [G, D]])) # Prepare state dict, which holds things like epoch\",\n \"loaded, prepare the training function train = train_fns.create_train_fn( G, D, GD, z_, y_,\",\n \"set to eval # For D, which typically doesn't have BN, this shouldn't\"\n]","isConversational":false}}},{"rowIdx":4086,"cells":{"ngram":{"kind":"list like","value":["% settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile","def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir =","zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s'","rights reserved. # __END_LICENSE__ import os import time import random import shutil from","os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix)","glob import glob import traceback import sys from geocamUtil import FileUtil from django.conf","'%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files =","settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile =","sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return","settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles:","os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir) return zipFile","Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os import time","import os import time import random import shutil from glob import glob import","as represented by the #Administrator of the National Aeronautics and Space Administration. #All","= getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d)","random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for","os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix):","def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix)","= time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and","= os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir) return","time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not","getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def","%s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod","deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files:","files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except","(settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' %","% settings.TMP_DIR) now = time.time() for f in files: if (now - os.stat(f).st_ctime","not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not","Administration. #All rights reserved. # __END_LICENSE__ import os import time import random import","glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if (now -","the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os","finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s","United States Government, as represented by the #Administrator of the National Aeronautics and","f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink","try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]'","Space Administration. #All rights reserved. # __END_LICENSE__ import os import time import random","'%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now =","(c) 2015, United States Government, as represented by the #Administrator of the National","import shutil from glob import glob import traceback import sys from geocamUtil import","from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s'","for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')):","FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' %","% zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir)))","import random import shutil from glob import glob import traceback import sys from","time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now","__END_LICENSE__ import os import time import random import shutil from glob import glob","d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles()","# __BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the #Administrator","d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir","suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles():","suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f","print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d","def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r","files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if","except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f","go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def","glob import traceback import sys from geocamUtil import FileUtil from django.conf import settings","(now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc()","zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir)","#Administrator of the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__","#Copyright (c) 2015, United States Government, as represented by the #Administrator of the","import sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''):","OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def","initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd()","in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f)","reserved. # __END_LICENSE__ import os import time import random import shutil from glob","States Government, as represented by the #Administrator of the National Aeronautics and Space","oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir)","unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR)","sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix)","return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir","could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not","def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' %","settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16),","def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in","= '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' %","represented by the #Administrator of the National Aeronautics and Space Administration. #All rights","makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip","now = time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS","if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d","os import time import random import shutil from glob import glob import traceback","not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def","makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR)","traceback import sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix,","and Space Administration. #All rights reserved. # __END_LICENSE__ import os import time import","settings.TMP_DIR) now = time.time() for f in files: if (now - os.stat(f).st_ctime >","the #Administrator of the National Aeronautics and Space Administration. #All rights reserved. #","__BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the #Administrator of","and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could","f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try:","= glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if (now","% (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*'","Government, as represented by the #Administrator of the National Aeronautics and Space Administration.","%s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir):","from glob import glob import traceback import sys from geocamUtil import FileUtil from",">> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d =","os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' %","#All rights reserved. # __END_LICENSE__ import os import time import random import shutil","% random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time()","- os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print","by the #Administrator of the National Aeronautics and Space Administration. #All rights reserved.","return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR))","traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix):","return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files","<reponame>geocam/geocamUtilWeb<gh_stars>1-10 # __BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the","from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'),","FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix,","2015, United States Government, as represented by the #Administrator of the National Aeronautics","deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip'","% f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw","geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' %","'%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile,","# __END_LICENSE__ import os import time import random import shutil from glob import","django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x'","import time import random import shutil from glob import glob import traceback import","time import random import shutil from glob import glob import traceback import sys","prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR)","of the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import","import traceback import sys from geocamUtil import FileUtil from django.conf import settings def","> settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr,","os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >>","shutil from glob import glob import traceback import sys from geocamUtil import FileUtil","import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR,","not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR):","'[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if","random import shutil from glob import glob import traceback import sys from geocamUtil","import glob import traceback import sys from geocamUtil import FileUtil from django.conf import","getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return","FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return","import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' %","National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os import","f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s'","if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError:"],"string":"[\n \"% settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile\",\n \"def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir =\",\n \"zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s'\",\n \"rights reserved. # __END_LICENSE__ import os import time import random import shutil from\",\n \"os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix)\",\n \"glob import glob import traceback import sys from geocamUtil import FileUtil from django.conf\",\n \"'%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files =\",\n \"settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile =\",\n \"sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return\",\n \"settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles:\",\n \"os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir) return zipFile\",\n \"Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os import time\",\n \"import os import time import random import shutil from glob import glob import\",\n \"as represented by the #Administrator of the National Aeronautics and Space Administration. #All\",\n \"= getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d)\",\n \"random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for\",\n \"os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix):\",\n \"def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix)\",\n \"= time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and\",\n \"= os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir) return\",\n \"time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not\",\n \"getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def\",\n \"%s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod\",\n \"deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files:\",\n \"files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except\",\n \"(settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' %\",\n \"% settings.TMP_DIR) now = time.time() for f in files: if (now - os.stat(f).st_ctime\",\n \"not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not\",\n \"Administration. #All rights reserved. # __END_LICENSE__ import os import time import random import\",\n \"glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if (now -\",\n \"the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os\",\n \"finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s\",\n \"United States Government, as represented by the #Administrator of the National Aeronautics and\",\n \"f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink\",\n \"try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]'\",\n \"Space Administration. #All rights reserved. # __END_LICENSE__ import os import time import random\",\n \"'%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now =\",\n \"(c) 2015, United States Government, as represented by the #Administrator of the National\",\n \"import shutil from glob import glob import traceback import sys from geocamUtil import\",\n \"from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s'\",\n \"for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')):\",\n \"FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' %\",\n \"% zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir)))\",\n \"import random import shutil from glob import glob import traceback import sys from\",\n \"time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now\",\n \"__END_LICENSE__ import os import time import random import shutil from glob import glob\",\n \"d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles()\",\n \"# __BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the #Administrator\",\n \"d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir\",\n \"suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles():\",\n \"suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f\",\n \"print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d\",\n \"def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r\",\n \"files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if\",\n \"except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f\",\n \"go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def\",\n \"glob import traceback import sys from geocamUtil import FileUtil from django.conf import settings\",\n \"(now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc()\",\n \"zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir)\",\n \"#Administrator of the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__\",\n \"#Copyright (c) 2015, United States Government, as represented by the #Administrator of the\",\n \"import sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''):\",\n \"OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def\",\n \"initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd()\",\n \"in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f)\",\n \"reserved. # __END_LICENSE__ import os import time import random import shutil from glob\",\n \"States Government, as represented by the #Administrator of the National Aeronautics and Space\",\n \"oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile, os.path.basename(zipDir))) os.chdir(oldDir) shutil.rmtree(zipDir)\",\n \"unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR)\",\n \"sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix)\",\n \"return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir\",\n \"could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not\",\n \"def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' %\",\n \"settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16),\",\n \"def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time() for f in\",\n \"= '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' %\",\n \"represented by the #Administrator of the National Aeronautics and Space Administration. #All rights\",\n \"makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip\",\n \"now = time.time() for f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS\",\n \"if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d\",\n \"os import time import random import shutil from glob import glob import traceback\",\n \"not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def\",\n \"makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR)\",\n \"traceback import sys from geocamUtil import FileUtil from django.conf import settings def getTempName(prefix,\",\n \"and Space Administration. #All rights reserved. # __END_LICENSE__ import os import time import\",\n \"settings.TMP_DIR) now = time.time() for f in files: if (now - os.stat(f).st_ctime >\",\n \"the #Administrator of the National Aeronautics and Space Administration. #All rights reserved. #\",\n \"__BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the #Administrator of\",\n \"and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could\",\n \"f in files: if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try:\",\n \"= glob('%s/*' % settings.TMP_DIR) now = time.time() for f in files: if (now\",\n \"% (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*'\",\n \"Government, as represented by the #Administrator of the National Aeronautics and Space Administration.\",\n \"%s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir):\",\n \"from glob import glob import traceback import sys from geocamUtil import FileUtil from\",\n \">> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d =\",\n \"os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' %\",\n \"#All rights reserved. # __END_LICENSE__ import os import time import random import shutil\",\n \"% random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR) now = time.time()\",\n \"- os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print\",\n \"by the #Administrator of the National Aeronautics and Space Administration. #All rights reserved.\",\n \"return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR))\",\n \"traceback.print_exc() print >> sys.stderr, '[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix):\",\n \"return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files\",\n \"<reponame>geocam/geocamUtilWeb<gh_stars>1-10 # __BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the\",\n \"from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'),\",\n \"FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix,\",\n \"2015, United States Government, as represented by the #Administrator of the National Aeronautics\",\n \"deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip'\",\n \"% f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw\",\n \"geocamUtil import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' %\",\n \"'%s.zip' % zipDir oldDir = os.getcwd() os.chdir(os.path.dirname(settings.TMP_DIR)) os.system('zip -r %s %s' % (zipFile,\",\n \"# __END_LICENSE__ import os import time import random import shutil from glob import\",\n \"django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x'\",\n \"import time import random import shutil from glob import glob import traceback import\",\n \"time import random import shutil from glob import glob import traceback import sys\",\n \"prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' % random.getrandbits(16), suffix) def deleteStaleFiles(): files = glob('%s/*' % settings.TMP_DIR)\",\n \"of the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import\",\n \"import traceback import sys from geocamUtil import FileUtil from django.conf import settings def\",\n \"> settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >> sys.stderr,\",\n \"os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError: traceback.print_exc() print >>\",\n \"shutil from glob import glob import traceback import sys from geocamUtil import FileUtil\",\n \"import FileUtil from django.conf import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR,\",\n \"not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR):\",\n \"'[tempfiles.deleteStaleFiles: could not unlink %s]' % f def makeTempDir(prefix): d = getTempName(prefix) if\",\n \"random import shutil from glob import glob import traceback import sys from geocamUtil\",\n \"import glob import traceback import sys from geocamUtil import FileUtil from django.conf import\",\n \"getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return\",\n \"FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s' % settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return\",\n \"import settings def getTempName(prefix, suffix=''): return '%s/%s-%s-%s%s' % (settings.TMP_DIR, prefix, time.strftime('%Y-%m-%d-%H%M'), '%04x' %\",\n \"National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ import os import\",\n \"f def makeTempDir(prefix): d = getTempName(prefix) if not os.path.exists(settings.TMP_DIR): FileUtil.mkdirP(settings.TMP_DIR) os.system('chmod go+rw %s'\",\n \"if (now - os.stat(f).st_ctime > settings.GEOCAM_UTIL_DELETE_TMP_FILE_WAIT_SECONDS and not f.endswith('/README.txt')): try: os.unlink(f) except OSError:\"\n]","isConversational":false}}},{"rowIdx":4087,"cells":{"ngram":{"kind":"list like","value":["we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10]","k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while","def __init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList =","lon) or (x * 2 + 1 == lon and HL[2*x] >= val)","the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i","2 + 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] <","percolation until 0 reached or father is bigger while i // 2 >","#initialise un tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize","assert tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste si","i * 2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError:","self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates","self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if","a bien le minimum avec delmin assert tau.delMin() is None x = []","self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a","if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i * 2","self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until","len(alist) + 1# + 1 self.heapList = [0] + alist # enlever le","tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList)","[0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >=","integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un","tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer)","corriger les bugs;\" # - envoyer le diff ou le dépôt git par","assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau","@settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1","mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]:","tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def","(HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon):","ou le dépôt git par email.\"\"\" import hypothesis from hypothesis import given, settings","gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15)","= self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist):","avec L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x","tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4]","HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau","IndexError: print(\"Empty heap. Nothing is changed. None is returned.\") return self.currentSize = self.currentSize","range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test that we","16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15)","\", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap()","hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap:","= [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient","self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a list,","1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie qu'on a","self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i * 2 +","None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau =","+= [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1):","rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on","// 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i]","self.heapList = [0] + alist # enlever le [:] while (i < self.currentSize):","tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def","= tmp i //= 2 def insert(self,k): #inserting a new value into the","index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] =","* 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is the","smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] =","@given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is","is None x = [] tau.buildHeap(K) for l in L:#teste si 1 suite","= [0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward","la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father","returned.\") return if i * 2 + 1 >= self.currentSize: return i *","L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\")","+= 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or","de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les","tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L)","- envoyer le diff ou le dépôt git par email.\"\"\" import hypothesis from","for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList ==","+ 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have a","2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index","erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture; # -","tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\")","bugs;\" # - envoyer le diff ou le dépôt git par email.\"\"\" import","#inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize +","val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def","= [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert","tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then","assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x","self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap","i = mc def minChild(self,i): if i * 2 >= self.currentSize or i","== 0: print(\"No Child. None is returned.\") return if i * 2 +",">= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize ==","given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires","test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L)","@given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def","tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize =","= self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new",">= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap()","[0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList ==","couverture; # - corriger les bugs;\" # - envoyer le diff ou le","assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10]","heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i *","2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i","percDown(self,i): while (i * 2) < self.currentSize:#while I have a child mc =","assert x == sorted(L + K)#verifie qu'on a bien le minimum avec delmin","tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18","le dépôt git par email.\"\"\" import hypothesis from hypothesis import given, settings from","tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] =","tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K):","None is returned.\") return if i * 2 + 1 >= self.currentSize: return","par email.\"\"\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers,","child mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i] >","return rval def buildHeap(self,alist): #creates a whole heap from a list, by percolating","+ 1): for _ in range(len(L)): tau.percDown(x) #then we test that we got","binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element 0","self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed. None is returned.\") return self.currentSize","assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList)","test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L","tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L):","range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L)","a child mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i]","i * 2 + 1 >= self.currentSize: return i * 2 else: if","L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x =","hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self):","d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille de la","assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() ==","#upward percolation until 0 reached or father is bigger while i // 2","tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10]","[0] + alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i","assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100)","@settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i):","in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap()","avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l in","tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for _ in","2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def","and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init():","and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i //","def minChild(self,i): if i * 2 >= self.currentSize or i == 0: print(\"No","test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau =","2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i //","def percDown(self,i): while (i * 2) < self.currentSize:#while I have a child mc","tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert","bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]:","< self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index of","@given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] =","- 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a","[0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList","tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau =","= mc def minChild(self,i): if i * 2 >= self.currentSize or i ==","* 2 else: return i * 2 + 1 def delMin(self): try: rval","tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap()","BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements de","les elements assert x == sorted(L + K)#verifie qu'on a bien le minimum","heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father is bigger","// 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i","- corriger les bugs;\" # - envoyer le diff ou le dépôt git","1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x","tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for _","* 2 + 1 > lon) or (x * 2 + 1 ==","delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed. None","i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else:","self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp =","tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les","tous les elements assert x == sorted(L + K)#verifie qu'on a bien le","= gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert","heap. Nothing is changed. None is returned.\") return self.currentSize = self.currentSize - 1","from a list, by percolating all its elements i = 1 self.currentSize =","is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i]","except IndexError: print(\"Empty heap. Nothing is changed. None is returned.\") return self.currentSize =","assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i):","== gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize","tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for","tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste si 1","tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert","2 + 1 == lon and HL[2*x] >= val) or (HL[2*x] >= val","self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 >","gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize ==","rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed. None is returned.\")","x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test","# Ce fichier contient (au moins) cinq erreurs. # Instructions: # - tester","dépôt git par email.\"\"\" import hypothesis from hypothesis import given, settings from hypothesis.strategies","def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize =","if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp","// 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting","+ 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i)","18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def","qu'on a bien le minimum avec delmin assert tau.delMin() is None x =","== [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList","new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def","its elements i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList","atteindre 100% de couverture; # - corriger les bugs;\" # - envoyer le","self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have","sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers())","#print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i):",">= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList)","tmp i //= 2 def insert(self,k): #inserting a new value into the heap","@given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) +","tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient la structure","0: print(\"No Child. None is returned.\") return if i * 2 + 1","@given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert","or father is bigger while i // 2 > 0 and self.heapList[i] <","1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): #","assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1]","== sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000)","self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i]","tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap()","changed. None is returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize]","else: return i * 2 + 1 def delMin(self): try: rval = self.heapList[1]","+= [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU","= BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers()))","les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire","= self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed. None is returned.\") return","is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau","list, by percolating all its elements i = 1 self.currentSize = len(alist) +","1 self.heapList = [0] + alist # enlever le [:] while (i <","self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2) <","#structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec","[] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient la","None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400)","= 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert","jusqu'à atteindre 100% de couverture; # - corriger les bugs;\" # - envoyer","2 >= self.currentSize or i == 0: print(\"No Child. None is returned.\") return","x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x","+ 1# + 1 self.heapList = [0] + alist # enlever le [:]","test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers())","1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0] + alist","all its elements i = 1 self.currentSize = len(alist) + 1# + 1","#then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def","return if i * 2 + 1 >= self.currentSize: return i * 2","alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1","2 + 1 > lon) or (x * 2 + 1 == lon","0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant) def","that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L +=","@given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k","i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0]","returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return","tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None","and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def","1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return","assert ((x * 2 + 1 > lon) or (x * 2 +","or i == 0: print(\"No Child. None is returned.\") return if i *","== lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >=","well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap()","+ 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing","for _ in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered","100% de couverture; # - corriger les bugs;\" # - envoyer le diff","[0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation","le minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for","avec un element 0 self.heapList = [0] self.currentSize = 1#taille de la liste","2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a","< self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 +","* 2 >= self.currentSize or i == 0: print(\"No Child. None is returned.\")","< self.heapList[i*2+1]: return i * 2 else: return i * 2 + 1","1 > lon) or (x * 2 + 1 == lon and HL[2*x]","tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100)","(x * 2 + 1 == lon and HL[2*x] >= val) or (HL[2*x]","== min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None","buildHeap(self,alist): #creates a whole heap from a list, by percolating all its elements","= BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0])","[] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L","def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert","= 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0","is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers())","self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i","tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList ==","whole heap from a list, by percolating all its elements i = 1","binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille de","mc def minChild(self,i): if i * 2 >= self.currentSize or i == 0:","self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a list, by","is changed. None is returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1] =","= [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x ==","+ 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]:","x = [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression","tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >=","class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire","+ K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is None","tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16","Nothing is changed. None is returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1]","_ in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap","tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) ==","+ 1 self.heapList = [0] + alist # enlever le [:] while (i",">= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau =","< self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] =","= self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i *","len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) +","i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp =","== 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize","1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole","= 1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0] +","BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15]","[:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x","self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def","or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in","test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList)","def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on","def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert","tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None)","def buildHeap(self,alist): #creates a whole heap from a list, by percolating all its","1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1)","moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture;","of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc]","tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap()","* 2 + 1 == lon and HL[2*x] >= val) or (HL[2*x] >=","test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not","2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] =","#creates a whole heap from a list, by percolating all its elements i","# - tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\"","__init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList = [0]","@settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i","if i * 2 + 1 >= self.currentSize: return i * 2 else:","= self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp","= len(alist) + 1# + 1 self.heapList = [0] + alist # enlever","self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new value into","minChild(self,i): if i * 2 >= self.currentSize or i == 0: print(\"No Child.","else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i *","BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L)","fichier contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre","elements i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList =","self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k):","in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin() tau.insert(l) assert_goodheap(tau,tau.currentSize)","def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is","assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList","insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize","a list, by percolating all its elements i = 1 self.currentSize = len(alist)","1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached","@settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in","delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste","= i tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)):","import integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise","= self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i):","self.currentSize or i == 0: print(\"No Child. None is returned.\") return if i","by percolating all its elements i = 1 self.currentSize = len(alist) + 1#","bien le minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K)","def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1)","for l in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin()","de couverture; # - corriger les bugs;\" # - envoyer le diff ou","# - envoyer le diff ou le dépôt git par email.\"\"\" import hypothesis","return i * 2 else: return i * 2 + 1 def delMin(self):","min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1])","a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1","@given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16","== [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8]","= BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements","Ce fichier contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à","def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L)","import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists class","we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L):","tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille","is bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i //","val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap()","hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de","try: rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed. None is","= BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert","print(\"No Child. None is returned.\") return if i * 2 + 1 >=","# assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers())","return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval","> lon) or (x * 2 + 1 == lon and HL[2*x] >=","i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon)","assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i))","cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture; #","sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def","= 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8)","father is bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i","* 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return","a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau =","def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x *","a whole heap from a list, by percolating all its elements i =","elements assert x == sorted(L + K)#verifie qu'on a bien le minimum avec","I have a child mc = self.minChild(i)#mc is the index of the smallest","# enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1 def","= BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L","from hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure","i tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x)","tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma =","in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test that","@settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None","#print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau =","@settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1)","have a child mc = self.minChild(i)#mc is the index of the smallest if","K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize >","tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17)","from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers def","assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def",">= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i","assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x * 2","assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8]","None is returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop()",">= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for","got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau","= 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000)","16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize =","* 2 + 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2]","tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L)","x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau =","#for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau","= BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in","assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert","the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i]","BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer):","x == sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert","self.heapList[mc] = tmp i = mc def minChild(self,i): if i * 2 >=","val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x","> self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i =","while i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp","Child. None is returned.\") return if i * 2 + 1 >= self.currentSize:","tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie","d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList","tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element","= [0] + alist # enlever le [:] while (i < self.currentSize): self.percUp(i)","1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have a child","1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing is","percUp(self,i): #upward percolation until 0 reached or father is bigger while i //","self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1","[10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for","(i * 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is","while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L +","range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()))","in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1)","[10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \",","sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is","def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1","if i * 2 >= self.currentSize or i == 0: print(\"No Child. None","envoyer le diff ou le dépôt git par email.\"\"\" import hypothesis from hypothesis","print(\"Empty heap. Nothing is changed. None is returned.\") return self.currentSize = self.currentSize -","// 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2","BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L +=","+ 1 == lon and HL[2*x] >= val) or (HL[2*x] >= val and","None x = [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/","self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new value","de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or","value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i):","2 else: return i * 2 + 1 def delMin(self): try: rval =","def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList =","None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for","# Instructions: # - tester jusqu'à atteindre 100% de couverture; # - corriger","== 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize","self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //=","self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while","(i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2","until 0 reached or father is bigger while i // 2 > 0","BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin()","> 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2]","2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize","def delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty heap. Nothing is changed.","or (x * 2 + 1 == lon and HL[2*x] >= val) or","assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16)","self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2)","= tmp i = mc def minChild(self,i): if i * 2 >= self.currentSize","the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc]","i //= 2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k)","== sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin()","= self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from","diff ou le dépôt git par email.\"\"\" import hypothesis from hypothesis import given,","def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau","x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0]","tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is","print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17","tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\" # -","i * 2 else: return i * 2 + 1 def delMin(self): try:","self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2]","self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc","enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL):","test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:]","tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L)","assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin()","is returned.\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1)","* 2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty","elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous","le [:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert","retire tous les elements assert x == sorted(L + K)#verifie qu'on a bien",">= self.currentSize or i == 0: print(\"No Child. None is returned.\") return if","de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un","rval def buildHeap(self,alist): #creates a whole heap from a list, by percolating all","git par email.\"\"\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import","i * 2 >= self.currentSize or i == 0: print(\"No Child. None is","tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert","is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec","return i * 2 + 1 def delMin(self): try: rval = self.heapList[1] except","in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize","settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers","1 == lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1]","#print(\"sorted\",sorted(L),\"\\n\") #print(\"TAU \", tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau","le diff ou le dépôt git par email.\"\"\" import hypothesis from hypothesis import","= self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i","minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l","reached or father is bigger while i // 2 > 0 and self.heapList[i]","K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is None x","assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize ==","for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = []","2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\"Empty heap.","= BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def","tau.heapList,\"\\n\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert","percolating all its elements i = 1 self.currentSize = len(alist) + 1# +","lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas","2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize","les bugs;\" # - envoyer le diff ou le dépôt git par email.\"\"\"","self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i *","lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val))","tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers()))","0 reached or father is bigger while i // 2 > 0 and","un tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize =","K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements","gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList","+ 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1):","@settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList","into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while","1# + 1 self.heapList = [0] + alist # enlever le [:] while","(au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de","# - corriger les bugs;\" # - envoyer le diff ou le dépôt","tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in","BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers","1): for _ in range(len(L)): tau.percDown(x) #then we test that we got a","i == 0: print(\"No Child. None is returned.\") return if i * 2","tmp i = mc def minChild(self,i): if i * 2 >= self.currentSize or","heap from a list, by percolating all its elements i = 1 self.currentSize","[0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau","assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau =","self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i * 2","test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute","assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas","un element 0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList","assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma","((x * 2 + 1 > lon) or (x * 2 + 1","liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father is","l in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin() tau.insert(l)","+ alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i +=","assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert","is returned.\") return if i * 2 + 1 >= self.currentSize: return i","17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17]","tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp","BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1)","tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit","while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x *","not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L","tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements de K","element 0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant)","HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon):","return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2","for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we","- tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\" #","= self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I","+ 1 > lon) or (x * 2 + 1 == lon and","//= 2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize)","self.currentSize = len(alist) + 1# + 1 self.heapList = [0] + alist #","== [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3]","0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i","= BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:]","test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for","BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None)","> 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie qu'on","def percUp(self,i): #upward percolation until 0 reached or father is bigger while i","in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert","tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L)","tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList ==","email.\"\"\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists","tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize","while (i * 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc","import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas","Instructions: # - tester jusqu'à atteindre 100% de couverture; # - corriger les","(invariant) def percUp(self,i): #upward percolation until 0 reached or father is bigger while","== len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L)","= 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2","self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i):","L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) +","construit avec L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize)","self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index of the","self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i","heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert","assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3]","self.heapList[i*2+1]: return i * 2 else: return i * 2 + 1 def","contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100%","tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x","1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize =","range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize"],"string":"[\n \"we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10]\",\n \"k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while\",\n \"def __init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList =\",\n \"lon) or (x * 2 + 1 == lon and HL[2*x] >= val)\",\n \"the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i\",\n \"2 + 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] <\",\n \"percolation until 0 reached or father is bigger while i // 2 >\",\n \"#initialise un tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize\",\n \"assert tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste si\",\n \"i * 2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError:\",\n \"self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates\",\n \"self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if\",\n \"a bien le minimum avec delmin assert tau.delMin() is None x = []\",\n \"self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a\",\n \"if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i * 2\",\n \"self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until\",\n \"len(alist) + 1# + 1 self.heapList = [0] + alist # enlever le\",\n \"tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList)\",\n \"[0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >=\",\n \"integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un\",\n \"tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer)\",\n \"corriger les bugs;\\\" # - envoyer le diff ou le dépôt git par\",\n \"assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau\",\n \"@settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1\",\n \"mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]:\",\n \"tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def\",\n \"(HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon):\",\n \"ou le dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings\",\n \"gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15)\",\n \"= self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist):\",\n \"avec L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x\",\n \"tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4]\",\n \"HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau\",\n \"IndexError: print(\\\"Empty heap. Nothing is changed. None is returned.\\\") return self.currentSize = self.currentSize\",\n \"range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test that we\",\n \"16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15)\",\n \"\\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap()\",\n \"hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap:\",\n \"= [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient\",\n \"self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a list,\",\n \"1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie qu'on a\",\n \"self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i * 2 +\",\n \"None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau =\",\n \"+= [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1):\",\n \"rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on\",\n \"// 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i]\",\n \"self.heapList = [0] + alist # enlever le [:] while (i < self.currentSize):\",\n \"tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def\",\n \"= tmp i //= 2 def insert(self,k): #inserting a new value into the\",\n \"index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] =\",\n \"* 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is the\",\n \"smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] =\",\n \"@given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is\",\n \"is None x = [] tau.buildHeap(K) for l in L:#teste si 1 suite\",\n \"= [0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward\",\n \"la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father\",\n \"returned.\\\") return if i * 2 + 1 >= self.currentSize: return i *\",\n \"L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\")\",\n \"+= 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or\",\n \"de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les\",\n \"tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L)\",\n \"- envoyer le diff ou le dépôt git par email.\\\"\\\"\\\" import hypothesis from\",\n \"for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList ==\",\n \"+ 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have a\",\n \"2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index\",\n \"erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture; # -\",\n \"tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\")\",\n \"bugs;\\\" # - envoyer le diff ou le dépôt git par email.\\\"\\\"\\\" import\",\n \"#inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize +\",\n \"val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def\",\n \"= [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert\",\n \"tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then\",\n \"assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x\",\n \"self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap\",\n \"i = mc def minChild(self,i): if i * 2 >= self.currentSize or i\",\n \"== 0: print(\\\"No Child. None is returned.\\\") return if i * 2 +\",\n \">= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize ==\",\n \"given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires\",\n \"test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L)\",\n \"@given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def\",\n \"tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize =\",\n \"= self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new\",\n \">= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap()\",\n \"[0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList ==\",\n \"couverture; # - corriger les bugs;\\\" # - envoyer le diff ou le\",\n \"assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10]\",\n \"heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i *\",\n \"2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i\",\n \"percDown(self,i): while (i * 2) < self.currentSize:#while I have a child mc =\",\n \"assert x == sorted(L + K)#verifie qu'on a bien le minimum avec delmin\",\n \"tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18\",\n \"le dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings from\",\n \"tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] =\",\n \"tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K):\",\n \"None is returned.\\\") return if i * 2 + 1 >= self.currentSize: return\",\n \"par email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers,\",\n \"child mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i] >\",\n \"return rval def buildHeap(self,alist): #creates a whole heap from a list, by percolating\",\n \"+ 1): for _ in range(len(L)): tau.percDown(x) #then we test that we got\",\n \"binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element 0\",\n \"self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed. None is returned.\\\") return self.currentSize\",\n \"assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList)\",\n \"test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L\",\n \"tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L):\",\n \"range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L)\",\n \"a child mc = self.minChild(i)#mc is the index of the smallest if self.heapList[i]\",\n \"i * 2 + 1 >= self.currentSize: return i * 2 else: if\",\n \"L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x =\",\n \"hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self):\",\n \"d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille de la\",\n \"assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() ==\",\n \"#upward percolation until 0 reached or father is bigger while i // 2\",\n \"tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10]\",\n \"[0] + alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i\",\n \"assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100)\",\n \"@settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i):\",\n \"in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap()\",\n \"avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l in\",\n \"tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for _ in\",\n \"2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def\",\n \"and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init():\",\n \"and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i //\",\n \"def minChild(self,i): if i * 2 >= self.currentSize or i == 0: print(\\\"No\",\n \"test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau =\",\n \"2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i //\",\n \"def percDown(self,i): while (i * 2) < self.currentSize:#while I have a child mc\",\n \"tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert\",\n \"bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]:\",\n \"< self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index of\",\n \"@given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] =\",\n \"- 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a\",\n \"[0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList\",\n \"tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau =\",\n \"= mc def minChild(self,i): if i * 2 >= self.currentSize or i ==\",\n \"* 2 else: return i * 2 + 1 def delMin(self): try: rval\",\n \"tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap()\",\n \"BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements de\",\n \"les elements assert x == sorted(L + K)#verifie qu'on a bien le minimum\",\n \"heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father is bigger\",\n \"// 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i\",\n \"- corriger les bugs;\\\" # - envoyer le diff ou le dépôt git\",\n \"1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x\",\n \"tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for _\",\n \"* 2 + 1 > lon) or (x * 2 + 1 ==\",\n \"delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed. None\",\n \"i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else:\",\n \"self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp =\",\n \"tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les\",\n \"tous les elements assert x == sorted(L + K)#verifie qu'on a bien le\",\n \"= gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert\",\n \"heap. Nothing is changed. None is returned.\\\") return self.currentSize = self.currentSize - 1\",\n \"from a list, by percolating all its elements i = 1 self.currentSize =\",\n \"is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i]\",\n \"except IndexError: print(\\\"Empty heap. Nothing is changed. None is returned.\\\") return self.currentSize =\",\n \"assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i):\",\n \"== gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize\",\n \"tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for\",\n \"tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste si 1\",\n \"tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert\",\n \"2 + 1 == lon and HL[2*x] >= val) or (HL[2*x] >= val\",\n \"self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 >\",\n \"gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize ==\",\n \"rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed. None is returned.\\\")\",\n \"x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test\",\n \"# Ce fichier contient (au moins) cinq erreurs. # Instructions: # - tester\",\n \"dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings from hypothesis.strategies\",\n \"def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize =\",\n \"if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp\",\n \"// 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting\",\n \"+ 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i)\",\n \"18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def\",\n \"qu'on a bien le minimum avec delmin assert tau.delMin() is None x =\",\n \"== [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList\",\n \"new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def\",\n \"its elements i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList\",\n \"atteindre 100% de couverture; # - corriger les bugs;\\\" # - envoyer le\",\n \"self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have\",\n \"sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers())\",\n \"#print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i):\",\n \">= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList)\",\n \"tmp i //= 2 def insert(self,k): #inserting a new value into the heap\",\n \"@given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) +\",\n \"tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient la structure\",\n \"0: print(\\\"No Child. None is returned.\\\") return if i * 2 + 1\",\n \"@given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert\",\n \"or father is bigger while i // 2 > 0 and self.heapList[i] <\",\n \"1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): #\",\n \"assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1]\",\n \"== sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000)\",\n \"self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i]\",\n \"tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap()\",\n \"changed. None is returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize]\",\n \"else: return i * 2 + 1 def delMin(self): try: rval = self.heapList[1]\",\n \"+= [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU\",\n \"= BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers()))\",\n \"les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire\",\n \"= self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed. None is returned.\\\") return\",\n \"is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau\",\n \"list, by percolating all its elements i = 1 self.currentSize = len(alist) +\",\n \"1 self.heapList = [0] + alist # enlever le [:] while (i <\",\n \"self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2) <\",\n \"#structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec\",\n \"[] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression maintient la\",\n \"None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400)\",\n \"= 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert\",\n \"jusqu'à atteindre 100% de couverture; # - corriger les bugs;\\\" # - envoyer\",\n \"2 >= self.currentSize or i == 0: print(\\\"No Child. None is returned.\\\") return\",\n \"x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x\",\n \"+ 1# + 1 self.heapList = [0] + alist # enlever le [:]\",\n \"test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers())\",\n \"1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0] + alist\",\n \"all its elements i = 1 self.currentSize = len(alist) + 1# + 1\",\n \"#then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def\",\n \"return if i * 2 + 1 >= self.currentSize: return i * 2\",\n \"alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1\",\n \"2 + 1 > lon) or (x * 2 + 1 == lon\",\n \"0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant) def\",\n \"that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L +=\",\n \"@given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k\",\n \"i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0]\",\n \"returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return\",\n \"tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None\",\n \"and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def\",\n \"1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return\",\n \"assert ((x * 2 + 1 > lon) or (x * 2 +\",\n \"or i == 0: print(\\\"No Child. None is returned.\\\") return if i *\",\n \"== lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >=\",\n \"well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap()\",\n \"+ 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing\",\n \"for _ in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered\",\n \"100% de couverture; # - corriger les bugs;\\\" # - envoyer le diff\",\n \"[0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation\",\n \"le minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for\",\n \"avec un element 0 self.heapList = [0] self.currentSize = 1#taille de la liste\",\n \"2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a\",\n \"< self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 +\",\n \"* 2 >= self.currentSize or i == 0: print(\\\"No Child. None is returned.\\\")\",\n \"< self.heapList[i*2+1]: return i * 2 else: return i * 2 + 1\",\n \"1 > lon) or (x * 2 + 1 == lon and HL[2*x]\",\n \"tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100)\",\n \"(x * 2 + 1 == lon and HL[2*x] >= val) or (HL[2*x]\",\n \"== min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None\",\n \"buildHeap(self,alist): #creates a whole heap from a list, by percolating all its elements\",\n \"= BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0])\",\n \"[] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L\",\n \"def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert\",\n \"= 1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0\",\n \"is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers())\",\n \"self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i\",\n \"tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList ==\",\n \"whole heap from a list, by percolating all its elements i = 1\",\n \"binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille de\",\n \"mc def minChild(self,i): if i * 2 >= self.currentSize or i == 0:\",\n \"self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from a list, by\",\n \"is changed. None is returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1] =\",\n \"= [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x ==\",\n \"+ 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]:\",\n \"x = [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/ suppression\",\n \"tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >=\",\n \"class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire\",\n \"+ K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is None\",\n \"tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16\",\n \"Nothing is changed. None is returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1]\",\n \"_ in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap\",\n \"tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) ==\",\n \"+ 1 self.heapList = [0] + alist # enlever le [:] while (i\",\n \">= val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau =\",\n \"< self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2] =\",\n \"= self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i *\",\n \"len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) +\",\n \"i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp =\",\n \"== 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize\",\n \"1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole\",\n \"= 1 self.currentSize = len(alist) + 1# + 1 self.heapList = [0] +\",\n \"BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15]\",\n \"[:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x\",\n \"self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def\",\n \"or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x in\",\n \"test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList)\",\n \"def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on\",\n \"def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert\",\n \"tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None)\",\n \"def buildHeap(self,alist): #creates a whole heap from a list, by percolating all its\",\n \"1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1)\",\n \"moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture;\",\n \"of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc]\",\n \"tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap()\",\n \"* 2 + 1 == lon and HL[2*x] >= val) or (HL[2*x] >=\",\n \"test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not\",\n \"2]: tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] =\",\n \"#creates a whole heap from a list, by percolating all its elements i\",\n \"# - tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\\\"\",\n \"__init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList = [0]\",\n \"@settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i\",\n \"if i * 2 + 1 >= self.currentSize: return i * 2 else:\",\n \"= self.minChild(i)#mc is the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp\",\n \"= len(alist) + 1# + 1 self.heapList = [0] + alist # enlever\",\n \"self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new value into\",\n \"minChild(self,i): if i * 2 >= self.currentSize or i == 0: print(\\\"No Child.\",\n \"else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return i *\",\n \"BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L)\",\n \"fichier contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre\",\n \"elements i = 1 self.currentSize = len(alist) + 1# + 1 self.heapList =\",\n \"self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k):\",\n \"in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin() tau.insert(l) assert_goodheap(tau,tau.currentSize)\",\n \"def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is\",\n \"assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList\",\n \"insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize\",\n \"a list, by percolating all its elements i = 1 self.currentSize = len(alist)\",\n \"1#taille de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached\",\n \"@settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in\",\n \"delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l in L:#teste\",\n \"= i tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)):\",\n \"import integers, lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise\",\n \"= self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i):\",\n \"self.currentSize or i == 0: print(\\\"No Child. None is returned.\\\") return if i\",\n \"by percolating all its elements i = 1 self.currentSize = len(alist) + 1#\",\n \"bien le minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K)\",\n \"def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1)\",\n \"for l in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin()\",\n \"de couverture; # - corriger les bugs;\\\" # - envoyer le diff ou\",\n \"# - envoyer le diff ou le dépôt git par email.\\\"\\\"\\\" import hypothesis\",\n \"return i * 2 else: return i * 2 + 1 def delMin(self):\",\n \"min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1])\",\n \"a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1\",\n \"@given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16\",\n \"== [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8]\",\n \"= BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements\",\n \"Ce fichier contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à\",\n \"def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L)\",\n \"import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists class\",\n \"we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L):\",\n \"tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize = 1#taille\",\n \"is bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i //\",\n \"val)) def assert_goodheap(tau,lon): for x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap()\",\n \"hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de\",\n \"try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed. None is\",\n \"= BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert\",\n \"print(\\\"No Child. None is returned.\\\") return if i * 2 + 1 >=\",\n \"# assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers())\",\n \"return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval\",\n \"> lon) or (x * 2 + 1 == lon and HL[2*x] >=\",\n \"i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon)\",\n \"assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i))\",\n \"cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture; #\",\n \"sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def\",\n \"= 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8)\",\n \"father is bigger while i // 2 > 0 and self.heapList[i] < self.heapList[i\",\n \"* 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2 else: return\",\n \"a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau =\",\n \"def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x *\",\n \"a whole heap from a list, by percolating all its elements i =\",\n \"elements assert x == sorted(L + K)#verifie qu'on a bien le minimum avec\",\n \"I have a child mc = self.minChild(i)#mc is the index of the smallest\",\n \"# enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1 def\",\n \"= BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L\",\n \"from hypothesis import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure\",\n \"i tau.percDown(1) for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x)\",\n \"tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma =\",\n \"in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we test that\",\n \"@settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin() is None\",\n \"#print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau =\",\n \"@settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1)\",\n \"have a child mc = self.minChild(i)#mc is the index of the smallest if\",\n \"K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize >\",\n \"tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17)\",\n \"from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers def\",\n \"assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def\",\n \">= self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i\",\n \"assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1 > lon) or (x * 2\",\n \"assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8]\",\n \"None is returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop()\",\n \">= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for\",\n \"got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau\",\n \"= 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000)\",\n \"16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize =\",\n \"* 2 + 1 >= self.currentSize: return i * 2 else: if self.heapList[i*2]\",\n \"tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L)\",\n \"x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau =\",\n \"#for x in range(1,len(L) + 1): # assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) @given(lists(elements=integers()),integers()) @settings(max_examples=1000) def test_insert(L,i): tau\",\n \"= BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in\",\n \"assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert\",\n \"the index of the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i]\",\n \"BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer):\",\n \"x == sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert\",\n \"self.heapList[mc] = tmp i = mc def minChild(self,i): if i * 2 >=\",\n \"val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon): for x\",\n \"> self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i =\",\n \"while i // 2 > 0 and self.heapList[i] < self.heapList[i // 2]: tmp\",\n \"Child. None is returned.\\\") return if i * 2 + 1 >= self.currentSize:\",\n \"tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie\",\n \"d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element 0 self.heapList\",\n \"tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un element\",\n \"= [0] + alist # enlever le [:] while (i < self.currentSize): self.percUp(i)\",\n \"1 def percDown(self,i): while (i * 2) < self.currentSize:#while I have a child\",\n \"1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is\",\n \"percUp(self,i): #upward percolation until 0 reached or father is bigger while i //\",\n \"self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2 + 1\",\n \"[10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) + 1): for\",\n \"(i * 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc is\",\n \"while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L +\",\n \"range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()))\",\n \"in range(len(L)): tau.percDown(x) #then we test that we got a well-ordered heap assert_goodheap(tau,len(L)+1)\",\n \"[10] tau = BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\",\",\n \"sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is\",\n \"def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1\",\n \"if i * 2 >= self.currentSize or i == 0: print(\\\"No Child. None\",\n \"envoyer le diff ou le dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis\",\n \"print(\\\"Empty heap. Nothing is changed. None is returned.\\\") return self.currentSize = self.currentSize -\",\n \"// 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //= 2\",\n \"BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L +=\",\n \"+ 1 == lon and HL[2*x] >= val) or (HL[2*x] >= val and\",\n \"None x = [] tau.buildHeap(K) for l in L:#teste si 1 suite d'insertion/\",\n \"self.heapList[i] self.heapList[i] = tmp i //= 2 def insert(self,k): #inserting a new value\",\n \"de la liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or\",\n \"value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i):\",\n \"2 else: return i * 2 + 1 def delMin(self): try: rval =\",\n \"def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList =\",\n \"None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for\",\n \"# Instructions: # - tester jusqu'à atteindre 100% de couverture; # - corriger\",\n \"== 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize\",\n \"self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i //=\",\n \"self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while\",\n \"(i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x * 2\",\n \"until 0 reached or father is bigger while i // 2 > 0\",\n \"BinHeap() assert tau.delMin() is None tau.buildHeap(L) #print(L) #print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin()\",\n \"> 0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2]\",\n \"2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize\",\n \"def delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap. Nothing is changed.\",\n \"or (x * 2 + 1 == lon and HL[2*x] >= val) or\",\n \"assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16)\",\n \"self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while (i * 2)\",\n \"= tmp i = mc def minChild(self,i): if i * 2 >= self.currentSize\",\n \"the smallest if self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc]\",\n \"i //= 2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k)\",\n \"== sorted(L + K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin()\",\n \"= self.heapList[self.currentSize] self.heapList.pop() self.percDown(1) return rval def buildHeap(self,alist): #creates a whole heap from\",\n \"diff ou le dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis import given,\",\n \"def test_insert(L,i): tau = BinHeap() tau.buildHeap(L) tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau\",\n \"x in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0]\",\n \"tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert tau.minChild(abs(i)) is\",\n \"print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17\",\n \"tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\\\" # -\",\n \"i * 2 else: return i * 2 + 1 def delMin(self): try:\",\n \"self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i // 2]\",\n \"self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i = mc\",\n \"enlever le [:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL):\",\n \"test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:]\",\n \"tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L)\",\n \"assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert tau.delMin()\",\n \"is returned.\\\") return self.currentSize = self.currentSize - 1 self.heapList[1] = self.heapList[self.currentSize] self.heapList.pop() self.percDown(1)\",\n \"* 2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty\",\n \"elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous\",\n \"le [:] while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert\",\n \"retire tous les elements assert x == sorted(L + K)#verifie qu'on a bien\",\n \">= self.currentSize or i == 0: print(\\\"No Child. None is returned.\\\") return if\",\n \"de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers avec un\",\n \"rval def buildHeap(self,alist): #creates a whole heap from a list, by percolating all\",\n \"git par email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import\",\n \"i * 2 >= self.currentSize or i == 0: print(\\\"No Child. None is\",\n \"tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize = 17 tau.percUp(16) assert\",\n \"is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec\",\n \"return i * 2 + 1 def delMin(self): try: rval = self.heapList[1] except\",\n \"in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize\",\n \"settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas binaires d'entiers\",\n \"1 == lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1]\",\n \"#print(\\\"sorted\\\",sorted(L),\\\"\\\\n\\\") #print(\\\"TAU \\\", tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau\",\n \"le diff ou le dépôt git par email.\\\"\\\"\\\" import hypothesis from hypothesis import\",\n \"= self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp i\",\n \"minimum avec delmin assert tau.delMin() is None x = [] tau.buildHeap(K) for l\",\n \"reached or father is bigger while i // 2 > 0 and self.heapList[i]\",\n \"K)#verifie qu'on a bien le minimum avec delmin assert tau.delMin() is None x\",\n \"assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize ==\",\n \"for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = []\",\n \"2 + 1 def delMin(self): try: rval = self.heapList[1] except IndexError: print(\\\"Empty heap.\",\n \"= BinHeap() assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def\",\n \"tau.heapList,\\\"\\\\n\\\") assert tau.delMin() == min(L) @given(lists(elements=integers()),integers()) @settings(max_examples=400) def test_minChild(L,i): tau = BinHeap() assert\",\n \"percolating all its elements i = 1 self.currentSize = len(alist) + 1# +\",\n \"lists class BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas\",\n \"2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3] assert tau.currentsize == 16 tau.heapList.append(8) tau.currentsize\",\n \"les bugs;\\\" # - envoyer le diff ou le dépôt git par email.\\\"\\\"\\\"\",\n \"self.currentSize: return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i *\",\n \"lon and HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val))\",\n \"tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17] >= tau.heapList[8] assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers()))\",\n \"0 reached or father is bigger while i // 2 > 0 and\",\n \"un tas binaire d'entiers avec un element 0 self.heapList = [0] self.currentSize =\",\n \"K assert_goodheap(tau,tau.currentSize) x = [] while tau.currentSize > 1:x.append(tau.delMin())#on retire tous les elements\",\n \"gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList\",\n \"+ 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L) + 1):\",\n \"@settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize = 16 tau.heapList\",\n \"into the heap self.heapList.append(k) self.percUp(self.currentSize) self.currentSize = self.currentSize + 1 def percDown(self,i): while\",\n \"1# + 1 self.heapList = [0] + alist # enlever le [:] while\",\n \"(au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de\",\n \"# - corriger les bugs;\\\" # - envoyer le diff ou le dépôt\",\n \"tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in\",\n \"BinHeap: #structure de tas binaires d'entiers def __init__(self): #initialise un tas binaire d'entiers\",\n \"1): for _ in range(len(L)): tau.percDown(x) #then we test that we got a\",\n \"i == 0: print(\\\"No Child. None is returned.\\\") return if i * 2\",\n \"tmp i = mc def minChild(self,i): if i * 2 >= self.currentSize or\",\n \"heap from a list, by percolating all its elements i = 1 self.currentSize\",\n \"[0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau\",\n \"assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau =\",\n \"self.heapList[mc] self.heapList[mc] = tmp i = mc def minChild(self,i): if i * 2\",\n \"test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute\",\n \"assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas\",\n \"un element 0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList\",\n \"assert tau.heapList == [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma\",\n \"((x * 2 + 1 > lon) or (x * 2 + 1\",\n \"liste heapList (invariant) def percUp(self,i): #upward percolation until 0 reached or father is\",\n \"l in L:#teste si 1 suite d'insertion/ suppression maintient la structure tau.delMin() tau.insert(l)\",\n \"+ alist # enlever le [:] while (i < self.currentSize): self.percUp(i) i +=\",\n \"assert tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert\",\n \"is returned.\\\") return if i * 2 + 1 >= self.currentSize: return i\",\n \"17 tau.percUp(16) assert tau.heapList == [0,1,3,2,8,99,2,2,9,9,103,102,3,2,3,3,10] tau.heapList.append(integer) tau.currentsize = 18 tau.percUp(17) assert tau.heapList[17]\",\n \"tmp = self.heapList[i // 2] self.heapList[i // 2] = self.heapList[i] self.heapList[i] = tmp\",\n \"BinHeap() tau.buildHeap(L) assert tau.currentSize == len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1)\",\n \"tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit\",\n \"while (i < self.currentSize): self.percUp(i) i += 1 def assert_isheaplist(x,val,lon,HL): assert ((x *\",\n \"not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_general(L,K): tau = BinHeap() tau.buildHeap(L)#tas construit avec L\",\n \"tau.buildHeap(L)#tas construit avec L for k in K:tau.insert(k)#on rajoute les elements de K\",\n \"element 0 self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant)\",\n \"HL[2*x] >= val) or (HL[2*x] >= val and HL[2*x+1] >= val)) def assert_goodheap(tau,lon):\",\n \"return i * 2 else: if self.heapList[i*2] < self.heapList[i*2+1]: return i * 2\",\n \"for x in range(1,len(L) + 1): for _ in range(len(L)): tau.percDown(x) #then we\",\n \"- tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;\\\" #\",\n \"= self.currentSize + 1 def percDown(self,i): while (i * 2) < self.currentSize:#while I\",\n \"+ 1 > lon) or (x * 2 + 1 == lon and\",\n \"//= 2 def insert(self,k): #inserting a new value into the heap self.heapList.append(k) self.percUp(self.currentSize)\",\n \"self.currentSize = len(alist) + 1# + 1 self.heapList = [0] + alist #\",\n \"== [0] assert tau.currentSize == 1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3]\",\n \"0 and self.heapList[i] < self.heapList[i // 2]: tmp = self.heapList[i // 2] self.heapList[i\",\n \"= BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:]\",\n \"test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for\",\n \"BinHeap() assert tau.minChild(abs(i)) is None tau.buildHeap(2*L+[0,1]) assert tau.minChild(len(L)+1) is not None @given(lists(elements=integers()),lists(elements=integers())) @settings(max_examples=400,deadline=None)\",\n \"> 1:x.append(tau.delMin())#on retire tous les elements assert x == sorted(L + K)#verifie qu'on\",\n \"def percUp(self,i): #upward percolation until 0 reached or father is bigger while i\",\n \"in range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert\",\n \"tau.insert(i) assert_goodheap(tau,len(L)+1) @given(lists(elements=integers()),integers()) @settings(max_examples=100) def test_percDown(L,i): tau = BinHeap() L += [10] tau.buildHeap(L)\",\n \"tau = BinHeap() tau.currentsize = 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList ==\",\n \"email.\\\"\\\"\\\" import hypothesis from hypothesis import given, settings from hypothesis.strategies import integers, lists\",\n \"tau.heapList[8] >= tau.heapList[4] @given(lists(elements=integers())) @settings(max_examples=1000) def test_build(L): tau = BinHeap() tau.buildHeap(L) assert tau.currentSize\",\n \"while (i * 2) < self.currentSize:#while I have a child mc = self.minChild(i)#mc\",\n \"import given, settings from hypothesis.strategies import integers, lists class BinHeap: #structure de tas\",\n \"Instructions: # - tester jusqu'à atteindre 100% de couverture; # - corriger les\",\n \"(invariant) def percUp(self,i): #upward percolation until 0 reached or father is bigger while\",\n \"== len(L) + 1 assert sorted(tau.heapList) == sorted(L+[0]) assert_goodheap(tau,len(L)+1) #for x in range(1,len(L)\",\n \"= 16 tau.heapList = gamma[:] tau.percUp(15) assert tau.heapList == gamma[:] tau.heapList[15] = 2\",\n \"self.heapList = [0] self.currentSize = 1#taille de la liste heapList (invariant) def percUp(self,i):\",\n \"L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x in range(1,len(L) +\",\n \"construit avec L for k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize)\",\n \"self.currentSize:#while I have a child mc = self.minChild(i)#mc is the index of the\",\n \"self.heapList[i] > self.heapList[mc]: tmp = self.heapList[i] self.heapList[i] = self.heapList[mc] self.heapList[mc] = tmp i\",\n \"heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10] tau = BinHeap() assert\",\n \"assert tau.heapList == gamma[:] tau.heapList[15] = 2 tau.percUp(15) print(tau.heapList) assert tau.heapList == [0,1,3,2,9,99,2,2,10,9,103,102,3,2,3,3]\",\n \"self.heapList[i*2+1]: return i * 2 else: return i * 2 + 1 def\",\n \"contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100%\",\n \"tau = BinHeap() L += [10] tau.buildHeap(L) tau.heapList[1] = i tau.percDown(1) for x\",\n \"1 @given(integers()) @settings(max_examples=100) def test_percup(integer): gamma = [0,1,3,2,9,99,2,3,10,9,103,102,3,2,3,3] tau = BinHeap() tau.currentsize =\",\n \"range(1,lon): assert_isheaplist(x,tau.heapList[x],tau.currentSize,tau.heapList) def test_init(): tau = BinHeap() assert tau.heapList == [0] assert tau.currentSize\"\n]","isConversational":false}}},{"rowIdx":4088,"cells":{"ngram":{"kind":"list like","value":["Use dict to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation':","the events from given table flux,det,weighted,smeared = params for c in table.columns: if","tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins =","distribution, for each reaction channel or the sum of them. \"\"\" import io","'.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates","= tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1",": str Name of detector. If ``\"all\"``, will use all detectors supported by","* MeV # 1MeV # Loop over sampled times. for i, t in","osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR])","snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in","= tend nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time()","(set of) detector(s). These event rates are given as a function of the","file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results","collated files, and .png's made for this snewpy run. Returns ------- dict Dictionary","if t >= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend)","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s,","(for each detector and for each time bin). The output tables allow to","SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input =","the table to have only channel column levels = list(table.columns.names) levels.remove('channel') t =","verbose : bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool","in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file,","flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings","smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the","tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\",","output gives the number of events detected as a function of energy for",": int or float Distance to supernova in kpc. output_filename : str or","order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params):","Choose flavor transformation. Use dict to associate the transformation name with its class.","model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for","ending_index = [next(j for j, t in enumerate(model_tend) if t >= tend)] #","= t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the","in the previous step with the cross-sections for the interaction channels happening in","tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is","time. * **Collating SNOwGLoBES outputs:** This step puts together all the interaction channels","to do this, either generate a time series or a fluence file. This","return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino","in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec","over them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed","flavor transformation. Use dict to associate the transformation name with its class. flavor_transformation_dict","astropy import units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation","#drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the","else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file =","aNuMu aNuTau') # Generate energy + number flux table. for j, E in","the effective mass of the detector as well as a smearing matrix describing","1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file)","time bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This","from tarfile and sets up lists of paths and fluxfilenames for later use","result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving","+ tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] =","all detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose output,","remaining columns the number of events for each interaction channel in the detector.","d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This version will","associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL),","= {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for","smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)')","os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux","for name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum","interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given","as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input","nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in","output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This version will subsample","---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path :","'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO':","= np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables =","for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j","compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir =","subsample the times in a supernova model, produce energy tables expected by SNOwGLoBES,","generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files from","mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False,","result is a compressed .tar file containing all individual input files. * **Running","together all the interaction channels and time bins evaluated by SNOwGLoBES in a","len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None,","osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j","time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if","def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates","params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10)","neutrino energy spectrum and neutrino time distribution, for each reaction channel or the","the results for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T","flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested","as tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {} for","NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation =","+ '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf:","the cross-sections for the interaction channels happening in various detectors supported by SNOwGLoBES.","energy = np.linspace(0, 100, 501) * MeV # Loop over sampled times. for","**patterns): #rearrange the table to have only channel column levels = list(table.columns.names) levels.remove('channel')","the times in a supernova model, produce energy tables expected by SNOwGLoBES, and","if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read","len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as","the neutrino flux at each time step, the latter will compute the integrated","(if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin,","= model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension)","snewpy.flavor_transformation documentation for possible values. d : int or float Distance to supernova","= snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0]","bin and for each interaction channel. verbose : bool Whether to generate verbose","or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory","= {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename","osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR])","in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not","# formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()}","* MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s,","= tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f","output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file","aNuTau') # Generate energy + number flux table. for j, E in enumerate(energy):","them. \"\"\" import io import logging import os import re import tarfile from","import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface","detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile","Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] +","detector_input : str Name of detector. If ``\"all\"``, will use all detectors supported","or None Name of output file. If ``None``, will be based on input","= list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input:","Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity or None","list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files,","Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for","['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to","tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) >","flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time.","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and","bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return","s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save","full time window of the model if tstart is None: tstart = snmodel.get_time()[0]","= SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input","and interaction channels. There are three basic steps to using SNOwGLoBES from SNEWPY:","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s =","header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data =","flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c","E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux","sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence","supernova model, produce energy tables expected by SNOwGLoBES, and compress the output into","inputs are None: full time window of the model if tstart is None:","with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation.","with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if","u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over sampled times.","NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir,","False, it gives as output the plot of the energy distribution for each","do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params for c","NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path)","(if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1,","and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns,","file containing neutrino flux information from supernova model. model_type : str Format of","class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED),","files, and .png's made for this snewpy run. Returns ------- dict Dictionary of","t[name]=t_agg #fill the column #return table with the original levels order t =","from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways to","= logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series","#save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in","each interaction channel. verbose : bool Whether to generate verbose output, e.g. for","detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots or returns","SNOwGLoBES in a single file (for each detector and for each time bin).","t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel","+ '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info =","It supports many different neutrino detectors, detector materials and interaction channels. There are","len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem","0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if","def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux files and","'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if","transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values.","These event rates are given as a function of the neutrino energy and",".tar file containing all individual input files. * **Running SNOwGLoBES:** This step convolves","at each time step, the latter will compute the integrated neutrino flux (fluence)","supernova model. model_type : str Format of input file. Matches the name of","with tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}')","materials and interaction channels. There are three basic steps to using SNOwGLoBES from","for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save","levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels","def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in","SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways","as tf: #creates file in tar archive that gives information on parameters output","times dt = tb-ta #first time bin of model in requested interval osc_spectra","+ '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir,","as mpl import matplotlib.pyplot as plt import numpy as np from astropy import","column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column","supernova script inside SNOwGLoBES, which outputs calculated event rates expected for a given","snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin","in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename","channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed.","a single file (for each detector and for each time bin). The output","interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is","ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel","If ``None``, will be based on input file name. tstart : astropy.Quantity or","step convolves the fluence generated in the previous step with the cross-sections for","#get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and save","None Length of time slices. Returns ------- str Path of compressed .tar file","model_type) # Choose flavor transformation. Use dict to associate the transformation name with","TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt))",">= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if t","contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates","The output tables allow to build the detected neutrino energy spectrum and neutrino","model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root +","energy for each interaction channel, integrated in a given time window (or time","interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for","in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2","containing all individual input files. * **Running SNOwGLoBES:** This step convolves the fluence","- tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] +","bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp:","#This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict","np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir,","os import re import tarfile from pathlib import Path from tempfile import TemporaryDirectory","= params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10)","+ 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname","step, the latter will compute the integrated neutrino flux (fluence) in the time","> tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t >=","simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux files and configures","= 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0","u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino","result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'}","output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir():","aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base","with the cross-sections for the interaction channels happening in various detectors supported by","model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta:","if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _","'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s","= tb-ta #first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]],","in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = []","detector materials and interaction channels. There are three basic steps to using SNOwGLoBES","= \".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type","'.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename)","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s =","for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in","to integrate over, or list of end times of the time series bins.","sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str):","0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared'","tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy","deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges","if inputs are None: full time window of the model if tstart is","> 1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ =","t = times[i] dt = tb-ta else: ta = tstart tb = tend","over sampled times. for i in range(nbin): if nbin > 1: ta =","event rates expected for a given (set of) detector(s). These event rates are","number of events for each interaction channel in the detector. \"\"\" def aggregate_channels(table,","a tarfile. Parameters ---------- model_path : str Input file containing neutrino flux information","plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino","supports many different neutrino detectors, detector materials and interaction channels. There are three","tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl","for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base =","filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)","channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels","if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s)","files in SNOwGLoBES format. This version will subsample the times in a supernova","MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV #","table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots","the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See","{'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv","------- dict Dictionary of data tables: One table per time bin; each table","(ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate","``\"all\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it","times in a supernova model, produce energy tables expected by SNOwGLoBES, and compress","detector and for each time bin). The output tables allow to build the","energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence","be based on input file name. ntbins : int Number of time slices.","deltat : astropy.Quantity or None Length of time slices. Returns ------- str Path","or None End of time interval to integrate over, or list of end","snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30,","= tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin","Name of detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. verbose","based on input file name. tstart : astropy.Quantity or None Start of time","+ number flux table. for j, E in enumerate(energy): for flavor in Flavor:","by SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path :","time window of the model if tstart is None: tstart = snmodel.get_time()[0] tend","nbin > 1: ta = tstart[i] tb = tend[i] t = times[i] dt","the neutrino flux files and configures and runs the supernova script inside SNOwGLoBES,","first column the energy bins, in the remaining columns the number of events","t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return","channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items():","_ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s,","matplotlib.pyplot as plt import numpy as np from astropy import units as u","tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det","output files from SNOwGLoBES, collated files, and .png's made for this snewpy run.","this snewpy run. Returns ------- dict Dictionary of data tables: One table per","are None: full time window of the model if tstart is None: tstart","events from given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max()","snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin","results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables","gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6","#intermediate time bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i],","event rates from a given input supernova neutrino flux. It supports many different","energy spectrum and neutrino time distribution, for each reaction channel or the sum","re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def","str Path of compressed .tar file with neutrino flux data. \"\"\" model_class =","enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend)","energy bins, in the remaining columns the number of events for each interaction","= snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s","channels. There are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating","t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j,","np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j","snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor","None: full time window of the model if tstart is None: tstart =","as input the supernova simulation model. The first will evaluate the neutrino flux","int or float Distance to supernova in kpc. output_filename : str or None","rates are given as a function of the neutrino energy and time, for","model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times),","w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}'","output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data","This step puts together all the interaction channels and time bins evaluated by","smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if","up #default if inputs are None: full time window of the model if","on input file name. ntbins : int Number of time slices. Will be","i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table","The output gives the number of events detected as a function of energy","tf: #creates file in tar archive that gives information on parameters output =","to file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _","given. deltat : astropy.Quantity or None Length of time slices. Returns ------- str","borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux}","SNOwGLoBES output files and generates plots or returns a data table. Parameters ----------","from SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns -------","kpc. output_filename : str or None Name of output file. If ``None``, will","= next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] =","the plot of the energy distribution for each time bin and for each","for i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if","= f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else:","int Number of time slices. Will be ignored if ``deltat`` is also given.","for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a","f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+' '.join(list(table.columns))","detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. verbose : bool","detector(s). These event rates are given as a function of the neutrino energy","table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best',","cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy","\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info,","is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0:","io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV","for j, t in enumerate(model_tend) if t >= tend)] # Generate output. if","getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the transformation name","energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in","= 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None: tfname","is installed. tarball_path : str Path of compressed .tar file produced e.g. by","the number of events detected as a function of energy for each interaction","of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path)","if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt","either generate a time series or a fluence file. This is done taking","extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1,","will evaluate the neutrino flux at each time step, the latter will compute","[next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j","will compute the integrated neutrino flux (fluence) in the time bin. The result","up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir:","for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where","timings up #default if inputs are None: full time window of the model","else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if","= {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e':","table and output to file in tar archive. output = '\\n'.join(table).encode('ascii') extension =","simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {} #save","with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header =","'+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] =","mpl import matplotlib.pyplot as plt import numpy as np from astropy import units","energy distribution for each time bin and for each interaction channel. verbose :","osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else:","bin). The output tables allow to build the detected neutrino energy spectrum and","params): #plotting the events from given table flux,det,weighted,smeared = params for c in","tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file","t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events","AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)}","latter will compute the integrated neutrino flux (fluence) in the time bin. The","over, or list of end times of the time series bins. Returns -------","# Generate energy + number flux table. for j, E in enumerate(energy): for","bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the","units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import *","model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in","and .png's made for this snewpy run. Returns ------- dict Dictionary of data","channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and save to","bins. Returns ------- str Path of compressed .tar file with neutrino flux data.","tb-ta #first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy,","module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event","a supernova model, produce energy tables expected by SNOwGLoBES, and compress the output","for each interaction channel, integrated in a given time window (or time bin),","and compress the output into a tarfile. Parameters ---------- model_path : str Input","the time series bins. Returns ------- str Path of compressed .tar file with","If ``None``, will be based on input file name. ntbins : int Number","logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time","if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file)","in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False,","flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV /","------- str Path of compressed .tar file with neutrino flux data. \"\"\" model_class","the energy distribution for each time bin and for each interaction channel. verbose","with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return","import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from","basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:**","for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files","# 1MeV # Loop over sampled times. for i, t in enumerate(times): osc_spectra","detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as output the","the latter will compute the integrated neutrino flux (fluence) in the time bin.","produce energy tables expected by SNOwGLoBES, and compress the output into a tarfile.","= tb-ta else: ta = tstart tb = tend t = times dt","SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for debugging. \"\"\"","flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if","or None Start of time interval to integrate over, or list of start","sampled times. for i in range(nbin): if nbin > 1: ta = tstart[i]","None End of time interval to integrate over, or list of end times","= flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up","name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO':","time bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1):","filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d,","flux (fluence) in the time bin. The result is a compressed .tar file","directory where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file","Format of input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`.","astropy.Quantity or None Start of time interval to integrate over, or list of","slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt","#rearrange the table to have only channel column levels = list(table.columns.names) levels.remove('channel') t","osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec","* **Generating input files for SNOwGLoBES:** There are two ways to do this,","str Path to directory where SNOwGLoBES is installed. tarball_path : str Path of","(tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:]","for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {}","to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is","will be based on input file name. tstart : astropy.Quantity or None Start","supported by SNOwGLoBES. It takes into account the effective mass of the detector","= model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64)","ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] +=","SNOwGLoBES format. This version will subsample the times in a supernova model, produce","extension = \".dat\" if output_filename is not None: if nbin > 1: filename","of start times of the time series bins. tend : astropy.Quantity or None","each time bin and for each interaction channel. verbose : bool Whether to","str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d :","ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES format. This version will subsample","times of the time series bins. Returns ------- str Path of compressed .tar","'.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname),","NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(),","flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int or float","detector_input = [detector_input] result = {} #Extracts data from tarfile and sets up","in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2)","matrix describing the energy-dependent detection efficiency. The output gives the number of events",": bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove","archive that gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV","osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s =","configures and runs the supernova script inside SNOwGLoBES, which outputs calculated event rates","given time window (or time bin), or in a snapshot in time. *","Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV)","plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title","= osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3","by SNOwGLoBES. It takes into account the effective mass of the detector as","tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}') return","t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table =","the condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path","time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory","import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy as","with the condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed'","t1 = tend nbin = 1 times = 0.5*(tstart + tend) model_times =","save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True)","= t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table","axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels order t","gives as output the plot of the energy distribution for each time bin","np.linspace(0, 100, 501) * MeV # Loop over sampled times. for i in","'\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100,","= model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2'","t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from given table","ways to do this, either generate a time series or a fluence file.","cross-sections for the interaction channels happening in various detectors supported by SNOwGLoBES. It","input file name. tstart : astropy.Quantity or None Start of time interval to","os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to 30 time","except: t0 = tstart t1 = tend nbin = 1 times = 0.5*(tstart","is a compressed .tar file containing all individual input files. * **Running SNOwGLoBES:**","all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as output","for each reaction channel or the sum of them. \"\"\" import io import","'w:bz2') as tf: #creates file in tar archive that gives information on parameters","will use all detectors supported by SNOwGLoBES. verbose : bool Whether to generate","neutrino flux information from supernova model. model_type : str Format of input file.","channel or the sum of them. \"\"\" import io import logging import os","* dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV))","if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy","as input the neutrino flux files and configures and runs the supernova script","of output file. If ``None``, will be based on input file name. tstart","dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of","\"\"\"Generate time series files in SNOwGLoBES format. This version will subsample the times","in a given time window (or time bin), or in a snapshot in","files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with","s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir,","in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor:","None Start of time interval to integrate over, or list of start times",": str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d","energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled","The first will evaluate the neutrino flux at each time step, the latter","+ transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s,","is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style","or ``generate_fluence()``. detector_input : str Name of detector. If ``\"all\"``, will use all","compute the integrated neutrino flux (fluence) in the time bin. The result is","if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input]","tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w","def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s =","energy tables expected by SNOwGLoBES, and compress the output into a tarfile. Parameters","as a smearing matrix describing the energy-dependent detection efficiency. The output gives the","snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else:","NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table. for","in tar archive that gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'),","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to","and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str","the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of","graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events')","loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected'","extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax,","= list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data",": astropy.Quantity or None End of time interval to integrate over, or list","Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu","len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin","Dictionary of data tables: One table per time bin; each table contains in","to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for","else: ta = tstart tb = tend t = times dt = tb-ta","a smearing matrix describing the energy-dependent detection efficiency. The output gives the number","---------- model_path : str Input file containing neutrino flux information from supernova model.","deltat=None): \"\"\"Generate time series files in SNOwGLoBES format. This version will subsample the","a given (set of) detector(s). These event rates are given as a function","#last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy,","Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output","of the detector as well as a smearing matrix describing the energy-dependent detection","in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is not","information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 *","e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files,","* u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop","a given input supernova neutrino flux. It supports many different neutrino detectors, detector","for i in range(nbin): if nbin > 1: ta = tstart[i] tb =","slices. Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity or","rates from a given input supernova neutrino flux. It supports many different neutrino","tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) +","e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\"all\"``,","t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart","= os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.' +","the time series bins. tend : astropy.Quantity or None End of time interval","<https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova neutrino flux.","of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int or","It takes into account the effective mass of the detector as well as","mass of the detector as well as a smearing matrix describing the energy-dependent","a function of energy for each interaction channel, integrated in a given time","tarfile and sets up lists of paths and fluxfilenames for later use with","run. Returns ------- dict Dictionary of data tables: One table per time bin;","import units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import","describing the energy-dependent detection efficiency. The output gives the number of events detected","dt = tb-ta else: ta = tstart tb = tend t = times","tb = tend[i] t = times[i] dt = tb-ta else: ta = tstart","column the energy bins, in the remaining columns the number of events for","be ignored if ``deltat`` is also given. deltat : astropy.Quantity or None Length","info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d,","collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots","do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed","Number of time slices. Will be ignored if ``deltat`` is also given. deltat","['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename =","np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse","account the effective mass of the detector as well as a smearing matrix","dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *=","output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename =","remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and .png's made","results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp","integrated in a given time window (or time bin), or in a snapshot","the first column the energy bins, in the remaining columns the number of","per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate","return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence","= getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the transformation","logging.debug(s) # Encode energy/flux table and output to file in tar archive. output","to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO':","fluence generated in the previous step with the cross-sections for the interaction channels","tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename","= t.filter(like=pattern) #sum over them and save to a separate column t_agg =","results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for","original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def","io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the","snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import","table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern)","tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None):","a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES","If ``\"all\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If False,","in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval","in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for","distribution for each time bin and for each interaction channel. verbose : bool","results for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index","output file. If ``None``, will be based on input file name. ntbins :","neutrino detectors, detector materials and interaction channels. There are three basic steps to","in the first column the energy bins, in the remaining columns the number","step with the cross-sections for the interaction channels happening in various detectors supported","in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5,","of end times of the time series bins. Returns ------- str Path of","Loop over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy,","#Make a tarfile with the condensed data files and plots output_name = Path(tarball_path).stem","output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) #","None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip","= os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to 30","step puts together all the interaction channels and time bins evaluated by SNOwGLoBES","tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {} for flux,t","_ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.'","output_filename : str or None Name of output file. If ``None``, will be","else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root","os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.' + transformation_type","table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j]","different neutrino detectors, detector materials and interaction channels. There are three basic steps","filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d,","of the neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir","interval to integrate over, or list of start times of the time series","tb-ta else: ta = tstart tb = tend t = times dt =","sets up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as","aNuE aNuMu aNuTau') # Generate energy + number flux table. for j, E","model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if","None Name of output file. If ``None``, will be based on input file","files and configures and runs the supernova script inside SNOwGLoBES, which outputs calculated","or list of start times of the time series bins. tend : astropy.Quantity","is also given. deltat : astropy.Quantity or None Length of time slices. Returns","flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('#","dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s","for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s","neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use","osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 *","if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root,","which outputs calculated event rates expected for a given (set of) detector(s). These","#default if inputs are None: full time window of the model if tstart","in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to","= model_class(model_path) # Subsample the model time. Default to 30 time slices. tmin","ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges =","data from tarfile and sets up lists of paths and fluxfilenames for later","= model_class(model_path) #set the timings up #default if inputs are None: full time","with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that gives","str Name of detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES.","into account the effective mass of the detector as well as a smearing","+ 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive","file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str","= '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0,","model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info","tables expected by SNOwGLoBES, and compress the output into a tarfile. Parameters ----------","import re import tarfile from pathlib import Path from tempfile import TemporaryDirectory import","interaction channels. There are three basic steps to using SNOwGLoBES from SNEWPY: *","None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else:","of them. \"\"\" import io import logging import os import re import tarfile","info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path,","file. If ``None``, will be based on input file name. tstart : astropy.Quantity","interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt","import matplotlib.pyplot as plt import numpy as np from astropy import units as","its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO':",": int Number of time slices. Will be ignored if ``deltat`` is also","(4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s,","name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over","of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor","This step convolves the fluence generated in the previous step with the cross-sections","generate verbose output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input ==","column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get","for each time bin). The output tables allow to build the detected neutrino","= np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if","Generate energy + number flux table. for j, E in enumerate(energy): for flavor","model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1,","1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV #","generate a time series or a fluence file. This is done taking as","output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for","the model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax =","with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL),","output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ =","0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 =","model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This","c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and","#fill the column #return table with the original levels order t = t.unstack(levels)","= 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared':","Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence =","that gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV =","#tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results =","model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1]","starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i]","by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results","flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$","each time step, the latter will compute the integrated neutrino flux (fluence) in","ta = tstart tb = tend t = times dt = tb-ta #first","#plotting the events from given table flux,det,weighted,smeared = params for c in table.columns:","in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results","bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for","= snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat","on input file name. tstart : astropy.Quantity or None Start of time interval","snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger","30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s)","dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy +","== 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result =","tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1])","range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if","_ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1)","channel. verbose : bool Whether to generate verbose output, e.g. for debugging. remove_generated_files:","each interaction channel in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table","> 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in","> 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0","results, for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as","of output file. If ``None``, will be based on input file name. ntbins","function of energy for each interaction channel, integrated in a given time window","logging import os import re import tarfile from pathlib import Path from tempfile","to build the detected neutrino energy spectrum and neutrino time distribution, for each","next(j for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index =","= 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data])","neutrino flux (fluence) in the time bin. The result is a compressed .tar","table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per","SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns ------- dict","and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path,","tarfile. Parameters ---------- model_path : str Input file containing neutrino flux information from","\"\"\"Generate fluence files in SNOwGLoBES format. This version will subsample the times in",": str Path to directory where SNOwGLoBES is installed. tarball_path : str Path","for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors)","Parameters ---------- model_path : str Input file containing neutrino flux information from supernova","detectors supported by SNOwGLoBES. It takes into account the effective mass of the","= output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if","to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg","str Input file containing neutrino flux information from supernova model. model_type : str","tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from","integrated neutrino flux (fluence) in the time bin. The result is a compressed","flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in","= {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for","``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected","= times dt = tb-ta #first time bin of model in requested interval","or a fluence file. This is done taking as input the supernova simulation","list of end times of the time series bins. Returns ------- str Path","window (or time bin), or in a snapshot in time. * **Collating SNOwGLoBES","from supernova model. model_type : str Format of input file. Matches the name","= next(j for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index","installed. tarball_path : str Path of compressed .tar file produced e.g. by ``generate_time_series()``","in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t >","= '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not None: if nbin >","times[i] dt = tb-ta else: ta = tstart tb = tend t =","= np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled times.","in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t in","of time slices. Will be ignored if ``deltat`` is also given. deltat :","501) * MeV # Loop over sampled times. for i in range(nbin): if","tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t >= tend)]","end times of the time series bins. Returns ------- str Path of compressed","allow to build the detected neutrino energy spectrum and neutrino time distribution, for","< model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta)","0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0]","# Subsample the model time. Default to 30 time slices. tmin = snmodel.get_time()[0]","d : int or float Distance to supernova in kpc. output_filename : str","Length of time slices. Returns ------- str Path of compressed .tar file with","integrate over, or list of start times of the time series bins. tend","containing neutrino flux information from supernova model. model_type : str Format of input","tarball_path : str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or","= os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs are","script inside SNOwGLoBES, which outputs calculated event rates expected for a given (set","tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label","generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path","function of the neutrino energy and time, for each interaction channel. Parameters ----------","j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j,","= snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time","channel in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table to have","output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not None: if nbin","= os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s,","tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and","mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates","in flux_files),res)) # save result to file for re-use in collate() cache_file =","all the interaction channels and time bins evaluated by SNOwGLoBES in a single","complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title}","is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip","if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for","= flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model","ta = tstart[i] tb = tend[i] t = times[i] dt = tb-ta else:","files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det]","event rates are given as a function of the neutrino energy and time,","tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots or","0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else","def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res =","``\"all\"``, will use all detectors supported by SNOwGLoBES. verbose : bool Whether to","Subsample the model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax","taking as input the supernova simulation model. The first will evaluate the neutrino","astropy.Quantity or None Length of time slices. Returns ------- str Path of compressed","for old-style results, for backward compatibiity results = {} #save collated files: with","m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return","Distance to supernova in kpc. output_filename : str or None Name of output","= \".dat\" if output_filename is not None: if nbin > 1: filename =","flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict","series bins. tend : astropy.Quantity or None End of time interval to integrate","time interval to integrate over, or list of end times of the time","the interaction channels and time bins evaluated by SNOwGLoBES in a single file","table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text","list of start times of the time series bins. tend : astropy.Quantity or","tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output.","{cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent',","patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and","100, 501) * MeV # 1MeV # Loop over sampled times. for i,","SNOwGLoBES. It takes into account the effective mass of the detector as well","'.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates","snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor","will use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives","series bins. Returns ------- str Path of compressed .tar file with neutrino flux","tables allow to build the detected neutrino energy spectrum and neutrino time distribution,","return t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared =","'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO':","in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j, t","dict Dictionary of data tables: One table per time bin; each table contains","a fluence file. This is done taking as input the supernova simulation model.","os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs are None:","Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar","each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES","j, t in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename","table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the","osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV","'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that","detected as a function of energy for each interaction channel, integrated in a","and time bins evaluated by SNOwGLoBES in a single file (for each detector","files for SNOwGLoBES:** There are two ways to do this, either generate a","output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy =","1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)):","detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If","nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _","file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is","= int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s,","data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally","filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w')","model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1])","steps to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There","for det in tables: results[det] = {} for flux,t in tables[det].items(): t =","not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax -","SNOwGLoBES outputs:** This step puts together all the interaction channels and time bins","leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) #","to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files","based on input file name. ntbins : int Number of time slices. Will","on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg","+ '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf:","open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy","it gives as output the plot of the energy distribution for each time","data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz')","files and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir :","{} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for","this, either generate a time series or a fluence file. This is done","input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type :","documentation for possible values. d : int or float Distance to supernova in","in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']:","for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV","input files for SNOwGLoBES:** There are two ways to do this, either generate","bins evaluated by SNOwGLoBES in a single file (for each detector and for","output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name)","= (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times =","Name of output file. If ``None``, will be based on input file name.","Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _","tstart[i] tb = tend[i] t = times[i] dt = tb-ta else: ta =","output file. If ``None``, will be based on input file name. tstart :","tend : astropy.Quantity or None End of time interval to integrate over, or","Name of detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. skip_plots:","(GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy'","# Encode energy/flux table and output to file in tar archive. output =","* u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over sampled","= tstart[i] tb = tend[i] t = times[i] dt = tb-ta else: ta","corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation","output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as","tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input","os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files","t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t","bin. The result is a compressed .tar file containing all individual input files.","= m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res","neutrino flux at each time step, the latter will compute the integrated neutrino","np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled times. for","the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel","data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is","the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t","the previous step with the cross-sections for the interaction channels happening in various","{'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename =","tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output","NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table.","series files in SNOwGLoBES format. This version will subsample the times in a","times of the time series bins. tend : astropy.Quantity or None End of","gives the number of events detected as a function of energy for each","times. for i in range(nbin): if nbin > 1: ta = tstart[i] tb","evaluated by SNOwGLoBES in a single file (for each detector and for each","paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as","using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two","to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp =","SNOwGLoBES:** This step convolves the fluence generated in the previous step with the","table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c),","NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table. for j,","temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]])","flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the","= snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i","not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) #","detected neutrino energy spectrum and neutrino time distribution, for each reaction channel or","spectrum and neutrino time distribution, for each reaction channel or the sum of","verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots or returns a data","model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64)","snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1","else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root","model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate","timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu",":py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible","Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables","tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables","present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d)","for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in","SNOwGLoBES. skip_plots: bool If False, it gives as output the plot of the","and runs the supernova script inside SNOwGLoBES, which outputs calculated event rates expected","Encode energy/flux table and output to file in tar archive. output = '\\n'.join(table).encode('ascii')","as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+'","file containing all individual input files. * **Running SNOwGLoBES:** This step convolves the","output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input","'.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar","= output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname","pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot","bbox_inches='tight') #Make a tarfile with the condensed data files and plots output_name =","= snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for","np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename","SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path : str","do this, either generate a time series or a fluence file. This is","if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det)","model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i]","Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s","table.append(s) logging.debug(s) # Encode energy/flux table and output to file in tar archive.","= [next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index =","= Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar:","deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt =","tedges[:-1]) # Generate output. if output_filename is not None: tfname = output_filename +","Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s","astropy.Quantity or None End of time interval to integrate over, or list of","output tables allow to build the detected neutrino energy spectrum and neutrino time","*= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j in","file. This is done taking as input the supernova simulation model. The first","Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt","energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path","separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the","= model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] =","result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for re-use in","list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from","= output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file in","snewpy run. Returns ------- dict Dictionary of data tables: One table per time","t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from","= t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from given","This is done taking as input the supernova simulation model. The first will","+ \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output)","output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension","**Collating SNOwGLoBES outputs:** This step puts together all the interaction channels and time","One table per time bin; each table contains in the first column the","the interaction channels happening in various detectors supported by SNOwGLoBES. It takes into","Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path","of data tables: One table per time bin; each table contains in the","next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j","verbose=False): \"\"\"Takes as input the neutrino flux files and configures and runs the","TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np","t >= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if","expected by SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path","enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is not None:","the integrated neutrino flux (fluence) in the time bin. The result is a","isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile and sets","parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy","else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model","debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and .png's","tend t = times dt = tb-ta #first time bin of model in","table contains in the first column the energy bins, in the remaining columns","interaction channels happening in various detectors supported by SNOwGLoBES. It takes into account","model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default","t.filter(like=pattern) #sum over them and save to a separate column t_agg = t_sel.sum(axis='columns')","import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy","in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor:","* MeV # Loop over sampled times. for i in range(nbin): if nbin","# Loop over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t,","of detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. verbose :","if ``deltat`` is also given. deltat : astropy.Quantity or None Length of time","flux information from supernova model. model_type : str Format of input file. Matches","plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file","/= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s =","bin; each table contains in the first column the energy bins, in the","+ transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2')","'.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output))","coding: utf-8 -*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES","time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if","of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation.","bins, in the remaining columns the number of events for each interaction channel","Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat","files from SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns","#read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s,","import numpy as np from astropy import units as u from tqdm.auto import","time step, the latter will compute the integrated neutrino flux (fluence) in the","d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES format. This version","model_class(model_path) #set the timings up #default if inputs are None: full time window","files. * **Running SNOwGLoBES:** This step convolves the fluence generated in the previous","of compressed .tar file with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type)","s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X])","interaction channel in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table to","to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are","SNOwGLoBES:** There are two ways to do this, either generate a time series","TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file =","a function of the neutrino energy and time, for each interaction channel. Parameters","to directory where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar","of time interval to integrate over, or list of end times of the","model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with","= tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type,","= 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index","Remove the output files from SNOwGLoBES, collated files, and .png's made for this","if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s)","cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return","flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at","requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor]","inside SNOwGLoBES, which outputs calculated event rates expected for a given (set of)","get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups()","use all detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose","collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result)","tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin =","* **Collating SNOwGLoBES outputs:** This step puts together all the interaction channels and","the sum of them. \"\"\" import io import logging import os import re","columns the number of events for each interaction channel in the detector. \"\"\"","as np from astropy import units as u from tqdm.auto import tqdm import","the column #return table with the original levels order t = t.unstack(levels) t","by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\"all\"``, will","'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname =","re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}')","plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title =","io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate","Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra","tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This version will subsample the times","which contain `like` t_sel = t.filter(like=pattern) #sum over them and save to a","energy-dependent detection efficiency. The output gives the number of events detected as a","text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output","'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #","for this snewpy run. Returns ------- dict Dictionary of data tables: One table","det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for re-use","[] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos","from given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() >","= deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1)","build the detected neutrino energy spectrum and neutrino time distribution, for each reaction","tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 =","= np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] =","transformation. See snewpy.flavor_transformation documentation for possible values. d : int or float Distance","tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux files","if output_filename is not None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else:","flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta)","detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for","flux table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] =","len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except:","snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path,","debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O')","i in range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i]","various detectors supported by SNOwGLoBES. It takes into account the effective mass of","dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend)","tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt =","strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\","energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor:","``generate_fluence()``. detector_input : str Name of detector. If ``\"all\"``, will use all detectors","#dict for old-style results, for backward compatibiity results = {} #save collated files:","ending_index[i] = next(j for j, t in enumerate(model_tend) if t >= tend[i]) else:","output to file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root,","expected for a given (set of) detector(s). These event rates are given as","from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced","str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input","transformation. Use dict to associate the transformation name with its class. flavor_transformation_dict =","mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if","plt import numpy as np from astropy import units as u from tqdm.auto","snmodel = model_class(model_path) # Subsample the model time. Default to 30 time slices.","in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t in","returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where","detectors, detector materials and interaction channels. There are three basic steps to using","* from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__)","of) detector(s). These event rates are given as a function of the neutrino","# Generate output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2'","where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file produced","time window (or time bin), or in a snapshot in time. * **Collating","j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt","= np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False:","+ '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info =","= simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {}","There are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input","only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in","input file name. ntbins : int Number of time slices. Will be ignored",": bool Whether to generate verbose output, e.g. for debugging. \"\"\" sng =","1MeV # Loop over sampled times. for i, t in enumerate(times): osc_spectra =","detection efficiency. The output gives the number of events detected as a function","time series or a fluence file. This is done taking as input the","evaluate the neutrino flux at each time step, the latter will compute the","extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def","Returns ------- str Path of compressed .tar file with neutrino flux data. \"\"\"","\"\"\"Takes as input the neutrino flux files and configures and runs the supernova","previous step with the cross-sections for the interaction channels happening in various detectors","flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model","tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension","for each interaction channel. verbose : bool Whether to generate verbose output, e.g.","tb = tend t = times dt = tb-ta #first time bin of","can estimate detected event rates from a given input supernova neutrino flux. It","j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for","= '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root +","Whether to generate verbose output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if","file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector.","> 1: ta = tstart[i] tb = tend[i] t = times[i] dt =","tar archive that gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output))","for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /=","table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number","sum of them. \"\"\" import io import logging import os import re import","str or None Name of output file. If ``None``, will be based on","model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index =","flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default","tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)","formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()}","output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES format. This version will","Loop over sampled times. for i in range(nbin): if nbin > 1: ta","to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the","input files. * **Running SNOwGLoBES:** This step convolves the fluence generated in the","bool If False, it gives as output the plot of the energy distribution","t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t","model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in","events detected as a function of energy for each interaction channel, integrated in","= snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt)","= tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path,","detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel column","tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return","model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with","what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward","\"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif","= tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files","model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i]","= list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file","from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl import","**Running SNOwGLoBES:** This step convolves the fluence generated in the previous step with","dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time","tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) +","det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result","= snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for","# strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type +","Start of time interval to integrate over, or list of start times of","utf-8 -*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_","lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete","of events detected as a function of energy for each interaction channel, integrated",": str Input file containing neutrino flux information from supernova model. model_type :","output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root,","convolves the fluence generated in the previous step with the cross-sections for the","= aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s]","= model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] =","{weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)')","'\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not None: if nbin > 1:",": str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``.","time bin; each table contains in the first column the energy bins, in","import os import re import tarfile from pathlib import Path from tempfile import","\".dat\" if output_filename is not None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension","of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str","= [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in","output to file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if","time series files in SNOwGLoBES format. This version will subsample the times in",": str Format of input file. Matches the name of the corresponding class","t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params","flux. It supports many different neutrino detectors, detector materials and interaction channels. There","os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type","t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table =","storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file,","= 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # Loop","strip extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0,","'\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1)","tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for","happening in various detectors supported by SNOwGLoBES. It takes into account the effective","ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir,","osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR])","#creates file in tar archive that gives information on parameters output = '\\n'.join(map(str,","None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin)","to generate verbose output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input","plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats","= np.linspace(0, 100, 501) * MeV # Loop over sampled times. for i","model_type : str Format of input file. Matches the name of the corresponding","or the sum of them. \"\"\" import io import logging import os import","of the time series bins. Returns ------- str Path of compressed .tar file","= 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0","\"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate","nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i","Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int","index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results","tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not None:","plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the","processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original","{} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this","0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) #","# Choose flavor transformation. Use dict to associate the transformation name with its","SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path : str","for each time bin and for each interaction channel. verbose : bool Whether","all individual input files. * **Running SNOwGLoBES:** This step convolves the fluence generated","\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info,","enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 *","snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV","for j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for","into a tarfile. Parameters ---------- model_path : str Input file containing neutrino flux","bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra","verbose : bool Whether to generate verbose output, e.g. for debugging. \"\"\" sng","tstart tb = tend t = times dt = tb-ta #first time bin","from astropy import units as u from tqdm.auto import tqdm import snewpy.models from","Path of compressed .tar file with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn,","start times of the time series bins. tend : astropy.Quantity or None End","to integrate over, or list of start times of the time series bins.","in range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i] t","generated in the previous step with the cross-sections for the interaction channels happening","+ \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output)","extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0,","'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected","the output files from SNOwGLoBES, collated files, and .png's made for this snewpy","neutrino time distribution, for each reaction channel or the sum of them. \"\"\"","class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation","int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s,","bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step","plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'),","requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation)","functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from","transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size =","in kpc. output_filename : str or None Name of output file. If ``None``,","flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor:","for each interaction channel in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the","= tstart tb = tend t = times dt = tb-ta #first time","by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for debugging.","for possible values. d : int or float Distance to supernova in kpc.","and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to","* 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s =","d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar","\"\"\" import io import logging import os import re import tarfile from pathlib","i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] =","info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir,","the remaining columns the number of events for each interaction channel in the","time distribution, for each reaction channel or the sum of them. \"\"\" import","MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X])","'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation","supported by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for","for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat'","io import logging import os import re import tarfile from pathlib import Path","in the time bin. The result is a compressed .tar file containing all","SNOwGLoBES, which outputs calculated event rates expected for a given (set of) detector(s).","times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence =","time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation)","in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt","#set the timings up #default if inputs are None: full time window of","for f in flux_files),res)) # save result to file for re-use in collate()","SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file produced e.g.","SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova","t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return","c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\",","at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE","plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph","the energy-dependent detection efficiency. The output gives the number of events detected as","return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True):","to file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename","t in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is","flux at each time step, the latter will compute the integrated neutrino flux","detector as well as a smearing matrix describing the energy-dependent detection efficiency. The","E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt *","extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) +","for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to","t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels order","= snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1]","filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) #","End of time interval to integrate over, or list of end times of","f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+' '.join(list(table.columns)) data","model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info","output files and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir","number flux table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor]","not None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension","tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor,","'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else:","file name. ntbins : int Number of time slices. Will be ignored if","/ (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s =","Path(tempdir) for det in tables: results[det] = {} for flux,t in tables[det].items(): t","compressed .tar file containing all individual input files. * **Running SNOwGLoBES:** This step","neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str","tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input =","{det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy","nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart =","t >= tend)] # Generate output. if output_filename is not None: tfname =","model_path : str Input file containing neutrino flux information from supernova model. model_type","simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c):","or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts","output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname =","= [detector_input] result = {} #Extracts data from tarfile and sets up lists","in a single file (for each detector and for each time bin). The","for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1]","as a function of the neutrino energy and time, for each interaction channel.","the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300,","transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as","temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]],","SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate","1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over","f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else: return","elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile and","for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau","starting_index[i] = next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i]","#save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results","dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and plots output_name","time bins evaluated by SNOwGLoBES in a single file (for each detector and","is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax","ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j,","tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in","len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1 times =","if t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if","range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i] t =","a given time window (or time bin), or in a snapshot in time.","Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive:","to have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for","model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j],","by SNOwGLoBES in a single file (for each detector and for each time","detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux files and configures and runs","effective mass of the detector as well as a smearing matrix describing the","t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t","range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t > tstart[i])","first will evaluate the neutrino flux at each time step, the latter will","= tend t = times dt = tb-ta #first time bin of model","None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if","model. model_type : str Format of input file. Matches the name of the","tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that gives information","/ (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) #","#optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png'","return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output","{cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by:","(GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage","# save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] +","if t >= tend)] # Generate output. if output_filename is not None: tfname","transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501)","enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend)","This version will subsample the times in a supernova model, produce energy tables","'.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename)","'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set","tend)] # Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else:","the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}')","generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format.","tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {} #save collated","= model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times),","outputs calculated event rates expected for a given (set of) detector(s). These event","time interval to integrate over, or list of start times of the time","info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False):","i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t","for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index =","reaction channel or the sum of them. \"\"\" import io import logging import","integrate over, or list of end times of the time series bins. Returns","the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy()","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s","in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval","= table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel =","nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1","each table contains in the first column the energy bins, in the remaining","output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root","> 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0)","for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for","if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s",".tar file with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose","os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins,","neutrino flux. It supports many different neutrino detectors, detector materials and interaction channels.","-*- coding: utf-8 -*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES.","events for each interaction channel in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange","from a given input supernova neutrino flux. It supports many different neutrino detectors,","input the neutrino flux files and configures and runs the supernova script inside","= model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2'","enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j, t in","flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor]","for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log')","or list of end times of the time series bins. Returns ------- str","tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table","plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to","flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in","from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None,","produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If","Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar:","import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None):","of the model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1]","(if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins,","results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight')","input the supernova simulation model. The first will evaluate the neutrino flux at","0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index =","1: ta = tstart[i] tb = tend[i] t = times[i] dt = tb-ta","tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type,","transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED),","compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name","fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir)","tfname), 'w:bz2') as tf: #creates file in tar archive that gives information on","else: starting_index = [next(j for j, t in enumerate(model_tend) if t > tstart)]","= len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None,","data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s","slices. Returns ------- str Path of compressed .tar file with neutrino flux data.","else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events')","tarfile with the condensed data files and plots output_name = Path(tarball_path).stem output_name =","as output the plot of the energy distribution for each time bin and","model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1]","model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to","temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table =","data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to","'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file","If False, it gives as output the plot of the energy distribution for","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s)","Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input :","supernova neutrino flux. It supports many different neutrino detectors, detector materials and interaction","file with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor","estimate detected event rates from a given input supernova neutrino flux. It supports","as plt import numpy as np from astropy import units as u from","desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res))","from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def","information from supernova model. model_type : str Format of input file. Matches the","import io import logging import os import re import tarfile from pathlib import","`like` t_sel = t.filter(like=pattern) #sum over them and save to a separate column","and configures and runs the supernova script inside SNOwGLoBES, which outputs calculated event","\"\"\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate","Generate output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else:","in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file)","table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin","input supernova neutrino flux. It supports many different neutrino detectors, detector materials and","file name. tstart : astropy.Quantity or None Start of time interval to integrate","the supernova script inside SNOwGLoBES, which outputs calculated event rates expected for a","list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain","efficiency. The output gives the number of events detected as a function of","plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results","+ '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir,","dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) /","range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor]","flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence':","each interaction channel, integrated in a given time window (or time bin), or","False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with","file. If ``None``, will be based on input file name. ntbins : int","of events for each interaction channel in the detector. \"\"\" def aggregate_channels(table, **patterns):","'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel =","of time interval to integrate over, or list of start times of the","output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ =","given as a function of the neutrino energy and time, for each interaction","for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] *","channel, integrated in a given time window (or time bin), or in a","else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES","logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def","1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j])","flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1)","neutrino flux files and configures and runs the supernova script inside SNOwGLoBES, which","osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode","allow_pickle=True).tolist() #This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input)","calculated event rates expected for a given (set of) detector(s). These event rates","lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with","as well as a smearing matrix describing the energy-dependent detection efficiency. The output","ntbins : int Number of time slices. Will be ignored if ``deltat`` is","= table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if","each time bin). The output tables allow to build the detected neutrino energy","tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the","for SNOwGLoBES:** There are two ways to do this, either generate a time","MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d,","# Loop over sampled times. for i in range(nbin): if nbin > 1:","= np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t","`SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova neutrino","'.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname),","save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy'",".png's made for this snewpy run. Returns ------- dict Dictionary of data tables:","skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a","MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s","'.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)')","c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5,","have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern","tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t >= tend[i])","'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path))","else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s","model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root +","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s","Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name","the time bin. The result is a compressed .tar file containing all individual","window of the model if tstart is None: tstart = snmodel.get_time()[0] tend =","= dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins","re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res","\"\"\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels","= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table","contain `like` t_sel = t.filter(like=pattern) #sum over them and save to a separate","+ tedges[:-1]) # Generate output. if output_filename is not None: tfname = output_filename","f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f:","model, produce energy tables expected by SNOwGLoBES, and compress the output into a","transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES format. This","be based on input file name. tstart : astropy.Quantity or None Start of","results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s))","interaction channel, integrated in a given time window (or time bin), or in","model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if","model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs","name. ntbins : int Number of time slices. Will be ignored if ``deltat``","= len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1 times","> tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t >=","AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL),","as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from","det in tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e')","t = times dt = tb-ta #first time bin of model in requested","interaction channel. verbose : bool Whether to generate verbose output, e.g. for debugging.","for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and","given input supernova neutrino flux. It supports many different neutrino detectors, detector materials","times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None:","flux_files),res)) # save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')]","present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d)","detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g.","re import tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib","use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat'))","+ transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2')","supernova in kpc. output_filename : str or None Name of output file. If","tend[i] t = times[i] dt = tb-ta else: ta = tstart tb =","file (for each detector and for each time bin). The output tables allow","also given. deltat : astropy.Quantity or None Length of time slices. Returns -------","detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file","three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files for","the output into a tarfile. Parameters ---------- model_path : str Input file containing","def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params for","'+res return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c)","s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux","Path to directory where SNOwGLoBES is installed. tarball_path : str Path of compressed","model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\","= table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot","in a supernova model, produce energy tables expected by SNOwGLoBES, and compress the","file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ =","t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting","osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output","skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots or returns a","time series bins. Returns ------- str Path of compressed .tar file with neutrino","'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name]","\"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}') return results","takes into account the effective mass of the detector as well as a","{smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction","dt = tb-ta #first time bin of model in requested interval osc_spectra =","data tables: One table per time bin; each table contains in the first","model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1):","'.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output))","(tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at","contains in the first column the energy bins, in the remaining columns the","table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed.","for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = []","by SNOwGLoBES. skip_plots: bool If False, it gives as output the plot of","snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins","\".dat\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type +","rates expected for a given (set of) detector(s). These event rates are given","0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None: tfname =","= {} #Extracts data from tarfile and sets up lists of paths and","TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t,","output_filename is not None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename","matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from astropy","levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table,","result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta","axis=1) return t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared","See snewpy.flavor_transformation documentation for possible values. d : int or float Distance to","model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1:","'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {}","in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together","import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger =","= tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result","the model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try:","or None Length of time slices. Returns ------- str Path of compressed .tar","ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in","is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file)","if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor]","None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0","in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy,","the detected neutrino energy spectrum and neutrino time distribution, for each reaction channel","import tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as","SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways to do","in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1]","bins. tend : astropy.Quantity or None End of time interval to integrate over,","**Generating input files for SNOwGLoBES:** There are two ways to do this, either","time bin). The output tables allow to build the detected neutrino energy spectrum","+ '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label =","runs the supernova script inside SNOwGLoBES, which outputs calculated event rates expected for","verbose output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all':","them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels","flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested","tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if t >","with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det] =","t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with","'.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data}","name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor","and sets up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes')","dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not","logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to","for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor","filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data","output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)","the neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir :","= Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created","not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension","per time bin; each table contains in the first column the energy bins,","and neutrino time distribution, for each reaction channel or the sum of them.","#Extracts data from tarfile and sets up lists of paths and fluxfilenames for","transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size =","if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path,","TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {}","= [next(j for j, t in enumerate(model_tend) if t >= tend)] # Generate","values. d : int or float Distance to supernova in kpc. output_filename :","skip_plots: bool If False, it gives as output the plot of the energy","np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t in","if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in","from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables =","the supernova simulation model. The first will evaluate the neutrino flux at each","tempdir = Path(tempdir) for det in tables: results[det] = {} for flux,t in","NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample","as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False)","model. The first will evaluate the neutrino flux at each time step, the","plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading","= '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) #","detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result","'+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label,","(or time bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:**","a time series or a fluence file. This is done taking as input","to supernova in kpc. output_filename : str or None Name of output file.","from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import","= {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']:","time bin. The result is a compressed .tar file containing all individual input","of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra =","over, or list of start times of the time series bins. tend :","Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from","MeV # Loop over sampled times. for i in range(nbin): if nbin >","supernova simulation model. The first will evaluate the neutrino flux at each time","energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of","gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\'+flv if bar: bar='\\\\'+bar s = f'${bar}{{\\\\nu}}_{flv}$","output, e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated","in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them",">= tend)] # Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2'","given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1:","= output_filename+\"_\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip","the detector as well as a smearing matrix describing the energy-dependent detection efficiency.","in tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for","over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation)","tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in","TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0:","= t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files","0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s,","transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This version","1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin >","t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file","for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir:","time series bins. tend : astropy.Quantity or None End of time interval to","{} #Extracts data from tarfile and sets up lists of paths and fluxfilenames","version will subsample the times in a supernova model, produce energy tables expected","numpy as np from astropy import units as u from tqdm.auto import tqdm","filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g'))","if nbin > 1: ta = tstart[i] tb = tend[i] t = times[i]","smearing matrix describing the energy-dependent detection efficiency. The output gives the number of","table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5,","100, 501) * MeV # Loop over sampled times. for i in range(nbin):","requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt =","is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile","osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth","model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index","tend nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart","possible values. d : int or float Distance to supernova in kpc. output_filename","try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin =","= output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename","detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts","in SNOwGLoBES format. This version will subsample the times in a supernova model,","model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the","energy/flux table and output to file in tar archive. output = '\\n'.join(table).encode('ascii') extension","are given as a function of the neutrino energy and time, for each","``None``, will be based on input file name. ntbins : int Number of","produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity","j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in","present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin,","table per time bin; each table contains in the first column the energy","osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra =","ignored if ``deltat`` is also given. deltat : astropy.Quantity or None Length of","import logging import os import re import tarfile from pathlib import Path from","of detector. If ``\"all\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool","u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop over","verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES,","and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar:","in the detector. \"\"\" def aggregate_channels(table, **patterns): #rearrange the table to have only","similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results,","EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('#","nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir,","s = f'${bar}{{\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c]","<gh_stars>0 # -*- coding: utf-8 -*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting","detected event rates from a given input supernova neutrino flux. It supports many","archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" model_file_root, _ = os.path.splitext(model_file) filename =","the timings up #default if inputs are None: full time window of the","given (set of) detector(s). These event rates are given as a function of","tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors',","= model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times),","done taking as input the supernova simulation model. The first will evaluate the","a tarfile with the condensed data files and plots output_name = Path(tarball_path).stem output_name","# strip extension (if present) tfname = model_file_root + '.' + transformation_type +","#sum over them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop","plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and plots","+= temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra(","of input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type","t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend","dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for","[detector_input] result = {} #Extracts data from tarfile and sets up lists of","later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files =","snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together all the","tstart t1 = tend nbin = 1 times = 0.5*(tstart + tend) model_times","[next(j for j, t in enumerate(model_tend) if t >= tend)] # Generate output.","(fluence) in the time bin. The result is a compressed .tar file containing","of time slices. Returns ------- str Path of compressed .tar file with neutrino","in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence","of energy for each interaction channel, integrated in a given time window (or","time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None:","return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def","each reaction channel or the sum of them. \"\"\" import io import logging","model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES format.","strip extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin,","= Path(tempdir) for det in tables: results[det] = {} for flux,t in tables[det].items():","tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 =","old-style results, for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes')","t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t","condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path =","for the interaction channels happening in various detectors supported by SNOwGLoBES. It takes","(model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j in range(starting_index[i]+1,","generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files in SNOwGLoBES","t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with","``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\"all\"``, will use","results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in","Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type,","temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last","made for this snewpy run. Returns ------- dict Dictionary of data tables: One","from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES",": astropy.Quantity or None Length of time slices. Returns ------- str Path of","time bin and for each interaction channel. verbose : bool Whether to generate","``None``, will be based on input file name. tstart : astropy.Quantity or None","The result is a compressed .tar file containing all individual input files. *","There are two ways to do this, either generate a time series or","'{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s =","collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables:","a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill","+= temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table","t_sel = t.filter(like=pattern) #sum over them and save to a separate column t_agg","If ``\"all\"``, will use all detectors supported by SNOwGLoBES. verbose : bool Whether","a compressed .tar file containing all individual input files. * **Running SNOwGLoBES:** This","time slices. Returns ------- str Path of compressed .tar file with neutrino flux","cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist()","plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction'","channels happening in various detectors supported by SNOwGLoBES. It takes into account the","file in tar archive that gives information on parameters output = '\\n'.join(map(str, transformation_type)).encode('ascii')","well as a smearing matrix describing the energy-dependent detection efficiency. The output gives","results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir)","for a given (set of) detector(s). These event rates are given as a","#first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation)","= {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(),","if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make","float Distance to supernova in kpc. output_filename : str or None Name of","times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend =","= re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\nu}_x+e^-$'} def gen_label(m):","= 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV","puts together all the interaction channels and time bins evaluated by SNOwGLoBES in","bool Remove the output files from SNOwGLoBES, collated files, and .png's made for",": str or None Name of output file. If ``None``, will be based","NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the","are two ways to do this, either generate a time series or a","plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')]","bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt","f in flux_files),res)) # save result to file for re-use in collate() cache_file","plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\")","will be based on input file name. ntbins : int Number of time","and for each interaction channel. verbose : bool Whether to generate verbose output,","to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation","1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend","dict to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(),","interaction channels and time bins evaluated by SNOwGLoBES in a single file (for","this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE","plot of the energy distribution for each time bin and for each interaction","simulation model. The first will evaluate the neutrino flux at each time step,","tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 =","interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] +=","aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels = list(table.columns.names)","channels and time bins evaluated by SNOwGLoBES in a single file (for each","flux files and configures and runs the supernova script inside SNOwGLoBES, which outputs","Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \"w:gz\") as tar: for","= tend[i] t = times[i] dt = tb-ta else: ta = tstart tb","osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file in tar","column #return table with the original levels order t = t.unstack(levels) t =","= len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes","compressed .tar file with neutrino flux data. \"\"\" model_class = getattr(snewpy.models.ccsn, model_type) #","if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5),","\"\"\"Collates SNOwGLoBES output files and generates plots or returns a data table. Parameters",".tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of","MeV # 1MeV # Loop over sampled times. for i, t in enumerate(times):","model_class(model_path) # Subsample the model time. Default to 30 time slices. tmin =","np from astropy import units as u from tqdm.auto import tqdm import snewpy.models","# -*- coding: utf-8 -*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with","e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input =","name. tstart : astropy.Quantity or None Start of time interval to integrate over,","+ '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is","bool Whether to generate verbose output, e.g. for debugging. \"\"\" sng = SNOwGLoBES(SNOwGLoBESdir)","np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300)","Input file containing neutrino flux information from supernova model. model_type : str Format","= os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) +","dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times","individual input files. * **Running SNOwGLoBES:** This step convolves the fluence generated in","if t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if","table to have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels)","tarball_path, detector_input=\"all\", verbose=False): \"\"\"Takes as input the neutrino flux files and configures and","in the remaining columns the number of events for each interaction channel in","energy + number flux table. for j, E in enumerate(energy): for flavor in","transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as","the energy bins, in the remaining columns the number of events for each","= tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format","fluence file. This is done taking as input the supernova simulation model. The","archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\" if output_filename is not None: if","table with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1)","is not None: if nbin > 1: filename = output_filename+\"_\"+str(i)+extension else: filename =","enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('#","j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j,","= times[i] dt = tb-ta else: ta = tstart tb = tend t","files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header","of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt <","interval to integrate over, or list of end times of the time series","or float Distance to supernova in kpc. output_filename : str or None Name","in time. * **Collating SNOwGLoBES outputs:** This step puts together all the interaction","+ transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size","import Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as","format. This version will subsample the times in a supernova model, produce energy","output into a tarfile. Parameters ---------- model_path : str Input file containing neutrino",": astropy.Quantity or None Start of time interval to integrate over, or list","many different neutrino detectors, detector materials and interaction channels. There are three basic","number of events detected as a function of energy for each interaction channel,","tables: One table per time bin; each table contains in the first column","import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type,","tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0]","series or a fluence file. This is done taking as input the supernova","the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO':","{'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence':","#format the results for the output header = 'Energy '+' '.join(list(table.columns)) data =","of the time series bins. tend : astropy.Quantity or None End of time","from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import","osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {}","Returns ------- dict Dictionary of data tables: One table per time bin; each","in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau')","-*- \"\"\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can","res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for","re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\"all\", skip_plots=False, verbose=False, remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files","use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as","= f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as","return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay',","neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') #","logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \"\"\"Generate time series files","is done taking as input the supernova simulation model. The first will evaluate","are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files","fluence files in SNOwGLoBES format. This version will subsample the times in a","result = {} #Extracts data from tarfile and sets up lists of paths","two ways to do this, either generate a time series or a fluence","'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type]","'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file in","= tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This","the number of events for each interaction channel in the detector. \"\"\" def","with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input,","remove_generated_files=True): \"\"\"Collates SNOwGLoBES output files and generates plots or returns a data table.","starting_index = [next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index","of the energy distribution for each time bin and for each interaction channel.","# Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root,","will subsample the times in a supernova model, produce energy tables expected by","compress the output into a tarfile. Parameters ---------- model_path : str Input file","def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES","as a function of energy for each interaction channel, integrated in a given","tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) *","and output to file in tar archive. output = '\\n'.join(table).encode('ascii') extension = \".dat\"","tstart : astropy.Quantity or None Start of time interval to integrate over, or","{} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det","``deltat`` is also given. deltat : astropy.Quantity or None Length of time slices.","a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together all","d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname)","def aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels =","str Format of input file. Matches the name of the corresponding class in","tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what","#return table with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names,","tstart=None, tend=None): \"\"\"Generate fluence files in SNOwGLoBES format. This version will subsample the","and for each time bin). The output tables allow to build the detected","501) * MeV # 1MeV # Loop over sampled times. for i, t","* **Running SNOwGLoBES:** This step convolves the fluence generated in the previous step","outputs:** This step puts together all the interaction channels and time bins evaluated","single file (for each detector and for each time bin). The output tables","Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events')","output the plot of the energy distribution for each time bin and for","levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like`","t0 = tstart t1 = tend nbin = 1 times = 0.5*(tstart +","each detector and for each time bin). The output tables allow to build","backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir","* MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E])","with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input","snmodel = model_class(model_path) #set the timings up #default if inputs are None: full","in various detectors supported by SNOwGLoBES. It takes into account the effective mass","= model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension)","= tstart t1 = tend nbin = 1 times = 0.5*(tstart + tend)","the fluence generated in the previous step with the cross-sections for the interaction","supported by SNOwGLoBES. skip_plots: bool If False, it gives as output the plot","time slices. Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity"],"string":"[\n \"Use dict to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation':\",\n \"the events from given table flux,det,weighted,smeared = params for c in table.columns: if\",\n \"tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins =\",\n \"distribution, for each reaction channel or the sum of them. \\\"\\\"\\\" import io\",\n \"'.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates\",\n \"= tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1\",\n \": str Name of detector. If ``\\\"all\\\"``, will use all detectors supported by\",\n \"* MeV # 1MeV # Loop over sampled times. for i, t in\",\n \"osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR])\",\n \"snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in\",\n \"= tend nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time()\",\n \"(set of) detector(s). These event rates are given as a function of the\",\n \"file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results\",\n \"collated files, and .png's made for this snewpy run. Returns ------- dict Dictionary\",\n \"if t >= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend)\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s,\",\n \"(for each detector and for each time bin). The output tables allow to\",\n \"SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input =\",\n \"the table to have only channel column levels = list(table.columns.names) levels.remove('channel') t =\",\n \"verbose : bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool\",\n \"in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file,\",\n \"flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings\",\n \"smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the\",\n \"tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\",\",\n \"output gives the number of events detected as a function of energy for\",\n \": int or float Distance to supernova in kpc. output_filename : str or\",\n \"order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params):\",\n \"Choose flavor transformation. Use dict to associate the transformation name with its class.\",\n \"model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for\",\n \"ending_index = [next(j for j, t in enumerate(model_tend) if t >= tend)] #\",\n \"= t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the\",\n \"in the previous step with the cross-sections for the interaction channels happening in\",\n \"tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is\",\n \"time. * **Collating SNOwGLoBES outputs:** This step puts together all the interaction channels\",\n \"to do this, either generate a time series or a fluence file. This\",\n \"return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino\",\n \"in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec\",\n \"over them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed\",\n \"flavor transformation. Use dict to associate the transformation name with its class. flavor_transformation_dict\",\n \"astropy import units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation\",\n \"#drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the\",\n \"else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file =\",\n \"aNuMu aNuTau') # Generate energy + number flux table. for j, E in\",\n \"the effective mass of the detector as well as a smearing matrix describing\",\n \"1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file)\",\n \"time bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This\",\n \"from tarfile and sets up lists of paths and fluxfilenames for later use\",\n \"result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving\",\n \"+ tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] =\",\n \"all detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose output,\",\n \"remaining columns the number of events for each interaction channel in the detector.\",\n \"d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version will\",\n \"associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL),\",\n \"= {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for\",\n \"smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)')\",\n \"os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux\",\n \"for name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum\",\n \"interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given\",\n \"as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input\",\n \"nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in\",\n \"output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version will subsample\",\n \"---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path :\",\n \"'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO':\",\n \"= np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables =\",\n \"for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j\",\n \"compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir =\",\n \"subsample the times in a supernova model, produce energy tables expected by SNOwGLoBES,\",\n \"generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files from\",\n \"mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False,\",\n \"result is a compressed .tar file containing all individual input files. * **Running\",\n \"together all the interaction channels and time bins evaluated by SNOwGLoBES in a\",\n \"len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None,\",\n \"osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j\",\n \"time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if\",\n \"def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates\",\n \"params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10)\",\n \"neutrino energy spectrum and neutrino time distribution, for each reaction channel or the\",\n \"the results for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T\",\n \"flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested\",\n \"as tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {} for\",\n \"NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation =\",\n \"+ '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf:\",\n \"the cross-sections for the interaction channels happening in various detectors supported by SNOwGLoBES.\",\n \"energy = np.linspace(0, 100, 501) * MeV # Loop over sampled times. for\",\n \"**patterns): #rearrange the table to have only channel column levels = list(table.columns.names) levels.remove('channel')\",\n \"the times in a supernova model, produce energy tables expected by SNOwGLoBES, and\",\n \"if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read\",\n \"len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as\",\n \"the neutrino flux at each time step, the latter will compute the integrated\",\n \"(if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin,\",\n \"= model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension)\",\n \"snewpy.flavor_transformation documentation for possible values. d : int or float Distance to supernova\",\n \"= snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0]\",\n \"bin and for each interaction channel. verbose : bool Whether to generate verbose\",\n \"or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory\",\n \"= {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename\",\n \"osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR])\",\n \"in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not\",\n \"# formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()}\",\n \"* MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s,\",\n \"= tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f\",\n \"output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file\",\n \"aNuTau') # Generate energy + number flux table. for j, E in enumerate(energy):\",\n \"them. \\\"\\\"\\\" import io import logging import os import re import tarfile from\",\n \"import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface\",\n \"detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile\",\n \"Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] +\",\n \"detector_input : str Name of detector. If ``\\\"all\\\"``, will use all detectors supported\",\n \"or None Name of output file. If ``None``, will be based on input\",\n \"= list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input:\",\n \"Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity or None\",\n \"list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files,\",\n \"Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for\",\n \"['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to\",\n \"tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) >\",\n \"flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time.\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and\",\n \"bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return\",\n \"s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save\",\n \"full time window of the model if tstart is None: tstart = snmodel.get_time()[0]\",\n \"= SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input\",\n \"and interaction channels. There are three basic steps to using SNOwGLoBES from SNEWPY:\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s =\",\n \"header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data =\",\n \"flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c\",\n \"E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux\",\n \"sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence\",\n \"supernova model, produce energy tables expected by SNOwGLoBES, and compress the output into\",\n \"inputs are None: full time window of the model if tstart is None:\",\n \"with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation.\",\n \"with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if\",\n \"u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over sampled times.\",\n \"NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir,\",\n \"False, it gives as output the plot of the energy distribution for each\",\n \"do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params for c\",\n \"NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path)\",\n \"(if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1,\",\n \"and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns,\",\n \"file containing neutrino flux information from supernova model. model_type : str Format of\",\n \"class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED),\",\n \"files, and .png's made for this snewpy run. Returns ------- dict Dictionary of\",\n \"t[name]=t_agg #fill the column #return table with the original levels order t =\",\n \"from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways to\",\n \"= logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series\",\n \"#save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in\",\n \"each interaction channel. verbose : bool Whether to generate verbose output, e.g. for\",\n \"detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or returns\",\n \"SNOwGLoBES in a single file (for each detector and for each time bin).\",\n \"t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel\",\n \"+ '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info =\",\n \"It supports many different neutrino detectors, detector materials and interaction channels. There are\",\n \"len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem\",\n \"0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if\",\n \"def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files and\",\n \"'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if\",\n \"transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values.\",\n \"These event rates are given as a function of the neutrino energy and\",\n \".tar file containing all individual input files. * **Running SNOwGLoBES:** This step convolves\",\n \"at each time step, the latter will compute the integrated neutrino flux (fluence)\",\n \"supernova model. model_type : str Format of input file. Matches the name of\",\n \"with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}')\",\n \"materials and interaction channels. There are three basic steps to using SNOwGLoBES from\",\n \"for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save\",\n \"levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels\",\n \"def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in\",\n \"SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways\",\n \"as tf: #creates file in tar archive that gives information on parameters output\",\n \"times dt = tb-ta #first time bin of model in requested interval osc_spectra\",\n \"+ '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir,\",\n \"as mpl import matplotlib.pyplot as plt import numpy as np from astropy import\",\n \"column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column\",\n \"supernova script inside SNOwGLoBES, which outputs calculated event rates expected for a given\",\n \"snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin\",\n \"in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename\",\n \"channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed.\",\n \"a single file (for each detector and for each time bin). The output\",\n \"interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is\",\n \"ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel\",\n \"If ``None``, will be based on input file name. tstart : astropy.Quantity or\",\n \"step convolves the fluence generated in the previous step with the cross-sections for\",\n \"#get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and save\",\n \"None Length of time slices. Returns ------- str Path of compressed .tar file\",\n \"model_type) # Choose flavor transformation. Use dict to associate the transformation name with\",\n \"TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt))\",\n \">= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if t\",\n \"contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates\",\n \"The output tables allow to build the detected neutrino energy spectrum and neutrino\",\n \"model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root +\",\n \"energy for each interaction channel, integrated in a given time window (or time\",\n \"interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for\",\n \"in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2\",\n \"containing all individual input files. * **Running SNOwGLoBES:** This step convolves the fluence\",\n \"- tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] +\",\n \"bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp:\",\n \"#This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict\",\n \"np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir,\",\n \"os import re import tarfile from pathlib import Path from tempfile import TemporaryDirectory\",\n \"= params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10)\",\n \"+ 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname\",\n \"step, the latter will compute the integrated neutrino flux (fluence) in the time\",\n \"> tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t >=\",\n \"simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files and configures\",\n \"= 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0\",\n \"u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino\",\n \"result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'}\",\n \"output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir():\",\n \"aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base\",\n \"with the cross-sections for the interaction channels happening in various detectors supported by\",\n \"model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta:\",\n \"if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _\",\n \"'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s\",\n \"= tb-ta #first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]],\",\n \"in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = []\",\n \"detector materials and interaction channels. There are three basic steps to using SNOwGLoBES\",\n \"= \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type\",\n \"'.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename)\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s =\",\n \"for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in\",\n \"to integrate over, or list of end times of the time series bins.\",\n \"sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str):\",\n \"0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared'\",\n \"tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy\",\n \"deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges\",\n \"if inputs are None: full time window of the model if tstart is\",\n \"> 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ =\",\n \"t = times[i] dt = tb-ta else: ta = tstart tb = tend\",\n \"over sampled times. for i in range(nbin): if nbin > 1: ta =\",\n \"event rates expected for a given (set of) detector(s). These event rates are\",\n \"number of events for each interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table,\",\n \"a tarfile. Parameters ---------- model_path : str Input file containing neutrino flux information\",\n \"plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino\",\n \"supports many different neutrino detectors, detector materials and interaction channels. There are three\",\n \"tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl\",\n \"for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base =\",\n \"filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)\",\n \"channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels\",\n \"if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s)\",\n \"files in SNOwGLoBES format. This version will subsample the times in a supernova\",\n \"MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV #\",\n \"table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots\",\n \"the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See\",\n \"{'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv\",\n \"------- dict Dictionary of data tables: One table per time bin; each table\",\n \"(ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate\",\n \"``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it\",\n \"times in a supernova model, produce energy tables expected by SNOwGLoBES, and compress\",\n \"detector and for each time bin). The output tables allow to build the\",\n \"energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence\",\n \"be based on input file name. ntbins : int Number of time slices.\",\n \"deltat : astropy.Quantity or None Length of time slices. Returns ------- str Path\",\n \"or None End of time interval to integrate over, or list of end\",\n \"snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30,\",\n \"= tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin\",\n \"Name of detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. verbose\",\n \"based on input file name. tstart : astropy.Quantity or None Start of time\",\n \"+ number flux table. for j, E in enumerate(energy): for flavor in Flavor:\",\n \"by SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path :\",\n \"time window of the model if tstart is None: tstart = snmodel.get_time()[0] tend\",\n \"nbin > 1: ta = tstart[i] tb = tend[i] t = times[i] dt\",\n \"the neutrino flux files and configures and runs the supernova script inside SNOwGLoBES,\",\n \"first column the energy bins, in the remaining columns the number of events\",\n \"t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return\",\n \"channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items():\",\n \"_ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s,\",\n \"matplotlib.pyplot as plt import numpy as np from astropy import units as u\",\n \"tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det\",\n \"output files from SNOwGLoBES, collated files, and .png's made for this snewpy run.\",\n \"this snewpy run. Returns ------- dict Dictionary of data tables: One table per\",\n \"are None: full time window of the model if tstart is None: tstart\",\n \"events from given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max()\",\n \"snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin\",\n \"results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables\",\n \"gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6\",\n \"#intermediate time bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i],\",\n \"event rates from a given input supernova neutrino flux. It supports many different\",\n \"energy spectrum and neutrino time distribution, for each reaction channel or the sum\",\n \"re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def\",\n \"str Path of compressed .tar file with neutrino flux data. \\\"\\\"\\\" model_class =\",\n \"enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend)\",\n \"energy bins, in the remaining columns the number of events for each interaction\",\n \"= snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s\",\n \"channels. There are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating\",\n \"t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j,\",\n \"np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j\",\n \"snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor\",\n \"None: full time window of the model if tstart is None: tstart =\",\n \"as input the supernova simulation model. The first will evaluate the neutrino flux\",\n \"int or float Distance to supernova in kpc. output_filename : str or None\",\n \"rates are given as a function of the neutrino energy and time, for\",\n \"model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times),\",\n \"w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}'\",\n \"output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data\",\n \"This step puts together all the interaction channels and time bins evaluated by\",\n \"smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if\",\n \"up #default if inputs are None: full time window of the model if\",\n \"on input file name. ntbins : int Number of time slices. Will be\",\n \"i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table\",\n \"The output gives the number of events detected as a function of energy\",\n \"tf: #creates file in tar archive that gives information on parameters output =\",\n \"to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _\",\n \"given. deltat : astropy.Quantity or None Length of time slices. Returns ------- str\",\n \"borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux}\",\n \"SNOwGLoBES output files and generates plots or returns a data table. Parameters ----------\",\n \"from SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns -------\",\n \"kpc. output_filename : str or None Name of output file. If ``None``, will\",\n \"= next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] =\",\n \"the plot of the energy distribution for each time bin and for each\",\n \"for i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if\",\n \"= f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else:\",\n \"int Number of time slices. Will be ignored if ``deltat`` is also given.\",\n \"for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a\",\n \"f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+' '.join(list(table.columns))\",\n \"detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. verbose : bool\",\n \"detector(s). These event rates are given as a function of the neutrino energy\",\n \"table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best',\",\n \"cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy\",\n \"\\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info,\",\n \"is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0:\",\n \"io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV\",\n \"for j, t in enumerate(model_tend) if t >= tend)] # Generate output. if\",\n \"getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the transformation name\",\n \"energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in\",\n \"= 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None: tfname\",\n \"is installed. tarball_path : str Path of compressed .tar file produced e.g. by\",\n \"the number of events detected as a function of energy for each interaction\",\n \"of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path)\",\n \"if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt\",\n \"either generate a time series or a fluence file. This is done taking\",\n \"extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1,\",\n \"will evaluate the neutrino flux at each time step, the latter will compute\",\n \"[next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j\",\n \"will compute the integrated neutrino flux (fluence) in the time bin. The result\",\n \"up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir:\",\n \"for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where\",\n \"timings up #default if inputs are None: full time window of the model\",\n \"else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if\",\n \"= {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e':\",\n \"table and output to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension =\",\n \"simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {} #save\",\n \"with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header =\",\n \"'+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] =\",\n \"mpl import matplotlib.pyplot as plt import numpy as np from astropy import units\",\n \"energy distribution for each time bin and for each interaction channel. verbose :\",\n \"osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else:\",\n \"bin). The output tables allow to build the detected neutrino energy spectrum and\",\n \"params): #plotting the events from given table flux,det,weighted,smeared = params for c in\",\n \"tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file\",\n \"t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events\",\n \"AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)}\",\n \"latter will compute the integrated neutrino flux (fluence) in the time bin. The\",\n \"over, or list of end times of the time series bins. Returns -------\",\n \"# Generate energy + number flux table. for j, E in enumerate(energy): for\",\n \"bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the\",\n \"units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import *\",\n \"model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in\",\n \"and .png's made for this snewpy run. Returns ------- dict Dictionary of data\",\n \"channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and save to\",\n \"bins. Returns ------- str Path of compressed .tar file with neutrino flux data.\",\n \"tb-ta #first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy,\",\n \"module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event\",\n \"a supernova model, produce energy tables expected by SNOwGLoBES, and compress the output\",\n \"for each interaction channel, integrated in a given time window (or time bin),\",\n \"and compress the output into a tarfile. Parameters ---------- model_path : str Input\",\n \"the time series bins. Returns ------- str Path of compressed .tar file with\",\n \"If ``None``, will be based on input file name. ntbins : int Number\",\n \"logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time\",\n \"if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file)\",\n \"in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False,\",\n \"flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV /\",\n \"------- str Path of compressed .tar file with neutrino flux data. \\\"\\\"\\\" model_class\",\n \"the energy distribution for each time bin and for each interaction channel. verbose\",\n \"with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return\",\n \"import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from\",\n \"basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:**\",\n \"for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files\",\n \"# 1MeV # Loop over sampled times. for i, t in enumerate(times): osc_spectra\",\n \"detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as output the\",\n \"the latter will compute the integrated neutrino flux (fluence) in the time bin.\",\n \"produce energy tables expected by SNOwGLoBES, and compress the output into a tarfile.\",\n \"= tb-ta else: ta = tstart tb = tend t = times dt\",\n \"SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for debugging. \\\"\\\"\\\"\",\n \"flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if\",\n \"or None Start of time interval to integrate over, or list of start\",\n \"sampled times. for i in range(nbin): if nbin > 1: ta = tstart[i]\",\n \"None End of time interval to integrate over, or list of end times\",\n \"= flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up\",\n \"name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO':\",\n \"time bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1):\",\n \"filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d,\",\n \"flux (fluence) in the time bin. The result is a compressed .tar file\",\n \"directory where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file\",\n \"Format of input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`.\",\n \"astropy.Quantity or None Start of time interval to integrate over, or list of\",\n \"slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt\",\n \"#rearrange the table to have only channel column levels = list(table.columns.names) levels.remove('channel') t\",\n \"osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec\",\n \"* **Generating input files for SNOwGLoBES:** There are two ways to do this,\",\n \"str Path to directory where SNOwGLoBES is installed. tarball_path : str Path of\",\n \"(tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:]\",\n \"for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {}\",\n \"to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is\",\n \"will be based on input file name. tstart : astropy.Quantity or None Start\",\n \"supported by SNOwGLoBES. It takes into account the effective mass of the detector\",\n \"= model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64)\",\n \"ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] +=\",\n \"SNOwGLoBES format. This version will subsample the times in a supernova model, produce\",\n \"extension = \\\".dat\\\" if output_filename is not None: if nbin > 1: filename\",\n \"of start times of the time series bins. tend : astropy.Quantity or None\",\n \"each time bin and for each interaction channel. verbose : bool Whether to\",\n \"str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d :\",\n \"ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This version will subsample\",\n \"times of the time series bins. Returns ------- str Path of compressed .tar\",\n \"'.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname),\",\n \"NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(),\",\n \"flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int or float\",\n \"detector_input = [detector_input] result = {} #Extracts data from tarfile and sets up\",\n \"in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2)\",\n \"matrix describing the energy-dependent detection efficiency. The output gives the number of events\",\n \": bool Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove\",\n \"archive that gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV\",\n \"osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s =\",\n \"configures and runs the supernova script inside SNOwGLoBES, which outputs calculated event rates\",\n \"given time window (or time bin), or in a snapshot in time. *\",\n \"Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV)\",\n \"plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title\",\n \"= osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3\",\n \"by SNOwGLoBES. It takes into account the effective mass of the detector as\",\n \"tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}') return\",\n \"t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table =\",\n \"the condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path\",\n \"time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory\",\n \"import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy as\",\n \"with the condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed'\",\n \"t1 = tend nbin = 1 times = 0.5*(tstart + tend) model_times =\",\n \"save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True)\",\n \"= t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table\",\n \"axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels order t\",\n \"gives as output the plot of the energy distribution for each time bin\",\n \"np.linspace(0, 100, 501) * MeV # Loop over sampled times. for i in\",\n \"'\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100,\",\n \"= model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2'\",\n \"t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from given table\",\n \"ways to do this, either generate a time series or a fluence file.\",\n \"cross-sections for the interaction channels happening in various detectors supported by SNOwGLoBES. It\",\n \"input file name. tstart : astropy.Quantity or None Start of time interval to\",\n \"os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to 30 time\",\n \"except: t0 = tstart t1 = tend nbin = 1 times = 0.5*(tstart\",\n \"is a compressed .tar file containing all individual input files. * **Running SNOwGLoBES:**\",\n \"all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as output\",\n \"for each reaction channel or the sum of them. \\\"\\\"\\\" import io import\",\n \"'w:bz2') as tf: #creates file in tar archive that gives information on parameters\",\n \"will use all detectors supported by SNOwGLoBES. verbose : bool Whether to generate\",\n \"neutrino flux information from supernova model. model_type : str Format of input file.\",\n \"channel or the sum of them. \\\"\\\"\\\" import io import logging import os\",\n \"* dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV))\",\n \"if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy\",\n \"as input the neutrino flux files and configures and runs the supernova script\",\n \"of output file. If ``None``, will be based on input file name. tstart\",\n \"dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of\",\n \"\\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This version will subsample the times\",\n \"in a given time window (or time bin), or in a snapshot in\",\n \"files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with\",\n \"s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir,\",\n \"in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor:\",\n \"None Start of time interval to integrate over, or list of start times\",\n \": str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d\",\n \"energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled\",\n \"The first will evaluate the neutrino flux at each time step, the latter\",\n \"+ transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s,\",\n \"is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style\",\n \"or ``generate_fluence()``. detector_input : str Name of detector. If ``\\\"all\\\"``, will use all\",\n \"compute the integrated neutrino flux (fluence) in the time bin. The result is\",\n \"if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input]\",\n \"tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w\",\n \"def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s =\",\n \"energy tables expected by SNOwGLoBES, and compress the output into a tarfile. Parameters\",\n \"as a smearing matrix describing the energy-dependent detection efficiency. The output gives the\",\n \"snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else:\",\n \"NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table. for\",\n \"in tar archive that gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'),\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to\",\n \"and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str\",\n \"the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of\",\n \"graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events')\",\n \"loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected'\",\n \"extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax,\",\n \"= list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data\",\n \": astropy.Quantity or None End of time interval to integrate over, or list\",\n \"Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu\",\n \"len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin\",\n \"Dictionary of data tables: One table per time bin; each table contains in\",\n \"to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for\",\n \"else: ta = tstart tb = tend t = times dt = tb-ta\",\n \"a smearing matrix describing the energy-dependent detection efficiency. The output gives the number\",\n \"---------- model_path : str Input file containing neutrino flux information from supernova model.\",\n \"deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This version will subsample the\",\n \"a given (set of) detector(s). These event rates are given as a function\",\n \"#last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy,\",\n \"Whether to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output\",\n \"of the detector as well as a smearing matrix describing the energy-dependent detection\",\n \"in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is not\",\n \"information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 *\",\n \"e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files,\",\n \"* u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop\",\n \"a given input supernova neutrino flux. It supports many different neutrino detectors, detector\",\n \"for i in range(nbin): if nbin > 1: ta = tstart[i] tb =\",\n \"slices. Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity or\",\n \"rates from a given input supernova neutrino flux. It supports many different neutrino\",\n \"tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) +\",\n \"e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\\\"all\\\"``,\",\n \"t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart\",\n \"= os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.' +\",\n \"the time series bins. tend : astropy.Quantity or None End of time interval\",\n \"<https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova neutrino flux.\",\n \"of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int or\",\n \"It takes into account the effective mass of the detector as well as\",\n \"mass of the detector as well as a smearing matrix describing the energy-dependent\",\n \"a function of energy for each interaction channel, integrated in a given time\",\n \"tarfile and sets up lists of paths and fluxfilenames for later use with\",\n \"run. Returns ------- dict Dictionary of data tables: One table per time bin;\",\n \"import units as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import\",\n \"describing the energy-dependent detection efficiency. The output gives the number of events detected\",\n \"dt = tb-ta else: ta = tstart tb = tend t = times\",\n \"tb = tend[i] t = times[i] dt = tb-ta else: ta = tstart\",\n \"column the energy bins, in the remaining columns the number of events for\",\n \"be ignored if ``deltat`` is also given. deltat : astropy.Quantity or None Length\",\n \"info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d,\",\n \"collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots\",\n \"do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed\",\n \"Number of time slices. Will be ignored if ``deltat`` is also given. deltat\",\n \"['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename =\",\n \"np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse\",\n \"account the effective mass of the detector as well as a smearing matrix\",\n \"dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *=\",\n \"output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename =\",\n \"remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and .png's made\",\n \"results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp\",\n \"integrated in a given time window (or time bin), or in a snapshot\",\n \"the first column the energy bins, in the remaining columns the number of\",\n \"per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate\",\n \"return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence\",\n \"= getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the transformation\",\n \"logging.debug(s) # Encode energy/flux table and output to file in tar archive. output\",\n \"to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO':\",\n \"fluence generated in the previous step with the cross-sections for the interaction channels\",\n \"tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename\",\n \"= t.filter(like=pattern) #sum over them and save to a separate column t_agg =\",\n \"results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for\",\n \"original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def\",\n \"io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the\",\n \"snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import\",\n \"table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern)\",\n \"tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None):\",\n \"a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES\",\n \"If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If False,\",\n \"in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval\",\n \"in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for\",\n \"distribution for each time bin and for each interaction channel. verbose : bool\",\n \"results for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index\",\n \"output file. If ``None``, will be based on input file name. ntbins :\",\n \"neutrino detectors, detector materials and interaction channels. There are three basic steps to\",\n \"in the first column the energy bins, in the remaining columns the number\",\n \"step with the cross-sections for the interaction channels happening in various detectors supported\",\n \"in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5,\",\n \"of end times of the time series bins. Returns ------- str Path of\",\n \"Loop over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy,\",\n \"#Make a tarfile with the condensed data files and plots output_name = Path(tarball_path).stem\",\n \"output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) #\",\n \"None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip\",\n \"= os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to 30\",\n \"step puts together all the interaction channels and time bins evaluated by SNOwGLoBES\",\n \"tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {} for flux,t\",\n \"_ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.'\",\n \"output_filename : str or None Name of output file. If ``None``, will be\",\n \"else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root\",\n \"os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root + '.' + transformation_type\",\n \"table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j]\",\n \"different neutrino detectors, detector materials and interaction channels. There are three basic steps\",\n \"filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d,\",\n \"of the neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir\",\n \"interval to integrate over, or list of start times of the time series\",\n \"tb-ta else: ta = tstart tb = tend t = times dt =\",\n \"sets up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as\",\n \"aNuE aNuMu aNuTau') # Generate energy + number flux table. for j, E\",\n \"model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if\",\n \"None Name of output file. If ``None``, will be based on input file\",\n \"files and configures and runs the supernova script inside SNOwGLoBES, which outputs calculated\",\n \"or list of start times of the time series bins. tend : astropy.Quantity\",\n \"is also given. deltat : astropy.Quantity or None Length of time slices. Returns\",\n \"flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('#\",\n \"dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s\",\n \"for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s\",\n \"neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use\",\n \"osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 *\",\n \"if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root,\",\n \"which outputs calculated event rates expected for a given (set of) detector(s). These\",\n \"#default if inputs are None: full time window of the model if tstart\",\n \"in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to\",\n \"= model_class(model_path) # Subsample the model time. Default to 30 time slices. tmin\",\n \"ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges =\",\n \"data from tarfile and sets up lists of paths and fluxfilenames for later\",\n \"= model_class(model_path) #set the timings up #default if inputs are None: full time\",\n \"with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that gives\",\n \"str Name of detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES.\",\n \"into account the effective mass of the detector as well as a smearing\",\n \"+ 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive\",\n \"file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str\",\n \"= '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0,\",\n \"model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info\",\n \"tables expected by SNOwGLoBES, and compress the output into a tarfile. Parameters ----------\",\n \"import re import tarfile from pathlib import Path from tempfile import TemporaryDirectory import\",\n \"interaction channels. There are three basic steps to using SNOwGLoBES from SNEWPY: *\",\n \"None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else:\",\n \"of them. \\\"\\\"\\\" import io import logging import os import re import tarfile\",\n \"info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path,\",\n \"file. If ``None``, will be based on input file name. tstart : astropy.Quantity\",\n \"interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt\",\n \"import matplotlib.pyplot as plt import numpy as np from astropy import units as\",\n \"its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO':\",\n \": int Number of time slices. Will be ignored if ``deltat`` is also\",\n \"(4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s,\",\n \"name,pattern in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over\",\n \"of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor\",\n \"This step convolves the fluence generated in the previous step with the cross-sections\",\n \"generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input ==\",\n \"column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get\",\n \"for each time bin). The output tables allow to build the detected neutrino\",\n \"= np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if\",\n \"Generate energy + number flux table. for j, E in enumerate(energy): for flavor\",\n \"model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1,\",\n \"1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV #\",\n \"generate a time series or a fluence file. This is done taking as\",\n \"output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for\",\n \"the model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax =\",\n \"with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL),\",\n \"output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ =\",\n \"0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 =\",\n \"model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This\",\n \"c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and\",\n \"#fill the column #return table with the original levels order t = t.unstack(levels)\",\n \"= 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared':\",\n \"Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence =\",\n \"that gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV =\",\n \"#tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results =\",\n \"model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1]\",\n \"starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i]\",\n \"by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results\",\n \"flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$\",\n \"each time step, the latter will compute the integrated neutrino flux (fluence) in\",\n \"ta = tstart tb = tend t = times dt = tb-ta #first\",\n \"#plotting the events from given table flux,det,weighted,smeared = params for c in table.columns:\",\n \"in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results\",\n \"bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for\",\n \"= snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat\",\n \"on input file name. tstart : astropy.Quantity or None Start of time interval\",\n \"snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger\",\n \"30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s)\",\n \"dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy +\",\n \"== 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result =\",\n \"tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1])\",\n \"range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if\",\n \"_ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1)\",\n \"channel. verbose : bool Whether to generate verbose output, e.g. for debugging. remove_generated_files:\",\n \"each interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table\",\n \"> 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in\",\n \"> 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0\",\n \"results, for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as\",\n \"of output file. If ``None``, will be based on input file name. ntbins\",\n \"function of energy for each interaction channel, integrated in a given time window\",\n \"logging import os import re import tarfile from pathlib import Path from tempfile\",\n \"to build the detected neutrino energy spectrum and neutrino time distribution, for each\",\n \"next(j for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index =\",\n \"= 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data])\",\n \"neutrino flux (fluence) in the time bin. The result is a compressed .tar\",\n \"table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per\",\n \"SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns ------- dict\",\n \"and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path,\",\n \"tarfile. Parameters ---------- model_path : str Input file containing neutrino flux information from\",\n \"\\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version will subsample the times in\",\n \": str Path to directory where SNOwGLoBES is installed. tarball_path : str Path\",\n \"for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors)\",\n \"Parameters ---------- model_path : str Input file containing neutrino flux information from supernova\",\n \"detectors supported by SNOwGLoBES. It takes into account the effective mass of the\",\n \"= output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if\",\n \"to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg\",\n \"str Input file containing neutrino flux information from supernova model. model_type : str\",\n \"tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from\",\n \"integrated neutrino flux (fluence) in the time bin. The result is a compressed\",\n \"flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in\",\n \"= {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for\",\n \"``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected\",\n \"= times dt = tb-ta #first time bin of model in requested interval\",\n \"or a fluence file. This is done taking as input the supernova simulation\",\n \"list of end times of the time series bins. Returns ------- str Path\",\n \"window (or time bin), or in a snapshot in time. * **Collating SNOwGLoBES\",\n \"from supernova model. model_type : str Format of input file. Matches the name\",\n \"= next(j for j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index\",\n \"installed. tarball_path : str Path of compressed .tar file produced e.g. by ``generate_time_series()``\",\n \"in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t >\",\n \"= '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not None: if nbin >\",\n \"times[i] dt = tb-ta else: ta = tstart tb = tend t =\",\n \"= np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled times.\",\n \"in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t in\",\n \"of time slices. Will be ignored if ``deltat`` is also given. deltat :\",\n \"501) * MeV # Loop over sampled times. for i in range(nbin): if\",\n \"tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t >= tend)]\",\n \"end times of the time series bins. Returns ------- str Path of compressed\",\n \"allow to build the detected neutrino energy spectrum and neutrino time distribution, for\",\n \"< model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta)\",\n \"0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0]\",\n \"# Subsample the model time. Default to 30 time slices. tmin = snmodel.get_time()[0]\",\n \"d : int or float Distance to supernova in kpc. output_filename : str\",\n \"Length of time slices. Returns ------- str Path of compressed .tar file with\",\n \"integrate over, or list of start times of the time series bins. tend\",\n \"containing neutrino flux information from supernova model. model_type : str Format of input\",\n \"tarball_path : str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or\",\n \"= os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs are\",\n \"script inside SNOwGLoBES, which outputs calculated event rates expected for a given (set\",\n \"tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label\",\n \"generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path\",\n \"function of the neutrino energy and time, for each interaction channel. Parameters ----------\",\n \"j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j,\",\n \"= snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time\",\n \"channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to have\",\n \"output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not None: if nbin\",\n \"= os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s,\",\n \"tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and\",\n \"mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates\",\n \"in flux_files),res)) # save result to file for re-use in collate() cache_file =\",\n \"all the interaction channels and time bins evaluated by SNOwGLoBES in a single\",\n \"complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title}\",\n \"is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip\",\n \"if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for\",\n \"= flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model\",\n \"ta = tstart[i] tb = tend[i] t = times[i] dt = tb-ta else:\",\n \"files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det]\",\n \"event rates are given as a function of the neutrino energy and time,\",\n \"tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or\",\n \"0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else\",\n \"def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res =\",\n \"``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. verbose : bool Whether to\",\n \"Subsample the model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax\",\n \"taking as input the supernova simulation model. The first will evaluate the neutrino\",\n \"astropy.Quantity or None Length of time slices. Returns ------- str Path of compressed\",\n \"for old-style results, for backward compatibiity results = {} #save collated files: with\",\n \"m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return\",\n \"Distance to supernova in kpc. output_filename : str or None Name of output\",\n \"= \\\".dat\\\" if output_filename is not None: if nbin > 1: filename =\",\n \"flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict\",\n \"series bins. tend : astropy.Quantity or None End of time interval to integrate\",\n \"time interval to integrate over, or list of end times of the time\",\n \"the interaction channels and time bins evaluated by SNOwGLoBES in a single file\",\n \"table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text\",\n \"list of start times of the time series bins. tend : astropy.Quantity or\",\n \"tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output.\",\n \"{cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent',\",\n \"patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them and\",\n \"100, 501) * MeV # 1MeV # Loop over sampled times. for i,\",\n \"SNOwGLoBES. It takes into account the effective mass of the detector as well\",\n \"'.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates\",\n \"snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor\",\n \"will use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives\",\n \"series bins. Returns ------- str Path of compressed .tar file with neutrino flux\",\n \"tables allow to build the detected neutrino energy spectrum and neutrino time distribution,\",\n \"return t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared =\",\n \"'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO':\",\n \"in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j, t\",\n \"dict Dictionary of data tables: One table per time bin; each table contains\",\n \"a fluence file. This is done taking as input the supernova simulation model.\",\n \"os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs are None:\",\n \"Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar\",\n \"each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES\",\n \"j, t in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename\",\n \"table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the\",\n \"osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV\",\n \"'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that\",\n \"detected as a function of energy for each interaction channel, integrated in a\",\n \"and time bins evaluated by SNOwGLoBES in a single file (for each detector\",\n \"files for SNOwGLoBES:** There are two ways to do this, either generate a\",\n \"output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy =\",\n \"1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)):\",\n \"detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool If\",\n \"nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _\",\n \"file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is\",\n \"= int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s,\",\n \"data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally\",\n \"filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w')\",\n \"model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1])\",\n \"steps to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There\",\n \"for det in tables: results[det] = {} for flux,t in tables[det].items(): t =\",\n \"not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax -\",\n \"SNOwGLoBES outputs:** This step puts together all the interaction channels and time bins\",\n \"leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) #\",\n \"to generate verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files\",\n \"based on input file name. ntbins : int Number of time slices. Will\",\n \"on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg\",\n \"+ '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf:\",\n \"open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy\",\n \"it gives as output the plot of the energy distribution for each time\",\n \"data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz')\",\n \"files and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir :\",\n \"{} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for\",\n \"this, either generate a time series or a fluence file. This is done\",\n \"input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type :\",\n \"documentation for possible values. d : int or float Distance to supernova in\",\n \"in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']:\",\n \"for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV\",\n \"input files for SNOwGLoBES:** There are two ways to do this, either generate\",\n \"bins evaluated by SNOwGLoBES in a single file (for each detector and for\",\n \"output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name)\",\n \"= (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times =\",\n \"Name of output file. If ``None``, will be based on input file name.\",\n \"Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _\",\n \"tstart[i] tb = tend[i] t = times[i] dt = tb-ta else: ta =\",\n \"output file. If ``None``, will be based on input file name. tstart :\",\n \"tend : astropy.Quantity or None End of time interval to integrate over, or\",\n \"Name of detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. skip_plots:\",\n \"(GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy'\",\n \"# Encode energy/flux table and output to file in tar archive. output =\",\n \"* u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over sampled\",\n \"= tstart[i] tb = tend[i] t = times[i] dt = tb-ta else: ta\",\n \"corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation\",\n \"output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as\",\n \"tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input\",\n \"os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files\",\n \"t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t\",\n \"bin. The result is a compressed .tar file containing all individual input files.\",\n \"= m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res\",\n \"neutrino flux at each time step, the latter will compute the integrated neutrino\",\n \"np.linspace(0, 100, 501) * MeV # 1MeV # Loop over sampled times. for\",\n \"the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel\",\n \"data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is\",\n \"the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t\",\n \"the previous step with the cross-sections for the interaction channels happening in various\",\n \"{'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename =\",\n \"tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output\",\n \"NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table.\",\n \"series files in SNOwGLoBES format. This version will subsample the times in a\",\n \"times of the time series bins. tend : astropy.Quantity or None End of\",\n \"gives the number of events detected as a function of energy for each\",\n \"times. for i in range(nbin): if nbin > 1: ta = tstart[i] tb\",\n \"evaluated by SNOwGLoBES in a single file (for each detector and for each\",\n \"paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as\",\n \"using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are two\",\n \"to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp =\",\n \"SNOwGLoBES:** This step convolves the fluence generated in the previous step with the\",\n \"table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c),\",\n \"NuTau aNuE aNuMu aNuTau') # Generate energy + number flux table. for j,\",\n \"temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]])\",\n \"flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the\",\n \"= snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i\",\n \"not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) #\",\n \"detected neutrino energy spectrum and neutrino time distribution, for each reaction channel or\",\n \"spectrum and neutrino time distribution, for each reaction channel or the sum of\",\n \"verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or returns a data\",\n \"model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64)\",\n \"snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1\",\n \"else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname = model_file_root\",\n \"model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate\",\n \"timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu\",\n \":py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation for possible\",\n \"Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables\",\n \"tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by: #tables\",\n \"present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d)\",\n \"for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in\",\n \"SNOwGLoBES. skip_plots: bool If False, it gives as output the plot of the\",\n \"and runs the supernova script inside SNOwGLoBES, which outputs calculated event rates expected\",\n \"Encode energy/flux table and output to file in tar archive. output = '\\\\n'.join(table).encode('ascii')\",\n \"as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+'\",\n \"file containing all individual input files. * **Running SNOwGLoBES:** This step convolves the\",\n \"output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input\",\n \"'.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar\",\n \"= output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname\",\n \"pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot\",\n \"bbox_inches='tight') #Make a tarfile with the condensed data files and plots output_name =\",\n \"= snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for\",\n \"np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename\",\n \"SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path : str\",\n \"do this, either generate a time series or a fluence file. This is\",\n \"if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det)\",\n \"model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i]\",\n \"Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s\",\n \"table.append(s) logging.debug(s) # Encode energy/flux table and output to file in tar archive.\",\n \"= [next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index =\",\n \"= Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar:\",\n \"deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt =\",\n \"tedges[:-1]) # Generate output. if output_filename is not None: tfname = output_filename +\",\n \"Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s\",\n \"astropy.Quantity or None End of time interval to integrate over, or list of\",\n \"output tables allow to build the detected neutrino energy spectrum and neutrino time\",\n \"*= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j in\",\n \"file. This is done taking as input the supernova simulation model. The first\",\n \"Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt\",\n \"energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path\",\n \"separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the\",\n \"= model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] =\",\n \"result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for re-use in\",\n \"list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from\",\n \"= output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in\",\n \"snewpy run. Returns ------- dict Dictionary of data tables: One table per time\",\n \"t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from\",\n \"= t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting the events from given\",\n \"This is done taking as input the supernova simulation model. The first will\",\n \"+ \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output)\",\n \"output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension\",\n \"**Collating SNOwGLoBES outputs:** This step puts together all the interaction channels and time\",\n \"One table per time bin; each table contains in the first column the\",\n \"the interaction channels happening in various detectors supported by SNOwGLoBES. It takes into\",\n \"Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed. tarball_path\",\n \"of data tables: One table per time bin; each table contains in the\",\n \"next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j\",\n \"verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files and configures and runs the\",\n \"TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np\",\n \"t >= tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if\",\n \"expected by SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path\",\n \"enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is not None:\",\n \"the integrated neutrino flux (fluence) in the time bin. The result is a\",\n \"isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile and sets\",\n \"parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy\",\n \"else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model\",\n \"debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and .png's\",\n \"tend t = times dt = tb-ta #first time bin of model in\",\n \"table contains in the first column the energy bins, in the remaining columns\",\n \"interaction channels happening in various detectors supported by SNOwGLoBES. It takes into account\",\n \"model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default\",\n \"t.filter(like=pattern) #sum over them and save to a separate column t_agg = t_sel.sum(axis='columns')\",\n \"import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy\",\n \"in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor:\",\n \"* MeV # Loop over sampled times. for i in range(nbin): if nbin\",\n \"# Loop over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t,\",\n \"of detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. verbose :\",\n \"if ``deltat`` is also given. deltat : astropy.Quantity or None Length of time\",\n \"flux information from supernova model. model_type : str Format of input file. Matches\",\n \"plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file\",\n \"/= (tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s =\",\n \"bin; each table contains in the first column the energy bins, in the\",\n \"+ transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2')\",\n \"'.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output))\",\n \"coding: utf-8 -*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES\",\n \"time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if\",\n \"of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation.\",\n \"bins, in the remaining columns the number of events for each interaction channel\",\n \"Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat\",\n \"files from SNOwGLoBES, collated files, and .png's made for this snewpy run. Returns\",\n \"#read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s,\",\n \"import numpy as np from astropy import units as u from tqdm.auto import\",\n \"time step, the latter will compute the integrated neutrino flux (fluence) in the\",\n \"d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This version\",\n \"model_class(model_path) #set the timings up #default if inputs are None: full time window\",\n \"files. * **Running SNOwGLoBES:** This step convolves the fluence generated in the previous\",\n \"of compressed .tar file with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type)\",\n \"s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X])\",\n \"interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to\",\n \"to using SNOwGLoBES from SNEWPY: * **Generating input files for SNOwGLoBES:** There are\",\n \"SNOwGLoBES:** There are two ways to do this, either generate a time series\",\n \"TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file =\",\n \"a function of the neutrino energy and time, for each interaction channel. Parameters\",\n \"to directory where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar\",\n \"of time interval to integrate over, or list of end times of the\",\n \"model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with\",\n \"= tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type,\",\n \"= 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index\",\n \"Remove the output files from SNOwGLoBES, collated files, and .png's made for this\",\n \"if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s)\",\n \"cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return\",\n \"flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at\",\n \"requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor]\",\n \"inside SNOwGLoBES, which outputs calculated event rates expected for a given (set of)\",\n \"get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups()\",\n \"use all detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose\",\n \"collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result)\",\n \"tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin =\",\n \"* **Collating SNOwGLoBES outputs:** This step puts together all the interaction channels and\",\n \"the sum of them. \\\"\\\"\\\" import io import logging import os import re\",\n \"columns the number of events for each interaction channel in the detector. \\\"\\\"\\\"\",\n \"as np from astropy import units as u from tqdm.auto import tqdm import\",\n \"the column #return table with the original levels order t = t.unstack(levels) t\",\n \"by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\\\"all\\\"``, will\",\n \"'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname =\",\n \"re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}')\",\n \"plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title =\",\n \"io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate\",\n \"Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra\",\n \"tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version will subsample the times\",\n \"which contain `like` t_sel = t.filter(like=pattern) #sum over them and save to a\",\n \"energy-dependent detection efficiency. The output gives the number of events detected as a\",\n \"text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output\",\n \"'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #\",\n \"for this snewpy run. Returns ------- dict Dictionary of data tables: One table\",\n \"det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for re-use\",\n \"[] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos\",\n \"from given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() >\",\n \"= deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1)\",\n \"build the detected neutrino energy spectrum and neutrino time distribution, for each reaction\",\n \"tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 =\",\n \"= np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] =\",\n \"transformation. See snewpy.flavor_transformation documentation for possible values. d : int or float Distance\",\n \"tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files\",\n \"if output_filename is not None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else:\",\n \"flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta)\",\n \"detector_input = tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for\",\n \"flux table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] =\",\n \"len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except:\",\n \"snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path,\",\n \"debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O')\",\n \"i in range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i]\",\n \"various detectors supported by SNOwGLoBES. It takes into account the effective mass of\",\n \"dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend)\",\n \"tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt =\",\n \"strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\\",\n \"energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor:\",\n \"``generate_fluence()``. detector_input : str Name of detector. If ``\\\"all\\\"``, will use all detectors\",\n \"#dict for old-style results, for backward compatibiity results = {} #save collated files:\",\n \"ending_index[i] = next(j for j, t in enumerate(model_tend) if t >= tend[i]) else:\",\n \"output to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root,\",\n \"expected for a given (set of) detector(s). These event rates are given as\",\n \"from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced\",\n \"str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input\",\n \"transformation. Use dict to associate the transformation name with its class. flavor_transformation_dict =\",\n \"mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if\",\n \"plt import numpy as np from astropy import units as u from tqdm.auto\",\n \"snmodel = model_class(model_path) # Subsample the model time. Default to 30 time slices.\",\n \"in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t in\",\n \"returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where\",\n \"detectors, detector materials and interaction channels. There are three basic steps to using\",\n \"* from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__)\",\n \"of) detector(s). These event rates are given as a function of the neutrino\",\n \"# Generate output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2'\",\n \"where SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file produced\",\n \"time window (or time bin), or in a snapshot in time. * **Collating\",\n \"j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt\",\n \"= np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False:\",\n \"+ '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info =\",\n \"= simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {}\",\n \"There are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input\",\n \"only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in\",\n \"input file name. ntbins : int Number of time slices. Will be ignored\",\n \": bool Whether to generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng =\",\n \"1MeV # Loop over sampled times. for i, t in enumerate(times): osc_spectra =\",\n \"detection efficiency. The output gives the number of events detected as a function\",\n \"time series or a fluence file. This is done taking as input the\",\n \"evaluate the neutrino flux at each time step, the latter will compute the\",\n \"extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def\",\n \"Returns ------- str Path of compressed .tar file with neutrino flux data. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Takes as input the neutrino flux files and configures and runs the supernova\",\n \"previous step with the cross-sections for the interaction channels happening in various detectors\",\n \"flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model\",\n \"tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension\",\n \"for each interaction channel. verbose : bool Whether to generate verbose output, e.g.\",\n \"tb = tend t = times dt = tb-ta #first time bin of\",\n \"can estimate detected event rates from a given input supernova neutrino flux. It\",\n \"j, t in enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for\",\n \"= '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root +\",\n \"Whether to generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if\",\n \"file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector.\",\n \"> 1: ta = tstart[i] tb = tend[i] t = times[i] dt =\",\n \"tar archive that gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output))\",\n \"for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /=\",\n \"table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') # Generate energy + number\",\n \"sum of them. \\\"\\\"\\\" import io import logging import os import re import\",\n \"str or None Name of output file. If ``None``, will be based on\",\n \"model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index =\",\n \"flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default\",\n \"tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)\",\n \"formats complete graph smear_title = 'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()}\",\n \"output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This version will\",\n \"Loop over sampled times. for i in range(nbin): if nbin > 1: ta\",\n \"to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the\",\n \"input files. * **Running SNOwGLoBES:** This step convolves the fluence generated in the\",\n \"bool If False, it gives as output the plot of the energy distribution\",\n \"t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for j, t\",\n \"model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in\",\n \"events detected as a function of energy for each interaction channel, integrated in\",\n \"= snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins = int((tmax-tmin)/dt)\",\n \"= tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path,\",\n \"detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel column\",\n \"tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return\",\n \"model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with\",\n \"what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward\",\n \"\\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif\",\n \"= tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files\",\n \"model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i]\",\n \"= list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file\",\n \"from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as mpl import\",\n \"**Running SNOwGLoBES:** This step convolves the fluence generated in the previous step with\",\n \"dt = dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time\",\n \"tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) +\",\n \"det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result\",\n \"= snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for\",\n \"# strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type +\",\n \"Start of time interval to integrate over, or list of start times of\",\n \"utf-8 -*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_\",\n \"lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete\",\n \"of events detected as a function of energy for each interaction channel, integrated\",\n \": str Input file containing neutrino flux information from supernova model. model_type :\",\n \"output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root,\",\n \"convolves the fluence generated in the previous step with the cross-sections for the\",\n \"= aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table = t[w][s]\",\n \"= model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1): model_tstart[i] =\",\n \"{weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)')\",\n \"'\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not None: if nbin > 1:\",\n \": str Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``.\",\n \"time bin; each table contains in the first column the energy bins, in\",\n \"import os import re import tarfile from pathlib import Path from tempfile import\",\n \"\\\".dat\\\" if output_filename is not None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension\",\n \"of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str\",\n \"= [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in\",\n \"output to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if\",\n \"time series files in SNOwGLoBES format. This version will subsample the times in\",\n \": str Format of input file. Matches the name of the corresponding class\",\n \"t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params\",\n \"flux. It supports many different neutrino detectors, detector materials and interaction channels. There\",\n \"os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type\",\n \"t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table =\",\n \"storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file,\",\n \"= 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # Loop\",\n \"strip extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0,\",\n \"'\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1)\",\n \"tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False) for\",\n \"happening in various detectors supported by SNOwGLoBES. It takes into account the effective\",\n \"ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir,\",\n \"osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR])\",\n \"#creates file in tar archive that gives information on parameters output = '\\\\n'.join(map(str,\",\n \"None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin)\",\n \"to generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input\",\n \"plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats\",\n \"= np.linspace(0, 100, 501) * MeV # Loop over sampled times. for i\",\n \"model_type : str Format of input file. Matches the name of the corresponding\",\n \"or the sum of them. \\\"\\\"\\\" import io import logging import os import\",\n \"of the time series bins. Returns ------- str Path of compressed .tar file\",\n \"= 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0\",\n \"\\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate\",\n \"nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i\",\n \"Name of flavor transformation. See snewpy.flavor_transformation documentation for possible values. d : int\",\n \"index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results\",\n \"tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not None:\",\n \"plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the\",\n \"processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original\",\n \"{} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this\",\n \"0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) #\",\n \"# Choose flavor transformation. Use dict to associate the transformation name with its\",\n \"SNOwGLoBES, and compress the output into a tarfile. Parameters ---------- model_path : str\",\n \"for each time bin and for each interaction channel. verbose : bool Whether\",\n \"all individual input files. * **Running SNOwGLoBES:** This step convolves the fluence generated\",\n \"\\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info,\",\n \"enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt * 0.2 *\",\n \"snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV\",\n \"for j, t in enumerate(model_tend) if t > tstart)] ending_index = [next(j for\",\n \"into a tarfile. Parameters ---------- model_path : str Input file containing neutrino flux\",\n \"bins of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra\",\n \"verbose : bool Whether to generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng\",\n \"tstart tb = tend t = times dt = tb-ta #first time bin\",\n \"from astropy import units as u from tqdm.auto import tqdm import snewpy.models from\",\n \"Path of compressed .tar file with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn,\",\n \"start times of the time series bins. tend : astropy.Quantity or None End\",\n \"to integrate over, or list of start times of the time series bins.\",\n \"in range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i] t\",\n \"generated in the previous step with the cross-sections for the interaction channels happening\",\n \"+ \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output)\",\n \"extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0,\",\n \"'Interaction' if smeared=='unsmeared' else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected\",\n \"the output files from SNOwGLoBES, collated files, and .png's made for this snewpy\",\n \"neutrino time distribution, for each reaction channel or the sum of them. \\\"\\\"\\\"\",\n \"class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor transformation. See snewpy.flavor_transformation documentation\",\n \"int((tmax-tmin)/dt) else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s,\",\n \"bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step\",\n \"plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'),\",\n \"requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation)\",\n \"functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from\",\n \"transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size =\",\n \"in kpc. output_filename : str or None Name of output file. If ``None``,\",\n \"flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor:\",\n \"for each interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the\",\n \"= tstart tb = tend t = times dt = tb-ta #first time\",\n \"by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for debugging.\",\n \"for possible values. d : int or float Distance to supernova in kpc.\",\n \"and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str Path to\",\n \"* 0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s =\",\n \"d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar\",\n \"\\\"\\\"\\\" import io import logging import os import re import tarfile from pathlib\",\n \"i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] =\",\n \"info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir,\",\n \"the remaining columns the number of events for each interaction channel in the\",\n \"time distribution, for each reaction channel or the sum of them. \\\"\\\"\\\" import\",\n \"MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X])\",\n \"'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation\",\n \"supported by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g. for\",\n \"for s in ['smeared','unsmeared']: table = t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat'\",\n \"io import logging import os import re import tarfile from pathlib import Path\",\n \"in the time bin. The result is a compressed .tar file containing all\",\n \"SNOwGLoBES, which outputs calculated event rates expected for a given (set of) detector(s).\",\n \"times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence =\",\n \"time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation)\",\n \"in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt\",\n \"#set the timings up #default if inputs are None: full time window of\",\n \"for f in flux_files),res)) # save result to file for re-use in collate()\",\n \"SNOwGLoBES is installed. tarball_path : str Path of compressed .tar file produced e.g.\",\n \"SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova\",\n \"t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return\",\n \"c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\",\",\n \"at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE\",\n \"plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph\",\n \"the energy-dependent detection efficiency. The output gives the number of events detected as\",\n \"return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True):\",\n \"to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename\",\n \"t in enumerate(model_tend) if t >= tend)] # Generate output. if output_filename is\",\n \"flux at each time step, the latter will compute the integrated neutrino flux\",\n \"detector as well as a smearing matrix describing the energy-dependent detection efficiency. The\",\n \"E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] * dt *\",\n \"extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) +\",\n \"for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to\",\n \"t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with the original levels order\",\n \"= snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1]\",\n \"filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) #\",\n \"End of time interval to integrate over, or list of end times of\",\n \"f.write(table.to_string(float_format='%23.15g')) #format the results for the output header = 'Energy '+' '.join(list(table.columns)) data\",\n \"model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info\",\n \"output files and generates plots or returns a data table. Parameters ---------- SNOwGLoBESdir\",\n \"number flux table. for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor]\",\n \"not None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension\",\n \"tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor,\",\n \"'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else:\",\n \"file name. ntbins : int Number of time slices. Will be ignored if\",\n \"/ (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s =\",\n \"Path(tempdir) for det in tables: results[det] = {} for flux,t in tables[det].items(): t\",\n \"compressed .tar file containing all individual input files. * **Running SNOwGLoBES:** This step\",\n \"neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir : str\",\n \"tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input =\",\n \"{det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy\",\n \"nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart =\",\n \"t >= tend)] # Generate output. if output_filename is not None: tfname =\",\n \"model_path : str Input file containing neutrino flux information from supernova model. model_type\",\n \"simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c):\",\n \"or in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts\",\n \"output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) tfname =\",\n \"= [detector_input] result = {} #Extracts data from tarfile and sets up lists\",\n \"in a single file (for each detector and for each time bin). The\",\n \"for i in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1]\",\n \"as a function of the neutrino energy and time, for each interaction channel.\",\n \"the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300,\",\n \"transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as\",\n \"temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]],\",\n \"SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate\",\n \"1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # Loop over\",\n \"f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else: return\",\n \"elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts data from tarfile and\",\n \"for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau\",\n \"starting_index[i] = next(j for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i]\",\n \"#save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results\",\n \"dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and plots output_name\",\n \"time bins evaluated by SNOwGLoBES in a single file (for each detector and\",\n \"is not None: dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax\",\n \"ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j,\",\n \"tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in\",\n \"len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1 times =\",\n \"if t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if\",\n \"range(nbin): if nbin > 1: ta = tstart[i] tb = tend[i] t =\",\n \"a given time window (or time bin), or in a snapshot in time.\",\n \"Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive:\",\n \"to have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for\",\n \"model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j],\",\n \"by SNOwGLoBES in a single file (for each detector and for each time\",\n \"detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files and configures and runs\",\n \"effective mass of the detector as well as a smearing matrix describing the\",\n \"t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t\",\n \"range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t > tstart[i])\",\n \"first will evaluate the neutrino flux at each time step, the latter will\",\n \"= tend t = times dt = tb-ta #first time bin of model\",\n \"None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if\",\n \"model. model_type : str Format of input file. Matches the name of the\",\n \"tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in tar archive that gives information\",\n \"/ (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) #\",\n \"#optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png'\",\n \"return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output\",\n \"{cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what produced by:\",\n \"(GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage\",\n \"# save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] +\",\n \"if t >= tend)] # Generate output. if output_filename is not None: tfname\",\n \"transformation_type)).encode('ascii') tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501)\",\n \"enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend)\",\n \"This version will subsample the times in a supernova model, produce energy tables\",\n \"'.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename)\",\n \"'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set\",\n \"tend)] # Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else:\",\n \"the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}')\",\n \"generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format.\",\n \"tarball_path,detector_input) #dict for old-style results, for backward compatibiity results = {} #save collated\",\n \"= model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times),\",\n \"outputs calculated event rates expected for a given (set of) detector(s). These event\",\n \"time interval to integrate over, or list of start times of the time\",\n \"info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False):\",\n \"i in range(len(tstart)): starting_index[i] = next(j for j, t in enumerate(model_tend) if t\",\n \"for the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index =\",\n \"reaction channel or the sum of them. \\\"\\\"\\\" import io import logging import\",\n \"integrate over, or list of end times of the time series bins. Returns\",\n \"the output header = 'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy()\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s\",\n \"in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval\",\n \"= table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like` t_sel =\",\n \"nbin = len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1\",\n \"each table contains in the first column the energy bins, in the remaining\",\n \"output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root\",\n \"> 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0)\",\n \"for j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for\",\n \"if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s\",\n \".tar file with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose\",\n \"os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins,\",\n \"neutrino flux. It supports many different neutrino detectors, detector materials and interaction channels.\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES.\",\n \"events for each interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange\",\n \"from a given input supernova neutrino flux. It supports many different neutrino detectors,\",\n \"input the neutrino flux files and configures and runs the supernova script inside\",\n \"= model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2'\",\n \"enumerate(model_tend) if t >= tend[i]) else: starting_index = [next(j for j, t in\",\n \"flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor]\",\n \"for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log')\",\n \"or list of end times of the time series bins. Returns ------- str\",\n \"tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in ['smeared','unsmeared']: table\",\n \"plots or returns a data table. Parameters ---------- SNOwGLoBESdir : str Path to\",\n \"flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']: for s in\",\n \"from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None,\",\n \"produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If\",\n \"Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar:\",\n \"import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None):\",\n \"of the model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1]\",\n \"(if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins,\",\n \"results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight')\",\n \"input the supernova simulation model. The first will evaluate the neutrino flux at\",\n \"0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index =\",\n \"1: ta = tstart[i] tb = tend[i] t = times[i] dt = tb-ta\",\n \"tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type,\",\n \"transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED),\",\n \"compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name\",\n \"fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir)\",\n \"tfname), 'w:bz2') as tf: #creates file in tar archive that gives information on\",\n \"else: starting_index = [next(j for j, t in enumerate(model_tend) if t > tstart)]\",\n \"= len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None,\",\n \"data table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s\",\n \"slices. Returns ------- str Path of compressed .tar file with neutrino flux data.\",\n \"else 'Detected' plt.title(f'{flux} {det.capitalize()} {weighted.capitalize()} {smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events')\",\n \"tarfile with the condensed data files and plots output_name = Path(tarball_path).stem output_name =\",\n \"as output the plot of the energy distribution for each time bin and\",\n \"model time. Default to 30 time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1]\",\n \"model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample the model time. Default to\",\n \"temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table =\",\n \"data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to\",\n \"'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file\",\n \"If False, it gives as output the plot of the energy distribution for\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s)\",\n \"Path of compressed .tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input :\",\n \"supernova neutrino flux. It supports many different neutrino detectors, detector materials and interaction\",\n \"file with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor\",\n \"estimate detected event rates from a given input supernova neutrino flux. It supports\",\n \"as plt import numpy as np from astropy import units as u from\",\n \"desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res))\",\n \"from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def\",\n \"information from supernova model. model_type : str Format of input file. Matches the\",\n \"import io import logging import os import re import tarfile from pathlib import\",\n \"`like` t_sel = t.filter(like=pattern) #sum over them and save to a separate column\",\n \"and configures and runs the supernova script inside SNOwGLoBES, which outputs calculated event\",\n \"\\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate\",\n \"Generate output. if output_filename is not None: tfname = output_filename + 'kpc.tar.bz2' else:\",\n \"in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file)\",\n \"table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin\",\n \"input supernova neutrino flux. It supports many different neutrino detectors, detector materials and\",\n \"file name. tstart : astropy.Quantity or None Start of time interval to integrate\",\n \"the supernova script inside SNOwGLoBES, which outputs calculated event rates expected for a\",\n \"list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain\",\n \"efficiency. The output gives the number of events detected as a function of\",\n \"plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results\",\n \"+ '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir,\",\n \"dt = deltat ntbins = int((tmax-tmin)/dt) else: dt = (tmax - tmin) /\",\n \"range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor]\",\n \"flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence':\",\n \"each interaction channel, integrated in a given time window (or time bin), or\",\n \"False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with\",\n \"file. If ``None``, will be based on input file name. ntbins : int\",\n \"of events for each interaction channel in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns):\",\n \"'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel =\",\n \"of time interval to integrate over, or list of start times of the\",\n \"output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ =\",\n \"given as a function of the neutrino energy and time, for each interaction\",\n \"for j, E in enumerate(energy): for flavor in Flavor: osc_fluence[flavor] = osc_spectra[flavor][j] *\",\n \"channel, integrated in a given time window (or time bin), or in a\",\n \"else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES\",\n \"logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def\",\n \"1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j])\",\n \"flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1)\",\n \"neutrino flux files and configures and runs the supernova script inside SNOwGLoBES, which\",\n \"osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode\",\n \"allow_pickle=True).tolist() #This output is similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input)\",\n \"calculated event rates expected for a given (set of) detector(s). These event rates\",\n \"lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with\",\n \"as well as a smearing matrix describing the energy-dependent detection efficiency. The output\",\n \"ntbins : int Number of time slices. Will be ignored if ``deltat`` is\",\n \"= table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if\",\n \"each time bin). The output tables allow to build the detected neutrino energy\",\n \"tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the\",\n \"for SNOwGLoBES:** There are two ways to do this, either generate a time\",\n \"MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d,\",\n \"# Loop over sampled times. for i in range(nbin): if nbin > 1:\",\n \"= np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t\",\n \"`SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova neutrino\",\n \"'.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname),\",\n \"save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy'\",\n \".png's made for this snewpy run. Returns ------- dict Dictionary of data tables:\",\n \"skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a\",\n \"MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s\",\n \"'.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)')\",\n \"c in table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5,\",\n \"have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels) for name,pattern\",\n \"tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t >= tend[i])\",\n \"'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path))\",\n \"else: dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s\",\n \"model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename = model_file_root +\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s\",\n \"Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name\",\n \"the time bin. The result is a compressed .tar file containing all individual\",\n \"window of the model if tstart is None: tstart = snmodel.get_time()[0] tend =\",\n \"= dt else: for flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins\",\n \"re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m): flv,bar,Nuc,num,res\",\n \"\\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels\",\n \"= '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table\",\n \"contain `like` t_sel = t.filter(like=pattern) #sum over them and save to a separate\",\n \"+ tedges[:-1]) # Generate output. if output_filename is not None: tfname = output_filename\",\n \"f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f:\",\n \"model, produce energy tables expected by SNOwGLoBES, and compress the output into a\",\n \"transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format. This\",\n \"be based on input file name. tstart : astropy.Quantity or None Start of\",\n \"results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s))\",\n \"interaction channel, integrated in a given time window (or time bin), or in\",\n \"model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if\",\n \"model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the timings up #default if inputs\",\n \"name. ntbins : int Number of time slices. Will be ignored if ``deltat``\",\n \"= len(tstart/u.s) except: t0 = tstart t1 = tend nbin = 1 times\",\n \"> tstart[i]) ending_index[i] = next(j for j, t in enumerate(model_tend) if t >=\",\n \"AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL),\",\n \"as u from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from\",\n \"det in tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e')\",\n \"t = times dt = tb-ta #first time bin of model in requested\",\n \"interaction channel. verbose : bool Whether to generate verbose output, e.g. for debugging.\",\n \"for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated files, and\",\n \"given input supernova neutrino flux. It supports many different neutrino detectors, detector materials\",\n \"times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None:\",\n \"flux_files),res)) # save result to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')]\",\n \"present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d)\",\n \"detectors supported by SNOwGLoBES. verbose : bool Whether to generate verbose output, e.g.\",\n \"re import tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib\",\n \"use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat'))\",\n \"+ transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin, tmax, ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2')\",\n \"supernova in kpc. output_filename : str or None Name of output file. If\",\n \"tend[i] t = times[i] dt = tb-ta else: ta = tstart tb =\",\n \"file (for each detector and for each time bin). The output tables allow\",\n \"also given. deltat : astropy.Quantity or None Length of time slices. Returns -------\",\n \"detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file\",\n \"three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files for\",\n \"the output into a tarfile. Parameters ---------- model_path : str Input file containing\",\n \"def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared = params for\",\n \"'+res return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c)\",\n \"s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux\",\n \"Path to directory where SNOwGLoBES is installed. tarball_path : str Path of compressed\",\n \"model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\\",\n \"= table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot\",\n \"in a supernova model, produce energy tables expected by SNOwGLoBES, and compress the\",\n \"file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ =\",\n \"t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table, params): #plotting\",\n \"osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output\",\n \"skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or returns a\",\n \"time series bins. Returns ------- str Path of compressed .tar file with neutrino\",\n \"'Energy '+' '.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name]\",\n \"\\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created archive: {output_path}') return results\",\n \"takes into account the effective mass of the detector as well as a\",\n \"{smear_title} Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction\",\n \"dt = tb-ta #first time bin of model in requested interval osc_spectra =\",\n \"data tables: One table per time bin; each table contains in the first\",\n \"model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0] for i in range(1, len(model_times), 1):\",\n \"'.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output))\",\n \"(tb-ta) osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at\",\n \"contains in the first column the energy bins, in the remaining columns the\",\n \"table. Parameters ---------- SNOwGLoBESdir : str Path to directory where SNOwGLoBES is installed.\",\n \"for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table = []\",\n \"by SNOwGLoBES. skip_plots: bool If False, it gives as output the plot of\",\n \"snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins\",\n \"\\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type +\",\n \"rates expected for a given (set of) detector(s). These event rates are given\",\n \"0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not None: tfname =\",\n \"= {} #Extracts data from tarfile and sets up lists of paths and\",\n \"TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t,\",\n \"output_filename is not None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename\",\n \"matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from astropy\",\n \"levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1) return t def do_plot(table,\",\n \"result) return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta\",\n \"axis=1) return t def do_plot(table, params): #plotting the events from given table flux,det,weighted,smeared\",\n \"See snewpy.flavor_transformation documentation for possible values. d : int or float Distance to\",\n \"model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1:\",\n \"'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {}\",\n \"in a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together\",\n \"import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger =\",\n \"= tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result\",\n \"the model if tstart is None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try:\",\n \"or None Length of time slices. Returns ------- str Path of compressed .tar\",\n \"ntbins, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file in\",\n \"is not None: tfname = output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file)\",\n \"if dt < model_tend[starting_index[i]]-ta: dt = dt else: for flavor in Flavor: osc_spectra[flavor]\",\n \"None: tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0\",\n \"in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy,\",\n \"the detected neutrino energy spectrum and neutrino time distribution, for each reaction channel\",\n \"import tarfile from pathlib import Path from tempfile import TemporaryDirectory import matplotlib as\",\n \"SNEWPY: * **Generating input files for SNOwGLoBES:** There are two ways to do\",\n \"in range(1, len(model_times), 1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1]\",\n \"bins. tend : astropy.Quantity or None End of time interval to integrate over,\",\n \"**Generating input files for SNOwGLoBES:** There are two ways to do this, either\",\n \"time bin). The output tables allow to build the detected neutrino energy spectrum\",\n \"+ '.npy' logging.info(f'Saving simulation results to {cache_file}') np.save(cache_file, result) return result re_chan_label =\",\n \"runs the supernova script inside SNOwGLoBES, which outputs calculated event rates expected for\",\n \"verbose output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all':\",\n \"them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels\",\n \"flavor in Flavor: osc_spectra[flavor] *= (model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested\",\n \"tend[i]) else: starting_index = [next(j for j, t in enumerate(model_tend) if t >\",\n \"with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det] =\",\n \"t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with\",\n \"'.join(list(table.columns)) data = table.to_numpy().T index = table.index.to_numpy() data = np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data}\",\n \"name of the corresponding class in :py:mod:`snewpy.models`. transformation_type : str Name of flavor\",\n \"and sets up lists of paths and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes')\",\n \"dt/u.s)*u.s times = 0.5*(tedges[1:] + tedges[:-1]) # Generate output. if output_filename is not\",\n \"logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to\",\n \"for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor\",\n \"filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data\",\n \"output_filename + 'kpc.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present)\",\n \"the neutrino energy and time, for each interaction channel. Parameters ---------- SNOwGLoBESdir :\",\n \"= Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for file in tempdir.iterdir(): tar.add(file,arcname=output_name+'/'+file.name) logging.info(f'Created\",\n \"not None: tfname = output_filename+'.tar.bz2' else: model_file_root, _ = os.path.splitext(model_file) # strip extension\",\n \"per time bin; each table contains in the first column the energy bins,\",\n \"and neutrino time distribution, for each reaction channel or the sum of them.\",\n \"#Extracts data from tarfile and sets up lists of paths and fluxfilenames for\",\n \"transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size =\",\n \"if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path,\",\n \"TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables: results[det] = {}\",\n \"= [next(j for j, t in enumerate(model_tend) if t >= tend)] # Generate\",\n \"values. d : int or float Distance to supernova in kpc. output_filename :\",\n \"skip_plots: bool If False, it gives as output the plot of the energy\",\n \"np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for j, t in\",\n \"if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in\",\n \"from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables =\",\n \"the supernova simulation model. The first will evaluate the neutrino flux at each\",\n \"tempdir = Path(tempdir) for det in tables: results[det] = {} for flux,t in\",\n \"NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) # Subsample\",\n \"as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors', leave=False)\",\n \"model. The first will evaluate the neutrino flux at each time step, the\",\n \"plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading\",\n \"= '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) #\",\n \"detector_input == 'all': detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result\",\n \"'+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label,\",\n \"(or time bin), or in a snapshot in time. * **Collating SNOwGLoBES outputs:**\",\n \"a time series or a fluence file. This is done taking as input\",\n \"to supernova in kpc. output_filename : str or None Name of output file.\",\n \"from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as plt import\",\n \"= {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in ['weighted','unweighted']:\",\n \"time bin. The result is a compressed .tar file containing all individual input\",\n \"of model in requested interval for j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra =\",\n \"over, or list of start times of the time series bins. tend :\",\n \"Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from\",\n \"MeV # Loop over sampled times. for i in range(nbin): if nbin >\",\n \"supernova simulation model. The first will evaluate the neutrino flux at each time\",\n \"energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of\",\n \"gen_label(m): flv,bar,Nuc,num,res = m.groups() if flv!='e': flv='\\\\\\\\'+flv if bar: bar='\\\\\\\\'+bar s = f'${bar}{{\\\\\\\\nu}}_{flv}$\",\n \"output, e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES, collated\",\n \"in patterns.items(): #get channels which contain `like` t_sel = t.filter(like=pattern) #sum over them\",\n \">= tend)] # Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2'\",\n \"given table flux,det,weighted,smeared = params for c in table.columns: if table[c].max() > 0.1:\",\n \"= output_filename+\\\"_\\\"+str(i)+extension else: filename = output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip\",\n \"the detector as well as a smearing matrix describing the energy-dependent detection efficiency.\",\n \"in tables: results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for\",\n \"over sampled times. for i, t in enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation)\",\n \"tqdm(detector_input, desc='Detectors', leave=False) for det in detector_input: res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in\",\n \"TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0:\",\n \"= t[w][s] filename_base = f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files\",\n \"0.2 * MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s,\",\n \"transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version\",\n \"1): model_tstart[i] = 0.5*(model_times[i]+model_times[i-1]) model_tend[i-1] = model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin >\",\n \"t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as tf: #creates file\",\n \"for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir:\",\n \"time series bins. tend : astropy.Quantity or None End of time interval to\",\n \"{} #Extracts data from tarfile and sets up lists of paths and fluxfilenames\",\n \"version will subsample the times in a supernova model, produce energy tables expected\",\n \"numpy as np from astropy import units as u from tqdm.auto import tqdm\",\n \"filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g'))\",\n \"if nbin > 1: ta = tstart[i] tb = tend[i] t = times[i]\",\n \"smearing matrix describing the energy-dependent detection efficiency. The output gives the number of\",\n \"table.columns: if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5,\",\n \"100, 501) * MeV # Loop over sampled times. for i in range(nbin):\",\n \"requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt < model_tend[starting_index[i]]-ta: dt =\",\n \"is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile\",\n \"osc_fluence = {} table = [] table.append('# TBinMid={:g}sec TBinWidth={:g}s EBinWidth=0.2MeV Fluence at Earth\",\n \"model_tstart[i] model_tend[len(model_times)-1] = model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index\",\n \"tend nbin = 1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart\",\n \"possible values. d : int or float Distance to supernova in kpc. output_filename\",\n \"try: if len(tstart/u.s) > 0: t0 = tstart[0] t1 = tend[-1] nbin =\",\n \"= output_filename+extension else: model_file_root, _ = os.path.splitext(model_file) # strip extension (if present) filename\",\n \"detector_input = list(sng.detectors) detector_input.remove('d2O') elif isinstance(detector_input,str): detector_input = [detector_input] result = {} #Extracts\",\n \"in SNOwGLoBES format. This version will subsample the times in a supernova model,\",\n \"model_class = getattr(snewpy.models.ccsn, model_type) # Choose flavor transformation. Use dict to associate the\",\n \"energy/flux table and output to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension\",\n \"are given as a function of the neutrino energy and time, for each\",\n \"``None``, will be based on input file name. ntbins : int Number of\",\n \"produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results, for backward compatibiity\",\n \"j in range(starting_index[i]+1, ending_index[i], 1): temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in\",\n \"present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(t0, t1, nbin,\",\n \"table per time bin; each table contains in the first column the energy\",\n \"osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra =\",\n \"ignored if ``deltat`` is also given. deltat : astropy.Quantity or None Length of\",\n \"import logging import os import re import tarfile from pathlib import Path from\",\n \"of detector. If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. skip_plots: bool\",\n \"u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV # Loop over\",\n \"verbose output, e.g. for debugging. remove_generated_files: bool Remove the output files from SNOwGLoBES,\",\n \"and fluxfilenames for later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar:\",\n \"in the detector. \\\"\\\"\\\" def aggregate_channels(table, **patterns): #rearrange the table to have only\",\n \"similar to what produced by: #tables = simulate(SNOwGLoBESdir, tarball_path,detector_input) #dict for old-style results,\",\n \"EBinWidth=0.2MeV Fluence at Earth for this timebin in neutrinos per cm^2'.format(t, dt)) table.append('#\",\n \"nbin, d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir,\",\n \"s = f'${bar}{{\\\\\\\\nu}}_{flv}$ '+f'${{}}^{{{num}}}{Nuc}$ '+res return s if c in mapp: return mapp[c]\",\n \"<gh_stars>0 # -*- coding: utf-8 -*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting\",\n \"detected event rates from a given input supernova neutrino flux. It supports many\",\n \"archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" model_file_root, _ = os.path.splitext(model_file) filename =\",\n \"the timings up #default if inputs are None: full time window of the\",\n \"given (set of) detector(s). These event rates are given as a function of\",\n \"tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input, desc='Detectors',\",\n \"= model_times[-1] if nbin > 1: starting_index = np.zeros(len(times), dtype=np.int64) ending_index = np.zeros(len(times),\",\n \"done taking as input the supernova simulation model. The first will evaluate the\",\n \"a tarfile with the condensed data files and plots output_name = Path(tarball_path).stem output_name\",\n \"# strip extension (if present) tfname = model_file_root + '.' + transformation_type +\",\n \"#sum over them and save to a separate column t_agg = t_sel.sum(axis='columns') #drop\",\n \"plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make a tarfile with the condensed data files and plots\",\n \"+= temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last time bin of model in requested interval temp_spectra = snmodel.get_transformed_spectra(\",\n \"of input file. Matches the name of the corresponding class in :py:mod:`snewpy.models`. transformation_type\",\n \"t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 = tend\",\n \"dtype=np.int64) ending_index = np.zeros(len(times), dtype=np.int64) for i in range(len(tstart)): starting_index[i] = next(j for\",\n \"[detector_input] result = {} #Extracts data from tarfile and sets up lists of\",\n \"later use with TemporaryDirectory(prefix='snowglobes') as tempdir: with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files =\",\n \"snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together all the\",\n \"tstart t1 = tend nbin = 1 times = 0.5*(tstart + tend) model_times\",\n \"[next(j for j, t in enumerate(model_tend) if t >= tend)] # Generate output.\",\n \"(fluence) in the time bin. The result is a compressed .tar file containing\",\n \"of time slices. Returns ------- str Path of compressed .tar file with neutrino\",\n \"in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence\",\n \"of energy for each interaction channel, integrated in a given time window (or\",\n \"time slices. tmin = snmodel.get_time()[0] tmax = snmodel.get_time()[-1] if deltat is not None:\",\n \"return s if c in mapp: return mapp[c] else: return re_chan_label.sub(gen_label, c) def\",\n \"each reaction channel or the sum of them. \\\"\\\"\\\" import io import logging\",\n \"model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES format.\",\n \"strip extension (if present) tfname = model_file_root + '.' + transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(tmin,\",\n \"= Path(tempdir) for det in tables: results[det] = {} for flux,t in tables[det].items():\",\n \"tstart = snmodel.get_time()[0] tend = snmodel.get_time()[-1] try: if len(tstart/u.s) > 0: t0 =\",\n \"old-style results, for backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes')\",\n \"t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if t\",\n \"condensed data files and plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path =\",\n \"for the interaction channels happening in various detectors supported by SNOwGLoBES. It takes\",\n \"(model_tend[starting_index[i]]-ta) #intermediate time bins of model in requested interval for j in range(starting_index[i]+1,\",\n \"generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files in SNOwGLoBES\",\n \"t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill the column #return table with\",\n \"``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of detector. If ``\\\"all\\\"``, will use\",\n \"results[det] = {} for flux,t in tables[det].items(): t = aggregate_channels(t,nc='nc_',e='_e') for w in\",\n \"Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type,\",\n \"temp_spectra = snmodel.get_transformed_spectra(model_times[j], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] += temp_spectra[flavor]*(model_tend[j]-model_tstart[j]) #last\",\n \"made for this snewpy run. Returns ------- dict Dictionary of data tables: One\",\n \"from snewpy.flavor_transformation import * from snewpy.neutrino import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES\",\n \": astropy.Quantity or None Length of time slices. Returns ------- str Path of\",\n \"time bin and for each interaction channel. verbose : bool Whether to generate\",\n \"``None``, will be based on input file name. tstart : astropy.Quantity or None\",\n \"The result is a compressed .tar file containing all individual input files. *\",\n \"There are two ways to do this, either generate a time series or\",\n \"'{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E]) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X]) s =\",\n \"collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det in tables:\",\n \"a separate column t_agg = t_sel.sum(axis='columns') #drop processed channels t.drop(t_sel.columns, axis='columns',inplace=True) t[name]=t_agg #fill\",\n \"+= temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {} table\",\n \"t_sel = t.filter(like=pattern) #sum over them and save to a separate column t_agg\",\n \"If ``\\\"all\\\"``, will use all detectors supported by SNOwGLoBES. verbose : bool Whether\",\n \"a compressed .tar file containing all individual input files. * **Running SNOwGLoBES:** This\",\n \"time slices. Returns ------- str Path of compressed .tar file with neutrino flux\",\n \"cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist()\",\n \"plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5), loc='best', borderaxespad=0) # formats complete graph smear_title = 'Interaction'\",\n \"channels happening in various detectors supported by SNOwGLoBES. It takes into account the\",\n \"file in tar archive that gives information on parameters output = '\\\\n'.join(map(str, transformation_type)).encode('ascii')\",\n \"well as a smearing matrix describing the energy-dependent detection efficiency. The output gives\",\n \"results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir)\",\n \"for a given (set of) detector(s). These event rates are given as a\",\n \"#first time bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation)\",\n \"= {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(),\",\n \"if skip_plots is False: plt.figure(dpi=300) do_plot(table,(flux,det,w,s)) filename = tempdir/f'{filename_base}_log_plot.png' plt.savefig(filename.with_suffix('.png'), dpi=300, bbox_inches='tight') #Make\",\n \"float Distance to supernova in kpc. output_filename : str or None Name of\",\n \"times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend =\",\n \"= re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay', 'e':r'${\\\\nu}_x+e^-$'} def gen_label(m):\",\n \"= 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) * MeV # 1MeV\",\n \"puts together all the interaction channels and time bins evaluated by SNOwGLoBES in\",\n \"bool Remove the output files from SNOwGLoBES, collated files, and .png's made for\",\n \": str or None Name of output file. If ``None``, will be based\",\n \"NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type] model_dir, model_file = os.path.split(os.path.abspath(model_path)) snmodel = model_class(model_path) #set the\",\n \"are two ways to do this, either generate a time series or a\",\n \"plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events') #read the results from storage cache_file = tarball_path[:tarball_path.rfind('.tar')]\",\n \"bin of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt\",\n \"f in flux_files),res)) # save result to file for re-use in collate() cache_file\",\n \"plots output_name = Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\")\",\n \"will be based on input file name. ntbins : int Number of time\",\n \"and for each interaction channel. verbose : bool Whether to generate verbose output,\",\n \"to file for re-use in collate() cache_file = tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Saving simulation\",\n \"1 times = 0.5*(tstart + tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend\",\n \"dict to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(),\",\n \"interaction channels and time bins evaluated by SNOwGLoBES in a single file (for\",\n \"this timebin in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE\",\n \"plot of the energy distribution for each time bin and for each interaction\",\n \"simulation model. The first will evaluate the neutrino flux at each time step,\",\n \"tstart[0] t1 = tend[-1] nbin = len(tstart/u.s) except: t0 = tstart t1 =\",\n \"interval temp_spectra = snmodel.get_transformed_spectra( model_times[ending_index[i]], energy, flavor_transformation) for flavor in Flavor: osc_spectra[flavor] +=\",\n \"aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels = list(table.columns.names)\",\n \"channels and time bins evaluated by SNOwGLoBES in a single file (for each\",\n \"flux files and configures and runs the supernova script inside SNOwGLoBES, which outputs\",\n \"Path(tarball_path).stem output_name = output_name[:output_name.rfind('.tar')]+'_SNOprocessed' output_path = Path(tarball_path).parent/(output_name+'.tar.gz') with tarfile.open(output_path, \\\"w:gz\\\") as tar: for\",\n \"= tend[i] t = times[i] dt = tb-ta else: ta = tstart tb\",\n \"osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file in tar\",\n \"column #return table with the original levels order t = t.unstack(levels) t =\",\n \"= len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname) def simulate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes\",\n \"compressed .tar file with neutrino flux data. \\\"\\\"\\\" model_class = getattr(snewpy.models.ccsn, model_type) #\",\n \"if table[c].max() > 0.1: plt.plot(table[c],drawstyle='steps',label=get_channel_label(c), lw=1) plt.xlim(right=0.10) plt.ylim(bottom=0.10) plt.yscale('log') plt.legend(bbox_to_anchor=(0.5, 0.5, 0.5, 0.5),\",\n \"\\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or returns a data table. Parameters\",\n \".tar file produced e.g. by ``generate_time_series()`` or ``generate_fluence()``. detector_input : str Name of\",\n \"MeV # 1MeV # Loop over sampled times. for i, t in enumerate(times):\",\n \"model_class(model_path) # Subsample the model time. Default to 30 time slices. tmin =\",\n \"np from astropy import units as u from tqdm.auto import tqdm import snewpy.models\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with\",\n \"e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir) if detector_input == 'all': detector_input =\",\n \"name. tstart : astropy.Quantity or None Start of time interval to integrate over,\",\n \"+ '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is\",\n \"bool Whether to generate verbose output, e.g. for debugging. \\\"\\\"\\\" sng = SNOwGLoBES(SNOwGLoBESdir)\",\n \"np.concatenate([[index],data]) results[filename.name] = {'header':header,'data':data} #optionally plot the results if skip_plots is False: plt.figure(dpi=300)\",\n \"Input file containing neutrino flux information from supernova model. model_type : str Format\",\n \"= os.path.splitext(model_file) # strip extension (if present) filename = model_file_root + '.tbin{:01d}.'.format(i+1) +\",\n \"dt = (tmax - tmin) / (ntbins+1) tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s times\",\n \"individual input files. * **Running SNOwGLoBES:** This step convolves the fluence generated in\",\n \"if t > tstart)] ending_index = [next(j for j, t in enumerate(model_tend) if\",\n \"table to have only channel column levels = list(table.columns.names) levels.remove('channel') t = table.stack(levels)\",\n \"tarball_path, detector_input=\\\"all\\\", verbose=False): \\\"\\\"\\\"Takes as input the neutrino flux files and configures and\",\n \"in the remaining columns the number of events for each interaction channel in\",\n \"energy + number flux table. for j, E in enumerate(energy): for flavor in\",\n \"transformation_type + '.{:.3f},{:.3f},{:d}-{:.1f}'.format(t0, t1, nbin, d) + 'kpc.tar.bz2' with tarfile.open(os.path.join(model_dir, tfname), 'w:bz2') as\",\n \"the energy bins, in the remaining columns the number of events for each\",\n \"= tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format\",\n \"fluence file. This is done taking as input the supernova simulation model. The\",\n \"archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\" if output_filename is not None: if\",\n \"table with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names, axis=1)\",\n \"is not None: if nbin > 1: filename = output_filename+\\\"_\\\"+str(i)+extension else: filename =\",\n \"enumerate(times): osc_spectra = snmodel.get_transformed_spectra(t, energy, flavor_transformation) osc_fluence = {} table = [] table.append('#\",\n \"j, t in enumerate(model_tend) if t > tstart[i]) ending_index[i] = next(j for j,\",\n \"= times[i] dt = tb-ta else: ta = tstart tb = tend t\",\n \"files with open(filename,'w') as f: f.write(table.to_string(float_format='%23.15g')) #format the results for the output header\",\n \"of model in requested interval osc_spectra = snmodel.get_transformed_spectra(model_times[starting_index[i]], energy, flavor_transformation) if dt <\",\n \"interval to integrate over, or list of end times of the time series\",\n \"or float Distance to supernova in kpc. output_filename : str or None Name\",\n \"in time. * **Collating SNOwGLoBES outputs:** This step puts together all the interaction\",\n \"+ transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension) info = tarfile.TarInfo(name=filename) info.size\",\n \"import Path from tempfile import TemporaryDirectory import matplotlib as mpl import matplotlib.pyplot as\",\n \"format. This version will subsample the times in a supernova model, produce energy\",\n \"output into a tarfile. Parameters ---------- model_path : str Input file containing neutrino\",\n \": astropy.Quantity or None Start of time interval to integrate over, or list\",\n \"many different neutrino detectors, detector materials and interaction channels. There are three basic\",\n \"number of events detected as a function of energy for each interaction channel,\",\n \"tables: One table per time bin; each table contains in the first column\",\n \"import Flavor, MassHierarchy from snewpy.snowglobes_interface import SNOwGLoBES logger = logging.getLogger(__name__) def generate_time_series(model_path, model_type,\",\n \"tend) model_times = snmodel.get_time() model_tstart = model_times*1.0 model_tend = model_times*1.0 model_tstart[0] = model_times[0]\",\n \"series or a fluence file. This is done taking as input the supernova\",\n \"the transformation name with its class. flavor_transformation_dict = {'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO':\",\n \"{'NoTransformation': NoTransformation(), 'AdiabaticMSW_NMO': AdiabaticMSW(mh=MassHierarchy.NORMAL), 'AdiabaticMSW_IMO': AdiabaticMSW(mh=MassHierarchy.INVERTED), 'NonAdiabaticMSWH_NMO': NonAdiabaticMSWH(mh=MassHierarchy.NORMAL), 'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence':\",\n \"#format the results for the output header = 'Energy '+' '.join(list(table.columns)) data =\",\n \"of the time series bins. tend : astropy.Quantity or None End of time\",\n \"from tqdm.auto import tqdm import snewpy.models from snewpy.flavor_transformation import * from snewpy.neutrino import\",\n \"osc_spectra[flavor] += temp_spectra[flavor]*(tb-model_tstart[ending_index[i]]) for flavor in Flavor: osc_spectra[flavor] /= (tb-ta) osc_fluence = {}\",\n \"Returns ------- dict Dictionary of data tables: One table per time bin; each\",\n \"in neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau')\",\n \"-*- \\\"\\\"\\\"The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can\",\n \"res=sng.run(flux_files, det) result[det]=dict(zip((f.stem for f in flux_files),res)) # save result to file for\",\n \"re_chan_label.sub(gen_label, c) def collate(SNOwGLoBESdir, tarball_path, detector_input=\\\"all\\\", skip_plots=False, verbose=False, remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files\",\n \"use all detectors supported by SNOwGLoBES. skip_plots: bool If False, it gives as\",\n \"= f'{flux}_{det}_events_{s}_{w}' filename = tempdir/f'Collated_{filename_base}.dat' #save results to text files with open(filename,'w') as\",\n \"return result re_chan_label = re.compile(r'nu(e|mu|tau)(bar|)_([A-Z][a-z]*)(\\\\d*)_?(.*)') def get_channel_label(c): mapp = {'nc':'NeutralCurrent', 'ibd':'Inverse Beta Decay',\",\n \"neutrinos per cm^2'.format(t, dt)) table.append('# E(GeV) NuE NuMu NuTau aNuE aNuMu aNuTau') #\",\n \"logging.getLogger(__name__) def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None): \\\"\\\"\\\"Generate time series files\",\n \"is done taking as input the supernova simulation model. The first will evaluate\",\n \"are three basic steps to using SNOwGLoBES from SNEWPY: * **Generating input files\",\n \"fluence files in SNOwGLoBES format. This version will subsample the times in a\",\n \"result = {} #Extracts data from tarfile and sets up lists of paths\",\n \"two ways to do this, either generate a time series or a fluence\",\n \"'NonAdiabaticMSWH_IMO': NonAdiabaticMSWH(mh=MassHierarchy.INVERTED), 'TwoFlavorDecoherence': TwoFlavorDecoherence(), 'ThreeFlavorDecoherence': ThreeFlavorDecoherence(), 'NeutrinoDecay_NMO': NeutrinoDecay(mh=MassHierarchy.NORMAL), 'NeutrinoDecay_IMO': NeutrinoDecay(mh=MassHierarchy.INVERTED)} flavor_transformation = flavor_transformation_dict[transformation_type]\",\n \"'{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_X_BAR]) table.append(s) logging.debug(s) # Encode energy/flux table and output to file in\",\n \"= tarball_path[:tarball_path.rfind('.tar')] + '.npy' logging.info(f'Reading tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This\",\n \"the number of events for each interaction channel in the detector. \\\"\\\"\\\" def\",\n \"with tarfile.open(tarball_path) as tar: tar.extractall(tempdir) flux_files = list(Path(tempdir).glob('*.dat')) if len(detector_input)>0: detector_input = tqdm(detector_input,\",\n \"remove_generated_files=True): \\\"\\\"\\\"Collates SNOwGLoBES output files and generates plots or returns a data table.\",\n \"starting_index = [next(j for j, t in enumerate(model_tend) if t > tstart)] ending_index\",\n \"of the energy distribution for each time bin and for each interaction channel.\",\n \"# Generate output. if output_filename is not None: tfname = output_filename+'.tar.bz2' else: model_file_root,\",\n \"will subsample the times in a supernova model, produce energy tables expected by\",\n \"compress the output into a tarfile. Parameters ---------- model_path : str Input file\",\n \"def generate_fluence(model_path, model_type, transformation_type, d, output_filename=None, tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES\",\n \"as a function of energy for each interaction channel, integrated in a given\",\n \"tf.addfile(tarfile.TarInfo(name='parameterinfo'), io.BytesIO(output)) MeV = 1.60218e-6 * u.erg energy = np.linspace(0, 100, 501) *\",\n \"and output to file in tar archive. output = '\\\\n'.join(table).encode('ascii') extension = \\\".dat\\\"\",\n \"tstart : astropy.Quantity or None Start of time interval to integrate over, or\",\n \"{} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir = Path(tempdir) for det\",\n \"``deltat`` is also given. deltat : astropy.Quantity or None Length of time slices.\",\n \"a snapshot in time. * **Collating SNOwGLoBES outputs:** This step puts together all\",\n \"d, extension) info = tarfile.TarInfo(name=filename) info.size = len(output) tf.addfile(info, io.BytesIO(output)) return os.path.join(model_dir, tfname)\",\n \"def aggregate_channels(table, **patterns): #rearrange the table to have only channel column levels =\",\n \"str Format of input file. Matches the name of the corresponding class in\",\n \"tables from {cache_file}') tables = np.load(cache_file, allow_pickle=True).tolist() #This output is similar to what\",\n \"#return table with the original levels order t = t.unstack(levels) t = t.reorder_levels(table.columns.names,\",\n \"tstart=None, tend=None): \\\"\\\"\\\"Generate fluence files in SNOwGLoBES format. This version will subsample the\",\n \"and for each time bin). The output tables allow to build the detected\",\n \"501) * MeV # 1MeV # Loop over sampled times. for i, t\",\n \"* **Running SNOwGLoBES:** This step convolves the fluence generated in the previous step\",\n \"outputs:** This step puts together all the interaction channels and time bins evaluated\",\n \"single file (for each detector and for each time bin). The output tables\",\n \"Events') if smeared=='smeared': plt.xlabel('Detected Energy (GeV)') plt.ylabel('Events') else: plt.xlabel('Neutrino Energy (GeV)') plt.ylabel('Interaction Events')\",\n \"output the plot of the energy distribution for each time bin and for\",\n \"levels.remove('channel') t = table.stack(levels) for name,pattern in patterns.items(): #get channels which contain `like`\",\n \"t0 = tstart t1 = tend nbin = 1 times = 0.5*(tstart +\",\n \"each detector and for each time bin). The output tables allow to build\",\n \"backward compatibiity results = {} #save collated files: with TemporaryDirectory(prefix='snowglobes') as tempdir: tempdir\",\n \"* MeV / (4.*np.pi*(d*1000*3.086e+18)**2) s = '{:17.8E}'.format(E/(1e3 * MeV)) s = '{}{:17.8E}'.format(s, osc_fluence[Flavor.NU_E])\",\n \"with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input\",\n \"snmodel = model_class(model_path) #set the timings up #default if inputs are None: full\",\n \"in various detectors supported by SNOwGLoBES. It takes into account the effective mass\",\n \"= model_file_root + '.tbin{:01d}.'.format(i+1) + transformation_type + \\\\ '.{:.3f},{:.3f},{:01d}-{:.1f}kpc{}'.format(tmin/u.s, tmax/u.s, ntbins, d, extension)\",\n \"= tstart t1 = tend nbin = 1 times = 0.5*(tstart + tend)\",\n \"the fluence generated in the previous step with the cross-sections for the interaction\",\n \"supported by SNOwGLoBES. skip_plots: bool If False, it gives as output the plot\",\n \"time slices. Will be ignored if ``deltat`` is also given. deltat : astropy.Quantity\"\n]","isConversational":false}}},{"rowIdx":4089,"cells":{"ngram":{"kind":"list like","value":["(1 - dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss","k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def","offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb","...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states,",".long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states)","= data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma","= self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma","next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss","networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists =","n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) *","network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists","__init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module,","self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma *","* (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self,","z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z =","proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1),","(lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub +","target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj *","states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad():","= torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z","if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with","element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z:","next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards +","= data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z =","def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks:","* network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda()","rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value,","F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\"","target_z = rewards + (1 - dones) * n_step_gamma * next_z distance =","+ offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1),","DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(","-> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub","element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float","= target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma","= rewards + (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z,","states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset = (","torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb =","1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset =","next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma =","offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection(","= (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist","...]: network, target_network = networks states, actions, rewards, next_states, dones = data batch_size","n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z,","return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda:","return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor,","0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute","target_z = rewards + (1 - dones) * n_step_gamma * network.support target_z =","= next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1","nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double","- z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance)","1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones)","return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 *",") -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones","target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma **","= torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float =","self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return","target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist =","hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...],","= self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma","networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network =","...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states,","torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0):","network, target_network = networks states, actions, rewards, next_states, dones = data batch_size =","dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor:","import DictConfig import torch import torch.nn as nn import torch.nn.functional as F from","b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb +","actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))),","with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q =","b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long()","__call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network,","log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self,","rewards, next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size","network, target_network = networks states, actions, rewards, next_states, dones = data q_value =","Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], )","loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self,","( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) )","n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau -","next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones)","/ network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda:","= target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs()","def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]:","= offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z =","= self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1)","= states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long()","__call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network,","self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma *","- b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1)","= 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() -","DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,","= b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb","= b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda()","k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss):","states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1)","next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size -","log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset:","as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def","Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones = data","+ (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(),","target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() *","= torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists)","(next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z *","batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists =","...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states,","next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 -","* self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor],","next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() <","= data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) *","rewards + (1 - dones) * n_step_gamma * next_z distance = target_z -","dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = (","network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True)","max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj","proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0,","element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile","= torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network:","data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions,","= F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression","use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor,","def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 *","next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards +","distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float()",") if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)]","use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor,","(1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj","* k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):","actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)]","offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z","element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):","dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network,",") -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long()","target_q = rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss =","List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k","target_network = networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1,","class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,","0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda:","\"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)","self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad():","* n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss","torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k))","torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\"","= networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions)","import List, Tuple from omegaconf import DictConfig import torch import torch.nn as nn","states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))),","Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks","Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards,","networks states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset =","omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional as F","...]: network, target_network = networks states, actions, rewards, next_states, dones = data z_dists","dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return","offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states)","* n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau","k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,","Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards,","Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] )","as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute","CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)","torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma","* x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51","-> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones =","self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b","= network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions =","torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss):","= z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z","1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5","z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss","ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0,","torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 -","* (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b","dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1)","network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b =","torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z =","- 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset","(x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params:","dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return","** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * next_z","Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks","+ (1 - dones) * n_step_gamma * next_z distance = target_z - z_dists","torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z","self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k:","@staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5","torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if","proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_(","bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...],","- dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj =",") return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig,","proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1)","float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs()","actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q","-(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z:","= torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1","- (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return","actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q","C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(","proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) ) return proj_dist","dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z","keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs()","self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss",") proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) ) return","import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class","+ (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max)","torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards","z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions","0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub","...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states,","self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network","quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def","rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with","torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub =","self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * network.support target_z","target_network = networks states, actions, rewards, next_states, dones = data batch_size = states.size(0)","(distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss","(target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist =","\"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def","rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q =","network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist","** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma * next_q","(ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b -",".unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists","data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states)","nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z","data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions,","element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k,","b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_(","- dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss =","from typing import List, Tuple from omegaconf import DictConfig import torch import torch.nn","Tuple from omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional",".expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists =","offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z","= rewards + (1 - dones) * n_step_gamma * next_z distance = target_z","network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2),","z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1]","from omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional as","class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,","target_network = networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states)","torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min)","- dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" )","bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...]","<= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class","DictConfig import torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss","offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size,","actions, rewards, next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0,","= torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z","double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def","= proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) )","import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda:","target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self,","if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float()","network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))),","x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\"","* next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach()","* n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z,","** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * network.support","next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step","quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss =","hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) ->","min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss =","= network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma **","= networks states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset","network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if","Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):","0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def","data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms,","dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1)","* log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor,","torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss","target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def","class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,","batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size)","torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss","network, target_network = networks states, actions, rewards, next_states, dones = data z_dists =","\"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)","def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]:","q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma","- network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size())","self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network","networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network =","self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * next_z distance","b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) )","torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z *","def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) ->","regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(","= rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss(","* next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss):","element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, )","(1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\"","q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def","huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2),","import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN","DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data:","n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) *","( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1,","next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad():","next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z -","with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions]","network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist","next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute","data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma =","= networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists","target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) /","List, Tuple from omegaconf import DictConfig import torch import torch.nn as nn import","from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params:","...]: network, target_network = networks states, actions, rewards, next_states, dones = data q_value","reduction=\"none\" ) return element_wise_loss class QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params:","* network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset)","typing import List, Tuple from omegaconf import DictConfig import torch import torch.nn as","= ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms)","k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k *","network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step","(batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda:","use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor,","rewards + (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min,","next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states),","rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self, hyper_params: DictConfig,","networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with","QRLoss(Loss): \"\"\"Compute quantile regression loss\"\"\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,","lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist =","torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module,","hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) ->","dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards","return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <=","= -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor,","F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \"\"\"Compute double DQN loss\"\"\" def __init__(self,","- 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def __init__(self, hyper_params: DictConfig,","import torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import","= ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss,","self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() -","n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\"none\" ) return element_wise_loss class","< 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod",").abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x:"],"string":"[\n \"(1 - dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss\",\n \"k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def\",\n \"offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb\",\n \"...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states,\",\n \".long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states)\",\n \"= data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma\",\n \"= self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma\",\n \"next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss\",\n \"networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists =\",\n \"n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) *\",\n \"network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists\",\n \"__init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module,\",\n \"self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma *\",\n \"* (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self,\",\n \"z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z =\",\n \"proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1),\",\n \"(lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub +\",\n \"target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj *\",\n \"states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad():\",\n \"= torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z\",\n \"if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with\",\n \"element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z:\",\n \"next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards +\",\n \"= data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z =\",\n \"def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks:\",\n \"* network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda()\",\n \"rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value,\",\n \"F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\"\",\n \"target_z = rewards + (1 - dones) * n_step_gamma * next_z distance =\",\n \"+ offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1),\",\n \"DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(\",\n \"-> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub\",\n \"element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float\",\n \"= target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma\",\n \"= rewards + (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z,\",\n \"states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset = (\",\n \"torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb =\",\n \"1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset =\",\n \"next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma =\",\n \"offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection(\",\n \"= (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist\",\n \"...]: network, target_network = networks states, actions, rewards, next_states, dones = data batch_size\",\n \"n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z,\",\n \"return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda:\",\n \"return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor,\",\n \"0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute\",\n \"target_z = rewards + (1 - dones) * n_step_gamma * network.support target_z =\",\n \"= next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1\",\n \"nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double\",\n \"- z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance)\",\n \"1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones)\",\n \"return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 *\",\n \") -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones\",\n \"target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma **\",\n \"= torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float =\",\n \"self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return\",\n \"target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist =\",\n \"hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...],\",\n \"= self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma\",\n \"networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network =\",\n \"...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states,\",\n \"torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0):\",\n \"network, target_network = networks states, actions, rewards, next_states, dones = data batch_size =\",\n \"dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor:\",\n \"import DictConfig import torch import torch.nn as nn import torch.nn.functional as F from\",\n \"b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb +\",\n \"actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))),\",\n \"with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q =\",\n \"b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long()\",\n \"__call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network,\",\n \"log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self,\",\n \"rewards, next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size\",\n \"network, target_network = networks states, actions, rewards, next_states, dones = data q_value =\",\n \"Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], )\",\n \"loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self,\",\n \"( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) )\",\n \"n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau -\",\n \"next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones)\",\n \"/ network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda:\",\n \"= target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs()\",\n \"def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]:\",\n \"= offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z =\",\n \"= self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1)\",\n \"= states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long()\",\n \"__call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network,\",\n \"self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma *\",\n \"- b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1)\",\n \"= 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() -\",\n \"DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,\",\n \"= b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb\",\n \"= b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda()\",\n \"k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss):\",\n \"states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1)\",\n \"next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size -\",\n \"log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset:\",\n \"as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def\",\n \"Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones = data\",\n \"+ (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(),\",\n \"target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() *\",\n \"= torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists)\",\n \"(next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z *\",\n \"batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists =\",\n \"...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states,\",\n \"next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 -\",\n \"* self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor],\",\n \"next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() <\",\n \"= data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) *\",\n \"rewards + (1 - dones) * n_step_gamma * next_z distance = target_z -\",\n \"dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = (\",\n \"network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True)\",\n \"max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj\",\n \"proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0,\",\n \"element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile\",\n \"= torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network:\",\n \"data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions,\",\n \"= F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression\",\n \"use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor,\",\n \"def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 *\",\n \"next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards +\",\n \"distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float()\",\n \") if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)]\",\n \"use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor,\",\n \"(1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj\",\n \"* k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):\",\n \"actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)]\",\n \"offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z\",\n \"element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):\",\n \"dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network,\",\n \") -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long()\",\n \"target_q = rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss =\",\n \"List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k\",\n \"target_network = networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1,\",\n \"class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,\",\n \"0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda:\",\n \"\\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)\",\n \"self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad():\",\n \"* n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss\",\n \"torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k))\",\n \"torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\"\",\n \"= networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions)\",\n \"import List, Tuple from omegaconf import DictConfig import torch import torch.nn as nn\",\n \"states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))),\",\n \"Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks\",\n \"Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards,\",\n \"networks states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset =\",\n \"omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional as F\",\n \"...]: network, target_network = networks states, actions, rewards, next_states, dones = data z_dists\",\n \"dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return\",\n \"offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states)\",\n \"* n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau\",\n \"k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,\",\n \"Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards,\",\n \"Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] )\",\n \"as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute\",\n \"CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)\",\n \"torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma\",\n \"* x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51\",\n \"-> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states, dones =\",\n \"self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b\",\n \"= network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions =\",\n \"torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss):\",\n \"= z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z\",\n \"1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs() - 0.5\",\n \"z_dists quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss\",\n \"ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0,\",\n \"torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1 -\",\n \"* (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b\",\n \"dones = data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1)\",\n \"network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b =\",\n \"torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z =\",\n \"- 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset\",\n \"(x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params:\",\n \"dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return\",\n \"** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * next_z\",\n \"Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network = networks\",\n \"+ (1 - dones) * n_step_gamma * next_z distance = target_z - z_dists\",\n \"torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z\",\n \"self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k:\",\n \"@staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5\",\n \"torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if\",\n \"proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_(\",\n \"bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...],\",\n \"- dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj =\",\n \") return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig,\",\n \"proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1)\",\n \"float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k * (x.abs()\",\n \"actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q\",\n \"-(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z:\",\n \"= torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards + (1\",\n \"- (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return\",\n \"actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q\",\n \"C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(\",\n \"proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) ) return proj_dist\",\n \"dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z\",\n \"keepdim=True) return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs()\",\n \"self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss\",\n \") proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) ) return\",\n \"import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class\",\n \"+ (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max)\",\n \"torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_q = rewards\",\n \"z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions\",\n \"0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub\",\n \"...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions, rewards, next_states,\",\n \"self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]: network, target_network\",\n \"quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def\",\n \"rewards, next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with\",\n \"torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb = b.floor().long() ub =\",\n \"self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * network.support target_z\",\n \"target_network = networks states, actions, rewards, next_states, dones = data batch_size = states.size(0)\",\n \"(distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss\",\n \"(target_z - network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist =\",\n \"\\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def\",\n \"rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q =\",\n \"network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist\",\n \"** self.hyper_params.n_step target_q = rewards + (1 - dones) * n_step_gamma * next_q\",\n \"(ub.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b -\",\n \".unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists\",\n \"data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states)\",\n \"nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z\",\n \"data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network = networks states, actions,\",\n \"element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k,\",\n \"b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_(\",\n \"- dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss =\",\n \"from typing import List, Tuple from omegaconf import DictConfig import torch import torch.nn\",\n \"Tuple from omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional\",\n \".expand(batch_size, network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists =\",\n \"offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z\",\n \"= rewards + (1 - dones) * n_step_gamma * next_z distance = target_z\",\n \"network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2),\",\n \"z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1]\",\n \"from omegaconf import DictConfig import torch import torch.nn as nn import torch.nn.functional as\",\n \"class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,\",\n \"target_network = networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states)\",\n \"torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min)\",\n \"- dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" )\",\n \"bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...]\",\n \"<= k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) class\",\n \"DictConfig import torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss\",\n \"offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size,\",\n \"actions, rewards, next_states, dones = data batch_size = states.size(0) offset = ( torch.linspace(0,\",\n \"= torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z\",\n \"double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def\",\n \"= proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() - b)).view(-1) )\",\n \"import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda:\",\n \"target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self,\",\n \"if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float()\",\n \"network.num_atoms) ) if self.use_cuda: offset = offset.cuda() z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))),\",\n \"x.pow(2), k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\"\",\n \"* next_z distance = target_z - z_dists quantile_huber_loss = ( network.tau - (distance.detach()\",\n \"* n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z,\",\n \"** self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * network.support\",\n \"next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step\",\n \"quantile_huber_loss = ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss =\",\n \"hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) ->\",\n \"min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss =\",\n \"= network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma **\",\n \"= networks states, actions, rewards, next_states, dones = data batch_size = states.size(0) offset\",\n \"network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if\",\n \"Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool):\",\n \"0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def\",\n \"data batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms,\",\n \"dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1)\",\n \"* log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor,\",\n \"torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss\",\n \"target_z, offset) log_dist = torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def\",\n \"class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self,\",\n \"batch_size = states.size(0) offset = ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size)\",\n \"torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist = torch.log(z_dists) element_wise_loss\",\n \"network, target_network = networks states, actions, rewards, next_states, dones = data z_dists =\",\n \"\\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda)\",\n \"def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) -> Tuple[torch.Tensor, ...]:\",\n \"q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma\",\n \"- network.v_min) / network.delta_z lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size())\",\n \"self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network\",\n \"networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...] ) -> Tuple[torch.Tensor, ...]: network, target_network =\",\n \"self.hyper_params.n_step target_z = rewards + (1 - dones) * n_step_gamma * next_z distance\",\n \"b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (ub + offset).view(-1), (next_z * (b - lb.float())).view(-1) )\",\n \"torch.zeros(next_z.size()) if self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z *\",\n \"def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) ->\",\n \"regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__(\",\n \"= rewards + (1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss(\",\n \"* next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss):\",\n \"element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, )\",\n \"(1 - dones) * n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\"\",\n \"q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def\",\n \"huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2),\",\n \"import torch.nn.functional as F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN\",\n \"DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data:\",\n \"n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards + (1 - dones) *\",\n \"( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1,\",\n \"next_states, dones = data z_dists = network.forward(states) z_dists = z_dists[list(range(states.size(0))), actions.view(-1)] with torch.no_grad():\",\n \"next_z: torch.Tensor, target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z -\",\n \"with torch.no_grad(): next_z = target_network.forward(next_states) next_actions = torch.max(next_z.mean(2), dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions]\",\n \"network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset) log_dist\",\n \"next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute\",\n \"data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma =\",\n \"= networks states, actions, rewards, next_states, dones = data z_dists = network.forward(states) z_dists\",\n \"target_z: torch.Tensor, offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) /\",\n \"List, Tuple from omegaconf import DictConfig import torch import torch.nn as nn import\",\n \"from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params:\",\n \"...]: network, target_network = networks states, actions, rewards, next_states, dones = data q_value\",\n \"reduction=\\\"none\\\" ) return element_wise_loss class QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params:\",\n \"* network.support target_z = torch.clamp(target_z, min=network.v_min, max=network.v_max) target_proj = self.dist_projection(network, next_z, target_z, offset)\",\n \"typing import List, Tuple from omegaconf import DictConfig import torch import torch.nn as\",\n \"= ( torch.linspace(0, (batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms)\",\n \"k: float = 1.0): return torch.where(x.abs() <= k, 0.5 * x.pow(2), k *\",\n \"network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states), 1)[0].unsqueeze(1) n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step\",\n \"(batch_size - 1) * network.num_atoms, batch_size) .long() .unsqueeze(1) .expand(batch_size, network.num_atoms) ) if self.use_cuda:\",\n \"use_cuda: bool): Loss.__init__(self, hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor,\",\n \"rewards + (1 - dones) * n_step_gamma * network.support target_z = torch.clamp(target_z, min=network.v_min,\",\n \"next_states, dones = data q_value = network.forward(states).gather(1, actions) with torch.no_grad(): next_q = torch.max(target_network.forward(next_states),\",\n \"rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig,\",\n \"networks states, actions, rewards, next_states, dones = data q_value = network.forward(states).gather(1, actions) with\",\n \"QRLoss(Loss): \\\"\\\"\\\"Compute quantile regression loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig, use_cuda: bool): Loss.__init__(self, hyper_params,\",\n \"lb = b.floor().long() ub = b.ceil().long() proj_dist = torch.zeros(next_z.size()) if self.use_cuda: proj_dist =\",\n \"torch.log(z_dists) element_wise_loss = -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module,\",\n \"hyper_params, use_cuda) def __call__( self, networks: Tuple[nn.Module, ...], data: Tuple[torch.Tensor, ...], ) ->\",\n \"dim=1)[1] next_z = next_z[list(range(states.size(0))), next_actions] n_step_gamma = self.hyper_params.gamma ** self.hyper_params.n_step target_z = rewards\",\n \"return element_wise_loss @staticmethod def huber_loss(x: List[torch.Tensor], k: float = 1.0): return torch.where(x.abs() <=\",\n \"= -(target_proj * log_dist).sum(1) return element_wise_loss def dist_projection( self, network: nn.Module, next_z: torch.Tensor,\",\n \"F from rlcycle.common.abstract.loss import Loss class DQNLoss(Loss): \\\"\\\"\\\"Compute double DQN loss\\\"\\\"\\\" def __init__(self,\",\n \"- 0.5 * k)) class CategoricalLoss(Loss): \\\"\\\"\\\"Compute C51 loss\\\"\\\"\\\" def __init__(self, hyper_params: DictConfig,\",\n \"import torch import torch.nn as nn import torch.nn.functional as F from rlcycle.common.abstract.loss import\",\n \"= ( network.tau - (distance.detach() < 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss,\",\n \"self.use_cuda: proj_dist = proj_dist.cuda() proj_dist.view(-1).index_add_( 0, (lb + offset).view(-1), (next_z * (ub.float() -\",\n \"n_step_gamma * next_q element_wise_loss = F.smooth_l1_loss( q_value, target_q.detach(), reduction=\\\"none\\\" ) return element_wise_loss class\",\n \"< 0).float() ).abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod\",\n \").abs() * self.huber_loss(distance) element_wise_loss = torch.mean(quantile_huber_loss, dim=1, keepdim=True) return element_wise_loss @staticmethod def huber_loss(x:\"\n]","isConversational":false}}},{"rowIdx":4090,"cells":{"ngram":{"kind":"list like","value":["= self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f","font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94,","[] # unmasked full images self.imgs_pred = [] self.cc = [] for index,","- b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2","images in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) !=","in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in","report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1])","in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of","elif event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom +=",":, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] =","2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1]","resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 -","in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5))","RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk","c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h,","self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index]))","text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img]))","parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m',","self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc = []","parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand","Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w","self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom,","images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280,","in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates):","self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img])","tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np","= [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images","= [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p =","zoomer(self, event): if event.num == 4 or event.delta == 120 or event.keysym ==","* 0.94, font=(\"Courier\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w","* imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img])","2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h -","import numpy as np from PIL import Image, ImageTk import probgf.media as media","zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if","img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img","index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w),","= (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img",":, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)):","0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] #","/ 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h","root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors","with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for","/ 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle","1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if","for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1,","50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks)","0.94, font=(\"Courier\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w /","else: if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom ==","35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h /","are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R',","int(h / 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\",","img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img)","self.img_w = int(h * 0.68) # width of each displayed image self.imgs_orig_m =","else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x,","{:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100:","self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2","2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None):","timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h,","number of images in {} than days in the report {}!'.format(args.masks, args.report)) root","0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) #","with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with","h_logos = int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root,","self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks =","buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS))","image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h,","self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images","self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h *","+ self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def","parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r',","= self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img","self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer)","+ self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom","img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left,","errors # setup images self.img_w = int(h * 0.68) # width of each","Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56,","1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error !=","+ self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if","2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x +","which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images')","right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing","full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred","self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom /","% (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos) root.focus_set()","* 0.12, h * 0.94, font=(\"Courier\", int(h / 50)), text='') # image timeline","__init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates","self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos /","self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag =","= self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img]","root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m,","int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h *","enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates,","0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text = \"Show masks\",","text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a =","0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w","0.0 else 'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2,","self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f /","* 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons","= int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x","self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3)","100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) +","text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)),","= img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w,","allows to interactively view spatio-temporal gap filling results\"\"\" import os import argparse from","if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] -","== 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240:","- w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info","if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than days","import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL","+ b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height()","self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are","img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img","self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons","int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 -","img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right","h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h *","self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser","datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN,","in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in","image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img],","data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window","self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1","event.delta == 360: self.zoom += 60 else: if self.zoom - 20 >= 100:","h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors","/ zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2)","os import argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk,","/ 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h","# full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig]","visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w","= [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600),","[ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v =","= min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom","self.dates = dates self.errors = errors # setup images self.img_w = int(h *","b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h","range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for","self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom =","self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\",","text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)),","[] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1],","- self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not","image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{}","def zoomer(self, event): if event.num == 4 or event.delta == 120 or event.keysym","imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) /","self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset","right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width',","2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self,","= int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img]","self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True","e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\",","else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or","= int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f","if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x =","self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for","img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for","error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates):","0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img])","as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) %","/ self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f /","self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom =","root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\",","errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in","if event.num == 4 or event.delta == 120 or event.keysym == 'plus': self.zoom","acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height')","3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and","of images in {} than days in the report {}!'.format(args.masks, args.report)) root =","self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover","shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown","self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self,","width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for","/ 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w","int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\"Courier\",","int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) +","resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0),","root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False","os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day,","left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right')","+ self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f /","img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0),","def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self,","0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h *","255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) *","self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img]","help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int,","'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif event.delta","= ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos),","= self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} -","self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w /","spatio-temporal gap filling results\"\"\" import os import argparse from datetime import datetime, timedelta","h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy())","cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom ==","results\"\"\" import os import argparse from datetime import datetime, timedelta from tkinter import","in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates","[] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w,","% len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig)","h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full","button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self,","zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN)","self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h / 30)), text='')","self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text","image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2","event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom += 60","int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None):","click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100","in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right =","args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img","%d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0","of images in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m)","from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as","/ self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh()","= 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button","\"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset","0.12, h * 0.94, font=(\"Courier\", int(h / 50)), text='') # image timeline imagelist_h","gap filling results\"\"\" import os import argparse from datetime import datetime, timedelta from","image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos),","' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o)","delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d","0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width","sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks,","- self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f",":, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in","self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w","Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o,","255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h *","+ b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2","/ 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y =","view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\",","len(dates): raise RuntimeError('Different number of images in {} than days in the report","self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh()","self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w,","event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event):","in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img","* 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit)","font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19,","for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for","= Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w *","/ 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x","def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 -","information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data","3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h","4 or event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif","imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) #","font=(\"Courier\", int(h / 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\",","# width of each displayed image self.imgs_orig_m = [] # masked full images","os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])]","click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next()","2, h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w /","if error != 0.0 else 'n.a. ' for error in report[:, 5]] logos","2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y +","-= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x =","* logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS))","False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave)","def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle =","= min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom","Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos","int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\",","/ 600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :]","- event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)),","self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image","20 elif event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom","root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\",","/ np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos = int(h","dates self.errors = errors # setup images self.img_w = int(h * 0.68) #","default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand images') parser.add_argument('-y',","img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year,","1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors","[] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :,","in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise","estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='')","100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event):",":, :] = 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index,","/ 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)),","self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with","range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error","text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta ==","logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos),","Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img]","in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) +","+= 60 else: if self.zoom - 20 >= 100: self.zoom -= 20 if","np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a)","int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos),","int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f /","= 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button","0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h *","'--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width')","self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100,","/ len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index","cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom","to interactively view spatio-temporal gap filling results\"\"\" import os import argparse from datetime","h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text =","+ 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)):","Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image,","b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 -","self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom -= 20","self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\",","= ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1]","= int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y","interactively view spatio-temporal gap filling results\"\"\" import os import argparse from datetime import","2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f","60 else: if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom","* 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image","self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle","mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the","False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates","event): self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self, root,","img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w),","img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w),","# bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask)","== 4 or event.delta == 120 or event.keysym == 'plus': self.zoom += 20","as np from PIL import Image, ImageTk import probgf.media as media class MainWindow():","event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom","the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W',","* 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h","logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2","c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h","Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w","len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in","parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on","self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w -","def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self,","= img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2)","font=(\"Courier\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred))","/ 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w","5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number","self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w -","root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\",","or event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta","os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])]","/ 600)):, :, :] = 255 c_list[index, :, :int(w / 600), :] =","else 'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3]","on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on","root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\",","self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif event.delta ==","= [] self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc","for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for","ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] *","# images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v =","int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list =","font=(\"Courier\", int(h / 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root,","* logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS))","/ 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width()","- b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2,","range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index,","self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size)","self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/',",":] = 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :,","window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h *",">= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self,","bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\",","text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h / 50)),","= parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in","self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def","* 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70,","ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img])","!= 0.0 else 'n.a. ' for error in report[:, 5]] logos = [media.logo1,","self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f /","* logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 +","50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a","= True def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h,","100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1]","== 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event):","numpy as np from PIL import Image, ImageTk import probgf.media as media class","imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images","np from PIL import Image, ImageTk import probgf.media as media class MainWindow(): def","Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w),","NORMAL import numpy as np from PIL import Image, ImageTk import probgf.media as","the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the","h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9,","b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1","= Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit","probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1)","Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2","self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization","self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def","not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom","3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w /","if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than days","images self.img_w = int(h * 0.68) # width of each displayed image self.imgs_orig_m","import Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img","self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] =","self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right,","self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud","= np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h -","self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w /","event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def","images in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) !=","self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f","/ 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w","self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image","# text labels and logos h_logos = int(h / 17) b_logos = int(w","prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event):","self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button =","- self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom","media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig)","= 0 # text labels and logos h_logos = int(h / 17) b_logos","help='directory with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory","self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img =","full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] #","def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self,","% len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y +","(event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev()","int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h *","for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for","event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag","text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text","number of images in {} than days in the report {}!'.format(args.left, args.report)) if","/ 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h","days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number","/ 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w","= dates self.errors = errors # setup images self.img_w = int(h * 0.68)",":, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h,","from PIL import Image, ImageTk import probgf.media as media class MainWindow(): def next(self,","== 240: self.zoom += 40 elif event.delta == 360: self.zoom += 60 else:","self.zoom += 40 elif event.delta == 360: self.zoom += 60 else: if self.zoom","of each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m =","= False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos):","reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y =","setup images self.img_w = int(h * 0.68) # width of each displayed image","font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19,","= self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w","self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f","b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 -","in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255","int(w / 600)):, :, :] = 255 c_list[index, :, :int(w / 600), :]","= 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h","- int(w / 600)):, :, :] = 255 c_list[index, :, :int(w / 600),","height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2","event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40","image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v","self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom)","self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser =","in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for","labels and logos h_logos = int(h / 17) b_logos = int(w / 100)","/ 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not","img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 =","- int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2","text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w","= np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list =","h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 =","imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m","images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left","[(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in","class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh()","next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None):","self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94,","== 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0","17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white')","imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] =","command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy)","datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import","root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button","int(h / 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h","== 'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif","self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1",":, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w /","= ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1]","imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3),","h * 0.94, font=(\"Courier\", int(h / 50)), text='') # image timeline imagelist_h =","= Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p,","h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9,","self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y","self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w -","self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f =","'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if","{} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise","root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height,","text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)),","self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h / 50)), text='')","* 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM:","int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] -","/ zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom)","import os import argparse from datetime import datetime, timedelta from tkinter import Canvas,","self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4","def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o,","parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args","the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width,","/ 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h","* 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0]","default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o","import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img +","self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors,","default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/',","error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of","= self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root,","= self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h / 50)), text='') #","600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :]","img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img","Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w","img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 =","\"\"\"viewer application which allows to interactively view spatio-temporal gap filling results\"\"\" import os","img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1]","buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next)","imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h,","/ 600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :,","self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\",","default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left,","self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev)",":] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5,","self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w /","self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f /","<gh_stars>1-10 \"\"\"viewer application which allows to interactively view spatio-temporal gap filling results\"\"\" import","index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] =","\"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50,","imgs_m, dates, errors, logos): self.dates = dates self.errors = errors # setup images","+ 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1)","= Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]),","/ logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() /","0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and","img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img)","timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error)","int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y =","each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = []","+ self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED)","images in {} than days in the report {}!'.format(args.masks, args.report)) root = Tk()","self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter):","len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than days in","days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\"","zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) +","b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 +","dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS))","self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not","def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2","elif event.delta == 360: self.zoom += 60 else: if self.zoom - 20 >=","toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False","of images in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p)","self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta == 120 or","event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x","= 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def","float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom /","100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button =","/ logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]),","- self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)),","root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter)","ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img","2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() /","event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f","* 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h","imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :,","# unmasked full images self.imgs_pred = [] self.cc = [] for index, img","* 0.68) # width of each displayed image self.imgs_orig_m = [] # masked","= (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button:","img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x,","raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.masks,","= (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x","Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image","media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than","imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors #","/ 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self,","50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h /","- 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x,","np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :]","self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w,","= int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8')","SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk import probgf.media","alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and","2, h * 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w *","int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h)","self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56,","logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos),","= \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text =","'--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img","not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f","width of each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m","import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from","= [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0))","self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w),",":] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255","def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) /","2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0","= [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day","/ 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2)","than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different","np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos =","than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer')","{:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta == 120","/ 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f","3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text","# setup images self.img_w = int(h * 0.68) # width of each displayed","* 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56,","in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in","self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\",","self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 =","(self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x +","errors, logos): self.dates = dates self.errors = errors # setup images self.img_w =","int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos","/ 2, h * 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w","root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\",","2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h /","+ b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h / 25)),","self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image =",":int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):,","and error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year","self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self,","0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h","self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1)","args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than","+= 20 elif event.delta == 240: self.zoom += 40 elif event.delta == 360:","drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 -","= [] # masked full images self.imgs_pred_m = [] self.imgs_orig = [] #","self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2","/ 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h","for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a.","self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom /","/ 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width()","['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in report[:, 5]]","image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling","h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w *","self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x,","self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left',","self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory","100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if","'--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and","self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img]","state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform()","0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform)","'--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m', '--masks',","parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window","= \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text","root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\",","not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 =","self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 +","= np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) -","for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition')","self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos /","resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img","root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave)","width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img)","[ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h","2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle =","import argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button,","np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w","from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED,","root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave)","media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {}","event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f","self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2","containing date and error information for the right hand images') parser.add_argument('-y', '--year', type=int,","date and error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018,","* 0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\",","= self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom","font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h","type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in","/ 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\"Courier\", int(h","240: self.zoom += 40 elif event.delta == 360: self.zoom += 60 else: if","- int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list",":] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list","help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information","RuntimeError('Different number of images in {} than days in the report {}!'.format(args.right, args.report))","masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset view\",","35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\"Courier\", int(h /","event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if","text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\", int(h / 30)),","self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer)","'--left', default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r', '--right',","help='report containing date and error information for the right hand images') parser.add_argument('-y', '--year',","= self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x,","/ 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 -","0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button =",":] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255","2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w /","255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h -","+ b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2","/ 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1","\"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text =","len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y)","= False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def","root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter)","images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask","self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2","0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h *","False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self,","+ self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 =","text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num ==","in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = []","command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset view\", command=self.reset_transform,","int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2","with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with","self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform()","MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def","{}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {}","100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom))","2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f","imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 #","default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height',","int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos),","argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED,","/ self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2))","event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img","self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi)","images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images","images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w,","def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates =","on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv',","Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors,","and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\",","Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import","width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS))","(imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :, :int(w /","= 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred)","logos h_logos = int(h / 17) b_logos = int(w / 100) self.canvas =","self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer')","(self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\",","resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels","h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2,","parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date","1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])]","self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\",","root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\",","self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h / 50)), text='') # image","- event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)),","- self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 -","PIL import Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None):","logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of","full images self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p):","0 # text labels and logos h_logos = int(h / 17) b_logos =","img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img","int(h * 0.68) # width of each displayed image self.imgs_orig_m = [] #","30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h /","c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :, :int(w","and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w),","= ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image =","self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f /","/ logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]),","index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists =","text labels and logos h_logos = int(h / 17) b_logos = int(w /","the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images","self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x","imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report","= False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\",","2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f /","{}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root,","are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are","= np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :,","self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def","self.b_reset = Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED)","self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w),","self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info,","self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w),","default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error","+ timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors =","unmasked full images self.imgs_pred = [] self.cc = [] for index, img in","* 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda","= \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w *","= self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img]","min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom /","2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() /","for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left =","for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS))","parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for","if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1","= [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9,","int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\"Courier\",","zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f /",":int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):,","for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :]","= [] # unmasked full images self.imgs_pred = [] self.cc = [] for","self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in","self.zoomer) root.bind(\"-\", self.zoomer) self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\",","= 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index,","360: self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom -=","self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta","self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred = []","root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\",","the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report","len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh()","= [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report =","/ zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else:","- cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if","(self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img -","event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta ==","/ 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h /","self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2","c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w","/ 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y =","logos): self.dates = dates self.errors = errors # setup images self.img_w = int(h","args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width,","* 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h","raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.left,","h * 0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12,","command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h","(args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos) root.focus_set() root.mainloop()","self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh()","- estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom,","- self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f","600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w *","[Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report,","image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks","np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos = int(h /","self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w,","2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle:","self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] *","def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y","self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event):","image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig =","self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 =","img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1)","self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y,","logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3","height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for","self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not","font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13,","600)):, :, :] = 255 c_list[index, :, :int(w / 600), :] = 255","RuntimeError('Different number of images in {} than days in the report {}!'.format(args.masks, args.report))","if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than days","/ zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom)","255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :,","= ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in report[:,","imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index,","event): if event.num == 4 or event.delta == 120 or event.keysym == 'plus':","{}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {}","self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE","= self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS)","* 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset)","refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f","- np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos","self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x","self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos","- w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h","if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def","0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self,","image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images","!= len(dates): raise RuntimeError('Different number of images in {} than days in the","self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right)","40 elif event.delta == 360: self.zoom += 60 else: if self.zoom - 20","type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args()","report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in","/ 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9,","self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0","hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int,","[Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img)","which allows to interactively view spatio-temporal gap filling results\"\"\" import os import argparse","- self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w","not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x","Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos),","dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for","int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for","/ 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y,","[Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img)","= int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w,","= int(h * 0.68) # width of each displayed image self.imgs_orig_m = []","image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}%","dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y')","w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info =","0.13, font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h *","# images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred]","[] # masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked","self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__)","report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height))","imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index","DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk import","self.curr_img = 0 # text labels and logos h_logos = int(h / 17)","self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num","20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0]","self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 -","self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated","images self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w,","for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w,","day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. '","days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number","view spatio-temporal gap filling results\"\"\" import os import argparse from datetime import datetime,","lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next)","w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors =","argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the left')","for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for","and logos h_logos = int(h / 17) b_logos = int(w / 100) self.canvas","self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL)","min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom /","displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig","- 20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh()","= [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v","3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h),","self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image =","0.68) # width of each displayed image self.imgs_orig_m = [] # masked full","self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w -","/ 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\"Courier\", int(h","Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img =","c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w /","np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b","self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1","self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self, root, w,","if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom == 100:","self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel)","self.errors = errors # setup images self.img_w = int(h * 0.68) # width","self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer) root.bind(\"-\", self.zoomer)","self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which","self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f /","/ 2, h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w","self.over_button = False self.b_masks.bind(\"<Enter>\", self.button_enter) self.b_masks.bind(\"<Leave>\", self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter)","in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\"%dx%d+0+0\" %","which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which","1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index,","root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos)","type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H',","Button(root, font=(\"Courier\", int(h / 50)), text = \"Reset view\", command=self.reset_transform, state=DISABLED) self.b_quit =","masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images","help='directory with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory","images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred =","True def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p,","ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] *","or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom +=","event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y = 0","logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos),","ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height()","self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 +","= errors # setup images self.img_w = int(h * 0.68) # width of","alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img =","help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img)","images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right","self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x =","1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y","zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y","of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720,","int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x =","imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors # setup","report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different","self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2)","in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of","in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates):","h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() /","2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() /","Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed')","# image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred),","ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list,","50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root, font=(\"Courier\", int(h / 50)),","report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in","self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left)","w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h /","image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text = \"Show masks\", command=self.toggle_mask)","= [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m =","parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])]","= self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% -","enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1],","self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\", self.drag_roi) root.bind(\"+\", self.zoomer)","RuntimeError('Different number of images in {} than days in the report {}!'.format(args.left, args.report))","len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than days in","'--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand images')","default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/',","{} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise","# masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full","= not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100","error != 0.0 else 'n.a. ' for error in report[:, 5]] logos =","self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False","self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] *","* 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\"Courier\", int(h / 50)), text = \"Show","Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30,","self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown","logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width()","keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\", self.toggle_mask) root.bind(\"<Right>\", self.next) root.bind(\"<Left>\", self.prev)","shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report',","Filling Viewer') root.geometry(\"%dx%d+0+0\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates,","self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom =","0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f /","self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\",","/ 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack()","self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos","int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\",","25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\"Courier\", int(h /","self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig = []","h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1)","* 0.19, font=(\"Courier\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h","self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img]","b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() /","len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than days in","sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right,","click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event):","h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind","self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in","= ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img])","self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer)","/ 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height()","{} than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling","/ 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94,","def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button:","in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in","self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f /","self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS)","{}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else:","= [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0))","self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5))","/ 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y","for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists","self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0","= Button(root, font=(\"Courier\", int(h / 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset =","imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p","- 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error","for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',',","self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Predicted')","event.num == 4 or event.delta == 120 or event.keysym == 'plus': self.zoom +=","for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img])","self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle","= argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the","root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\", self.zoomer) root.bind(\"<Button-5>\", self.zoomer) root.bind(\"<MouseWheel>\", self.zoomer) root.bind(\"<B2-Motion>\",","1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) %","dates, errors, logos): self.dates = dates self.errors = errors # setup images self.img_w","for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) !=","application which allows to interactively view spatio-temporal gap filling results\"\"\" import os import","Button(root, font=(\"Courier\", int(h / 50)), text = \"Show masks\", command=self.toggle_mask) self.b_reset = Button(root,","def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False def","filling results\"\"\" import os import argparse from datetime import datetime, timedelta from tkinter","%Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else","event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f","timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy","[] self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc =","self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h *","(imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3))))","window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys","= float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom","+= 40 elif event.delta == 360: self.zoom += 60 else: if self.zoom -","self.b_quit = Button(root, font=(\"Courier\", int(h / 50)), text = \"Quit\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w","/ 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos /","button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m,","3.9, h * 0.19, font=(\"Courier\", int(h / 35)), text='Observed') self.canvas.create_text(w - w /","sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates =","20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def","in {} than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap","self.button_leave) self.b_reset.bind(\"<Enter>\", self.button_enter) self.b_reset.bind(\"<Leave>\", self.button_leave) self.b_quit.bind(\"<Enter>\", self.button_enter) self.b_quit.bind(\"<Leave>\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l',","[media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in","100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 -","= self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x,","b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\"Courier\", int(h / 25)), text='Gap","args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than","self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94,","help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o =","'--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args =","= 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h","number of images in {} than days in the report {}!'.format(args.right, args.report)) if","self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y,","than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different","root.bind(\"<Left>\", self.prev) root.bind(\"<Down>\", self.next) root.bind(\"<Up>\", self.prev) root.bind(\"<Button-3>\", self.click_right) root.bind(\"<Button-1>\", self.click_left) root.bind(\"<Button-2>\", self.click_wheel) root.bind(\"<Button-4>\",","self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y","img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] =","= int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 =","/ 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def","600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] =","the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images","MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED)","== 360: self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom","img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 =","- (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06,","120 or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom","0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda e:","raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.right,","Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL","window=self.b_quit) # bind buttons and keys root.bind(\"q\", lambda e: self.canvas.master.destroy()) root.bind(\"r\", self.reset_transform) root.bind(\"m\",","if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self):"],"string":"[\n \"= self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f\",\n \"font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94,\",\n \"[] # unmasked full images self.imgs_pred = [] self.cc = [] for index,\",\n \"- b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2\",\n \"images in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) !=\",\n \"in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in\",\n \"report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1])\",\n \"in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of\",\n \"elif event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom +=\",\n \":, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] =\",\n \"2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1]\",\n \"resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 -\",\n \"in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5))\",\n \"RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk\",\n \"c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h,\",\n \"self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index]))\",\n \"text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img]))\",\n \"parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m',\",\n \"self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc = []\",\n \"parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand\",\n \"Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w\",\n \"self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom,\",\n \"images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280,\",\n \"in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates):\",\n \"self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img])\",\n \"tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np\",\n \"= [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images\",\n \"= [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p =\",\n \"zoomer(self, event): if event.num == 4 or event.delta == 120 or event.keysym ==\",\n \"* 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w\",\n \"* imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img])\",\n \"2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h -\",\n \"import numpy as np from PIL import Image, ImageTk import probgf.media as media\",\n \"zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if\",\n \"img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img\",\n \"index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w),\",\n \"= (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img\",\n \":, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)):\",\n \"0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] #\",\n \"/ 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h\",\n \"root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors\",\n \"with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for\",\n \"/ 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle\",\n \"1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if\",\n \"for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1,\",\n \"50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks)\",\n \"0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w /\",\n \"else: if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom ==\",\n \"35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h /\",\n \"are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R',\",\n \"int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\",\",\n \"img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img)\",\n \"self.img_w = int(h * 0.68) # width of each displayed image self.imgs_orig_m =\",\n \"else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x,\",\n \"{:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100:\",\n \"self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2\",\n \"2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None):\",\n \"timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h,\",\n \"number of images in {} than days in the report {}!'.format(args.masks, args.report)) root\",\n \"0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) #\",\n \"with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with\",\n \"h_logos = int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root,\",\n \"self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks =\",\n \"buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS))\",\n \"image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h,\",\n \"self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images\",\n \"self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h *\",\n \"+ self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def\",\n \"parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r',\",\n \"= self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img\",\n \"self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer)\",\n \"+ self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom\",\n \"img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left,\",\n \"errors # setup images self.img_w = int(h * 0.68) # width of each\",\n \"Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56,\",\n \"1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error !=\",\n \"+ self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if\",\n \"2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x +\",\n \"which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images')\",\n \"right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing\",\n \"full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred\",\n \"self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom /\",\n \"% (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos) root.focus_set()\",\n \"* 0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline\",\n \"__init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates\",\n \"self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos /\",\n \"self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag =\",\n \"= self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img]\",\n \"root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m,\",\n \"int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h *\",\n \"enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates,\",\n \"0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\",\",\n \"text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a =\",\n \"0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w\",\n \"0.0 else 'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2,\",\n \"self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f /\",\n \"* 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons\",\n \"= int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x\",\n \"self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3)\",\n \"100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) +\",\n \"text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)),\",\n \"= img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w,\",\n \"allows to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse from\",\n \"if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] -\",\n \"== 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240:\",\n \"- w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info\",\n \"if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than days\",\n \"import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL\",\n \"+ b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height()\",\n \"self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are\",\n \"img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img\",\n \"self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons\",\n \"int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 -\",\n \"img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right\",\n \"h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h *\",\n \"self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser\",\n \"datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN,\",\n \"in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in\",\n \"image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img],\",\n \"data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window\",\n \"self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1\",\n \"event.delta == 360: self.zoom += 60 else: if self.zoom - 20 >= 100:\",\n \"h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors\",\n \"/ zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2)\",\n \"os import argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk,\",\n \"/ 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h\",\n \"# full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig]\",\n \"visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w\",\n \"= [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600),\",\n \"[ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v =\",\n \"= min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom\",\n \"self.dates = dates self.errors = errors # setup images self.img_w = int(h *\",\n \"b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h\",\n \"range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for\",\n \"self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom =\",\n \"self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\",\",\n \"text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)),\",\n \"[] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1],\",\n \"- self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not\",\n \"image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{}\",\n \"def zoomer(self, event): if event.num == 4 or event.delta == 120 or event.keysym\",\n \"imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) /\",\n \"self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset\",\n \"right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width',\",\n \"2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self,\",\n \"= int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img]\",\n \"self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True\",\n \"e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\",\",\n \"else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or\",\n \"= int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f\",\n \"if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x =\",\n \"self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for\",\n \"img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for\",\n \"error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates):\",\n \"0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img])\",\n \"as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) %\",\n \"/ self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f /\",\n \"self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom =\",\n \"root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\",\",\n \"errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in\",\n \"if event.num == 4 or event.delta == 120 or event.keysym == 'plus': self.zoom\",\n \"acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height')\",\n \"3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and\",\n \"of images in {} than days in the report {}!'.format(args.masks, args.report)) root =\",\n \"self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover\",\n \"shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown\",\n \"self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self,\",\n \"width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for\",\n \"/ 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w\",\n \"int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\\\"Courier\\\",\",\n \"int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) +\",\n \"resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0),\",\n \"root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False\",\n \"os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day,\",\n \"left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right')\",\n \"+ self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f /\",\n \"img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0),\",\n \"def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self,\",\n \"0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h *\",\n \"255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) *\",\n \"self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img]\",\n \"help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int,\",\n \"'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif event.delta\",\n \"= ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos),\",\n \"= self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} -\",\n \"self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w /\",\n \"spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse from datetime import datetime, timedelta\",\n \"h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy())\",\n \"cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom ==\",\n \"results\\\"\\\"\\\" import os import argparse from datetime import datetime, timedelta from tkinter import\",\n \"in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates\",\n \"[] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w,\",\n \"% len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig)\",\n \"h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full\",\n \"button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self,\",\n \"zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN)\",\n \"self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='')\",\n \"self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text\",\n \"image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2\",\n \"event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom += 60\",\n \"int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None):\",\n \"click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100\",\n \"in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right =\",\n \"args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img\",\n \"%d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0\",\n \"of images in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m)\",\n \"from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as\",\n \"/ self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh()\",\n \"= 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button\",\n \"\\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset\",\n \"0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline imagelist_h\",\n \"gap filling results\\\"\\\"\\\" import os import argparse from datetime import datetime, timedelta from\",\n \"image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos),\",\n \"' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o)\",\n \"delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d\",\n \"0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width\",\n \"sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks,\",\n \"- self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f\",\n \":, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in\",\n \"self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w\",\n \"Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o,\",\n \"255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h *\",\n \"+ b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2\",\n \"/ 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y =\",\n \"view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\",\",\n \"len(dates): raise RuntimeError('Different number of images in {} than days in the report\",\n \"self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh()\",\n \"self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w,\",\n \"event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event):\",\n \"in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img\",\n \"* 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit)\",\n \"font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19,\",\n \"for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for\",\n \"= Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w *\",\n \"/ 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x\",\n \"def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 -\",\n \"information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data\",\n \"3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h\",\n \"4 or event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif\",\n \"imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) #\",\n \"font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\",\",\n \"# width of each displayed image self.imgs_orig_m = [] # masked full images\",\n \"os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])]\",\n \"click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next()\",\n \"2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w /\",\n \"if error != 0.0 else 'n.a. ' for error in report[:, 5]] logos\",\n \"2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y +\",\n \"-= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x =\",\n \"* logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS))\",\n \"False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave)\",\n \"def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle =\",\n \"= min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom\",\n \"Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos\",\n \"int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\",\",\n \"/ 600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :]\",\n \"- event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)),\",\n \"self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image\",\n \"20 elif event.delta == 240: self.zoom += 40 elif event.delta == 360: self.zoom\",\n \"root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\",\",\n \"/ np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos = int(h\",\n \"dates self.errors = errors # setup images self.img_w = int(h * 0.68) #\",\n \"default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand images') parser.add_argument('-y',\",\n \"img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year,\",\n \"1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors\",\n \"[] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :,\",\n \"in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise\",\n \"estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='')\",\n \"100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event):\",\n \":, :] = 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index,\",\n \"/ 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)),\",\n \"self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with\",\n \"range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error\",\n \"text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta ==\",\n \"logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos),\",\n \"Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img]\",\n \"in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) +\",\n \"+= 60 else: if self.zoom - 20 >= 100: self.zoom -= 20 if\",\n \"np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a)\",\n \"int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos),\",\n \"int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f /\",\n \"= 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button\",\n \"0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h *\",\n \"'--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width')\",\n \"self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100,\",\n \"/ len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index\",\n \"cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom\",\n \"to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse from datetime\",\n \"h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text =\",\n \"+ 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)):\",\n \"Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image,\",\n \"b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 -\",\n \"self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom -= 20\",\n \"self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\",\",\n \"= ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1]\",\n \"= int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y\",\n \"interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse from datetime import\",\n \"2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f\",\n \"60 else: if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom\",\n \"* 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image\",\n \"self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle\",\n \"mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the\",\n \"False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates\",\n \"event): self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self, root,\",\n \"img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w),\",\n \"img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w),\",\n \"# bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask)\",\n \"== 4 or event.delta == 120 or event.keysym == 'plus': self.zoom += 20\",\n \"as np from PIL import Image, ImageTk import probgf.media as media class MainWindow():\",\n \"event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom\",\n \"the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W',\",\n \"* 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h\",\n \"logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2\",\n \"c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h\",\n \"Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w\",\n \"len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in\",\n \"parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on\",\n \"self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w -\",\n \"def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self,\",\n \"= img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2)\",\n \"font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred))\",\n \"/ 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w\",\n \"5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number\",\n \"self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w -\",\n \"root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\",\",\n \"or event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta\",\n \"os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])]\",\n \"/ 600)):, :, :] = 255 c_list[index, :, :int(w / 600), :] =\",\n \"else 'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3]\",\n \"on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on\",\n \"root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\",\",\n \"self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif event.delta ==\",\n \"= [] self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc\",\n \"for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for\",\n \"ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] *\",\n \"# images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v =\",\n \"int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list =\",\n \"font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root,\",\n \"* logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS))\",\n \"/ 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width()\",\n \"- b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2,\",\n \"range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index,\",\n \"self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size)\",\n \"self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/',\",\n \":] = 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :,\",\n \"window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h *\",\n \">= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self,\",\n \"bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\",\",\n \"text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)),\",\n \"= parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in\",\n \"self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def\",\n \"* 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70,\",\n \"ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img])\",\n \"!= 0.0 else 'n.a. ' for error in report[:, 5]] logos = [media.logo1,\",\n \"self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f /\",\n \"* logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 +\",\n \"50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a\",\n \"= True def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h,\",\n \"100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1]\",\n \"== 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event):\",\n \"numpy as np from PIL import Image, ImageTk import probgf.media as media class\",\n \"imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images\",\n \"np from PIL import Image, ImageTk import probgf.media as media class MainWindow(): def\",\n \"Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w),\",\n \"NORMAL import numpy as np from PIL import Image, ImageTk import probgf.media as\",\n \"the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the\",\n \"h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9,\",\n \"b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1\",\n \"= Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit\",\n \"probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1)\",\n \"Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2\",\n \"self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization\",\n \"self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def\",\n \"not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom\",\n \"3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w /\",\n \"if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than days\",\n \"images self.img_w = int(h * 0.68) # width of each displayed image self.imgs_orig_m\",\n \"import Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img\",\n \"self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] =\",\n \"self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right,\",\n \"self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud\",\n \"= np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h -\",\n \"self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w /\",\n \"event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def\",\n \"images in {} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) !=\",\n \"self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f\",\n \"/ 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w\",\n \"self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image\",\n \"# text labels and logos h_logos = int(h / 17) b_logos = int(w\",\n \"prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event):\",\n \"self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button =\",\n \"- self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom\",\n \"media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig)\",\n \"= 0 # text labels and logos h_logos = int(h / 17) b_logos\",\n \"help='directory with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory\",\n \"self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img =\",\n \"full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] #\",\n \"def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self,\",\n \"% len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y +\",\n \"(event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button: self.prev()\",\n \"int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h *\",\n \"for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for\",\n \"event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag\",\n \"text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text\",\n \"number of images in {} than days in the report {}!'.format(args.left, args.report)) if\",\n \"/ 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h\",\n \"days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number\",\n \"/ 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w\",\n \"= dates self.errors = errors # setup images self.img_w = int(h * 0.68)\",\n \":, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h,\",\n \"from PIL import Image, ImageTk import probgf.media as media class MainWindow(): def next(self,\",\n \"== 240: self.zoom += 40 elif event.delta == 360: self.zoom += 60 else:\",\n \"self.zoom += 40 elif event.delta == 360: self.zoom += 60 else: if self.zoom\",\n \"of each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m =\",\n \"= False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos):\",\n \"reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y =\",\n \"setup images self.img_w = int(h * 0.68) # width of each displayed image\",\n \"font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19,\",\n \"= self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w\",\n \"self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f\",\n \"b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 -\",\n \"in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255\",\n \"int(w / 600)):, :, :] = 255 c_list[index, :, :int(w / 600), :]\",\n \"= 255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h\",\n \"- int(w / 600)):, :, :] = 255 c_list[index, :, :int(w / 600),\",\n \"height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2\",\n \"event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40\",\n \"image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v\",\n \"self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom)\",\n \"self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser =\",\n \"in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for\",\n \"labels and logos h_logos = int(h / 17) b_logos = int(w / 100)\",\n \"/ 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not\",\n \"img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 =\",\n \"- int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2\",\n \"text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w\",\n \"= np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list =\",\n \"h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 =\",\n \"imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m\",\n \"images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left\",\n \"[(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in\",\n \"class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh()\",\n \"next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None):\",\n \"self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94,\",\n \"== 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0\",\n \"17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white')\",\n \"imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] =\",\n \"command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy)\",\n \"datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import\",\n \"root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button\",\n \"int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h\",\n \"== 'plus': self.zoom += 20 elif event.delta == 240: self.zoom += 40 elif\",\n \"self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1\",\n \":, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w /\",\n \"= ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1]\",\n \"imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3),\",\n \"h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image timeline imagelist_h =\",\n \"= Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p,\",\n \"h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9,\",\n \"self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y\",\n \"self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w -\",\n \"self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f =\",\n \"'n.a. ' for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if\",\n \"{} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise\",\n \"root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height,\",\n \"text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)),\",\n \"self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='')\",\n \"* 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM:\",\n \"int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] -\",\n \"/ zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom)\",\n \"import os import argparse from datetime import datetime, timedelta from tkinter import Canvas,\",\n \"self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4\",\n \"def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o,\",\n \"parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args\",\n \"the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width,\",\n \"/ 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h\",\n \"* 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f = self.imgs_orig[0].size[0]\",\n \"default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o\",\n \"import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img +\",\n \"self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors,\",\n \"default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/',\",\n \"error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of\",\n \"= self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root,\",\n \"= self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') #\",\n \"600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :]\",\n \"img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img\",\n \"Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w\",\n \"img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 =\",\n \"\\\"\\\"\\\"viewer application which allows to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os\",\n \"img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1]\",\n \"buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next)\",\n \"imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h,\",\n \"/ 600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):, :,\",\n \"self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\",\",\n \"default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left,\",\n \"self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev)\",\n \":] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5,\",\n \"self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w /\",\n \"self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f /\",\n \"<gh_stars>1-10 \\\"\\\"\\\"viewer application which allows to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import\",\n \"index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :] =\",\n \"\\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50,\",\n \"imgs_m, dates, errors, logos): self.dates = dates self.errors = errors # setup images\",\n \"+ 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1)\",\n \"= Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]),\",\n \"/ logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() /\",\n \"0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and\",\n \"img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img)\",\n \"timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error)\",\n \"int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y =\",\n \"each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = []\",\n \"+ self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED)\",\n \"images in {} than days in the report {}!'.format(args.masks, args.report)) root = Tk()\",\n \"self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter):\",\n \"len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than days in\",\n \"days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\"\",\n \"zoom) self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) +\",\n \"b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 +\",\n \"dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS))\",\n \"self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not\",\n \"def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2\",\n \"elif event.delta == 360: self.zoom += 60 else: if self.zoom - 20 >=\",\n \"toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False\",\n \"of images in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p)\",\n \"self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta == 120 or\",\n \"event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x\",\n \"= 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def\",\n \"float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom /\",\n \"100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button =\",\n \"/ logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]),\",\n \"- self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)),\",\n \"root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter)\",\n \"ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img\",\n \"2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() /\",\n \"event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f\",\n \"* 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h\",\n \"imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :,\",\n \"# unmasked full images self.imgs_pred = [] self.cc = [] for index, img\",\n \"* 0.68) # width of each displayed image self.imgs_orig_m = [] # masked\",\n \"= (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if not self.over_button:\",\n \"img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x,\",\n \"raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.masks,\",\n \"= (self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x\",\n \"Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image\",\n \"media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than\",\n \"imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors #\",\n \"/ 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle self.refresh() def reset_transform(self,\",\n \"50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h /\",\n \"- 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x,\",\n \"np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :]\",\n \"self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w,\",\n \"= int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8')\",\n \"SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk import probgf.media\",\n \"alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and\",\n \"2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w *\",\n \"int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h)\",\n \"self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h * 0.56,\",\n \"logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos),\",\n \"= \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text =\",\n \"'--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img\",\n \"not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f\",\n \"width of each displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m\",\n \"import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from\",\n \"= [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0))\",\n \"self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w),\",\n \":] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255\",\n \"def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom = float(self.zoom) /\",\n \"2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0\",\n \"= [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day\",\n \"/ 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2)\",\n \"than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different\",\n \"np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos =\",\n \"than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer')\",\n \"{:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta == 120\",\n \"/ 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f\",\n \"3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text\",\n \"# setup images self.img_w = int(h * 0.68) # width of each displayed\",\n \"* 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56,\",\n \"in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in\",\n \"self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\",\",\n \"self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 =\",\n \"(self.curr_img - 1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x +\",\n \"errors, logos): self.dates = dates self.errors = errors # setup images self.img_w =\",\n \"int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos\",\n \"/ 2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w\",\n \"root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\",\",\n \"2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h /\",\n \"+ b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)),\",\n \"self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image =\",\n \":int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):,\",\n \"and error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year\",\n \"self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self,\",\n \"0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h\",\n \"self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1)\",\n \"args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {} than\",\n \"+= 20 elif event.delta == 240: self.zoom += 40 elif event.delta == 360:\",\n \"drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 -\",\n \"= [] # masked full images self.imgs_pred_m = [] self.imgs_orig = [] #\",\n \"self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2\",\n \"/ 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h\",\n \"for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a.\",\n \"self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom /\",\n \"/ 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width()\",\n \"['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in report[:, 5]]\",\n \"image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling\",\n \"h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w *\",\n \"self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x,\",\n \"self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left',\",\n \"self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory\",\n \"100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if\",\n \"'--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and\",\n \"self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img]\",\n \"state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform()\",\n \"0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform)\",\n \"'--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are shown on the right') parser.add_argument('-m', '--masks',\",\n \"parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window\",\n \"= \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text\",\n \"root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\",\",\n \"not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 =\",\n \"self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() / 2 +\",\n \"= np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) -\",\n \"for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition')\",\n \"self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos /\",\n \"resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img\",\n \"root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave)\",\n \"width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img)\",\n \"[ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h\",\n \"2 - self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle =\",\n \"import argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button,\",\n \"np.array(imagelist_a) c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w\",\n \"from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED,\",\n \"root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave)\",\n \"media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {}\",\n \"event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f\",\n \"self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2\",\n \"containing date and error information for the right hand images') parser.add_argument('-y', '--year', type=int,\",\n \"date and error information for the right hand images') parser.add_argument('-y', '--year', type=int, default=2018,\",\n \"* 0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\",\",\n \"= self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom\",\n \"font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h\",\n \"type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in\",\n \"/ 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h\",\n \"240: self.zoom += 40 elif event.delta == 360: self.zoom += 60 else: if\",\n \"- int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list\",\n \":] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list\",\n \"help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information\",\n \"RuntimeError('Different number of images in {} than days in the report {}!'.format(args.right, args.report))\",\n \"masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\",\",\n \"35)), text='Observed') self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h /\",\n \"event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event): if\",\n \"text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\", int(h / 30)),\",\n \"self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer)\",\n \"'--left', default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r', '--right',\",\n \"help='report containing date and error information for the right hand images') parser.add_argument('-y', '--year',\",\n \"= self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x,\",\n \"/ 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 -\",\n \"0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button =\",\n \":] = 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255\",\n \"2 - b_logos), int(h - (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w /\",\n \"255 c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h -\",\n \"+ b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2\",\n \"/ 50)), text='') # image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1\",\n \"\\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text =\",\n \"len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y)\",\n \"= False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def\",\n \"root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter)\",\n \"images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask\",\n \"self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2\",\n \"0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w / 3.9, h *\",\n \"False self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self,\",\n \"+ self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 =\",\n \"text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num ==\",\n \"in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = []\",\n \"command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform,\",\n \"int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2\",\n \"with images which are shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with\",\n \"self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform()\",\n \"MainWindow(): def next(self, event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def\",\n \"{}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {}\",\n \"100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom))\",\n \"2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f\",\n \"imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 #\",\n \"default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height',\",\n \"int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos),\",\n \"argparse from datetime import datetime, timedelta from tkinter import Canvas, Tk, Button, RAISED,\",\n \"/ self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2))\",\n \"event=None): self.curr_img = (self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img\",\n \"self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi)\",\n \"images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images\",\n \"images and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w,\",\n \"def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates =\",\n \"on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv',\",\n \"Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors,\",\n \"and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\",\",\n \"Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL import\",\n \"width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS))\",\n \"(imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :, :int(w /\",\n \"= 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred)\",\n \"logos h_logos = int(h / 17) b_logos = int(w / 100) self.canvas =\",\n \"self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer')\",\n \"(self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\",\",\n \"resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels\",\n \"h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2,\",\n \"parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date\",\n \"1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])]\",\n \"self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\",\",\n \"root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\",\",\n \"self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h / 50)), text='') # image\",\n \"- event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)),\",\n \"- self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y, 0 -\",\n \"PIL import Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None):\",\n \"logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of\",\n \"full images self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p):\",\n \"0 # text labels and logos h_logos = int(h / 17) b_logos =\",\n \"img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right, img) for img\",\n \"int(h * 0.68) # width of each displayed image self.imgs_orig_m = [] #\",\n \"30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h /\",\n \"c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :, :int(w\",\n \"and buttons self.img_w_f = self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w),\",\n \"= ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image =\",\n \"self.shift_y = min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f /\",\n \"/ logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]),\",\n \"index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists =\",\n \"text labels and logos h_logos = int(h / 17) b_logos = int(w /\",\n \"the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images\",\n \"self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x\",\n \"imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report\",\n \"= False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\",\",\n \"2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f /\",\n \"{}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root,\",\n \"are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which are\",\n \"= np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :,\",\n \"self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def\",\n \"self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED)\",\n \"self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w),\",\n \"self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info,\",\n \"self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w),\",\n \"default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error\",\n \"+ timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors =\",\n \"unmasked full images self.imgs_pred = [] self.cc = [] for index, img in\",\n \"* 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda\",\n \"= \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w *\",\n \"= self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img]\",\n \"min(max(self.start_drag[1] - event.y, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom /\",\n \"2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() /\",\n \"for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left =\",\n \"for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS))\",\n \"parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for\",\n \"if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1\",\n \"= [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9,\",\n \"int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13, font=(\\\"Courier\\\",\",\n \"zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f /\",\n \":int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w / 600)):,\",\n \"for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index, :int(w / 600), :, :]\",\n \"= [] # unmasked full images self.imgs_pred = [] self.cc = [] for\",\n \"self.imgs_orig[0].size[0] # full image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in\",\n \"self.zoomer) root.bind(\\\"-\\\", self.zoomer) self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\",\",\n \"= 255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index,\",\n \"360: self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom -=\",\n \"self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num == 4 or event.delta\",\n \"self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred = []\",\n \"root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\",\",\n \"the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report\",\n \"len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) % len(self.imgs_orig) self.refresh()\",\n \"= [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report =\",\n \"/ zoom) if not self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else:\",\n \"- cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if\",\n \"(self.curr_img + 1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img -\",\n \"event.delta == 120 or event.keysym == 'plus': self.zoom += 20 elif event.delta ==\",\n \"/ 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h /\",\n \"self.next() def refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2\",\n \"c_list[index, :, :int(w / 600), :] = 255 c_list[index, :, (imagelist_h - int(w\",\n \"/ 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.shift_y =\",\n \"logos): self.dates = dates self.errors = errors # setup images self.img_w = int(h\",\n \"args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width,\",\n \"* 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h\",\n \"raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.left,\",\n \"h * 0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12,\",\n \"command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h\",\n \"(args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos) root.focus_set() root.mainloop()\",\n \"self.zoom / 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh()\",\n \"- estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom,\",\n \"- self.img_w_f / zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f\",\n \"600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w *\",\n \"[Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report,\",\n \"image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks\",\n \"np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos = int(h /\",\n \"self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w,\",\n \"2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom) if not self.mask_toggle:\",\n \"self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] *\",\n \"def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y\",\n \"self.zoom = 100 self.shift_x = 0 self.shift_y = 0 self.refresh() def button_enter(self, event):\",\n \"image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig =\",\n \"self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 =\",\n \"img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1)\",\n \"self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y,\",\n \"logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3\",\n \"height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for\",\n \"self.shift_y) def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not\",\n \"font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h * 0.13,\",\n \"600)):, :, :] = 255 c_list[index, :, :int(w / 600), :] = 255\",\n \"RuntimeError('Different number of images in {} than days in the report {}!'.format(args.masks, args.report))\",\n \"if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than days\",\n \"/ zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f / zoom)\",\n \"255 c_list[index, (imagelist_h - int(w / 600)):, :, :] = 255 c_list[index, :,\",\n \"= ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. ' for error in report[:,\",\n \"imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index in range(len(self.imgs_pred)): c_list = np.array(imagelist_a) c_list[index,\",\n \"event): if event.num == 4 or event.delta == 120 or event.keysym == 'plus':\",\n \"{}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in {}\",\n \"self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE\",\n \"= self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS)\",\n \"* 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset)\",\n \"refresh(self): zoom = float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f\",\n \"- np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos h_logos\",\n \"self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x\",\n \"self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0 # text labels and logos\",\n \"- w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h\",\n \"if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def\",\n \"0 self.shift_y = 0 self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self,\",\n \"image width self.imgs_orig_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images\",\n \"!= len(dates): raise RuntimeError('Different number of images in {} than days in the\",\n \"self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right)\",\n \"40 elif event.delta == 360: self.zoom += 60 else: if self.zoom - 20\",\n \"type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args()\",\n \"report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in\",\n \"/ 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img]) self.i_right = self.canvas.create_image(w - w / 3.9,\",\n \"self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img = 0\",\n \"hand images') parser.add_argument('-y', '--year', type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int,\",\n \"[Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m = [Image.open(img)\",\n \"which allows to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse\",\n \"- self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2) self.canvas.create_image(int(w\",\n \"not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x\",\n \"Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos),\",\n \"dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y') for\",\n \"int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for\",\n \"/ 2 - self.img_w_f / self.zoom / 2)) self.shift_y = min(max(self.start_drag[1] - event.y,\",\n \"[Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img)\",\n \"= int(h / 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w,\",\n \"= int(h * 0.68) # width of each displayed image self.imgs_orig_m = []\",\n \"image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}%\",\n \"dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b %d %Y')\",\n \"w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info =\",\n \"0.13, font=(\\\"Courier\\\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h *\",\n \"# images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_pred]\",\n \"[] # masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked\",\n \"self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__)\",\n \"report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height))\",\n \"imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists = [] for index\",\n \"DISABLED, SUNKEN, NORMAL import numpy as np from PIL import Image, ImageTk import\",\n \"self.curr_img = 0 # text labels and logos h_logos = int(h / 17)\",\n \"self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL) def zoomer(self, event): if event.num\",\n \"20 if self.zoom == 100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0]\",\n \"self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y = int(self.img_w_f / 2 -\",\n \"self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated\",\n \"images self.imgs_pred = [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w,\",\n \"for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w,\",\n \"day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else 'n.a. '\",\n \"days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number\",\n \"view spatio-temporal gap filling results\\\"\\\"\\\" import os import argparse from datetime import datetime,\",\n \"lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next)\",\n \"w, h, imgs_p, imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors =\",\n \"argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the left')\",\n \"for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for\",\n \"and logos h_logos = int(h / 17) b_logos = int(w / 100) self.canvas\",\n \"self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else: self.canvas.itemconfig(self.zoom, text='ZOOM: {:3d}%'.format(self.zoom)) self.b_reset.config(state=NORMAL)\",\n \"min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom /\",\n \"displayed image self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig\",\n \"- 20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh()\",\n \"= [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in self.imgs_orig] # images for visualization self.imgs_pred_v\",\n \"3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h),\",\n \"self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image =\",\n \"0.68) # width of each displayed image self.imgs_orig_m = [] # masked full\",\n \"self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w -\",\n \"/ 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94, font=(\\\"Courier\\\", int(h\",\n \"Image, ImageTk import probgf.media as media class MainWindow(): def next(self, event=None): self.curr_img =\",\n \"c_list[index, :int(w / 600), :, :] = 255 c_list[index, (imagelist_h - int(w /\",\n \"np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates = [(datetime(args.year, 1, 1) + timedelta(int(report[day, 1]) - 1)).strftime('%b\",\n \"self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img] img2 = self.imgs_pred_m[self.curr_img] img1\",\n \"self.over_button = True def button_leave(self, enter): self.over_button = False def __init__(self, root, w,\",\n \"if self.zoom - 20 >= 100: self.zoom -= 20 if self.zoom == 100:\",\n \"self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel)\",\n \"self.errors = errors # setup images self.img_w = int(h * 0.68) # width\",\n \"self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer) root.bind(\\\"-\\\", self.zoomer)\",\n \"self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which\",\n \"self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 - int(self.img_w_f /\",\n \"/ 2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w\",\n \"self.over_button = False self.b_masks.bind(\\\"<Enter>\\\", self.button_enter) self.b_masks.bind(\\\"<Leave>\\\", self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter)\",\n \"in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" %\",\n \"which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory with images which\",\n \"1 imagelist_a = np.zeros((len(self.imgs_pred), imagelist_h, imagelist_h, 3), dtype='uint8') for index in range(len(self.imgs_pred)): imagelist_a[index,\",\n \"root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates, errors, logos)\",\n \"type=int, default=2018, help='year of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H',\",\n \"Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Reset view\\\", command=self.reset_transform, state=DISABLED) self.b_quit =\",\n \"masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images\",\n \"help='directory with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/', help='directory\",\n \"images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full images self.imgs_pred =\",\n \"True def button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p,\",\n \"ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos / logos[1].size[1] *\",\n \"or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom +=\",\n \"event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0 self.shift_y = 0\",\n \"logos[1].size[1] * logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos),\",\n \"ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height()\",\n \"self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 +\",\n \"= errors # setup images self.img_w = int(h * 0.68) # width of\",\n \"alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.cc.append(1 - np.count_nonzero(np.array(imgs_m[index])) / np.array(imgs_m[index]).size) self.curr_img =\",\n \"help='window height') args = parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img)\",\n \"images') parser.add_argument('-R', '--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right\",\n \"self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x =\",\n \"1) % len(self.imgs_orig) self.refresh() def click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y\",\n \"zoom / 2) + self.shift_x self.end_x = int(self.start_x + self.img_w_f / zoom) self.start_y\",\n \"of data acquisition') parser.add_argument('-W', '--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720,\",\n \"int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_x self.end_x =\",\n \"imgs_o, imgs_m, dates, errors, logos): self.dates = dates self.errors = errors # setup\",\n \"report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different\",\n \"self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height() / 2 + b_logos), image=self.logo2)\",\n \"in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of\",\n \"in {} than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates):\",\n \"h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() /\",\n \"2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() /\",\n \"Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed')\",\n \"# image timeline imagelist_h = int(self.img_w / len(self.imgs_pred)) + 1 imagelist_a = np.zeros((len(self.imgs_pred),\",\n \"ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list,\",\n \"50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root, font=(\\\"Courier\\\", int(h / 50)),\",\n \"report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images in\",\n \"self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left)\",\n \"w / 3.9, h * 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h /\",\n \"image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask)\",\n \"= [Image.open(img) for img in sorted([os.path.join(args.right, img) for img in os.listdir(args.right)])] imgs_m =\",\n \"parser.parse_args() imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])]\",\n \"= self.imgs_pred_v[self.curr_img]) self.canvas.itemconfig(self.i_list, image = self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% -\",\n \"enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0)) self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1],\",\n \"self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\", self.drag_roi) root.bind(\\\"+\\\", self.zoomer)\",\n \"RuntimeError('Different number of images in {} than days in the report {}!'.format(args.left, args.report))\",\n \"len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than days in\",\n \"'--report', default='report_lin_spatial_clouds0_2.csv', help='report containing date and error information for the right hand images')\",\n \"default='imgs/original/', help='directory with images which are shown on the left') parser.add_argument('-r', '--right', default='imgs/pred_outline_lin_spatial_clouds0_2/',\",\n \"{} than days in the report {}!'.format(args.right, args.report)) if len(imgs_m) != len(dates): raise\",\n \"# masked full images self.imgs_pred_m = [] self.imgs_orig = [] # unmasked full\",\n \"= not self.mask_toggle self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100\",\n \"error != 0.0 else 'n.a. ' for error in report[:, 5]] logos =\",\n \"self.refresh() def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False\",\n \"self.imagelists[self.curr_img]) self.canvas.itemconfig(self.day_info, text='{} - cloud cover {:06.2f}% - estimated MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] *\",\n \"* 0.56, image=self.imgs_pred_v[self.curr_img]) self.b_masks = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show\",\n \"Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30,\",\n \"self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown\",\n \"logos[1].size[0]), h_logos), Image.ANTIALIAS)) self.logo3 = ImageTk.PhotoImage(logos[2].resize((int(h_logos / logos[2].size[1] * logos[2].size[0]), h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width()\",\n \"keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\", self.toggle_mask) root.bind(\\\"<Right>\\\", self.next) root.bind(\\\"<Left>\\\", self.prev)\",\n \"shown on the right') parser.add_argument('-m', '--masks', default='imgs/mask/', help='directory with mask images') parser.add_argument('-R', '--report',\",\n \"Filling Viewer') root.geometry(\\\"%dx%d+0+0\\\" % (args.width, args.height)) MainWindow(root, args.width, args.height, imgs_p, imgs_o, imgs_m, dates,\",\n \"self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self): zoom =\",\n \"0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f /\",\n \"self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\",\",\n \"/ 17) b_logos = int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack()\",\n \"self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos / logos[0].size[1] * logos[0].size[0]), h_logos), Image.ANTIALIAS)) self.logo2 = ImageTk.PhotoImage(logos[1].resize((int(h_logos\",\n \"int(h / 25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\",\",\n \"25)), text='Gap Filling Viewer') self.canvas.create_text(w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h /\",\n \"self.imgs_orig_m = [] # masked full images self.imgs_pred_m = [] self.imgs_orig = []\",\n \"h_logos), Image.ANTIALIAS)) self.canvas.create_image(int(self.logo1.width() / 2 + b_logos), int(self.logo1.height() / 2 + b_logos), image=self.logo1)\",\n \"* 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted') self.day_info = self.canvas.create_text(w / 2, h\",\n \"self.mask_toggle: self.b_masks.config(relief=RAISED) img1 = self.imgs_orig[self.curr_img] img2 = self.imgs_pred[self.curr_img] else: self.b_masks.config(relief=SUNKEN) img1 = self.imgs_orig_m[self.curr_img]\",\n \"b_logos), image=self.logo2) self.canvas.create_image(int(w - self.logo3.width() / 2 - b_logos), int(h - (self.logo3.height() /\",\n \"len(imgs_o) != len(dates): raise RuntimeError('Different number of images in {} than days in\",\n \"sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p = [Image.open(img) for img in sorted([os.path.join(args.right,\",\n \"click_wheel(self, event): self.start_drag = (event.x + self.shift_x, event.y + self.shift_y) def click_left(self, event):\",\n \"h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind\",\n \"self.imgs_orig] # images for visualization self.imgs_pred_v = [ImageTk.PhotoImage(img.resize((self.img_w, self.img_w), Image.ANTIALIAS)) for img in\",\n \"= ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] = ImageTk.PhotoImage(img2) self.canvas.itemconfig(self.i_left, image = self.imgs_orig_v[self.curr_img]) self.canvas.itemconfig(self.i_right, image = self.imgs_pred_v[self.curr_img])\",\n \"self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer)\",\n \"/ 2 + b_logos), image=self.logo1) self.canvas.create_image(int(w - self.logo2.width() / 2 - b_logos), int(self.logo2.height()\",\n \"{} than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap Filling\",\n \"/ 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94,\",\n \"def click_left(self, event): if not self.over_button: self.prev() def click_right(self, event): if not self.over_button:\",\n \"in os.listdir(args.right)])] imgs_m = [Image.open(img) for img in sorted([os.path.join(args.masks, img) for img in\",\n \"self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y + self.img_w_f /\",\n \"self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS)\",\n \"{}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED) else:\",\n \"= [] self.cc = [] for index, img in enumerate(imgs_p): self.imgs_orig.append(imgs_o[index].resize((self.img_w, self.img_w), resample=0))\",\n \"self.imgs_pred.append(img.resize((self.img_w, self.img_w), resample=0)) self.imgs_orig_m.append(Image.blend(self.imgs_orig[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5)) self.imgs_pred_m.append(Image.blend(self.imgs_pred[-1], imgs_m[index].convert(mode='RGB').resize((self.img_w, self.img_w), resample=0), alpha=.5))\",\n \"/ 2 - self.img_w_f / zoom / 2) + self.shift_y self.end_y = int(self.start_y\",\n \"for index in range(len(self.imgs_pred)): imagelist_a[index, :, :, :] = np.array(self.imgs_pred[index].resize((imagelist_h, imagelist_h), Image.ANTIALIAS)) self.imagelists\",\n \"self.refresh() def reset_transform(self, event=None): self.mask_toggle = False self.zoom = 100 self.shift_x = 0\",\n \"= Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset =\",\n \"imgs_o = [Image.open(img) for img in sorted([os.path.join(args.left, img) for img in os.listdir(args.left)])] imgs_p\",\n \"- 1)).strftime('%b %d %Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error\",\n \"for img in sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',',\",\n \"self.canvas.create_text(w - w / 3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Predicted')\",\n \"event.num == 4 or event.delta == 120 or event.keysym == 'plus': self.zoom +=\",\n \"for img in self.imgs_pred] self.i_left = self.canvas.create_image(w / 3.9, h * 0.56, image=self.imgs_orig_v[self.curr_img])\",\n \"self.img_w_f / self.zoom / 2)) self.refresh() def toggle_mask(self, event=None): self.mask_toggle = not self.mask_toggle\",\n \"= argparse.ArgumentParser(description=__doc__) parser.add_argument('-l', '--left', default='imgs/original/', help='directory with images which are shown on the\",\n \"root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\", self.zoomer) root.bind(\\\"<Button-5>\\\", self.zoomer) root.bind(\\\"<MouseWheel>\\\", self.zoomer) root.bind(\\\"<B2-Motion>\\\",\",\n \"1) % len(self.imgs_orig) self.refresh() def prev(self, event=None): self.curr_img = (self.curr_img - 1) %\",\n \"dates, errors, logos): self.dates = dates self.errors = errors # setup images self.img_w\",\n \"for error in report[:, 5]] logos = [media.logo1, media.logo2, media.logo3] if len(imgs_o) !=\",\n \"application which allows to interactively view spatio-temporal gap filling results\\\"\\\"\\\" import os import\",\n \"Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Show masks\\\", command=self.toggle_mask) self.b_reset = Button(root,\",\n \"def button_enter(self, event): self.over_button = True def button_leave(self, enter): self.over_button = False def\",\n \"filling results\\\"\\\"\\\" import os import argparse from datetime import datetime, timedelta from tkinter\",\n \"%Y') for day in range(report.shape[0])] errors = ['{:4.1f}'.format(error) if error != 0.0 else\",\n \"event.x, 0 - int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)), int(self.img_w_f\",\n \"timedelta from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy\",\n \"[] self.imgs_orig = [] # unmasked full images self.imgs_pred = [] self.cc =\",\n \"self.reset_transform() self.canvas.create_window(w * 0.30, h * 0.94, window=self.b_masks) self.canvas.create_window(w * 0.50, h *\",\n \"(imagelist_h - int(w / 600)):, :] = 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3))))\",\n \"window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94, window=self.b_quit) # bind buttons and keys\",\n \"= float(self.zoom) / 100 self.start_x = int(self.img_w_f / 2 - self.img_w_f / zoom\",\n \"+= 40 elif event.delta == 360: self.zoom += 60 else: if self.zoom -\",\n \"self.b_quit = Button(root, font=(\\\"Courier\\\", int(h / 50)), text = \\\"Quit\\\", command=self.canvas.master.destroy) self.reset_transform() self.canvas.create_window(w\",\n \"/ 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 = ImageTk.PhotoImage(logos[0].resize((int(h_logos /\",\n \"button_leave(self, enter): self.over_button = False def __init__(self, root, w, h, imgs_p, imgs_o, imgs_m,\",\n \"3.9, h * 0.19, font=(\\\"Courier\\\", int(h / 35)), text='Observed') self.canvas.create_text(w - w /\",\n \"sorted([os.path.join(args.masks, img) for img in os.listdir(args.masks)])] report = np.genfromtxt(args.report, delimiter=',', dtype=float)[1:-1] dates =\",\n \"20 >= 100: self.zoom -= 20 if self.zoom == 100: self.reset_transform() self.refresh() def\",\n \"in {} than days in the report {}!'.format(args.masks, args.report)) root = Tk() root.title('Gap\",\n \"self.button_leave) self.b_reset.bind(\\\"<Enter>\\\", self.button_enter) self.b_reset.bind(\\\"<Leave>\\\", self.button_leave) self.b_quit.bind(\\\"<Enter>\\\", self.button_enter) self.b_quit.bind(\\\"<Leave>\\\", self.button_leave) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('-l',\",\n \"[media.logo1, media.logo2, media.logo3] if len(imgs_o) != len(dates): raise RuntimeError('Different number of images in\",\n \"100: self.reset_transform() self.refresh() def drag_roi(self, event): self.shift_x = min(max(self.start_drag[0] - event.x, 0 -\",\n \"= self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x,\",\n \"b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06, font=(\\\"Courier\\\", int(h / 25)), text='Gap\",\n \"args.report)) if len(imgs_m) != len(dates): raise RuntimeError('Different number of images in {} than\",\n \"self.canvas.create_window(w * 0.50, h * 0.94, window=self.b_reset) self.canvas.create_window(w * 0.70, h * 0.94,\",\n \"help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args = parser.parse_args() imgs_o =\",\n \"'--width', type=int, default=1280, help='window width') parser.add_argument('-H', '--height', type=int, default=720, help='window height') args =\",\n \"= 255 self.imagelists.append(ImageTk.PhotoImage(Image.fromarray(c_list.reshape(len(self.imgs_pred) * imagelist_h, imagelist_h, 3)))) self.i_list = self.canvas.create_image(w * 0.5, h\",\n \"number of images in {} than days in the report {}!'.format(args.right, args.report)) if\",\n \"self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 = img2.crop((self.start_x, self.start_y,\",\n \"than days in the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different\",\n \"root.bind(\\\"<Left>\\\", self.prev) root.bind(\\\"<Down>\\\", self.next) root.bind(\\\"<Up>\\\", self.prev) root.bind(\\\"<Button-3>\\\", self.click_right) root.bind(\\\"<Button-1>\\\", self.click_left) root.bind(\\\"<Button-2>\\\", self.click_wheel) root.bind(\\\"<Button-4>\\\",\",\n \"self.start_y = int(self.img_w_f / 2 - self.img_w_f / zoom / 2) + self.shift_y\",\n \"img2 = img2.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) self.imgs_orig_v[self.curr_img] = ImageTk.PhotoImage(img1) self.imgs_pred_v[self.curr_img] =\",\n \"= int(w / 100) self.canvas = Canvas(root, width=w, height=h) self.canvas.pack() self.canvas.configure(background='white') self.logo1 =\",\n \"/ 2)), int(self.img_w_f / 2 - self.img_w_f / self.zoom / 2)) self.refresh() def\",\n \"600), :] = 255 c_list[index, :, (imagelist_h - int(w / 600)):, :] =\",\n \"the report {}!'.format(args.left, args.report)) if len(imgs_p) != len(dates): raise RuntimeError('Different number of images\",\n \"MAE {}'.format(self.dates[self.curr_img], self.cc[self.curr_img] * 100, self.errors[self.curr_img])) if self.zoom == 100: self.canvas.itemconfig(self.zoom, text='') self.b_reset.config(state=DISABLED)\",\n \"== 360: self.zoom += 60 else: if self.zoom - 20 >= 100: self.zoom\",\n \"img2 = self.imgs_pred_m[self.curr_img] img1 = img1.crop((self.start_x, self.start_y, self.end_x, self.end_y)).resize((self.img_w, self.img_w), Image.ANTIALIAS) img2 =\",\n \"- (self.logo3.height() / 2 + b_logos)), image=self.logo3) self.canvas.create_text(w / 2, h * 0.06,\",\n \"120 or event.keysym == 'plus': self.zoom += 20 elif event.delta == 240: self.zoom\",\n \"0.70, h * 0.94, window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda e:\",\n \"raise RuntimeError('Different number of images in {} than days in the report {}!'.format(args.right,\",\n \"Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL import numpy as np from PIL\",\n \"window=self.b_quit) # bind buttons and keys root.bind(\\\"q\\\", lambda e: self.canvas.master.destroy()) root.bind(\\\"r\\\", self.reset_transform) root.bind(\\\"m\\\",\",\n \"if not self.over_button: self.prev() def click_right(self, event): if not self.over_button: self.next() def refresh(self):\"\n]","isConversational":false}}},{"rowIdx":4091,"cells":{"ngram":{"kind":"list like","value":["# 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', #","# because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ###","= True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/',","WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test import TestCase","# 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', #","to stub out tests for doExpressCheckoutPayment and friends # because they're behind paypal's","'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname':","'2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None',","St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2':","# 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', #","'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z',","# # def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field","'/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1',","doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request: #","test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city':","= { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl':","self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and","ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client import Client","def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and friends","= self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00',","\"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #","'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid':","they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment #","'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = {","'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data","'012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self):","'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST)","### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom':","# \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses = { # # @@@","# \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data): # return self.responses[\"DoDirectPayment\"]","'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode':","'1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate':","have to stub out tests for doExpressCheckoutPayment and friends # because they're behind","django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers","'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype':","= { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME':","from django.http import QueryDict from django.test import TestCase from django.test.client import Client from","\"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data): # return self.responses[\"DoDirectPayment\"] class","settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def","def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase):","self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80,","\"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests","} # # def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self):","paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to","'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890',","# 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', #","'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode':","requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt':","data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname':","'100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate':","= DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'}","# 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', #","== \"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',","True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl':","u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build':","# {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', #","# 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', #","request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING':","'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD',","CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding","'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt':","1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data))","some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self,","data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data): #","def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG","data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San","'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',","u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt':","'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999',","import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client):","from django.forms import ValidationError from django.http import QueryDict from django.test import TestCase from","'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>',","'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction':","'', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults)","tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj =","django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test","'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1',","'10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment',","{ 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC',","# 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', #","'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def","= { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state':","# 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', #","# @@@ Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } #","PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv':","import QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields import","Used to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies,","Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item","# 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} #","'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request)","'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG =","DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError,","# responses = { # # @@@ Need some reals data here. #","import ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client import","'10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street':","= settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom':","80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST =","Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): #","paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request:","'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return","settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom',","behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal","return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError,","'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star',","'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ)","'848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z',","'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\",","# 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success',","# def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field =","generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/',","PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG","self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street':","'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason':","# 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #","class RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ =","'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip':","CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request","'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data","'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # #","self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname':","class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455')","django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class","from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used","settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict","'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have","environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass","class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug =","TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP,","self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1","'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token':","self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real","'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory()","environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP):","= { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9',","DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses = { #","self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at","# 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', #","<gh_stars>1-10 #!/usr/bin/python # -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi","testing PayPalWPP.\"\"\" # responses = { # # @@@ Need some reals data","environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '',","'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST","data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment","= CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): #","PayPalWPP.\"\"\" # responses = { # # @@@ Need some reals data here.","self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl':","import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test import","django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http","'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests","PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self, **request):","'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out","'2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', # 'version': '54.0'}","PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid':","responses = { # # @@@ Need some reals data here. # \"DoDirectPayment\":","@@@ Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # #","u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4',","'0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None',","St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2':","# 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response:","_request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309')","# 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', #","'037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data =","'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',}","paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def","'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack':","RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class","QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField","field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self):","return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass #","test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = {","data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main","'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self):","'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus':","Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309',","data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100","-*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError","'10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for","coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms","'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' }","class for testing PayPalWPP.\"\"\" # responses = { # # @@@ Need some","nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt':","# 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', #","import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self,","'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US',","'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): #","because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment","from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from","\"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses = { # # @@@ Need","= self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self):","# PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', #","'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',}","Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname',","'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token':","# We'll have to stub out tests for doExpressCheckoutPayment and friends # because","'95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data))","self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data","to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO':","'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA',","'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG","'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid':","data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver',","pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses = { # #","} environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class","{'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname':","'0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout',","'012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self):","def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St',","Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34',","u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'}","PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ","'', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF","def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St',","self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', #","and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack ==","{ 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9', 'state':","'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa',","testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def","def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField()","import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import","u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct':","test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and friends #","DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1',","self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia","'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl':","tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data)","'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def","'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037',","'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug","RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses","self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv',","= RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for","'999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll","WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy","{ 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/'","out tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj","'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub","'375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed',","def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment,","# 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', #","'1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug","objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '',","'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA',","from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from","# # @@@ Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # }","'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V","CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class","{ # # @@@ Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", #","**request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME':","'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail',","'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', }","'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def","blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item =","'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa',","'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress':","# PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', #","'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: #","u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale',","RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing","We'll have to stub out tests for doExpressCheckoutPayment and friends # because they're","'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def","'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self):","friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\")","Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019',","'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp =","# 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', #","REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses =","at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95',","setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG =","RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ = {","class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" # responses = {","reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data):","for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item)","from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects.","# 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', #","'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic',","'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT':","'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype':","'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF =","django.http import QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields","= { # # @@@ Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\",","} self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data =","import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import","# # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077',","= {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave',","# } # # def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def","'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp':","\"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase):","from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError","'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item)","'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL':","u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal","# Used to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE':","def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD':","'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V',","'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype':","self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/',","'1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to","#!/usr/bin/python # -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import","data): # return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type,","'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip':","CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal.","# 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #","'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', # 'version':","Need some reals data here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def","'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress':","'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant',","'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\")","stub out tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors.","real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = {","'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', #","field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting","'9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp","# -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest","'4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname':","request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET',","'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt':","Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019',","'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype':","test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city':","PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv':","'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode':","def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data =","from django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from","u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',","{'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method':","'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next':","self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\") self.assertRaises(ValidationError, CreditCardField().clean,","# {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', #","# 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', #","'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct':","-*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from","{'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode':","here. # \"DoDirectPayment\": \"\"\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\"\"\", # } # # def _request(self, data): # return","'10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59',","'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime':","# 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00',","import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate","# return self.responses[\"DoDirectPayment\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \"Visa\")","'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00',","# Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True","settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next':","= RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\" #","django.forms import ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client","for testing PayPalWPP.\"\"\" # responses = { # # @@@ Need some reals","'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item)","{ 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver',","utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import","doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack","REQUEST = RF.get(\"/pay/\", REMOTE_ADDR=\"127.0.0.1:8000\") class DummyPayPalWPP(PayPalWPP): pass # \"\"\"Dummy class for testing PayPalWPP.\"\"\"","'street': '1 Main St', 'city': u'San Jos\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131',","'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', #"],"string":"[\n \"# 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', #\",\n \"# because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ###\",\n \"= True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/',\",\n \"WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test import TestCase\",\n \"# 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', #\",\n \"to stub out tests for doExpressCheckoutPayment and friends # because they're behind paypal's\",\n \"'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname':\",\n \"'2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None',\",\n \"St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2':\",\n \"# 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', #\",\n \"'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z',\",\n \"# # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field\",\n \"'/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1',\",\n \"doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: #\",\n \"test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city':\",\n \"= { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl':\",\n \"self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and\",\n \"ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client import Client\",\n \"def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and friends\",\n \"= self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00',\",\n \"\\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #\",\n \"'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid':\",\n \"they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment #\",\n \"'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = {\",\n \"'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data\",\n \"'012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self):\",\n \"'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST)\",\n \"### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom':\",\n \"# \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@\",\n \"# \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"]\",\n \"'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode':\",\n \"'1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate':\",\n \"have to stub out tests for doExpressCheckoutPayment and friends # because they're behind\",\n \"django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers\",\n \"'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype':\",\n \"= { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME':\",\n \"from django.http import QueryDict from django.test import TestCase from django.test.client import Client from\",\n \"\\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class\",\n \"settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def\",\n \"def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase):\",\n \"self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80,\",\n \"\\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests\",\n \"} # # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self):\",\n \"paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to\",\n \"'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890',\",\n \"# 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', #\",\n \"'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode':\",\n \"requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt':\",\n \"data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname':\",\n \"'100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate':\",\n \"= DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'}\",\n \"# 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', #\",\n \"== \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',\",\n \"True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl':\",\n \"u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build':\",\n \"# {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', #\",\n \"# 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', #\",\n \"request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING':\",\n \"'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD',\",\n \"CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding\",\n \"'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt':\",\n \"1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data))\",\n \"some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self,\",\n \"data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data): #\",\n \"def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG\",\n \"data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San\",\n \"'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',\",\n \"u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt':\",\n \"'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999',\",\n \"import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client):\",\n \"from django.forms import ValidationError from django.http import QueryDict from django.test import TestCase from\",\n \"'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>',\",\n \"'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction':\",\n \"'', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults)\",\n \"tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj =\",\n \"django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test\",\n \"'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1',\",\n \"'10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment',\",\n \"{ 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC',\",\n \"# 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', #\",\n \"'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def\",\n \"= { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state':\",\n \"# 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', #\",\n \"# @@@ Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } #\",\n \"PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv':\",\n \"import QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields import\",\n \"Used to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies,\",\n \"Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item\",\n \"# 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} #\",\n \"'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request)\",\n \"'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG =\",\n \"DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError,\",\n \"# responses = { # # @@@ Need some reals data here. #\",\n \"import ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client import\",\n \"'10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street':\",\n \"= settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom':\",\n \"80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST =\",\n \"Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): #\",\n \"paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request:\",\n \"'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return\",\n \"settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom',\",\n \"behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal\",\n \"return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError,\",\n \"'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star',\",\n \"'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ)\",\n \"'848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z',\",\n \"'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\",\",\n \"# 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success',\",\n \"# def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field =\",\n \"generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/',\",\n \"PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG\",\n \"self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street':\",\n \"'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason':\",\n \"# 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #\",\n \"class RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ =\",\n \"'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip':\",\n \"CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request\",\n \"'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data\",\n \"'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # #\",\n \"self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname':\",\n \"class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455')\",\n \"django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class\",\n \"from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used\",\n \"settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict\",\n \"'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have\",\n \"environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass\",\n \"class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug =\",\n \"TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP,\",\n \"self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1\",\n \"'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token':\",\n \"self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real\",\n \"'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory()\",\n \"environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP):\",\n \"= { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9',\",\n \"DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses = { #\",\n \"self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at\",\n \"# 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', #\",\n \"<gh_stars>1-10 #!/usr/bin/python # -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi\",\n \"testing PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@ Need some reals data\",\n \"environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '',\",\n \"'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST\",\n \"data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment\",\n \"= CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): #\",\n \"PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@ Need some reals data here.\",\n \"self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl':\",\n \"import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from django.test import\",\n \"django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http\",\n \"'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests\",\n \"PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self, **request):\",\n \"'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out\",\n \"'2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', # 'version': '54.0'}\",\n \"PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid':\",\n \"responses = { # # @@@ Need some reals data here. # \\\"DoDirectPayment\\\":\",\n \"@@@ Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # #\",\n \"u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4',\",\n \"'0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None',\",\n \"St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2':\",\n \"# 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response:\",\n \"_request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309')\",\n \"# 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', #\",\n \"'037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data =\",\n \"'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',}\",\n \"paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def\",\n \"'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack':\",\n \"RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class\",\n \"QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField\",\n \"field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self):\",\n \"return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass #\",\n \"test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = {\",\n \"data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main\",\n \"'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self):\",\n \"'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus':\",\n \"Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309',\",\n \"data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100\",\n \"-*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError\",\n \"'10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub out tests for\",\n \"coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms\",\n \"'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' }\",\n \"class for testing PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@ Need some\",\n \"nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt':\",\n \"# 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', #\",\n \"import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self,\",\n \"'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US',\",\n \"'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): #\",\n \"because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment\",\n \"from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from\",\n \"\\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@ Need\",\n \"= self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self):\",\n \"# PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', #\",\n \"'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',}\",\n \"Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname',\",\n \"'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token':\",\n \"# We'll have to stub out tests for doExpressCheckoutPayment and friends # because\",\n \"'95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data))\",\n \"self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data\",\n \"to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO':\",\n \"'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA',\",\n \"'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG\",\n \"'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid':\",\n \"data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver',\",\n \"pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses = { # #\",\n \"} environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class\",\n \"{'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname':\",\n \"'0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout',\",\n \"'012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self):\",\n \"def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia St',\",\n \"Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34',\",\n \"u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'}\",\n \"PayPalError class RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ\",\n \"'', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF\",\n \"def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St',\",\n \"self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\") ### DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', #\",\n \"and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack ==\",\n \"{ 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state':\",\n \"'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa',\",\n \"testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def\",\n \"def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField()\",\n \"import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import\",\n \"u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct':\",\n \"test_setExpressCheckout(self): # We'll have to stub out tests for doExpressCheckoutPayment and friends #\",\n \"DoExpressCheckoutPayment # PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1',\",\n \"self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail', 'street': '100 Georgia\",\n \"'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl':\",\n \"tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data)\",\n \"'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def\",\n \"'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037',\",\n \"'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug\",\n \"RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses\",\n \"self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv',\",\n \"= RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for\",\n \"'999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll\",\n \"WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy\",\n \"{ 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/'\",\n \"out tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj\",\n \"'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to stub\",\n \"'375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed',\",\n \"def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment,\",\n \"# 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', #\",\n \"'1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal. self.old_debug\",\n \"objects. def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '',\",\n \"'Epic', 'lastname': 'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA',\",\n \"from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import QueryDict from\",\n \"# # @@@ Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # }\",\n \"'Fail', 'street': '100 Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V\",\n \"CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class\",\n \"{ # # @@@ Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", #\",\n \"**request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME':\",\n \"'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic', 'lastname': 'Fail',\",\n \"'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', }\",\n \"'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def\",\n \"blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item =\",\n \"'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa',\",\n \"'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress':\",\n \"# PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', #\",\n \"'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: #\",\n \"u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale',\",\n \"RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing\",\n \"We'll have to stub out tests for doExpressCheckoutPayment and friends # because they're\",\n \"'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def\",\n \"'cancelurl': 'http://www.example.com/cancel/' } self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self):\",\n \"friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \\\"Success\\\")\",\n \"Georgia St', 'city': 'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019',\",\n \"'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp =\",\n \"# 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', #\",\n \"REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses =\",\n \"at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = { 'amt': '9.95',\",\n \"setUp(self): # Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG =\",\n \"RequestFactory(Client): # Used to generate request objects. def request(self, **request): environ = {\",\n \"class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" # responses = {\",\n \"reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data):\",\n \"for doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item)\",\n \"from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate request objects.\",\n \"# 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'payerid': u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', #\",\n \"'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname': 'Epic',\",\n \"'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT':\",\n \"'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype':\",\n \"'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL': 'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF =\",\n \"django.http import QueryDict from django.test import TestCase from django.test.client import Client from paypal.pro.fields\",\n \"= { # # @@@ Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\",\",\n \"} self.wpp = DummyPayPalWPP(REQUEST) def tearDown(self): settings.DEBUG = self.old_debug def test_doDirectPayment_missing_params(self): data =\",\n \"import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import ValidationError from django.http import\",\n \"# # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', # 'build': '848077',\",\n \"= {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data = { 'firstname': 'Brave',\",\n \"# } # # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def\",\n \"'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00', # 'timestamp':\",\n \"\\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase):\",\n \"from django.test.client import Client from paypal.pro.fields import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError\",\n \"'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item)\",\n \"'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver', 'SERVER_PORT': 80, 'SERVER_PROTOCOL':\",\n \"u'BN5JZ2V7MLEV4', # 'paymentaction': 'Sale', # 'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal\",\n \"# Used to generate request objects. def request(self, **request): environ = { 'HTTP_COOKIE':\",\n \"def request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD':\",\n \"'None', # 'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V',\",\n \"'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype':\",\n \"self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/',\",\n \"'1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertFalse(self.wpp.doDirectPayment(data)) def test_setExpressCheckout(self): # We'll have to\",\n \"#!/usr/bin/python # -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import\",\n \"data): # return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type,\",\n \"'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip':\",\n \"CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting real requests at PayPal.\",\n \"# 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', #\",\n \"'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype': 'expresscheckout', # 'version':\",\n \"Need some reals data here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def\",\n \"'countrycode': 'US', 'zip': '95131', 'expdate': '012019', 'cvv2': '037', 'acct': '4797503429879309', 'creditcardtype': 'visa', 'ipaddress':\",\n \"'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant',\",\n \"'HTTP/1.1', } environ.update(self.defaults) environ.update(request) return WSGIRequest(environ) RF = RequestFactory() REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\")\",\n \"stub out tests for doExpressCheckoutPayment and friends # because they're behind paypal's doors.\",\n \"real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True self.item = {\",\n \"'returnurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', #\",\n \"field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean, '1234567890123455') class PayPalWPPTest(TestCase): def setUp(self): # Avoding blasting\",\n \"'9.95', 'inv': 'inv', 'custom': 'custom', 'next': 'http://www.example.com/next/', 'returnurl': 'http://www.example.com/pay/', 'cancelurl': 'http://www.example.com/cancel/' } self.wpp\",\n \"# -*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest\",\n \"'4797503429879309', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item) self.assertTrue(self.wpp.doDirectPayment(data)) def test_doDirectPayment_invalid(self): data = { 'firstname':\",\n \"request(self, **request): environ = { 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET',\",\n \"'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt':\",\n \"Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131', 'expdate': '012019',\",\n \"'taxamt': '0.00', # 'timestamp': '2009-03-04T20:56:09Z', # 'token': '<PASSWORD>', # 'transactionid': '3TG42202A7335864V', # 'transactiontype':\",\n \"test_doDirectPayment_valid(self): data = { 'firstname': 'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city':\",\n \"PayPal Request: # {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv':\",\n \"'Brave', 'lastname': 'Star', 'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode':\",\n \"def test_doDirectPayment_missing_params(self): data = {'firstname': 'Chewbacca'} self.assertRaises(PayPalError, self.wpp.doDirectPayment, data) def test_doDirectPayment_valid(self): data =\",\n \"from django.test import TestCase from django.test.client import Client from paypal.pro.fields import CreditCardField from\",\n \"u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname',\",\n \"{'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method':\",\n \"'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', # 'method': 'DoExpressCheckoutPayment', # 'next':\",\n \"self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\") self.assertRaises(ValidationError, CreditCardField().clean,\",\n \"# {'amt': '10.00', # 'cancelurl': u'http://xxx.xxx.xxx.xxx/deploy/480/upgrade/?upgrade=cname', # 'custom': u'website_id=480&cname=1', # 'inv': u'website-480-cname', #\",\n \"# 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', #\",\n \"'Vancouver', 'state': 'BC', 'countrycode': 'CA', 'zip': 'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct':\",\n \"-*- coding: utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from\",\n \"{'ack': 'Success', # 'amt': '10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode':\",\n \"here. # \\\"DoDirectPayment\\\": \\\"\\\"\\\"ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&l_longmessage0=Security+header+is+not+valid&version=54.0&build=854529&l_errorcode0=&correlationid=\\\"\\\"\\\", # } # # def _request(self, data): # return\",\n \"'10.00', # 'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59',\",\n \"'build': '848077', # 'correlationid': '375f4773c3d34', # 'currencycode': 'USD', # 'feeamt': '0.59', # 'ordertime':\",\n \"# 'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00',\",\n \"import CreditCardField from paypal.pro.helpers import PayPalWPP, PayPalError class RequestFactory(Client): # Used to generate\",\n \"# return self.responses[\\\"DoDirectPayment\\\"] class CreditCardFieldTest(TestCase): def testCreditCardField(self): field = CreditCardField() field.clean('4797503429879309') self.assertEquals(field.card_type, \\\"Visa\\\")\",\n \"'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', # 'reasoncode': 'None', # 'taxamt': '0.00',\",\n \"# Avoding blasting real requests at PayPal. self.old_debug = settings.DEBUG settings.DEBUG = True\",\n \"settings.DEBUG = True self.item = { 'amt': '9.95', 'inv': 'inv', 'custom': 'custom', 'next':\",\n \"= RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\" #\",\n \"django.forms import ValidationError from django.http import QueryDict from django.test import TestCase from django.test.client\",\n \"for testing PayPalWPP.\\\"\\\"\\\" # responses = { # # @@@ Need some reals\",\n \"'V6V 1V1', 'expdate': '012019', 'cvv2': '999', 'acct': '1234567890', 'creditcardtype': 'visa', 'ipaddress': '10.0.1.199',} data.update(self.item)\",\n \"{ 'HTTP_COOKIE': self.cookies, 'PATH_INFO': '/', 'QUERY_STRING': '', 'REQUEST_METHOD': 'GET', 'SCRIPT_NAME': '', 'SERVER_NAME': 'testserver',\",\n \"utf-8 -*- from django.conf import settings from django.core.handlers.wsgi import WSGIRequest from django.forms import\",\n \"doExpressCheckoutPayment and friends # because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack\",\n \"REQUEST = RF.get(\\\"/pay/\\\", REMOTE_ADDR=\\\"127.0.0.1:8000\\\") class DummyPayPalWPP(PayPalWPP): pass # \\\"\\\"\\\"Dummy class for testing PayPalWPP.\\\"\\\"\\\"\",\n \"'street': '1 Main St', 'city': u'San Jos\\\\xe9', 'state': 'CA', 'countrycode': 'US', 'zip': '95131',\",\n \"'token': u'<PASSWORD>'} # # PayPal Response: # {'ack': 'Success', # 'amt': '10.00', #\"\n]","isConversational":false}}},{"rowIdx":4092,"cells":{"ngram":{"kind":"list like","value":["elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m","Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i","me_j=me_j+1 else: break m = int(input()) grid = [] for i in range(0,","19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n):","on Mon Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2","j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN')","range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j","me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input())","if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP')","print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else:","7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in","@author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p'","for i in range(n): if 'p' in grid[i]: pe_i=i for j in range(n):","range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0):","pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)):","pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else:","| (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0):","-*- coding: utf-8 -*- \"\"\" Created on Mon Dec 7 19:46:40 2020 @author:","break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT')","me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break","else: break m = int(input()) grid = [] for i in range(0, m):","coding: utf-8 -*- \"\"\" Created on Mon Dec 7 19:46:40 2020 @author: Intel","utf-8 -*- \"\"\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \"\"\"","in range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]:","grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) |","displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for","break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0):","while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1","if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1","print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = []","in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1","if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break","else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid","print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m =","'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break","(me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT')","Mon Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for","me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for j","me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for j in","# -*- coding: utf-8 -*- \"\"\" Created on Mon Dec 7 19:46:40 2020","-*- \"\"\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \"\"\" def","\"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]:","i in range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if","Created on Mon Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2","def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i","Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in","'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1","if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid =","break m = int(input()) grid = [] for i in range(0, m): grid.append(input().strip())","for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0):","print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for i in","me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for","m = int(input()) grid = [] for i in range(0, m): grid.append(input().strip()) displayPathtoPrincess(m,grid)","in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i)","elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for i","2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if","\"\"\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid):"],"string":"[\n \"elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m\",\n \"Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i\",\n \"me_j=me_j+1 else: break m = int(input()) grid = [] for i in range(0,\",\n \"19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n):\",\n \"on Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2\",\n \"j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN')\",\n \"range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j\",\n \"me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input())\",\n \"if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP')\",\n \"print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else:\",\n \"7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in\",\n \"@author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p'\",\n \"for i in range(n): if 'p' in grid[i]: pe_i=i for j in range(n):\",\n \"range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0):\",\n \"pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)):\",\n \"pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else:\",\n \"| (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0):\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020 @author:\",\n \"break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT')\",\n \"me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break\",\n \"else: break m = int(input()) grid = [] for i in range(0, m):\",\n \"coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020 @author: Intel\",\n \"utf-8 -*- \\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\"\",\n \"in range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]:\",\n \"grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) |\",\n \"displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for\",\n \"break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0):\",\n \"while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1\",\n \"if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1\",\n \"print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = []\",\n \"in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1\",\n \"if 'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break\",\n \"else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid\",\n \"print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m =\",\n \"'p' in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break\",\n \"(me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT')\",\n \"Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for\",\n \"me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for j\",\n \"me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i for j in\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020\",\n \"-*- \\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def\",\n \"\\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]:\",\n \"i in range(n): if 'p' in grid[i]: pe_i=i for j in range(n): if\",\n \"Created on Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2\",\n \"def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in grid[i]: pe_i=i\",\n \"Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if 'p' in\",\n \"'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0): print('DOWN') me_i=me_i+1 elif(me_i-pe_i>0): print('UP') me_i=me_i-1\",\n \"if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid =\",\n \"break m = int(input()) grid = [] for i in range(0, m): grid.append(input().strip())\",\n \"for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i) | (me_j!=pe_j)): if(me_i-pe_i<0):\",\n \"print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for i in\",\n \"me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for\",\n \"m = int(input()) grid = [] for i in range(0, m): grid.append(input().strip()) displayPathtoPrincess(m,grid)\",\n \"in grid[i]: pe_i=i for j in range(n): if 'p'==grid[i][j]: pe_j=j break break while((me_i!=pe_i)\",\n \"elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m = int(input()) grid = [] for i\",\n \"2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i in range(n): if\",\n \"\\\"\\\"\\\" Created on Mon Dec 7 19:46:40 2020 @author: Intel \\\"\\\"\\\" def displayPathtoPrincess(n,grid):\"\n]","isConversational":false}}},{"rowIdx":4093,"cells":{"ngram":{"kind":"list like","value":["import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None,","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype:","ids not to be plotted, defaults to None, :type blacklist: set, optional :param","optional :param loc_coordinates: Either of \"up\" | \"down\". If \"up\", plot ticks to","whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = []",":param ax: Axis on which to plot contact map, defaults to None. :type","gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names: left_border =","Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to be plotted.","plotting, defaults to \"normal\". :type g2_id: str, optional :param plot_gene_names: If True, the","Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value =","{} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color =","''' ax = ax if ax is not None else plt.gca() patches_dict =","None): color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [","break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping","Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type","Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)]","range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0]","is underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple of","ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color =","+ 1.5. max_y_pos defines the \\ number of stacked genes. 2. patch_list is","extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of","ax = ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap)",".3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list = None tick_positions =","else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand","not j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) >","= max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5),","1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from","int :param r_end: End position of region to be plotted. :type r_end: int",":type TX_pos: int :param direction: Direction of the genomic part that is translocated.",":type color_minus: str, optional :param ax: Axis on which to plot contact map,",":type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of stacked","[lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]]","position of the region to be plotted. :type start: int :param end: End",":type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome:","end, bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap :param","else 0. for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries ==","plotted. :type chrom_r: str :param start_r: Start position of the region to be","ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start,","size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\",","motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start","defaults to \"#80b1d3\". :type color_g2: str, optional :param g1_id: ID of g1 used","keys = names of segments, and values patch :rtype: dict ''' ax =","to \"g\". :type color_gain: str, optional :param color_loss: Plot color of copy number","optional :param alpha: Alpha value of the rectangle, representing the region to be","ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)","bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap :param methylation_bed:","0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance =","minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract","ax = None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation","def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that plots methylation values as","== \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect)","], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls],","mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2","] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i]","Start position 3. end position 4. Beta Value :type meth_calles: iterator :param chrom:","\"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list = None tick_positions","y_pos_dict: Dictionary with keys = gene ids and values = y position \\","= [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names:","values of all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame`","this ratio is underwent, the genes will be stacked, default is 0.1. :type","region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression","int :param color: Color of the rectangles representing the regions to be plotted,","= open(input_filename, \"r\") cnv_bed_list = [] ploidy = None for line in input_file:","distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons for i","i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if False make",":param rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation: int,","not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward = True elif(strand ==","or minus stranded genes is plotted, False otherwise. Default is False. :type plot_legend:","bin_size: int, optional :param ax: Axis to be used for plotting, defaults to","ax is not None else plt.gca() max_signal = 0 left = [] height","with the following entries: 1. Chromsome 2. Start position 3. end position 4.","ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for tick","np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches","be plotted. :type chrom: str :param start: Start position of region to be","plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function","None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2):","extension): extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\":",":type end_r: int :param color: Color of the arc, defaults to \"k\". :type","= (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in","ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\",","= height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end)","binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i","-.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color","gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1):","blacklist): continue # Define color for gene plotting strand = str(i[5]) color =","of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes.","markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [","None :type vmin: float, optional :param vmax: Maximal value of intensity range to","motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of",":type offset: int, optional :param merge: Number of elements to be merged. If","iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param","[int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes =","\"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None,","index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not","ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper:","color_gain: str, optional :param color_loss: Plot color of copy number losses, defaults to",":param chrom: Chromosome of region to be plotted. :type chrom: str :param start:","to \"#80b1d3\". :type color_plus: str, optional :param color_minus: Color of plus stranded TF","int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth","\"#80b1d3\". :type color_plus: str, optional :param color_minus: Color of plus stranded TF regions,","'''Function that plots links between genomic regions as arcs. :param links_bed: Iterator, that","in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1]","linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks","start + offset left += [start] height += [value] left_merged = [] height_merged","\"b\". :type color: str, optional :param offset: Length of intervals, defaults to None.","determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of","gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name =","ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax","the following entries: 1. Chromosome 2. Start position 3. End position :type regions:","chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None): '''Function that plots links between","gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to","of the rectangle, representing the region to be plotted, defaults to 1. :type","as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the","str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y =","considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap:","color_neutral: Plot color of copy number neutral regions, defaults to \"k\". :type color_neutral:","plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation values",":param plot_gene_ids: If True, all gene ids will be included in the plot,","= True elif(strand == \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse","are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True,","= 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for","= {} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name",":type upper: bool, optional :param loc_coordinates: Either of \"up\" | \"down\". If \"up\",","'''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs","optional :param plot_points: If True, a point per expression value is plotted in","= Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color","binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap cmap =","used for legend plotting, defaults to \"tumor\". :type g1_id: str, optional :param g2_id:","and values patch :rtype: dict ''' ax = ax if ax is not","Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = []","str :param start: Start position of the region to be plotted. :type start:","intervals, defaults to None. :type offset: int, optional :param merge: Number of elements",":param end: End position of region to be plotted. :type end: int :param","genome that is translocated. :param chrom_r: Chromosome of the region to be plotted.","of the region to be plotted. :type end_r: int :param TX_pos: Position of","position of the region to be plotted. :type start: str :param end: End","object containing regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool`","< cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0)","ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map","% merge == 0 and not (i == 0)): left_merged += [lefts[0]] lefts","methylation values, defaults to \"k\". :type color: str, optional :param ax: Axis of","None. :type colors: str, optional :param ids: IDs used for legend plotting, defaults","gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None","max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start,","if ax is not None else plt.gca() patches_dict = {} for segment in","start: Start position on chromosome. :type start: int :param end: End position on","max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\ number of stacked","int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks","facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0)","Axis on which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","a legend describing plus or minus stranded genes is plotted, False otherwise. Default","bin position = int(element[1]) if(position < start or position > end): continue n_meth","coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing to be","head_length if(strand == \"-\"): arrow_start = motif_end arrow_end = motif_start color = color_minus","current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension)","color field is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename:","interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for","boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops =","head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as arrows,","plot_points: If True, a point per expression value is plotted in addition to","offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start,","extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"}","False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max y","end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not","optional :param y_max: Max y value in the gene plot. If not set,","color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in ticks","gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions","regions, defaults to \"#fb8072\". :type color_minus: str, optional :param ax: Axis on which","if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03),","extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops","plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a","{} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name =","that plots genomic regions as simple rectangles. :param regions: Iterator containig list-like elements","tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev)","codes) patch = PathPatch(path, facecolor = \"None\", edgecolor = color, lw = lw)","optional :param edge_color: Color of region edge. If False, no edge is plotted,","patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function","value is plotted in addition to the boxplot, no points are plotted otherwise,","= None if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None","gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True,","y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max","genomic part that is translocated. Either of \"left\" (upstream), or \"right\" (downstream), defaults","else plt.gca() c = 0 for region in regions: if(not edgecolor): current_color =","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is","= left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1","linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3,","End position :type regions: iterator :param start: Start position of the region to","genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down","ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool object :param input_filename: Full path","Plot color of copy number gains, defaults to \"g\". :type color_gain: str, optional","color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None):","plots bedGraph like iterators. :param chip_signals: Iterator for which each element is a",":param legend_loc: Location of the legend. Either of \"lower left\", \"lower right\", \"upper","[] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left = left_merged","plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False)","r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param","\" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for","codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point,","TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color,","base pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes, as","= None if(not len(genes_in_region) == 0): # Determine y positions of genes for","region. :type start: int :param end: End position of the genomic region. :type","optional :param colors: List of colors used for plotting samples expression. The number","+ end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in","of region to be plotted. :type r_end: int :param ax: Axis of plot","= None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for","legend plotting, defaults to None. Number of ids must be the same as","end_r: End position of the region to be plotted. :type end_r: int :param",":type color_loss: str, optional :param color_neutral: Plot color of copy number neutral regions,","region to be plotted. :type end_r: int :param TX_pos: Position of the translocation.","max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand ==","plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene","the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position","in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5,","(equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch)","returned :rtype: None ''' ax = ax if ax is not None else","two genes, as ratio of ax width, such that two genes are plotted","extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a","region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene plot is","if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos =","list ''' ax = ax if ax is not None else plt.gca() #","= [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for i in binned_meth_calls","samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary","a translocation event as a bar, showing the part of the genome that","in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance >","more ticks. :type upper: bool, optional :param loc_coordinates: Either of \"up\" | \"down\".","is underwent, the genes will be stacked, default is 0.1. :type distance_ratio: float,","containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing","start or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin","Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0],","right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None):","= Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([],","optional. :param ax: Axis on which to plot, defaults to None. :type ax:","g_id = ids[g] else: g_id = \"group \"+str(g) if(not g_id in patch_description_list): patch_list","translocation. :type TX_pos: int :param direction: Direction of the genomic part that is","gene ids not to be plotted, default to None. :type blacklist: set, optional","== 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension","= ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for","ids :param gene_name_mapping_file: Path to a tab separated file, for which the first","i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and","color: Color of the arc, defaults to \"k\". :type color: str, optional. :param","not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) #",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults","+= [patch] patch_description_list += [\"reverse strand\"] met_reverse = True # Plot Gene Names","axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\",","linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),","list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE","end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax = None,","continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1","g_id = \"group \"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list +=","standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values)))","m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect","0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], [])",":param plot_points: If True, a point per expression value is plotted in addition","\"-\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length","Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of","plus stranded TF regions, defaults to \"#80b1d3\". :type color_plus: str, optional :param color_minus:","of the region to be plotted. :type end: int :param color: Color of","if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for","+= [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean)))","region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in","Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position","region edge. If False, no edge is plotted, defaults to False. :type edge_color:","equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points +=","used for plotting samples expression. The number of colors must be the same","color: str, optional :param offset: Length of intervals, defaults to None. :type offset:","average_list = [ j for j in [meth_before, meth_after] if not j ==",":type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed:","exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed:","0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+","False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of","= gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start,","i == len(binned_average_meth)-1 else \"NA\") average_list = [ j for j in [meth_before,",":type ids: list, optional. :param plot_gene_names: True if gene names shall be plotted,","optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else","if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2,","def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\",","merge == 0 and not (i == 0)): left_merged += [lefts[0]] lefts =","chrom: str :param start: Start position of region to be plotted. :type start:","<= end): ticks += [current_tick] current_tick = current_tick + tick_size scale = None","containing the expression values of all samples (columns: sample ids; index: gene ids).","<= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size","g1 used for legend plotting, defaults to \"tumor\". :type g1_id: str, optional :param","= gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name","to be plotted. :type chrom_r: str :param start_r: Chromosomal start position of the","2. Start position 3. end position 4. Number methylated cytosines 5. Number unmethylated","= list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values =","{} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start =","object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list","= contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor","for which the first column is a ensemble gene id, and the second","g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal)","direction: Direction of distance equalization (top_down | bottom_up), defaults to \"top_down\". :type direction:","as a bar, showing the part of the genome that is translocated. :param","linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],","color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions of","= color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt',","region to be plotted. :type r_end: int :param ax: Axis of plot :type","Dictionary with keys = names of segments, and values patch :rtype: dict '''","of the legend. Either of \"lower left\", \"lower right\", \"upper left\", \"upper right\",","float :return: Tuple of 1. max_y_pos: Defines the number of stacked genes. 2.","\"#cbebc4\". :type color: str, optional :param edge_color: Color of region edge. If False,","plotted. :type end: int :param bin_size: size of bin to average methylation values,","6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to","defaults to False. :type plot_points: bool, optional :param alpha: Alpha value for the","defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy","with the following elements: 1. Chromosome region1 2. Start region1 3. End region1","color of the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return:","int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1])","strand\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region:","color: str, optional :param edge_color: Color of region edge. If False, no edge","ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param","(e.g. tumor and normal) on a gene region scale retaining the position of","two genes are plotted side by side. If this ratio is underwent, the","plot_gene_ids: If True, all gene ids will be included in the plot, False","None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension =","meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\") average_list = [","0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i",".06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list += [patch]","lw=1, color=\"k\", ax = None): '''Function that plots links between genomic regions as","of bars, defaults to \"b\". :type color: str, optional :param offset: Length of","plotting CNVs, defaults to \"bwr\". :type cmap: str, optional :param max_dev: Maximal deviation","distances, defaults to \"k\". :type color: str, optional :param direction: Direction of distance","Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold:","= [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts +=","and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward = True elif(strand","(columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing","one. Defaults to 0. :type overhang: float, optional :param color_plus: Color of plus","location == \"bottom\" the pyramid points downwards, defaults to top, :type location: str,","None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path to","color = color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch","otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point per expression","the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis.","> y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]]","== \"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values","color for gene plotting strand = str(i[5]) color = color_forward if(strand == \"-\"):","import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import","def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots","gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue #","the expression values of g2 samples (columns: sample ids; index: gene ids) :type","default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two",":param ax: Axis where the plot is drawn, defaults to None. :type ax:","= float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn >","exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1.","None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \"left\"): TX_start","edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha)","in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos","int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int,","capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename):","[(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO,",":param color_plus: Color of plus stranded TF regions, defaults to \"#80b1d3\". :type color_plus:","optional :param color_gain: Plot color of copy number gains, defaults to \"g\". :type",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or minus stranded","== \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point,","= int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points)","revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults to 0.","cmap: Name of the colormap to be used for plotting HiC intensities, defaults","i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2])",":type max_dev: float, optional :param ax: Axis used for plotting, defaults to None.","optional :param vmin: Minimal value of intensity range to be plotted, defaults to",":class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1:","genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points:","in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end])","gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color)","= gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist):","cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap",":param y_max: Max y value in the gene plot. If not set, then","plot_gene_ids: bool, optional :param y_max: Max y value in the gene plot. If","plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing","the region to be plotted. :type start: str :param end: End position of","plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location","= \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close()","overhang means triangular shape). Can be negative or greater than one. Defaults to","if(not colors is None): color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points):","plot, defaults to None. :type max_dev: float, optional :param ax: Axis used for","def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\") files and converts them","be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance between","str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse border_distance_down = region_border_down-start","on a gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool`","= True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True","if gene names shall be plotted, False otherwise, defaults to True. :type plot_gene_names:","ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev","and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing","> cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)","cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4])","float, optional :param color_gain: Plot color of copy number gains, defaults to \"g\".","blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border =","linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse =","object containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End","[np.mean(heights)] heights = [] offset = merge*offset left = left_merged height = height_merged","arcs from unequal distances of genomic segments to equal distances. :param genomic_segments: List","region to be plotted. :type chrom_r: str :param start_r: Start position of the","for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1)","Direction of distance equalization (top_down | bottom_up), defaults to \"top_down\". :type direction: str,","segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start:","height += [value] left_merged = [] height_merged = [] if(not merge is None):",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax = ax","def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix segments","smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev","(each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param","the max y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool`","as the number of groups. :type ids: list, optional. :param plot_gene_names: True if",":param color: Color of lines equalizing distances, defaults to \"k\". :type color: str,","Minimal deviation from ploidy to be considered as a CNV, defaults to 0.7.","elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev),","== 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start,","of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region","and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break","position of region to be plotted. :type end: int :param segment_size: Size of",":param color_minus: Color of plus stranded TF regions, defaults to \"#fb8072\". :type color_minus:","return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False,","if(not len(genes_in_region) == 0): # Determine y positions of genes for plotting max_y_pos,","patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names","= int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] +=","else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint","plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either","and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene","Path to tabixed bed file containing (chrom, start, end, name, score, strand, start,","code for plus stranded genes, default is \"#80b1d3\". :type color_plus: str, optional. :param","len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension ==","ticks = [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick","else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin","plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in","''' ax = ax if ax is not None else plt.gca() max_signal =","the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy","max_y_pos = y_max # Plot Exons for i in exons_in_region: start = int(i[1])","drawn on the ax. :rtype: list \"\"\" ax = ax if ax is","direction, else if \"down\", plot ticks to lower direction, defaults to \"up\". :type","equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points","vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact maps as pyramid plots","mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)]","merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which each","marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r,","chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom: str","Value to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of","not i == len(binned_average_meth)-1 else \"NA\") average_list = [ j for j in",".2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point,","[left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"):","End position 4. Value to be plotted as bar :type chip_signals: iterator :param","ax if ax is not None else plt.gca() max_dist = 0 for e","vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ]","head_length: Length of the arrow in bp (depends on the region that is","in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5,","the pyramid points downwards, defaults to top, :type location: str, optional :param ax:","= int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos =","gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter += 1 continue","Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax","= len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [","Location of the legend. Either of \"lower left\", \"lower right\", \"upper left\", \"upper","False. :type plot_points: bool, optional :param alpha: Alpha value for the background color","for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]),","the genomic region. :type end: int :param color: Color of lines equalizing distances,","= norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0),","in blacklist): counter += 1 continue n_groups = len(groups) for g in range(n_groups):","'''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields","= ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not","else if False make more ticks. :type upper: bool, optional :param loc_coordinates: Either","on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults to","as proportion of the arrow, defaults to 0.2 :type head_width: float, optional :param","vertices = [] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices","value for the background color of the boxplots boxes, defaults to 0.5. :type","of ids must be the same as the number of groups. :type ids:","of region to be plotted. :type end: int :param color: Color of points","if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor,","'''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path to a","+= 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return","None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color =","plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([],",":param g2_id: ID of g2 used for legend plotting, defaults to \"normal\". :type","str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5","[5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start),","# Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0]","str, optional :param alpha: Alpha value of the rectangle, representing the region to","region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should not","None): max_y_pos = y_max # Plot Exons for i in exons_in_region: start =","math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False,","= 0 left = [] height = [] for signal in chip_signals: start","ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax =","is not None else plt.gca() binned_meth_calls = [ [0, 0] for i in","color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as arrows, indicating their directionality.","expression (e.g. tumor and normal) on a gene region scale equalizing the position","e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]]","If False, no edge is plotted, defaults to False. :type edge_color: str, optional","binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for i in","is \"lower right\". :type legend_loc: str, optional :param color_plus: Color code for plus","offset: Length of intervals, defaults to None. :type offset: int, optional :param merge:","plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for","region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not","of region to be plotted. :type end: int :param bin_size: size of bin","motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \"-\"): arrow_start =",":return: Nothing to be returned :rtype: None ''' # Use given axis for","= None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls.","of coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing to","idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in",":return: Nothing to be returned. :rtype: None ''' ax = ax if ax","segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2.","plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\",","defaults to 0. :type rotation: int, optional :return: Nothing to be returned. :rtype:","region: list :param groups: List of lists containing the IDs of the different","of the region to be plotted. :type start: int :param end: End position","ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean])))","boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list =","defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting","of g2 used for legend plotting, defaults to \"normal\". :type g2_id: str, optional",":param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed:","elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"],","bool, optional :param plot_points: If True, a point per expression value is plotted","color=\"k\", ax = None): '''Function that plots arcs from unequal distances of genomic","position of region to be plotted. :type end: int :param color: Color of","= int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn =","left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' #","is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two genes,","blacklist: set, optional :param ax: Axis used for plotting, defaults to None. :type","patch :rtype: dict ''' ax = ax if ax is not None else","introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"):","expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting","not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for","in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border)","1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor,","Either of \"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional :param","''' ax = ax if ax is not None else plt.gca() max_dist =","i == 0 else \"NA\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1","stacked genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance","the number of groups. :type ids: list, optional. :param plot_gene_names: True if gene","contains bed-like structured lists with the following elements: 1. Chromosome region1 2. Start","if ax is not None else plt.gca() TX_start = TX_pos TX_end = end_r","is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap","r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals:","elements to be merged. If this value is not equal to 0, than","0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy number gains,","ids[g] else: g_id = \"group \"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]]","\"#fb8072\". :type color_g1: str, optional :param color_g2: Color used for plotting g2 samples","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype:","plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must be",":type segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted. :type","fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed:","\"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax is","y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and","start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None",":param genomic_segments: List of segments for which distances shall be equalized (each segment","y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3]","patch_list = None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0",":param vmin: Minimal value of intensity range to be plotted, defaults to None","offset: int, optional :param merge: Number of elements to be merged. If this","color = None if(not colors is None): color = colors[g] else: color =","color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], [])","patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in","= first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size","is the list of patches drawn on the ax. 3. patch_description_list is the","[] ploidy = None for line in input_file: if(line[:7] == \"#ploidy\"): ploidy =",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax","meth_after] if not j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if","TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start,","\"up\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to","an plotted, defaults to 0. :type merge: int, optional :return: Nothing to be","not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end =","dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for line in gene_name_mapping_file:","object containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param","color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates","blacklist: Set containing gene ids not to be plotted, default to None. :type","str :return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file =","rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect","first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks","'''Function that determines the max y position for gene plotting via function plotGenes.","of the genomic part that is translocated. Either of \"left\" (upstream), or \"right\"","c = 0 for region in regions: if(not edgecolor): current_color = color rect","arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF","genes is plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param","str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1,","ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional :param",".4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions,","0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end =","or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom =","{\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\":","optional :param color_plus: Color of plus stranded TF regions, defaults to \"#80b1d3\". :type","r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like iterators.","the ax. :rtype: list \"\"\" ax = ax if ax is not None","Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size)","ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None): '''Function that","True :type plot_gene_ids: bool, optional :param y_max: Max y value in the gene",":param overhang: Fraction that the arrow is swept back (0 overhang means triangular","position on chromosome. :type start: int :param end: End position on chromosome. :type","= (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\") average_list = [ j","with the following entries: 1. Chromosome 2. Start position 3. End position :type","= pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get gene names and regions","region to be plotted. :type end_r: int :param color: Color of the arc,","distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e.","expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted, defaults","plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r,","list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not ids is None):","plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0,","ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups,","in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e","segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points =",":type g1_id: str, optional :param g2_id: ID of g2 used for legend plotting,","make less ticks, else if False make more ticks. :type upper: bool, optional","plotted. :type end: int :param color: Color of the rectangles representing the regions","[0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for","\"k.\", markersize=3) g_id = None if(not ids is None): g_id = ids[g] else:","int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], [])","which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of","+ extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values","None): '''Function that plots links between genomic regions as arcs. :param links_bed: Iterator,","patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that","containing: 1. Chromosome 2. Start postion 3. End position 4. Value to be","gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type","introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward","genes. :type gene_mid_points: list :param region: List containing the region to be plotted","cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed:","gene_names_map: dict. :param blacklist: Set containing gene ids not to be plotted, default","bedGraph like iterators. :param chip_signals: Iterator for which each element is a list-ike","None else plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1])","split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed,","is not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end","genomic region. :type start: int :param end: End position of the genomic region.","def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for","[2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),","and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing","from ploidy to plot, defaults to None. :type max_dev: float, optional :param ax:","chrom_r: Chromosome of the region to be plotted. :type chrom_r: str :param start_r:","left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset","\"top\" | \"bottom\". If location == \"top\", the pyramid points upwards, else if","y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j","True, all gene ids will be included in the plot, False otherwise, default","end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact maps",":param vmax: Maximal value of intensity range to be plotted, defaults to None.","ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i],","directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be","0. :type rotation: int, optional :return: Nothing to be returned. :rtype: None '''","for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: #","Size of the segments for which contacts were called. :type segment_size: int :param","plotted. :type end: int :param head_width: Width of the arrow head as proportion",".3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean = []","optional :param plot_gene_ids: If True, all gene ids will be included in the","[ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e","values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids","'''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which each element","bp (depends on the region that is plotted), defaults to 1000. :type head_length:","None ''' ax = ax if ax is not None else plt.gca() contact_map_index1",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \"b\". :type color: str,",":param plot_gene_names: True if gene names shall be plotted, False otherwise, defaults to","ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller","in [meth_before, meth_after] if not j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/","plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get gene names","Size of region to be plotted in base pairs. :type region_size: int :param","form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of the","is not equal to 0, than merge elements will be averaged an plotted,","(i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id =","None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as","of \"lower left\", \"lower right\", \"upper left\", \"upper right\", default is \"lower right\".","or \"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional :param log_transformed: If True","\"Greys\". :type cmap: str, optional :param vmin: Minimal value of intensity range to","not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)]","Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax","1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\",","genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed file containing","= int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev","int :param color: Color of the arc, defaults to \"k\". :type color: str,",":class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param","If True, the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs","== \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of","list of descriptions for the patches \\ drawn on the ax. :rtype: list","Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is","int :param distance_ratio: Minimal distance between two genes, as ratio of ax width,","= current_tick + tick_size scale = None if(first_tick > 1000000): scale = \"Mb\"","abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2","g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border,","list-like elements with the following entries: 1. Chromosome 2. Start position 3. End","+= [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) #","rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end))",":class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object","str, optional :param ax: Axis on which to plot contact map, defaults to","start: int :param end: End position on chromosome. :type end: int :param ploidy:","list :param region: List containing the region to be plotted ([<chrom>, <start>, <end>]).","plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax","location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint","= link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes","<start>, <end>]). :type region: list :param groups: List of lists containing the IDs","verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5,","plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\",","ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered as","import pandas as pnd import numpy as np import tabix import matplotlib.ticker as","= Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds","ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom,","r_start: int :param r_end: End position of region to be plotted. :type r_end:","color_g2: str, optional :param g1_id: ID of g1 used for legend plotting, defaults","values containing center positions of genes. :type gene_mid_points: list :param region: List containing","representing methylation values, defaults to \"k\". :type color: str, optional :param ax: Axis","0 else \"NA\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\")","gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list,","max_signal): max_signal = value if(not offset is None): end = start + offset","color: Color of the rectangles representing the regions to be plotted, defaults to","the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","be plotted. :type start: int :param end: Chromosomal end position of region to","= int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type =",">= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True,","True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list","binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth","optional :return: Nothing to be returned :rtype: None ''' ax = ax if","== 0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict =","to be plotted, defaults to None, :type blacklist: set, optional :param ax: (default:","gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center","ax = ax if ax is not None else plt.gca() n_entries = len(meth_calls[0])","Plot color of copy number neutral regions, defaults to \"k\". :type color_neutral: str,","showing the part of the genome that is translocated. :param chrom_r: Chromosome of","''' ax = ax if ax is not None else plt.gca() # Calculate","[meth_before, meth_after] if not j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list)))","color=color_plus dx = head_length if(strand == \"-\"): arrow_start = motif_end arrow_end = motif_start","[]) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000,","average methylation values, defaults to 1000. :type bin_size: int, optional :param ax: Axis","which the first column is a ensemble gene id, and the second column","bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not","1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops,","regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists with the","than merge elements will be averaged an plotted, defaults to 0. :type merge:","1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4):","''' ax = ax if ax is not None else plt.gca() for motif","colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name,","end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation event as a","Defaults to 0. :type overhang: float, optional :param color_plus: Color of plus stranded","if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None):","+= 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border","right\". :type legend_loc: str, optional :param color_plus: Color code for plus stranded genes,","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \"b\". :type color:","edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\",","1000. :type bin_size: int, optional :param ax: Axis to be used for plotting,","digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000","to be plotted. :type start_r: int :param end_r: Chromosomal end positiont of the","None ''' ax = ax if ax is not None else plt.gca() max_signal","in a linea fashion. :param chrom: Chromosome of the region to be plotted.","default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on which","otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used for plotting","postion 3. End position 4. Value to be plotted as bar :type chip_signals:","and gene_name in blacklist): counter += 1 continue n_groups = len(groups) for g",":param end_r: Chromosomal end positiont of the region to be plotted. :type end_r:","to average methylation values, defaults to 1000. :type bin_size: int, optional :param ax:","[(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch","0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin #","str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown,","plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g.",":type end: int :param color: Color of the genomic scales elements, defaults to","+= [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for","be plotted. :type start: int :param end: End position of the region to","values, defaults to \"k\". :type color: str, optional :param ax: Axis of plot,","plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to","ax=None): '''Function that plots HiC contact maps as pyramid plots :param contact_map: Matrix","ax if ax is not None else plt.gca() binned_meth_calls = [ [0, 0]",":type end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int :param","ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower","samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe`","''' ax = ax if ax is not None else plt.gca() binned_meth_calls =",":type start: str :param end: End position of the region to be plotted.","defaults to \"#fb8072\". :type color_minus: str, optional :param ax: Axis on which to","plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance between two","overhang: float, optional :param color_plus: Color of plus stranded TF regions, defaults to","methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3.","region_size, distance_ratio): '''Function that determines the max y position for gene plotting via","i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels():","edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def","shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on","in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict","y value in the gene plot. If not set, then y_max is the","region to be plotted. :type end: int :param color: Color of points representing","pyramid points upwards, else if location == \"bottom\" the pyramid points downwards, defaults","be plotted. :type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for","0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension =","patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the","== 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m","position 3. end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome","plotted. :type chrom: str :param start: Start position of the region to be","current_tick + tick_size scale = None if(first_tick > 1000000): scale = \"Mb\" else:","= ax if ax is not None else plt.gca() c = 0 for",":type blacklist: list, optional :param plot_gene_ids: If True, all gene ids will be","cnv_threshold: Minimal deviation from ploidy to be considered as a CNV, defaults to","= int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end =","ids not to be plotted, default to None. :type blacklist: set, optional :param","for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start,","cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e)","border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size)","if False make more ticks. :type upper: bool, optional :param loc_coordinates: Either of","\"r\". :type color_loss: str, optional :param color_neutral: Plot color of copy number neutral","copy number losses, defaults to \"r\". :type color_loss: str, optional :param color_neutral: Plot","SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool.","plots methylation values as dot plots. :param meth_calls: Iterator containing list-like elements with","merge elements will be averaged an plotted, defaults to 0. :type merge: int,","Color of the rectangles representing the regions to be plotted, defaults to \"#cbebc4\".","exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for i","segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact maps as","methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must be included:","ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i","blacklist: Set containing gene ids not to be plotted, defaults to None, :type","TF regions, defaults to \"#80b1d3\". :type color_plus: str, optional :param color_minus: Color of",":param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to","= TX_pos TX_end = end_r if(direction == \"left\"): TX_start = start_r TX_end =","the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene","gene_name_mapping_file: Path to a tab separated file, for which the first column is","if not i == 0 else \"NA\") meth_after = (binned_average_meth[i+1] if not i","fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None):","be plotted. :type end: int :param segment_size: Size of the segments for which","groups. :type ids: list, optional. :param plot_gene_names: True if gene names shall be","ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing","{\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list =","in meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color, marker=\".\",","and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i","ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist:","= ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos],","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise, defaults","genes, default is \"#fb8072\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list,","keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param","digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i","ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None): '''Function","+= [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ ==",":param upper: If True, make less ticks, else if False make more ticks.","= [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos","and normal) on a gene region scale equalizing the position of genes. :param","rectangles. :param regions: Iterator containig list-like elements with the following entries: 1. Chromosome","True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1])","] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start,","intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none')","i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else","optional :param rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation:","str, optional :param color_plus: Color code for plus stranded genes, default is \"#80b1d3\".","+= 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border +","for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys","else plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2","plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for","be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of","position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end):","strand\"] met_forward = True elif(strand == \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list","color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label =",":return: Tuple of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict:","[]) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that plots","edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy ==","GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values","if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color","max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object","ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars,","expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids; index:","ax. 3. patch_description_list is the list of descriptions for the patches \\ drawn","for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = []","to decreasing order. Else, coordinates stay in increasing order, defaults to False. :type",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \"b\". :type","extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"}","ax: Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","g2 samples expression, defaults to \"#80b1d3\". :type color_g2: str, optional :param g1_id: ID","int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1],","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5,","as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from","plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location ==","contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor =","the expression values of g1 samples (columns: sample ids; index: gene ids) :type","ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with","color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax","chrom: Chromosome of the region to be plotted. :type chrom: str :param start:","Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy, 5. True","= ax if ax is not None else plt.gca() TX_start = TX_pos TX_end","c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def","max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y","score, strand, start, end, color). The color field is used to determine the","False met_reverse = False # Plot Introns for i in introns_in_region: start =",":class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids; index: gene","between two genes, as ratio of ax width, such that two genes are",".3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1,","TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions","[genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos =","of genomic segments to equal distances. :param genomic_segments: List of segments for which","ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower","cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults to \"bwr\".","boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops = {\"color\":","containig list-like elements with the following entries: 1. Chromosome 2. Start position 3.","from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for","signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints.","0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]]","strings, defaults to 0. :type rotation: int, optional :return: Nothing to be returned.","segment_size: Size of the segments for which contacts were called. :type segment_size: int","direction=\"top_down\", color=\"k\", ax = None): '''Function that plots arcs from unequal distances of","4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end,","used for plotting g1 samples expression, defaults to \"#fb8072\". :type color_g1: str, optional","or greater than one. Defaults to 0. :type overhang: float, optional :param color_plus:","int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth","to be plotted. :type end: int :param head_width: Width of the arrow head","returned. :rtype: None ''' ax = ax if ax is not None else","placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else",":class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base pairs. :type","end: str :param ax: Axis used for plotting, defaults to None. :type ax:","== 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2],","marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls","bool, optional :param distance_ratio: Minimal distance between two genes, as ratio of ax",">= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops,","None and gene_name in blacklist): counter += 1 continue n_groups = len(groups) for",":type plot_gene_ids: bool, optional :param y_max: Max y value in the gene plot.","end: End position of region to be plotted. :type end: int :param bin_size:","\"normal\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs","= list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2)","Chromosome of the region to be plotted. :type chrom_r: str :param start_r: Chromosomal","capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i","color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict","1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect =","percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3,","tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks =","direction: str, optional. :param ax: Axis on which to plot, defaults to None.","class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample ids; index:","for j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.)","expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :])","[3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy","PathPatch(path, facecolor = \"None\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False)","TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region","is False. :type plot_legend: bool, optional :param legend_loc: Location of the legend. Either","i in range(len(left)): if(i % merge == 0 and not (i == 0)):","edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75],","= (binned_average_meth[i-1] if not i == 0 else \"NA\") meth_after = (binned_average_meth[i+1] if","gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter","cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome: str","be plotted. :type r_start: int :param r_end: End position of region to be","cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect =","cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev =","be plotted. :type end_r: int :param TX_pos: Position of the translocation. :type TX_pos:","[ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for","flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\":","= colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color,","''' ax = ax if ax is not None else plt.gca() contact_map_index1 =","int :param end_r: Chromosomal end positiont of the region to be plotted. :type","edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def","start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal","\"bwr\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot,","Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\", edgecolor = color, lw =","if location == \"bottom\" the pyramid points downwards, defaults to top, :type location:","plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick","True, make less ticks, else if False make more ticks. :type upper: bool,","str, optional :param g2_id: ID of g2 used for legend plotting, defaults to","patch_description_list += [\"forward strand\"] met_forward = True elif(strand == \"-\" and not(met_reverse)): patch_list","current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth","distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos","genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed:","patches \\ drawn on the ax. :rtype: list \"\"\" ax = ax if","to \"tumor\". :type g1_id: str, optional :param g2_id: ID of g2 used for","the following elements: 1. Chromosome region1 2. Start region1 3. End region1 4.","minus stranded genes is plotted, False otherwise. Default is False. :type plot_legend: bool,","containing (chrom, start, end, name, score, strand, start, end, color). The color field","no points are plotted otherwise, defaults to False. :type plot_points: bool, optional :param","plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot","of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param","location == \"top\", the pyramid points upwards, else if location == \"bottom\" the","Plot color of copy number losses, defaults to \"r\". :type color_loss: str, optional","PathPatch import matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed,","to be plotted. :type chrom_r: str :param start_r: Start position of the region","in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean","shall be equalized (each segment is of the form [<chrom>, <start>, <end>]) :type","(upstream), or \"right\" (downstream), defaults to \"left\". :type direction: str, optional :param color:","\\ drawn on the ax. :rtype: list \"\"\" ax = ax if ax","which to plot CNVs. :type chromosome: str :param start: Start position on chromosome.","position of the region to be plotted. :type end: int :param color: Color","plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.)","object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool`","plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev =","each element is a list-ike object containing: 1. Chromosome 2. Start postion 3.","import matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed,","alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\",","direction: str, optional :param color: Color of the bar representing the translocation, defaults",":class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer",":param region: List containing the region to be plotted ([<chrom>, <start>, <end>]). :type","start: Start position of the region to be plotted. :type start: int :param","in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] =","gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names +=","If this value is not equal to 0, than merge elements will be","position :type regions: iterator :param start: Start position of the region to be","color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1",":type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` '''","list, optional :param plot_gene_ids: If True, all gene ids will be included in","plots. :param meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome","[bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\")","\"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom ==","Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch","[0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in","break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] +=","-1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev))","end, color=\"k\", ax=None): '''Function that plots methylation values as dot plots. :param meth_calls:","ax=None): '''Function that plots a translocation event as a bar, showing the part","Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend:","files and converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq","+= [\"reverse strand\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i","None and gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand","stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on",":param g1_id: ID of g1 used for legend plotting, defaults to \"tumor\". :type",":param color: Color of points representing methylation values, defaults to \"k\". :type color:","+= [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for","motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be plotted. :type","the gene plot. If not set, then y_max is the max number of","[]) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\") files and converts","3. patch_description_list is the list of descriptions for the patches \\ drawn on","regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color,","Set containing gene ids not to be plotted, default to None. :type blacklist:","Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color =","patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax =","None. :type vmax: float, optional :param location: Either of \"top\" | \"bottom\". If","to 0. :type merge: int, optional :return: Nothing to be returned. :rtype: None","in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"):","to None. Number of ids must be the same as the number of","True, the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are","10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick","None else plt.gca() # Calculate midpoints of original and distance equalized segments n_segments","= motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \"-\"): arrow_start","region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean])))","start_r: int :param end_r: Chromosomal end positiont of the region to be plotted.","of the arrow head as proportion of the arrow, defaults to 0.2 :type","plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param colors:","be plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis of","bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name]","ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start,","cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <=","\"\"\"Function for plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool`","int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict):","for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list,","\"Mb\" else: scale=\"Kb\" digits_to_round = None divisor = None if(scale == \"Mb\"): digits_to_round","for minus stranded genes, default is \"#fb8072\". :type color_minus: str, optional. :return: Tuple",":type genomic_segments: list :param start: Start position of the genomic region. :type start:","# Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist","End position of the region to be plotted. :type end: int :param color:","the arc, defaults to \"k\". :type color: str, optional. :param ax: Axis where","end: int :param bin_size: size of bin to average methylation values, defaults to","genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type","region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort()","= binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap cmap","triangular shape). Can be negative or greater than one. Defaults to 0. :type","distance between two genes, as ratio of ax width, such that two genes","introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the","int :param end: End position of the region to be plotted. :type end:","genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists with","plotted. :type r_start: int :param r_end: End position of region to be plotted.","to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings,","by side. If this ratio is underwent, the genes will be stacked. :type","\"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\",","defaults to False. :type edge_color: str, optional :param alpha: Alpha value of the","end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point","= gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up,","for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3])",":param ax: Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([","plot, default is None :type blacklist: list, optional :param plot_gene_ids: If True, all","no edge is plotted, defaults to False. :type edge_color: str, optional :param alpha:","max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect =",":type blacklist: set, optional :param ax: Axis used for plotting, defaults to None.","str, optional :param vmin: Minimal value of intensity range to be plotted, defaults",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or","= ax if ax is not None else plt.gca() # Calculate midpoints of",":param edge_color: Color of region edge. If False, no edge is plotted, defaults","(depends on the region that is plotted), defaults to 1000. :type head_length: int,","plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC","= {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\",","1.5. max_y_pos defines the \\ number of stacked genes. 2. patch_list is the","digits_to_round = None divisor = None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor","is plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction that","optional :param vmax: Maximal value of intensity range to be plotted, defaults to","], [ float(m[4]) for m in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1,","Start position on chromosome. :type start: int :param end: End position on chromosome.","patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\"","plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed,","tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3],","of g1 used for legend plotting, defaults to \"tumor\". :type g1_id: str, optional","iterators. :param chip_signals: Iterator for which each element is a list-ike object containing:",":param color_neutral: Plot color of copy number neutral regions, defaults to \"k\". :type","strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length",".2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\",","rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation: int, optional",":param chrom: Chromosome of the region to be plotted. :type chrom: str :param","the background color of the boxplots boxes, defaults to 0.5. :type alpha: float,","is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev =","Either of \"left\" (upstream), or \"right\" (downstream), defaults to \"left\". :type direction: str,","be the same as the number of groups. :type ids: list, optional. :param","side by side. If this ratio is underwent, the genes will be stacked.","= True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start =","gene_name in blacklist): counter += 1 continue n_groups = len(groups) for g in",":type plot_legend: bool, optional :param legend_loc: Location of the legend. Either of \"lower","ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2.,","if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for i in","defaults to \"k\". :type color: str, optional. :param ax: Axis where the plot","ax is not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1])","Default is False. :type plot_legend: bool, optional :param legend_loc: Location of the legend.","<start>, <end>]) :type genomic_segments: list :param start: Start position of the genomic region.","| bottom_up), defaults to \"top_down\". :type direction: str, optional. :param ax: Axis on","of the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots","= int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse =","int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5)","alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal) on a","+= 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if","float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom =",":class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted. :type motifs_bed:","return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max","None, :type blacklist: set, optional :param ax: (default: None) Axis used for plotting,","barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in","j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices",":param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type","defaults to 0.2 :type head_width: float, optional :param head_length: Length of the arrow","else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \"left\"): TX_start =","e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None):","if(vmin is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9)","ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points,","if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size))",":class:`pybedtools.BedTool` :param start: Start position of the region to be plotted. :type start:","] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes)","# Define color for gene plotting strand = str(i[5]) color = color_forward if(strand","map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.","is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region =","right\", default is \"lower right\". :type legend_loc: str, optional :param color_plus: Color code","left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False,","all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist:","to be plotted. :type end_r: int :param color: Color of the arc, defaults","vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact maps as pyramid plots :param","width, such that two genes are plotted side by side. If this ratio","import pybedtools import pandas as pnd import numpy as np import tabix import","[] vertices = [] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO]","for line in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] ==",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and values patch","color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\",","\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick","is not None else plt.gca() # Get gene names and regions genes_in_region_bed =","linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold,",":param r_end: End position of region to be plotted. :type r_end: int :param","distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 +","Number of ids must be the same as the number of groups. :type","for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([","if(not ids is None): g_id = ids[g] else: g_id = \"group \"+str(g) if(not","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if False","same as the number of groups. :type ids: list, optional. :param plot_gene_names: True","is not None else plt.gca() max_dist = 0 for e in links_bed: link_pos1","facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end])","plt import pybedtools import pandas as pnd import numpy as np import tabix",":type rotation: int, optional :return: Nothing to be returned. :rtype: None ''' ax","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False",":param regions: Iterator containig list-like elements with the following entries: 1. Chromosome 2.","colormap to be used for plotting HiC intensities, defaults to \"Greys\". :type cmap:","to be plotted, defaults to None. :type vmax: float, optional :param location: Either","else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01","rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = []","motif_end color=color_plus dx = head_length if(strand == \"-\"): arrow_start = motif_end arrow_end =","y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0,","else: g_id = \"group \"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list","flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else","on the ax. :rtype: list \"\"\" ax = ax if ax is not","\"up\", plot ticks to upper direction, else if \"down\", plot ticks to lower","gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y,","blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos =",":param r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param r_start:","start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0)",":type gene_mid_points: list :param region: List containing the region to be plotted ([<chrom>,","and values = y position \\ of gene. :rtype: tuple ''' sort_indices =","gene expression (e.g. tumor and normal) on a gene region scale equalizing the","gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing","plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\",","genomic scales elements, defaults to \"k\". :type color: str, optional :param ax: Axis","standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax","end, color). The color field is used to determine the color for plotting","Full path to ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing","\"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list = None","Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\",","groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function","used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If","continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos","Start region1 3. End region1 4. Chromosome region2 5. Start region2 6. End","to be plotted. :type end: int :param segment_size: Size of the segments for","ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper):","start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function that plots arcs from unequal","ticks to upper direction, else if \"down\", plot ticks to lower direction, defaults","optional :param g2_id: ID of g2 used for legend plotting, defaults to \"normal\".","in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border","flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color","plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max","4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold,",":param links_bed: Iterator, that contains bed-like structured lists with the following elements: 1.","= value if(not offset is None): end = start + offset left +=","values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the","i in region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed,","Matrix that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param","the IDs of the different groups. :type groups: list :param gene_names_map: Dictionary with","\"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6],","\"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8),","start position of region to be plotted. :type start: int :param end: Chromosomal","region that is plotted), defaults to 1000. :type head_length: int, optional :param overhang:","plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned","Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","the number of groups, defaults to None. :type colors: str, optional :param ids:","([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists containing the","be plotted. :type r_chrom: str :param r_start: Start position of region to be","stay in increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation:","as the number of groups, defaults to None. :type colors: str, optional :param","len(genes_in_region) == 0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict","\"#80b1d3\". :type color_g2: str, optional :param g1_id: ID of g1 used for legend",":param loc_coordinates: Either of \"up\" | \"down\". If \"up\", plot ticks to upper","optional :param ax: (default: None) Axis used for plotting, defaults to None. :type","plot_gene_names: bool, optional :param position_gene_names: Either of \"top\", or \"bottom\", defaults to \"bottom\".","[ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for i in binned_meth_calls ]","plot_legend: If True plot legend, False otherwise, defaults to False. :type plot_legend: bool,","plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like","else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border =","drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.","= names of segments, and values patch :rtype: dict ''' ax = ax","used for plotting CNVs, defaults to \"bwr\". :type cmap: str, optional :param max_dev:",":param end: End position on chromosome. :type end: int :param ploidy: Assumed ploidy","plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool`","int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2,","= color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt',","representing the translocation, defaults to \"k\". :type color: str, optional :param ax: Axis","int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end","for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color,","2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be","u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens =","that is plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction","> 0 else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n",":param distance_ratio: Minimal distance between two genes, as ratio of ax width, such","[(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes =","of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to","\"k\", \"linewidth\": .3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean","start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label","blacklist: set, optional :param ax: (default: None) Axis used for plotting, defaults to","be merged. If this value is not equal to 0, than merge elements","legend plotting, defaults to \"tumor\". :type g1_id: str, optional :param g2_id: ID of","the number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids","optional :param color_neutral: Plot color of copy number neutral regions, defaults to \"k\".","= head_length if(strand == \"-\"): arrow_start = motif_end arrow_end = motif_start color =","segment_size: int :param cmap: Name of the colormap to be used for plotting","end): ticks += [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' #","linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold,",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise, defaults to","input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end,","structured lists with the following elements: 1. Chromosome region1 2. Start region1 3.","int :param end: End position of the genomic region. :type end: int :param","None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i","= None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a","defaults to 0. :type merge: int, optional :return: Nothing to be returned. :rtype:","genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param","0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that","str, optional. :param color_minus: Color code for minus stranded genes, default is \"#fb8072\".","head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as arrows, indicating","blacklist is None and gene_map[gene_name] in blacklist): continue # Define color for gene","len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border","which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene","cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect)","Minimal distance between two genes, as ratio of ax width, such that two","int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1","(end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1):","[1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),","optional :return: Nothing to be returned. :rtype: None ''' # Use given axis","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6,","patch = PathPatch(path, facecolor = \"None\", edgecolor = color, lw = lw) ax.add_patch(patch)","plotted, defaults to 0. :type merge: int, optional :return: Nothing to be returned.","a list-ike object containing: 1. Chromosome 2. Start postion 3. End position 4.","equal distances. :param genomic_segments: List of segments for which distances shall be equalized","positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not","tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left')","False. :type plot_legend: bool, optional :param colors: List of colors used for plotting","tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__","plotting strand = str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse","int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <=","Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)):","exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values =","for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2])","= start + offset left += [start] height += [value] left_merged = []",":class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool`","3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy ==","exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 =","not None else plt.gca() c = 0 for region in regions: if(not edgecolor):","0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region,","to lower direction, defaults to \"up\". :type loc_coordinates: str, optional :param revert_coordinates: If","] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin # Define","region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function","[]) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that","Dictionary with keys = gene ids and values = y position \\ of","Can be negative or greater than one. Defaults to 0. :type overhang: float,","start, end, bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap","= gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue","<= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start,","medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\":","\"left\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2,","range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i]","color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc,","Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map:","\" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for","TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction of the","sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the","be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted","extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist):","defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy","of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of","to be plotted, default to None. :type blacklist: set, optional :param ax: Axis","Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to","is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes","exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i","not None else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1))","lines equalizing distances, defaults to \"k\". :type color: str, optional :param direction: Direction",":type cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults","lists with the following elements: 1. Chromosome region1 2. Start region1 3. End","defaults to None, :type blacklist: set, optional :param ax: (default: None) Axis used","None): end = start + offset left += [start] height += [value] left_merged","+= [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos","a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used","be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis","continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0]","= list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ ==","[gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine","linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function","= colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"):","region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get gene names and","if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions +=","gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for","is not None else plt.gca() # Calculate midpoints of original and distance equalized","ax if ax is not None else plt.gca() max_signal = 0 left =","else plt.gca() # Calculate midpoints of original and distance equalized segments n_segments =","defaults to \"b\". :type color: str, optional :param offset: Length of intervals, defaults","region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List","[] counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border","end_r: int :param color: Color of the arc, defaults to \"k\". :type color:","to be plotted. :type r_end: int :param ax: Axis of plot :type ax:","overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as arrows, indicating their","input_filename: Full path to ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object","+ extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean","else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start),","= ax if ax is not None else plt.gca() max_signal = 0 left","= determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max #","= exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for","end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end,","for legend plotting, defaults to \"normal\". :type g2_id: str, optional :param plot_gene_names: If","position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and","number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and","(midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path =","blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border =","be plotted, defaults to None. :type vmax: float, optional :param location: Either of","color_g1: Color used for plotting g1 samples expression, defaults to \"#fb8072\". :type color_g1:","r_chrom: str :param r_start: Start position of region to be plotted. :type r_start:","{\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\",","side. If this ratio is underwent, the genes will be stacked, default is","\"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\",",":type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom:","ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def","them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \"most_important\" file :type","\"#fb8072\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1.","of intensity range to be plotted, defaults to None :type vmin: float, optional","translocation event as a bar, showing the part of the genome that is","the \\ number of stacked genes. 2. patch_list is the list of patches","if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color","Chromosomal end position of region to be plotted. :type end: int :param segment_size:","5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) ==","optional. :param color_minus: Color code for minus stranded genes, default is \"#fb8072\". :type","to be plotted. :type end_r: int :param TX_pos: Position of the translocation. :type","colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none',","be plotted, default to None. :type blacklist: set, optional :param ax: Axis used","Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\":","to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values","(float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing #","ax is not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1])",":rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy = None","of the different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL","the genomic region. :type start: int :param end: End position of the genomic","y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2.,","number of groups. :type ids: list, optional. :param plot_gene_names: True if gene names","plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha value",":param segment_size: Size of the segments for which contacts were called. :type segment_size:","= standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in","if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain","max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start >",":param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of","in the gene plot. If not set, then y_max is the max number","facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location ==","1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos,","region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2]","1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border +","''' # Use given axis for plotting ax = ax if ax is","genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2]","the genome that is translocated. :param chrom_r: Chromosome of the region to be","used for legend plotting, defaults to \"normal\". :type g2_id: str, optional :param plot_gene_names:","boxplot, no points are plotted otherwise, defaults to False. :type plot_points: bool, optional","for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3])","file containing (chrom, start, end, name, score, strand, start, end, color). The color","0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([],","color_g2: Color used for plotting g2 samples expression, defaults to \"#80b1d3\". :type color_g2:","g2 used for legend plotting, defaults to \"normal\". :type g2_id: str, optional :param","max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([],",":param alpha: Alpha value of the rectangle, representing the region to be plotted,","2. Start position 3. End position :type regions: iterator :param start: Start position","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise,","plotting ax = ax if ax is not None else plt.gca() colors =","= str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start,",":class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample","meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def","for plotting grouped gene expression (e.g. tumor and normal) on a gene region","str, optional :param color_loss: Plot color of copy number losses, defaults to \"r\".","gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i","of copy number gains, defaults to \"g\". :type color_gain: str, optional :param color_loss:","1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True,","2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"],","cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap)","gene names shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool,","list :param groups: List of lists containing the IDs of the different groups.","and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border =","color_minus: Color of plus stranded TF regions, defaults to \"#fb8072\". :type color_minus: str,","plotted. :type start: int :param end: Chromosomal end position of region to be","y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file:","1. :type alpha: float, optional. :param ax: Axis of plot, defaults to None.","start_r: int :param end_r: End position of the region to be plotted. :type","color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False","HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not",":type color: str, optional :param edge_color: Color of region edge. If False, no","[\"forward strand\"] met_forward = True elif(strand == \"-\" and not(met_reverse)): patch_list += [patch]","= y position \\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for","current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn","expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1.","True legend is plotted, False otherwise, defaults to False. :type plot_legend: bool :param","event as a bar, showing the part of the genome that is translocated.","TF regions, defaults to \"#fb8072\". :type color_minus: str, optional :param ax: Axis on","if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else:","optional :param offset: Length of intervals, defaults to None. :type offset: int, optional","exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i","= motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width,","if ax is not None else plt.gca() for interval in cnvs_bed: current_start =","str :param start_r: Start position of the region to be plotted. :type start_r:","indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited","gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a","to be considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional","line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map","size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list,","region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns:","of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos",":type y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as ratio","in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and","start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object","is plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha value","else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r,","be plotted. :type start_r: int :param end_r: Chromosomal end positiont of the region","plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5,","[] if(not merge is None): heights = [] lefts = [] for i","plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color,","end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None):","1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale","Iterator containig list-like elements with the following entries: 1. Chromosome 2. Start position","midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path","float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev","3. End position 4. Value to be plotted as bar :type chip_signals: iterator","plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots","showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id,","equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)]","values as heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom,","str, optional :param edge_color: Color of region edge. If False, no edge is","for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not","bed-like structured lists with the following elements: 1. Chromosome region1 2. Start region1","HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping.","as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted.","linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)],","sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval in","segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256.","scale=\"Kb\" digits_to_round = None divisor = None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size))","start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments","ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse = False","(\"*most_important*\") files and converts them to pybedtools.BedTool object :param input_filename: Full path to","colors must be the same as the number of groups, defaults to None.",":param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else the","gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for line in gene_name_mapping_file: split_line =","is plotted in addition to the boxplot, no points are plotted otherwise, defaults","lower direction, defaults to \"up\". :type loc_coordinates: str, optional :param revert_coordinates: If True,","in range(len(left)): if(i % merge == 0 and not (i == 0)): left_merged","direction, defaults to \"up\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates","the expression values of all samples (columns: sample ids; index: gene ids). :type","color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as arrows, indicating their directionality. :param","of intensity range to be plotted, defaults to None. :type vmax: float, optional","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None '''","int, optional :param merge: Number of elements to be merged. If this value","list-like elements with the following entries: 1. Chromsome 2. Start position 3. end","if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False)","if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] ==","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4],","> end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)):","Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm =","position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2.","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments,","position of the region to be plotted. :type start_r: int :param end_r: Chromosomal","the different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE","input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5]","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend,","= str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand","created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that","whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color = colors[g]","region to be plotted. :type start: int :param end: End position of region","translocated. Either of \"left\" (upstream), or \"right\" (downstream), defaults to \"left\". :type direction:","linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in","greater than one. Defaults to 0. :type overhang: float, optional :param color_plus: Color","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names",":type color: str, optional. :param ax: Axis where the plot is drawn, defaults","for the background color of the boxplots boxes, defaults to 0.5. :type alpha:","str :param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","int :param end_r: End position of the region to be plotted. :type end_r:","as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed,","for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None):","of region to be plotted. :type r_chrom: str :param r_start: Start position of","current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start,","expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids; index:","counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \"","False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used for","to be plotted, defaults to \"#cbebc4\". :type color: str, optional :param edge_color: Color","head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size,","in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value >","float(m[4]) for m in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0,","between gene ids :param gene_name_mapping_file: Path to a tab separated file, for which","hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot`","value of the rectangle, representing the region to be plotted, defaults to 1.","\"upper right\", default is \"lower right\". :type legend_loc: str, optional :param color_plus: Color","max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param","float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\",",":class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax","points representing methylation values, defaults to \"k\". :type color: str, optional :param ax:","equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1])","for j in [meth_before, meth_after] if not j == \"NA\" ] binned_average_meth_no_missing +=","ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean]))) for","y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as ratio of","None else plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2","len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else:","colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0,","path to ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs","\"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype:","] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]],","if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True,","from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots",":param head_width: Width of the arrow head as proportion of the arrow, defaults","location: str, optional :param ax: Axis on which to plot contact map, defaults",":type vmax: float, optional :param location: Either of \"top\" | \"bottom\". If location","start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments as","positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2)","boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot`","Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2 5. Start","g1 samples expression, defaults to \"#fb8072\". :type color_g1: str, optional :param color_g2: Color","createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path to","linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r,","i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices","is None :type blacklist: list, optional :param plot_gene_ids: If True, all gene ids","as dot plots. :param meth_calls: Iterator containing list-like elements with the following entries:","capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt',","import PathPatch import matplotlib.cm as cm import matplotlib import tabix import math def","alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]),","left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values =","(range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range:","not None else plt.gca() max_dist = 0 for e in links_bed: link_pos1 =","ax = ax if ax is not None else plt.gca() # Calculate midpoints","''' ax = ax if ax is not None else plt.gca() TX_start =","defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate","strand = str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse border_distance_down","coordinates in a linea fashion. :param chrom: Chromosome of the region to be","= int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in","4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],","list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1.","\"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :]","= str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse y =","Position of the translocation. :type TX_pos: int :param direction: Direction of the genomic","If True, a point per expression value is plotted in addition to the","< distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label","color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] =","end_r if(direction == \"left\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start,","[] patch_description_list = [] met_forward = False met_reverse = False # Plot Introns","distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label =","will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the","+= n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for","of the arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length",".8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r,","max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename):","int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name]","= [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions","methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End, Methylated Cs,","left = left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor","color: color of bars, defaults to \"b\". :type color: str, optional :param offset:","of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2.,","for which to plot CNVs. :type chromosome: str :param start: Start position on","vmin: float, optional :param vmax: Maximal value of intensity range to be plotted,","= [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(","else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for","gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0]",":type segment_size: int :param cmap: Name of the colormap to be used for","chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ]","plotting ax = ax if ax is not None else plt.gca() for interval","= [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names","gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name","= [] for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value","start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i","line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0]","optional :param color: color of bars, defaults to \"b\". :type color: str, optional","values per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.)","from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from","= exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size =","capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list = None tick_positions = []","cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that","in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 =",":param cmap: Name of the colormap to be used for plotting HiC intensities,","\"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"): continue","than one. Defaults to 0. :type overhang: float, optional :param color_plus: Color of","= int(element[1]) if(position < start or position > end): continue n_meth = int(element[3])","[left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"):",":param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample","= {} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color","gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values)","determines the max y position for gene plotting via function plotGenes. :param genes_bed:","float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot","is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10)))","groups: List of lists containing the IDs of the different groups. :type groups:","else if location == \"bottom\" the pyramid points downwards, defaults to top, :type","of the translocation. :type TX_pos: int :param direction: Direction of the genomic part","= Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5,","if ax is not None else plt.gca() c = 0 for region in","[\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax","edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as simple rectangles.","= color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None):","fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the","which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param","gene ids :param gene_name_mapping_file: Path to a tab separated file, for which the","for element in methylation_bed: # Determine bin position = int(element[1]) if(position < start","midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY,","plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\")","len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4]))","of lines equalizing distances, defaults to \"k\". :type color: str, optional :param direction:","= str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define","be negative or greater than one. Defaults to 0. :type overhang: float, optional","converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \"most_important\" file","genes, as ratio of ax width, such that two genes are plotted side","representing the regions to be plotted, defaults to \"#cbebc4\". :type color: str, optional","plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True,","cmap: str, optional :param vmin: Minimal value of intensity range to be plotted,",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' # Use given",":type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of",":return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\",","vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap)","[]) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size,","1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor = None if(scale","end: End position of the region to be plotted. :type end: str :param","chrom: Chromosome of region to be plotted. :type chrom: str :param start: Start","plot ticks to lower direction, defaults to \"up\". :type loc_coordinates: str, optional :param","of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and values","chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal):",":rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for line in","= introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward =","99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2)","distances of genomic segments to equal distances. :param genomic_segments: List of segments for","the following entries: 1. Chromsome 2. Start position 3. end position 4. Number","= int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal =","linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [","str, optional :param color_g2: Color used for plotting g2 samples expression, defaults to","used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype:","= matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect =","chrom: str :param start: Start position of the region to be plotted. :type","introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX",":param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param","return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None,","widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not","\"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06,","plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make","regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start:","GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot was","[start] height += [value] left_merged = [] height_merged = [] if(not merge is","str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing order. Else,","genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict =",".5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4,","<= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch)","expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample ids;","color of bars, defaults to \"b\". :type color: str, optional :param offset: Length","defaults to \"bwr\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy","of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for","range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = []","for legend plotting, defaults to \"tumor\". :type g1_id: str, optional :param g2_id: ID","the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend:","= color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none',","[] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]]","is None): end = start + offset left += [start] height += [value]","region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \"top_down\"):","scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions.","to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs,","gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1])","of the bar representing the translocation, defaults to \"k\". :type color: str, optional","str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults to None.","in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5,","that plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object","height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not","tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border =","optional :param log_transformed: If True use log transformed values for plotting, non-transformed values","will be averaged an plotted, defaults to 0. :type merge: int, optional :return:",":param TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction of",":type end: int :param bin_size: size of bin to average methylation values, defaults","= [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2","for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i","for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3])","exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end","else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get gene","ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df:","= left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor =","to \"up\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted","region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart,","bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def",":return: Dictionary with keys = names of segments, and values patch :rtype: dict","to 0. :type overhang: float, optional :param color_plus: Color of plus stranded TF","tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn","None. :type blacklist: set, optional :param ax: Axis used for plotting, defaults to","ax if ax is not None else plt.gca() for interval in cnvs_bed: current_start","= ids[g] else: g_id = \"group \"+str(g) if(not g_id in patch_description_list): patch_list +=","by side. If this ratio is underwent, the genes will be stacked, default",":param chip_signals: Iterator for which each element is a list-ike object containing: 1.","legend plotting, defaults to \"normal\". :type g2_id: str, optional :param plot_gene_names: If True,","arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length of the",":param end_r: End position of the region to be plotted. :type end_r: int","where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","of the region to be plotted. :type chrom_r: str :param start_r: Chromosomal start","if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in","gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]]","linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold,","first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick","+= 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color,","linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward",":param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids;","Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the","linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1,","else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin","gene plotting strand = str(i[5]) color = color_forward if(strand == \"-\"): color =","plotted. :type r_chrom: str :param r_start: Start position of region to be plotted.","if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end],","is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color","= Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and","end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\",","else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first","genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between","0.1. :type distance_ratio: float, optional :param ax: Axes instance on which the genes","tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev)","(binned_average_meth[i-1] if not i == 0 else \"NA\") meth_after = (binned_average_meth[i+1] if not","defaults to \"normal\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO","that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start:","is None and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1):","Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path =","3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5])","plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in m","the colormap to be used for plotting HiC intensities, defaults to \"Greys\". :type","End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region","4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start","with keys = names of segments, and values patch :rtype: dict ''' ax","not equal to 0, than merge elements will be averaged an plotted, defaults","position 3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or","ax is not None else plt.gca() max_dist = 0 for e in links_bed:","None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border","bottom_up), defaults to \"top_down\". :type direction: str, optional. :param ax: Axis on which","Length of the arrow in bp (depends on the region that is plotted),","= False # Plot Introns for i in introns_in_region: start = int(i[1]) end","ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL","ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"]","in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n):","contact maps as pyramid plots :param contact_map: Matrix that contains the intensity values","defaults to None. :type max_dev: float, optional :param ax: Axis used for plotting,","Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy Number) :type","ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq","List of equalized region midpoints. :rtype: list ''' ax = ax if ax","tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1,","facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom,","= 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates","is not None else plt.gca() max_signal = 0 left = [] height =","== 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights =","float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\")","n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing +=","Chromosomal start position of region to be plotted. :type start: int :param end:","plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be","object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one","+ (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1,","plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation event","= merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color =","size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops =","colors: str, optional :param ids: IDs used for legend plotting, defaults to None.","of stacked genes. 2. patch_list is the list of patches drawn on the","open(gene_name_mapping_filename, \"r\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id","in different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start,","if(i % merge == 0 and not (i == 0)): left_merged += [lefts[0]]","negative or greater than one. Defaults to 0. :type overhang: float, optional :param","default is \"#fb8072\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list,","if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean]))) for tick in","\"left\". :type direction: str, optional :param color: Color of the bar representing the","= ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos],","rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]),","plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed,","= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8),","for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict =","elements, defaults to \"k\". :type color: str, optional :param ax: Axis of plot,","([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5)","Or 1. Chromsome 2. Start position 3. end position 4. Beta Value :type","If True plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional","\"k\". :type color: str, optional :param ax: Axis of plot, defaults to None.","region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3])","link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices =","such that two genes are plotted side by side. If this ratio is","Determine bin position = int(element[1]) if(position < start or position > end): continue","2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0,","= [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i]","for plotting g2 samples expression, defaults to \"#80b1d3\". :type color_g2: str, optional :param",":type r_chrom: str :param r_start: Start position of region to be plotted. :type","position_gene_names: str, optional :param log_transformed: If True use log transformed values for plotting,","copy number gains, defaults to \"g\". :type color_gain: str, optional :param color_loss: Plot","End position of the region to be plotted. :type end_r: int :param TX_pos:","not None else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed,","if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 <","sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys:","height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def","float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold):","sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed =","\"down\", plot ticks to lower direction, defaults to \"up\". :type loc_coordinates: str, optional","TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing","Minimal value of intensity range to be plotted, defaults to None :type vmin:","Start position of the region to be plotted. :type start: int :param end:","Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1)","Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults",":param end: Chromosomal end position of region to be plotted. :type end: int",":class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size","tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])]","the arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length of",":type color: str, optional :param direction: Direction of distance equalization (top_down | bottom_up),","= int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels","split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\"","Set containing gene ids not to be plotted, defaults to None, :type blacklist:","determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot","is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True)","plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels():","mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for line","0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point,","in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6)","Nothing to be returned :rtype: None ''' ax = ax if ax is","with keys = gene ids and values = y position \\ of gene.","2. Start Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy","= [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path","blacklist is None and gene_name in blacklist): counter += 1 continue n_groups =","= color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect =","is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2.","if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i],","of all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param","ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function that","of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small",":param cnv_threshold: Minimal deviation from ploidy to be considered as a CNV, defaults","of the region to be plotted. :type end: str :param ax: Axis used","str :param chrom: Chromosome of the region to be plotted. :type chrom: str","r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color:","defaults to \"g\". :type color_gain: str, optional :param color_loss: Plot color of copy","= gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter += 1","defaults to \"top_down\". :type direction: str, optional. :param ax: Axis on which to","color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y","on chromosome. :type start: int :param end: End position on chromosome. :type end:","is translocated. :param chrom_r: Chromosome of the region to be plotted. :type chrom_r:","Define color for gene plotting strand = str(i[5]) color = color_forward if(strand ==","genes will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines","else if \"down\", plot ticks to lower direction, defaults to \"up\". :type loc_coordinates:","for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List","If True, a legend describing plus or minus stranded genes is plotted, False","retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend","of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation","which distances shall be equalized (each segment is of the form [<chrom>, <start>,","None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2])","methylation values as dot plots. :param meth_calls: Iterator containing list-like elements with the","= TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r,","= region_right_border if(not blacklist is None and gene_name in blacklist): counter += 1","values of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame`","= {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None patch_description_list","heights = [] offset = merge*offset left = left_merged height = height_merged plt.bar(left,","which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of",":class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should not be","ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix","right\", \"upper left\", \"upper right\", default is \"lower right\". :type legend_loc: str, optional","= None): '''Functions that plots genomic regions as simple rectangles. :param regions: Iterator",":type end: int :param color: Color of the rectangles representing the regions to","hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1,","int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx =","= rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome,","in bp (depends on the region that is plotted), defaults to 1000. :type","facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None,","binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\"","(midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO,","link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point","= int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn <","vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3]","plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists containing","position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes.","the ax. 3. patch_description_list is the list of descriptions for the patches \\","True elif(strand == \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"]","= None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved","ax if ax is not None else plt.gca() c = 0 for region","(equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices =","color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object","end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of","pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes, as ratio","region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension =",":type plot_gene_names: bool, optional :param position_gene_names: Either of \"top\", or \"bottom\", defaults to",":type end: int :param color: Color of points representing methylation values, defaults to","patch_list = [] patch_description_list = [] met_forward = False met_reverse = False #","y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed:",":type links_bed: iterator :param chrom_r: Chromosome of the region to be plotted. :type","== \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom,","if(not blacklist is None and gene_name in blacklist): counter += 1 continue n_groups","legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List","start_r: Chromosomal start position of the region to be plotted. :type start_r: int","\"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3}","3. End region1 4. Chromosome region2 5. Start region2 6. End region2 :type","of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start","entries: 1. Chromsome 2. Start position 3. end position 4. Number methylated cytosines","[binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else \"NA\") meth_after",":return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position +",":return: Nothing to be returned :rtype: None ''' ax = ax if ax","\"+\" and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward = True","i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,","region_right_border if(not blacklist is None and gene_name in blacklist): counter += 1 continue","revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates stay in","Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\", edgecolor","for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax","number of groups, defaults to None. :type colors: str, optional :param ids: IDs","split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list +=","Chromosome 2. Start postion 3. End position 4. Value to be plotted as","ax is not None else plt.gca() # Calculate midpoints of original and distance","Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] #","whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for","max y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object","otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location of the","[int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for","distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function that plots arcs from","return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\",","start, end, color). The color field is used to determine the color for","to pybedtools.BedTool object :param input_filename: Full path to ACESeq \"most_important\" file :type input_filename:","<gh_stars>0 import matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy","tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from","= region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension","else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) #","else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is","[ j for j in [meth_before, meth_after] if not j == \"NA\" ]","containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list =","[] met_forward = False met_reverse = False # Plot Introns for i in","[start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i","import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from","gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be","list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i","to be plotted. :type chrom: str :param start: Start position of the region","int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue","regions as simple rectangles. :param regions: Iterator containig list-like elements with the following","genes that should not be shown on the plot, default is None :type","start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick]","distances. :param genomic_segments: List of segments for which distances shall be equalized (each","are reverted to decreasing order. Else, coordinates stay in increasing order, defaults to","plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None): '''Function that plots links",":type chromosome: str :param start: Start position on chromosome. :type start: int :param","input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file","shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param","edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1,","max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def","path = Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\", edgecolor = color,","= 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = []","divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ]","= [] ploidy = None for line in input_file: if(line[:7] == \"#ploidy\"): ploidy","gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol",":type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered","plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 =","of the genomic region. :type start: int :param end: End position of the","be plotted. :type start_r: int :param end_r: End position of the region to","ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)],","Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome","column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the","length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None,","and converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \"most_important\"","end: int :param segment_size: Size of the segments for which contacts were called.","None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2]","True use log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool,","values of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame`","1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return","is None and gene_map[gene_name] in blacklist): continue # Define color for gene plotting","ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that","region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist:","plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used","\"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list","GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set","distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of stacked genes.","elif(current_extension < extension): extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops","in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev","plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7,","6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1,","not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end =","genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {}","for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in","introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is","list-ike object containing: 1. Chromosome 2. Start postion 3. End position 4. Value","ax is not None else plt.gca() # Get gene names and regions genes_in_region_bed","g1_id: ID of g1 used for legend plotting, defaults to \"tumor\". :type g1_id:","binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] ==","(equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color,","int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i","plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \"b\".","gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend):","structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start,",":type head_length: int, optional :param overhang: Fraction that the arrow is swept back","color). The color field is used to determine the color for plotting (R,G,B).","ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn","== \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse =","color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]],","for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to","plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True):","None ''' ax = ax if ax is not None else plt.gca() c",":param start: Chromosomal start position of region to be plotted. :type start: int","gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border =","i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in","be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to","is not None else plt.gca() patches_dict = {} for segment in segments_list: segment_start","color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom,","center positions of genes. :type gene_mid_points: list :param region: List containing the region","plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end,","plot. If not set, then y_max is the max number of stacked genes,","ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be","{\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\":","entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy,","plotting samples expression. The number of colors must be the same as the","None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved =","gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border =","= gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border","end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None):","position of the region to be plotted. :type end: str :param ax: Axis","that plots arcs from unequal distances of genomic segments to equal distances. :param","\"linewidth\": .3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean =","end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome","i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine","edge_color: str, optional :param alpha: Alpha value of the rectangle, representing the region","coordinates are reverted to decreasing order. Else, coordinates stay in increasing order, defaults","exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 =","be used for plotting HiC intensities, defaults to \"Greys\". :type cmap: str, optional","in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist","location=\"top\", ax=None): '''Function that plots HiC contact maps as pyramid plots :param contact_map:","chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation values as","methylation values, defaults to 1000. :type bin_size: int, optional :param ax: Axis to","containing list-like elements with the following entries: 1. Chromsome 2. Start position 3.","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less","return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None):","= [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if","end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list +=","regions to be plotted, defaults to \"#cbebc4\". :type color: str, optional :param edge_color:","to be returned :rtype: None ''' # Use given axis for plotting ax","0. :type merge: int, optional :return: Nothing to be returned. :rtype: None '''","color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1,","# Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1,","as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import","plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting","\"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor =","left = [] height = [] for signal in chip_signals: start = int(signal[1])","chrom_r: str :param start_r: Start position of the region to be plotted. :type","optional. :param plot_gene_names: True if gene names shall be plotted, False otherwise, defaults","pyramid points downwards, defaults to top, :type location: str, optional :param ax: Axis","optional :param ax: Axes instance on which the genes are plotted, default is","if(strand == \"+\" and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward","\"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0.","start position of the region to be plotted. :type start_r: int :param end_r:","underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple of 1.","plots arcs from unequal distances of genomic segments to equal distances. :param genomic_segments:","end: End position of the region to be plotted. :type end: int :param","# Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn","if ax is not None else plt.gca() binned_meth_calls = [ [0, 0] for","i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1)","color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region","= float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn =","= list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >=","right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.):","Color of points representing methylation values, defaults to \"k\". :type color: str, optional","Chromosome of region to be plotted. :type chrom: str :param start: Start position","{\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\":",":class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3.","= left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border +","regions, defaults to \"#80b1d3\". :type color_plus: str, optional :param color_minus: Color of plus","creates a mapping between gene ids :param gene_name_mapping_file: Path to a tab separated","to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \"top\", or \"bottom\",",".3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops","contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax =","the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot","plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls","int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus","plotted. :type end_r: int :param color: Color of the arc, defaults to \"k\".","rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3,","for plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed",":param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene plot",":param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2,",":param revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates stay","''' ax = ax if ax is not None else plt.gca() c =","expression values of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2:","ax if ax is not None else plt.gca() TX_start = TX_pos TX_end =","losses, defaults to \"r\". :type color_loss: str, optional :param color_neutral: Plot color of","= [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2","file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool`","exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i","str :param start: Start position of region to be plotted. :type start: int","None else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ]","color: str, optional :param ax: Axis of plot, defaults to None. :type ax:","linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ],","\"lower right\". :type legend_loc: str, optional :param color_plus: Color code for plus stranded","Start position 3. End position :type regions: iterator :param start: Start position of","be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param","if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define color for","False. :type edge_color: str, optional :param alpha: Alpha value of the rectangle, representing","to be returned. :rtype: None ''' # Use given axis for plotting ax","not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse = True # Plot","dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color,","= (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect","+= [height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged +=","bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])],","if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension):","defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \"top\", or","# Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name","False make more ticks. :type upper: bool, optional :param loc_coordinates: Either of \"up\"","the region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups:","= None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path","matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size,","distance equalization (top_down | bottom_up), defaults to \"top_down\". :type direction: str, optional. :param","def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation","ax is not None else plt.gca() patches_dict = {} for segment in segments_list:","Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm,","list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else:","of descriptions for the patches \\ drawn on the ax. :rtype: list \"\"\"",":type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted.","links_bed: Iterator, that contains bed-like structured lists with the following elements: 1. Chromosome","stranded genes, default is \"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color code","deviation from ploidy to plot, defaults to None. :type max_dev: float, optional :param","are plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha","returned. :rtype: None ''' # Use given axis for plotting ax = ax",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' # Use given","= 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end","if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in","plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned","None): '''Functions that plots genomic regions as simple rectangles. :param regions: Iterator containig","ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in","in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and","to be returned :rtype: None ''' ax = ax if ax is not","= ax if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region","left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.]","sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the","ids is None): g_id = ids[g] else: g_id = \"group \"+str(g) if(not g_id","rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3])","end: int :param color: Color of points representing methylation values, defaults to \"k\".","segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted. :type chrom:","for plotting HiC intensities, defaults to \"Greys\". :type cmap: str, optional :param vmin:","189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5)","to plot, defaults to None. :type max_dev: float, optional :param ax: Axis used","cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else:","str :param end: End position of the region to be plotted. :type end:","= int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx","region to be plotted, defaults to 1. :type alpha: float, optional. :param ax:","int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [","to be plotted. :type end: str :param ax: Axis used for plotting, defaults","start: int :param end: End position of region to be plotted. :type end:","ids: IDs used for legend plotting, defaults to None. Number of ids must","groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values","is a list-ike object containing: 1. Chromosome 2. Start postion 3. End position","expression, defaults to \"#80b1d3\". :type color_g2: str, optional :param g1_id: ID of g1","to None. :type blacklist: set, optional :param ax: Axis used for plotting, defaults","plot_legend: If True legend is plotted, False otherwise, defaults to False. :type plot_legend:","patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2])","is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map =",":return: Nothing to be returned. :rtype: None ''' # Use given axis for","binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap","\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick","current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c","4. Value to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome","3. End Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed:","True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \"top\", or \"bottom\", defaults","to be returned. :rtype: None ''' ax = ax if ax is not","elements: 1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2","+= [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth =","color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks =","if not j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list)","float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing","reads CNVs from ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool object :param","be plotted, defaults to \"#cbebc4\". :type color: str, optional :param edge_color: Color of","range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1])","+= [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2])","not to be plotted, defaults to None, :type blacklist: set, optional :param ax:","plotted, defaults to None. :type vmax: float, optional :param location: Either of \"top\"","= plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for","List of segments for which distances shall be equalized (each segment is of","color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0)","IDs of the different groups. :type groups: list :param gene_names_map: Dictionary with keys:","[g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([","plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6)","[\"reverse strand\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in","for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None","== \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\",","gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is","as arcs. :param links_bed: Iterator, that contains bed-like structured lists with the following","= i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1","gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file","i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None","region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops","chromosome. :type start: int :param end: End position on chromosome. :type end: int","equalized (each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list","int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5)","for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0.","= {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\",","end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region","Path to a tab separated file, for which the first column is a","None): '''Function that plots arcs from unequal distances of genomic segments to equal","containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed:","gene names, for genes that should not be shown on the plot, default","if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label,",":class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed:","head as proportion of the arrow, defaults to 0.2 :type head_width: float, optional","(bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id","= False met_reverse = False # Plot Introns for i in introns_in_region: start","for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names +=","optional :param merge: Number of elements to be merged. If this value is","= max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label","if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold):","Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name =",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if","y_max # Plot Exons for i in exons_in_region: start = int(i[1]) end =","-1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start,","of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position","color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns exons of genes. :param genes_bed:","Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3])","color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is","defaults to False. :type plot_legend: bool, optional :param colors: List of colors used","ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def","position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to","point per expression value is plotted in addition to the boxplot, no points","1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None):","Start position of region to be plotted. :type start: int :param end: End","Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm","midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0],","to be merged. If this value is not equal to 0, than merge","g_id = None if(not ids is None): g_id = ids[g] else: g_id =","lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r,","List containing the region to be plotted ([<chrom>, <start>, <end>]). :type region: list","chrom, start, end, ax = None): '''Function for plotting genomix segments in different","is None): max_y_pos = y_max # Plot Exons for i in exons_in_region: start","ax = ax if ax is not None else plt.gca() TX_start = TX_pos","ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression","SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not to be","+= [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i]",":type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults to","False # Plot Introns for i in introns_in_region: start = int(i[1]) end =","gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos","ax = None): '''Function that plots links between genomic regions as arcs. :param","[[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of","= float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops","end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a","defaults to 1. :type alpha: float, optional. :param ax: Axis of plot, defaults","int(element[1]) if(position < start or position > end): continue n_meth = int(element[3]) n_unmeth","names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions =",".4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\",","\"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops =","of region to be plotted. :type chrom: str :param start: Start position of","markersize=3) g_id = None if(not ids is None): g_id = ids[g] else: g_id","shape). Can be negative or greater than one. Defaults to 0. :type overhang:","end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic","+= [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights +=","distance_ratio: Minimal distance between two genes, as ratio of ax width, such that","tab separated file, for which the first column is a ensemble gene id,","rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\",","links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist):","to \"bottom\". :type position_gene_names: str, optional :param log_transformed: If True use log transformed","optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults to \"top_down\".","color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end,","= int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point =","cnv_threshold: float, optional :param color_gain: Plot color of copy number gains, defaults to",".5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([],","start: int :param end: Chromosomal end position of region to be plotted. :type","import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None,","capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list += [patch] patch_description_list +=",":param ax: (default: None) Axis used for plotting, defaults to None. :type ax:","[] heights += [height[i]] lefts += [left[i]] if(not i % merge == 0):","of region to be plotted. :type r_start: int :param r_end: End position of","Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to","def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation",":param color_plus: Color code for plus stranded genes, default is \"#80b1d3\". :type color_plus:","== \"-\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start =","[ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color,","] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not ids is None): g_id","equalized region midpoints. :rtype: list ''' ax = ax if ax is not","plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax =","pyramid plots :param contact_map: Matrix that contains the intensity values of HiC contacts.","= int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist =","ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 =","equal to 0, than merge elements will be averaged an plotted, defaults to","= gene ids and values = y position \\ of gene. :rtype: tuple","= split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list","arrow_start = motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start,","\"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None):","None ''' ax = ax if ax is not None else plt.gca() max_dist","make more ticks. :type upper: bool, optional :param loc_coordinates: Either of \"up\" |","plots links between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like","of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param","open(input_filename, \"r\") cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7]",":type region_size: int :param distance_ratio: Minimal distance between two genes, as ratio of","bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type","meth_before = (binned_average_meth[i-1] if not i == 0 else \"NA\") meth_after = (binned_average_meth[i+1]","int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is","gene id, and the second column is the HUGO gene name :type gene_name_mapping_file:","iterator :param start: Start position of the region to be plotted. :type start:","plotted. :type end: int :param segment_size: Size of the segments for which contacts","range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint =","return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None,","to top, :type location: str, optional :param ax: Axis on which to plot","[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i,",":type meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type chrom:","str, optional :param color: Color of the bar representing the translocation, defaults to","[Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\",","in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval","for the patches \\ drawn on the ax. :rtype: list \"\"\" ax =","motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx,",":rtype: list ''' ax = ax if ax is not None else plt.gca()","decreasing order. Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates:",":type start: int :param end: End position of region to be plotted. :type","(binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\") average_list = [ j for","ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax","plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction that the","list \"\"\" ax = ax if ax is not None else plt.gca() genes_in_region","= int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth =","int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None","False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location of","= int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and","tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left')","[ float(m[4]) for m in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5)","6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2,","widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors","y_max: Max y value in the gene plot. If not set, then y_max","color_forward if(strand == \"-\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up):","float, optional :param head_length: Length of the arrow in bp (depends on the","in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points +=","be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists","'''Function that plots a translocation event as a bar, showing the part of","ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def","plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions","0 and not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged","\"-\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up","to None, :type blacklist: set, optional :param ax: (default: None) Axis used for","= color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000,","to \"#cbebc4\". :type color: str, optional :param edge_color: Color of region edge. If","optional :param g1_id: ID of g1 used for legend plotting, defaults to \"tumor\".","head_length: int, optional :param overhang: Fraction that the arrow is swept back (0","None): g_id = ids[g] else: g_id = \"group \"+str(g) if(not g_id in patch_description_list):","optional :param color: Color of the bar representing the translocation, defaults to \"k\".","separated file, for which the first column is a ensemble gene id, and","plotting HiC intensities, defaults to \"Greys\". :type cmap: str, optional :param vmin: Minimal","expression values of all samples (columns: sample ids; index: gene ids). :type expression_df:","i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops,","a linea fashion. :param chrom: Chromosome of the region to be plotted. :type","= float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than","int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5)","if ax is not None else plt.gca() max_signal = 0 left = []","Either of \"top\" | \"bottom\". If location == \"top\", the pyramid points upwards,","y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing","to be plotted. :type r_chrom: str :param r_start: Start position of region to","int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color","= Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], [])","plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand =","cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool`","to \"k\". :type color: str, optional :param direction: Direction of distance equalization (top_down","less ticks, else if False make more ticks. :type upper: bool, optional :param","+= n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0","loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param","containing regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param","== \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start,","4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to be","[] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions +=","default is None :type blacklist: list, optional :param plot_gene_ids: If True, all gene","to \"#80b1d3\". :type color_g2: str, optional :param g1_id: ID of g1 used for",":param blacklist: Set containing gene ids not to be plotted, defaults to None,","via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type","optional :param head_length: Length of the arrow in bp (depends on the region","used for legend plotting, defaults to None. Number of ids must be the","Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j]","\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter +=","patch_list is the list of patches drawn on the ax. 3. patch_description_list is",":type distance_ratio: float, optional :param ax: Axes instance on which the genes are","= tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type ==","start_r, end_r, TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation event as","i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not","medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color =","if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not","4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45)","is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral","'''Function that plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool`","to None. :type offset: int, optional :param merge: Number of elements to be","tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start,","end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs as","a point per expression value is plotted in addition to the boxplot, no","like iterators. :param chip_signals: Iterator for which each element is a list-ike object","is None): color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions =","0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = []","the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start","the genes will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos:",":param direction: Direction of the genomic part that is translocated. Either of \"left\"","to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy number","if(not blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border","values = y position \\ of gene. :rtype: tuple ''' sort_indices = [int(idx)","positions of genes. :type gene_mid_points: list :param region: List containing the region to","in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist","which each element is a list-ike object containing: 1. Chromosome 2. Start postion","= ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2","line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom ==","as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome,","distance_ratio: float, optional :param ax: Axes instance on which the genes are plotted,","object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values","g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal) on","[\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2,","# Use given axis for plotting ax = ax if ax is not",":param gene_name_mapping_file: Path to a tab separated file, for which the first column","samples expression. The number of colors must be the same as the number","genomic region. :type end: int :param color: Color of lines equalizing distances, defaults","is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start","color=\"k\", ax = None): '''Function that plots links between genomic regions as arcs.","region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name","plot CNVs. :type chromosome: str :param start: Start position on chromosome. :type start:","None ''' ax = ax if ax is not None else plt.gca() TX_start","If this ratio is underwent, the genes will be stacked. :type distance_ratio: float","= Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7])","reverted to decreasing order. Else, coordinates stay in increasing order, defaults to False.",":type start: int :param end: End position on chromosome. :type end: int :param","meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type chrom: str","2. y_pos_dict: Dictionary with keys = gene ids and values = y position","plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list",":class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE","color: Color of lines equalizing distances, defaults to \"k\". :type color: str, optional","methylation_bed: # Determine bin position = int(element[1]) if(position < start or position >","used for plotting g2 samples expression, defaults to \"#80b1d3\". :type color_g2: str, optional","normal) on a gene region scale equalizing the position of genes. :param genes_bed:","CNVs. :type chromosome: str :param start: Start position on chromosome. :type start: int","was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None","position of the region to be plotted. :type end_r: int :param TX_pos: Position","[(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path =","segments for which distances shall be equalized (each segment is of the form","[bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean)))","Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs.",":param location: Either of \"top\" | \"bottom\". If location == \"top\", the pyramid","[chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in","to 0, than merge elements will be averaged an plotted, defaults to 0.","def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that plots","plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for","if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values =","Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned","that two genes are plotted side by side. If this ratio is underwent,",":param plot_legend: If True legend is plotted, False otherwise, defaults to False. :type","= [] offset = merge*offset left = left_merged height = height_merged plt.bar(left, height,","0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = []","in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices =",":class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool`","optional :param upper: If True, make less ticks, else if False make more","tabixed bed file containing (chrom, start, end, name, score, strand, start, end, color).","color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) >","plot_legend: bool, optional :param legend_loc: Location of the legend. Either of \"lower left\",",":param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param","< region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name =","== \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start,","= color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions",":param groups: List of lists containing the IDs of the different groups. :type","to be plotted. :type start_r: int :param end_r: End position of the region","color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start,","Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\", edgecolor =","matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm","0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors =","Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param","region: List containing the region to be plotted ([<chrom>, <start>, <end>]). :type region:","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax =","of region to be plotted. :type start: int :param end: End position of","None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param","end = start + offset left += [start] height += [value] left_merged =","+= [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) <","plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function","3. End position :type regions: iterator :param start: Start position of the region","in base pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes,","= None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round","contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted. :type","following entries: 1. Chromosome 2. Start position 3. End position :type regions: iterator","ax: (default: None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","\"\"\" ax = ax if ax is not None else plt.gca() genes_in_region =","Chromosome of the region to be plotted. :type chrom: str :param start: Start","None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m","chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV","(link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch =","= None if(not colors is None): color = colors[g] else: color = standard_colors[g]","bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color","if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points","== 0.) else 0. for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5)","descriptions for the patches \\ drawn on the ax. :rtype: list \"\"\" ax",":]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in","default to None. :type blacklist: set, optional :param ax: Axis used for plotting,","be plotted. :type end: int :param bin_size: size of bin to average methylation","of \"top\" | \"bottom\". If location == \"top\", the pyramid points upwards, else","plots genomic regions as simple rectangles. :param regions: Iterator containig list-like elements with","rectangles representing the regions to be plotted, defaults to \"#cbebc4\". :type color: str,","defaults to None :type vmin: float, optional :param vmax: Maximal value of intensity","None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for gene_name","3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :]","HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression","float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\".\",","elements with the following entries: 1. Chromosome 2. Start position 3. End position","containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region,","None ''' ax = ax if ax is not None else plt.gca() tick_size","extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values =","left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 =","1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location","Name of the colormap to be used for plotting HiC intensities, defaults to","region to be plotted. :type end: int :param head_width: Width of the arrow","None :type blacklist: list, optional :param plot_gene_ids: If True, all gene ids will","strand = str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse y","= max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5])","gene ids will be included in the plot, False otherwise, default is True","counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border =","= None if(not ids is None): g_id = ids[g] else: g_id = \"group","be included in the plot, False otherwise, default is True :type plot_gene_ids: bool,","for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,","(midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ]","= color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c +=","start: Start position of region to be plotted. :type start: int :param end:","a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\",","genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names","ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color,","ax = ax if ax is not None else plt.gca() genes_in_region = genes_bed","color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5)","str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border =",":type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param","g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2:","legend is plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1:","int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered as a","the arrow head as proportion of the arrow, defaults to 0.2 :type head_width:","on the plot, default is None :type blacklist: list, optional :param plot_gene_ids: If","[] for i in range(len(left)): if(i % merge == 0 and not (i","int :param color: Color of points representing methylation values, defaults to \"k\". :type",":type start_r: int :param end_r: End position of the region to be plotted.","gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] =","Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255.,","for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end =","None. Number of ids must be the same as the number of groups.","value of intensity range to be plotted, defaults to None :type vmin: float,","float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color","+= 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for","+ (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name,","ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for","Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position 4.","[int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point","object containing: 1. Chromosome 2. Start postion 3. End position 4. Value to","legend_loc: str, optional :param color_plus: Color code for plus stranded genes, default is","None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea","for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list,","patches drawn on the ax. 3. patch_description_list is the list of descriptions for","plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy ==","color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing","bar, showing the part of the genome that is translocated. :param chrom_r: Chromosome","for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be","continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.))","position of the genomic region. :type end: int :param color: Color of lines","in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i],","optional :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","= int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list '''","of the region to be plotted. :type start_r: int :param end_r: Chromosomal end",":type chrom: str :param start: Start position of region to be plotted. :type","digits_to_round))+scale for i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0],","is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1,","\"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return","color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine","max number of stacked genes, default is None. :type y_max: bool, optional :param","ax width, such that two genes are plotted side by side. If this","TX_pos: int :param direction: Direction of the genomic part that is translocated. Either","1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start,","to \"#fb8072\". :type color_minus: str, optional :param ax: Axis on which to plot","for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = []","distances shall be equalized (each segment is of the form [<chrom>, <start>, <end>])","the region to be plotted. :type end: str :param ax: Axis used for","TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool`","else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort()","dx = head_length if(strand == \"-\"): arrow_start = motif_end arrow_end = motif_start color","widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i","edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start,","float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops =","upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion.","element in methylation_bed: # Determine bin position = int(element[1]) if(position < start or",".2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5)","for i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\",","color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start","int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not","True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot","= split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2,","arrow in bp (depends on the region that is plotted), defaults to 1000.","legend describing plus or minus stranded genes is plotted, False otherwise. Default is","end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap","genomic coordinates in a linea fashion. :param chrom: Chromosome of the region to","of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to","int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn,","Axis where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","translocation, defaults to \"k\". :type color: str, optional :param ax: Axis of plot,","= Path(vertices, codes) patch = PathPatch(path, facecolor = \"None\", edgecolor = color, lw","to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region","''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for line in gene_name_mapping_file: split_line","segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type","[]) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that plots methylation values","codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5],","in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed,","ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in","edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax =","be plotted. :type chrom_r: str :param start_r: Start position of the region to",":type edge_color: str, optional :param alpha: Alpha value of the rectangle, representing the","< len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list ''' ax =","plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns exons","Start position of the genomic region. :type start: int :param end: End position","for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end):","of segments for which distances shall be equalized (each segment is of the","# Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm","5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position","i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color,","in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1)","Number of elements to be merged. If this value is not equal to","of the segments for which contacts were called. :type segment_size: int :param cmap:","\"#e5c494\", \"#bbbbbb\"] ax = ax if ax is not None else plt.gca() region_bed","and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i <","def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function that plots arcs","small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\":","# Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names =","left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in","samples expression, defaults to \"#fb8072\". :type color_g1: str, optional :param color_g2: Color used","len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border +","ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g.","= ax if ax is not None else plt.gca() binned_meth_calls = [ [0,","PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def","the region to be plotted. :type end_r: int :param color: Color of the","region to be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal","side by side. If this ratio is underwent, the genes will be stacked,","ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1])","plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to be","if(segment_type == \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color)","position 4. Value to be plotted as bar :type chip_signals: iterator :param r_chrom:","the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden.","in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border","tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"):",":type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base",":type alpha: float, optional. :param ax: Axis of plot, defaults to None. :type","[[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i","of segments, and values patch :rtype: dict ''' ax = ax if ax","Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be","introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for","plot_legend: bool, optional :param colors: List of colors used for plotting samples expression.",":type end: int :param segment_size: Size of the segments for which contacts were","of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed:","number losses, defaults to \"r\". :type color_loss: str, optional :param color_neutral: Plot color","position of the region to be plotted. :type start_r: int :param end_r: End","color of copy number neutral regions, defaults to \"k\". :type color_neutral: str, optional","of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type","else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev","bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ]",":param position_gene_names: Either of \"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names: str,","(downstream), defaults to \"left\". :type direction: str, optional :param color: Color of the","ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0.,","expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for","CNVs, defaults to \"bwr\". :type cmap: str, optional :param max_dev: Maximal deviation from","not None else plt.gca() patches_dict = {} for segment in segments_list: segment_start =","codes = [] vertices = [] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO,","in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect","g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.]","> end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter","[] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing","loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing order.","region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \"","arc, defaults to \"k\". :type color: str, optional. :param ax: Axis where the","max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point =","hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False,","color_plus: Color of plus stranded TF regions, defaults to \"#80b1d3\". :type color_plus: str,","defaults to \"up\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are","cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact maps as pyramid","of the genome that is translocated. :param chrom_r: Chromosome of the region to","the part of the genome that is translocated. :param chrom_r: Chromosome of the","ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"): continue split_line","colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression","the genomic scales elements, defaults to \"k\". :type color: str, optional :param ax:","== 0 else \"NA\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else","plot_points: bool, optional :param alpha: Alpha value for the background color of the",".8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices","[g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not","y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = []",":param colors: List of colors used for plotting samples expression. The number of",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and","ax if ax is not None else plt.gca() # Get gene names and","max_dev: float, optional :param ax: Axis used for plotting, defaults to None. :type","1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys","region1 2. Start region1 3. End region1 4. Chromosome region2 5. Start region2","defaults to False. :type plot_legend: bool :param color_g1: Color used for plotting g1","Alpha value for the background color of the boxplots boxes, defaults to 0.5.","if(not offset is None): end = start + offset left += [start] height","plotted, default to None. :type blacklist: set, optional :param ax: Axis used for",":type end: str :param ax: Axis used for plotting, defaults to None. :type","(equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0),","ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6)","genes, default is \"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color code for","= split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed,","ax is not None else plt.gca() c = 0 for region in regions:","if(position < start or position > end): continue n_meth = int(element[3]) n_unmeth =","j for j in [meth_before, meth_after] if not j == \"NA\" ] binned_average_meth_no_missing","= (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin","plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene","= end_r if(direction == \"left\"): TX_start = start_r TX_end = TX_pos rect =","Plot average methylation values per bin # Define Colormap cmap = cm.bwr norm","else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start","counter += 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos =","drawn on the ax. 3. patch_description_list is the list of descriptions for the","ax = None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename:","# Plot average methylation values per bin # Define Colormap cmap = cm.bwr","(2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]]","for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\"","is a ensemble gene id, and the second column is the HUGO gene","range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4,","n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ],","Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else:","Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def","start_r: Start position of the region to be plotted. :type start_r: int :param","100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates ==","plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \"left\"): TX_start = start_r","if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1","input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy = None for line in","be plotted. :type end: int :param head_width: Width of the arrow head as","= [] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for","else \"NA\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\") average_list","str, optional. :param ax: Axis on which to plot, defaults to None. :type","groups, defaults to None. :type colors: str, optional :param ids: IDs used for","for which each element is a list-ike object containing: 1. Chromosome 2. Start","boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype:","for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue","position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size)","ids: list, optional. :param plot_gene_names: True if gene names shall be plotted, False","contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be","ax is not None else plt.gca() binned_meth_calls = [ [0, 0] for i","region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region","start, end, color=\"k\", ax=None): '''Function that plots methylation values as dot plots. :param","containing the expression values of g2 samples (columns: sample ids; index: gene ids)","= genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down =","region to be plotted. :type end: int :param segment_size: Size of the segments","patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1","not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start","import numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import","defaults to \"#fb8072\". :type color_g1: str, optional :param color_g2: Color used for plotting","= interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i","= [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes)","[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point,","the plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max:","height_merged += [np.mean(heights)] heights = [] offset = merge*offset left = left_merged height","for m in meth_calls ], [ float(m[4]) for m in m in meth_calls],","whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = None","end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color,","to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome","to plot CNVs. :type chromosome: str :param start: Start position on chromosome. :type","end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as","first column is a ensemble gene id, and the second column is the","# Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick =","r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting","\"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\",","not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3]))","split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]),","[ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element","range(len(left)): if(i % merge == 0 and not (i == 0)): left_merged +=","met_forward = True elif(strand == \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list +=","from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import","\"NA\") average_list = [ j for j in [meth_before, meth_after] if not j","10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick =","''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax =","averaged an plotted, defaults to 0. :type merge: int, optional :return: Nothing to","[] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not","= [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points","flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0],","regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for","same as the number of groups, defaults to None. :type colors: str, optional","id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {} for","max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue","in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1]","y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect)","plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates","defaults to \"#cbebc4\". :type color: str, optional :param edge_color: Color of region edge.","dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6])","path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value","patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0","int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5])","introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus","'''Function that plots arcs from unequal distances of genomic segments to equal distances.","gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend):",":class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax =","containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map","as heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start,","extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 =",":type merge: int, optional :return: Nothing to be returned. :rtype: None ''' ax","end: int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy:","[] gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names: left_border","ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color =","elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2),","number neutral regions, defaults to \"k\". :type color_neutral: str, optional :param ax: Axis","of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param","default is \"lower right\". :type legend_loc: str, optional :param color_plus: Color code for","GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame`",":type direction: str, optional :param color: Color of the bar representing the translocation,",":param log_transformed: If True use log transformed values for plotting, non-transformed values otherwise.","color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\",","len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation","y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures,","str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define color","HiC contact maps as pyramid plots :param contact_map: Matrix that contains the intensity","% merge == 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)]","norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size,","+= 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed,","position on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults","Colormap used for plotting CNVs, defaults to \"bwr\". :type cmap: str, optional :param","for i in region]), from_string=True) # Get gene names and regions genes_in_region_bed =","object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe`","in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not ids is","gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs,","plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that plots methylation","= matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin","4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in","== \"-\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx =","int :param bin_size: size of bin to average methylation values, defaults to 1000.","iterator :param chrom: Chromosome of region to be plotted. :type chrom: str :param","a bar, showing the part of the genome that is translocated. :param chrom_r:","keys = gene ids and values = y position \\ of gene. :rtype:","color_loss: Plot color of copy number losses, defaults to \"r\". :type color_loss: str,","of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha}","segment_type = str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0),","1. Chromsome 2. Start position 3. end position 4. Beta Value :type meth_calles:","x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values,","plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels():","None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following","will be included in the plot, False otherwise, default is True :type plot_gene_ids:","= region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) <","= [] patch_description_list = [] met_forward = False met_reverse = False # Plot","is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]]","equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type",":param start: Start position of the region to be plotted. :type start: int","heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End,","Length of intervals, defaults to None. :type offset: int, optional :param merge: Number","for plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object","= False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not","None if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor","= [] met_forward = False met_reverse = False # Plot Introns for i","that plots genomic coordinates in a linea fashion. :param chrom: Chromosome of the","for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with","if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average","ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which","expression value is plotted in addition to the boxplot, no points are plotted","upper direction, else if \"down\", plot ticks to lower direction, defaults to \"up\".","color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0)","= Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0,","= 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick","gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down])","calls. Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs.","if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for tick in","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list ''' ax","gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8","[4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),",":param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional","tumor and normal) on a gene region scale equalizing the position of genes.","] counter = 0 for element in methylation_bed: # Determine bin position =","genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool`","= ax if ax is not None else plt.gca() patches_dict = {} for","for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True","names shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional","ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments,","float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev):","Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1],","\\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1]","HiC intensities, defaults to \"Greys\". :type cmap: str, optional :param vmin: Minimal value","for plotting g1 samples expression, defaults to \"#fb8072\". :type color_g1: str, optional :param","patch_description_list = [] met_forward = False met_reverse = False # Plot Introns for","for i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before","linea fashion. :param chrom: Chromosome of the region to be plotted. :type chrom:","Axes instance on which the genes are plotted, default is None. :type ax:","+= [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) #",":param alpha: Alpha value for the background color of the boxplots boxes, defaults",":type end: int :param color: Color of lines equalizing distances, defaults to \"k\".","ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None","Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5])","otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \"top\",","start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points +=","ploidy of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal","as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot","of copy number neutral regions, defaults to \"k\". :type color_neutral: str, optional :param","plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None '''","region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]),","Iterator containing list-like elements with the following entries: 1. Chromsome 2. Start position","0. for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4):","the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of","def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True,","segments to equal distances. :param genomic_segments: List of segments for which distances shall","Color code for plus stranded genes, default is \"#80b1d3\". :type color_plus: str, optional.","default is True :type plot_gene_ids: bool, optional :param y_max: Max y value in",":param direction: Direction of distance equalization (top_down | bottom_up), defaults to \"top_down\". :type","gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired",":type color_g1: str, optional :param color_g2: Color used for plotting g2 samples expression,","= int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] +=","i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist =","to upper direction, else if \"down\", plot ticks to lower direction, defaults to","of region to be plotted in base pairs. :type region_size: int :param distance_ratio:","1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO,","plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\",","capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse","defaults to 1000. :type bin_size: int, optional :param ax: Axis to be used","that plots bedGraph like iterators. :param chip_signals: Iterator for which each element is","ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\",","for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must","merged. If this value is not equal to 0, than merge elements will","TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons","must be the same as the number of groups, defaults to None. :type","plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF","3. end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of","ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2","binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0",":type cmap: str, optional :param vmin: Minimal value of intensity range to be","unequal distances of genomic segments to equal distances. :param genomic_segments: List of segments","while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size scale =","TX_start = TX_pos TX_end = end_r if(direction == \"left\"): TX_start = start_r TX_end","SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed.","else plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end","of patches drawn on the ax. 3. patch_description_list is the list of descriptions","continue # Define color for gene plotting strand = str(i[5]) color = color_forward","return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids","plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys =","int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot","heights = [] heights += [height[i]] lefts += [left[i]] if(not i % merge","Either of \"lower left\", \"lower right\", \"upper left\", \"upper right\", default is \"lower","for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates","used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to","color of copy number losses, defaults to \"r\". :type color_loss: str, optional :param","contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal","''' input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy = None for line","plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter =","Rotational angle of coordinate strings, defaults to 0. :type rotation: int, optional :return:","color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color,","addition to the boxplot, no points are plotted otherwise, defaults to False. :type",":] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2",":type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be plotted.","for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in","blacklist is None and gene_name in blacklist): counter += 1 continue if(counter <","methylation values per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0.,","Tuple of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary","ax. :rtype: list \"\"\" ax = ax if ax is not None else",">= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups),","str, optional :param offset: Length of intervals, defaults to None. :type offset: int,","for legend plotting, defaults to None. Number of ids must be the same","= (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1,","back (0 overhang means triangular shape). Can be negative or greater than one.","\"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax is not",":class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted. :type start:","color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color,","that contains bed-like structured lists with the following elements: 1. Chromosome region1 2.","segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name, score, strand,","end: End position on chromosome. :type end: int :param ploidy: Assumed ploidy of","+ (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO,","plotted. :type end: int :param color: Color of points representing methylation values, defaults","upwards, else if location == \"bottom\" the pyramid points downwards, defaults to top,","1. Chromosome 2. Start position 3. End position :type regions: iterator :param start:","color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color,","not i == 0 else \"NA\") meth_after = (binned_average_meth[i+1] if not i ==","motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end","i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name]","max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1])","= [] if(not merge is None): heights = [] lefts = [] for","float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i ==","ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and","in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list,","blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for","color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in","is not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction ==","matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in","is plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color","np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict","+ tick_size scale = None if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\"","containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing","genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in","(region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons","If location == \"top\", the pyramid points upwards, else if location == \"bottom\"","= [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes","\"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional :param log_transformed: If True use","that plots a translocation event as a bar, showing the part of the","if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list += [","from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names","color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt',","as ratio of ax width, such that two genes are plotted side by","ax=None): '''Function that plots TF motifs as arrows, indicating their directionality. :param motifs_bed:","to be plotted. :type start: int :param end: End position of region to","a gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object","to the boxplot, no points are plotted otherwise, defaults to False. :type plot_points:","the region to be plotted. :type start_r: int :param end_r: Chromosomal end positiont","vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices,","== \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i ==","genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type genes_bed:","> max_signal): max_signal = value if(not offset is None): end = start +","of the arrow in bp (depends on the region that is plotted), defaults","upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks","stranded TF regions, defaults to \"#fb8072\". :type color_minus: str, optional :param ax: Axis","that plots HiC contact maps as pyramid plots :param contact_map: Matrix that contains","height = height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start,","of \"up\" | \"down\". If \"up\", plot ticks to upper direction, else if","patch_description_list += [\"reverse strand\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for","ticks = [ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse()","in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)","1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([],","if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops","Chromsome 2. Start position 3. end position 4. Beta Value :type meth_calles: iterator","[.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation)","be plotted. :type end_r: int :param color: Color of the arc, defaults to","used for plotting HiC intensities, defaults to \"Greys\". :type cmap: str, optional :param",":param contact_map: Matrix that contains the intensity values of HiC contacts. :type contact_map:","gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing","Chromosome 2. Start position 3. End position :type regions: iterator :param start: Start","line in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\"","CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome,","\"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0",":type colors: str, optional :param ids: IDs used for legend plotting, defaults to","\"NA\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \"NA\") average_list =","stranded genes is plotted, False otherwise. Default is False. :type plot_legend: bool, optional","TX_pos TX_end = end_r if(direction == \"left\"): TX_start = start_r TX_end = TX_pos","[]) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax","plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not ids is None): g_id =","vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start,","color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as simple","rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param chrom: Chromosome","] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start,","= [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] =","the first column is a ensemble gene id, and the second column is","range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i)))",".8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5)","defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` '''","= line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return","plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name","= region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region)","i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not","link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist","\"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if","interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn","from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\":","segments, and values patch :rtype: dict ''' ax = ax if ax is","to None. :type vmax: float, optional :param location: Either of \"top\" | \"bottom\".","Exons for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name =","rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)])","be plotted. :type end: int :param color: Color of the rectangles representing the","tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position","= float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"): continue split_line =","region1 3. End region1 4. Chromosome region2 5. Start region2 6. End region2","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and values","= [] lefts = [] for i in range(len(left)): if(i % merge ==","== 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6)","expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5):","to be plotted. :type start: str :param end: End position of the region","color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start),","tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list,","if(line[0] == \"#\" or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev =","midpoints. :rtype: list ''' ax = ax if ax is not None else","r_end: End position of region to be plotted. :type r_end: int :param ax:",":type color_gain: str, optional :param color_loss: Plot color of copy number losses, defaults","size of bin to average methylation values, defaults to 1000. :type bin_size: int,","= {\"color\": \"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\":","lists containing the IDs of the different groups. :type groups: list :param gene_names_map:","side. If this ratio is underwent, the genes will be stacked. :type distance_ratio:","optional :param position_gene_names: Either of \"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names:","start, end, name, score, strand, start, end, color). The color field is used","patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position","list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i","gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None,","float, optional :param location: Either of \"top\" | \"bottom\". If location == \"top\",","merge is None): heights = [] lefts = [] for i in range(len(left)):","n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1]","plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval","color_gain: Plot color of copy number gains, defaults to \"g\". :type color_gain: str,","Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If",":type color_g2: str, optional :param g1_id: ID of g1 used for legend plotting,","plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic","bool, optional :param colors: List of colors used for plotting samples expression. The","str :param start_r: Chromosomal start position of the region to be plotted. :type","== 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4])","== len(binned_average_meth)-1 else \"NA\") average_list = [ j for j in [meth_before, meth_after]","\"linewidth\": .3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean =","number of stacked genes, default is None. :type y_max: bool, optional :param distance_ratio:",":class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of genes. :type","region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None","elements with the following entries: 1. Chromsome 2. Start position 3. end position","be averaged an plotted, defaults to 0. :type merge: int, optional :return: Nothing","n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0]","non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point","r_start: Start position of region to be plotted. :type r_start: int :param r_end:","distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 +","[6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0,","bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])],","showfliers=False) color = None if(not colors is None): color = colors[g] else: color","not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) #","codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point,",":param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs.","linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold,","color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i","\"bottom\" the pyramid points downwards, defaults to top, :type location: str, optional :param","plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names","positiont of the region to be plotted. :type end_r: int :param color: Color","Nothing to be returned. :rtype: None ''' # Use given axis for plotting","''' ax = ax if ax is not None else plt.gca() n_entries =","linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3,","merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color = color,","split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals,","counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist","start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that plots","List of colors used for plotting samples expression. The number of colors must","link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes =","cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None,","Start postion 3. End position 4. Value to be plotted as bar :type","Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not","1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops,","1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq","gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i]","optional :param alpha: Alpha value for the background color of the boxplots boxes,","y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend):","rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot","IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing",":param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name, score,","= {\"color\": \"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\",","ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be","if \"down\", plot ticks to lower direction, defaults to \"up\". :type loc_coordinates: str,","Color used for plotting g1 samples expression, defaults to \"#fb8072\". :type color_g1: str,","i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2","color: str, optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults","g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map:","max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start =","matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy as np","of the region to be plotted. :type chrom_r: str :param start_r: Start position","should not be shown on the plot, default is None :type blacklist: list,","first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size scale","region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param","the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id",":type end: int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type","minus stranded genes, default is \"#fb8072\". :type color_minus: str, optional. :return: Tuple of","= ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color","bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name]","m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start,","regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions","pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get gene names and regions genes_in_region_bed","End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to be","edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], [])","equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0):","plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1.","Iterator for which each element is a list-ike object containing: 1. Chromosome 2.","optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\",","that plots links between genomic regions as arcs. :param links_bed: Iterator, that contains",":type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values:","to None. :type colors: str, optional :param ids: IDs used for legend plotting,","> max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2):","genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of genes.","plus or minus stranded genes is plotted, False otherwise. Default is False. :type","= expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0,","\"bottom\". :type position_gene_names: str, optional :param log_transformed: If True use log transformed values","str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels =","right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.)","of elements to be merged. If this value is not equal to 0,","plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or minus","-1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4,","values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If","int :param direction: Direction of the genomic part that is translocated. Either of","tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list,","plotting, defaults to \"tumor\". :type g1_id: str, optional :param g2_id: ID of g2","int :param color: Color of the genomic scales elements, defaults to \"k\". :type","int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs","ax: Axis to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","to be plotted. :type end: int :param color: Color of points representing methylation",":rtype: None ''' ax = ax if ax is not None else plt.gca()","0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that","+= [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes","plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0],","of colors must be the same as the number of groups, defaults to","plt.gca() c = 0 for region in regions: if(not edgecolor): current_color = color","patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names):","is swept back (0 overhang means triangular shape). Can be negative or greater","groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO","if(direction == \"left\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4),","plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\",","to None. :type max_dev: float, optional :param ax: Axis used for plotting, defaults","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6],","If not set, then y_max is the max number of stacked genes, default","5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)])","and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse = True #","if(not blacklist is None and gene_name in blacklist): counter += 1 continue if(counter","ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2.,","vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)):","for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)","be plotted. :type chrom: str :param start: Start position of the region to","Color of plus stranded TF regions, defaults to \"#fb8072\". :type color_minus: str, optional","1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\ number of","int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value","6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed,","part that is translocated. Either of \"left\" (upstream), or \"right\" (downstream), defaults to","of the region to be plotted. :type end_r: int :param color: Color of","of integer values containing center positions of genes. :type gene_mid_points: list :param region:","genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3],","midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO,","for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3])","If True legend is plotted, False otherwise, defaults to False. :type plot_legend: bool","field is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str","object containing the one region, for which the gene plot is created. :type","max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point","if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size","== \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else","midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO,","== 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\",","tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy","\"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \"+\"]","right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border","value is not equal to 0, than merge elements will be averaged an","color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down","Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn =","ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region","of copy number losses, defaults to \"r\". :type color_loss: str, optional :param color_neutral:","to \"k\". :type color_neutral: str, optional :param ax: Axis used for plotting. :type","= color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5)","vmin: Minimal value of intensity range to be plotted, defaults to None :type","continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene","[0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation)","== \"+\" and not(met_forward)): patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward =","bool, optional :param position_gene_names: Either of \"top\", or \"bottom\", defaults to \"bottom\". :type","binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else","max_y_position + 1.5. max_y_pos defines the \\ number of stacked genes. 2. patch_list","the second column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary","arrow_end = motif_end color=color_plus dx = head_length if(strand == \"-\"): arrow_start = motif_end","motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length,","counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1]))","for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i","genomic_segments: list :param start: Start position of the genomic region. :type start: int","= cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap =","ax = ax if ax is not None else plt.gca() patches_dict = {}",":param color_loss: Plot color of copy number losses, defaults to \"r\". :type color_loss:","fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8])","color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\",","max_signal = value if(not offset is None): end = start + offset left","right_border = region_right_border if(not blacklist is None and gene_name in blacklist): counter +=","Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0,","if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist",":param start: Start position of the region to be plotted. :type start: str","y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function",":class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample","bool :param color_g1: Color used for plotting g1 samples expression, defaults to \"#fb8072\".","alpha: Alpha value of the rectangle, representing the region to be plotted, defaults","Plot Introns for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name","[4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold,","None if(not len(genes_in_region) == 0): # Determine y positions of genes for plotting","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False","of the region to be plotted. :type start: str :param end: End position","of gene names, for genes that should not be shown on the plot,","pybedtools import pandas as pnd import numpy as np import tabix import matplotlib.ticker","ax = ax if ax is not None else plt.gca() max_dist = 0","linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0)","= int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\")","the pyramid points upwards, else if location == \"bottom\" the pyramid points downwards,","matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path","if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none',","patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse = True # Plot Gene","end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward","[] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border",":class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy = None for","defines the \\ number of stacked genes. 2. patch_list is the list of","+= [value] left_merged = [] height_merged = [] if(not merge is None): heights","intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position","linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in ticks ]",":type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary '''","\\ number of stacked genes. 2. patch_list is the list of patches drawn","= 0 for element in methylation_bed: # Determine bin position = int(element[1]) if(position","tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__",":return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename,","i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {}","max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else:","bars, defaults to \"b\". :type color: str, optional :param offset: Length of intervals,","end: int :param color: Color of the rectangles representing the regions to be","cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end","GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not to","ax = ax if ax is not None else plt.gca() for interval in","of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max","merge == 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights","to \"bwr\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy to","if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None):","loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None,","upper: If True, make less ticks, else if False make more ticks. :type","minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None","that reads CNVs from ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool object","alpha: Alpha value for the background color of the boxplots boxes, defaults to","that is translocated. Either of \"left\" (upstream), or \"right\" (downstream), defaults to \"left\".","heights = [] lefts = [] for i in range(len(left)): if(i % merge","1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\",","the region to be plotted. :type chrom_r: str :param start_r: Start position of",":] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1","id, and the second column is the HUGO gene name :type gene_name_mapping_file: str","optional. :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5,","with following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation",":class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else plt.gca() #","the list of descriptions for the patches \\ drawn on the ax. :rtype:","End position of the region to be plotted. :type end: str :param ax:","met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start","Start Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy Number)","y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict","= color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False,","if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1),","if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name","color = color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect","gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting",":rtype: list \"\"\" ax = ax if ax is not None else plt.gca()","genes will be stacked, default is 0.1. :type distance_ratio: float, optional :param ax:","bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id]","\"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax is not None","= [] height = [] for signal in chip_signals: start = int(signal[1]) end","(link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3,","exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__","value of intensity range to be plotted, defaults to None. :type vmax: float,","= color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([],","start, end, ax = None): '''Function for plotting genomix segments in different colors","genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be","instance on which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set","else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function","color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type","expression_values = np.log2([i if i >= 1. else 1. for i in exp_values])","\"top_down\". :type direction: str, optional. :param ax: Axis on which to plot, defaults","if ax is not None else plt.gca() for motif in motifs_bed: motif_start =","for region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75],","ploidy to be considered as a CNV, defaults to 0.7. :type cnv_threshold: float,","region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)):","str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect =","= [] height_merged = [] if(not merge is None): heights = [] lefts","[] offset = merge*offset left = left_merged height = height_merged plt.bar(left, height, offset,","bar representing the translocation, defaults to \"k\". :type color: str, optional :param ax:",":return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax","optional :param location: Either of \"top\" | \"bottom\". If location == \"top\", the","segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1.","counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names):","1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start,","list of integer values containing center positions of genes. :type gene_mid_points: list :param","None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]), from_string=True) # Get","region to be plotted. :type start_r: int :param end_r: End position of the","\"upper left\", \"upper right\", default is \"lower right\". :type legend_loc: str, optional :param","= np.log2([i if i >= 1. else 1. for i in exp_values]) bplot","gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand = str(i[5])","plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed file","if(value > max_signal): max_signal = value if(not offset is None): end = start","= [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that",":class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\")","> max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect","== \"bottom\" the pyramid points downwards, defaults to top, :type location: str, optional","boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1.","region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2]","be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome:","color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return","Axis to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","ax = None): '''Function that plots arcs from unequal distances of genomic segments","Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5.","((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is","= hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None,","gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map","elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for","None if(not colors is None): color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color)","linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4],","] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if","points upwards, else if location == \"bottom\" the pyramid points downwards, defaults to","(default: None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene","range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if","to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","{\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list =","def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path","set, optional :param ax: Axis used for plotting, defaults to None. :type ax:","\"#bbbbbb\"] ax = ax if ax is not None else plt.gca() region_bed =","ID of g1 used for legend plotting, defaults to \"tumor\". :type g1_id: str,","# Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops =","Color code for minus stranded genes, default is \"#fb8072\". :type color_minus: str, optional.","TX_pos, direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation event as a bar,","if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed","{} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol =","< -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color =","''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed","0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path,","= len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]]","column is a ensemble gene id, and the second column is the HUGO","plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\",","if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 =","loc_coordinates: Either of \"up\" | \"down\". If \"up\", plot ticks to upper direction,","''' ax = ax if ax is not None else plt.gca() # Get","not set, then y_max is the max number of stacked genes, default is","End position of region to be plotted. :type r_end: int :param ax: Axis","plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.","ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None):","region to be plotted. :type r_chrom: str :param r_start: Start position of region","tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return","def plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function","describing plus or minus stranded genes is plotted, False otherwise. Default is False.","+= [patch] patch_description_list += [\"forward strand\"] met_forward = True elif(strand == \"-\" and","expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :])","is not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end","int, optional :param ax: Axis to be used for plotting, defaults to None.","\"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in","end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i","optional :param overhang: Fraction that the arrow is swept back (0 overhang means","position of region to be plotted. :type start: int :param end: End position","region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1):","genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2])","be returned :rtype: None ''' ax = ax if ax is not None","of the arc, defaults to \"k\". :type color: str, optional. :param ax: Axis","= [] vertices = [] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO,","ticks to lower direction, defaults to \"up\". :type loc_coordinates: str, optional :param revert_coordinates:",":param ax: Axes instance on which the genes are plotted, default is None.","to \"normal\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE","5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to","the list of patches drawn on the ax. 3. patch_description_list is the list","+= [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset =","the region to be plotted. :type chrom_r: str :param start_r: Chromosomal start position","and normal) on a gene region scale retaining the position of genes. :param","index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values","\"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4,","Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool, optional","meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in",".3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list = [] tick_positions =","0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices,","len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i","groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and","= int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for","[left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts = []","else: meth_before = (binned_average_meth[i-1] if not i == 0 else \"NA\") meth_after =",":type gene_names_map: dict. :param blacklist: Set containing gene ids not to be plotted,","= ax if ax is not None else plt.gca() # Get gene names","exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values",":param r_start: Start position of region to be plotted. :type r_start: int :param","Chromosome of the region to be plotted. :type chrom_r: str :param start_r: Start","None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\"color\":","False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist","[] for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value =","if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] =","Color used for plotting g2 samples expression, defaults to \"#80b1d3\". :type color_g2: str,","dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns:","optional :param ax: Axis on which to plot contact map, defaults to None.","None else plt.gca() c = 0 for region in regions: if(not edgecolor): current_color","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks,",":param blacklist: List of gene names, for genes that should not be shown","= {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list",":param color: color of bars, defaults to \"b\". :type color: str, optional :param","chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param start: Start","ax = ax if ax is not None else plt.gca() max_signal = 0","optional :param ax: Axis to be used for plotting, defaults to None. :type","color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1","max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list =","equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \"top_down\"): codes = [Path.MOVETO,","gene ids not to be plotted, defaults to None, :type blacklist: set, optional","if ax is not None else plt.gca() max_dist = 0 for e in","tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255.,","following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from","where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\ number","not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1,","gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map = {}","= cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin]))","\"None\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([],","integer values containing center positions of genes. :type gene_mid_points: list :param region: List","to \"k\". :type color: str, optional. :param ax: Axis where the plot is","ax if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries ==","to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized","Nothing to be returned. :rtype: None ''' ax = ax if ax is","[gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values","points are plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha:","Either of \"up\" | \"down\". If \"up\", plot ticks to upper direction, else","else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2","[]) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\",","0.) else 0. for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries","entries: 1. Chromosome 2. Start position 3. End position :type regions: iterator :param","\"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax is not None else","for plotting ax = ax if ax is not None else plt.gca() for","this ratio is underwent, the genes will be stacked. :type distance_ratio: float :return:","Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start,","True, a legend describing plus or minus stranded genes is plotted, False otherwise.","range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean +=","1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1]))","top, :type location: str, optional :param ax: Axis on which to plot contact","gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal","Fraction that the arrow is swept back (0 overhang means triangular shape). Can","+= 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter(","stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of","= motif_end color=color_plus dx = head_length if(strand == \"-\"): arrow_start = motif_end arrow_end","\"bottom\". If location == \"top\", the pyramid points upwards, else if location ==","color_plus: str, optional. :param color_minus: Color code for minus stranded genes, default is","markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos,","matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib","to \"b\". :type color: str, optional :param offset: Length of intervals, defaults to","plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not","thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold,","= int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist):","| \"bottom\". If location == \"top\", the pyramid points upwards, else if location","in methylation_bed: # Determine bin position = int(element[1]) if(position < start or position","else \"NA\") average_list = [ j for j in [meth_before, meth_after] if not","genomic_segments: List of segments for which distances shall be equalized (each segment is","else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point =","== \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"):","linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end,","ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region,","samples expression, defaults to \"#80b1d3\". :type color_g2: str, optional :param g1_id: ID of","region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to be plotted.","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None '''","[]) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\") files","optional :return: Nothing to be returned. :rtype: None ''' ax = ax if","plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function","= int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth","to be plotted, defaults to None :type vmin: float, optional :param vmax: Maximal","tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for gene_name in","horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1,","contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map,","color_minus: Color code for minus stranded genes, default is \"#fb8072\". :type color_minus: str,","genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If","optional :param color_loss: Plot color of copy number losses, defaults to \"r\". :type","to \"r\". :type color_loss: str, optional :param color_neutral: Plot color of copy number","capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list =","colors is None): color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions","Color of plus stranded TF regions, defaults to \"#80b1d3\". :type color_plus: str, optional","elif(strand == \"-\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse","to be plotted. :type chrom: str :param start: Start position of region to",":type blacklist: set, optional :param ax: (default: None) Axis used for plotting, defaults","called. :type segment_size: int :param cmap: Name of the colormap to be used","= [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not","with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict.","y_max is None): max_y_pos = y_max # Plot Exons for i in exons_in_region:","number of colors must be the same as the number of groups, defaults","from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy","color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns exons of genes. :param","= [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None):","to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be","\"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle","i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points","2. Start postion 3. End position 4. Value to be plotted as bar","0 left = [] height = [] for signal in chip_signals: start =","None ''' ax = ax if ax is not None else plt.gca() n_entries","(region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO]","pybedtools.BedTool object :param input_filename: Full path to ACESeq \"most_important\" file :type input_filename: str","following entries: 1. Chromsome 2. Start position 3. end position 4. Number methylated","[gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine",":type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing","in region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True,","i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for","for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,","iterator :param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str","position = int(element[1]) if(position < start or position > end): continue n_meth =","defaults to \"tumor\". :type g1_id: str, optional :param g2_id: ID of g2 used","Plot Exons for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name","be stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance","plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0,","tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <=","plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else the GENE","plotted, defaults to None, :type blacklist: set, optional :param ax: (default: None) Axis","from ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool object :param input_filename: Full","[[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates","segments for which contacts were called. :type segment_size: int :param cmap: Name of","contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0","chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\"","else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end)","equalizing distances, defaults to \"k\". :type color: str, optional :param direction: Direction of","the plot, default is None :type blacklist: list, optional :param plot_gene_ids: If True,","color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start,","ax if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region =","default is \"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color code for minus","def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph","end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter +=","of the region to be plotted. :type end: int :param head_width: Width of","horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1)","is the max number of stacked genes, default is None. :type y_max: bool,","optional :param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","be returned :rtype: None ''' # Use given axis for plotting ax =","region to be plotted. :type chrom_r: str :param start_r: Chromosomal start position of","readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\") files and converts them to","'''Functions that plots genomic regions as simple rectangles. :param regions: Iterator containig list-like","intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.)","is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed:","= expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0,","= {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start","tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color,","if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if","chrom_r: str :param start_r: Chromosomal start position of the region to be plotted.","lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r]) plt.ylim([0,","bool, optional :param loc_coordinates: Either of \"up\" | \"down\". If \"up\", plot ticks","neutral regions, defaults to \"k\". :type color_neutral: str, optional :param ax: Axis used","else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth)","end positiont of the region to be plotted. :type end_r: int :param color:","patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names ==","\"r\") cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7] ==","means triangular shape). Can be negative or greater than one. Defaults to 0.","to 0. :type rotation: int, optional :return: Nothing to be returned. :rtype: None","color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss","True, a point per expression value is plotted in addition to the boxplot,","lefts += [left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts","left_merged = [] height_merged = [] if(not merge is None): heights = []","to be used for plotting HiC intensities, defaults to \"Greys\". :type cmap: str,","Color of lines equalizing distances, defaults to \"k\". :type color: str, optional :param","i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] =","is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on which the","contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for","i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio):","If True, all gene ids will be included in the plot, False otherwise,","included in the plot, False otherwise, default is True :type plot_gene_ids: bool, optional","patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions)))","norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for","colors used for plotting samples expression. The number of colors must be the","to be plotted. :type end: int :param color: Color of the rectangles representing","for i in range(len(left)): if(i % merge == 0 and not (i ==","in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i","default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend","in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices =","plotted, defaults to None :type vmin: float, optional :param vmax: Maximal value of","or \"right\" (downstream), defaults to \"left\". :type direction: str, optional :param color: Color","(float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else","1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],","\"k\". :type color: str, optional. :param ax: Axis where the plot is drawn,",":type color_plus: str, optional. :param color_minus: Color code for minus stranded genes, default","if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in","genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type","(mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes)","\"#fb8072\". :type color_minus: str, optional :param ax: Axis on which to plot contact","region2 5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome","str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def","plotted. :type chrom_r: str :param start_r: Chromosomal start position of the region to","be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to","to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors","if not i == len(binned_average_meth)-1 else \"NA\") average_list = [ j for j","second column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing","int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev","> 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor = None","gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1)","of distance equalization (top_down | bottom_up), defaults to \"top_down\". :type direction: str, optional.","Start position of the region to be plotted. :type start_r: int :param end_r:","'''Function that plots methylation values as dot plots. :param meth_calls: Iterator containing list-like","a tab separated file, for which the first column is a ensemble gene","head_width: float, optional :param head_length: Length of the arrow in bp (depends on","# Plot Introns for i in introns_in_region: start = int(i[1]) end = int(i[2])","value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None):","else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for","pandas as pnd import numpy as np import tabix import matplotlib.ticker as ticker","colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if","= plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location","int :param TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction","plot_legend: bool :param color_g1: Color used for plotting g1 samples expression, defaults to","str :param r_start: Start position of region to be plotted. :type r_start: int","Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type",":return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename,","contact_map_index2) if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: #","1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,","color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for","else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2),","optional :param revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates","is None): g_id = ids[g] else: g_id = \"group \"+str(g) if(not g_id in",":rtype: None ''' # Use given axis for plotting ax = ax if",":param color_minus: Color code for minus stranded genes, default is \"#fb8072\". :type color_minus:","range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in","y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] =","= right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension","CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for","plotted side by side. If this ratio is underwent, the genes will be","set, optional :param ax: (default: None) Axis used for plotting, defaults to None.","break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start,","if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter +=","int :param color: Color of lines equalizing distances, defaults to \"k\". :type color:","direction=\"right\", color=\"k\", ax=None): '''Function that plots a translocation event as a bar, showing","plotted. :type start_r: int :param end_r: Chromosomal end positiont of the region to",":type overhang: float, optional :param color_plus: Color of plus stranded TF regions, defaults",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults to False.","Chromosome for which to plot CNVs. :type chromosome: str :param start: Start position","str, optional :param log_transformed: If True use log transformed values for plotting, non-transformed","0. :type overhang: float, optional :param color_plus: Color of plus stranded TF regions,","counter = 0 for element in methylation_bed: # Determine bin position = int(element[1])","The color field is used to determine the color for plotting (R,G,B). :type","start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions",":param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted.","dot plots. :param meth_calls: Iterator containing list-like elements with the following entries: 1.","chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2],","paired gene expression (e.g. tumor and normal) on a gene region scale retaining","capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False,","in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and","contacts were called. :type segment_size: int :param cmap: Name of the colormap to","ratio is underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple","current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick +","original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments >","vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range","+= [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"):","plot_gene_names: True if gene names shall be plotted, False otherwise, defaults to True.","= motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5,","str, optional :param color_neutral: Plot color of copy number neutral regions, defaults to","= color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch =","increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle","[] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick","of genes. :type gene_mid_points: list :param region: List containing the region to be","on which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","| \"down\". If \"up\", plot ticks to upper direction, else if \"down\", plot","plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to","region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos","c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5,","2. patch_list is the list of patches drawn on the ax. 3. patch_description_list","path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return","equalization (top_down | bottom_up), defaults to \"top_down\". :type direction: str, optional. :param ax:","g2_id: ID of g2 used for legend plotting, defaults to \"normal\". :type g2_id:",":param color: Color of the bar representing the translocation, defaults to \"k\". :type","equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in","blacklist): counter += 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos","positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if","log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param",":param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End, Methylated","\"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom,","ax is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i) for i in region]),","HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to","segments in different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom,","= [ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels)","0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],","log_transformed: If True use log transformed values for plotting, non-transformed values otherwise. :type","plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start +",":class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom: str :param","scales elements, defaults to \"k\". :type color: str, optional :param ax: Axis of","[] height = [] for signal in chip_signals: start = int(signal[1]) end =","[] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map","left += [start] height += [value] left_merged = [] height_merged = [] if(not","= ax if ax is not None else plt.gca() max_dist = 0 for","on the ax. 3. patch_description_list is the list of descriptions for the patches","of stacked genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal","ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \"",":param color: Color of the rectangles representing the regions to be plotted, defaults","None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2])",":type vmin: float, optional :param vmax: Maximal value of intensity range to be","None divisor = None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000",":type start: int :param end: End position of the region to be plotted.","color: str, optional. :param ax: Axis where the plot is drawn, defaults to","gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None","Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted.","rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start,",".3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean = []","to 1000. :type bin_size: int, optional :param ax: Axis to be used for","list :param start: Start position of the genomic region. :type start: int :param","different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end,","1. Chromsome 2. Start position 3. end position 4. Number methylated cytosines 5.",":param max_dev: Maximal deviation from ploidy to plot, defaults to None. :type max_dev:","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of","color_neutral: str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","number gains, defaults to \"g\". :type color_gain: str, optional :param color_loss: Plot color","end: int :param color: Color of the genomic scales elements, defaults to \"k\".","elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]]","position_gene_names: Either of \"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional","pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\",","plots genomic coordinates in a linea fashion. :param chrom: Chromosome of the region","current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) #","if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\".\", linestyle='None',","region to be plotted. :type start: int :param end: Chromosomal end position of","copy number neutral regions, defaults to \"k\". :type color_neutral: str, optional :param ax:","alpha=1, ax = None): '''Functions that plots genomic regions as simple rectangles. :param","to 1000. :type head_length: int, optional :param overhang: Fraction that the arrow is","optional :param color_minus: Color of plus stranded TF regions, defaults to \"#fb8072\". :type","The number of colors must be the same as the number of groups,","head_width: Width of the arrow head as proportion of the arrow, defaults to","plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5,","== \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom","str, optional :param g1_id: ID of g1 used for legend plotting, defaults to","+= [left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts =","to be plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis","= int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1]","be returned. :rtype: None ''' # Use given axis for plotting ax =","on the region that is plotted), defaults to 1000. :type head_length: int, optional","names, for genes that should not be shown on the plot, default is","start: Start position of the genomic region. :type start: int :param end: End","[] height_merged = [] if(not merge is None): heights = [] lefts =","rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([],","end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param","= float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\":","left\", \"upper right\", default is \"lower right\". :type legend_loc: str, optional :param color_plus:","if(not i % merge == 0): left_merged += [lefts[0]] lefts = [] height_merged","ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start,","4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\",","[\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7,","patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False","fashion. :param chrom: Chromosome of the region to be plotted. :type chrom: str","= [] heights += [height[i]] lefts += [left[i]] if(not i % merge ==","Maximal value of intensity range to be plotted, defaults to None. :type vmax:","i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for","= colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)*","str, optional :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as","patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border,","from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix import","if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\",","None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up","in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for","plus stranded TF regions, defaults to \"#fb8072\". :type color_minus: str, optional :param ax:","intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch","Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\")",":type legend_loc: str, optional :param color_plus: Color code for plus stranded genes, default","ID of g2 used for legend plotting, defaults to \"normal\". :type g2_id: str,","= [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \"NA\"):","e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]]","in the plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param","and the second column is the HUGO gene name :type gene_name_mapping_file: str :return:","patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed,","plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def","coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool, optional :param","genes are plotted side by side. If this ratio is underwent, the genes","0 else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n =","[((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start):","region midpoints. :rtype: list ''' ax = ax if ax is not None",":type r_start: int :param r_end: End position of region to be plotted. :type","to False. :type plot_points: bool, optional :param alpha: Alpha value for the background","optional :param legend_loc: Location of the legend. Either of \"lower left\", \"lower right\",","If this ratio is underwent, the genes will be stacked, default is 0.1.","midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start)","intensity range to be plotted, defaults to None :type vmin: float, optional :param","midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.),","None ''' # Use given axis for plotting ax = ax if ax","== \"#\" or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy","[]) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\",","int, optional :return: Nothing to be returned. :rtype: None ''' ax = ax","color_plus: Color code for plus stranded genes, default is \"#80b1d3\". :type color_plus: str,",":type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values:","= gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in blacklist):","cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end])","intensities, defaults to \"Greys\". :type cmap: str, optional :param vmin: Minimal value of","log_transformed: bool, optional :param plot_points: If True, a point per expression value is","2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold,","in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border =","n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else","Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type","n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions","2. Start position 3. end position 4. Beta Value :type meth_calles: iterator :param","region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) ==","swept back (0 overhang means triangular shape). Can be negative or greater than","plots a translocation event as a bar, showing the part of the genome","patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\","object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of","plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\",","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' # Use","defaults to top, :type location: str, optional :param ax: Axis on which to","the region to be plotted. :type chrom: str :param start: Start position of","if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border",":param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","(intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path,","else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter","y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name]","region to be plotted. :type end: int :param color: Color of the genomic","bool, optional :param y_max: Max y value in the gene plot. If not","+= [start] height += [value] left_merged = [] height_merged = [] if(not merge","arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0,","[current_tick] current_tick = current_tick + tick_size scale = None if(first_tick > 1000000): scale",".3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list =","elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in","Chromosome of region to be plotted. :type r_chrom: str :param r_start: Start position","4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy","stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and values =",":param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str :param","for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is","(midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO,","= \"group \"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id]",".0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start,","cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7] == \"#ploidy\"):","defaults to \"#80b1d3\". :type color_plus: str, optional :param color_minus: Color of plus stranded","revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param chrom:","len(binned_average_meth)-1 else \"NA\") average_list = [ j for j in [meth_before, meth_after] if","the region to be plotted. :type start_r: int :param end_r: End position of","the rectangle, representing the region to be plotted, defaults to 1. :type alpha:","None. :type offset: int, optional :param merge: Number of elements to be merged.","= float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None): end","plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV","IDs used for legend plotting, defaults to None. Number of ids must be","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus","int :param segment_size: Size of the segments for which contacts were called. :type",":type start_r: int :param end_r: Chromosomal end positiont of the region to be","plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\"--\",","\"-\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\"reverse strand\"] met_reverse = True","transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points:","will be stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes","positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not","int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect","ax = None): '''Functions that plots genomic regions as simple rectangles. :param regions:","for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to","current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c","ax = ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10)))","of ax width, such that two genes are plotted side by side. If","divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [","= list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for","of plus stranded TF regions, defaults to \"#fb8072\". :type color_minus: str, optional :param","colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed])","optional :param cnv_threshold: Minimal deviation from ploidy to be considered as a CNV,","= i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) >","False otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List of","End Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool`","range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine bin position","\"g\". :type color_gain: str, optional :param color_loss: Plot color of copy number losses,","location: Either of \"top\" | \"bottom\". If location == \"top\", the pyramid points","r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param r_start: Start","0, than merge elements will be averaged an plotted, defaults to 0. :type","= Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end])","If \"up\", plot ticks to upper direction, else if \"down\", plot ticks to","max_y_pos defines the \\ number of stacked genes. 2. patch_list is the list","heights += [height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged","motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus","region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is","position of region to be plotted. :type r_start: int :param r_end: End position","cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool`","region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False,","end_r, lw=1, color=\"k\", ax = None): '''Function that plots links between genomic regions","the region to be plotted. :type end_r: int :param TX_pos: Position of the","to be plotted. :type start: int :param end: Chromosomal end position of region","bool, optional :param alpha: Alpha value for the background color of the boxplots","import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None,","be plotted. :type end: int :param color: Color of the genomic scales elements,","e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance","tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow",":type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5","to False. :type plot_legend: bool, optional :param colors: List of colors used for","to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region","n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = []","that the arrow is swept back (0 overhang means triangular shape). Can be","expression. The number of colors must be the same as the number of","\"k\". :type color: str, optional :param direction: Direction of distance equalization (top_down |","end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax = None):","ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45)","None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for","defaults to \"r\". :type color_loss: str, optional :param color_neutral: Plot color of copy","\"right\" (downstream), defaults to \"left\". :type direction: str, optional :param color: Color of","4], [\"0\", \"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0,","+= [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not i","= (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.),","of lists containing the IDs of the different groups. :type groups: list :param","Start position 3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines","'''Function for plotting grouped gene expression (e.g. tumor and normal) on a gene","following elements: 1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome","number of stacked genes. 2. patch_list is the list of patches drawn on","4. Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator :param","exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool`","arcs. :param links_bed: Iterator, that contains bed-like structured lists with the following elements:","\"k\", \"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3}","left\", \"lower right\", \"upper left\", \"upper right\", default is \"lower right\". :type legend_loc:","= line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom","plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\",","0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per","to 1. :type alpha: float, optional. :param ax: Axis of plot, defaults to","> distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos):","bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border","SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of",":rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in","0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i","int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev):","blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting","between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists","patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved):","Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary","on a gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool`","= start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt',","to 0.2 :type head_width: float, optional :param head_length: Length of the arrow in","== 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights =","+= [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension","else: scale=\"Kb\" digits_to_round = None divisor = None if(scale == \"Mb\"): digits_to_round =","arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \"-\"):","in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None, merge=None): '''Function","gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX","position of region to be plotted. :type r_end: int :param ax: Axis of","ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\"#cbebc4\", edgecolor=False, alpha=1, ax","plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal) on","of region to be plotted. :type end: int :param segment_size: Size of the","in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for","dict. :param blacklist: Set containing gene ids not to be plotted, default to","patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >=","be equalized (each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments:","shown on the plot, default is None :type blacklist: list, optional :param plot_gene_ids:","for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\"b\", offset=None,","ax = ax if ax is not None else plt.gca() for motif in","max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2",":type regions: iterator :param start: Start position of the region to be plotted.","divisor = None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else:","if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 =","== max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break","plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted.","end: End position of the genomic region. :type end: int :param color: Color","int :param end: Chromosomal end position of region to be plotted. :type end:","max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label =","bin_size: size of bin to average methylation values, defaults to 1000. :type bin_size:","\"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3],","the translocation. :type TX_pos: int :param direction: Direction of the genomic part that","loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function","ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"):","str :param start: Start position on chromosome. :type start: int :param end: End",":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax = ax","= contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax","= None): '''Function that plots arcs from unequal distances of genomic segments to","HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type","= 0 for region in regions: if(not edgecolor): current_color = color rect =","\"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3}","offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which",".4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward =","3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],","= ax if ax is not None else plt.gca() for interval in cnvs_bed:","that plots methylation values as dot plots. :param meth_calls: Iterator containing list-like elements","float, optional :param vmax: Maximal value of intensity range to be plotted, defaults","end position of region to be plotted. :type end: int :param segment_size: Size","heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2.","End position of region to be plotted. :type end: int :param color: Color","= [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick =","plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1,","region to be plotted. :type start_r: int :param end_r: Chromosomal end positiont of","in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,","extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\"color\": \"k\",","= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value =","tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1),","alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1])","plotted. :type end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int","y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list","for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3])","of groups. :type ids: list, optional. :param plot_gene_names: True if gene names shall","ids and values = y position \\ of gene. :rtype: tuple ''' sort_indices","cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold):","True if gene names shall be plotted, False otherwise, defaults to True. :type","max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys =",".4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2,","IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object","== \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name,","Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([],","Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes","region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse","optional. :param ax: Axis where the plot is drawn, defaults to None. :type","color: Color of the genomic scales elements, defaults to \"k\". :type color: str,","on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List","to equal distances. :param genomic_segments: List of segments for which distances shall be","be plotted. :type end: int :param color: Color of points representing methylation values,","ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list = [] ploidy =","wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens","is the list of descriptions for the patches \\ drawn on the ax.","List of gene names, for genes that should not be shown on the","= max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def","use log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional","region_size: int :param distance_ratio: Minimal distance between two genes, as ratio of ax",":type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all",":type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom:",":param ids: IDs used for legend plotting, defaults to None. Number of ids","legend. Either of \"lower left\", \"lower right\", \"upper left\", \"upper right\", default is","if (float(i[0])+float(i[1])) > 0 else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing =","(start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin =","exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up","if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2),","for m in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1])","[gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1",":type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param","plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix segments in","is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None","proportion of the arrow, defaults to 0.2 :type head_width: float, optional :param head_length:","counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border","int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not","+= [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick > 1000000):","1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2 5.","regions: Iterator containig list-like elements with the following entries: 1. Chromosome 2. Start","= color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c +=","GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names:","chrom, start, end, color=\"k\", ax=None): '''Function that plots methylation values as dot plots.","lefts = [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left","= left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values","in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices]","else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else","ploidy to plot, defaults to None. :type max_dev: float, optional :param ax: Axis","\"left\" (upstream), or \"right\" (downstream), defaults to \"left\". :type direction: str, optional :param","= color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down =","as pnd import numpy as np import tabix import matplotlib.ticker as ticker from","region to be plotted. :type end: int :param bin_size: size of bin to",":type color_neutral: str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing","max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines","plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a","boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None):","Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up),","extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension","genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border =","== \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor","plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend","def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for gene","= str(i[5]) color = color_forward if(strand == \"-\"): color = color_reverse border_distance_down =","gene_mid_points: list :param region: List containing the region to be plotted ([<chrom>, <start>,","set, then y_max is the max number of stacked genes, default is None.","y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos +=","ticks, else if False make more ticks. :type upper: bool, optional :param loc_coordinates:","\"k\". :type color_neutral: str, optional :param ax: Axis used for plotting. :type ax:","Iterator, that contains bed-like structured lists with the following elements: 1. Chromosome region1",":param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object","tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold):","deviation from ploidy to be considered as a CNV, defaults to 0.7. :type","strand, start, end, color). The color field is used to determine the color","ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None,","ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45)","TX_end = end_r if(direction == \"left\"): TX_start = start_r TX_end = TX_pos rect","== \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3,","met_reverse = False # Plot Introns for i in introns_in_region: start = int(i[1])","Max y value in the gene plot. If not set, then y_max is","is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param","else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in","to False. :type plot_legend: bool :param color_g1: Color used for plotting g1 samples","a ensemble gene id, and the second column is the HUGO gene name","n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments:","= color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev)","as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap","overhang: Fraction that the arrow is swept back (0 overhang means triangular shape).","ax: Axis on which to plot contact map, defaults to None. :type ax:","ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else:","sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None):","edge is plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha","used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom:","elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2,","import matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy as","TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r])","] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ]","per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m","CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of","of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be","color_g1: str, optional :param color_g2: Color used for plotting g2 samples expression, defaults","plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise,","start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in","+= [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1 = list(exp_values_g1) else:","gene plot. If not set, then y_max is the max number of stacked","None): heights = [] lefts = [] for i in range(len(left)): if(i %","Nothing to be returned :rtype: None ''' # Use given axis for plotting","facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]),","\"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def","contact_map: Matrix that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame`",":type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted,","segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], [])","region to be plotted. :type chrom: str :param start: Start position of the","patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.)","of \"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional :param log_transformed:","\"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\"0\", \"2\",","color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else:",":class:`pybedtools.BedTool` object containing the one region, for which the gene plot is created.","start: str :param end: End position of the region to be plotted. :type","if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\", \"2\",","j == \"NA\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0","simple rectangles. :param regions: Iterator containig list-like elements with the following entries: 1.","to tabixed bed file containing (chrom, start, end, name, score, strand, start, end,","defaults to \"k\". :type color: str, optional :param ax: Axis of plot, defaults","gene expression (e.g. tumor and normal) on a gene region scale retaining the","split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close()","current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255.,","[patch] patch_description_list += [\"reverse strand\"] met_reverse = True # Plot Gene Names if(plot_gene_ids):","Start position of region to be plotted. :type r_start: int :param r_end: End","the translocation, defaults to \"k\". :type color: str, optional :param ax: Axis of","bed file containing (chrom, start, end, name, score, strand, start, end, color). The","plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed,","the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the","continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension)","not None else plt.gca() # Calculate midpoints of original and distance equalized segments","= None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension",":param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list","is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing",":type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should","gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\",","chip_signals: Iterator for which each element is a list-ike object containing: 1. Chromosome",":param chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param start:","plotting g1 samples expression, defaults to \"#fb8072\". :type color_g1: str, optional :param color_g2:","region1 4. Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator","all gene ids will be included in the plot, False otherwise, default is",":type cnv_threshold: float, optional :param color_gain: Plot color of copy number gains, defaults","which contacts were called. :type segment_size: int :param cmap: Name of the colormap","chromosome: str :param start: Start position on chromosome. :type start: int :param end:","= str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y","equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \"top_down\"): codes","values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point per","plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], [])","returned :rtype: None ''' # Use given axis for plotting ax = ax","of the region to be plotted. :type start_r: int :param end_r: End position","a gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object","not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed","= [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\",","(columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with","tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"):","to \"Greys\". :type cmap: str, optional :param vmin: Minimal value of intensity range","underwent, the genes will be stacked, default is 0.1. :type distance_ratio: float, optional","ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end,","gene_mid_points: list of integer values containing center positions of genes. :type gene_mid_points: list",":param offset: Length of intervals, defaults to None. :type offset: int, optional :param",":param bin_size: size of bin to average methylation values, defaults to 1000. :type","pnd import numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches","alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\",",".3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else:","else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up =","is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene","defaults to \"left\". :type direction: str, optional :param color: Color of the bar","per expression value is plotted in addition to the boxplot, no points are","position of region to be plotted. :type end: int :param bin_size: size of","of plus stranded TF regions, defaults to \"#80b1d3\". :type color_plus: str, optional :param","determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for gene plotting","split_line[1].split(\".\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map,",":param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool`","plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha value of","angle of coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing","scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor = None if(scale ==","str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions","2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start,","+= [np.mean(heights)] heights = [] offset = merge*offset left = left_merged height =","if(not y_max is None): max_y_pos = y_max # Plot Exons for i in","end, ax = None): '''Function for plotting genomix segments in different colors :param","end: int :param head_width: Width of the arrow head as proportion of the","be plotted, defaults to None :type vmin: float, optional :param vmax: Maximal value","def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting","gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2])",":param end: End position of the region to be plotted. :type end: int","optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position","None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start","color=\"k\", ax=None): '''Function that plots a translocation event as a bar, showing the",":type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which","of colors used for plotting samples expression. The number of colors must be","= ax if ax is not None else plt.gca() for motif in motifs_bed:","range to be plotted, defaults to None. :type vmax: float, optional :param location:","[3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),",":param color_gain: Plot color of copy number gains, defaults to \"g\". :type color_gain:","rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else:","translocated. :param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str","not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2,","to \"#fb8072\". :type color_g1: str, optional :param color_g2: Color used for plotting g2","be plotted, defaults to None, :type blacklist: set, optional :param ax: (default: None)","> 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values",":param end: End position of the genomic region. :type end: int :param color:","is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be","\"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed):","optional :return: Nothing to be returned :rtype: None ''' # Use given axis","== \"left\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start,","of the genomic scales elements, defaults to \"k\". :type color: str, optional :param","defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype:","methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom: str","values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of","matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True,","region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons for","overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end,","y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not","be the same as the number of groups, defaults to None. :type colors:","List of lists containing the IDs of the different groups. :type groups: list","downwards, defaults to top, :type location: str, optional :param ax: Axis on which","\"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color code for minus stranded genes,","elements will be averaged an plotted, defaults to 0. :type merge: int, optional","that is translocated. :param chrom_r: Chromosome of the region to be plotted. :type","in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i","1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that","containing gene ids not to be plotted, default to None. :type blacklist: set,","i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border","the arrow is swept back (0 overhang means triangular shape). Can be negative","print(ploidy) if(line[0] == \"#\" or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev","plotted. :type start: int :param end: End position of the region to be",":type color_plus: str, optional :param color_minus: Color of plus stranded TF regions, defaults","<end>]). :type region: list :param groups: List of lists containing the IDs of","None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine","= [] gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names:","== \"top\"): ax.xaxis.set_ticks_position(\"top\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i","meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome 2. Start","plt.gca() # Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments)","used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with","= ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax","legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns exons of","\"top\", or \"bottom\", defaults to \"bottom\". :type position_gene_names: str, optional :param log_transformed: If","i % merge == 0): left_merged += [lefts[0]] lefts = [] height_merged +=","cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1,","arrow head as proportion of the arrow, defaults to 0.2 :type head_width: float,","blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene","None for line in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0]","dict ''' ax = ax if ax is not None else plt.gca() patches_dict","m in meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color,","False, no edge is plotted, defaults to False. :type edge_color: str, optional :param","plotted. :type end: int :param color: Color of the genomic scales elements, defaults","< extension): extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3} flierprops =","right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns exons of genes.","None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round =","in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], [])","((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None","189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start),","1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls,","[] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border","in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn =","False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults","patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is","name, score, strand, start, end, color). The color field is used to determine","if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end,","of \"left\" (upstream), or \"right\" (downstream), defaults to \"left\". :type direction: str, optional","region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list,","class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted, defaults to","head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start,","list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1.","cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)","plotted. :type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","given axis for plotting ax = ax if ax is not None else","the genomic part that is translocated. Either of \"left\" (upstream), or \"right\" (downstream),","genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of","exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\",","= ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color =","ax = ax if ax is not None else plt.gca() binned_meth_calls = [","plotted. :type end: str :param ax: Axis used for plotting, defaults to None.","gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in blacklist): counter",":param start_r: Start position of the region to be plotted. :type start_r: int","position of the region to be plotted. :type end: int :param head_width: Width","else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops,","to be plotted. :type start: int :param end: End position of the region","If True, coordinates are reverted to decreasing order. Else, coordinates stay in increasing","genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes.","[ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing","edge. If False, no edge is plotted, defaults to False. :type edge_color: str,","Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size =","color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color,","else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops,","== \"top\", the pyramid points upwards, else if location == \"bottom\" the pyramid","is not None else plt.gca() c = 0 for region in regions: if(not","name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary","\"up\" | \"down\". If \"up\", plot ticks to upper direction, else if \"down\",","for genes that should not be shown on the plot, default is None","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\", linewidth=.5) elif(ploidy == 4):","<end>]) :type genomic_segments: list :param start: Start position of the genomic region. :type","to ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from","color_loss: str, optional :param color_neutral: Plot color of copy number neutral regions, defaults","Chromsome 2. Start position 3. end position 4. Number methylated cytosines 5. Number","= int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in","else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between","ploidy = None for line in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1])","plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function","if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio):","j in [meth_before, meth_after] if not j == \"NA\" ] binned_average_meth_no_missing += [","color=\"k\", ax=None): '''Function that plots methylation values as dot plots. :param meth_calls: Iterator","\"group \"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\"boxes\"][0]] patch_description_list += [g_id] counter","plots HiC contact maps as pyramid plots :param contact_map: Matrix that contains the","plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads","order. Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool,","[] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name","end, direction=\"top_down\", color=\"k\", ax = None): '''Function that plots arcs from unequal distances","y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break","meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m","0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch =","that should not be shown on the plot, default is None :type blacklist:","End position on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor,","= -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) <","e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points","color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \"+\" and not(met_forward)): patch_list += [patch] patch_description_list","== \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False)","optional :param ids: IDs used for legend plotting, defaults to None. Number of","defaults to \"Greys\". :type cmap: str, optional :param vmin: Minimal value of intensity","color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor","this value is not equal to 0, than merge elements will be averaged","plotted. :type start_r: int :param end_r: End position of the region to be",":rtype: dict ''' ax = ax if ax is not None else plt.gca()","genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base pairs.","binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) >","[[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos]","grouped gene expression (e.g. tumor and normal) on a gene region scale equalizing","If True, make less ticks, else if False make more ticks. :type upper:","else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,","1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,","float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold","of bin to average methylation values, defaults to 1000. :type bin_size: int, optional","on which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional","right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter +=","gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0]","is None and gene_name in blacklist): counter += 1 continue n_groups = len(groups)","gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples","to \"left\". :type direction: str, optional :param color: Color of the bar representing","be plotted. :type start: int :param end: End position of region to be","capprops=capprops, showfliers=False) color = None if(not colors is None): color = colors[g] else:","the genes will be stacked, default is 0.1. :type distance_ratio: float, optional :param","legend_loc: Location of the legend. Either of \"lower left\", \"lower right\", \"upper left\",","= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2),","position of region to be plotted. :type start: int :param end: Chromosomal end","patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border)","to be plotted. :type end: int :param color: Color of the genomic scales","and gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand =",":class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene","be returned. :rtype: None ''' ax = ax if ax is not None","Value :type meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type",":param region_size: Size of region to be plotted in base pairs. :type region_size:","= (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch =","vmax: Maximal value of intensity range to be plotted, defaults to None. :type","gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of","containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region","g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be","edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none',","path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start,","intensity range to be plotted, defaults to None. :type vmax: float, optional :param","function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed:",":param cmap: Colormap used for plotting CNVs, defaults to \"bwr\". :type cmap: str,","left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor = color)","region to be plotted. :type chrom: str :param start: Start position of region","= [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor =","medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1.","defaults to None. :type colors: str, optional :param ids: IDs used for legend","if(strand == \"-\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start","color_forward if(strand == \"-\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start,","int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset is","the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of","None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap =","1. Chromosome 2. Start postion 3. End position 4. Value to be plotted","exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type","genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e","\"down\". If \"up\", plot ticks to upper direction, else if \"down\", plot ticks","order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of","height = [] for signal in chip_signals: start = int(signal[1]) end = int(signal[2])","containing the one region, for which the gene plot is created. :type region_bed:","extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and","plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor","= ax if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries","plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal) on a","to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of","\"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1],",":param plot_legend: If True plot legend, False otherwise, defaults to False. :type plot_legend:","height_merged = [] if(not merge is None): heights = [] lefts = []","head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map,","if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5):","exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 =","and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = []","current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if","link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2,","[] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names","if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist",":param start: Start position of region to be plotted. :type start: int :param","end, name, score, strand, start, end, color). The color field is used to","gains, defaults to \"g\". :type color_gain: str, optional :param color_loss: Plot color of","color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([], [])","the region to be plotted. :type start: int :param end: End position of","ratio is underwent, the genes will be stacked, default is 0.1. :type distance_ratio:","of the rectangles representing the regions to be plotted, defaults to \"#cbebc4\". :type","ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([","end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)):","tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return","containing the region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param","considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain:","\"r\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id =","ax: Axes instance on which the genes are plotted, default is None. :type","ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size =","plotCoordinates(chrom, start, end, color=\"k\", ax = None, upper=True, loc_coordinates=\"up\", revert_coordinates=False, rotation=0): '''Function that","for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is","color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([],","Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value","list of patches drawn on the ax. 3. patch_description_list is the list of","matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed,","distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments","5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of","plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2,","i in range(n): if(not binned_average_meth[i] == \"NA\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before =","codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor","end: int :param color: Color of lines equalizing distances, defaults to \"k\". :type","True, coordinates are reverted to decreasing order. Else, coordinates stay in increasing order,","names of segments, and values patch :rtype: dict ''' ax = ax if","defaults to \"k\". :type color_neutral: str, optional :param ax: Axis used for plotting.","[0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1)",":type bin_size: int, optional :param ax: Axis to be used for plotting, defaults",":param color: Color of the arc, defaults to \"k\". :type color: str, optional.","ticks += [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick >","lefts = [] for i in range(len(left)): if(i % merge == 0 and","then y_max is the max number of stacked genes, default is None. :type","the max number of stacked genes, default is None. :type y_max: bool, optional","'''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following","ax = ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size","size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is","size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function","otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max y value","interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i ==","None ''' ax = ax if ax is not None else plt.gca() for","gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border =","0 for element in methylation_bed: # Determine bin position = int(element[1]) if(position <","genomic regions as simple rectangles. :param regions: Iterator containig list-like elements with the","= region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction ==","ids must be the same as the number of groups. :type ids: list,","line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0] gene_map[ensembl_gene_id]","to \"k\". :type color: str, optional :param ax: Axis of plot, defaults to","the same as the number of groups. :type ids: list, optional. :param plot_gene_names:","ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def","= {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol","Methylation calls. Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated",":param start: Start position on chromosome. :type start: int :param end: End position","if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0)","for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color =","ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids","float, optional. :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","= gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions","and not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged +=","gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names,","colors: List of colors used for plotting samples expression. The number of colors","color = colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+","to a tab separated file, for which the first column is a ensemble","1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function","be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names:","End region1 4. Chromosome region2 5. Start region2 6. End region2 :type links_bed:","float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\"color\": \"k\", \"linewidth\": .3}","g1_id: str, optional :param g2_id: ID of g2 used for legend plotting, defaults","exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1)","n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \"NA\" for i","plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end])","is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops =","plots :param contact_map: Matrix that contains the intensity values of HiC contacts. :type","str, optional :param color_minus: Color of plus stranded TF regions, defaults to \"#fb8072\".","containing gene ids not to be plotted, defaults to None, :type blacklist: set,","= [ j for j in [meth_before, meth_after] if not j == \"NA\"","for plotting paired gene expression (e.g. tumor and normal) on a gene region",":]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else","End position of the region to be plotted. :type end: int :param head_width:","is \"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color code for minus stranded","plotting grouped gene expression (e.g. tumor and normal) on a gene region scale","showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in","== \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\",","y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio)","= Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect =","expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO",":param color_g2: Color used for plotting g2 samples expression, defaults to \"#80b1d3\". :type","if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor =","< start or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4])","if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1","patch_description_list is the list of descriptions for the patches \\ drawn on the","offset = merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color",".3} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops =","[ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3)",":type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` '''","current_tick = current_tick + tick_size scale = None if(first_tick > 1000000): scale =","= int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and","plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that plots methylation values as dot",":param start: Start position of the genomic region. :type start: int :param end:","to be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance","\"k\", \"linewidth\": .3, \"alpha\":alpha} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\":","(float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in m in","linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2):","is plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc:","genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed:","included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom:","different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs,","plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None):","False. :type plot_legend: bool, optional :param legend_loc: Location of the legend. Either of","\"tumor\". :type g1_id: str, optional :param g2_id: ID of g2 used for legend","be shown on the plot, default is None :type blacklist: list, optional :param",":type plot_legend: bool :param color_g1: Color used for plotting g1 samples expression, defaults","region to be plotted. :type r_start: int :param r_end: End position of region","CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4.",":param head_length: Length of the arrow in bp (depends on the region that","in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start =","for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name]","Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if","\"#ffd92f\", \"#e5c494\", \"#bbbbbb\"] ax = ax if ax is not None else plt.gca()","if i >= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values,","extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean +=","= [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO,","capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list = [] tick_positions = []",":param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids;",":type location: str, optional :param ax: Axis on which to plot contact map,","plot ticks to upper direction, else if \"down\", plot ticks to lower direction,","[gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border","return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting","position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool`","codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else","color_g2=\"#80b1d3\", g1_id=\"tumor\", g2_id=\"normal\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and",":class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed:","of equalized region midpoints. :rtype: list ''' ax = ax if ax is","ax: Axis where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,","to be plotted. :type r_start: int :param r_end: End position of region to","else plt.gca() max_signal = 0 left = [] height = [] for signal","list, optional. :param plot_gene_names: True if gene names shall be plotted, False otherwise,","are hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype:","start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\", ax=None): '''Function that plots TF motifs",">= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list","optional :return: Dictionary with keys = names of segments, and values patch :rtype:","the boxplot, no points are plotted otherwise, defaults to False. :type plot_points: bool,","distance_ratio): '''Function that determines the max y position for gene plotting via function","extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops","as pyramid plots :param contact_map: Matrix that contains the intensity values of HiC","= int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist):",":type chrom_r: str :param start_r: Chromosomal start position of the region to be","is None): heights = [] lefts = [] for i in range(len(left)): if(i","= genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in",":type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\",","m in m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([],","plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\"k\", ax=None): '''Function that","= np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range =",":param input_filename: Full path to ACESeq \"most_important\" file :type input_filename: str :return: :class:`pybedtools.BedTool`","(i == 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights","tick_size scale = None if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round","-.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([],","for m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([","gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions +=","i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels():","i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates ==","None. :type max_dev: float, optional :param ax: Axis used for plotting, defaults to",":param blacklist: Set containing gene ids not to be plotted, default to None.","start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots HiC contact","End position of region to be plotted. :type end: int :param bin_size: size","gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension","= expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0,","3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1.",":type start: int :param end: End position of the genomic region. :type end:","edge_color: Color of region edge. If False, no edge is plotted, defaults to","alpha: float, optional. :param ax: Axis of plot, defaults to None. :type ax:","[patch] patch_description_list += [\"forward strand\"] met_forward = True elif(strand == \"-\" and not(met_reverse)):","Introns for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name =","= PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points",":param color_g1: Color used for plotting g1 samples expression, defaults to \"#fb8072\". :type","cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start Position,","the legend. Either of \"lower left\", \"lower right\", \"upper left\", \"upper right\", default","i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1)","merge: int, optional :return: Nothing to be returned. :rtype: None ''' ax =","optional :return: List of equalized region midpoints. :rtype: list ''' ax = ax","0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed:","plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\"#80b1d3\", color_minus=\"#fb8072\",","ensemble gene id, and the second column is the HUGO gene name :type","position of the genomic region. :type start: int :param end: End position of","max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end","color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i],","end]) plt.yticks([], []) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], [])","left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights","> 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points +=","fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None,","containing the IDs of the different groups. :type groups: list :param gene_names_map: Dictionary","of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True,","than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >=","0 for region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]),","gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1):","if ax is not None else plt.gca() # Calculate midpoints of original and","on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax","be plotted. :type end: str :param ax: Axis used for plotting, defaults to","float, optional :param ax: Axes instance on which the genes are plotted, default","containing the expression values of g1 samples (columns: sample ids; index: gene ids)","defaults to 1000. :type head_length: int, optional :param overhang: Fraction that the arrow","'''Function for plotting paired gene expression (e.g. tumor and normal) on a gene","expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :])","Direction of the genomic part that is translocated. Either of \"left\" (upstream), or","of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with","ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\"bottom\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped","value in the gene plot. If not set, then y_max is the max","color of copy number gains, defaults to \"g\". :type color_gain: str, optional :param","< link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance),","optional :param cmap: Colormap used for plotting CNVs, defaults to \"bwr\". :type cmap:","if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region",":return: List of equalized region midpoints. :rtype: list ''' ax = ax if","== \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i","chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments","defaults to None. :type offset: int, optional :param merge: Number of elements to","np.log2([i if i >= 1. else 1. for i in exp_values]) bplot =","containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns","chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults to 2.","end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int :param direction:","not be shown on the plot, default is None :type blacklist: list, optional","capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2):","max_dev: Maximal deviation from ploidy to plot, defaults to None. :type max_dev: float,","1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,","if(revert_coordinates): ticks = [ start + end-i for i in ticks ] ticks.reverse()",":type plot_points: bool, optional :param alpha: Alpha value for the background color of","plotting g2 samples expression, defaults to \"#80b1d3\". :type color_g2: str, optional :param g1_id:","upper: bool, optional :param loc_coordinates: Either of \"up\" | \"down\". If \"up\", plot","\"top\", the pyramid points upwards, else if location == \"bottom\" the pyramid points","and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the","= [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2])","loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines","i >= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos],","]+[1]) segment_type = str(segment[3]) if(segment_type == \"R\"): color = (1,1,1,1) rect = Rectangle((segment_start,","otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha value for","blacklist: list, optional :param plot_gene_ids: If True, all gene ids will be included","value if(not offset is None): end = start + offset left += [start]","= Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <=","the region to be plotted. :type end: int :param head_width: Width of the","plus stranded genes, default is \"#80b1d3\". :type color_plus: str, optional. :param color_minus: Color","start: Start position of the region to be plotted. :type start: str :param","ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5,","'''Function that plots HiC contact maps as pyramid plots :param contact_map: Matrix that","exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is","for which distances shall be equalized (each segment is of the form [<chrom>,","containing center positions of genes. :type gene_mid_points: list :param region: List containing the","'''Function that reads CNVs from ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool","# Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down-","Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path",":type plot_legend: bool, optional :param colors: List of colors used for plotting samples","normal) on a gene region scale retaining the position of genes. :param genes_bed:","CNVs from ACESeq (\"*most_important*\") files and converts them to pybedtools.BedTool object :param input_filename:","is the max_y_position + 1.5. max_y_pos defines the \\ number of stacked genes.","if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df,","be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number","in increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational","ticks. :type upper: bool, optional :param loc_coordinates: Either of \"up\" | \"down\". If","plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region","in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine bin","gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype:","that determines the max y position for gene plotting via function plotGenes. :param","as plt import pybedtools import pandas as pnd import numpy as np import","object :param input_filename: Full path to ACESeq \"most_important\" file :type input_filename: str :return:","start: Chromosomal start position of region to be plotted. :type start: int :param","region to be plotted. :type end: int :param color: Color of the rectangles","ax = ax if ax is not None else plt.gca() # Get gene","CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \"r\") cnv_bed_list = []","left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else:",":type region: list :param groups: List of lists containing the IDs of the","plotting paired gene expression (e.g. tumor and normal) on a gene region scale","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4,","gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]),","if ax is not None else plt.gca() # Get gene names and regions","current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn","\"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax","Width of the arrow head as proportion of the arrow, defaults to 0.2",":class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids; index: gene","= color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): #","genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size:","float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None): end =","not None else plt.gca() max_signal = 0 left = [] height = []","matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2.,","scale = None if(first_tick > 1000000): scale = \"Mb\" else: scale=\"Kb\" digits_to_round =",":param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:","codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\") plt.xlim([start, end]) plt.ylim([0, 1])","plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be","the same as the number of groups, defaults to None. :type colors: str,","ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function for plotting gene structures, i.e. introns","from ploidy to be considered as a CNV, defaults to 0.7. :type cnv_threshold:","representing the region to be plotted, defaults to 1. :type alpha: float, optional.","optional :param distance_ratio: Minimal distance between two genes, as ratio of ax width,","(R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted.","split_line[2], str(ploidy_dev), split_line[5], \"+\"] ] input_file.close() return pybedtools.BedTool(\"\\n\".join([\"\\t\".join(e) for e in cnv_bed_list]), from_string=True)",":type position_gene_names: str, optional :param log_transformed: If True use log transformed values for","introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing","maps as pyramid plots :param contact_map: Matrix that contains the intensity values of","genes. 2. y_pos_dict: Dictionary with keys = gene ids and values = y","= max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand","to \"top_down\". :type direction: str, optional. :param ax: Axis on which to plot,","containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End Position,","= 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance","the patches \\ drawn on the ax. :rtype: list \"\"\" ax = ax","rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect)","start: int :param end: End position of the genomic region. :type end: int","\"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list = [] tick_positions","bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m =","y_max is the max number of stacked genes, default is None. :type y_max:","None ''' ax = ax if ax is not None else plt.gca() binned_meth_calls","float, optional :param color_plus: Color of plus stranded TF regions, defaults to \"#80b1d3\".","right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension <","defaults to \"k\". :type color: str, optional :param direction: Direction of distance equalization",":class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param","= lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r])","exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size",":param end: End position of the region to be plotted. :type end: str","\"lower left\", \"lower right\", \"upper left\", \"upper right\", default is \"lower right\". :type","stranded TF regions, defaults to \"#80b1d3\". :type color_plus: str, optional :param color_minus: Color","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' # Use","if location == \"top\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define","import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import","in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else:","else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which","values patch :rtype: dict ''' ax = ax if ax is not None","Color of the genomic scales elements, defaults to \"k\". :type color: str, optional","bool, optional :param legend_loc: Location of the legend. Either of \"lower left\", \"lower","region_size: Size of region to be plotted in base pairs. :type region_size: int","= [] counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0]",":param gene_mid_points: list of integer values containing center positions of genes. :type gene_mid_points:","Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list =","ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax =","for which contacts were called. :type segment_size: int :param cmap: Name of the","\"R\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type]","plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end,","one region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param","'''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed","plotted, defaults to \"#cbebc4\". :type color: str, optional :param edge_color: Color of region","colors[g] else: color = standard_colors[g] bplot[\"boxes\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3)))","which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is","element is a list-ike object containing: 1. Chromosome 2. Start postion 3. End","ax is not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction","color_plus: str, optional :param color_minus: Color of plus stranded TF regions, defaults to","for plus stranded genes, default is \"#80b1d3\". :type color_plus: str, optional. :param color_minus:","# Determine bin position = int(element[1]) if(position < start or position > end):","for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\") ensembl_gene_id = split_line[0].split(\".\")[0] hugo_gene_symbol = split_line[1].split(\".\")[0]","end_r: Chromosomal end positiont of the region to be plotted. :type end_r: int","merge: Number of elements to be merged. If this value is not equal","= ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev","if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt',","(j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1])","-.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates ==","plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.","position 3. End position :type regions: iterator :param start: Start position of the","midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices,","(0 overhang means triangular shape). Can be negative or greater than one. Defaults","index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE",":type chrom: str :param start: Start position of the region to be plotted.","End position of the genomic region. :type end: int :param color: Color of","= max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list","Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix","optional :param color_plus: Color code for plus stranded genes, default is \"#80b1d3\". :type","'''Function that plots genomic coordinates in a linea fashion. :param chrom: Chromosome of","str, optional :param ids: IDs used for legend plotting, defaults to None. Number","verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i],","(e.g. tumor and normal) on a gene region scale equalizing the position of","code for minus stranded genes, default is \"#fb8072\". :type color_minus: str, optional. :return:","2. Start region1 3. End region1 4. Chromosome region2 5. Start region2 6.","int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\",\") ]+[1]) segment_type = str(segment[3])","int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\"0\", \"1\",","def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\", ax = None): '''Function that plots","file, for which the first column is a ensemble gene id, and the","+= [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ ==","mapping between gene ids :param gene_name_mapping_file: Path to a tab separated file, for","# Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size","+ 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name,",":type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples","optional :param max_dev: Maximal deviation from ploidy to plot, defaults to None. :type","links_bed: iterator :param chrom_r: Chromosome of the region to be plotted. :type chrom_r:","str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end], [0,","(i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2.,","cmap: Colormap used for plotting CNVs, defaults to \"bwr\". :type cmap: str, optional","= len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for","region to be plotted. :type start: str :param end: End position of the","str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the","Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], [])",":type chrom_r: str :param start_r: Start position of the region to be plotted.",":param start_r: Chromosomal start position of the region to be plotted. :type start_r:","linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy ==","m in meth_calls], color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2.","from unequal distances of genomic segments to equal distances. :param genomic_segments: List of","= -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True)","optional :param plot_legend: If True plot legend, False otherwise, defaults to False. :type","Color of the bar representing the translocation, defaults to \"k\". :type color: str,",":type head_width: float, optional :param head_length: Length of the arrow in bp (depends","optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing","in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \"top\" else range(contact_map_index1,","expression values of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1:","= None divisor = None if(scale == \"Mb\"): digits_to_round = int(6-np.log10(tick_size)) divisor =","Maximal deviation from ploidy to plot, defaults to None. :type max_dev: float, optional","region to be plotted. :type end: str :param ax: Axis used for plotting,","for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0","for plotting ax = ax if ax is not None else plt.gca() colors","patch_list += [patch] patch_description_list += [\"forward strand\"] met_forward = True elif(strand == \"-\"","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None","optional :param plot_legend: If True, a legend describing plus or minus stranded genes","plt.yticks([0, 2, 4, 6], [\"0\", \"2\", \"4\", \"6\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome,","scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and",":type log_transformed: bool, optional :param plot_points: If True, a point per expression value","+= [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else \"NA\")","in blacklist): continue # Define color for gene plotting strand = str(i[5]) color","list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"):","for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is","color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function","plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from","= region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map","\"linewidth\": .3} flierprops = {\"color\": \"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted,","-1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold):","plotted. :type start: str :param end: End position of the region to be","for gene plotting strand = str(i[5]) color = color_forward if(strand == \"-\"): color","as simple rectangles. :param regions: Iterator containig list-like elements with the following entries:","region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower right\", color_plus=\"#80b1d3\", color_minus=\"#fb8072\"): \"\"\"Function",":type direction: str, optional. :param ax: Axis on which to plot, defaults to","in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0,","bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter","== \"24\"): chrom = \"Y\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5],","bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax =","region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i]","points downwards, defaults to top, :type location: str, optional :param ax: Axis on","print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \"up\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\"-\",","plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1])","plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\"k\",","color = color_forward if(strand == \"-\"): color = color_reverse border_distance_down = region_border_down-start if(start","ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is","patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None,",":rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else plt.gca()","exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)","is True :type plot_gene_ids: bool, optional :param y_max: Max y value in the","left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for","from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import","gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name","be plotted. :type start: str :param end: End position of the region to","\"k\", \"linewidth\": .3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean","ax = ax if ax is not None else plt.gca() c = 0","ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path","for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries:","max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions of genes","= start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks +=",":param ax: Axis to be used for plotting, defaults to None. :type ax:","continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"):","False. :type plot_legend: bool :param color_g1: Color used for plotting g1 samples expression,","gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\"#fb8072\", color_g2=\"#80b1d3\", g1_id=\"tumor\",","float, optional :param cmap: Colormap used for plotting CNVs, defaults to \"bwr\". :type","stacked genes. 2. patch_list is the list of patches drawn on the ax.","current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], [])","ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r]) plt.ylim([0, max_dist/2])","plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\"k\", ax =","of intervals, defaults to None. :type offset: int, optional :param merge: Number of","= len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions +=","cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults to","= link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices","y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch)",":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax","[lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset","TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect)","lefts = [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts","color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0):","matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches","j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value)","[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point,","plt.gca() max_signal = 0 left = [] height = [] for signal in","ploidy=2, cnv_threshold=0.7, cmap=\"bwr\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param","a mapping between gene ids :param gene_name_mapping_file: Path to a tab separated file,","genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name]","will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return:","True plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param","= \"None\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False) ax.spines[\"right\"].set_visible(False)","equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)-","def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\"Greys\", vmin=None, vmax=None, location=\"top\", ax=None): '''Function that plots","color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3,","ax=None): '''Function that plots methylation values as dot plots. :param meth_calls: Iterator containing","plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in","\"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for","[value] left_merged = [] height_merged = [] if(not merge is None): heights =","= [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions)))","the segments for which contacts were called. :type segment_size: int :param cmap: Name","= str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border","end: End position of region to be plotted. :type end: int :param color:","expression (e.g. tumor and normal) on a gene region scale retaining the position","segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in","float, optional :param ax: Axis used for plotting, defaults to None. :type ax:","str, optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults to","TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of","plotted. :type start: int :param end: End position of region to be plotted.","ploidy=2, cnv_threshold=0.7, color_gain=\"g\", color_loss=\"r\", color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param cnvs_bed:","genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to","int :param end: End position on chromosome. :type end: int :param ploidy: Assumed","Chromosomal end positiont of the region to be plotted. :type end_r: int :param","in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5)","None else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True,","linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end])","plotting, defaults to None. Number of ids must be the same as the","rectangle, representing the region to be plotted, defaults to 1. :type alpha: float,","= [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left =","tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \"up\"):","m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for","Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick","color: Color of points representing methylation values, defaults to \"k\". :type color: str,","the rectangles representing the regions to be plotted, defaults to \"#cbebc4\". :type color:","= max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1])","0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None): '''Function that plots",":type color: str, optional :param offset: Length of intervals, defaults to None. :type","contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None):","0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\", color=color, linewidth=1) if(revert_coordinates):","genes. 2. patch_list is the list of patches drawn on the ax. 3.","optional :param color_g2: Color used for plotting g2 samples expression, defaults to \"#80b1d3\".","region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point =","end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in","1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \" \" for i","plot_legend: If True, a legend describing plus or minus stranded genes is plotted,","in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy) if(line[0] == \"#\" or","ax if ax is not None else plt.gca() for motif in motifs_bed: motif_start","= [] for i in range(len(left)): if(i % merge == 0 and not","of region edge. If False, no edge is plotted, defaults to False. :type","plotted. :type chrom: str :param start: Start position of region to be plotted.","''' ax = ax if ax is not None else plt.gca() tick_size =","= None for line in input_file: if(line[:7] == \"#ploidy\"): ploidy = float(line.rstrip().split(\":\")[1]) print(ploidy)","= color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label","Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r:","CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries:","genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool`","the region to be plotted, defaults to 1. :type alpha: float, optional. :param","of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments","None else plt.gca() max_signal = 0 left = [] height = [] for","of the region to be plotted. :type chrom: str :param start: Start position","to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to","import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import","contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be","1000. :type head_length: int, optional :param overhang: Fraction that the arrow is swept","= y_max # Plot Exons for i in exons_in_region: start = int(i[1]) end","defaults to \"bottom\". :type position_gene_names: str, optional :param log_transformed: If True use log","Color of region edge. If False, no edge is plotted, defaults to False.","Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome","contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2.,","ax = ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\"\\t\".join([str(i)","1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4.","plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\"--\",","= 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor),",":type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults to",":type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will","must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool`","offset left += [start] height += [value] left_merged = [] height_merged = []","color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang,","for plotting CNVs, defaults to \"bwr\". :type cmap: str, optional :param max_dev: Maximal","else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i","log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal)","of points representing methylation values, defaults to \"k\". :type color: str, optional :param","int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\"--\", linewidth=.5)","int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else:","np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1),","to be plotted. :type end: int :param bin_size: size of bin to average","or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin =","of the genomic region. :type end: int :param color: Color of lines equalizing","were called. :type segment_size: int :param cmap: Name of the colormap to be","int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue","gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name]","color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy","1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy, 5.","= [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start,","border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not","axis for plotting ax = ax if ax is not None else plt.gca()","= [] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices =","position \\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in","[]) if(loc_coordinates == \"up\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False)","= \"Mb\" else: scale=\"Kb\" digits_to_round = None divisor = None if(scale == \"Mb\"):","vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes =","height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom,","cytosines Or 1. Chromsome 2. Start position 3. end position 4. Beta Value",".2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end,","defaults to None. Number of ids must be the same as the number","to None :type vmin: float, optional :param vmax: Maximal value of intensity range","the region that is plotted), defaults to 1000. :type head_length: int, optional :param",":param color: Color of the genomic scales elements, defaults to \"k\". :type color:","Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene","the one region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool`","None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else","range to be plotted, defaults to None :type vmin: float, optional :param vmax:",":type end: int :param head_width: Width of the arrow head as proportion of","patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border","= int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in range(len(gene_regions)):","region to be plotted. :type start: int :param end: End position of the","plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis of plot,","barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage","= float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \"23\"): chrom=\"X\" elif(chrom == \"24\"): chrom","offset is None): end = start + offset left += [start] height +=","expression, defaults to \"#fb8072\". :type color_g1: str, optional :param color_g2: Color used for","2], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)],",":type color: str, optional :param ax: Axis of plot, defaults to None. :type","optional :param plot_legend: If True legend is plotted, False otherwise, defaults to False.","== 0 and not (i == 0)): left_merged += [lefts[0]] lefts = []","segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i","= open(gene_name_mapping_filename, \"r\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\"\\t\")","= matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \"top\"): ax.set_ylim(0, (end-start)/2.) else:","containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object","= color_forward if(strand == \"-\"): color = color_reverse border_distance_down = region_border_down-start if(start <","str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand ==","expression_values, \"k.\", markersize=3) g_id = None if(not ids is None): g_id = ids[g]","\"#\" or line[:5] == \"chrom\"): continue split_line = line.rstrip().split(\"\\t\") ploidy_dev = float(split_line[5])-ploidy chrom","i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \"k.\", markersize=3) g_id = None if(not ids","int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance","color_minus: str, optional :param ax: Axis on which to plot contact map, defaults","gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0","0.2 :type head_width: float, optional :param head_length: Length of the arrow in bp","max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos =","be plotted. :type chrom_r: str :param start_r: Chromosomal start position of the region","< cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev","\"lower right\", \"upper left\", \"upper right\", default is \"lower right\". :type legend_loc: str,","in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m","bin to average methylation values, defaults to 1000. :type bin_size: int, optional :param",":rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\"#66c2a5\", \"#fc8d62\", \"#8da0cb\", \"#ec87c2\", \"#a6d854\", \"#ffd92f\", \"#e5c494\", \"#bbbbbb\"]","= None): '''Function that plots links between genomic regions as arcs. :param links_bed:","of region to be plotted. :type start: int :param end: Chromosomal end position","of groups, defaults to None. :type colors: str, optional :param ids: IDs used","= equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \"top_down\"): codes =","= float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) <","str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file =","tumor and normal) on a gene region scale retaining the position of genes.","plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end =","to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None","else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha)",":param meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome 2.","binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not","distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch","linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start),","str, optional. :param ax: Axis where the plot is drawn, defaults to None.","plotted in addition to the boxplot, no points are plotted otherwise, defaults to","regions: iterator :param start: Start position of the region to be plotted. :type","[ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \"up\"): plt.plot([start, end],","\"k\", \"linewidth\": .3} capprops={\"color\": \"k\", \"linewidth\": .3} patch_list = [] patch_description_list = []","arrow is swept back (0 overhang means triangular shape). Can be negative or","alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4])","ids will be included in the plot, False otherwise, default is True :type","is \"#fb8072\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where","matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix import math","\"1\", \"2\", \"3\", \"4\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4,","meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color, marker=\".\", linestyle='None',","= [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for","ax if ax is not None else plt.gca() patches_dict = {} for segment","= region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label =","def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\"lower","= list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >=","the regions to be plotted, defaults to \"#cbebc4\". :type color: str, optional :param",":type start: int :param end: Chromosomal end position of region to be plotted.","If True use log transformed values for plotting, non-transformed values otherwise. :type log_transformed:","(region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path,","defaults to None. :type vmax: float, optional :param location: Either of \"top\" |","+= [\"forward strand\"] met_forward = True elif(strand == \"-\" and not(met_reverse)): patch_list +=","if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions,","the bar representing the translocation, defaults to \"k\". :type color: str, optional :param","ax if ax is not None else plt.gca() # Calculate midpoints of original","\"k\"} medianprops = {\"color\": \"k\", \"linewidth\": .3} whiskerprops = {\"color\": \"k\", \"linewidth\": .3}","genomic segments to equal distances. :param genomic_segments: List of segments for which distances","[] lefts = [] for i in range(len(left)): if(i % merge == 0","their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to","of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i]","linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\"center\", fontsize=5, color=color, verticalalignment=\"top\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], [])","of the colormap to be used for plotting HiC intensities, defaults to \"Greys\".","for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True,","[height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged += [lefts[0]]","region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction","that creates a mapping between gene ids :param gene_name_mapping_file: Path to a tab","the max_y_position + 1.5. max_y_pos defines the \\ number of stacked genes. 2.","(chrom, start, end, name, score, strand, start, end, color). The color field is",":param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool`","int :param end: End position of region to be plotted. :type end: int","values as dot plots. :param meth_calls: Iterator containing list-like elements with the following","ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults to","color_neutral=\"k\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs","stranded genes, default is \"#fb8072\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5,","regions, defaults to \"k\". :type color_neutral: str, optional :param ax: Axis used for","0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\"none\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def","[<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of the genomic","be considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param","part of the genome that is translocated. :param chrom_r: Chromosome of the region","[] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) >","ax=None, color=\"b\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator","(columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing","not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \"left\"):","max_signal = 0 left = [] height = [] for signal in chip_signals:","unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position 4. Beta","average methylation values per bin # Define Colormap cmap = cm.bwr norm =","blacklist: List of gene names, for genes that should not be shown on","whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]] patch_description_list","ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression","gene ids and values = y position \\ of gene. :rtype: tuple '''","fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], -.1,","Chromosomal start position of the region to be plotted. :type start_r: int :param","+ offset left += [start] height += [value] left_merged = [] height_merged =","Start position of the region to be plotted. :type start: str :param end:",":type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted","# Plot Exons for i in exons_in_region: start = int(i[1]) end = int(i[2])","end: Chromosomal end position of region to be plotted. :type end: int :param",":param merge: Number of elements to be merged. If this value is not",":type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples","< start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points","[] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names +=","linestyle=\"-\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i],","met_forward = False met_reverse = False # Plot Introns for i in introns_in_region:","= Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list","and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0):","Use given axis for plotting ax = ax if ax is not None","g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \"","values, defaults to 1000. :type bin_size: int, optional :param ax: Axis to be","background color of the boxplots boxes, defaults to 0.5. :type alpha: float, optional","vmax: float, optional :param location: Either of \"top\" | \"bottom\". If location ==","(end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\"top_down\", color=\"k\", ax = None):","plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos","+= [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \"Series\"): exp_values = list(exp_values) else:","not to be plotted, default to None. :type blacklist: set, optional :param ax:","int(end)], [5, 5], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\"--\", linewidth=.5)","int :param head_width: Width of the arrow head as proportion of the arrow,",":]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \"Series\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2","medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\"boxes\"][0].set_facecolor(color_g1) bplot_g2[\"boxes\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\"boxes\"][0], bplot_g2[\"boxes\"][0]]","= str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens]","y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3])","= PathPatch(path, facecolor = \"None\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False)","y position \\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx","bool, optional :param rotation: Rotational angle of coordinate strings, defaults to 0. :type","object containing the expression values of all samples (columns: sample ids; index: gene","if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point,",":param plot_legend: If True, a legend describing plus or minus stranded genes is","Alpha value of the rectangle, representing the region to be plotted, defaults to","elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start,","= distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2","the arrow in bp (depends on the region that is plotted), defaults to","the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed:","start_r, end_r, lw=1, color=\"k\", ax = None): '''Function that plots links between genomic","plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing","# Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]),","are plotted side by side. If this ratio is underwent, the genes will","color: Color of the bar representing the translocation, defaults to \"k\". :type color:","plt.plot([start, end], [0, 0], linestyle=\"-\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\"-\",","[] if(direction == \"top_down\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point,","for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border +","+ extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \"Series\"): exp_values_g1","plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\"*most_important*\") files and","if(not merge is None): heights = [] lefts = [] for i in","expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns:","[np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not i %","plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1","< len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension","must be the same as the number of groups. :type ids: list, optional.","facecolor = \"None\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\"bottom\"].set_visible(False) ax.spines[\"top\"].set_visible(False) ax.spines[\"left\"].set_visible(False)","plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\"right\", color=\"k\",","otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List of colors","the region to be plotted. :type end: int :param color: Color of the","motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted. :type","for plotting samples expression. The number of colors must be the same as","rotation: int, optional :return: Nothing to be returned. :rtype: None ''' ax =","(link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0),","the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \"r\") gene_map =","ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \" \" for i in gene_names_clean]))) for tick","to False. :type edge_color: str, optional :param alpha: Alpha value of the rectangle,","position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes","start: int :param end: End position of the region to be plotted. :type","in addition to the boxplot, no points are plotted otherwise, defaults to False.","if(strand == \"-\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx","gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3])","region. :type end: int :param color: Color of lines equalizing distances, defaults to","(1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start),","int :param cmap: Name of the colormap to be used for plotting HiC","(float(i[0])+float(i[1])) > 0 else \"NA\" for i in binned_meth_calls ] binned_average_meth_no_missing = []","None if(not ids is None): g_id = ids[g] else: g_id = \"group \"+str(g)","[] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points =","Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)]","= abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2","None and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border","numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle","direction: Direction of the genomic part that is translocated. Either of \"left\" (upstream),","int, optional :param overhang: Fraction that the arrow is swept back (0 overhang","(top_down | bottom_up), defaults to \"top_down\". :type direction: str, optional. :param ax: Axis","Color of the arc, defaults to \"k\". :type color: str, optional. :param ax:","is translocated. Either of \"left\" (upstream), or \"right\" (downstream), defaults to \"left\". :type","ratio of ax width, such that two genes are plotted side by side.","1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\"none\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\"off\")","0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults","for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax","if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size)","a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color","motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start","elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5)","color=color, marker=\".\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r,","links between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured","== 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\"--\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2],"],"string":"[\n \"import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None,\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype:\",\n \"ids not to be plotted, defaults to None, :type blacklist: set, optional :param\",\n \"optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to\",\n \"whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = []\",\n \":param ax: Axis on which to plot contact map, defaults to None. :type\",\n \"gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names: left_border =\",\n \"Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to be plotted.\",\n \"plotting, defaults to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If True, the\",\n \"Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value =\",\n \"{} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color =\",\n \"''' ax = ax if ax is not None else plt.gca() patches_dict =\",\n \"None): color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [\",\n \"break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping\",\n \"Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type\",\n \"Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)]\",\n \"range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0]\",\n \"is underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple of\",\n \"ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color =\",\n \"+ 1.5. max_y_pos defines the \\\\ number of stacked genes. 2. patch_list is\",\n \"extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of\",\n \"ax = ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap)\",\n \".3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list = None tick_positions =\",\n \"else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand\",\n \"not j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) >\",\n \"= max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5),\",\n \"1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from\",\n \"int :param r_end: End position of region to be plotted. :type r_end: int\",\n \":type TX_pos: int :param direction: Direction of the genomic part that is translocated.\",\n \":type color_minus: str, optional :param ax: Axis on which to plot contact map,\",\n \":type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of stacked\",\n \"[lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]]\",\n \"position of the region to be plotted. :type start: int :param end: End\",\n \":type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome:\",\n \"end, bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap :param\",\n \"else 0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries ==\",\n \"plotted. :type chrom_r: str :param start_r: Start position of the region to be\",\n \"ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start,\",\n \"size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\",\",\n \"motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start\",\n \"defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID of g1 used\",\n \"keys = names of segments, and values patch :rtype: dict ''' ax =\",\n \"to \\\"g\\\". :type color_gain: str, optional :param color_loss: Plot color of copy number\",\n \"optional :param alpha: Alpha value of the rectangle, representing the region to be\",\n \"ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)\",\n \"bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap :param methylation_bed:\",\n \"0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance =\",\n \"minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract\",\n \"ax = None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation\",\n \"def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation values as\",\n \"== \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect)\",\n \"], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls],\",\n \"mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2\",\n \"] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i]\",\n \"Start position 3. end position 4. Beta Value :type meth_calles: iterator :param chrom:\",\n \"\\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list = None tick_positions\",\n \"y_pos_dict: Dictionary with keys = gene ids and values = y position \\\\\",\n \"= [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names:\",\n \"values of all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame`\",\n \"this ratio is underwent, the genes will be stacked, default is 0.1. :type\",\n \"region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression\",\n \"int :param color: Color of the rectangles representing the regions to be plotted,\",\n \"= open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None for line in input_file:\",\n \"distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons for i\",\n \"i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if False make\",\n \":param rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation: int,\",\n \"not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand ==\",\n \"or minus stranded genes is plotted, False otherwise. Default is False. :type plot_legend:\",\n \"bin_size: int, optional :param ax: Axis to be used for plotting, defaults to\",\n \"ax is not None else plt.gca() max_signal = 0 left = [] height\",\n \"with the following entries: 1. Chromsome 2. Start position 3. end position 4.\",\n \"ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for tick\",\n \"np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches\",\n \"be plotted. :type chrom: str :param start: Start position of region to be\",\n \"plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function\",\n \"None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2):\",\n \"extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\":\",\n \":type end_r: int :param color: Color of the arc, defaults to \\\"k\\\". :type\",\n \"= (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in\",\n \"ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\",\",\n \"= height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end)\",\n \"binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i\",\n \"-.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color\",\n \"gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1):\",\n \"blacklist): continue # Define color for gene plotting strand = str(i[5]) color =\",\n \"of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes.\",\n \"markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [\",\n \"None :type vmin: float, optional :param vmax: Maximal value of intensity range to\",\n \"motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of\",\n \":type offset: int, optional :param merge: Number of elements to be merged. If\",\n \"iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param\",\n \"[int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes =\",\n \"\\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None,\",\n \"index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not\",\n \"ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper:\",\n \"color_gain: str, optional :param color_loss: Plot color of copy number losses, defaults to\",\n \":param chrom: Chromosome of region to be plotted. :type chrom: str :param start:\",\n \"to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color of plus stranded TF\",\n \"int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth\",\n \"\\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color of plus stranded TF regions,\",\n \"'''Function that plots links between genomic regions as arcs. :param links_bed: Iterator, that\",\n \"in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1]\",\n \"linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks\",\n \"start + offset left += [start] height += [value] left_merged = [] height_merged\",\n \"\\\"b\\\". :type color: str, optional :param offset: Length of intervals, defaults to None.\",\n \"determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of\",\n \"gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name =\",\n \"ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax\",\n \"the following entries: 1. Chromosome 2. Start position 3. End position :type regions:\",\n \"chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that plots links between\",\n \"gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to\",\n \"of the rectangle, representing the region to be plotted, defaults to 1. :type\",\n \"as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the\",\n \"str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y =\",\n \"considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap:\",\n \"color_neutral: Plot color of copy number neutral regions, defaults to \\\"k\\\". :type color_neutral:\",\n \"plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation values\",\n \":param plot_gene_ids: If True, all gene ids will be included in the plot,\",\n \"= True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse\",\n \"are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True,\",\n \"= 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for\",\n \"= {} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name\",\n \":type upper: bool, optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\",\",\n \"'''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs\",\n \"optional :param plot_points: If True, a point per expression value is plotted in\",\n \"= Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color\",\n \"binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap cmap =\",\n \"used for legend plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional :param g2_id:\",\n \"and values patch :rtype: dict ''' ax = ax if ax is not\",\n \"Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = []\",\n \"str :param start: Start position of the region to be plotted. :type start:\",\n \"intervals, defaults to None. :type offset: int, optional :param merge: Number of elements\",\n \":param end: End position of region to be plotted. :type end: int :param\",\n \"genome that is translocated. :param chrom_r: Chromosome of the region to be plotted.\",\n \"of the region to be plotted. :type end_r: int :param TX_pos: Position of\",\n \"position of the region to be plotted. :type start: str :param end: End\",\n \"object containing regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool`\",\n \"< cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0)\",\n \"ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map\",\n \"% merge == 0 and not (i == 0)): left_merged += [lefts[0]] lefts\",\n \"methylation values, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of\",\n \"None. :type colors: str, optional :param ids: IDs used for legend plotting, defaults\",\n \"gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None\",\n \"max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start,\",\n \"if ax is not None else plt.gca() patches_dict = {} for segment in\",\n \"start: Start position on chromosome. :type start: int :param end: End position on\",\n \"max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\\\ number of stacked\",\n \"int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks\",\n \"facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0)\",\n \"Axis on which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"a legend describing plus or minus stranded genes is plotted, False otherwise. Default\",\n \"bin position = int(element[1]) if(position < start or position > end): continue n_meth\",\n \"coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing to be\",\n \"head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color = color_minus\",\n \"current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension)\",\n \"color field is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename:\",\n \"interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for\",\n \"boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops =\",\n \"head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as arrows,\",\n \"plot_points: If True, a point per expression value is plotted in addition to\",\n \"offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start,\",\n \"extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"}\",\n \"False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max y\",\n \"end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not\",\n \"optional :param y_max: Max y value in the gene plot. If not set,\",\n \"color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in ticks\",\n \"gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions\",\n \"regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis on which\",\n \"if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03),\",\n \"extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops\",\n \"plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a\",\n \"{} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name =\",\n \"that plots genomic regions as simple rectangles. :param regions: Iterator containig list-like elements\",\n \"tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev)\",\n \"codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw = lw)\",\n \"optional :param edge_color: Color of region edge. If False, no edge is plotted,\",\n \"patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function\",\n \"value is plotted in addition to the boxplot, no points are plotted otherwise,\",\n \"= None if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None\",\n \"gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True,\",\n \"y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max\",\n \"genomic part that is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults\",\n \"else plt.gca() c = 0 for region in regions: if(not edgecolor): current_color =\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is\",\n \"= left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1\",\n \"linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3,\",\n \"End position :type regions: iterator :param start: Start position of the region to\",\n \"genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down\",\n \"ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object :param input_filename: Full path\",\n \"Plot color of copy number gains, defaults to \\\"g\\\". :type color_gain: str, optional\",\n \"color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None):\",\n \"plots bedGraph like iterators. :param chip_signals: Iterator for which each element is a\",\n \":param legend_loc: Location of the legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper\",\n \"[] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left = left_merged\",\n \"plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False)\",\n \"r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param\",\n \"\\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for\",\n \"codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point,\",\n \"TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color,\",\n \"base pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes, as\",\n \"= None if(not len(genes_in_region) == 0): # Determine y positions of genes for\",\n \"region. :type start: int :param end: End position of the genomic region. :type\",\n \"optional :param colors: List of colors used for plotting samples expression. The number\",\n \"+ end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in\",\n \"of region to be plotted. :type r_end: int :param ax: Axis of plot\",\n \"= None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for\",\n \"legend plotting, defaults to None. Number of ids must be the same as\",\n \"end_r: End position of the region to be plotted. :type end_r: int :param\",\n \":type color_loss: str, optional :param color_neutral: Plot color of copy number neutral regions,\",\n \"region to be plotted. :type end_r: int :param TX_pos: Position of the translocation.\",\n \"max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand ==\",\n \"plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene\",\n \"the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position\",\n \"in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5,\",\n \"(equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch)\",\n \"returned :rtype: None ''' ax = ax if ax is not None else\",\n \"two genes, as ratio of ax width, such that two genes are plotted\",\n \"extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a\",\n \"region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene plot is\",\n \"if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos =\",\n \"list ''' ax = ax if ax is not None else plt.gca() #\",\n \"= [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in binned_meth_calls\",\n \"samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary\",\n \"a translocation event as a bar, showing the part of the genome that\",\n \"in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance >\",\n \"more ticks. :type upper: bool, optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\".\",\n \"is underwent, the genes will be stacked, default is 0.1. :type distance_ratio: float,\",\n \"containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing\",\n \"start or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin\",\n \"Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0],\",\n \"right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None):\",\n \"= Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([],\",\n \"optional. :param ax: Axis on which to plot, defaults to None. :type ax:\",\n \"g_id = ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list\",\n \"translocation. :type TX_pos: int :param direction: Direction of the genomic part that is\",\n \"gene ids not to be plotted, default to None. :type blacklist: set, optional\",\n \"== 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension\",\n \"= ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for\",\n \"ids :param gene_name_mapping_file: Path to a tab separated file, for which the first\",\n \"i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and\",\n \"color: Color of the arc, defaults to \\\"k\\\". :type color: str, optional. :param\",\n \"not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) #\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults\",\n \"+= [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True # Plot Gene Names\",\n \"axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\",\",\n \"linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \"list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE\",\n \"end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None,\",\n \"continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1\",\n \"g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list +=\",\n \"standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values)))\",\n \"m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect\",\n \"0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], [])\",\n \":param plot_points: If True, a point per expression value is plotted in addition\",\n \"\\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length\",\n \"Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of\",\n \"plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus:\",\n \"of the region to be plotted. :type end: int :param color: Color of\",\n \"if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for\",\n \"+= [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean)))\",\n \"region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in\",\n \"Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position\",\n \"region edge. If False, no edge is plotted, defaults to False. :type edge_color:\",\n \"equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points +=\",\n \"used for plotting samples expression. The number of colors must be the same\",\n \"color: str, optional :param offset: Length of intervals, defaults to None. :type offset:\",\n \"average_list = [ j for j in [meth_before, meth_after] if not j ==\",\n \":type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed:\",\n \"exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed:\",\n \"0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+\",\n \"False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of\",\n \"= gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start,\",\n \"i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for j in [meth_before,\",\n \":type ids: list, optional. :param plot_gene_names: True if gene names shall be plotted,\",\n \"optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else\",\n \"if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2,\",\n \"def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\",\",\n \"merge == 0 and not (i == 0)): left_merged += [lefts[0]] lefts =\",\n \"chrom: str :param start: Start position of region to be plotted. :type start:\",\n \"<= end): ticks += [current_tick] current_tick = current_tick + tick_size scale = None\",\n \"containing the expression values of all samples (columns: sample ids; index: gene ids).\",\n \"<= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size\",\n \"g1 used for legend plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional :param\",\n \"= gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name\",\n \"to be plotted. :type chrom_r: str :param start_r: Chromosomal start position of the\",\n \"2. Start position 3. end position 4. Number methylated cytosines 5. Number unmethylated\",\n \"= list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values =\",\n \"{} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start =\",\n \"object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list\",\n \"= contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor\",\n \"for which the first column is a ensemble gene id, and the second\",\n \"g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal)\",\n \"direction: Direction of distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type direction:\",\n \"as a bar, showing the part of the genome that is translocated. :param\",\n \"linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \"color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions of\",\n \"= color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt',\",\n \"region to be plotted. :type r_end: int :param ax: Axis of plot :type\",\n \"Dictionary with keys = names of segments, and values patch :rtype: dict '''\",\n \"of the legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\",\",\n \"float :return: Tuple of 1. max_y_pos: Defines the number of stacked genes. 2.\",\n \"\\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color of region edge. If False,\",\n \"plotted. :type end: int :param bin_size: size of bin to average methylation values,\",\n \"6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to\",\n \"defaults to False. :type plot_points: bool, optional :param alpha: Alpha value for the\",\n \"defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy\",\n \"with the following elements: 1. Chromosome region1 2. Start region1 3. End region1\",\n \"color of the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return:\",\n \"int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1])\",\n \"strand\\\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region:\",\n \"color: str, optional :param edge_color: Color of region edge. If False, no edge\",\n \"ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param\",\n \"(e.g. tumor and normal) on a gene region scale retaining the position of\",\n \"two genes are plotted side by side. If this ratio is underwent, the\",\n \"plot_gene_ids: If True, all gene ids will be included in the plot, False\",\n \"None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension =\",\n \"meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [\",\n \"0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i\",\n \".06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch]\",\n \"lw=1, color=\\\"k\\\", ax = None): '''Function that plots links between genomic regions as\",\n \"of bars, defaults to \\\"b\\\". :type color: str, optional :param offset: Length of\",\n \"plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal deviation\",\n \"distances, defaults to \\\"k\\\". :type color: str, optional :param direction: Direction of distance\",\n \"Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold:\",\n \"= [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts +=\",\n \"and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand\",\n \"(columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing\",\n \"one. Defaults to 0. :type overhang: float, optional :param color_plus: Color of plus\",\n \"location == \\\"bottom\\\" the pyramid points downwards, defaults to top, :type location: str,\",\n \"None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path to\",\n \"color = color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch\",\n \"otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point per expression\",\n \"the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis.\",\n \"> y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]]\",\n \"== \\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values\",\n \"color for gene plotting strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"):\",\n \"import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import\",\n \"def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots\",\n \"gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue #\",\n \"the expression values of g2 samples (columns: sample ids; index: gene ids) :type\",\n \"default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two\",\n \":param ax: Axis where the plot is drawn, defaults to None. :type ax:\",\n \"= float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn >\",\n \"exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1.\",\n \"None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start\",\n \"edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha)\",\n \"in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos\",\n \"int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int,\",\n \"capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename):\",\n \"[(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO,\",\n \":param color_plus: Color of plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus:\",\n \"optional :param color_gain: Plot color of copy number gains, defaults to \\\"g\\\". :type\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or minus stranded\",\n \"== \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point,\",\n \"= int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points)\",\n \"revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults to 0.\",\n \"cmap: Name of the colormap to be used for plotting HiC intensities, defaults\",\n \"i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2])\",\n \":type max_dev: float, optional :param ax: Axis used for plotting, defaults to None.\",\n \"optional :param vmin: Minimal value of intensity range to be plotted, defaults to\",\n \":class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1:\",\n \"genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points:\",\n \"in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end])\",\n \"gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color)\",\n \"= gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist):\",\n \"cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap\",\n \":param y_max: Max y value in the gene plot. If not set, then\",\n \"plot_gene_ids: bool, optional :param y_max: Max y value in the gene plot. If\",\n \"plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing\",\n \"the region to be plotted. :type start: str :param end: End position of\",\n \"plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location\",\n \"= \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close()\",\n \"overhang means triangular shape). Can be negative or greater than one. Defaults to\",\n \"if(not colors is None): color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points):\",\n \"plot, defaults to None. :type max_dev: float, optional :param ax: Axis used for\",\n \"def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts them\",\n \"be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance between\",\n \"str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start\",\n \"on a gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool`\",\n \"= True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True\",\n \"if gene names shall be plotted, False otherwise, defaults to True. :type plot_gene_names:\",\n \"ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev\",\n \"and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing\",\n \"> cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)\",\n \"cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4])\",\n \"float, optional :param color_gain: Plot color of copy number gains, defaults to \\\"g\\\".\",\n \"blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border =\",\n \"linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse =\",\n \"object containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End\",\n \"[np.mean(heights)] heights = [] offset = merge*offset left = left_merged height = height_merged\",\n \"arcs from unequal distances of genomic segments to equal distances. :param genomic_segments: List\",\n \"region to be plotted. :type chrom_r: str :param start_r: Start position of the\",\n \"for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1)\",\n \"Direction of distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type direction: str,\",\n \"segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start:\",\n \"height += [value] left_merged = [] height_merged = [] if(not merge is None):\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax = ax\",\n \"def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix segments\",\n \"smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev\",\n \"(each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param\",\n \"the max y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool`\",\n \"as the number of groups. :type ids: list, optional. :param plot_gene_names: True if\",\n \":param color: Color of lines equalizing distances, defaults to \\\"k\\\". :type color: str,\",\n \"Minimal deviation from ploidy to be considered as a CNV, defaults to 0.7.\",\n \"elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev),\",\n \"== 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start,\",\n \"of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region\",\n \"and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break\",\n \"position of region to be plotted. :type end: int :param segment_size: Size of\",\n \":param color_minus: Color of plus stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus:\",\n \"return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False,\",\n \"if(not len(genes_in_region) == 0): # Determine y positions of genes for plotting max_y_pos,\",\n \"patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names\",\n \"= int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] +=\",\n \"else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint\",\n \"plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either\",\n \"and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene\",\n \"Path to tabixed bed file containing (chrom, start, end, name, score, strand, start,\",\n \"code for plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param\",\n \"len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension ==\",\n \"ticks = [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick\",\n \"else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin\",\n \"plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in\",\n \"''' ax = ax if ax is not None else plt.gca() max_signal =\",\n \"the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy\",\n \"max_y_pos = y_max # Plot Exons for i in exons_in_region: start = int(i[1])\",\n \"drawn on the ax. :rtype: list \\\"\\\"\\\" ax = ax if ax is\",\n \"direction, else if \\\"down\\\", plot ticks to lower direction, defaults to \\\"up\\\". :type\",\n \"equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points\",\n \"vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps as pyramid plots\",\n \"mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)]\",\n \"merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which each\",\n \"marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r,\",\n \"chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom: str\",\n \"Value to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of\",\n \"not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for j in\",\n \".2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point,\",\n \"[left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"):\",\n \"End position 4. Value to be plotted as bar :type chip_signals: iterator :param\",\n \"ax if ax is not None else plt.gca() max_dist = 0 for e\",\n \"vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ]\",\n \"head_length: Length of the arrow in bp (depends on the region that is\",\n \"in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5,\",\n \"the pyramid points downwards, defaults to top, :type location: str, optional :param ax:\",\n \"= int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos =\",\n \"gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter += 1 continue\",\n \"Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax\",\n \"= len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [\",\n \"Location of the legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper\",\n \"False. :type plot_points: bool, optional :param alpha: Alpha value for the background color\",\n \"for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]),\",\n \"the genomic region. :type end: int :param color: Color of lines equalizing distances,\",\n \"= norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0),\",\n \"in blacklist): counter += 1 continue n_groups = len(groups) for g in range(n_groups):\",\n \"'''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields\",\n \"= ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not\",\n \"else if False make more ticks. :type upper: bool, optional :param loc_coordinates: Either\",\n \"on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults to\",\n \"as proportion of the arrow, defaults to 0.2 :type head_width: float, optional :param\",\n \"vertices = [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices\",\n \"value for the background color of the boxplots boxes, defaults to 0.5. :type\",\n \"of ids must be the same as the number of groups. :type ids:\",\n \"of region to be plotted. :type end: int :param color: Color of points\",\n \"if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor,\",\n \"'''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path to a\",\n \"+= 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return\",\n \"None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color =\",\n \"plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([],\",\n \":param g2_id: ID of g2 used for legend plotting, defaults to \\\"normal\\\". :type\",\n \"str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5\",\n \"[5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start),\",\n \"# Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0]\",\n \"str, optional :param alpha: Alpha value of the rectangle, representing the region to\",\n \"region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should not\",\n \"None): max_y_pos = y_max # Plot Exons for i in exons_in_region: start =\",\n \"math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False,\",\n \"= 0 left = [] height = [] for signal in chip_signals: start\",\n \"ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax =\",\n \"is not None else plt.gca() binned_meth_calls = [ [0, 0] for i in\",\n \"color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as arrows, indicating their directionality.\",\n \"expression (e.g. tumor and normal) on a gene region scale equalizing the position\",\n \"e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]]\",\n \"If False, no edge is plotted, defaults to False. :type edge_color: str, optional\",\n \"binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in\",\n \"is \\\"lower right\\\". :type legend_loc: str, optional :param color_plus: Color code for plus\",\n \"offset: Length of intervals, defaults to None. :type offset: int, optional :param merge:\",\n \"plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for\",\n \"region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not\",\n \"of region to be plotted. :type end: int :param bin_size: size of bin\",\n \"motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start =\",\n \":return: Nothing to be returned :rtype: None ''' # Use given axis for\",\n \"= None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls.\",\n \"of coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing to\",\n \"idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in\",\n \":return: Nothing to be returned. :rtype: None ''' ax = ax if ax\",\n \"segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2.\",\n \"plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\",\",\n \"defaults to 0. :type rotation: int, optional :return: Nothing to be returned. :rtype:\",\n \"region: list :param groups: List of lists containing the IDs of the different\",\n \"of the region to be plotted. :type start: int :param end: End position\",\n \"ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean])))\",\n \"boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list =\",\n \"defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting\",\n \"of g2 used for legend plotting, defaults to \\\"normal\\\". :type g2_id: str, optional\",\n \":param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed:\",\n \"elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"],\",\n \"bool, optional :param plot_points: If True, a point per expression value is plotted\",\n \"color=\\\"k\\\", ax = None): '''Function that plots arcs from unequal distances of genomic\",\n \"position of region to be plotted. :type end: int :param color: Color of\",\n \"= int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn =\",\n \"left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' #\",\n \"is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two genes,\",\n \"blacklist: set, optional :param ax: Axis used for plotting, defaults to None. :type\",\n \"patch :rtype: dict ''' ax = ax if ax is not None else\",\n \"introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"):\",\n \"expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting\",\n \"not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for\",\n \"in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border)\",\n \"1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor,\",\n \"Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param\",\n \"''' ax = ax if ax is not None else plt.gca() max_dist =\",\n \"i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1\",\n \"stacked genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance\",\n \"the number of groups. :type ids: list, optional. :param plot_gene_names: True if gene\",\n \"contains bed-like structured lists with the following elements: 1. Chromosome region1 2. Start\",\n \"if ax is not None else plt.gca() TX_start = TX_pos TX_end = end_r\",\n \"is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap\",\n \"r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals:\",\n \"elements to be merged. If this value is not equal to 0, than\",\n \"0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy number gains,\",\n \"ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]]\",\n \"\\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color used for plotting g2 samples\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype:\",\n \"plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must be\",\n \":type segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted. :type\",\n \"fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed:\",\n \"\\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is\",\n \"y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and\",\n \"start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None\",\n \":param genomic_segments: List of segments for which distances shall be equalized (each segment\",\n \"y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3]\",\n \"patch_list = None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0\",\n \":param vmin: Minimal value of intensity range to be plotted, defaults to None\",\n \"offset: int, optional :param merge: Number of elements to be merged. If this\",\n \"color = None if(not colors is None): color = colors[g] else: color =\",\n \"color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], [])\",\n \"patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in\",\n \"= first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size\",\n \"is the list of patches drawn on the ax. 3. patch_description_list is the\",\n \"[] ploidy = None for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy =\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax\",\n \"meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if\",\n \"TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start,\",\n \"\\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to\",\n \"an plotted, defaults to 0. :type merge: int, optional :return: Nothing to be\",\n \"not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end =\",\n \"dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in gene_name_mapping_file:\",\n \"object containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param\",\n \"color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates\",\n \"blacklist: Set containing gene ids not to be plotted, default to None. :type\",\n \"str :return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file =\",\n \"rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect\",\n \"first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks\",\n \"'''Function that determines the max y position for gene plotting via function plotGenes.\",\n \"of the genomic part that is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\"\",\n \"c = 0 for region in regions: if(not edgecolor): current_color = color rect\",\n \"arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF\",\n \"genes is plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param\",\n \"str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1,\",\n \"ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional :param\",\n \".4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions,\",\n \"0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end =\",\n \"or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom =\",\n \"{\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \"optional :param color_plus: Color of plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type\",\n \"r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like iterators.\",\n \"the ax. :rtype: list \\\"\\\"\\\" ax = ax if ax is not None\",\n \"Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size)\",\n \"ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that\",\n \"True :type plot_gene_ids: bool, optional :param y_max: Max y value in the gene\",\n \":param overhang: Fraction that the arrow is swept back (0 overhang means triangular\",\n \"position on chromosome. :type start: int :param end: End position on chromosome. :type\",\n \"= (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j\",\n \"with the following entries: 1. Chromosome 2. Start position 3. End position :type\",\n \"= pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get gene names and regions\",\n \"region to be plotted. :type end_r: int :param color: Color of the arc,\",\n \"distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e.\",\n \"expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted, defaults\",\n \"plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r,\",\n \"list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids is None):\",\n \"plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0,\",\n \"ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups,\",\n \"in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e\",\n \"segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points =\",\n \":type g1_id: str, optional :param g2_id: ID of g2 used for legend plotting,\",\n \"make less ticks, else if False make more ticks. :type upper: bool, optional\",\n \"plotted. :type end: int :param color: Color of the rectangles representing the regions\",\n \"[0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for\",\n \"\\\"k.\\\", markersize=3) g_id = None if(not ids is None): g_id = ids[g] else:\",\n \"int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], [])\",\n \"which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of\",\n \"+ extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values\",\n \"None): '''Function that plots links between genomic regions as arcs. :param links_bed: Iterator,\",\n \"patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that\",\n \"containing: 1. Chromosome 2. Start postion 3. End position 4. Value to be\",\n \"gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type\",\n \"introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward\",\n \"genes. :type gene_mid_points: list :param region: List containing the region to be plotted\",\n \"cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed:\",\n \"gene_names_map: dict. :param blacklist: Set containing gene ids not to be plotted, default\",\n \"bedGraph like iterators. :param chip_signals: Iterator for which each element is a list-ike\",\n \"None else plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1])\",\n \"split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed,\",\n \"is not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end\",\n \"genomic region. :type start: int :param end: End position of the genomic region.\",\n \"def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for\",\n \"[2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \"and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing\",\n \"from ploidy to plot, defaults to None. :type max_dev: float, optional :param ax:\",\n \"chrom_r: Chromosome of the region to be plotted. :type chrom_r: str :param start_r:\",\n \"left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset\",\n \"\\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the pyramid points upwards, else if\",\n \"y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j\",\n \"True, all gene ids will be included in the plot, False otherwise, default\",\n \"end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps\",\n \":param vmax: Maximal value of intensity range to be plotted, defaults to None.\",\n \"ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i],\",\n \"directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be\",\n \"0. :type rotation: int, optional :return: Nothing to be returned. :rtype: None '''\",\n \"for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: #\",\n \"Size of the segments for which contacts were called. :type segment_size: int :param\",\n \"plotted. :type end: int :param head_width: Width of the arrow head as proportion\",\n \".3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean = []\",\n \"optional :param plot_gene_ids: If True, all gene ids will be included in the\",\n \"[ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e\",\n \"values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids\",\n \"'''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which each element\",\n \"bp (depends on the region that is plotted), defaults to 1000. :type head_length:\",\n \"None ''' ax = ax if ax is not None else plt.gca() contact_map_index1\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \\\"b\\\". :type color: str,\",\n \":param plot_gene_names: True if gene names shall be plotted, False otherwise, defaults to\",\n \"ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller\",\n \"in [meth_before, meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/\",\n \"plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get gene names\",\n \"Size of region to be plotted in base pairs. :type region_size: int :param\",\n \"form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of the\",\n \"is not equal to 0, than merge elements will be averaged an plotted,\",\n \"(i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id =\",\n \"None. :type y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as\",\n \"of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\".\",\n \"or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If True\",\n \"\\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal value of intensity range to\",\n \"not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)]\",\n \"Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax\",\n \"1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\",\",\n \"genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed file containing\",\n \"= int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev\",\n \"int :param color: Color of the arc, defaults to \\\"k\\\". :type color: str,\",\n \":class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param\",\n \"If True, the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs\",\n \"== \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of\",\n \"list of descriptions for the patches \\\\ drawn on the ax. :rtype: list\",\n \"Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is\",\n \"int :param distance_ratio: Minimal distance between two genes, as ratio of ax width,\",\n \"= current_tick + tick_size scale = None if(first_tick > 1000000): scale = \\\"Mb\\\"\",\n \"abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2\",\n \"g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border,\",\n \"list-like elements with the following entries: 1. Chromosome 2. Start position 3. End\",\n \"+= [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) #\",\n \"rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end))\",\n \":class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object\",\n \"str, optional :param ax: Axis on which to plot contact map, defaults to\",\n \"start: int :param end: End position on chromosome. :type end: int :param ploidy:\",\n \"list :param region: List containing the region to be plotted ([<chrom>, <start>, <end>]).\",\n \"plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax\",\n \"location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint\",\n \"= link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes\",\n \"<start>, <end>]). :type region: list :param groups: List of lists containing the IDs\",\n \"verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5,\",\n \"plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\",\",\n \"ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered as\",\n \"import pandas as pnd import numpy as np import tabix import matplotlib.ticker as\",\n \"= Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds\",\n \"ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom,\",\n \"r_start: int :param r_end: End position of region to be plotted. :type r_end:\",\n \"color_g2: str, optional :param g1_id: ID of g1 used for legend plotting, defaults\",\n \"values containing center positions of genes. :type gene_mid_points: list :param region: List containing\",\n \"representing methylation values, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis\",\n \"0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\")\",\n \"gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list,\",\n \"max_signal): max_signal = value if(not offset is None): end = start + offset\",\n \"color: Color of the rectangles representing the regions to be plotted, defaults to\",\n \"the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"be plotted. :type start: int :param end: Chromosomal end position of region to\",\n \"= int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type =\",\n \">= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True,\",\n \"True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list\",\n \"binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth\",\n \"optional :return: Nothing to be returned :rtype: None ''' ax = ax if\",\n \"== 0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict =\",\n \"to be plotted, defaults to None, :type blacklist: set, optional :param ax: (default:\",\n \"gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center\",\n \"ax = ax if ax is not None else plt.gca() n_entries = len(meth_calls[0])\",\n \"Plot color of copy number neutral regions, defaults to \\\"k\\\". :type color_neutral: str,\",\n \"showing the part of the genome that is translocated. :param chrom_r: Chromosome of\",\n \"''' ax = ax if ax is not None else plt.gca() # Calculate\",\n \"[meth_before, meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list)))\",\n \"color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start\",\n \"[]) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000,\",\n \"average methylation values, defaults to 1000. :type bin_size: int, optional :param ax: Axis\",\n \"which the first column is a ensemble gene id, and the second column\",\n \"bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not\",\n \"1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops,\",\n \"regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists with the\",\n \"than merge elements will be averaged an plotted, defaults to 0. :type merge:\",\n \"1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4):\",\n \"''' ax = ax if ax is not None else plt.gca() for motif\",\n \"colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name,\",\n \"end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation event as a\",\n \"Defaults to 0. :type overhang: float, optional :param color_plus: Color of plus stranded\",\n \"if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None):\",\n \"+= 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border\",\n \"right\\\". :type legend_loc: str, optional :param color_plus: Color code for plus stranded genes,\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \\\"b\\\". :type color:\",\n \"edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\",\",\n \"1000. :type bin_size: int, optional :param ax: Axis to be used for plotting,\",\n \"digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000\",\n \"to be plotted. :type start_r: int :param end_r: Chromosomal end positiont of the\",\n \"None ''' ax = ax if ax is not None else plt.gca() max_signal\",\n \"in a linea fashion. :param chrom: Chromosome of the region to be plotted.\",\n \"default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on which\",\n \"otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used for plotting\",\n \"postion 3. End position 4. Value to be plotted as bar :type chip_signals:\",\n \"and gene_name in blacklist): counter += 1 continue n_groups = len(groups) for g\",\n \":param end_r: Chromosomal end positiont of the region to be plotted. :type end_r:\",\n \"to average methylation values, defaults to 1000. :type bin_size: int, optional :param ax:\",\n \"[(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch\",\n \"0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin #\",\n \"str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown,\",\n \"plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g.\",\n \":type end: int :param color: Color of the genomic scales elements, defaults to\",\n \"+= [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for\",\n \"be plotted. :type start: int :param end: End position of the region to\",\n \"values, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of plot,\",\n \"plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to\",\n \"ax=None): '''Function that plots HiC contact maps as pyramid plots :param contact_map: Matrix\",\n \"ax if ax is not None else plt.gca() binned_meth_calls = [ [0, 0]\",\n \":type end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int :param\",\n \"ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower\",\n \"samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe`\",\n \"''' ax = ax if ax is not None else plt.gca() binned_meth_calls =\",\n \":type start: str :param end: End position of the region to be plotted.\",\n \"defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis on which to\",\n \"plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance between two\",\n \"overhang: float, optional :param color_plus: Color of plus stranded TF regions, defaults to\",\n \"methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3.\",\n \"region_size, distance_ratio): '''Function that determines the max y position for gene plotting via\",\n \"i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels():\",\n \"edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def\",\n \"shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on\",\n \"in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict\",\n \"y value in the gene plot. If not set, then y_max is the\",\n \"region to be plotted. :type end: int :param color: Color of points representing\",\n \"pyramid points upwards, else if location == \\\"bottom\\\" the pyramid points downwards, defaults\",\n \"be plotted. :type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for\",\n \"0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension =\",\n \"patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the\",\n \"== 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m\",\n \"position 3. end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome\",\n \"plotted. :type chrom: str :param start: Start position of the region to be\",\n \"current_tick + tick_size scale = None if(first_tick > 1000000): scale = \\\"Mb\\\" else:\",\n \"= ax if ax is not None else plt.gca() c = 0 for\",\n \":type blacklist: list, optional :param plot_gene_ids: If True, all gene ids will be\",\n \"cnv_threshold: Minimal deviation from ploidy to be considered as a CNV, defaults to\",\n \"= int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end =\",\n \"ids not to be plotted, default to None. :type blacklist: set, optional :param\",\n \"for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start,\",\n \"cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e)\",\n \"border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size)\",\n \"if False make more ticks. :type upper: bool, optional :param loc_coordinates: Either of\",\n \"\\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot color of copy number neutral\",\n \"copy number losses, defaults to \\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot\",\n \"SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool.\",\n \"plots methylation values as dot plots. :param meth_calls: Iterator containing list-like elements with\",\n \"merge elements will be averaged an plotted, defaults to 0. :type merge: int,\",\n \"Color of the rectangles representing the regions to be plotted, defaults to \\\"#cbebc4\\\".\",\n \"exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for i\",\n \"segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps as\",\n \"methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must be included:\",\n \"ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i\",\n \"blacklist: Set containing gene ids not to be plotted, defaults to None, :type\",\n \"TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color of\",\n \":param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to\",\n \"= TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end =\",\n \"the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene\",\n \"gene_name_mapping_file: Path to a tab separated file, for which the first column is\",\n \"if not i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i\",\n \"fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None):\",\n \"be plotted. :type end: int :param segment_size: Size of the segments for which\",\n \"groups. :type ids: list, optional. :param plot_gene_names: True if gene names shall be\",\n \"ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing\",\n \"{\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list =\",\n \"in meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color, marker=\\\".\\\",\",\n \"and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i\",\n \"ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist:\",\n \"= ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos],\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise, defaults\",\n \"genes, default is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list,\",\n \"keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param\",\n \"digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i\",\n \"ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function\",\n \"+= [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ ==\",\n \":param upper: If True, make less ticks, else if False make more ticks.\",\n \"= [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos\",\n \"and normal) on a gene region scale equalizing the position of genes. :param\",\n \"rectangles. :param regions: Iterator containig list-like elements with the following entries: 1. Chromosome\",\n \"True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1])\",\n \"] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start,\",\n \"intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none')\",\n \"i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else\",\n \"optional :param rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation:\",\n \"str, optional :param color_plus: Color code for plus stranded genes, default is \\\"#80b1d3\\\".\",\n \"+= 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border +\",\n \"for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys\",\n \"else plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2\",\n \"plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for\",\n \"be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of\",\n \"position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end):\",\n \"strand\\\"] met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list\",\n \"color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label =\",\n \":return: Tuple of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict:\",\n \"[]) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots\",\n \"edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy ==\",\n \"GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values\",\n \"if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color\",\n \"max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object\",\n \"ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars,\",\n \"expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids; index:\",\n \"ax. 3. patch_description_list is the list of descriptions for the patches \\\\ drawn\",\n \"for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = []\",\n \"to decreasing order. Else, coordinates stay in increasing order, defaults to False. :type\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \\\"b\\\". :type\",\n \"extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"}\",\n \"ax: Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID\",\n \"int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1],\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5,\",\n \"as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from\",\n \"plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location ==\",\n \"contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor =\",\n \"the expression values of g1 samples (columns: sample ids; index: gene ids) :type\",\n \"ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with\",\n \"color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax\",\n \"chrom: Chromosome of the region to be plotted. :type chrom: str :param start:\",\n \"Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy, 5. True\",\n \"= ax if ax is not None else plt.gca() TX_start = TX_pos TX_end\",\n \"c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def\",\n \"max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y\",\n \"score, strand, start, end, color). The color field is used to determine the\",\n \"False met_reverse = False # Plot Introns for i in introns_in_region: start =\",\n \":class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids; index: gene\",\n \"between two genes, as ratio of ax width, such that two genes are\",\n \".3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1,\",\n \"TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions\",\n \"[genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos =\",\n \"of genomic segments to equal distances. :param genomic_segments: List of segments for which\",\n \"ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower\",\n \"cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults to \\\"bwr\\\".\",\n \"boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\":\",\n \"containig list-like elements with the following entries: 1. Chromosome 2. Start position 3.\",\n \"from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for\",\n \"signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints.\",\n \"0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]]\",\n \"strings, defaults to 0. :type rotation: int, optional :return: Nothing to be returned.\",\n \"segment_size: Size of the segments for which contacts were called. :type segment_size: int\",\n \"direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots arcs from unequal distances of\",\n \"4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end,\",\n \"used for plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional\",\n \"or greater than one. Defaults to 0. :type overhang: float, optional :param color_plus:\",\n \"int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth\",\n \"to be plotted. :type end: int :param head_width: Width of the arrow head\",\n \"returned. :rtype: None ''' ax = ax if ax is not None else\",\n \"placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else\",\n \":class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base pairs. :type\",\n \"end: str :param ax: Axis used for plotting, defaults to None. :type ax:\",\n \"== 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2],\",\n \"marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls\",\n \"bool, optional :param distance_ratio: Minimal distance between two genes, as ratio of ax\",\n \">= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops,\",\n \"None and gene_name in blacklist): counter += 1 continue n_groups = len(groups) for\",\n \":type plot_gene_ids: bool, optional :param y_max: Max y value in the gene plot.\",\n \"end: End position of region to be plotted. :type end: int :param bin_size:\",\n \"\\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs\",\n \"= list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2)\",\n \"Chromosome of the region to be plotted. :type chrom_r: str :param start_r: Chromosomal\",\n \"capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i\",\n \"color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict\",\n \"1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect =\",\n \"percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3,\",\n \"tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks =\",\n \"direction: str, optional. :param ax: Axis on which to plot, defaults to None.\",\n \"class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample ids; index:\",\n \"for j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.)\",\n \"expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :])\",\n \"[3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy\",\n \"PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False)\",\n \"TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region\",\n \"is False. :type plot_legend: bool, optional :param legend_loc: Location of the legend. Either\",\n \"i in range(len(left)): if(i % merge == 0 and not (i == 0)):\",\n \"edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75],\",\n \"= (binned_average_meth[i-1] if not i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if\",\n \"gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter\",\n \"cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome: str\",\n \"be plotted. :type r_start: int :param r_end: End position of region to be\",\n \"cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect =\",\n \"cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev =\",\n \"be plotted. :type end_r: int :param TX_pos: Position of the translocation. :type TX_pos:\",\n \"[ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for\",\n \"flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\":\",\n \"= colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color,\",\n \"''' ax = ax if ax is not None else plt.gca() contact_map_index1 =\",\n \"int :param end_r: Chromosomal end positiont of the region to be plotted. :type\",\n \"edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def\",\n \"start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal\",\n \"\\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot,\",\n \"Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw =\",\n \"if location == \\\"bottom\\\" the pyramid points downwards, defaults to top, :type location:\",\n \"plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick\",\n \"True, make less ticks, else if False make more ticks. :type upper: bool,\",\n \"str, optional :param g2_id: ID of g2 used for legend plotting, defaults to\",\n \"patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list\",\n \"current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth\",\n \"distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos\",\n \"genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed:\",\n \"patches \\\\ drawn on the ax. :rtype: list \\\"\\\"\\\" ax = ax if\",\n \"to \\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID of g2 used for\",\n \"the following elements: 1. Chromosome region1 2. Start region1 3. End region1 4.\",\n \"minus stranded genes is plotted, False otherwise. Default is False. :type plot_legend: bool,\",\n \"containing (chrom, start, end, name, score, strand, start, end, color). The color field\",\n \"no points are plotted otherwise, defaults to False. :type plot_points: bool, optional :param\",\n \"plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot\",\n \"of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param\",\n \"location == \\\"top\\\", the pyramid points upwards, else if location == \\\"bottom\\\" the\",\n \"Plot color of copy number losses, defaults to \\\"r\\\". :type color_loss: str, optional\",\n \"PathPatch import matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed,\",\n \"to be plotted. :type chrom_r: str :param start_r: Start position of the region\",\n \"in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean\",\n \"shall be equalized (each segment is of the form [<chrom>, <start>, <end>]) :type\",\n \"(upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str, optional :param color:\",\n \"\\\\ drawn on the ax. :rtype: list \\\"\\\"\\\" ax = ax if ax\",\n \"which to plot CNVs. :type chromosome: str :param start: Start position on chromosome.\",\n \"position of the region to be plotted. :type end: int :param color: Color\",\n \"plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.)\",\n \"object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool`\",\n \"plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev =\",\n \"each element is a list-ike object containing: 1. Chromosome 2. Start postion 3.\",\n \"import matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed,\",\n \"alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\",\",\n \"direction: str, optional :param color: Color of the bar representing the translocation, defaults\",\n \":class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer\",\n \":param region: List containing the region to be plotted ([<chrom>, <start>, <end>]). :type\",\n \"start: Start position of the region to be plotted. :type start: int :param\",\n \"in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] =\",\n \"gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names +=\",\n \"If this value is not equal to 0, than merge elements will be\",\n \"position :type regions: iterator :param start: Start position of the region to be\",\n \"color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1\",\n \":type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` '''\",\n \"list, optional :param plot_gene_ids: If True, all gene ids will be included in\",\n \"plots. :param meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome\",\n \"[bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\")\",\n \"\\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom ==\",\n \"Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch\",\n \"[0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in\",\n \"break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] +=\",\n \"-1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev))\",\n \"end, color=\\\"k\\\", ax=None): '''Function that plots methylation values as dot plots. :param meth_calls:\",\n \"ax=None): '''Function that plots a translocation event as a bar, showing the part\",\n \"Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend:\",\n \"files and converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq\",\n \"+= [\\\"reverse strand\\\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i\",\n \"None and gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand\",\n \"stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on\",\n \":param g1_id: ID of g1 used for legend plotting, defaults to \\\"tumor\\\". :type\",\n \":param color: Color of points representing methylation values, defaults to \\\"k\\\". :type color:\",\n \"+= [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for\",\n \"motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be plotted. :type\",\n \"the gene plot. If not set, then y_max is the max number of\",\n \"[]) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts\",\n \"3. patch_description_list is the list of descriptions for the patches \\\\ drawn on\",\n \"regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color,\",\n \"Set containing gene ids not to be plotted, default to None. :type blacklist:\",\n \"Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color =\",\n \"patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax =\",\n \"None. :type vmax: float, optional :param location: Either of \\\"top\\\" | \\\"bottom\\\". If\",\n \"to 0. :type merge: int, optional :return: Nothing to be returned. :rtype: None\",\n \"in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"):\",\n \"to None. Number of ids must be the same as the number of\",\n \"True, the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are\",\n \"10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick\",\n \"None else plt.gca() # Calculate midpoints of original and distance equalized segments n_segments\",\n \"= motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start\",\n \"region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean])))\",\n \"start_r: int :param end_r: Chromosomal end positiont of the region to be plotted.\",\n \"of the arrow head as proportion of the arrow, defaults to 0.2 :type\",\n \"plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param colors:\",\n \"be plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis of\",\n \"bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name]\",\n \"ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start,\",\n \"cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <=\",\n \"\\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool`\",\n \"int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict):\",\n \"for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list,\",\n \"\\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale == \\\"Mb\\\"): digits_to_round\",\n \"for minus stranded genes, default is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple\",\n \":type genomic_segments: list :param start: Start position of the genomic region. :type start:\",\n \"# Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist\",\n \"End position of the region to be plotted. :type end: int :param color:\",\n \"the arc, defaults to \\\"k\\\". :type color: str, optional. :param ax: Axis where\",\n \"end: int :param bin_size: size of bin to average methylation values, defaults to\",\n \"genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type\",\n \"region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort()\",\n \"= binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap cmap\",\n \"triangular shape). Can be negative or greater than one. Defaults to 0. :type\",\n \"distance between two genes, as ratio of ax width, such that two genes\",\n \"introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the\",\n \"int :param end: End position of the region to be plotted. :type end:\",\n \"genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists with\",\n \"plotted. :type r_start: int :param r_end: End position of region to be plotted.\",\n \"to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings,\",\n \"by side. If this ratio is underwent, the genes will be stacked. :type\",\n \"\\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\",\",\n \"defaults to False. :type edge_color: str, optional :param alpha: Alpha value of the\",\n \"end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point\",\n \"= gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up,\",\n \"for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3])\",\n \":param ax: Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([\",\n \"plot, default is None :type blacklist: list, optional :param plot_gene_ids: If True, all\",\n \"no edge is plotted, defaults to False. :type edge_color: str, optional :param alpha:\",\n \"max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect =\",\n \":type blacklist: set, optional :param ax: Axis used for plotting, defaults to None.\",\n \"str, optional :param vmin: Minimal value of intensity range to be plotted, defaults\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or\",\n \"= ax if ax is not None else plt.gca() # Calculate midpoints of\",\n \":param edge_color: Color of region edge. If False, no edge is plotted, defaults\",\n \"(depends on the region that is plotted), defaults to 1000. :type head_length: int,\",\n \"plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC\",\n \"= {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\",\",\n \"1.5. max_y_pos defines the \\\\ number of stacked genes. 2. patch_list is the\",\n \"digits_to_round = None divisor = None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor\",\n \"is plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction that\",\n \"optional :param vmax: Maximal value of intensity range to be plotted, defaults to\",\n \"], [ float(m[4]) for m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1,\",\n \"Start position on chromosome. :type start: int :param end: End position on chromosome.\",\n \"patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\"\",\n \"plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed,\",\n \"tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3],\",\n \"of g1 used for legend plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional\",\n \"iterators. :param chip_signals: Iterator for which each element is a list-ike object containing:\",\n \":param color_neutral: Plot color of copy number neutral regions, defaults to \\\"k\\\". :type\",\n \"strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length\",\n \".2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\",\",\n \"rotation: Rotational angle of coordinate strings, defaults to 0. :type rotation: int, optional\",\n \":param chrom: Chromosome of the region to be plotted. :type chrom: str :param\",\n \"the background color of the boxplots boxes, defaults to 0.5. :type alpha: float,\",\n \"is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev =\",\n \"Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str,\",\n \"be the same as the number of groups. :type ids: list, optional. :param\",\n \"side by side. If this ratio is underwent, the genes will be stacked.\",\n \"= True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start =\",\n \"gene_name in blacklist): counter += 1 continue n_groups = len(groups) for g in\",\n \":type plot_legend: bool, optional :param legend_loc: Location of the legend. Either of \\\"lower\",\n \"ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2.,\",\n \"if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for i in\",\n \"defaults to \\\"k\\\". :type color: str, optional. :param ax: Axis where the plot\",\n \"ax is not None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1])\",\n \"Default is False. :type plot_legend: bool, optional :param legend_loc: Location of the legend.\",\n \"<start>, <end>]) :type genomic_segments: list :param start: Start position of the genomic region.\",\n \"| bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional. :param ax: Axis on\",\n \"of the boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots\",\n \"= int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse =\",\n \"int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal) on a\",\n \"+= 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if\",\n \"float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom =\",\n \":class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted. :type motifs_bed:\",\n \"return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max\",\n \"None, :type blacklist: set, optional :param ax: (default: None) Axis used for plotting,\",\n \"barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in\",\n \"j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices\",\n \":param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type\",\n \"defaults to 0.2 :type head_width: float, optional :param head_length: Length of the arrow\",\n \"else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start =\",\n \"e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None):\",\n \"if(vmin is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9)\",\n \"ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points,\",\n \"if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size))\",\n \":class:`pybedtools.BedTool` :param start: Start position of the region to be plotted. :type start:\",\n \"] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes)\",\n \"# Define color for gene plotting strand = str(i[5]) color = color_forward if(strand\",\n \"map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.\",\n \"is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region =\",\n \"right\\\", default is \\\"lower right\\\". :type legend_loc: str, optional :param color_plus: Color code\",\n \"left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False,\",\n \"all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist:\",\n \"to be plotted. :type end_r: int :param color: Color of the arc, defaults\",\n \"vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps as pyramid plots :param\",\n \"width, such that two genes are plotted side by side. If this ratio\",\n \"import pybedtools import pandas as pnd import numpy as np import tabix import\",\n \"[] vertices = [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO]\",\n \"for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] ==\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and values patch\",\n \"color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\",\",\n \"\\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick\",\n \"is not None else plt.gca() # Get gene names and regions genes_in_region_bed =\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold,\",\n \":param r_end: End position of region to be plotted. :type r_end: int :param\",\n \"distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 +\",\n \"Number of ids must be the same as the number of groups. :type\",\n \"for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([\",\n \"if(not ids is None): g_id = ids[g] else: g_id = \\\"group \\\"+str(g) if(not\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if False\",\n \"same as the number of groups. :type ids: list, optional. :param plot_gene_names: True\",\n \"is not None else plt.gca() max_dist = 0 for e in links_bed: link_pos1\",\n \"facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end])\",\n \"plt import pybedtools import pandas as pnd import numpy as np import tabix\",\n \":type rotation: int, optional :return: Nothing to be returned. :rtype: None ''' ax\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False\",\n \":param regions: Iterator containig list-like elements with the following entries: 1. Chromosome 2.\",\n \"colormap to be used for plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap:\",\n \"to be plotted, defaults to None. :type vmax: float, optional :param location: Either\",\n \"else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01\",\n \"rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = []\",\n \"motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end =\",\n \"y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0,\",\n \"else: g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list\",\n \"flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else\",\n \"on the ax. :rtype: list \\\"\\\"\\\" ax = ax if ax is not\",\n \"\\\"up\\\", plot ticks to upper direction, else if \\\"down\\\", plot ticks to lower\",\n \"gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y,\",\n \"blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos =\",\n \":param r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param r_start:\",\n \"start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0)\",\n \":type gene_mid_points: list :param region: List containing the region to be plotted ([<chrom>,\",\n \"and values = y position \\\\ of gene. :rtype: tuple ''' sort_indices =\",\n \"gene expression (e.g. tumor and normal) on a gene region scale equalizing the\",\n \"gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing\",\n \"plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\",\",\n \"genomic scales elements, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis\",\n \"standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax\",\n \"end, color). The color field is used to determine the color for plotting\",\n \"Full path to ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list = None\",\n \"Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\",\",\n \"groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function\",\n \"used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If\",\n \"continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos\",\n \"Start region1 3. End region1 4. Chromosome region2 5. Start region2 6. End\",\n \"to be plotted. :type end: int :param segment_size: Size of the segments for\",\n \"ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper):\",\n \"start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots arcs from unequal\",\n \"ticks to upper direction, else if \\\"down\\\", plot ticks to lower direction, defaults\",\n \"optional :param g2_id: ID of g2 used for legend plotting, defaults to \\\"normal\\\".\",\n \"in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border\",\n \"flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color\",\n \"plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max\",\n \"4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold,\",\n \":param links_bed: Iterator, that contains bed-like structured lists with the following elements: 1.\",\n \"= value if(not offset is None): end = start + offset left +=\",\n \"values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the\",\n \"i in region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed,\",\n \"Matrix that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param\",\n \"the IDs of the different groups. :type groups: list :param gene_names_map: Dictionary with\",\n \"\\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6],\",\n \"\\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8),\",\n \"start position of region to be plotted. :type start: int :param end: Chromosomal\",\n \"region that is plotted), defaults to 1000. :type head_length: int, optional :param overhang:\",\n \"plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned\",\n \"Axis on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"the number of groups, defaults to None. :type colors: str, optional :param ids:\",\n \"([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists containing the\",\n \"be plotted. :type r_chrom: str :param r_start: Start position of region to be\",\n \"stay in increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation:\",\n \"as the number of groups, defaults to None. :type colors: str, optional :param\",\n \"len(genes_in_region) == 0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict\",\n \"\\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID of g1 used for legend\",\n \":param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to upper\",\n \"optional :param ax: (default: None) Axis used for plotting, defaults to None. :type\",\n \"plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\".\",\n \"[ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in binned_meth_calls ]\",\n \"plot_legend: If True plot legend, False otherwise, defaults to False. :type plot_legend: bool,\",\n \"plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like\",\n \"else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border =\",\n \"drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.\",\n \"= names of segments, and values patch :rtype: dict ''' ax = ax\",\n \"used for plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional :param max_dev:\",\n \":param end: End position on chromosome. :type end: int :param ploidy: Assumed ploidy\",\n \"plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool`\",\n \"int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2,\",\n \"= color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt',\",\n \"representing the translocation, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis\",\n \"int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end\",\n \"for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color,\",\n \"2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be\",\n \"u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens =\",\n \"that is plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction\",\n \"> 0 else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n\",\n \":param distance_ratio: Minimal distance between two genes, as ratio of ax width, such\",\n \"[(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes =\",\n \"of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean\",\n \"start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label\",\n \"blacklist: set, optional :param ax: (default: None) Axis used for plotting, defaults to\",\n \"be merged. If this value is not equal to 0, than merge elements\",\n \"legend plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID of\",\n \"the number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids\",\n \"optional :param color_neutral: Plot color of copy number neutral regions, defaults to \\\"k\\\".\",\n \"= head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color =\",\n \"segment_size: int :param cmap: Name of the colormap to be used for plotting\",\n \"end): ticks += [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' #\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold,\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise, defaults to\",\n \"input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end,\",\n \"structured lists with the following elements: 1. Chromosome region1 2. Start region1 3.\",\n \"int :param end: End position of the genomic region. :type end: int :param\",\n \"None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i\",\n \"= None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a\",\n \"defaults to 0. :type merge: int, optional :return: Nothing to be returned. :rtype:\",\n \"genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param\",\n \"0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that\",\n \"str, optional. :param color_minus: Color code for minus stranded genes, default is \\\"#fb8072\\\".\",\n \"head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as arrows, indicating\",\n \"blacklist is None and gene_map[gene_name] in blacklist): continue # Define color for gene\",\n \"len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border\",\n \"which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene\",\n \"cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect)\",\n \"Minimal distance between two genes, as ratio of ax width, such that two\",\n \"int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1\",\n \"(end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1):\",\n \"[1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \"optional :return: Nothing to be returned. :rtype: None ''' # Use given axis\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6,\",\n \"patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch)\",\n \"plotted, defaults to 0. :type merge: int, optional :return: Nothing to be returned.\",\n \"a list-ike object containing: 1. Chromosome 2. Start postion 3. End position 4.\",\n \"equal distances. :param genomic_segments: List of segments for which distances shall be equalized\",\n \"positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not\",\n \"tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left')\",\n \"False. :type plot_legend: bool, optional :param colors: List of colors used for plotting\",\n \"tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__\",\n \"plotting strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse\",\n \"int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <=\",\n \"Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)):\",\n \"exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values =\",\n \"for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2])\",\n \"= start + offset left += [start] height += [value] left_merged = []\",\n \":class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool`\",\n \"3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy ==\",\n \"exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 =\",\n \"not None else plt.gca() c = 0 for region in regions: if(not edgecolor):\",\n \"0): # Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region,\",\n \"to lower direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If\",\n \"] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin # Define\",\n \"region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function\",\n \"[]) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that\",\n \"Dictionary with keys = gene ids and values = y position \\\\ of\",\n \"Can be negative or greater than one. Defaults to 0. :type overhang: float,\",\n \"start, end, bin_size=1000, ax = None): '''Function for plotting methylation values as heatmap\",\n \"= gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue\",\n \"<= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start,\",\n \"medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\":\",\n \"\\\"left\\\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2,\",\n \"range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i]\",\n \"color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc,\",\n \"Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map:\",\n \"\\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for\",\n \"TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction of the\",\n \"sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the\",\n \"be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted\",\n \"extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist):\",\n \"defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy\",\n \"of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of\",\n \"to be plotted, default to None. :type blacklist: set, optional :param ax: Axis\",\n \"Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to\",\n \"is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes\",\n \"exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i\",\n \"not None else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1))\",\n \"lines equalizing distances, defaults to \\\"k\\\". :type color: str, optional :param direction: Direction\",\n \":type cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults\",\n \"lists with the following elements: 1. Chromosome region1 2. Start region1 3. End\",\n \"defaults to None, :type blacklist: set, optional :param ax: (default: None) Axis used\",\n \"None): end = start + offset left += [start] height += [value] left_merged\",\n \"+= [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos\",\n \"a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used\",\n \"be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis\",\n \"continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0]\",\n \"= list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ ==\",\n \"[gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine\",\n \"linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function\",\n \"= colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"):\",\n \"region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get gene names and\",\n \"if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions +=\",\n \"gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for\",\n \"is not None else plt.gca() # Calculate midpoints of original and distance equalized\",\n \"ax if ax is not None else plt.gca() max_signal = 0 left =\",\n \"else plt.gca() # Calculate midpoints of original and distance equalized segments n_segments =\",\n \"defaults to \\\"b\\\". :type color: str, optional :param offset: Length of intervals, defaults\",\n \"region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List\",\n \"[] counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border\",\n \"end_r: int :param color: Color of the arc, defaults to \\\"k\\\". :type color:\",\n \"to be plotted. :type r_end: int :param ax: Axis of plot :type ax:\",\n \"overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as arrows, indicating their\",\n \"input_filename: Full path to ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object\",\n \"+ extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean\",\n \"else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start),\",\n \"= ax if ax is not None else plt.gca() max_signal = 0 left\",\n \"= determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max #\",\n \"= exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1. for\",\n \"end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end,\",\n \"for legend plotting, defaults to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If\",\n \"position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and\",\n \"number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and\",\n \"(midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path =\",\n \"blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border =\",\n \"be plotted, defaults to None. :type vmax: float, optional :param location: Either of\",\n \"color_g1: Color used for plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1:\",\n \"r_chrom: str :param r_start: Start position of region to be plotted. :type r_start:\",\n \"{\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\",\",\n \"side. If this ratio is underwent, the genes will be stacked, default is\",\n \"\\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\",\",\n \":type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type r_chrom:\",\n \"ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def\",\n \"them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \\\"most_important\\\" file :type\",\n \"\\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1.\",\n \"of intensity range to be plotted, defaults to None :type vmin: float, optional\",\n \"translocation event as a bar, showing the part of the genome that is\",\n \"the \\\\ number of stacked genes. 2. patch_list is the list of patches\",\n \"if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color\",\n \"Chromosomal end position of region to be plotted. :type end: int :param segment_size:\",\n \"5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) ==\",\n \"optional. :param color_minus: Color code for minus stranded genes, default is \\\"#fb8072\\\". :type\",\n \"to be plotted. :type end_r: int :param TX_pos: Position of the translocation. :type\",\n \"colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none',\",\n \"be plotted, default to None. :type blacklist: set, optional :param ax: Axis used\",\n \"Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\":\",\n \"to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values\",\n \"(float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing #\",\n \"ax is not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1])\",\n \":rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None\",\n \"of the different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL\",\n \"the genomic region. :type start: int :param end: End position of the genomic\",\n \"y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2.,\",\n \"number of groups. :type ids: list, optional. :param plot_gene_names: True if gene names\",\n \"plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha value\",\n \":param segment_size: Size of the segments for which contacts were called. :type segment_size:\",\n \"= standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in\",\n \"if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain\",\n \"max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start >\",\n \":param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of\",\n \"in the gene plot. If not set, then y_max is the max number\",\n \"facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location ==\",\n \"1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos,\",\n \"region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2]\",\n \"1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border +\",\n \"''' # Use given axis for plotting ax = ax if ax is\",\n \"genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2]\",\n \"the genome that is translocated. :param chrom_r: Chromosome of the region to be\",\n \"used for legend plotting, defaults to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names:\",\n \"boxplot, no points are plotted otherwise, defaults to False. :type plot_points: bool, optional\",\n \"for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3])\",\n \"file containing (chrom, start, end, name, score, strand, start, end, color). The color\",\n \"0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([],\",\n \"color_g2: Color used for plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2:\",\n \"g2 used for legend plotting, defaults to \\\"normal\\\". :type g2_id: str, optional :param\",\n \"max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([],\",\n \":param alpha: Alpha value of the rectangle, representing the region to be plotted,\",\n \"2. Start position 3. End position :type regions: iterator :param start: Start position\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise,\",\n \"plotting ax = ax if ax is not None else plt.gca() colors =\",\n \"= str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start,\",\n \":class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample\",\n \"meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def\",\n \"for plotting grouped gene expression (e.g. tumor and normal) on a gene region\",\n \"str, optional :param color_loss: Plot color of copy number losses, defaults to \\\"r\\\".\",\n \"gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i\",\n \"of copy number gains, defaults to \\\"g\\\". :type color_gain: str, optional :param color_loss:\",\n \"1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True,\",\n \"2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"],\",\n \"cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap)\",\n \"gene names shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool,\",\n \"list :param groups: List of lists containing the IDs of the different groups.\",\n \"and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border =\",\n \"color_minus: Color of plus stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str,\",\n \"plotted. :type start: int :param end: Chromosomal end position of region to be\",\n \"y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file:\",\n \"1. :type alpha: float, optional. :param ax: Axis of plot, defaults to None.\",\n \"start_r: int :param end_r: End position of the region to be plotted. :type\",\n \"color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False\",\n \"HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not\",\n \":type color: str, optional :param edge_color: Color of region edge. If False, no\",\n \"[\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch]\",\n \"= y position \\\\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for\",\n \"current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn\",\n \"expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else 1.\",\n \"True legend is plotted, False otherwise, defaults to False. :type plot_legend: bool :param\",\n \"event as a bar, showing the part of the genome that is translocated.\",\n \"TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis on\",\n \"if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else:\",\n \"optional :param offset: Length of intervals, defaults to None. :type offset: int, optional\",\n \"exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i\",\n \"= motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width,\",\n \"if ax is not None else plt.gca() for interval in cnvs_bed: current_start =\",\n \"str :param start_r: Start position of the region to be plotted. :type start_r:\",\n \"indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited\",\n \"gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a\",\n \"to be considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional\",\n \"line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map\",\n \"size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list,\",\n \"region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns:\",\n \"of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos\",\n \":type y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as ratio\",\n \"in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and\",\n \"start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object\",\n \"is plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha value\",\n \"else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r,\",\n \"be plotted. :type start_r: int :param end_r: Chromosomal end positiont of the region\",\n \"plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5,\",\n \"[] if(not merge is None): heights = [] lefts = [] for i\",\n \"plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color,\",\n \"end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None):\",\n \"1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale\",\n \"Iterator containig list-like elements with the following entries: 1. Chromosome 2. Start position\",\n \"midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path\",\n \"float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev\",\n \"3. End position 4. Value to be plotted as bar :type chip_signals: iterator\",\n \"plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots\",\n \"showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id,\",\n \"equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)]\",\n \"values as heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom,\",\n \"str, optional :param edge_color: Color of region edge. If False, no edge is\",\n \"for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not\",\n \"bed-like structured lists with the following elements: 1. Chromosome region1 2. Start region1\",\n \"HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping.\",\n \"as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted.\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)],\",\n \"sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval in\",\n \"segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256.\",\n \"scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size))\",\n \"start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments\",\n \"ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse = False\",\n \"(\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object :param input_filename: Full path to\",\n \"colors must be the same as the number of groups, defaults to None.\",\n \":param plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else the\",\n \"gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in gene_name_mapping_file: split_line =\",\n \"is plotted in addition to the boxplot, no points are plotted otherwise, defaults\",\n \"lower direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If True,\",\n \"in range(len(left)): if(i % merge == 0 and not (i == 0)): left_merged\",\n \"direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates\",\n \"the expression values of all samples (columns: sample ids; index: gene ids). :type\",\n \"color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as arrows, indicating their directionality. :param\",\n \"of intensity range to be plotted, defaults to None. :type vmax: float, optional\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None '''\",\n \"int, optional :param merge: Number of elements to be merged. If this value\",\n \"list-like elements with the following entries: 1. Chromsome 2. Start position 3. end\",\n \"if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False)\",\n \"if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] ==\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4],\",\n \"> end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)):\",\n \"Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm =\",\n \"position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2.\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments,\",\n \"position of the region to be plotted. :type start_r: int :param end_r: Chromosomal\",\n \"the different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE\",\n \"input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5]\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend,\",\n \"= str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand\",\n \"created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that\",\n \"whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color = colors[g]\",\n \"region to be plotted. :type start: int :param end: End position of region\",\n \"translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction:\",\n \"linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in\",\n \"greater than one. Defaults to 0. :type overhang: float, optional :param color_plus: Color\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names\",\n \":type color: str, optional. :param ax: Axis where the plot is drawn, defaults\",\n \"for the background color of the boxplots boxes, defaults to 0.5. :type alpha:\",\n \"str :param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"int :param end_r: End position of the region to be plotted. :type end_r:\",\n \"as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed,\",\n \"for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None):\",\n \"of region to be plotted. :type r_chrom: str :param r_start: Start position of\",\n \"current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start,\",\n \"expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids; index:\",\n \"counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\"\",\n \"False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used for\",\n \"to be plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color\",\n \"head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size,\",\n \"in chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value >\",\n \"float(m[4]) for m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0,\",\n \"between gene ids :param gene_name_mapping_file: Path to a tab separated file, for which\",\n \"hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot`\",\n \"value of the rectangle, representing the region to be plotted, defaults to 1.\",\n \"\\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc: str, optional :param color_plus: Color\",\n \"max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param\",\n \"float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\",\",\n \":class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax\",\n \"points representing methylation values, defaults to \\\"k\\\". :type color: str, optional :param ax:\",\n \"equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1])\",\n \"for j in [meth_before, meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing +=\",\n \"ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean]))) for\",\n \"y_max: bool, optional :param distance_ratio: Minimal distance between two genes, as ratio of\",\n \"None else plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2\",\n \"len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else:\",\n \"colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0,\",\n \"path to ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs\",\n \"\\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype:\",\n \"] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]],\",\n \"if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True,\",\n \"from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots\",\n \":param head_width: Width of the arrow head as proportion of the arrow, defaults\",\n \"location: str, optional :param ax: Axis on which to plot contact map, defaults\",\n \":type vmax: float, optional :param location: Either of \\\"top\\\" | \\\"bottom\\\". If location\",\n \"start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments as\",\n \"positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2)\",\n \"boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot`\",\n \"Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2 5. Start\",\n \"g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color\",\n \"createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path to\",\n \"linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r,\",\n \"i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices\",\n \"is None :type blacklist: list, optional :param plot_gene_ids: If True, all gene ids\",\n \"as dot plots. :param meth_calls: Iterator containing list-like elements with the following entries:\",\n \"capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt',\",\n \"import PathPatch import matplotlib.cm as cm import matplotlib import tabix import math def\",\n \"alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]),\",\n \"left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values =\",\n \"(range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range:\",\n \"not None else plt.gca() max_dist = 0 for e in links_bed: link_pos1 =\",\n \"ax = ax if ax is not None else plt.gca() # Calculate midpoints\",\n \"''' ax = ax if ax is not None else plt.gca() TX_start =\",\n \"defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate\",\n \"strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down\",\n \"coordinates in a linea fashion. :param chrom: Chromosome of the region to be\",\n \"= int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in\",\n \"4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \"list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1.\",\n \"\\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :]\",\n \"= str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse y =\",\n \"Position of the translocation. :type TX_pos: int :param direction: Direction of the genomic\",\n \"If True, a point per expression value is plotted in addition to the\",\n \"< distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label\",\n \"color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] =\",\n \"end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start,\",\n \"[] patch_description_list = [] met_forward = False met_reverse = False # Plot Introns\",\n \"distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label =\",\n \"will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the\",\n \"+= n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for\",\n \"of the arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length\",\n \".8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r,\",\n \"max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename):\",\n \"int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name]\",\n \"= [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions\",\n \"methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End, Methylated Cs,\",\n \"left = left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor\",\n \"color: color of bars, defaults to \\\"b\\\". :type color: str, optional :param offset:\",\n \"of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2.,\",\n \"for which to plot CNVs. :type chromosome: str :param start: Start position on\",\n \"vmin: float, optional :param vmax: Maximal value of intensity range to be plotted,\",\n \"= [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(\",\n \"else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for\",\n \"gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0]\",\n \":type segment_size: int :param cmap: Name of the colormap to be used for\",\n \"chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ]\",\n \"plotting ax = ax if ax is not None else plt.gca() for interval\",\n \"= [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names\",\n \"gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name\",\n \"= [] for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value\",\n \"start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i\",\n \"line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0]\",\n \"optional :param color: color of bars, defaults to \\\"b\\\". :type color: str, optional\",\n \"values per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.)\",\n \"from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from\",\n \"= exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size =\",\n \"capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list = None tick_positions = []\",\n \"cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that\",\n \"in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 =\",\n \":param cmap: Name of the colormap to be used for plotting HiC intensities,\",\n \"\\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue\",\n \"than one. Defaults to 0. :type overhang: float, optional :param color_plus: Color of\",\n \"= int(element[1]) if(position < start or position > end): continue n_meth = int(element[3])\",\n \"[left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"):\",\n \":param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample\",\n \"= {} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color\",\n \"gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values)\",\n \"determines the max y position for gene plotting via function plotGenes. :param genes_bed:\",\n \"float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot\",\n \"is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10)))\",\n \"groups: List of lists containing the IDs of the different groups. :type groups:\",\n \"else if location == \\\"bottom\\\" the pyramid points downwards, defaults to top, :type\",\n \"of the translocation. :type TX_pos: int :param direction: Direction of the genomic part\",\n \"= Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5,\",\n \"if ax is not None else plt.gca() c = 0 for region in\",\n \"[\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax\",\n \"edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as simple rectangles.\",\n \"= color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None):\",\n \"fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the\",\n \"which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param\",\n \"gene ids :param gene_name_mapping_file: Path to a tab separated file, for which the\",\n \"for element in methylation_bed: # Determine bin position = int(element[1]) if(position < start\",\n \"midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY,\",\n \"plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\")\",\n \"len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4]))\",\n \"of lines equalizing distances, defaults to \\\"k\\\". :type color: str, optional :param direction:\",\n \"= str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define\",\n \"be negative or greater than one. Defaults to 0. :type overhang: float, optional\",\n \"converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \\\"most_important\\\" file\",\n \"genes, as ratio of ax width, such that two genes are plotted side\",\n \"representing the regions to be plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional\",\n \"plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True,\",\n \"cmap: str, optional :param vmin: Minimal value of intensity range to be plotted,\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' # Use given\",\n \":type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of\",\n \":return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\",\",\n \"vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap)\",\n \"[]) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size,\",\n \"1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale\",\n \"end: End position of the region to be plotted. :type end: str :param\",\n \"chrom: Chromosome of region to be plotted. :type chrom: str :param start: Start\",\n \"plot ticks to lower direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional :param\",\n \"of stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and values\",\n \"chip_signals: start = int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal):\",\n \":rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in\",\n \"= introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward =\",\n \"99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2)\",\n \"distances of genomic segments to equal distances. :param genomic_segments: List of segments for\",\n \"the following entries: 1. Chromsome 2. Start position 3. end position 4. Number\",\n \"= int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal =\",\n \"linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [\",\n \"str, optional :param color_g2: Color used for plotting g2 samples expression, defaults to\",\n \"used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype:\",\n \"= matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect =\",\n \"chrom: str :param start: Start position of the region to be plotted. :type\",\n \"introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX\",\n \":param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param\",\n \"return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None,\",\n \"widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not\",\n \"\\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06,\",\n \"plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make\",\n \"regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start:\",\n \"GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot was\",\n \"[start] height += [value] left_merged = [] height_merged = [] if(not merge is\",\n \"str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing order. Else,\",\n \"genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict =\",\n \".5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4,\",\n \"<= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch)\",\n \"expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns: sample ids;\",\n \"color of bars, defaults to \\\"b\\\". :type color: str, optional :param offset: Length\",\n \"defaults to \\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy\",\n \"of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for\",\n \"range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = []\",\n \"for legend plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID\",\n \"the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend:\",\n \"= color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none',\",\n \"[] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]]\",\n \"is None): end = start + offset left += [start] height += [value]\",\n \"region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \\\"top_down\\\"):\",\n \"scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions.\",\n \"to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs,\",\n \"gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1])\",\n \"of the bar representing the translocation, defaults to \\\"k\\\". :type color: str, optional\",\n \"str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults to None.\",\n \"in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5,\",\n \"that plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object\",\n \"height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not\",\n \"tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border =\",\n \"optional :param log_transformed: If True use log transformed values for plotting, non-transformed values\",\n \"will be averaged an plotted, defaults to 0. :type merge: int, optional :return:\",\n \":param TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction of\",\n \":type end: int :param bin_size: size of bin to average methylation values, defaults\",\n \"= [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2\",\n \"for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i\",\n \"for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3])\",\n \"exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end\",\n \"else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get gene\",\n \"ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df:\",\n \"= left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor =\",\n \"to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted\",\n \"region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart,\",\n \"bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def\",\n \":return: Dictionary with keys = names of segments, and values patch :rtype: dict\",\n \"to 0. :type overhang: float, optional :param color_plus: Color of plus stranded TF\",\n \"tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn\",\n \"None. :type blacklist: set, optional :param ax: Axis used for plotting, defaults to\",\n \"ax if ax is not None else plt.gca() for interval in cnvs_bed: current_start\",\n \"= ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list +=\",\n \"by side. If this ratio is underwent, the genes will be stacked, default\",\n \":param chip_signals: Iterator for which each element is a list-ike object containing: 1.\",\n \"legend plotting, defaults to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If True,\",\n \"arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length of the\",\n \":param end_r: End position of the region to be plotted. :type end_r: int\",\n \"where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"of the region to be plotted. :type chrom_r: str :param start_r: Chromosomal start\",\n \"if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in\",\n \"gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]]\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold,\",\n \"first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick\",\n \"+= 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color,\",\n \"linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward\",\n \":param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids;\",\n \"Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the\",\n \"linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1,\",\n \"else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin\",\n \"gene plotting strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"): color =\",\n \"plotted. :type r_chrom: str :param r_start: Start position of region to be plotted.\",\n \"if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end],\",\n \"is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color\",\n \"= Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and\",\n \"end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\",\",\n \"else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first\",\n \"genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal distance between\",\n \"0.1. :type distance_ratio: float, optional :param ax: Axes instance on which the genes\",\n \"tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev)\",\n \"(binned_average_meth[i-1] if not i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not\",\n \"defaults to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO\",\n \"that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start:\",\n \"is None and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1):\",\n \"Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path =\",\n \"3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5])\",\n \"plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in m\",\n \"the colormap to be used for plotting HiC intensities, defaults to \\\"Greys\\\". :type\",\n \"End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region\",\n \"4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start\",\n \"with keys = names of segments, and values patch :rtype: dict ''' ax\",\n \"not equal to 0, than merge elements will be averaged an plotted, defaults\",\n \"position 3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or\",\n \"ax is not None else plt.gca() max_dist = 0 for e in links_bed:\",\n \"None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border\",\n \"bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional. :param ax: Axis on which\",\n \"Length of the arrow in bp (depends on the region that is plotted),\",\n \"= False # Plot Introns for i in introns_in_region: start = int(i[1]) end\",\n \"ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL\",\n \"ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"]\",\n \"in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n):\",\n \"contact maps as pyramid plots :param contact_map: Matrix that contains the intensity values\",\n \"defaults to None. :type max_dev: float, optional :param ax: Axis used for plotting,\",\n \"Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy Number) :type\",\n \"ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq\",\n \"List of equalized region midpoints. :rtype: list ''' ax = ax if ax\",\n \"tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1,\",\n \"facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom,\",\n \"= 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates\",\n \"is not None else plt.gca() max_signal = 0 left = [] height =\",\n \"== 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights =\",\n \"float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\")\",\n \"n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing +=\",\n \"Chromosomal start position of region to be plotted. :type start: int :param end:\",\n \"plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be\",\n \"object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one\",\n \"+ (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1,\",\n \"plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation event\",\n \"= merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color =\",\n \"size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops =\",\n \"colors: str, optional :param ids: IDs used for legend plotting, defaults to None.\",\n \"of stacked genes. 2. patch_list is the list of patches drawn on the\",\n \"open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id\",\n \"in different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start,\",\n \"if(i % merge == 0 and not (i == 0)): left_merged += [lefts[0]]\",\n \"negative or greater than one. Defaults to 0. :type overhang: float, optional :param\",\n \"default is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list,\",\n \"if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean]))) for tick in\",\n \"\\\"left\\\". :type direction: str, optional :param color: Color of the bar representing the\",\n \"= ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos],\",\n \"rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]),\",\n \"plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed,\",\n \"= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8),\",\n \"for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict =\",\n \"elements, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of plot,\",\n \"([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5)\",\n \"Or 1. Chromsome 2. Start position 3. end position 4. Beta Value :type\",\n \"If True plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional\",\n \"\\\"k\\\". :type color: str, optional :param ax: Axis of plot, defaults to None.\",\n \"region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3])\",\n \"link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices =\",\n \"such that two genes are plotted side by side. If this ratio is\",\n \"Determine bin position = int(element[1]) if(position < start or position > end): continue\",\n \"2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0,\",\n \"= [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i]\",\n \"for plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param\",\n \":type r_chrom: str :param r_start: Start position of region to be plotted. :type\",\n \"position_gene_names: str, optional :param log_transformed: If True use log transformed values for plotting,\",\n \"copy number gains, defaults to \\\"g\\\". :type color_gain: str, optional :param color_loss: Plot\",\n \"End position of the region to be plotted. :type end_r: int :param TX_pos:\",\n \"not None else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed,\",\n \"if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 <\",\n \"sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys:\",\n \"height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def\",\n \"float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold):\",\n \"sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed =\",\n \"\\\"down\\\", plot ticks to lower direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional\",\n \"TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing\",\n \"Minimal value of intensity range to be plotted, defaults to None :type vmin:\",\n \"Start position of the region to be plotted. :type start: int :param end:\",\n \"Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1)\",\n \"Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults\",\n \":param end: Chromosomal end position of region to be plotted. :type end: int\",\n \":class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size\",\n \"tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])]\",\n \"the arrow, defaults to 0.2 :type head_width: float, optional :param head_length: Length of\",\n \":type color: str, optional :param direction: Direction of distance equalization (top_down | bottom_up),\",\n \"= int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels\",\n \"split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\"\",\n \"Set containing gene ids not to be plotted, defaults to None, :type blacklist:\",\n \"determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot\",\n \"is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True)\",\n \"plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels():\",\n \"mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line\",\n \"0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point,\",\n \"in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6)\",\n \"Nothing to be returned :rtype: None ''' ax = ax if ax is\",\n \"with keys = gene ids and values = y position \\\\ of gene.\",\n \"2. Start Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy\",\n \"= [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path\",\n \"blacklist is None and gene_name in blacklist): counter += 1 continue n_groups =\",\n \"= color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect =\",\n \"is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2.\",\n \"if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i],\",\n \"of all samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param\",\n \"ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that\",\n \"of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small\",\n \":param cnv_threshold: Minimal deviation from ploidy to be considered as a CNV, defaults\",\n \"of the region to be plotted. :type end: str :param ax: Axis used\",\n \"str :param chrom: Chromosome of the region to be plotted. :type chrom: str\",\n \"r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color:\",\n \"defaults to \\\"g\\\". :type color_gain: str, optional :param color_loss: Plot color of copy\",\n \"= gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter += 1\",\n \"defaults to \\\"top_down\\\". :type direction: str, optional. :param ax: Axis on which to\",\n \"color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y\",\n \"on chromosome. :type start: int :param end: End position on chromosome. :type end:\",\n \"is translocated. :param chrom_r: Chromosome of the region to be plotted. :type chrom_r:\",\n \"Define color for gene plotting strand = str(i[5]) color = color_forward if(strand ==\",\n \"genes will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines\",\n \"else if \\\"down\\\", plot ticks to lower direction, defaults to \\\"up\\\". :type loc_coordinates:\",\n \"for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List\",\n \"If True, a legend describing plus or minus stranded genes is plotted, False\",\n \"retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend\",\n \"of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation\",\n \"which distances shall be equalized (each segment is of the form [<chrom>, <start>,\",\n \"None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2])\",\n \"methylation values as dot plots. :param meth_calls: Iterator containing list-like elements with the\",\n \"= TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r,\",\n \"= region_right_border if(not blacklist is None and gene_name in blacklist): counter += 1\",\n \"values of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame`\",\n \"= {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None patch_description_list\",\n \"heights = [] offset = merge*offset left = left_merged height = height_merged plt.bar(left,\",\n \"which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of\",\n \":class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should not be\",\n \"ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix\",\n \"right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc: str, optional\",\n \"= None): '''Functions that plots genomic regions as simple rectangles. :param regions: Iterator\",\n \":type end: int :param color: Color of the rectangles representing the regions to\",\n \"hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1,\",\n \"int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx =\",\n \"= rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome,\",\n \"in bp (depends on the region that is plotted), defaults to 1000. :type\",\n \"facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None,\",\n \"binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\"\",\n \"(midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO,\",\n \"link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point\",\n \"= int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn <\",\n \"vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3]\",\n \"plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists containing\",\n \"position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes.\",\n \"the ax. 3. patch_description_list is the list of descriptions for the patches \\\\\",\n \"True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"]\",\n \"= None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved\",\n \"ax if ax is not None else plt.gca() c = 0 for region\",\n \"(equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices =\",\n \"color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object\",\n \"end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of\",\n \"pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes, as ratio\",\n \"region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension =\",\n \":type plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to\",\n \":type end: int :param color: Color of points representing methylation values, defaults to\",\n \"patch_list = [] patch_description_list = [] met_forward = False met_reverse = False #\",\n \"y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval in genes_sorted_bed:\",\n \":type links_bed: iterator :param chrom_r: Chromosome of the region to be plotted. :type\",\n \"== \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom,\",\n \"if(not blacklist is None and gene_name in blacklist): counter += 1 continue n_groups\",\n \"legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List\",\n \"start_r: Chromosomal start position of the region to be plotted. :type start_r: int\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3}\",\n \"3. End region1 4. Chromosome region2 5. Start region2 6. End region2 :type\",\n \"of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start\",\n \"entries: 1. Chromsome 2. Start position 3. end position 4. Number methylated cytosines\",\n \"[binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else \\\"NA\\\") meth_after\",\n \":return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position +\",\n \":return: Nothing to be returned :rtype: None ''' ax = ax if ax\",\n \"\\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True\",\n \"i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,\",\n \"region_right_border if(not blacklist is None and gene_name in blacklist): counter += 1 continue\",\n \"revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates stay in\",\n \"Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor\",\n \"for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax\",\n \"number of groups, defaults to None. :type colors: str, optional :param ids: IDs\",\n \"split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list +=\",\n \"Chromosome 2. Start postion 3. End position 4. Value to be plotted as\",\n \"ax is not None else plt.gca() # Calculate midpoints of original and distance\",\n \"Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] #\",\n \"whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for\",\n \"max y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object\",\n \"otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location of the\",\n \"[int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for\",\n \"distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots arcs from\",\n \"return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\",\",\n \"start, end, color). The color field is used to determine the color for\",\n \"to pybedtools.BedTool object :param input_filename: Full path to ACESeq \\\"most_important\\\" file :type input_filename:\",\n \"<gh_stars>0 import matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy\",\n \"tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from\",\n \"= region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is None): extension\",\n \"else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) #\",\n \"else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension is\",\n \"[ j for j in [meth_before, meth_after] if not j == \\\"NA\\\" ]\",\n \"containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list =\",\n \"[] met_forward = False met_reverse = False # Plot Introns for i in\",\n \"[start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i\",\n \"import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from\",\n \"gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be\",\n \"list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i\",\n \"to be plotted. :type chrom: str :param start: Start position of the region\",\n \"int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue\",\n \"regions as simple rectangles. :param regions: Iterator containig list-like elements with the following\",\n \"genes that should not be shown on the plot, default is None :type\",\n \"start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick]\",\n \"distances. :param genomic_segments: List of segments for which distances shall be equalized (each\",\n \"are reverted to decreasing order. Else, coordinates stay in increasing order, defaults to\",\n \"plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that plots links\",\n \":type chromosome: str :param start: Start position on chromosome. :type start: int :param\",\n \"input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file\",\n \"shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param\",\n \"edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1,\",\n \"max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def\",\n \"path = Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor = color,\",\n \"= 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = []\",\n \"divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ]\",\n \"= [] ploidy = None for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy\",\n \"gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol\",\n \":type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered\",\n \"plt.gca() max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 =\",\n \"of the genomic region. :type start: int :param end: End position of the\",\n \"be plotted. :type start_r: int :param end_r: End position of the region to\",\n \"ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)],\",\n \"Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome\",\n \"column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the\",\n \"length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None,\",\n \"and converts them to pybedtools.BedTool object :param input_filename: Full path to ACESeq \\\"most_important\\\"\",\n \"end: int :param segment_size: Size of the segments for which contacts were called.\",\n \"None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2]\",\n \"True use log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool,\",\n \"values of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame`\",\n \"1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return\",\n \"is None and gene_map[gene_name] in blacklist): continue # Define color for gene plotting\",\n \"ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that\",\n \"region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist:\",\n \"plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color used\",\n \"\\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list\",\n \"GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set\",\n \"distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of stacked genes.\",\n \"elif(current_extension < extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops\",\n \"in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev\",\n \"plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7,\",\n \"6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1,\",\n \"not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end =\",\n \"genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {} y_level_dict = {}\",\n \"for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in\",\n \"introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is\",\n \"list-ike object containing: 1. Chromosome 2. Start postion 3. End position 4. Value\",\n \"ax is not None else plt.gca() # Get gene names and regions genes_in_region_bed\",\n \"g1_id: ID of g1 used for legend plotting, defaults to \\\"tumor\\\". :type g1_id:\",\n \"binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] ==\",\n \"(equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color,\",\n \"int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i\",\n \"plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to \\\"b\\\".\",\n \"gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend):\",\n \"structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start,\",\n \":type head_length: int, optional :param overhang: Fraction that the arrow is swept back\",\n \"color). The color field is used to determine the color for plotting (R,G,B).\",\n \"ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn\",\n \"== \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse =\",\n \"color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]],\",\n \"for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to\",\n \"plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True):\",\n \"None ''' ax = ax if ax is not None else plt.gca() c\",\n \":param start: Chromosomal start position of region to be plotted. :type start: int\",\n \"gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border =\",\n \"i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in\",\n \"be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to\",\n \"is not None else plt.gca() patches_dict = {} for segment in segments_list: segment_start\",\n \"color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom,\",\n \"center positions of genes. :type gene_mid_points: list :param region: List containing the region\",\n \"plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end,\",\n \"plot. If not set, then y_max is the max number of stacked genes,\",\n \"ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be\",\n \"{\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \"entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy,\",\n \"plotting samples expression. The number of colors must be the same as the\",\n \"None patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved =\",\n \"gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border =\",\n \"= gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border\",\n \"end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None):\",\n \"position of the region to be plotted. :type end: str :param ax: Axis\",\n \"that plots arcs from unequal distances of genomic segments to equal distances. :param\",\n \"\\\"linewidth\\\": .3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean =\",\n \"end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1. Chromsome\",\n \"i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine\",\n \"edge_color: str, optional :param alpha: Alpha value of the rectangle, representing the region\",\n \"coordinates are reverted to decreasing order. Else, coordinates stay in increasing order, defaults\",\n \"exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 =\",\n \"be used for plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap: str, optional\",\n \"in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist\",\n \"location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps as pyramid plots :param contact_map:\",\n \"chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation values as\",\n \"methylation values, defaults to 1000. :type bin_size: int, optional :param ax: Axis to\",\n \"containing list-like elements with the following entries: 1. Chromsome 2. Start position 3.\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less\",\n \"return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None):\",\n \"= [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if\",\n \"end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list +=\",\n \"regions to be plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional :param edge_color:\",\n \"to be returned :rtype: None ''' # Use given axis for plotting ax\",\n \"0. :type merge: int, optional :return: Nothing to be returned. :rtype: None '''\",\n \"color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1,\",\n \"# Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1,\",\n \"as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import\",\n \"plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting\",\n \"\\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor =\",\n \"left = [] height = [] for signal in chip_signals: start = int(signal[1])\",\n \"chrom_r: str :param start_r: Start position of the region to be plotted. :type\",\n \"optional. :param plot_gene_names: True if gene names shall be plotted, False otherwise, defaults\",\n \"pyramid points downwards, defaults to top, :type location: str, optional :param ax: Axis\",\n \"optional :param ax: Axes instance on which the genes are plotted, default is\",\n \"if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward\",\n \"\\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0.\",\n \"start position of the region to be plotted. :type start_r: int :param end_r:\",\n \"underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple of 1.\",\n \"plots arcs from unequal distances of genomic segments to equal distances. :param genomic_segments:\",\n \"end: End position of the region to be plotted. :type end: int :param\",\n \"# Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn\",\n \"if ax is not None else plt.gca() binned_meth_calls = [ [0, 0] for\",\n \"i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1)\",\n \"color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region\",\n \"= float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn =\",\n \"= list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >=\",\n \"right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.):\",\n \"Color of points representing methylation values, defaults to \\\"k\\\". :type color: str, optional\",\n \"Chromosome of region to be plotted. :type chrom: str :param start: Start position\",\n \"{\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \":class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3.\",\n \"= left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border +\",\n \"regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color of plus\",\n \"creates a mapping between gene ids :param gene_name_mapping_file: Path to a tab separated\",\n \"to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\",\",\n \".3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops\",\n \"contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax =\",\n \"the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot\",\n \"plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls\",\n \"int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus\",\n \"plotted. :type end_r: int :param color: Color of the arc, defaults to \\\"k\\\".\",\n \"rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3,\",\n \"for plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed\",\n \":param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene plot\",\n \":param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2,\",\n \":param revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates stay\",\n \"''' ax = ax if ax is not None else plt.gca() c =\",\n \"expression values of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2:\",\n \"ax if ax is not None else plt.gca() TX_start = TX_pos TX_end =\",\n \"losses, defaults to \\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot color of\",\n \"= [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2\",\n \"file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool`\",\n \"exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i\",\n \"str :param start: Start position of region to be plotted. :type start: int\",\n \"None else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ]\",\n \"color: str, optional :param ax: Axis of plot, defaults to None. :type ax:\",\n \"linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ],\",\n \"\\\"lower right\\\". :type legend_loc: str, optional :param color_plus: Color code for plus stranded\",\n \"Start position 3. End position :type regions: iterator :param start: Start position of\",\n \"be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param\",\n \"if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define color for\",\n \"False. :type edge_color: str, optional :param alpha: Alpha value of the rectangle, representing\",\n \"to be returned. :rtype: None ''' # Use given axis for plotting ax\",\n \"not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True # Plot\",\n \"dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color,\",\n \"= (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect\",\n \"+= [height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged +=\",\n \"bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])],\",\n \"if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension < extension):\",\n \"defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\", or\",\n \"# Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name\",\n \"False make more ticks. :type upper: bool, optional :param loc_coordinates: Either of \\\"up\\\"\",\n \"the region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups:\",\n \"= None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path\",\n \"matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size,\",\n \"distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional. :param\",\n \"def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting methylation\",\n \"ax is not None else plt.gca() patches_dict = {} for segment in segments_list:\",\n \"Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm,\",\n \"list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else:\",\n \"of descriptions for the patches \\\\ drawn on the ax. :rtype: list \\\"\\\"\\\"\",\n \":type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted.\",\n \"links_bed: Iterator, that contains bed-like structured lists with the following elements: 1. Chromosome\",\n \"stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color code\",\n \"deviation from ploidy to plot, defaults to None. :type max_dev: float, optional :param\",\n \"are plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha\",\n \"returned. :rtype: None ''' # Use given axis for plotting ax = ax\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' # Use given\",\n \"= 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end\",\n \"if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in\",\n \"plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned\",\n \"None): '''Functions that plots genomic regions as simple rectangles. :param regions: Iterator containig\",\n \"ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in\",\n \"in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and\",\n \"to be returned :rtype: None ''' ax = ax if ax is not\",\n \"= ax if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region\",\n \"left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.]\",\n \"sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the\",\n \"ids is None): g_id = ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id\",\n \"rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3])\",\n \"end: int :param color: Color of points representing methylation values, defaults to \\\"k\\\".\",\n \"segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted. :type chrom:\",\n \"for plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap: str, optional :param vmin:\",\n \"189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"to plot, defaults to None. :type max_dev: float, optional :param ax: Axis used\",\n \"cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else:\",\n \"str :param end: End position of the region to be plotted. :type end:\",\n \"= int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx\",\n \"region to be plotted, defaults to 1. :type alpha: float, optional. :param ax:\",\n \"int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [\",\n \"to be plotted. :type end: str :param ax: Axis used for plotting, defaults\",\n \"start: int :param end: End position of region to be plotted. :type end:\",\n \"ids: IDs used for legend plotting, defaults to None. Number of ids must\",\n \"groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values\",\n \"is a list-ike object containing: 1. Chromosome 2. Start postion 3. End position\",\n \"expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID of g1\",\n \"to None. :type blacklist: set, optional :param ax: Axis used for plotting, defaults\",\n \"plot_legend: If True legend is plotted, False otherwise, defaults to False. :type plot_legend:\",\n \"patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2])\",\n \"is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map =\",\n \":return: Nothing to be returned. :rtype: None ''' # Use given axis for\",\n \"binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per bin # Define Colormap\",\n \"\\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick\",\n \"current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c\",\n \"4. Value to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome\",\n \"3. End Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed:\",\n \"True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults\",\n \"to be returned. :rtype: None ''' ax = ax if ax is not\",\n \"elements: 1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2\",\n \"+= [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth =\",\n \"color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks =\",\n \"if not j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list)\",\n \"float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing\",\n \"reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object :param\",\n \"be plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color of\",\n \"range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1])\",\n \"+= [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2])\",\n \"not to be plotted, defaults to None, :type blacklist: set, optional :param ax:\",\n \"plotted, defaults to None. :type vmax: float, optional :param location: Either of \\\"top\\\"\",\n \"= plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for\",\n \"List of segments for which distances shall be equalized (each segment is of\",\n \"color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0)\",\n \"IDs of the different groups. :type groups: list :param gene_names_map: Dictionary with keys:\",\n \"[g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([\",\n \"plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6)\",\n \"[\\\"reverse strand\\\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in\",\n \"for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None\",\n \"== \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\",\",\n \"gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is\",\n \"as arcs. :param links_bed: Iterator, that contains bed-like structured lists with the following\",\n \"= i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1\",\n \"gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file\",\n \"i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None\",\n \"region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops\",\n \"chromosome. :type start: int :param end: End position on chromosome. :type end: int\",\n \"equalized (each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list\",\n \"int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0.\",\n \"= {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\",\",\n \"end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region\",\n \"Path to a tab separated file, for which the first column is a\",\n \"None): '''Function that plots arcs from unequal distances of genomic segments to equal\",\n \"containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed:\",\n \"gene names, for genes that should not be shown on the plot, default\",\n \"if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label,\",\n \":class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed:\",\n \"head as proportion of the arrow, defaults to 0.2 :type head_width: float, optional\",\n \"(bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id\",\n \"= False met_reverse = False # Plot Introns for i in introns_in_region: start\",\n \"for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names +=\",\n \"optional :param merge: Number of elements to be merged. If this value is\",\n \"= max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label\",\n \"if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold):\",\n \"Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name =\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else if\",\n \"y_max # Plot Exons for i in exons_in_region: start = int(i[1]) end =\",\n \"-1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start,\",\n \"of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position\",\n \"color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons of genes. :param genes_bed:\",\n \"Gene Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3])\",\n \"color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is\",\n \"defaults to False. :type plot_legend: bool, optional :param colors: List of colors used\",\n \"ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def\",\n \"position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to\",\n \"point per expression value is plotted in addition to the boxplot, no points\",\n \"1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None):\",\n \"Start position of region to be plotted. :type start: int :param end: End\",\n \"Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm\",\n \"midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0],\",\n \"to be merged. If this value is not equal to 0, than merge\",\n \"g_id = None if(not ids is None): g_id = ids[g] else: g_id =\",\n \"lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r,\",\n \"List containing the region to be plotted ([<chrom>, <start>, <end>]). :type region: list\",\n \"chrom, start, end, ax = None): '''Function for plotting genomix segments in different\",\n \"is None): max_y_pos = y_max # Plot Exons for i in exons_in_region: start\",\n \"ax = ax if ax is not None else plt.gca() TX_start = TX_pos\",\n \"ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression\",\n \"SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not to be\",\n \"+= [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i]\",\n \":type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults to\",\n \"False # Plot Introns for i in introns_in_region: start = int(i[1]) end =\",\n \"gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not max_y_pos\",\n \"ax = None): '''Function that plots links between genomic regions as arcs. :param\",\n \"[[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of\",\n \"= float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops\",\n \"end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a\",\n \"defaults to 1. :type alpha: float, optional. :param ax: Axis of plot, defaults\",\n \"int(element[1]) if(position < start or position > end): continue n_meth = int(element[3]) n_unmeth\",\n \"names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions =\",\n \".4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\",\",\n \"\\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops =\",\n \"of region to be plotted. :type chrom: str :param start: Start position of\",\n \"markersize=3) g_id = None if(not ids is None): g_id = ids[g] else: g_id\",\n \"shape). Can be negative or greater than one. Defaults to 0. :type overhang:\",\n \"end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic\",\n \"+= [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights +=\",\n \"distance_ratio: Minimal distance between two genes, as ratio of ax width, such that\",\n \"tab separated file, for which the first column is a ensemble gene id,\",\n \"rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\",\",\n \"links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist):\",\n \"to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If True use log transformed\",\n \"optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\".\",\n \"color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end,\",\n \"= int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point =\",\n \"cnv_threshold: float, optional :param color_gain: Plot color of copy number gains, defaults to\",\n \".5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([],\",\n \"start: int :param end: Chromosomal end position of region to be plotted. :type\",\n \"import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None,\",\n \"capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list +=\",\n \":param ax: (default: None) Axis used for plotting, defaults to None. :type ax:\",\n \"[] heights += [height[i]] lefts += [left[i]] if(not i % merge == 0):\",\n \"of region to be plotted. :type r_start: int :param r_end: End position of\",\n \"Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to\",\n \"def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation\",\n \":param color_plus: Color code for plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus:\",\n \"== \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start =\",\n \"[ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color,\",\n \"] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids is None): g_id\",\n \"equalized region midpoints. :rtype: list ''' ax = ax if ax is not\",\n \"plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax =\",\n \"pyramid plots :param contact_map: Matrix that contains the intensity values of HiC contacts.\",\n \"= int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist =\",\n \"ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 =\",\n \"equal to 0, than merge elements will be averaged an plotted, defaults to\",\n \"= gene ids and values = y position \\\\ of gene. :rtype: tuple\",\n \"= split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list\",\n \"arrow_start = motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start,\",\n \"\\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None):\",\n \"None ''' ax = ax if ax is not None else plt.gca() max_dist\",\n \"make more ticks. :type upper: bool, optional :param loc_coordinates: Either of \\\"up\\\" |\",\n \"plots links between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like\",\n \"of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param\",\n \"open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7]\",\n \":type region_size: int :param distance_ratio: Minimal distance between two genes, as ratio of\",\n \"bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be plotted. :type\",\n \"meth_before = (binned_average_meth[i-1] if not i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1]\",\n \"int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is\",\n \"gene id, and the second column is the HUGO gene name :type gene_name_mapping_file:\",\n \"iterator :param start: Start position of the region to be plotted. :type start:\",\n \"plotted. :type end: int :param segment_size: Size of the segments for which contacts\",\n \"range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint =\",\n \"return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None,\",\n \"to top, :type location: str, optional :param ax: Axis on which to plot\",\n \"[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i,\",\n \":type meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type chrom:\",\n \"str, optional :param color: Color of the bar representing the translocation, defaults to\",\n \"[Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\",\",\n \"in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for interval\",\n \"for the patches \\\\ drawn on the ax. :rtype: list \\\"\\\"\\\" ax =\",\n \"motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx,\",\n \":rtype: list ''' ax = ax if ax is not None else plt.gca()\",\n \"decreasing order. Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates:\",\n \":type start: int :param end: End position of region to be plotted. :type\",\n \"(binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for\",\n \"ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax\",\n \"plotted), defaults to 1000. :type head_length: int, optional :param overhang: Fraction that the\",\n \"list \\\"\\\"\\\" ax = ax if ax is not None else plt.gca() genes_in_region\",\n \"= int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth =\",\n \"int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None\",\n \"False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc: Location of\",\n \"= int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and\",\n \"tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left')\",\n \"[ float(m[4]) for m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5)\",\n \"6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2,\",\n \"widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors\",\n \"y_max: Max y value in the gene plot. If not set, then y_max\",\n \"color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up):\",\n \"float, optional :param head_length: Length of the arrow in bp (depends on the\",\n \"in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points +=\",\n \"be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of lists\",\n \"'''Function that plots a translocation event as a bar, showing the part of\",\n \"ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def\",\n \"plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions\",\n \"0 and not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged\",\n \"\\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up\",\n \"to None, :type blacklist: set, optional :param ax: (default: None) Axis used for\",\n \"= color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000,\",\n \"to \\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color of region edge. If\",\n \"optional :param g1_id: ID of g1 used for legend plotting, defaults to \\\"tumor\\\".\",\n \"head_length: int, optional :param overhang: Fraction that the arrow is swept back (0\",\n \"None): g_id = ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id in patch_description_list):\",\n \"optional :param color: Color of the bar representing the translocation, defaults to \\\"k\\\".\",\n \"separated file, for which the first column is a ensemble gene id, and\",\n \"plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal\",\n \"expression values of all samples (columns: sample ids; index: gene ids). :type expression_df:\",\n \"i >= 1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops,\",\n \"a linea fashion. :param chrom: Chromosome of the region to be plotted. :type\",\n \"= float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than\",\n \"int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"if ax is not None else plt.gca() max_signal = 0 left = []\",\n \"Either of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the pyramid points upwards,\",\n \"y position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing\",\n \"to be plotted. :type r_chrom: str :param r_start: Start position of region to\",\n \"int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color\",\n \"= Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], [])\",\n \"plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand =\",\n \"cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool`\",\n \"to \\\"k\\\". :type color: str, optional :param direction: Direction of distance equalization (top_down\",\n \"less ticks, else if False make more ticks. :type upper: bool, optional :param\",\n \"+= n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0\",\n \"loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param\",\n \"containing regions of the TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param\",\n \"== \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start,\",\n \"4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of region to be\",\n \"[] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions +=\",\n \"default is None :type blacklist: list, optional :param plot_gene_ids: If True, all gene\",\n \"to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID of g1 used for\",\n \":param blacklist: Set containing gene ids not to be plotted, defaults to None,\",\n \"via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type\",\n \"optional :param head_length: Length of the arrow in bp (depends on the region\",\n \"used for legend plotting, defaults to None. Number of ids must be the\",\n \"Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j]\",\n \"\\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter +=\",\n \"patch_list is the list of patches drawn on the ax. 3. patch_description_list is\",\n \":type distance_ratio: float, optional :param ax: Axes instance on which the genes are\",\n \"= tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type ==\",\n \"start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation event as\",\n \"i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not\",\n \"medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None): color =\",\n \"if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not\",\n \"4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45)\",\n \"is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral\",\n \"'''Function that plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool`\",\n \"to None. :type offset: int, optional :param merge: Number of elements to be\",\n \"tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start,\",\n \"end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs as\",\n \"a point per expression value is plotted in addition to the boxplot, no\",\n \"like iterators. :param chip_signals: Iterator for which each element is a list-ike object\",\n \"is None): color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions =\",\n \"0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = []\",\n \"the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start\",\n \"the genes will be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos:\",\n \":param direction: Direction of the genomic part that is translocated. Either of \\\"left\\\"\",\n \"to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of copy number\",\n \"if(not blacklist is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border\",\n \"values = y position \\\\ of gene. :rtype: tuple ''' sort_indices = [int(idx)\",\n \"positions of genes. :type gene_mid_points: list :param region: List containing the region to\",\n \"in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist\",\n \"which each element is a list-ike object containing: 1. Chromosome 2. Start postion\",\n \"= ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2\",\n \"line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom ==\",\n \"as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome,\",\n \"distance_ratio: float, optional :param ax: Axes instance on which the genes are plotted,\",\n \"object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values\",\n \"g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal) on\",\n \"[\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2,\",\n \"# Use given axis for plotting ax = ax if ax is not\",\n \":param gene_name_mapping_file: Path to a tab separated file, for which the first column\",\n \"samples expression. The number of colors must be the same as the number\",\n \"genomic region. :type end: int :param color: Color of lines equalizing distances, defaults\",\n \"is of the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start\",\n \"color=\\\"k\\\", ax = None): '''Function that plots links between genomic regions as arcs.\",\n \"region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name\",\n \"plot CNVs. :type chromosome: str :param start: Start position on chromosome. :type start:\",\n \"None ''' ax = ax if ax is not None else plt.gca() TX_start\",\n \"If this ratio is underwent, the genes will be stacked. :type distance_ratio: float\",\n \"= Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7])\",\n \"reverted to decreasing order. Else, coordinates stay in increasing order, defaults to False.\",\n \":type start: int :param end: End position on chromosome. :type end: int :param\",\n \"meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type chrom: str\",\n \"2. y_pos_dict: Dictionary with keys = gene ids and values = y position\",\n \"plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list\",\n \":class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE\",\n \"color: Color of lines equalizing distances, defaults to \\\"k\\\". :type color: str, optional\",\n \"methylation_bed: # Determine bin position = int(element[1]) if(position < start or position >\",\n \"used for plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional\",\n \"normal) on a gene region scale equalizing the position of genes. :param genes_bed:\",\n \"CNVs. :type chromosome: str :param start: Start position on chromosome. :type start: int\",\n \"was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None\",\n \"position of the region to be plotted. :type end_r: int :param TX_pos: Position\",\n \"[(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path =\",\n \"segments for which distances shall be equalized (each segment is of the form\",\n \"[bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean)))\",\n \"Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs.\",\n \":param location: Either of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the pyramid\",\n \"[chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in\",\n \"to 0, than merge elements will be averaged an plotted, defaults to 0.\",\n \"def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that plots\",\n \"plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for\",\n \"if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values =\",\n \"Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned\",\n \"that two genes are plotted side by side. If this ratio is underwent,\",\n \":param plot_legend: If True legend is plotted, False otherwise, defaults to False. :type\",\n \"= [] offset = merge*offset left = left_merged height = height_merged plt.bar(left, height,\",\n \"0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = []\",\n \"in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices =\",\n \":class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool`\",\n \"optional :param upper: If True, make less ticks, else if False make more\",\n \"tabixed bed file containing (chrom, start, end, name, score, strand, start, end, color).\",\n \"color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) >\",\n \"plot_legend: bool, optional :param legend_loc: Location of the legend. Either of \\\"lower left\\\",\",\n \":param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param\",\n \"< region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name =\",\n \"== \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start,\",\n \"= color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions\",\n \":param groups: List of lists containing the IDs of the different groups. :type\",\n \"to be plotted. :type start_r: int :param end_r: End position of the region\",\n \"color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start,\",\n \"Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor =\",\n \"matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm\",\n \"0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors =\",\n \"Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param\",\n \"region: List containing the region to be plotted ([<chrom>, <start>, <end>]). :type region:\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax =\",\n \"of region to be plotted. :type start: int :param end: End position of\",\n \"None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param\",\n \"end = start + offset left += [start] height += [value] left_merged =\",\n \"+= [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) <\",\n \"plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function\",\n \"3. End position :type regions: iterator :param start: Start position of the region\",\n \"in base pairs. :type region_size: int :param distance_ratio: Minimal distance between two genes,\",\n \"= None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round\",\n \"contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted. :type\",\n \"following entries: 1. Chromosome 2. Start position 3. End position :type regions: iterator\",\n \"ax: (default: None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"\\\"\\\"\\\" ax = ax if ax is not None else plt.gca() genes_in_region =\",\n \"Chromosome of the region to be plotted. :type chrom: str :param start: Start\",\n \"None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m\",\n \"chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV\",\n \"(link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch =\",\n \"= None if(not colors is None): color = colors[g] else: color = standard_colors[g]\",\n \"bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color\",\n \"if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points\",\n \"== 0.) else 0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5)\",\n \"descriptions for the patches \\\\ drawn on the ax. :rtype: list \\\"\\\"\\\" ax\",\n \":]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in\",\n \"default to None. :type blacklist: set, optional :param ax: Axis used for plotting,\",\n \"be plotted. :type end: int :param bin_size: size of bin to average methylation\",\n \"of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the pyramid points upwards, else\",\n \"plots genomic regions as simple rectangles. :param regions: Iterator containig list-like elements with\",\n \"rectangles representing the regions to be plotted, defaults to \\\"#cbebc4\\\". :type color: str,\",\n \"defaults to None :type vmin: float, optional :param vmax: Maximal value of intensity\",\n \"None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for gene_name\",\n \"3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :]\",\n \"HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression\",\n \"float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\\\".\\\",\",\n \"elements with the following entries: 1. Chromosome 2. Start position 3. End position\",\n \"containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region,\",\n \"None ''' ax = ax if ax is not None else plt.gca() tick_size\",\n \"extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values =\",\n \"left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 =\",\n \"1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location\",\n \"Name of the colormap to be used for plotting HiC intensities, defaults to\",\n \"region to be plotted. :type end: int :param head_width: Width of the arrow\",\n \"None :type blacklist: list, optional :param plot_gene_ids: If True, all gene ids will\",\n \"strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse y\",\n \"= max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5])\",\n \"gene ids will be included in the plot, False otherwise, default is True\",\n \"counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border =\",\n \"= None if(not ids is None): g_id = ids[g] else: g_id = \\\"group\",\n \"be included in the plot, False otherwise, default is True :type plot_gene_ids: bool,\",\n \"for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,\",\n \"(midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ]\",\n \"= color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c +=\",\n \"start: Start position of region to be plotted. :type start: int :param end:\",\n \"a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\",\",\n \"genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names\",\n \"ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color,\",\n \"ax = ax if ax is not None else plt.gca() genes_in_region = genes_bed\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border =\",\n \":type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param\",\n \"g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2:\",\n \"legend is plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1:\",\n \"int, optional :param cnv_threshold: Minimal deviation from ploidy to be considered as a\",\n \"the arrow head as proportion of the arrow, defaults to 0.2 :type head_width:\",\n \"on the plot, default is None :type blacklist: list, optional :param plot_gene_ids: If\",\n \"[] for i in range(len(left)): if(i % merge == 0 and not (i\",\n \"int :param color: Color of points representing methylation values, defaults to \\\"k\\\". :type\",\n \":type start_r: int :param end_r: End position of the region to be plotted.\",\n \"gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] =\",\n \"Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255.,\",\n \"for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end =\",\n \"None. Number of ids must be the same as the number of groups.\",\n \"value of intensity range to be plotted, defaults to None :type vmin: float,\",\n \"float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color\",\n \"+= 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for\",\n \"+ (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name,\",\n \"ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for\",\n \"Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position 4.\",\n \"[int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point\",\n \"object containing: 1. Chromosome 2. Start postion 3. End position 4. Value to\",\n \"legend_loc: str, optional :param color_plus: Color code for plus stranded genes, default is\",\n \"None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea\",\n \"for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list,\",\n \"patches drawn on the ax. 3. patch_description_list is the list of descriptions for\",\n \"plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy ==\",\n \"color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing\",\n \"bar, showing the part of the genome that is translocated. :param chrom_r: Chromosome\",\n \"for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be\",\n \"continue n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.))\",\n \"position of the genomic region. :type end: int :param color: Color of lines\",\n \"in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i],\",\n \"optional :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"= int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list '''\",\n \"of the region to be plotted. :type start_r: int :param end_r: Chromosomal end\",\n \":type chrom: str :param start: Start position of region to be plotted. :type\",\n \"digits_to_round))+scale for i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0],\",\n \"is None): vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1,\",\n \"\\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return\",\n \"color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): # Determine\",\n \"max number of stacked genes, default is None. :type y_max: bool, optional :param\",\n \"ax width, such that two genes are plotted side by side. If this\",\n \"TX_pos: int :param direction: Direction of the genomic part that is translocated. Either\",\n \"1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start,\",\n \"to \\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis on which to plot\",\n \"for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = []\",\n \"distances shall be equalized (each segment is of the form [<chrom>, <start>, <end>])\",\n \"the region to be plotted. :type end: str :param ax: Axis used for\",\n \"TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool`\",\n \"else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort()\",\n \"dx = head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color\",\n \"widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i\",\n \"edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start,\",\n \"float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops =\",\n \"upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion.\",\n \"element in methylation_bed: # Determine bin position = int(element[1]) if(position < start or\",\n \".2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5)\",\n \"for i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\",\",\n \"color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start\",\n \"int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0 and not\",\n \"True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot\",\n \"= split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2,\",\n \"arrow in bp (depends on the region that is plotted), defaults to 1000.\",\n \"legend describing plus or minus stranded genes is plotted, False otherwise. Default is\",\n \"end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap\",\n \"genomic coordinates in a linea fashion. :param chrom: Chromosome of the region to\",\n \"of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param color: color of bars, defaults to\",\n \"int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn,\",\n \"Axis where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"translocation, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of plot,\",\n \"= Path(vertices, codes) patch = PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw\",\n \"to be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region\",\n \"''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in gene_name_mapping_file: split_line\",\n \"segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type\",\n \"[]) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation values\",\n \"codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5],\",\n \"in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed,\",\n \"ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in\",\n \"edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax =\",\n \"be plotted. :type chrom_r: str :param start_r: Start position of the region to\",\n \":type edge_color: str, optional :param alpha: Alpha value of the rectangle, representing the\",\n \"< len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list ''' ax =\",\n \"plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons\",\n \"Start position of the genomic region. :type start: int :param end: End position\",\n \"for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end):\",\n \"of segments for which distances shall be equalized (each segment is of the\",\n \"# Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm\",\n \"5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position\",\n \"i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color,\",\n \"in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1)\",\n \"Number of elements to be merged. If this value is not equal to\",\n \"of the segments for which contacts were called. :type segment_size: int :param cmap:\",\n \"\\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is not None else plt.gca() region_bed\",\n \"and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i <\",\n \"def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots arcs\",\n \"small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \"# Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names =\",\n \"left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in\",\n \"samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color used\",\n \"len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border +\",\n \"ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g.\",\n \"= ax if ax is not None else plt.gca() binned_meth_calls = [ [0,\",\n \"PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def\",\n \"the region to be plotted. :type end_r: int :param color: Color of the\",\n \"region to be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal\",\n \"side by side. If this ratio is underwent, the genes will be stacked,\",\n \"ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1])\",\n \"plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to be\",\n \"if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color)\",\n \"position 4. Value to be plotted as bar :type chip_signals: iterator :param r_chrom:\",\n \"the HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden.\",\n \"in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border\",\n \"tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"):\",\n \":type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base\",\n \":type alpha: float, optional. :param ax: Axis of plot, defaults to None. :type\",\n \"[[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i\",\n \"of segments, and values patch :rtype: dict ''' ax = ax if ax\",\n \"Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be\",\n \"introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for\",\n \"plot_legend: bool, optional :param colors: List of colors used for plotting samples expression.\",\n \":type end: int :param segment_size: Size of the segments for which contacts were\",\n \"of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed:\",\n \"number losses, defaults to \\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot color\",\n \"position of the region to be plotted. :type start_r: int :param end_r: End\",\n \"color of copy number neutral regions, defaults to \\\"k\\\". :type color_neutral: str, optional\",\n \"of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type\",\n \"else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev\",\n \"bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ]\",\n \":param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str,\",\n \"(downstream), defaults to \\\"left\\\". :type direction: str, optional :param color: Color of the\",\n \"ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0.,\",\n \"expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for\",\n \"CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal deviation from\",\n \"not None else plt.gca() patches_dict = {} for segment in segments_list: segment_start =\",\n \"codes = [] vertices = [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO,\",\n \"in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect\",\n \"g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.]\",\n \"> end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter\",\n \"[] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing\",\n \"loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing order.\",\n \"region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\"\",\n \"arc, defaults to \\\"k\\\". :type color: str, optional. :param ax: Axis where the\",\n \"max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point =\",\n \"hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False,\",\n \"color_plus: Color of plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str,\",\n \"defaults to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are\",\n \"cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact maps as pyramid\",\n \"of the genome that is translocated. :param chrom_r: Chromosome of the region to\",\n \"the part of the genome that is translocated. :param chrom_r: Chromosome of the\",\n \"ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line\",\n \"colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression\",\n \"the genomic scales elements, defaults to \\\"k\\\". :type color: str, optional :param ax:\",\n \"== 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else\",\n \"plot_points: bool, optional :param alpha: Alpha value for the background color of the\",\n \".8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices\",\n \"[g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not\",\n \"y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = []\",\n \":param colors: List of colors used for plotting samples expression. The number of\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and\",\n \"ax if ax is not None else plt.gca() # Get gene names and\",\n \"max_dev: float, optional :param ax: Axis used for plotting, defaults to None. :type\",\n \"1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys\",\n \"region1 2. Start region1 3. End region1 4. Chromosome region2 5. Start region2\",\n \"defaults to False. :type plot_legend: bool :param color_g1: Color used for plotting g1\",\n \"Alpha value for the background color of the boxplots boxes, defaults to 0.5.\",\n \"if(not offset is None): end = start + offset left += [start] height\",\n \"plotted, default to None. :type blacklist: set, optional :param ax: Axis used for\",\n \":type end: str :param ax: Axis used for plotting, defaults to None. :type\",\n \"(equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0),\",\n \"ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6)\",\n \"genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color code for\",\n \"= split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed,\",\n \"ax is not None else plt.gca() c = 0 for region in regions:\",\n \"if(position < start or position > end): continue n_meth = int(element[3]) n_unmeth =\",\n \"j for j in [meth_before, meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing\",\n \"= (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin\",\n \"plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene\",\n \"= end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end = TX_pos rect =\",\n \"Plot average methylation values per bin # Define Colormap cmap = cm.bwr norm\",\n \"else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start\",\n \"counter += 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos =\",\n \"drawn on the ax. 3. patch_description_list is the list of descriptions for the\",\n \"ax = None): '''Function for plotting genomix segments in different colors :param segments_tabix_filename:\",\n \"# Plot average methylation values per bin # Define Colormap cmap = cm.bwr\",\n \"(2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]]\",\n \"for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\"\",\n \"is a ensemble gene id, and the second column is the HUGO gene\",\n \"range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4,\",\n \"n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ],\",\n \"Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else:\",\n \"Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def\",\n \"start_r: Start position of the region to be plotted. :type start_r: int :param\",\n \"100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates ==\",\n \"plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start = start_r\",\n \"if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1\",\n \"input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None for line in\",\n \"be plotted. :type end: int :param head_width: Width of the arrow head as\",\n \"= [] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for\",\n \"else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list\",\n \"str, optional. :param ax: Axis on which to plot, defaults to None. :type\",\n \"groups, defaults to None. :type colors: str, optional :param ids: IDs used for\",\n \"for which each element is a list-ike object containing: 1. Chromosome 2. Start\",\n \"boxplots boxes, defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype:\",\n \"for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue\",\n \"position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size)\",\n \"ids: list, optional. :param plot_gene_names: True if gene names shall be plotted, False\",\n \"contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to be\",\n \"ax is not None else plt.gca() binned_meth_calls = [ [0, 0] for i\",\n \"region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region\",\n \"start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation values as dot plots. :param\",\n \"containing the expression values of g2 samples (columns: sample ids; index: gene ids)\",\n \"= genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down =\",\n \"region to be plotted. :type end: int :param segment_size: Size of the segments\",\n \"patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1\",\n \"not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start\",\n \"import numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import\",\n \"defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color used for plotting\",\n \"= interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i\",\n \"= [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes)\",\n \"[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point,\",\n \"the plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param y_max:\",\n \"height_merged += [np.mean(heights)] heights = [] offset = merge*offset left = left_merged height\",\n \"for m in meth_calls ], [ float(m[4]) for m in m in meth_calls],\",\n \"whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = None\",\n \"end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color,\",\n \"to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome\",\n \"to plot CNVs. :type chromosome: str :param start: Start position on chromosome. :type\",\n \"end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as\",\n \"first column is a ensemble gene id, and the second column is the\",\n \"# Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick =\",\n \"r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function for plotting\",\n \"\\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\",\",\n \"not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3]))\",\n \"split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]),\",\n \"[ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element\",\n \"range(len(left)): if(i % merge == 0 and not (i == 0)): left_merged +=\",\n \"met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list +=\",\n \"from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import\",\n \"\\\"NA\\\") average_list = [ j for j in [meth_before, meth_after] if not j\",\n \"10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick =\",\n \"''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax =\",\n \"averaged an plotted, defaults to 0. :type merge: int, optional :return: Nothing to\",\n \"[] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not\",\n \"= [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points\",\n \"flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0],\",\n \"regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for\",\n \"same as the number of groups, defaults to None. :type colors: str, optional\",\n \"id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for\",\n \"max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue\",\n \"in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1]\",\n \"y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect)\",\n \"plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates\",\n \"defaults to \\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color of region edge.\",\n \"dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6])\",\n \"path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value\",\n \"patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0\",\n \"int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5])\",\n \"introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus\",\n \"'''Function that plots arcs from unequal distances of genomic segments to equal distances.\",\n \"gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend):\",\n \":class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax =\",\n \"containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map\",\n \"as heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start,\",\n \"extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 =\",\n \":type merge: int, optional :return: Nothing to be returned. :rtype: None ''' ax\",\n \"end: int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy:\",\n \"[] gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names: left_border\",\n \"ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color =\",\n \"elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2),\",\n \"number neutral regions, defaults to \\\"k\\\". :type color_neutral: str, optional :param ax: Axis\",\n \"of g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param\",\n \"default is \\\"lower right\\\". :type legend_loc: str, optional :param color_plus: Color code for\",\n \"GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame`\",\n \":type direction: str, optional :param color: Color of the bar representing the translocation,\",\n \":param log_transformed: If True use log transformed values for plotting, non-transformed values otherwise.\",\n \"color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\",\",\n \"len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation\",\n \"y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures,\",\n \"str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue # Define color\",\n \"HiC contact maps as pyramid plots :param contact_map: Matrix that contains the intensity\",\n \"% merge == 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)]\",\n \"norm, cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size,\",\n \"+= 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed,\",\n \"position on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults\",\n \"Colormap used for plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional :param\",\n \"for i in region]), from_string=True) # Get gene names and regions genes_in_region_bed =\",\n \"object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe`\",\n \"in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids is\",\n \"gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs,\",\n \"plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation\",\n \"= matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin\",\n \"4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in\",\n \"== \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx =\",\n \"int :param bin_size: size of bin to average methylation values, defaults to 1000.\",\n \"iterator :param chrom: Chromosome of region to be plotted. :type chrom: str :param\",\n \"a bar, showing the part of the genome that is translocated. :param chrom_r:\",\n \"keys = gene ids and values = y position \\\\ of gene. :rtype:\",\n \"color_loss: Plot color of copy number losses, defaults to \\\"r\\\". :type color_loss: str,\",\n \"of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha}\",\n \"segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0),\",\n \"1. Chromsome 2. Start position 3. end position 4. Beta Value :type meth_calles:\",\n \"x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values,\",\n \"plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels():\",\n \"None): '''Function for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following\",\n \"will be included in the plot, False otherwise, default is True :type plot_gene_ids:\",\n \"= region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) <\",\n \"= [] patch_description_list = [] met_forward = False met_reverse = False # Plot\",\n \"is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]]\",\n \"equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type\",\n \":param start: Start position of the region to be plotted. :type start: int\",\n \"heatmap :param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End,\",\n \"Length of intervals, defaults to None. :type offset: int, optional :param merge: Number\",\n \"for plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object\",\n \"= False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not\",\n \"None if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor\",\n \"= [] met_forward = False met_reverse = False # Plot Introns for i\",\n \"that plots genomic coordinates in a linea fashion. :param chrom: Chromosome of the\",\n \"for plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with\",\n \"if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average\",\n \"ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which\",\n \"expression value is plotted in addition to the boxplot, no points are plotted\",\n \"upper direction, else if \\\"down\\\", plot ticks to lower direction, defaults to \\\"up\\\".\",\n \"color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0)\",\n \"= Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0,\",\n \"= 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick\",\n \"gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down])\",\n \"calls. Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs.\",\n \"if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for tick in\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list ''' ax\",\n \"gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8\",\n \"[4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \":param ploidy: Assumed ploidy of tumor, defaults to 2. :type ploidy: int, optional\",\n \"tumor and normal) on a gene region scale equalizing the position of genes.\",\n \"] counter = 0 for element in methylation_bed: # Determine bin position =\",\n \"genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool`\",\n \"= ax if ax is not None else plt.gca() patches_dict = {} for\",\n \"for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True\",\n \"names shall be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional\",\n \"ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments,\",\n \"float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev):\",\n \"Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1],\",\n \"\\\\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1]\",\n \"HiC intensities, defaults to \\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal value\",\n \"for plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param\",\n \"patch_description_list = [] met_forward = False met_reverse = False # Plot Introns for\",\n \"for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before\",\n \"linea fashion. :param chrom: Chromosome of the region to be plotted. :type chrom:\",\n \"Axes instance on which the genes are plotted, default is None. :type ax:\",\n \"+= [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) #\",\n \":param alpha: Alpha value for the background color of the boxplots boxes, defaults\",\n \":type end: int :param color: Color of lines equalizing distances, defaults to \\\"k\\\".\",\n \"ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None\",\n \"Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5])\",\n \"otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names: Either of \\\"top\\\",\",\n \"start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points +=\",\n \"ploidy of tumor, defaults to 2. :type ploidy: int, optional :param cnv_threshold: Minimal\",\n \"as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot\",\n \"of copy number neutral regions, defaults to \\\"k\\\". :type color_neutral: str, optional :param\",\n \"plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None '''\",\n \"region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]),\",\n \"Iterator containing list-like elements with the following entries: 1. Chromsome 2. Start position\",\n \"0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4):\",\n \"the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of\",\n \"def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True,\",\n \"segments to equal distances. :param genomic_segments: List of segments for which distances shall\",\n \"Color code for plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional.\",\n \"default is True :type plot_gene_ids: bool, optional :param y_max: Max y value in\",\n \":param direction: Direction of distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type\",\n \"gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired\",\n \":type color_g1: str, optional :param color_g2: Color used for plotting g2 samples expression,\",\n \"= int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] +=\",\n \"i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist =\",\n \"to upper direction, else if \\\"down\\\", plot ticks to lower direction, defaults to\",\n \"of region to be plotted in base pairs. :type region_size: int :param distance_ratio:\",\n \"1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO,\",\n \"plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\",\",\n \"capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward = False met_reverse\",\n \"defaults to 1000. :type bin_size: int, optional :param ax: Axis to be used\",\n \"that plots bedGraph like iterators. :param chip_signals: Iterator for which each element is\",\n \"ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\",\",\n \"for plotting methylation values as heatmap :param methylation_bed: Methylation calls. Following fields must\",\n \"merged. If this value is not equal to 0, than merge elements will\",\n \"TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons\",\n \"must be the same as the number of groups, defaults to None. :type\",\n \"plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF\",\n \"3. end position 4. Beta Value :type meth_calles: iterator :param chrom: Chromosome of\",\n \"ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2\",\n \"binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0\",\n \":type cmap: str, optional :param vmin: Minimal value of intensity range to be\",\n \"unequal distances of genomic segments to equal distances. :param genomic_segments: List of segments\",\n \"while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size scale =\",\n \"TX_start = TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end\",\n \"SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed.\",\n \"else plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end\",\n \"of patches drawn on the ax. 3. patch_description_list is the list of descriptions\",\n \"continue # Define color for gene plotting strand = str(i[5]) color = color_forward\",\n \"return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids\",\n \"plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys =\",\n \"int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot\",\n \"heights = [] heights += [height[i]] lefts += [left[i]] if(not i % merge\",\n \"Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower\",\n \"for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates\",\n \"used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to\",\n \"color of copy number losses, defaults to \\\"r\\\". :type color_loss: str, optional :param\",\n \"contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal\",\n \"''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None for line\",\n \"plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter =\",\n \"Rotational angle of coordinate strings, defaults to 0. :type rotation: int, optional :return:\",\n \"color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color,\",\n \"addition to the boxplot, no points are plotted otherwise, defaults to False. :type\",\n \":] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2\",\n \":type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region to be plotted.\",\n \"for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in\",\n \"blacklist is None and gene_name in blacklist): counter += 1 continue if(counter <\",\n \"methylation values per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0.,\",\n \"Tuple of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary\",\n \"ax. :rtype: list \\\"\\\"\\\" ax = ax if ax is not None else\",\n \">= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups),\",\n \"str, optional :param offset: Length of intervals, defaults to None. :type offset: int,\",\n \"for legend plotting, defaults to None. Number of ids must be the same\",\n \"= (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1,\",\n \"back (0 overhang means triangular shape). Can be negative or greater than one.\",\n \"\\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is not\",\n \":class:`pandas.DataFrame` :param start: Chromosomal start position of region to be plotted. :type start:\",\n \"color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color,\",\n \"that contains bed-like structured lists with the following elements: 1. Chromosome region1 2.\",\n \"segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name, score, strand,\",\n \"end: End position on chromosome. :type end: int :param ploidy: Assumed ploidy of\",\n \"+ (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO,\",\n \"plotted. :type end: int :param color: Color of points representing methylation values, defaults\",\n \"upwards, else if location == \\\"bottom\\\" the pyramid points downwards, defaults to top,\",\n \"1. Chromosome 2. Start position 3. End position :type regions: iterator :param start:\",\n \"color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color,\",\n \"not i == 0 else \\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i ==\",\n \"motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end\",\n \"i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name]\",\n \"max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1])\",\n \"= [] if(not merge is None): heights = [] lefts = [] for\",\n \"float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i ==\",\n \"ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and\",\n \"in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list,\",\n \"blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for\",\n \"color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in\",\n \"is not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction ==\",\n \"matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in\",\n \"is plotted, False otherwise, defaults to False. :type plot_legend: bool :param color_g1: Color\",\n \"np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict\",\n \"+ tick_size scale = None if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\"\",\n \"containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing\",\n \"genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in\",\n \"(region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons\",\n \"If location == \\\"top\\\", the pyramid points upwards, else if location == \\\"bottom\\\"\",\n \"= [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes\",\n \"\\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If True use\",\n \"that plots a translocation event as a bar, showing the part of the\",\n \"if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [\",\n \"from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names\",\n \"color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt',\",\n \"as ratio of ax width, such that two genes are plotted side by\",\n \"ax=None): '''Function that plots TF motifs as arrows, indicating their directionality. :param motifs_bed:\",\n \"to be plotted. :type start: int :param end: End position of region to\",\n \"a gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object\",\n \"to the boxplot, no points are plotted otherwise, defaults to False. :type plot_points:\",\n \"the region to be plotted. :type start_r: int :param end_r: Chromosomal end positiont\",\n \"vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices,\",\n \"== \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i ==\",\n \"genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type genes_bed:\",\n \"> max_signal): max_signal = value if(not offset is None): end = start +\",\n \"of the arrow in bp (depends on the region that is plotted), defaults\",\n \"upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks\",\n \"stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis\",\n \"that plots HiC contact maps as pyramid plots :param contact_map: Matrix that contains\",\n \"height = height_merged plt.bar(left, height, offset, color = color, edgecolor = color) plt.xlim(r_start,\",\n \"of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to upper direction, else if\",\n \"patch_description_list += [\\\"reverse strand\\\"] met_reverse = True # Plot Gene Names if(plot_gene_ids): for\",\n \"ticks = [ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse()\",\n \"in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)\",\n \"1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([],\",\n \"if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops\",\n \"Chromsome 2. Start position 3. end position 4. Beta Value :type meth_calles: iterator\",\n \"[.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation)\",\n \"be plotted. :type end_r: int :param color: Color of the arc, defaults to\",\n \"used for plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap: str, optional :param\",\n \":param contact_map: Matrix that contains the intensity values of HiC contacts. :type contact_map:\",\n \"gene region scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing\",\n \"Chromosome 2. Start position 3. End position :type regions: iterator :param start: Start\",\n \"line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\"\",\n \"CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome,\",\n \"\\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0\",\n \":type colors: str, optional :param ids: IDs used for legend plotting, defaults to\",\n \"\\\"NA\\\") meth_after = (binned_average_meth[i+1] if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list =\",\n \"stranded genes is plotted, False otherwise. Default is False. :type plot_legend: bool, optional\",\n \"TX_pos TX_end = end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end = TX_pos\",\n \"[]) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax\",\n \"plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids is None): g_id =\",\n \"vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start,\",\n \"color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions as simple\",\n \"rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param chrom: Chromosome\",\n \"] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start,\",\n \"= [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] =\",\n \"the first column is a ensemble gene id, and the second column is\",\n \"range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i)))\",\n \".8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5)\",\n \"defaults to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` '''\",\n \"= line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return\",\n \"plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name\",\n \"= region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region)\",\n \"i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not\",\n \"link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist\",\n \"\\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if\",\n \"interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn\",\n \"from extension extension=extension-(region[2]-region[1])*.01 boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\":\",\n \"segments, and values patch :rtype: dict ''' ax = ax if ax is\",\n \"to None. :type vmax: float, optional :param location: Either of \\\"top\\\" | \\\"bottom\\\".\",\n \"Exons for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name =\",\n \"rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)])\",\n \"be plotted. :type end: int :param color: Color of the rectangles representing the\",\n \"tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position\",\n \"= float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line =\",\n \"region1 3. End region1 4. Chromosome region2 5. Start region2 6. End region2\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of segments, and values\",\n \"= [] lefts = [] for i in range(len(left)): if(i % merge ==\",\n \"== 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6)\",\n \"expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5):\",\n \"to be plotted. :type start: str :param end: End position of the region\",\n \"color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start),\",\n \"tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list,\",\n \"if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev =\",\n \"midpoints. :rtype: list ''' ax = ax if ax is not None else\",\n \"r_end: End position of region to be plotted. :type r_end: int :param ax:\",\n \":type color_gain: str, optional :param color_loss: Plot color of copy number losses, defaults\",\n \"size of bin to average methylation values, defaults to 1000. :type bin_size: int,\",\n \"= {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\":\",\n \"lists containing the IDs of the different groups. :type groups: list :param gene_names_map:\",\n \"side. If this ratio is underwent, the genes will be stacked. :type distance_ratio:\",\n \"optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names:\",\n \"start, end, name, score, strand, start, end, color). The color field is used\",\n \"patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position\",\n \"list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i\",\n \"gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None,\",\n \"float, optional :param location: Either of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\",\",\n \"merge is None): heights = [] lefts = [] for i in range(len(left)):\",\n \"n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1]\",\n \"plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval\",\n \"color_gain: Plot color of copy number gains, defaults to \\\"g\\\". :type color_gain: str,\",\n \"Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If\",\n \":type color_g2: str, optional :param g1_id: ID of g1 used for legend plotting,\",\n \"plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic\",\n \"bool, optional :param colors: List of colors used for plotting samples expression. The\",\n \"str :param start_r: Chromosomal start position of the region to be plotted. :type\",\n \"== 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4])\",\n \"== len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for j in [meth_before, meth_after]\",\n \"\\\"linewidth\\\": .3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean =\",\n \"number of stacked genes, default is None. :type y_max: bool, optional :param distance_ratio:\",\n \":class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of genes. :type\",\n \"region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None\",\n \"elements with the following entries: 1. Chromsome 2. Start position 3. end position\",\n \"be averaged an plotted, defaults to 0. :type merge: int, optional :return: Nothing\",\n \"n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter += 1 binned_meth_calls[current_bin][0]\",\n \"non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point\",\n \"r_start: Start position of region to be plotted. :type r_start: int :param r_end:\",\n \"distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 +\",\n \"[6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0,\",\n \"bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g1])],\",\n \"showfliers=False) color = None if(not colors is None): color = colors[g] else: color\",\n \"not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) #\",\n \"codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point,\",\n \":param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs.\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold,\",\n \"color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i\",\n \"\\\"bottom\\\" the pyramid points downwards, defaults to top, :type location: str, optional :param\",\n \"plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names\",\n \"positiont of the region to be plotted. :type end_r: int :param color: Color\",\n \"Nothing to be returned. :rtype: None ''' # Use given axis for plotting\",\n \"''' ax = ax if ax is not None else plt.gca() n_entries =\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3,\",\n \"merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color = color,\",\n \"split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals,\",\n \"counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist\",\n \"start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that plots\",\n \"List of colors used for plotting samples expression. The number of colors must\",\n \"link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes =\",\n \"cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None,\",\n \"Start postion 3. End position 4. Value to be plotted as bar :type\",\n \"Names if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not\",\n \"1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops,\",\n \"1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq\",\n \"gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i]\",\n \"optional :param alpha: Alpha value for the background color of the boxplots boxes,\",\n \"y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend):\",\n \"rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot\",\n \"IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing\",\n \":param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end, name, score,\",\n \"= {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\",\",\n \"ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be\",\n \"if \\\"down\\\", plot ticks to lower direction, defaults to \\\"up\\\". :type loc_coordinates: str,\",\n \"Color used for plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str,\",\n \"i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2\",\n \"color: str, optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults\",\n \"g2 samples (columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map:\",\n \"max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start =\",\n \"matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy as np\",\n \"of the region to be plotted. :type chrom_r: str :param start_r: Start position\",\n \"should not be shown on the plot, default is None :type blacklist: list,\",\n \"first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick + tick_size scale\",\n \"region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param\",\n \"the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id\",\n \":type end: int :param ploidy: Assumed ploidy of tumor, defaults to 2. :type\",\n \"minus stranded genes, default is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of\",\n \"= ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color\",\n \"bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name]\",\n \"m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start,\",\n \"regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions\",\n \"pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get gene names and regions genes_in_region_bed\",\n \"End region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to be\",\n \"edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], [])\",\n \"equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0):\",\n \"plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1.\",\n \"Iterator for which each element is a list-ike object containing: 1. Chromosome 2.\",\n \"optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\",\",\n \"that plots links between genomic regions as arcs. :param links_bed: Iterator, that contains\",\n \":type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values:\",\n \"to None. :type colors: str, optional :param ids: IDs used for legend plotting,\",\n \"> max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2):\",\n \"genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of integer values containing center positions of genes.\",\n \"plus or minus stranded genes is plotted, False otherwise. Default is False. :type\",\n \"= expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0,\",\n \"\\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If True use log transformed values\",\n \"str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels =\",\n \"right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.)\",\n \"of elements to be merged. If this value is not equal to 0,\",\n \"plotting CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus or minus\",\n \"-1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4,\",\n \"values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If\",\n \"int :param direction: Direction of the genomic part that is translocated. Either of\",\n \"tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list,\",\n \"plotting, defaults to \\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID of g2\",\n \"int :param color: Color of the genomic scales elements, defaults to \\\"k\\\". :type\",\n \"int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs\",\n \"ax: Axis to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"to be plotted. :type end: int :param color: Color of points representing methylation\",\n \":rtype: None ''' ax = ax if ax is not None else plt.gca()\",\n \"0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that\",\n \"+= [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes\",\n \"plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0],\",\n \"of colors must be the same as the number of groups, defaults to\",\n \"plt.gca() c = 0 for region in regions: if(not edgecolor): current_color = color\",\n \"patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names):\",\n \"is swept back (0 overhang means triangular shape). Can be negative or greater\",\n \"groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO\",\n \"if(direction == \\\"left\\\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4),\",\n \"plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\",\",\n \"to None. :type max_dev: float, optional :param ax: Axis used for plotting, defaults\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6],\",\n \"If not set, then y_max is the max number of stacked genes, default\",\n \"5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)])\",\n \"and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True #\",\n \"if(not blacklist is None and gene_name in blacklist): counter += 1 continue if(counter\",\n \"ax.boxplot([np.log2([i if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2.,\",\n \"vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for cbin in range(len(binned_average_meth)):\",\n \"for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)\",\n \"be plotted. :type chrom: str :param start: Start position of the region to\",\n \"Color of plus stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional\",\n \"1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\\\ number of\",\n \"int(signal[1]) end = int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value\",\n \"6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed,\",\n \"part that is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to\",\n \"of the region to be plotted. :type end_r: int :param color: Color of\",\n \"of integer values containing center positions of genes. :type gene_mid_points: list :param region:\",\n \"genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3],\",\n \"midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO,\",\n \"for motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3])\",\n \"If True legend is plotted, False otherwise, defaults to False. :type plot_legend: bool\",\n \"field is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str\",\n \"object containing the one region, for which the gene plot is created. :type\",\n \"max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point\",\n \"if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size\",\n \"== \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else\",\n \"midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO,\",\n \"== 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\",\",\n \"tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy\",\n \"\\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"]\",\n \"right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border\",\n \"value is not equal to 0, than merge elements will be averaged an\",\n \"color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down\",\n \"Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn =\",\n \"ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region\",\n \"of copy number losses, defaults to \\\"r\\\". :type color_loss: str, optional :param color_neutral:\",\n \"to \\\"k\\\". :type color_neutral: str, optional :param ax: Axis used for plotting. :type\",\n \"= color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5)\",\n \"vmin: Minimal value of intensity range to be plotted, defaults to None :type\",\n \"continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene\",\n \"[0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation)\",\n \"== \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward =\",\n \"bool, optional :param position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type\",\n \"binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else\",\n \"max_y_position + 1.5. max_y_pos defines the \\\\ number of stacked genes. 2. patch_list\",\n \"the second column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary\",\n \"arrow_end = motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start = motif_end\",\n \"motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length,\",\n \"counter += 1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1]))\",\n \"for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i\",\n \"genomic_segments: list :param start: Start position of the genomic region. :type start: int\",\n \"= cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap =\",\n \"ax = ax if ax is not None else plt.gca() patches_dict = {}\",\n \":param color_loss: Plot color of copy number losses, defaults to \\\"r\\\". :type color_loss:\",\n \"fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8])\",\n \"color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\",\",\n \"max_signal = value if(not offset is None): end = start + offset left\",\n \"right_border = region_right_border if(not blacklist is None and gene_name in blacklist): counter +=\",\n \"Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0,\",\n \"if(plot_gene_ids): for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist\",\n \":param start: Start position of the region to be plotted. :type start: str\",\n \"y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function\",\n \":class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample\",\n \"bool :param color_g1: Color used for plotting g1 samples expression, defaults to \\\"#fb8072\\\".\",\n \"alpha: Alpha value of the rectangle, representing the region to be plotted, defaults\",\n \"Plot Introns for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name\",\n \"[4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold,\",\n \"None if(not len(genes_in_region) == 0): # Determine y positions of genes for plotting\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False\",\n \"of the region to be plotted. :type start: str :param end: End position\",\n \"of gene names, for genes that should not be shown on the plot,\",\n \"pybedtools import pandas as pnd import numpy as np import tabix import matplotlib.ticker\",\n \"ax = ax if ax is not None else plt.gca() max_dist = 0\",\n \"linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0)\",\n \"= int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\")\",\n \"the pyramid points upwards, else if location == \\\"bottom\\\" the pyramid points downwards,\",\n \"matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path\",\n \"if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none',\",\n \"patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True # Plot Gene\",\n \"end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward\",\n \"[] tick_positions = [] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border\",\n \":class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy = None for\",\n \"defines the \\\\ number of stacked genes. 2. patch_list is the list of\",\n \"+= [value] left_merged = [] height_merged = [] if(not merge is None): heights\",\n \"intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position\",\n \"linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i in ticks ]\",\n \":type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary '''\",\n \"\\\\ number of stacked genes. 2. patch_list is the list of patches drawn\",\n \"= 0 for element in methylation_bed: # Determine bin position = int(element[1]) if(position\",\n \"tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__\",\n \":return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename,\",\n \"i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices] y_pos_dict = {}\",\n \"max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else:\",\n \"bars, defaults to \\\"b\\\". :type color: str, optional :param offset: Length of intervals,\",\n \"end: int :param color: Color of the rectangles representing the regions to be\",\n \"cytosines 5. Number unmethylated cytosines Or 1. Chromsome 2. Start position 3. end\",\n \"GENE SYMBOLs. :type gene_names_map: dict. :param blacklist: Set containing gene ids not to\",\n \"ax = ax if ax is not None else plt.gca() for interval in\",\n \"of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max\",\n \"merge == 0): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights\",\n \"to \\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal deviation from ploidy to\",\n \"if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None):\",\n \"loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None,\",\n \"upper: If True, make less ticks, else if False make more ticks. :type\",\n \"minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None\",\n \"that reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object\",\n \"alpha: Alpha value for the background color of the boxplots boxes, defaults to\",\n \"that is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\".\",\n \"heights = [] lefts = [] for i in range(len(left)): if(i % merge\",\n \"1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\",\",\n \"the region to be plotted. :type chrom_r: str :param start_r: Start position of\",\n \":] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1\",\n \"id, and the second column is the HUGO gene name :type gene_name_mapping_file: str\",\n \"optional. :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"gene_map is None): gene_name_label = gene_map[gene_name] y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5,\",\n \"with following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation\",\n \":class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else plt.gca() #\",\n \"the list of descriptions for the patches \\\\ drawn on the ax. :rtype:\",\n \"End position of the region to be plotted. :type end: str :param ax:\",\n \"met_reverse = True # Plot Gene Names if(plot_gene_ids): for i in genes_in_region: start\",\n \"Start Position, 3. End Position, 4. Deviation from ploidy, 5. True Copy Number)\",\n \"y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict\",\n \"= color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False,\",\n \"if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1),\",\n \"if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name\",\n \"color = color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect\",\n \"gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting\",\n \":rtype: list \\\"\\\"\\\" ax = ax if ax is not None else plt.gca()\",\n \"genes will be stacked, default is 0.1. :type distance_ratio: float, optional :param ax:\",\n \"bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id]\",\n \"\\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is not None\",\n \"= [] height = [] for signal in chip_signals: start = int(signal[1]) end\",\n \"(link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3,\",\n \"exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__\",\n \"value of intensity range to be plotted, defaults to None. :type vmax: float,\",\n \"= color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([],\",\n \"start, end, ax = None): '''Function for plotting genomix segments in different colors\",\n \"genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be\",\n \"instance on which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"samples (columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set\",\n \"else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function\",\n \"color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type\",\n \"expression_values = np.log2([i if i >= 1. else 1. for i in exp_values])\",\n \"\\\"top_down\\\". :type direction: str, optional. :param ax: Axis on which to plot, defaults\",\n \"if ax is not None else plt.gca() for motif in motifs_bed: motif_start =\",\n \"for region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]), -.75],\",\n \"ploidy to be considered as a CNV, defaults to 0.7. :type cnv_threshold: float,\",\n \"region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)):\",\n \"str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect =\",\n \"= [] height_merged = [] if(not merge is None): heights = [] lefts\",\n \"[] offset = merge*offset left = left_merged height = height_merged plt.bar(left, height, offset,\",\n \"bar representing the translocation, defaults to \\\"k\\\". :type color: str, optional :param ax:\",\n \":return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax\",\n \"optional :param location: Either of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the\",\n \"segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1.\",\n \"counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names):\",\n \"1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start,\",\n \"list of integer values containing center positions of genes. :type gene_mid_points: list :param\",\n \"None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]), from_string=True) # Get\",\n \"region to be plotted. :type start_r: int :param end_r: End position of the\",\n \"\\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc: str, optional :param\",\n \"= np.log2([i if i >= 1. else 1. for i in exp_values]) bplot\",\n \"gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand = str(i[5])\",\n \"plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed bed file\",\n \"if(value > max_signal): max_signal = value if(not offset is None): end = start\",\n \"= [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that\",\n \":class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\")\",\n \"> max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect\",\n \"== \\\"bottom\\\" the pyramid points downwards, defaults to top, :type location: str, optional\",\n \"boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1.\",\n \"region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2]\",\n \"be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome:\",\n \"color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return\",\n \"Axis to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"ax = None): '''Function that plots arcs from unequal distances of genomic segments\",\n \"Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5.\",\n \"((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is\",\n \"= hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None,\",\n \"gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map\",\n \"elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for\",\n \"None if(not colors is None): color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color)\",\n \"linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4],\",\n \"] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if\",\n \"points upwards, else if location == \\\"bottom\\\" the pyramid points downwards, defaults to\",\n \"(default: None) Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene\",\n \"range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if\",\n \"to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"{\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list =\",\n \"def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between gene ids :param gene_name_mapping_file: Path\",\n \"set, optional :param ax: Axis used for plotting, defaults to None. :type ax:\",\n \"\\\"#bbbbbb\\\"] ax = ax if ax is not None else plt.gca() region_bed =\",\n \"ID of g1 used for legend plotting, defaults to \\\"tumor\\\". :type g1_id: str,\",\n \"# Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops =\",\n \"Color code for minus stranded genes, default is \\\"#fb8072\\\". :type color_minus: str, optional.\",\n \"TX_pos, direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation event as a bar,\",\n \"if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed\",\n \"{} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol =\",\n \"< -1.*max_dev): tcn = -1.*max_dev elif(tcn > max_dev): tcn = max_dev color =\",\n \"''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed\",\n \"0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path,\",\n \"= len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]]\",\n \"column is a ensemble gene id, and the second column is the HUGO\",\n \"plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\",\",\n \"if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 =\",\n \"loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to upper direction,\",\n \"''' ax = ax if ax is not None else plt.gca() # Get\",\n \"not set, then y_max is the max number of stacked genes, default is\",\n \"End position of region to be plotted. :type r_end: int :param ax: Axis\",\n \"plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.\",\n \"ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None):\",\n \"region to be plotted. :type r_chrom: str :param r_start: Start position of region\",\n \"tick.set_size(6) for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return\",\n \"def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function\",\n \"describing plus or minus stranded genes is plotted, False otherwise. Default is False.\",\n \"+= [patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\" and\",\n \"expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :])\",\n \"is not None else plt.gca() for motif in motifs_bed: motif_start = int(motif[1]) motif_end\",\n \"int, optional :param ax: Axis to be used for plotting, defaults to None.\",\n \"\\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in\",\n \"end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i\",\n \"optional :param overhang: Fraction that the arrow is swept back (0 overhang means\",\n \"position of region to be plotted. :type start: int :param end: End position\",\n \"region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1):\",\n \"genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2])\",\n \"be returned :rtype: None ''' ax = ax if ax is not None\",\n \"of the arc, defaults to \\\"k\\\". :type color: str, optional. :param ax: Axis\",\n \"= [] vertices = [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO,\",\n \"ticks to lower direction, defaults to \\\"up\\\". :type loc_coordinates: str, optional :param revert_coordinates:\",\n \":param ax: Axes instance on which the genes are plotted, default is None.\",\n \"to \\\"normal\\\". :type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE\",\n \"5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome for which to\",\n \"the list of patches drawn on the ax. 3. patch_description_list is the list\",\n \"+= [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset =\",\n \"the region to be plotted. :type chrom_r: str :param start_r: Chromosomal start position\",\n \"and normal) on a gene region scale retaining the position of genes. :param\",\n \"index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values\",\n \"\\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4,\",\n \"Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool, optional\",\n \"meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m in\",\n \".3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list = [] tick_positions =\",\n \"0), (mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices,\",\n \"len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i\",\n \"groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and\",\n \"= int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for\",\n \"[left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts = []\",\n \"else: meth_before = (binned_average_meth[i-1] if not i == 0 else \\\"NA\\\") meth_after =\",\n \":type gene_names_map: dict. :param blacklist: Set containing gene ids not to be plotted,\",\n \"= ax if ax is not None else plt.gca() # Get gene names\",\n \"exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values\",\n \":param r_start: Start position of region to be plotted. :type r_start: int :param\",\n \"Chromosome of the region to be plotted. :type chrom_r: str :param start_r: Start\",\n \"None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\\\"color\\\":\",\n \"False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist\",\n \"[] for signal in chip_signals: start = int(signal[1]) end = int(signal[2]) value =\",\n \"if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i y_level_dict[i] =\",\n \"Color used for plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str,\",\n \"dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples (columns:\",\n \"optional :param ax: Axis on which to plot contact map, defaults to None.\",\n \"None else plt.gca() c = 0 for region in regions: if(not edgecolor): current_color\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks,\",\n \":param blacklist: List of gene names, for genes that should not be shown\",\n \"= {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list\",\n \":param color: color of bars, defaults to \\\"b\\\". :type color: str, optional :param\",\n \"chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param start: Start\",\n \"ax = ax if ax is not None else plt.gca() max_signal = 0\",\n \"optional :param ax: Axis to be used for plotting, defaults to None. :type\",\n \"color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1\",\n \"max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list =\",\n \"equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO,\",\n \"gene ids not to be plotted, defaults to None, :type blacklist: set, optional\",\n \"if ax is not None else plt.gca() max_dist = 0 for e in\",\n \"tcn-.1), current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255.,\",\n \"following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from\",\n \"where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\\\ number\",\n \"not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1,\",\n \"gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map = {}\",\n \"= cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin]))\",\n \"\\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([],\",\n \"integer values containing center positions of genes. :type gene_mid_points: list :param region: List\",\n \"to \\\"k\\\". :type color: str, optional. :param ax: Axis where the plot is\",\n \"ax if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries ==\",\n \"to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized\",\n \"Nothing to be returned. :rtype: None ''' ax = ax if ax is\",\n \"[gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values\",\n \"points are plotted otherwise, defaults to False. :type plot_points: bool, optional :param alpha:\",\n \"Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to upper direction, else\",\n \"else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2\",\n \"[]) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\",\",\n \"0.) else 0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries\",\n \"entries: 1. Chromosome 2. Start position 3. End position :type regions: iterator :param\",\n \"\\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is not None else\",\n \"for plotting ax = ax if ax is not None else plt.gca() for\",\n \"this ratio is underwent, the genes will be stacked. :type distance_ratio: float :return:\",\n \"Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start,\",\n \"True, a legend describing plus or minus stranded genes is plotted, False otherwise.\",\n \"range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean +=\",\n \"1 binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1]))\",\n \"top, :type location: str, optional :param ax: Axis on which to plot contact\",\n \"gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal\",\n \"Fraction that the arrow is swept back (0 overhang means triangular shape). Can\",\n \"+= 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter(\",\n \"stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number of\",\n \"= motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end\",\n \"\\\"bottom\\\". If location == \\\"top\\\", the pyramid points upwards, else if location ==\",\n \"color_plus: str, optional. :param color_minus: Color code for minus stranded genes, default is\",\n \"markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos,\",\n \"matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib\",\n \"to \\\"b\\\". :type color: str, optional :param offset: Length of intervals, defaults to\",\n \"plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not\",\n \"thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold,\",\n \"= int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist):\",\n \"| \\\"bottom\\\". If location == \\\"top\\\", the pyramid points upwards, else if location\",\n \"in methylation_bed: # Determine bin position = int(element[1]) if(position < start or position\",\n \"else \\\"NA\\\") average_list = [ j for j in [meth_before, meth_after] if not\",\n \"genomic_segments: List of segments for which distances shall be equalized (each segment is\",\n \"else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point =\",\n \"== \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"):\",\n \"linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end,\",\n \"ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region,\",\n \"samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id: ID of\",\n \"region2 :type links_bed: iterator :param chrom_r: Chromosome of the region to be plotted.\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None '''\",\n \"[]) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files\",\n \"optional :return: Nothing to be returned. :rtype: None ''' ax = ax if\",\n \"plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function\",\n \"= int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth\",\n \"to be plotted, defaults to None :type vmin: float, optional :param vmax: Maximal\",\n \"tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False for gene_name in\",\n \"horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1,\",\n \"contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map,\",\n \"color_minus: Color code for minus stranded genes, default is \\\"#fb8072\\\". :type color_minus: str,\",\n \"genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If\",\n \"optional :param color_loss: Plot color of copy number losses, defaults to \\\"r\\\". :type\",\n \"to \\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot color of copy number\",\n \"capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list =\",\n \"colors is None): color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions\",\n \"Color of plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional\",\n \"elif(strand == \\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse\",\n \"to be plotted. :type chrom: str :param start: Start position of region to\",\n \":type blacklist: set, optional :param ax: (default: None) Axis used for plotting, defaults\",\n \"called. :type segment_size: int :param cmap: Name of the colormap to be used\",\n \"= [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not\",\n \"with keys: ENSEMBL GENE IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict.\",\n \"y_max is None): max_y_pos = y_max # Plot Exons for i in exons_in_region:\",\n \"number of colors must be the same as the number of groups, defaults\",\n \"from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy\",\n \"color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons of genes. :param\",\n \"= [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None):\",\n \"to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be\",\n \"\\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle\",\n \"i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points\",\n \"2. Start postion 3. End position 4. Value to be plotted as bar\",\n \"0 left = [] height = [] for signal in chip_signals: start =\",\n \"None ''' ax = ax if ax is not None else plt.gca() n_entries\",\n \"(region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO]\",\n \"pybedtools.BedTool object :param input_filename: Full path to ACESeq \\\"most_important\\\" file :type input_filename: str\",\n \"following entries: 1. Chromsome 2. Start position 3. end position 4. Number methylated\",\n \"[gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine\",\n \":type loc_coordinates: str, optional :param revert_coordinates: If True, coordinates are reverted to decreasing\",\n \"in region]), from_string=True) # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True,\",\n \"i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for\",\n \"for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,\",\n \"iterator :param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str\",\n \"position = int(element[1]) if(position < start or position > end): continue n_meth =\",\n \"defaults to \\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID of g2 used\",\n \"Plot Exons for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name\",\n \"be stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes instance\",\n \"plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2): plt.ylim([0,\",\n \"tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <=\",\n \"plot_gene_names: If True, the HUGO GENE SYMBOLs will be shown, else the GENE\",\n \"plotted, defaults to None, :type blacklist: set, optional :param ax: (default: None) Axis\",\n \"from ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object :param input_filename: Full\",\n \"[[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates\",\n \"segments for which contacts were called. :type segment_size: int :param cmap: Name of\",\n \"contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0\",\n \"chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\"\",\n \"else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end)\",\n \"equalizing distances, defaults to \\\"k\\\". :type color: str, optional :param direction: Direction of\",\n \"the plot, default is None :type blacklist: list, optional :param plot_gene_ids: If True,\",\n \"color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start,\",\n \"ax if ax is not None else plt.gca() genes_in_region = genes_bed exons_in_region =\",\n \"default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color code for minus\",\n \"def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph\",\n \"end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin = int((position-start)/bin_size) counter +=\",\n \"of the region to be plotted. :type end: int :param head_width: Width of\",\n \"horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1)\",\n \"is the max number of stacked genes, default is None. :type y_max: bool,\",\n \"optional :param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"to be used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"be returned :rtype: None ''' # Use given axis for plotting ax =\",\n \"region to be plotted. :type chrom_r: str :param start_r: Chromosomal start position of\",\n \"readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts them to\",\n \"'''Functions that plots genomic regions as simple rectangles. :param regions: Iterator containig list-like\",\n \"intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.)\",\n \"is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed:\",\n \"= expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0,\",\n \"= {} max_y_pos = 0 for interval in genes_sorted_bed: gene_name = interval[3] gene_start\",\n \"tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color,\",\n \"if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if\",\n \"chrom_r: str :param start_r: Chromosomal start position of the region to be plotted.\",\n \"lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r]) plt.ylim([0,\",\n \"bool, optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks\",\n \"neutral regions, defaults to \\\"k\\\". :type color_neutral: str, optional :param ax: Axis used\",\n \"else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth)\",\n \"end positiont of the region to be plotted. :type end_r: int :param color:\",\n \"patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names ==\",\n \"\\\"r\\\") cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7] ==\",\n \"means triangular shape). Can be negative or greater than one. Defaults to 0.\",\n \"to 0. :type rotation: int, optional :return: Nothing to be returned. :rtype: None\",\n \"color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss\",\n \"True, a point per expression value is plotted in addition to the boxplot,\",\n \"lefts += [left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts\",\n \"left_merged = [] height_merged = [] if(not merge is None): heights = []\",\n \"to be used for plotting HiC intensities, defaults to \\\"Greys\\\". :type cmap: str,\",\n \"Color of lines equalizing distances, defaults to \\\"k\\\". :type color: str, optional :param\",\n \"i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] =\",\n \"is 0.1. :type distance_ratio: float, optional :param ax: Axes instance on which the\",\n \"contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for\",\n \"i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio):\",\n \"If True, all gene ids will be included in the plot, False otherwise,\",\n \"included in the plot, False otherwise, default is True :type plot_gene_ids: bool, optional\",\n \"patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions)))\",\n \"norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m = matplotlib.cm.ScalarMappable(norm = norm, cmap = cmap) for\",\n \"colors used for plotting samples expression. The number of colors must be the\",\n \"to be plotted. :type end: int :param color: Color of the rectangles representing\",\n \"for i in range(len(left)): if(i % merge == 0 and not (i ==\",\n \"in genes_sorted_bed: gene_name = interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i\",\n \"default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend\",\n \"in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices =\",\n \"plotted, defaults to None :type vmin: float, optional :param vmax: Maximal value of\",\n \"or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str, optional :param color: Color\",\n \"(float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else\",\n \"1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \"\\\"k\\\". :type color: str, optional. :param ax: Axis where the plot is drawn,\",\n \":type color_plus: str, optional. :param color_minus: Color code for minus stranded genes, default\",\n \"if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in\",\n \"genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type\",\n \"(mid_point, distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes)\",\n \"\\\"#fb8072\\\". :type color_minus: str, optional :param ax: Axis on which to plot contact\",\n \"region2 5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome\",\n \"str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def\",\n \"plotted. :type chrom_r: str :param start_r: Chromosomal start position of the region to\",\n \"be plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to\",\n \"to 0.5. :type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors\",\n \"if not i == len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for j\",\n \"second column is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing\",\n \"int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn = -1.*max_dev\",\n \"> 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor = None\",\n \"gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1)\",\n \"of distance equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional.\",\n \"Start position of the region to be plotted. :type start_r: int :param end_r:\",\n \"'''Function that plots methylation values as dot plots. :param meth_calls: Iterator containing list-like\",\n \"a tab separated file, for which the first column is a ensemble gene\",\n \"head_width: float, optional :param head_length: Length of the arrow in bp (depends on\",\n \"# Plot Introns for i in introns_in_region: start = int(i[1]) end = int(i[2])\",\n \"value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None):\",\n \"else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for\",\n \"pandas as pnd import numpy as np import tabix import matplotlib.ticker as ticker\",\n \"colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if\",\n \"= plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location\",\n \"int :param TX_pos: Position of the translocation. :type TX_pos: int :param direction: Direction\",\n \"plot_legend: bool :param color_g1: Color used for plotting g1 samples expression, defaults to\",\n \"str :param r_start: Start position of region to be plotted. :type r_start: int\",\n \"Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type\",\n \":return: Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename,\",\n \"contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: #\",\n \"1. else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,\",\n \"color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for\",\n \"else: codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2),\",\n \"optional :param revert_coordinates: If True, coordinates are reverted to decreasing order. Else, coordinates\",\n \"is None): g_id = ids[g] else: g_id = \\\"group \\\"+str(g) if(not g_id in\",\n \":rtype: None ''' # Use given axis for plotting ax = ax if\",\n \":param color_minus: Color code for minus stranded genes, default is \\\"#fb8072\\\". :type color_minus:\",\n \"range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in\",\n \"y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] =\",\n \"= right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension\",\n \"CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for\",\n \"plotted side by side. If this ratio is underwent, the genes will be\",\n \"set, optional :param ax: (default: None) Axis used for plotting, defaults to None.\",\n \"break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start,\",\n \"if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter +=\",\n \"int :param color: Color of lines equalizing distances, defaults to \\\"k\\\". :type color:\",\n \"direction=\\\"right\\\", color=\\\"k\\\", ax=None): '''Function that plots a translocation event as a bar, showing\",\n \"plotted. :type start_r: int :param end_r: Chromosomal end positiont of the region to\",\n \":type overhang: float, optional :param color_plus: Color of plus stranded TF regions, defaults\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults to False.\",\n \"Chromosome for which to plot CNVs. :type chromosome: str :param start: Start position\",\n \"str, optional :param log_transformed: If True use log transformed values for plotting, non-transformed\",\n \"0. :type overhang: float, optional :param color_plus: Color of plus stranded TF regions,\",\n \"counter = 0 for element in methylation_bed: # Determine bin position = int(element[1])\",\n \"The color field is used to determine the color for plotting (R,G,B). :type\",\n \"start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None): '''Functions that plots genomic regions\",\n \":param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted.\",\n \"dot plots. :param meth_calls: Iterator containing list-like elements with the following entries: 1.\",\n \"chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2],\",\n \"paired gene expression (e.g. tumor and normal) on a gene region scale retaining\",\n \"capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False,\",\n \"in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and\",\n \"contacts were called. :type segment_size: int :param cmap: Name of the colormap to\",\n \"ratio is underwent, the genes will be stacked. :type distance_ratio: float :return: Tuple\",\n \"current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick +\",\n \"original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments >\",\n \"vmax = np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range\",\n \"+= [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"):\",\n \"plot_gene_names: True if gene names shall be plotted, False otherwise, defaults to True.\",\n \"= motif_end arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5,\",\n \"str, optional :param color_neutral: Plot color of copy number neutral regions, defaults to\",\n \"= color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 patch =\",\n \"increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle\",\n \"[] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick = current_tick\",\n \"of genes. :type gene_mid_points: list :param region: List containing the region to be\",\n \"on which the genes are plotted, default is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"| \\\"down\\\". If \\\"up\\\", plot ticks to upper direction, else if \\\"down\\\", plot\",\n \"plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to\",\n \"region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos\",\n \"c += 1 else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5,\",\n \"2. patch_list is the list of patches drawn on the ax. 3. patch_description_list\",\n \"path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return\",\n \"equalization (top_down | bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional. :param ax:\",\n \"g2_id: ID of g2 used for legend plotting, defaults to \\\"normal\\\". :type g2_id:\",\n \":param color: Color of the bar representing the translocation, defaults to \\\"k\\\". :type\",\n \"equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in\",\n \"blacklist): counter += 1 continue n_groups = len(groups) for g in range(n_groups): bplot_pos\",\n \"positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if\",\n \"log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param\",\n \":param methylation_bed: Methylation calls. Following fields must be included: Chrom, Start, End, Methylated\",\n \"\\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom,\",\n \"ax is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i) for i in region]),\",\n \"HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of region to\",\n \"segments in different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom,\",\n \"= [ start + end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels)\",\n \"0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \"log_transformed: If True use log transformed values for plotting, non-transformed values otherwise. :type\",\n \"plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start +\",\n \":class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom: str :param\",\n \"scales elements, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of\",\n \"[] height = [] for signal in chip_signals: start = int(signal[1]) end =\",\n \"[] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map\",\n \"left += [start] height += [value] left_merged = [] height_merged = [] if(not\",\n \"= ax if ax is not None else plt.gca() max_dist = 0 for\",\n \"on the ax. 3. patch_description_list is the list of descriptions for the patches\",\n \"of stacked genes, default is None. :type y_max: bool, optional :param distance_ratio: Minimal\",\n \"ax.set_xlim(region_left_border, region_right_border) if(position_gene_names == \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\"\",\n \":param color: Color of the rectangles representing the regions to be plotted, defaults\",\n \"None else plt.gca() for interval in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2])\",\n \":type vmin: float, optional :param vmax: Maximal value of intensity range to be\",\n \"None divisor = None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000\",\n \":type start: int :param end: End position of the region to be plotted.\",\n \"color: str, optional. :param ax: Axis where the plot is drawn, defaults to\",\n \"gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None\",\n \"Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted.\",\n \"rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed, chromosome, start,\",\n \".3} patch_list = None patch_description_list = None tick_positions = [] gene_names_clean = []\",\n \"to 1000. :type bin_size: int, optional :param ax: Axis to be used for\",\n \"list :param start: Start position of the genomic region. :type start: int :param\",\n \"different colors :param segments_tabix_filename: Path to tabixed bed file containing (chrom, start, end,\",\n \"1. Chromsome 2. Start position 3. end position 4. Number methylated cytosines 5.\",\n \":param max_dev: Maximal deviation from ploidy to plot, defaults to None. :type max_dev:\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with keys = names of\",\n \"color_neutral: str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"number gains, defaults to \\\"g\\\". :type color_gain: str, optional :param color_loss: Plot color\",\n \"end: int :param color: Color of the genomic scales elements, defaults to \\\"k\\\".\",\n \"elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]]\",\n \"position_gene_names: Either of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional\",\n \"pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\",\",\n \"plots genomic coordinates in a linea fashion. :param chrom: Chromosome of the region\",\n \"current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) #\",\n \"if not(float(m[3])+float(m[4]) == 0.) else 0. for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None',\",\n \"region to be plotted. :type start: int :param end: Chromosomal end position of\",\n \"copy number neutral regions, defaults to \\\"k\\\". :type color_neutral: str, optional :param ax:\",\n \"alpha=1, ax = None): '''Functions that plots genomic regions as simple rectangles. :param\",\n \"to 1000. :type head_length: int, optional :param overhang: Fraction that the arrow is\",\n \"optional :param color_minus: Color of plus stranded TF regions, defaults to \\\"#fb8072\\\". :type\",\n \"The number of colors must be the same as the number of groups,\",\n \"head_width: Width of the arrow head as proportion of the arrow, defaults to\",\n \"plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5,\",\n \"== \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom\",\n \"str, optional :param g1_id: ID of g1 used for legend plotting, defaults to\",\n \"+= [left[i]] if(not i % merge == 0): left_merged += [lefts[0]] lefts =\",\n \"to be plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis\",\n \"= int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1]\",\n \"be returned. :rtype: None ''' # Use given axis for plotting ax =\",\n \"on the region that is plotted), defaults to 1000. :type head_length: int, optional\",\n \"names, for genes that should not be shown on the plot, default is\",\n \"start: Start position of the genomic region. :type start: int :param end: End\",\n \"[] height_merged = [] if(not merge is None): heights = [] lefts =\",\n \"rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([],\",\n \"end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param\",\n \"= float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \"left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc: str, optional :param color_plus:\",\n \"if(not i % merge == 0): left_merged += [lefts[0]] lefts = [] height_merged\",\n \"ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start,\",\n \"4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\",\",\n \"[\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7,\",\n \"patch_description_list = None tick_positions = [] gene_names_clean = [] counter=0 patch_saved = False\",\n \"fashion. :param chrom: Chromosome of the region to be plotted. :type chrom: str\",\n \"= [] heights += [height[i]] lefts += [left[i]] if(not i % merge ==\",\n \"Maximal value of intensity range to be plotted, defaults to None. :type vmax:\",\n \"i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0 for\",\n \"= colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)*\",\n \"str, optional :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"import Arrow from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as\",\n \"patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border,\",\n \"from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix import\",\n \"if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\",\",\n \"None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up\",\n \"in segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for\",\n \"plus stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param ax:\",\n \"intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch\",\n \"Dictionary containing the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\")\",\n \":type legend_loc: str, optional :param color_plus: Color code for plus stranded genes, default\",\n \"ID of g2 used for legend plotting, defaults to \\\"normal\\\". :type g2_id: str,\",\n \"= [] n = len(binned_average_meth) for i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"):\",\n \"e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]]\",\n \"in the plot, False otherwise, default is True :type plot_gene_ids: bool, optional :param\",\n \"and the second column is the HUGO gene name :type gene_name_mapping_file: str :return:\",\n \"patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], []) return patches_dict def plotCNVs(cnvs_bed,\",\n \"plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def\",\n \"coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool, optional :param\",\n \"genes are plotted side by side. If this ratio is underwent, the genes\",\n \"0 else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n =\",\n \"[((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments: if(int(e[1]) < start):\",\n \"region midpoints. :rtype: list ''' ax = ax if ax is not None\",\n \":type r_start: int :param r_end: End position of region to be plotted. :type\",\n \"to False. :type plot_points: bool, optional :param alpha: Alpha value for the background\",\n \"optional :param legend_loc: Location of the legend. Either of \\\"lower left\\\", \\\"lower right\\\",\",\n \"If this ratio is underwent, the genes will be stacked, default is 0.1.\",\n \"midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start)\",\n \"intensity range to be plotted, defaults to None :type vmin: float, optional :param\",\n \"midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.),\",\n \"None ''' # Use given axis for plotting ax = ax if ax\",\n \"== \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy\",\n \"[]) return patches_dict def plotCNVs(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\",\",\n \"int, optional :return: Nothing to be returned. :rtype: None ''' ax = ax\",\n \"color_plus: Color code for plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str,\",\n \":type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values:\",\n \"= gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in blacklist):\",\n \"cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end])\",\n \"intensities, defaults to \\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal value of\",\n \"log_transformed: bool, optional :param plot_points: If True, a point per expression value is\",\n \"2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold,\",\n \"in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border =\",\n \"n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else\",\n \"Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type\",\n \"n_groups = len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions\",\n \"2. Start position 3. end position 4. Beta Value :type meth_calles: iterator :param\",\n \"region_border_down-region_border_up color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) ==\",\n \"swept back (0 overhang means triangular shape). Can be negative or greater than\",\n \"plots a translocation event as a bar, showing the part of the genome\",\n \"patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines the \\\\\",\n \"object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of\",\n \"plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\",\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' # Use\",\n \"defaults to top, :type location: str, optional :param ax: Axis on which to\",\n \"the region to be plotted. :type chrom: str :param start: Start position of\",\n \"if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border\",\n \":param ax: Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"(intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch = matplotlib.patches.PathPatch(path,\",\n \"else plt.gca() binned_meth_calls = [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter\",\n \"y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name]\",\n \"region to be plotted. :type end: int :param color: Color of the genomic\",\n \"bool, optional :param y_max: Max y value in the gene plot. If not\",\n \"+= [start] height += [value] left_merged = [] height_merged = [] if(not merge\",\n \"arrow_end = motif_start color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0,\",\n \"[current_tick] current_tick = current_tick + tick_size scale = None if(first_tick > 1000000): scale\",\n \".3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list =\",\n \"elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in\",\n \"Chromosome of region to be plotted. :type r_chrom: str :param r_start: Start position\",\n \"4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy\",\n \"stacked genes. 2. y_pos_dict: Dictionary with keys = gene ids and values =\",\n \":param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str :param\",\n \"for i in genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is\",\n \"(midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO,\",\n \"= \\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id]\",\n \".0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start,\",\n \"cnv_bed_list = [] ploidy = None for line in input_file: if(line[:7] == \\\"#ploidy\\\"):\",\n \"defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color of plus stranded\",\n \"revert_coordinates=False, rotation=0): '''Function that plots genomic coordinates in a linea fashion. :param chrom:\",\n \"len(binned_average_meth)-1 else \\\"NA\\\") average_list = [ j for j in [meth_before, meth_after] if\",\n \"the region to be plotted. :type start_r: int :param end_r: End position of\",\n \"the rectangle, representing the region to be plotted, defaults to 1. :type alpha:\",\n \"None. :type offset: int, optional :param merge: Number of elements to be merged.\",\n \"= float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None): end\",\n \"plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV\",\n \"IDs used for legend plotting, defaults to None. Number of ids must be\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing plus\",\n \"int :param segment_size: Size of the segments for which contacts were called. :type\",\n \":type start_r: int :param end_r: Chromosomal end positiont of the region to be\",\n \"plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\",\",\n \"\\\"-\\\" and not(met_reverse)): patch_list += [patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True\",\n \"transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points:\",\n \"will be stacked, default is 0.1. :type distance_ratio: float, optional :param ax: Axes\",\n \"positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not\",\n \"int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect\",\n \"ax = None): '''Functions that plots genomic regions as simple rectangles. :param regions:\",\n \"for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to\",\n \"current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c\",\n \"ax = ax if ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10)))\",\n \"of ax width, such that two genes are plotted side by side. If\",\n \"divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [\",\n \"= list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else 1. for\",\n \"of plus stranded TF regions, defaults to \\\"#fb8072\\\". :type color_minus: str, optional :param\",\n \"colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in cnvs_bed])\",\n \"optional :param cnv_threshold: Minimal deviation from ploidy to be considered as a CNV,\",\n \"= i y_level_dict[i] = [[gene_start, gene_end]] break elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) >\",\n \"False otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List of\",\n \"End Position, 4. Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool`\",\n \"range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine bin position\",\n \"\\\"g\\\". :type color_gain: str, optional :param color_loss: Plot color of copy number losses,\",\n \"location: Either of \\\"top\\\" | \\\"bottom\\\". If location == \\\"top\\\", the pyramid points\",\n \"r_chrom: Chromosome of region to be plotted. :type r_chrom: str :param r_start: Start\",\n \"0, than merge elements will be averaged an plotted, defaults to 0. :type\",\n \"= Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end])\",\n \"If \\\"up\\\", plot ticks to upper direction, else if \\\"down\\\", plot ticks to\",\n \"max_y_pos defines the \\\\ number of stacked genes. 2. patch_list is the list\",\n \"heights += [height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged\",\n \"motif_end = int(motif[2]) strand = str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus\",\n \"region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is\",\n \"position of region to be plotted. :type r_start: int :param r_end: End position\",\n \"cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool`\",\n \"region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False,\",\n \"end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that plots links between genomic regions\",\n \"the region to be plotted. :type end_r: int :param TX_pos: Position of the\",\n \"to be plotted. :type start: int :param end: Chromosomal end position of region\",\n \"bool, optional :param alpha: Alpha value for the background color of the boxplots\",\n \"import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None,\",\n \"be plotted. :type end: int :param color: Color of the genomic scales elements,\",\n \"e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance\",\n \"tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow\",\n \":type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5\",\n \"to False. :type plot_legend: bool, optional :param colors: List of colors used for\",\n \"to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of the region\",\n \"n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = []\",\n \"that the arrow is swept back (0 overhang means triangular shape). Can be\",\n \"expression. The number of colors must be the same as the number of\",\n \"\\\"k\\\". :type color: str, optional :param direction: Direction of distance equalization (top_down |\",\n \"end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax = None):\",\n \"ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45)\",\n \"None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for\",\n \"defaults to \\\"r\\\". :type color_loss: str, optional :param color_neutral: Plot color of copy\",\n \"\\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str, optional :param color: Color of\",\n \"4], [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0,\",\n \"+= [np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not i\",\n \"= (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.),\",\n \"of lists containing the IDs of the different groups. :type groups: list :param\",\n \"Start position 3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines\",\n \"'''Function for plotting grouped gene expression (e.g. tumor and normal) on a gene\",\n \"following elements: 1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome\",\n \"number of stacked genes. 2. patch_list is the list of patches drawn on\",\n \"4. Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator :param\",\n \"exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool`\",\n \"arcs. :param links_bed: Iterator, that contains bed-like structured lists with the following elements:\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3}\",\n \"left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc:\",\n \"= line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom\",\n \"plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\",\",\n \"0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values per\",\n \"to 1. :type alpha: float, optional. :param ax: Axis of plot, defaults to\",\n \"> distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos):\",\n \"bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border\",\n \"SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of\",\n \":rtype: tuple ''' sort_indices = [int(idx) for idx in np.argsort([i[1] for i in\",\n \"0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates): ticks = [ start + end-i for i\",\n \"int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev):\",\n \"blacklist=None, ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting\",\n \"between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured lists\",\n \"patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved):\",\n \"Axis used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary\",\n \"on a gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool`\",\n \"= start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt',\",\n \"to 0.2 :type head_width: float, optional :param head_length: Length of the arrow in\",\n \"== 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights =\",\n \"+= [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border = int(region_bed[0][1]) # Determine minimal extension\",\n \"else: scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale == \\\"Mb\\\"): digits_to_round =\",\n \"arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand == \\\"-\\\"):\",\n \"in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function\",\n \"gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX\",\n \"position of region to be plotted. :type r_end: int :param ax: Axis of\",\n \"ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end, color=\\\"#cbebc4\\\", edgecolor=False, alpha=1, ax\",\n \"plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal) on\",\n \"of region to be plotted. :type end: int :param segment_size: Size of the\",\n \"in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for\",\n \"dict. :param blacklist: Set containing gene ids not to be plotted, default to\",\n \"patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >=\",\n \"be equalized (each segment is of the form [<chrom>, <start>, <end>]) :type genomic_segments:\",\n \"shown on the plot, default is None :type blacklist: list, optional :param plot_gene_ids:\",\n \"for e in cnv_bed_list]), from_string=True) def plotChIPSignals(chip_signals, r_chrom, r_start, r_end, ax=None, color=\\\"b\\\", offset=None,\",\n \"ax = ax if ax is not None else plt.gca() for motif in\",\n \"max_dist = 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2\",\n \":type regions: iterator :param start: Start position of the region to be plotted.\",\n \"divisor = None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else:\",\n \"if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 =\",\n \"== max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break\",\n \"plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted.\",\n \"end: End position of the genomic region. :type end: int :param color: Color\",\n \"int :param end: Chromosomal end position of region to be plotted. :type end:\",\n \"max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label =\",\n \"bin_size: size of bin to average methylation values, defaults to 1000. :type bin_size:\",\n \"\\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3],\",\n \"the translocation. :type TX_pos: int :param direction: Direction of the genomic part that\",\n \"loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function\",\n \"ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"):\",\n \"str :param start: Start position on chromosome. :type start: int :param end: End\",\n \":class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' ax = ax\",\n \"= contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None): vmax\",\n \"= None): '''Function that plots arcs from unequal distances of genomic segments to\",\n \"HUGO GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type\",\n \"= 0 for region in regions: if(not edgecolor): current_color = color rect =\",\n \"\\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3}\",\n \"offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator for which\",\n \".4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list = [] met_forward =\",\n \"3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \"= ax if ax is not None else plt.gca() for interval in cnvs_bed:\",\n \"that plots methylation values as dot plots. :param meth_calls: Iterator containing list-like elements\",\n \"float, optional :param vmax: Maximal value of intensity range to be plotted, defaults\",\n \"end position of region to be plotted. :type end: int :param segment_size: Size\",\n \"heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2.\",\n \"End position of region to be plotted. :type end: int :param color: Color\",\n \"= [] current_tick = first_tick while(current_tick <= end): ticks += [current_tick] current_tick =\",\n \"plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4): plt.plot([int(start), int(end)], [1,\",\n \"region to be plotted. :type start_r: int :param end_r: Chromosomal end positiont of\",\n \"in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,\",\n \"extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\",\",\n \"= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value =\",\n \"tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1),\",\n \"alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1])\",\n \"plotted. :type end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int\",\n \"y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list\",\n \"for i in exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3])\",\n \"of groups. :type ids: list, optional. :param plot_gene_names: True if gene names shall\",\n \"ids and values = y position \\\\ of gene. :rtype: tuple ''' sort_indices\",\n \"cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold):\",\n \"True if gene names shall be plotted, False otherwise, defaults to True. :type\",\n \"max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys =\",\n \".4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2,\",\n \"IDs, and values: HUGO GENE SYMBOLs. :type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object\",\n \"== \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name,\",\n \"Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([],\",\n \"Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes\",\n \"region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up color_forward = color_plus color_reverse\",\n \"optional. :param ax: Axis where the plot is drawn, defaults to None. :type\",\n \"color: Color of the genomic scales elements, defaults to \\\"k\\\". :type color: str,\",\n \"on which to plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List\",\n \"to equal distances. :param genomic_segments: List of segments for which distances shall be\",\n \"be plotted. :type end: int :param color: Color of points representing methylation values,\",\n \"ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = [] ploidy =\",\n \"wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens\",\n \"is the list of descriptions for the patches \\\\ drawn on the ax.\",\n \"List of gene names, for genes that should not be shown on the\",\n \"= max_y_pos y_level_dict[max_y_pos] = [[gene_start, gene_end]] break else: continue return max_y_pos, y_pos_dict def\",\n \"use log transformed values for plotting, non-transformed values otherwise. :type log_transformed: bool, optional\",\n \"region_size: int :param distance_ratio: Minimal distance between two genes, as ratio of ax\",\n \":type gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all\",\n \":type methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom:\",\n \":param ids: IDs used for legend plotting, defaults to None. Number of ids\",\n \"legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is\",\n \"if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing =\",\n \"(start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin =\",\n \"exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size = region_border_down-region_border_up\",\n \"if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2),\",\n \"for m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1])\",\n \"[gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else: exp_values_g1\",\n \":type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param\",\n \"plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting genomix segments in\",\n \"is None and gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None\",\n \"proportion of the arrow, defaults to 0.2 :type head_width: float, optional :param head_length:\",\n \"counter += 1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border\",\n \"int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in range(len(gene_regions)): if(not\",\n \"+= [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick > 1000000):\",\n \"1. Chromosome region1 2. Start region1 3. End region1 4. Chromosome region2 5.\",\n \"regions: Iterator containig list-like elements with the following entries: 1. Chromosome 2. Start\",\n \"= color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c +=\",\n \"GENE SYMBOLs will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names:\",\n \"chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation values as dot plots.\",\n \"lefts = [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left\",\n \"= left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values\",\n \"in np.argsort([i[1] for i in genes_bed])] genes_sorted_bed = [genes_bed[i] for i in sort_indices]\",\n \"else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >= 1. else\",\n \"ploidy to plot, defaults to None. :type max_dev: float, optional :param ax: Axis\",\n \"\\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str, optional :param\",\n \"= color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start = region_border_up border_distance_down =\",\n \"as pnd import numpy as np import tabix import matplotlib.ticker as ticker from\",\n \"region to be plotted. :type end: int :param bin_size: size of bin to\",\n \":type color_neutral: str, optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing\",\n \"max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position + 1.5. max_y_pos defines\",\n \"plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a\",\n \"boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is None):\",\n \"Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up),\",\n \"extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension\",\n \"genes_in_region_bed: gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border =\",\n \"== \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor\",\n \"plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend\",\n \"def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for gene\",\n \"= str(i[5]) color = color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down =\",\n \"gene_mid_points: list :param region: List containing the region to be plotted ([<chrom>, <start>,\",\n \"set, then y_max is the max number of stacked genes, default is None.\",\n \"y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos +=\",\n \"ticks, else if False make more ticks. :type upper: bool, optional :param loc_coordinates:\",\n \"\\\"k\\\". :type color_neutral: str, optional :param ax: Axis used for plotting. :type ax:\",\n \"Iterator, that contains bed-like structured lists with the following elements: 1. Chromosome region1\",\n \":param introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object\",\n \"tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold):\",\n \"deviation from ploidy to be considered as a CNV, defaults to 0.7. :type\",\n \"strand, start, end, color). The color field is used to determine the color\",\n \"ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None, ax=None, plot_legend=False, colors=None, ids=None,\",\n \"ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45)\",\n \"TX_end = end_r if(direction == \\\"left\\\"): TX_start = start_r TX_end = TX_pos rect\",\n \"== \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3,\",\n \"met_reverse = False # Plot Introns for i in introns_in_region: start = int(i[1])\",\n \"Max y value in the gene plot. If not set, then y_max is\",\n \"is used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param\",\n \"else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([ (float(m[1])+float(m[2]))/2. for m in\",\n \"to False. :type plot_legend: bool :param color_g1: Color used for plotting g1 samples\",\n \"a ensemble gene id, and the second column is the HUGO gene name\",\n \"n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points = [] for e in genomic_segments:\",\n \"= color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev)\",\n \"as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param cmap: Colormap\",\n \"overhang: Fraction that the arrow is swept back (0 overhang means triangular shape).\",\n \"ax: Axis on which to plot contact map, defaults to None. :type ax:\",\n \"ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else:\",\n \"sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax is None):\",\n \"edge is plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha\",\n \"used to determine the color for plotting (R,G,B). :type segments_Tabix_filename: str :param chrom:\",\n \"elif(ploidy == 4): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2,\",\n \"import matplotlib.pyplot as plt import pybedtools import pandas as pnd import numpy as\",\n \"TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r])\",\n \"] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ]\",\n \"per bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m\",\n \"CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color of\",\n \"of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be\",\n \"color_g1: str, optional :param color_g2: Color used for plotting g2 samples expression, defaults\",\n \"plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted, False otherwise,\",\n \"start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in\",\n \"+= [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1 = list(exp_values_g1) else:\",\n \"gene plot. If not set, then y_max is the max number of stacked\",\n \"None): heights = [] lefts = [] for i in range(len(left)): if(i %\",\n \"Nothing to be returned :rtype: None ''' # Use given axis for plotting\",\n \"facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else: current_color = color rect = Rectangle([int(region[1]),\",\n \"\\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def\",\n \"contact_map: Matrix that contains the intensity values of HiC contacts. :type contact_map: :class:`pandas.DataFrame`\",\n \":type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted,\",\n \"segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start), int(end)) plt.ylim(0, 1) plt.yticks([], [])\",\n \"region to be plotted. :type chrom: str :param start: Start position of the\",\n \"patch = matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.)\",\n \"of \\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed:\",\n \"\\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\",\",\n \"color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else:\",\n \":class:`pybedtools.BedTool` object containing the one region, for which the gene plot is created.\",\n \"start: str :param end: End position of the region to be plotted. :type\",\n \"if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\", \\\"2\\\",\",\n \"j == \\\"NA\\\" ] binned_average_meth_no_missing += [ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0\",\n \"simple rectangles. :param regions: Iterator containig list-like elements with the following entries: 1.\",\n \"to tabixed bed file containing (chrom, start, end, name, score, strand, start, end,\",\n \"defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of plot, defaults\",\n \"gene expression (e.g. tumor and normal) on a gene region scale retaining the\",\n \"split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close()\",\n \"current_end-current_start, .2, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255.,\",\n \"[patch] patch_description_list += [\\\"reverse strand\\\"] met_reverse = True # Plot Gene Names if(plot_gene_ids):\",\n \"Start position of region to be plotted. :type r_start: int :param r_end: End\",\n \"the translocation, defaults to \\\"k\\\". :type color: str, optional :param ax: Axis of\",\n \"bed file containing (chrom, start, end, name, score, strand, start, end, color). The\",\n \"plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed,\",\n \"the region to be plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the\",\n \"continue if(extension is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension)\",\n \"not None else plt.gca() # Calculate midpoints of original and distance equalized segments\",\n \"= None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension\",\n \":param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list\",\n \"is None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True, a legend describing\",\n \":type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names, for genes that should\",\n \"gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\",\",\n \"chip_signals: Iterator for which each element is a list-ike object containing: 1. Chromosome\",\n \":param chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param start:\",\n \"plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2:\",\n \"region1 4. Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator\",\n \"all gene ids will be included in the plot, False otherwise, default is\",\n \":type cnv_threshold: float, optional :param color_gain: Plot color of copy number gains, defaults\",\n \"which contacts were called. :type segment_size: int :param cmap: Name of the colormap\",\n \"chromosome: str :param start: Start position on chromosome. :type start: int :param end:\",\n \"= str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name] y\",\n \"equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \\\"top_down\\\"): codes\",\n \"values otherwise. :type log_transformed: bool, optional :param plot_points: If True, a point per\",\n \"plt.text(start, y, gene_name_label, size=5, color = color) plt.xlim([region_border_up, region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], [])\",\n \"returned :rtype: None ''' # Use given axis for plotting ax = ax\",\n \"of the region to be plotted. :type start_r: int :param end_r: End position\",\n \"a gene region scale equalizing the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object\",\n \"not None else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed\",\n \"= [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\",\",\n \"(columns: sample ids; index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with\",\n \"tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"):\",\n \"to \\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal value of intensity range\",\n \"underwent, the genes will be stacked, default is 0.1. :type distance_ratio: float, optional\",\n \"ax.add_patch(rect) plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end,\",\n \"gene_mid_points: list of integer values containing center positions of genes. :type gene_mid_points: list\",\n \":param offset: Length of intervals, defaults to None. :type offset: int, optional :param\",\n \":param bin_size: size of bin to average methylation values, defaults to 1000. :type\",\n \"pnd import numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches\",\n \"alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\",\",\n \".3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else:\",\n \"else plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up =\",\n \"is the HUGO gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene\",\n \"defaults to \\\"left\\\". :type direction: str, optional :param color: Color of the bar\",\n \"per expression value is plotted in addition to the boxplot, no points are\",\n \"position of region to be plotted. :type end: int :param bin_size: size of\",\n \"of plus stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param\",\n \"determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines the max y position for gene plotting\",\n \"split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id] = hugo_gene_symbol gene_name_mapping_file.close() return gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map,\",\n \":param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool`\",\n \"plotted, defaults to False. :type edge_color: str, optional :param alpha: Alpha value of\",\n \"angle of coordinate strings, defaults to 0. :type rotation: int, optional :return: Nothing\",\n \"scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale ==\",\n \"str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions\",\n \"2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start,\",\n \"+= [np.mean(heights)] heights = [] offset = merge*offset left = left_merged height =\",\n \"if(not y_max is None): max_y_pos = y_max # Plot Exons for i in\",\n \"end, ax = None): '''Function for plotting genomix segments in different colors :param\",\n \"end: int :param head_width: Width of the arrow head as proportion of the\",\n \"be plotted, defaults to None :type vmin: float, optional :param vmax: Maximal value\",\n \"def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting\",\n \"gene_name_ens = str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2])\",\n \":param end: End position of the region to be plotted. :type end: int\",\n \"optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the max_y_position\",\n \"None): max_dev = max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start\",\n \"color=\\\"k\\\", ax=None): '''Function that plots a translocation event as a bar, showing the\",\n \":type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which\",\n \"of colors used for plotting samples expression. The number of colors must be\",\n \"= ax if ax is not None else plt.gca() for motif in motifs_bed:\",\n \"range to be plotted, defaults to None. :type vmax: float, optional :param location:\",\n \"[3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \":param color_gain: Plot color of copy number gains, defaults to \\\"g\\\". :type color_gain:\",\n \"rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor='none', alpha=alpha) c += 1 else:\",\n \"translocated. :param chrom_r: Chromosome of the region to be plotted. :type chrom_r: str\",\n \"not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2,\",\n \"to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color used for plotting g2\",\n \"be plotted, defaults to None, :type blacklist: set, optional :param ax: (default: None)\",\n \"> 0 else 0. ] binned_average_meth = binned_average_meth_no_missing # Plot average methylation values\",\n \":param end: End position of the genomic region. :type end: int :param color:\",\n \"is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be\",\n \"\\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed):\",\n \"optional :return: Nothing to be returned :rtype: None ''' # Use given axis\",\n \"== \\\"left\\\"): TX_start = start_r TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start,\",\n \"of the genomic scales elements, defaults to \\\"k\\\". :type color: str, optional :param\",\n \"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype:\",\n \"methylation_bed: :class:`pybedtools.BedTool` :param chrom: Chromosome of region to be plotted. :type chrom: str\",\n \"values of HiC contacts. :type contact_map: :class:`pandas.DataFrame` :param start: Chromosomal start position of\",\n \"matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True,\",\n \"region_border_up), distance_ratio) if(not y_max is None): max_y_pos = y_max # Plot Exons for\",\n \"overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end,\",\n \"y, gene_name_label, size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not\",\n \"be the same as the number of groups, defaults to None. :type colors:\",\n \"List of lists containing the IDs of the different groups. :type groups: list\",\n \"downwards, defaults to top, :type location: str, optional :param ax: Axis on which\",\n \"\\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color code for minus stranded genes,\",\n \"elements will be averaged an plotted, defaults to 0. :type merge: int, optional\",\n \"that is translocated. :param chrom_r: Chromosome of the region to be plotted. :type\",\n \"in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name] = i\",\n \"1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that\",\n \"containing gene ids not to be plotted, default to None. :type blacklist: set,\",\n \"i in range(len(gene_regions)): if(not blacklist is None and gene_names[i] in blacklist): continue left_border\",\n \"the arrow is swept back (0 overhang means triangular shape). Can be negative\",\n \"print(ploidy) if(line[0] == \\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev\",\n \"plotted. :type start: int :param end: End position of the region to be\",\n \":type color_plus: str, optional :param color_minus: Color of plus stranded TF regions, defaults\",\n \"<end>]). :type region: list :param groups: List of lists containing the IDs of\",\n \"None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine\",\n \"= [] gene_names_clean = [] counter=0 patch_saved = False for gene_name in gene_names:\",\n \"== \\\"top\\\"): ax.xaxis.set_ticks_position(\\\"top\\\") ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i\",\n \"meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome 2. Start\",\n \"plt.gca() # Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments)\",\n \"used for plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Dictionary with\",\n \"= ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None): vmin = 0 if(vmax\",\n \"legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons of\",\n \"\\\"top\\\", or \\\"bottom\\\", defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If\",\n \"i % merge == 0): left_merged += [lefts[0]] lefts = [] height_merged +=\",\n \"cmap = cmap) for cbin in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1,\",\n \"arrow head as proportion of the arrow, defaults to 0.2 :type head_width: float,\",\n \"blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene\",\n \"None for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0]\",\n \"dict ''' ax = ax if ax is not None else plt.gca() patches_dict\",\n \"m in meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color,\",\n \"False, no edge is plotted, defaults to False. :type edge_color: str, optional :param\",\n \"plotted. :type end: int :param color: Color of the genomic scales elements, defaults\",\n \"< extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} flierprops =\",\n \"right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns exons of genes.\",\n \"None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor = 1000000 else: digits_to_round =\",\n \"in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], [])\",\n \"((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None\",\n \"189./255., 0.5) if(ploidy == 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start),\",\n \"1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls,\",\n \"[] gene_names_clean = [] counter=0 for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border\",\n \"in cnvs_bed: current_start = int(interval[1]) current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn =\",\n \"False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults\",\n \"patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color = None if(not colors is\",\n \"name, score, strand, start, end, color). The color field is used to determine\",\n \"if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGenomicSegments(segments_list, chrom, start, end,\",\n \"of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type direction: str, optional\",\n \"region_border_down]) plt.ylim([0, max_y_pos+1.5]) plt.yticks([], []) if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list,\",\n \"class:`pandas.DataFrame` :param blacklist: Set containing gene ids not to be plotted, defaults to\",\n \"head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start,\",\n \"list(exp_values.iloc[0, :]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1.\",\n \"cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect)\",\n \"plotted. :type r_end: int :param ax: Axis of plot :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"given axis for plotting ax = ax if ax is not None else\",\n \"the genomic part that is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream),\",\n \"genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool` :param gene_mid_points: list of\",\n \"exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\",\",\n \"= ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) color =\",\n \"ax = ax if ax is not None else plt.gca() binned_meth_calls = [\",\n \"plotted. :type end: str :param ax: Axis used for plotting, defaults to None.\",\n \"gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name in blacklist): counter\",\n \":param start_r: Start position of the region to be plotted. :type start_r: int\",\n \"position of the region to be plotted. :type end: int :param head_width: Width\",\n \"else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops,\",\n \"to be plotted. :type start: int :param end: End position of the region\",\n \"If True, coordinates are reverted to decreasing order. Else, coordinates stay in increasing\",\n \"genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes.\",\n \"[ (float(sum(average_list))/ float(len(average_list))) if len(average_list) > 0 else 0. ] binned_average_meth = binned_average_meth_no_missing\",\n \"edge. If False, no edge is plotted, defaults to False. :type edge_color: str,\",\n \"Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size =\",\n \"color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color,\",\n \"else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops,\",\n \"== \\\"top\\\", the pyramid points upwards, else if location == \\\"bottom\\\" the pyramid\",\n \"is not None else plt.gca() c = 0 for region in regions: if(not\",\n \"name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype: dictionary\",\n \"\\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot ticks to upper direction, else if \\\"down\\\",\",\n \"for genes that should not be shown on the plot, default is None\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) elif(ploidy == 4):\",\n \"<end>]) :type genomic_segments: list :param start: Start position of the genomic region. :type\",\n \"to ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from\",\n \"color_loss: str, optional :param color_neutral: Plot color of copy number neutral regions, defaults\",\n \"Chromsome 2. Start position 3. end position 4. Number methylated cytosines 5. Number\",\n \"= int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in\",\n \"else: continue return max_y_pos, y_pos_dict def createGeneNameMap(gene_name_mapping_filename): '''Function that creates a mapping between\",\n \"ploidy = None for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1])\",\n \"plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function\",\n \"if(plot_legend): plt.legend(patch_list, patch_description_list, loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio):\",\n \"j in [meth_before, meth_after] if not j == \\\"NA\\\" ] binned_average_meth_no_missing += [\",\n \"color=\\\"k\\\", ax=None): '''Function that plots methylation values as dot plots. :param meth_calls: Iterator\",\n \"\\\"group \\\"+str(g) if(not g_id in patch_description_list): patch_list += [bplot[\\\"boxes\\\"][0]] patch_description_list += [g_id] counter\",\n \"plots HiC contact maps as pyramid plots :param contact_map: Matrix that contains the\",\n \"plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads\",\n \"order. Else, coordinates stay in increasing order, defaults to False. :type revert_coordinates: bool,\",\n \"[] patch_description_list = [] tick_positions = [] gene_names_clean = [] counter=0 for gene_name\",\n \"end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots arcs from unequal distances\",\n \"y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start, gene_end]] break\",\n \"meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m\",\n \"0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch =\",\n \"that should not be shown on the plot, default is None :type blacklist:\",\n \"End position on chromosome. :type end: int :param ploidy: Assumed ploidy of tumor,\",\n \"= -1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) <\",\n \"e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points\",\n \"color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand == \\\"+\\\" and not(met_forward)): patch_list += [patch] patch_description_list\",\n \"== \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False)\",\n \"optional :param ids: IDs used for legend plotting, defaults to None. Number of\",\n \"defaults to \\\"Greys\\\". :type cmap: str, optional :param vmin: Minimal value of intensity\",\n \"color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor\",\n \"this value is not equal to 0, than merge elements will be averaged\",\n \"plotted. :type start_r: int :param end_r: End position of the region to be\",\n \":rtype: dict ''' ax = ax if ax is not None else plt.gca()\",\n \"genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to be plotted in base pairs.\",\n \"binned_meth_calls[current_bin][0] += n_meth binned_meth_calls[current_bin][1] += n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) >\",\n \"[[gene_start, gene_end]] break elif(i == max_y_pos): max_y_pos += 1 y_pos_dict[gene_name] = max_y_pos y_level_dict[max_y_pos]\",\n \"grouped gene expression (e.g. tumor and normal) on a gene region scale equalizing\",\n \"If True, make less ticks, else if False make more ticks. :type upper:\",\n \"else 1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops,\",\n \"1. for i in exp_values_g1])], positions=[bplot_g1_pos], widths=extension/2., patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops,\",\n \"float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold\",\n \"of bin to average methylation values, defaults to 1000. :type bin_size: int, optional\",\n \"on which to plot contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional\",\n \"right_border = gene_mid_points[counter]+extension/2 if(not blacklist is None and gene_name in blacklist): counter +=\",\n \"gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0]\",\n \"is None and gene_name in blacklist): counter += 1 continue n_groups = len(groups)\",\n \"gene_names_map: dict. :param expression_df: class:`pandas.DataFrame` object containing the expression values of all samples\",\n \"to \\\"left\\\". :type direction: str, optional :param color: Color of the bar representing\",\n \"be plotted. :type start: int :param end: End position of region to be\",\n \"capprops=capprops, showfliers=False) color = None if(not colors is None): color = colors[g] else:\",\n \"the genes will be stacked, default is 0.1. :type distance_ratio: float, optional :param\",\n \"legend_loc: Location of the legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\",\",\n \"= [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2),\",\n \"position of region to be plotted. :type start: int :param end: Chromosomal end\",\n \"patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border)\",\n \"to be plotted. :type end: int :param color: Color of the genomic scales\",\n \"and gene_map[gene_name] in blacklist): continue # Define color for gene plotting strand =\",\n \":class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the one region, for which the gene\",\n \"be returned. :rtype: None ''' ax = ax if ax is not None\",\n \"Value :type meth_calles: iterator :param chrom: Chromosome of region to be plotted. :type\",\n \":param region_size: Size of region to be plotted in base pairs. :type region_size:\",\n \"= (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value) patch =\",\n \"vmax: Maximal value of intensity range to be plotted, defaults to None. :type\",\n \"gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of\",\n \"containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region\",\n \"g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will be\",\n \"edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect = Rectangle((current_start, tcn-.1), current_end-current_start, .2, color=color, edgecolor='none',\",\n \"path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start,\",\n \"intensity range to be plotted, defaults to None. :type vmax: float, optional :param\",\n \"function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed:\",\n \":param cmap: Colormap used for plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str,\",\n \"left_merged height = height_merged plt.bar(left, height, offset, color = color, edgecolor = color)\",\n \"region to be plotted. :type chrom: str :param start: Start position of region\",\n \"= [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor =\",\n \"medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1.\",\n \"defaults to None. :type colors: str, optional :param ids: IDs used for legend\",\n \"if(strand == \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start < region_border_up): start\",\n \"color_forward if(strand == \\\"-\\\"): color = color_reverse y = max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start,\",\n \"int(signal[2]) value = float(signal[3]) if(value > max_signal): max_signal = value if(not offset is\",\n \"the form [<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of\",\n \"None): vmin = 0 if(vmax is None): vmax = np.percentile(contact_map, 99.9) colormap =\",\n \"1. Chromosome 2. Start postion 3. End position 4. Value to be plotted\",\n \"exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns :type\",\n \"genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e\",\n \"\\\"down\\\". If \\\"up\\\", plot ticks to upper direction, else if \\\"down\\\", plot ticks\",\n \"order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational angle of\",\n \"height = [] for signal in chip_signals: start = int(signal[1]) end = int(signal[2])\",\n \"containing the one region, for which the gene plot is created. :type region_bed:\",\n \"extension of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and\",\n \"plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor\",\n \"= ax if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries\",\n \"plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and normal) on a\",\n \"to be plotted ([<chrom>, <start>, <end>]). :type region: list :param groups: List of\",\n \"\\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1],\",\n \":param plot_legend: If True plot legend, False otherwise, defaults to False. :type plot_legend:\",\n \"height_merged = [] if(not merge is None): heights = [] lefts = []\",\n \"head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def plotHiCContactMap(contact_map,\",\n \"if ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5):\",\n \"exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 =\",\n \"and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = []\",\n \"current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) # Smooth tcn, if\",\n \"link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance), (link_pos2,\",\n \"[] for e in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names\",\n \"if i >= 1. else 1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist\",\n \":param start: Start position of region to be plotted. :type start: int :param\",\n \"end, name, score, strand, start, end, color). The color field is used to\",\n \"gains, defaults to \\\"g\\\". :type color_gain: str, optional :param color_loss: Plot color of\",\n \"color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([], [])\",\n \"the region to be plotted. :type start: int :param end: End position of\",\n \"ratio is underwent, the genes will be stacked, default is 0.1. :type distance_ratio:\",\n \"of the rectangles representing the regions to be plotted, defaults to \\\"#cbebc4\\\". :type\",\n \"ax is not None else plt.gca() n_entries = len(meth_calls[0]) if(n_entries == 5): plt.plot([\",\n \"end-i for i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)):\",\n \"tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return\",\n \"containing the region to be plotted ([<chrom>, <start>, <end>]). :type region: list :param\",\n \"considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain:\",\n \"\\\"r\\\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id =\",\n \"ax: Axes instance on which the genes are plotted, default is None. :type\",\n \"ax is not None else plt.gca() tick_size = 10**math.ceil((np.log10((end-start)/10))) if(not upper): tick_size =\",\n \"plotCoordinates(chrom, start, end, color=\\\"k\\\", ax = None, upper=True, loc_coordinates=\\\"up\\\", revert_coordinates=False, rotation=0): '''Function that\",\n \"for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is\",\n \"color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([],\",\n \"Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value\",\n \"list of patches drawn on the ax. 3. patch_description_list is the list of\",\n \"matplotlib.cm as cm import matplotlib import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed,\",\n \"distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments\",\n \"5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r: Chromosome of\",\n \"plt.xticks([], []) plt.yticks([], []) plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2,\",\n \"i in range(n): if(not binned_average_meth[i] == \\\"NA\\\"): binned_average_meth_no_missing += [binned_average_meth[i]] else: meth_before =\",\n \"codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch = PathPatch(path, facecolor\",\n \"end: int :param color: Color of lines equalizing distances, defaults to \\\"k\\\". :type\",\n \"True, coordinates are reverted to decreasing order. Else, coordinates stay in increasing order,\",\n \"names of segments, and values patch :rtype: dict ''' ax = ax if\",\n \"defaults to \\\"k\\\". :type color_neutral: str, optional :param ax: Axis used for plotting.\",\n \"[0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1)\",\n \":type bin_size: int, optional :param ax: Axis to be used for plotting, defaults\",\n \":param color: Color of the arc, defaults to \\\"k\\\". :type color: str, optional.\",\n \"ticks += [current_tick] current_tick = current_tick + tick_size scale = None if(first_tick >\",\n \"lefts = [] for i in range(len(left)): if(i % merge == 0 and\",\n \"then y_max is the max number of stacked genes, default is None. :type\",\n \"the max number of stacked genes, default is None. :type y_max: bool, optional\",\n \"'''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following\",\n \"ax = ax if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size\",\n \"size=5, color = color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is\",\n \"size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function\",\n \"otherwise, default is True :type plot_gene_ids: bool, optional :param y_max: Max y value\",\n \"interval[3] gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i ==\",\n \"None ''' ax = ax if ax is not None else plt.gca() for\",\n \"gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border =\",\n \"0 for element in methylation_bed: # Determine bin position = int(element[1]) if(position <\",\n \"genomic regions as simple rectangles. :param regions: Iterator containig list-like elements with the\",\n \"= region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction ==\",\n \"ids must be the same as the number of groups. :type ids: list,\",\n \"line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0] gene_map[ensembl_gene_id]\",\n \"to \\\"k\\\". :type color: str, optional :param ax: Axis of plot, defaults to\",\n \"the same as the number of groups. :type ids: list, optional. :param plot_gene_names:\",\n \"ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def\",\n \"= {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol\",\n \"Methylation calls. Following fields must be included: Chrom, Start, End, Methylated Cs, Unmethylated\",\n \":param start: Start position on chromosome. :type start: int :param end: End position\",\n \"if(abs(ploidy_dev) > cnv_threshold): rect = Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0)\",\n \"for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color =\",\n \"ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing gene ids\",\n \"float, optional. :param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"= gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions\",\n \"and not (i == 0)): left_merged += [lefts[0]] lefts = [] height_merged +=\",\n \"gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param blacklist: List of gene names,\",\n \"colors: List of colors used for plotting samples expression. The number of colors\",\n \"color = colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+\",\n \"to a tab separated file, for which the first column is a ensemble\",\n \"1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function\",\n \"be plotted, False otherwise, defaults to True. :type plot_gene_names: bool, optional :param position_gene_names:\",\n \"End region1 4. Chromosome region2 5. Start region2 6. End region2 :type links_bed:\",\n \"float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3}\",\n \"g1_id: str, optional :param g2_id: ID of g2 used for legend plotting, defaults\",\n \"exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1)\",\n \"n_unmeth binned_average_meth = [ float(i[0])/(float(i[0])+float(i[1])) if (float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i\",\n \"plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end])\",\n \"is None): extension = float(current_extension) elif(current_extension < extension): extension = float(current_extension) boxprops =\",\n \"plots :param contact_map: Matrix that contains the intensity values of HiC contacts. :type\",\n \"str, optional :param color_minus: Color of plus stranded TF regions, defaults to \\\"#fb8072\\\".\",\n \"containing gene ids not to be plotted, defaults to None, :type blacklist: set,\",\n \"= [ j for j in [meth_before, meth_after] if not j == \\\"NA\\\"\",\n \"for plotting paired gene expression (e.g. tumor and normal) on a gene region\",\n \":]) expression_values = exp_values if(log_transformed): expression_values = np.log2([i if i >= 1. else\",\n \"End position of the region to be plotted. :type end: int :param head_width:\",\n \"is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color code for minus stranded\",\n \"plotting grouped gene expression (e.g. tumor and normal) on a gene region scale\",\n \"showfliers=False) bplot_g2 = ax.boxplot([np.log2([i if i >= 1. else 1. for i in\",\n \"== \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\",\",\n \"y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio)\",\n \"= Rectangle((current_start, tcn-.2), current_end-current_start, .4, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) else: rect =\",\n \"expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs, and values: HUGO\",\n \":param color_g2: Color used for plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type\",\n \"if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor =\",\n \"< start or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4])\",\n \"if ax is not None else plt.gca() contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1\",\n \"patch_description_list is the list of descriptions for the patches \\\\ drawn on the\",\n \"offset = merge*offset left = left_merged height = height_merged plt.bar(left, height, offset, color\",\n \".3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops =\",\n \"[ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3)\",\n \":type input_filename: str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` '''\",\n \"current_tick = current_tick + tick_size scale = None if(first_tick > 1000000): scale =\",\n \"= int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and\",\n \"plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that plots methylation values as dot\",\n \":param start: Start position of the genomic region. :type start: int :param end:\",\n \"to be plotted in base pairs. :type region_size: int :param distance_ratio: Minimal distance\",\n \"\\\"k\\\", \\\"linewidth\\\": .3, \\\"alpha\\\":alpha} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\":\",\n \"(float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4]) for m in m in\",\n \"linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy == 2):\",\n \"is plotted, False otherwise. Default is False. :type plot_legend: bool, optional :param legend_loc:\",\n \"genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in genes_in_region_bed:\",\n \"included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool` :param chrom:\",\n \"different groups. :type groups: list :param gene_names_map: Dictionary with keys: ENSEMBL GENE IDs,\",\n \"plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None):\",\n \"False. :type plot_legend: bool, optional :param legend_loc: Location of the legend. Either of\",\n \"\\\"tumor\\\". :type g1_id: str, optional :param g2_id: ID of g2 used for legend\",\n \"be shown on the plot, default is None :type blacklist: list, optional :param\",\n \":type plot_legend: bool :param color_g1: Color used for plotting g1 samples expression, defaults\",\n \"region to be plotted. :type r_start: int :param r_end: End position of region\",\n \"CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End Position, 4.\",\n \":param head_length: Length of the arrow in bp (depends on the region that\",\n \"in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start =\",\n \"for i in range(max_y_pos+1): if(i == 0 and not max_y_pos in y_level_dict): y_pos_dict[gene_name]\",\n \"Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if\",\n \"\\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"] ax = ax if ax is not None else plt.gca()\",\n \"if i >= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values,\",\n \"extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean +=\",\n \"= [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes = [Path.MOVETO,\",\n \"capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list = [] tick_positions = []\",\n \":param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns: sample ids;\",\n \":type location: str, optional :param ax: Axis on which to plot contact map,\",\n \"plot ticks to upper direction, else if \\\"down\\\", plot ticks to lower direction,\",\n \"[gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border\",\n \"return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for plotting\",\n \"position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed: :class:`pybedtools.BedTool`\",\n \"codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else\",\n \"color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\", g2_id=\\\"normal\\\", plot_gene_names=True): '''Function for plotting paired gene expression (e.g. tumor and\",\n \":class:`pybedtools.BedTool` object containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed:\",\n \"of equalized region midpoints. :rtype: list ''' ax = ax if ax is\",\n \"ax: Axis where the plot is drawn, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`,\",\n \"to be plotted. :type r_start: int :param r_end: End position of region to\",\n \"else plt.gca() max_signal = 0 left = [] height = [] for signal\",\n \"list, optional. :param plot_gene_names: True if gene names shall be plotted, False otherwise,\",\n \"are hidden. :type plot_gene_names: bool. :return: Axis on which plot was placed. :rtype:\",\n \"start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\", ax=None): '''Function that plots TF motifs\",\n \">= cnv_threshold): color=color_gain elif(ploidy_dev <= -1.*cnv_threshold): color = color_loss if(abs(ploidy_dev) > cnv_threshold): rect\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region midpoints. :rtype: list\",\n \"optional :return: Dictionary with keys = names of segments, and values patch :rtype:\",\n \"the boxplot, no points are plotted otherwise, defaults to False. :type plot_points: bool,\",\n \"distance_ratio): '''Function that determines the max y position for gene plotting via function\",\n \"extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region size from extension extension=extension-(region[2]-region[1])*.01 boxprops\",\n \"as pyramid plots :param contact_map: Matrix that contains the intensity values of HiC\",\n \"= int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist):\",\n \":type chrom_r: str :param start_r: Chromosomal start position of the region to be\",\n \"is None): heights = [] lefts = [] for i in range(len(left)): if(i\",\n \"= genes_bed.intersect(region_bed, wa=True, u=True).sort() gene_names = [] gene_regions = [] for e in\",\n \":type alpha: float, optional :return: Plots axis. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\",\",\n \"m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([],\",\n \"plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end, color=\\\"k\\\", ax=None): '''Function that\",\n \"= np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range =\",\n \":param input_filename: Full path to ACESeq \\\"most_important\\\" file :type input_filename: str :return: :class:`pybedtools.BedTool`\",\n \"(i == 0)): left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights\",\n \"tick_size scale = None if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round\",\n \"-.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], []) plt.yticks([],\",\n \"for m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([\",\n \"gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4. bplot_g2_pos = left_border + 3*(extension/4.) tick_positions +=\",\n \"i in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels():\",\n \"i in ticks ] ticks.reverse() tick_labels.reverse() print(tick_labels) for i in range(len(ticks)): if(loc_coordinates ==\",\n \"None. :type max_dev: float, optional :param ax: Axis used for plotting, defaults to\",\n \":param blacklist: Set containing gene ids not to be plotted, default to None.\",\n \"start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots HiC contact\",\n \"End position of region to be plotted. :type end: int :param bin_size: size\",\n \"gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension == 0.): continue if(extension\",\n \"= expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0,\",\n \"3. end position 4. Number methylated cytosines 5. Number unmethylated cytosines Or 1.\",\n \":type start: int :param end: End position of the genomic region. :type end:\",\n \"edge_color: Color of region edge. If False, no edge is plotted, defaults to\",\n \"alpha: float, optional. :param ax: Axis of plot, defaults to None. :type ax:\",\n \"[patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)):\",\n \"Introns for i in introns_in_region: start = int(i[1]) end = int(i[2]) gene_name =\",\n \"= PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points\",\n \":param color_g1: Color used for plotting g1 samples expression, defaults to \\\"#fb8072\\\". :type\",\n \"cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries: 1. Chromosome, 2. Start Position,\",\n \"the legend. Either of \\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default\",\n \"i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1)\",\n \"merge: int, optional :return: Nothing to be returned. :rtype: None ''' ax =\",\n \"optional :return: List of equalized region midpoints. :rtype: list ''' ax = ax\",\n \"0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed:\",\n \"plt.xlim([start, end]) plt.ylim([-1, 1]) def plotMotifDirections(motifs_bed, start, end, head_width=0.2, head_length=1000, overhang=0, color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\",\",\n \"ensemble gene id, and the second column is the HUGO gene name :type\",\n \"position of the genomic region. :type start: int :param end: End position of\",\n \"max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1]) current_end\",\n \"color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i],\",\n \"end]) plt.yticks([], []) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], [])\",\n \"left_merged += [lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] heights\",\n \"> 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points +=\",\n \"fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None, blacklist=None,\",\n \"containing the IDs of the different groups. :type groups: list :param gene_names_map: Dictionary\",\n \"of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True,\",\n \"than cnv_threshold if(abs(ploidy_dev) < cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >=\",\n \"0 for region in regions: if(not edgecolor): current_color = color rect = Rectangle([int(region[1]),\",\n \"gene_names[i] in blacklist): continue left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1):\",\n \"if ax is not None else plt.gca() # Calculate midpoints of original and\",\n \"on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax\",\n \"be plotted. :type end: str :param ax: Axis used for plotting, defaults to\",\n \"float, optional :param ax: Axes instance on which the genes are plotted, default\",\n \"containing the expression values of g1 samples (columns: sample ids; index: gene ids)\",\n \"defaults to 1000. :type head_length: int, optional :param overhang: Fraction that the arrow\",\n \"'''Function for plotting paired gene expression (e.g. tumor and normal) on a gene\",\n \"expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else: exp_values = list(exp_values.iloc[0, :])\",\n \"Direction of the genomic part that is translocated. Either of \\\"left\\\" (upstream), or\",\n \"of 1. max_y_pos: Defines the number of stacked genes. 2. y_pos_dict: Dictionary with\",\n \"ax=None, plot_legend=False, colors=None, ids=None, plot_gene_names=True, position_gene_names=\\\"bottom\\\", log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped\",\n \"value in the gene plot. If not set, then y_max is the max\",\n \"color of copy number gains, defaults to \\\"g\\\". :type color_gain: str, optional :param\",\n \"< link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point, distance),\",\n \"optional :param cmap: Colormap used for plotting CNVs, defaults to \\\"bwr\\\". :type cmap:\",\n \"if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage of region\",\n \":return: List of equalized region midpoints. :rtype: list ''' ax = ax if\",\n \"== \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i\",\n \"chromosome, start, end, ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments\",\n \"defaults to None. :type offset: int, optional :param merge: Number of elements to\",\n \"np.log2([i if i >= 1. else 1. for i in exp_values]) bplot =\",\n \"containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool` :param introns_bed: :class:`pybedtools.BedTool` object containing introns\",\n \"chromosome. :type end: int :param ploidy: Assumed ploidy of tumor, defaults to 2.\",\n \"end_r: int :param TX_pos: Position of the translocation. :type TX_pos: int :param direction:\",\n \"not be shown on the plot, default is None :type blacklist: list, optional\",\n \"capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2):\",\n \"max_dev: Maximal deviation from ploidy to plot, defaults to None. :type max_dev: float,\",\n \"1. for i in exp_values_g2])], positions=[bplot_g2_pos], widths=extension/2., patch_artist = True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops,\",\n \"if(revert_coordinates): ticks = [ start + end-i for i in ticks ] ticks.reverse()\",\n \":type plot_points: bool, optional :param alpha: Alpha value for the background color of\",\n \"plotting g2 samples expression, defaults to \\\"#80b1d3\\\". :type color_g2: str, optional :param g1_id:\",\n \"upper: bool, optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If \\\"up\\\", plot\",\n \"\\\"top\\\", the pyramid points upwards, else if location == \\\"bottom\\\" the pyramid points\",\n \"and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the\",\n \"= [] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2])\",\n \"loc=legend_loc, fontsize=5) return max_y_pos+1.5, patch_list, patch_description_list def determineYPosGene(genes_bed, region_size, distance_ratio): '''Function that determines\",\n \"i >= 1. else 1. for i in exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos],\",\n \"]+[1]) segment_type = str(segment[3]) if(segment_type == \\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start,\",\n \"otherwise, defaults to False. :type plot_points: bool, optional :param alpha: Alpha value for\",\n \"blacklist: list, optional :param plot_gene_ids: If True, all gene ids will be included\",\n \"value if(not offset is None): end = start + offset left += [start]\",\n \"= Path(vertices, codes) intensity_value = contact_map.iloc[i, j] intensity_value = (intensity_value/vmax if intensity_value <=\",\n \"the region to be plotted. :type end: int :param head_width: Width of the\",\n \"plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus: Color\",\n \"start: Start position of the region to be plotted. :type start: str :param\",\n \"ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5,\",\n \"'''Function that plots HiC contact maps as pyramid plots :param contact_map: Matrix that\",\n \"exons_in_region: start = int(i[1]) end = int(i[2]) gene_name = str(i[3]) if(not blacklist is\",\n \"for which distances shall be equalized (each segment is of the form [<chrom>,\",\n \"containing center positions of genes. :type gene_mid_points: list :param region: List containing the\",\n \"'''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool\",\n \"# Determine y positions of genes for plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down-\",\n \"Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path\",\n \":type plot_legend: bool, optional :param colors: List of colors used for plotting samples\",\n \"normal) on a gene region scale retaining the position of genes. :param genes_bed:\",\n \"CNVs from ACESeq (\\\"*most_important*\\\") files and converts them to pybedtools.BedTool object :param input_filename:\",\n \"is the max_y_position + 1.5. max_y_pos defines the \\\\ number of stacked genes.\",\n \"if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df,\",\n \"be stacked. :type distance_ratio: float :return: Tuple of 1. max_y_pos: Defines the number\",\n \"in increasing order, defaults to False. :type revert_coordinates: bool, optional :param rotation: Rotational\",\n \"ticks. :type upper: bool, optional :param loc_coordinates: Either of \\\"up\\\" | \\\"down\\\". If\",\n \"plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: List of equalized region\",\n \"in range(int(((end-start)/bin_size)+1)) ] counter = 0 for element in methylation_bed: # Determine bin\",\n \"gene name :type gene_name_mapping_file: str :return: Dictionary containing the gene id mapping. :rtype:\",\n \"that determines the max y position for gene plotting via function plotGenes. :param\",\n \"as plt import pybedtools import pandas as pnd import numpy as np import\",\n \"object :param input_filename: Full path to ACESeq \\\"most_important\\\" file :type input_filename: str :return:\",\n \"start: Chromosomal start position of region to be plotted. :type start: int :param\",\n \"region to be plotted. :type end: int :param color: Color of the rectangles\",\n \"ax = ax if ax is not None else plt.gca() # Get gene\",\n \"CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file = open(input_filename, \\\"r\\\") cnv_bed_list = []\",\n \"left_border = gene_regions[i][0] right_border = None if(i < len(gene_names)-1): right_border = gene_regions[i+1][0] else:\",\n \":type region: list :param groups: List of lists containing the IDs of the\",\n \"plotting paired gene expression (e.g. tumor and normal) on a gene region scale\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4,\",\n \"gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens] gene_regions += [[int(e[1]),\",\n \"if ax is not None else plt.gca() # Get gene names and regions\",\n \"current_end = int(interval[2]) ploidy_dev = float(interval[3]) tcn = float(interval[4]) if(tcn < -1.*max_dev): tcn\",\n \"\\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax\",\n \"Width of the arrow head as proportion of the arrow, defaults to 0.2\",\n \":class:`pandas.Dataframe` containing the expression values of g1 samples (columns: sample ids; index: gene\",\n \"= color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0): #\",\n \"genes_bed: :class:`pybedtools.BedTool` object containing genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size:\",\n \"float(signal[3]) if(value > max_signal): max_signal = value if(not offset is None): end =\",\n \"not None else plt.gca() max_signal = 0 left = [] height = []\",\n \"matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2.,\",\n \"scale = None if(first_tick > 1000000): scale = \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round =\",\n \":param ax: Axis of plot, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return:\",\n \"codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\") plt.xlim([start, end]) plt.ylim([0, 1])\",\n \"plotted as bar :type chip_signals: iterator :param r_chrom: Chromosome of region to be\",\n \"the same as the number of groups, defaults to None. :type colors: str,\",\n \"ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function for plotting gene structures, i.e. introns\",\n \"from ploidy to be considered as a CNV, defaults to 0.7. :type cnv_threshold:\",\n \"representing the region to be plotted, defaults to 1. :type alpha: float, optional.\",\n \"optional :param distance_ratio: Minimal distance between two genes, as ratio of ax width,\",\n \"(R,G,B). :type segments_Tabix_filename: str :param chrom: Chromosome of the region to be plotted.\",\n \"split_line[2], str(ploidy_dev), split_line[5], \\\"+\\\"] ] input_file.close() return pybedtools.BedTool(\\\"\\\\n\\\".join([\\\"\\\\t\\\".join(e) for e in cnv_bed_list]), from_string=True)\",\n \":type position_gene_names: str, optional :param log_transformed: If True use log transformed values for\",\n \"introns_bed: :class:`pybedtools.BedTool` object containing introns :type introns_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing\",\n \"maps as pyramid plots :param contact_map: Matrix that contains the intensity values of\",\n \"genes. 2. y_pos_dict: Dictionary with keys = gene ids and values = y\",\n \"= max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch) if(strand\",\n \"to \\\"top_down\\\". :type direction: str, optional. :param ax: Axis on which to plot,\",\n \"containing CNVs with following entries: 1. Chromosome, 2. Start Position, 3. End Position,\",\n \"= 0 for e in links_bed: link_pos1 = int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance\",\n \"the patches \\\\ drawn on the ax. :rtype: list \\\"\\\"\\\" ax = ax\",\n \"rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect)\",\n \"start: int :param end: End position of the genomic region. :type end: int\",\n \"\\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list = [] tick_positions\",\n \"bin # Define Colormap cmap = cm.bwr norm = matplotlib.colors.Normalize(vmin=0., vmax=1.) m =\",\n \"y_max is the max number of stacked genes, default is None. :type y_max:\",\n \"None ''' ax = ax if ax is not None else plt.gca() binned_meth_calls\",\n \"float, optional :param color_plus: Color of plus stranded TF regions, defaults to \\\"#80b1d3\\\".\",\n \"right_border-left_border if(current_extension == 0.): continue if(extension is None): extension = float(current_extension) elif(current_extension <\",\n \"defaults to \\\"k\\\". :type color: str, optional :param direction: Direction of distance equalization\",\n \":class:`pybedtools.BedTool` :param chromosome: Chromosome for which to plot CNVs. :type chromosome: str :param\",\n \"= lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r])\",\n \"exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1]) region_border_down = int(region_bed[0][2]) region_size\",\n \":param end: End position of the region to be plotted. :type end: str\",\n \"\\\"lower left\\\", \\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type\",\n \"stranded TF regions, defaults to \\\"#80b1d3\\\". :type color_plus: str, optional :param color_minus: Color\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None ''' # Use\",\n \"if location == \\\"top\\\" else range(contact_map_index1, contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define\",\n \"import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import\",\n \"in genes_in_region_bed: gene_name_ens = str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else:\",\n \"else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return: Axis on which\",\n \"values patch :rtype: dict ''' ax = ax if ax is not None\",\n \"Color of the genomic scales elements, defaults to \\\"k\\\". :type color: str, optional\",\n \"bool, optional :param legend_loc: Location of the legend. Either of \\\"lower left\\\", \\\"lower\",\n \"region_size: Size of region to be plotted in base pairs. :type region_size: int\",\n \"= [] counter=0 patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0]\",\n \":param gene_mid_points: list of integer values containing center positions of genes. :type gene_mid_points:\",\n \"Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list =\",\n \"ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax =\",\n \"for which contacts were called. :type segment_size: int :param cmap: Name of the\",\n \"\\\"R\\\"): color = (1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type]\",\n \"plt.xlim([start, end]) plt.ylim([0, 1]) plt.xticks([], []) plt.yticks([], []) def plotMethylationProfile(meth_calls, chrom, start, end,\",\n \"one region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool` :param\",\n \"'''Function for plotting genomix segments in different colors :param segments_tabix_filename: Path to tabixed\",\n \"plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional :param edge_color: Color of region\",\n \"colors[g] else: color = standard_colors[g] bplot[\\\"boxes\\\"][0].set_facecolor(color) if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3)))\",\n \"which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is\",\n \"element is a list-ike object containing: 1. Chromosome 2. Start postion 3. End\",\n \"ax is not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction\",\n \"color_plus: str, optional :param color_minus: Color of plus stranded TF regions, defaults to\",\n \"for plus stranded genes, default is \\\"#80b1d3\\\". :type color_plus: str, optional. :param color_minus:\",\n \"# Determine bin position = int(element[1]) if(position < start or position > end):\",\n \"for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\") ensembl_gene_id = split_line[0].split(\\\".\\\")[0] hugo_gene_symbol = split_line[1].split(\\\".\\\")[0]\",\n \"end_r: Chromosomal end positiont of the region to be plotted. :type end_r: int\",\n \"merge: Number of elements to be merged. If this value is not equal\",\n \"= ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev\",\n \"if(abs(ploidy_dev) < cnv_threshold): color=colors(.5) rect = Rectangle((current_start, .5), current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt',\",\n \"(j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2., midpoint[1])\",\n \"-.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], []) if(loc_coordinates ==\",\n \"plotting, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned.\",\n \"position 3. End position :type regions: iterator :param start: Start position of the\",\n \"midpoint[1]) ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(vertices,\",\n \"(0 overhang means triangular shape). Can be negative or greater than one. Defaults\",\n \"index: gene ids) :type expression_df_g2: :class:`pandas.DataFrame` :param gene_names_map: Dictionary with keys: ENSEMBL GENE\",\n \":type chrom: str :param start: Start position of the region to be plotted.\",\n \"End position of the genomic region. :type end: int :param color: Color of\",\n \"= max_y_pos-y_pos_dict[gene_name]+0.5 rect = Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list\",\n \"Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix\",\n \"optional :param color_plus: Color code for plus stranded genes, default is \\\"#80b1d3\\\". :type\",\n \"'''Function that plots genomic coordinates in a linea fashion. :param chrom: Chromosome of\",\n \"str, optional :param ids: IDs used for legend plotting, defaults to None. Number\",\n \"verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i],\",\n \"(e.g. tumor and normal) on a gene region scale equalizing the position of\",\n \"code for minus stranded genes, default is \\\"#fb8072\\\". :type color_minus: str, optional. :return:\",\n \"2. Start region1 3. End region1 4. Chromosome region2 5. Start region2 6.\",\n \"int(segment[2]) color = tuple([ float(i)/256. for i in str(segment[-1]).split(\\\",\\\") ]+[1]) segment_type = str(segment[3])\",\n \"int(end)]) if(ploidy == 2): plt.ylim([0, 4.5]) plt.yticks([0, 1, 2, 3, 4], [\\\"0\\\", \\\"1\\\",\",\n \"def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that plots\",\n \"file, for which the first column is a ensemble gene id, and the\",\n \"+= [left_border + extension/2.] gene_names_clean += [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ ==\",\n \"mapping between gene ids :param gene_name_mapping_file: Path to a tab separated file, for\",\n \"# Calculate midpoints of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size\",\n \"+ 3*(extension/4.) tick_positions += [left_border + extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name,\",\n \":type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1 samples\",\n \"optional :param max_dev: Maximal deviation from ploidy to plot, defaults to None. :type\",\n \"links_bed: iterator :param chrom_r: Chromosome of the region to be plotted. :type chrom_r:\",\n \"str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end], [0,\",\n \"(i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]-segment_size/2.), (midpoint[0]+segment_size/2., midpoint[1]), (midpoint[0], midpoint[1]+segment_size/2.), (midpoint[0]-segment_size/2.,\",\n \"cmap: Colormap used for plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional\",\n \"= len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0): equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for\",\n \"region to be plotted. :type start: str :param end: End position of the\",\n \"str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where 1. max_y_pos+1.5 is the\",\n \"Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha) c += 1 ax.add_patch(rect) plt.xticks([], [])\",\n \":type chrom_r: str :param start_r: Start position of the region to be plotted.\",\n \":param start_r: Chromosomal start position of the region to be plotted. :type start_r:\",\n \"linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.xlim([int(start), int(end)]) if(ploidy ==\",\n \"m in meth_calls], color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2.\",\n \"from unequal distances of genomic segments to equal distances. :param genomic_segments: List of\",\n \"= -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True)\",\n \"optional :param plot_legend: If True plot legend, False otherwise, defaults to False. :type\",\n \"Color of the bar representing the translocation, defaults to \\\"k\\\". :type color: str,\",\n \":type head_width: float, optional :param head_length: Length of the arrow in bp (depends\",\n \"optional :param ax: Axis used for plotting. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing\",\n \"in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1), contact_map_index2) if location == \\\"top\\\" else range(contact_map_index1,\",\n \"expression values of g1 samples (columns: sample ids; index: gene ids) :type expression_df_g1:\",\n \"= None divisor = None if(scale == \\\"Mb\\\"): digits_to_round = int(6-np.log10(tick_size)) divisor =\",\n \"Maximal deviation from ploidy to plot, defaults to None. :type max_dev: float, optional\",\n \"region to be plotted. :type end: str :param ax: Axis used for plotting,\",\n \"for i in sort_indices] y_pos_dict = {} y_level_dict = {} max_y_pos = 0\",\n \"for plotting ax = ax if ax is not None else plt.gca() colors\",\n \"patch_list += [patch] patch_description_list += [\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\"\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None\",\n \"optional :param plot_legend: If True, a legend describing plus or minus stranded genes\",\n \"plt.yticks([0, 2, 4, 6], [\\\"0\\\", \\\"2\\\", \\\"4\\\", \\\"6\\\"], size=6) plt.xticks(rotation=45) def plotCNVsHeat(cnvs_bed, chromosome,\",\n \"scale retaining the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TXstart, and\",\n \":type log_transformed: bool, optional :param plot_points: If True, a point per expression value\",\n \"+= [binned_average_meth[i]] else: meth_before = (binned_average_meth[i-1] if not i == 0 else \\\"NA\\\")\",\n \"in blacklist): continue # Define color for gene plotting strand = str(i[5]) color\",\n \"list(exp_values_g1) else: exp_values_g1 = list(exp_values_g1.iloc[0, :]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"):\",\n \"for gene_name in gene_names: left_border = gene_mid_points[counter]-extension/2 right_border = gene_mid_points[counter]+extension/2 if(not blacklist is\",\n \"color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom, start, end, bin_size=1000, ax = None): '''Function\",\n \"plt.ylim([.5, 1.5]) plt.xticks([], []) plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from\",\n \"= region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map\",\n \"\\\"linewidth\\\": .3} flierprops = {\\\"color\\\": \\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True legend is plotted,\",\n \"-1.*max_dev elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold):\",\n \"plotted. :type start: str :param end: End position of the region to be\",\n \"for gene plotting strand = str(i[5]) color = color_forward if(strand == \\\"-\\\"): color\",\n \"as simple rectangles. :param regions: Iterator containig list-like elements with the following entries:\",\n \"region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower right\\\", color_plus=\\\"#80b1d3\\\", color_minus=\\\"#fb8072\\\"): \\\"\\\"\\\"Function\",\n \":type direction: str, optional. :param ax: Axis on which to plot, defaults to\",\n \"in range(len(binned_average_meth)): rect = Rectangle((start+cbin*bin_size, 0), bin_size, 1, color=m.to_rgba(binned_average_meth[cbin])) ax.add_patch(rect) plt.xlim([start, end]) plt.ylim([0,\",\n \"bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter\",\n \"== \\\"24\\\"): chrom = \\\"Y\\\" cnv_bed_list += [ [chrom, split_line[1], split_line[2], str(ploidy_dev), split_line[5],\",\n \"bool. :return: Axis on which plot was placed. :rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax =\",\n \"region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i]\",\n \"points downwards, defaults to top, :type location: str, optional :param ax: Axis on\",\n \"print(tick_labels) for i in range(len(ticks)): if(loc_coordinates == \\\"up\\\"): plt.plot([ticks[i], ticks[i]], [0., .3], linestyle=\\\"-\\\",\",\n \"plt.gca() genes_in_region = genes_bed exons_in_region = exons_bed introns_in_region = introns_bed region_border_up = int(region_bed[0][1])\",\n \"plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) def plotLinksAsArcs(links_bed, chrom_r, start_r, end_r, lw=1, color=\\\"k\\\",\",\n \"color = color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start\",\n \"ax if ax is not None else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is\",\n \"patch_description_list, fontsize=5, loc='lower left') return ax def plotGeneExpressionEqualDist(genes_bed, gene_mid_points, region, expression_df, groups, gene_names_map=None,\",\n \":rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' ax = ax if ax is not None else plt.gca()\",\n \"exp_values]) bplot = ax.boxplot(expression_values, positions=[bplot_pos], widths=extension/float(n_groups), patch_artist=True, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False)\",\n \"is True :type plot_gene_ids: bool, optional :param y_max: Max y value in the\",\n \"left') return ax def plotGenomicSegments(segments_list, chrom, start, end, ax = None): '''Function for\",\n \"from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib.cm as cm import\",\n \"gene_names: left_border = gene_regions[counter][0] right_border = region_right_border if(not blacklist is None and gene_name\",\n \"be plotted. :type start: str :param end: End position of the region to\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list = [] tick_positions = [] gene_names_clean\",\n \"ax = ax if ax is not None else plt.gca() c = 0\",\n \"ticker from matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path\",\n \"for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries:\",\n \"max_y_pos = None if(not len(genes_in_region) == 0): # Determine y positions of genes\",\n \"= start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick while(current_tick <= end): ticks +=\",\n \":param ax: Axis to be used for plotting, defaults to None. :type ax:\",\n \"continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"):\",\n \"False. :type plot_legend: bool :param color_g1: Color used for plotting g1 samples expression,\",\n \"gene_map def plotGeneExpression(genes_bed, region_bed, expression_df_g1, expression_df_g2, gene_names_map, blacklist=None, ax=None, plot_legend=False, color_g1=\\\"#fb8072\\\", color_g2=\\\"#80b1d3\\\", g1_id=\\\"tumor\\\",\",\n \"float, optional :param cmap: Colormap used for plotting CNVs, defaults to \\\"bwr\\\". :type\",\n \"stacked genes. 2. patch_list is the list of patches drawn on the ax.\",\n \"current_end-current_start, 1, color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([int(start), int(end)]) plt.ylim([.5, 1.5]) plt.xticks([], [])\",\n \"ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False) plt.xticks([], []) plt.yticks([], []) plt.xlim([start_r, end_r]) plt.ylim([0, max_dist/2])\",\n \"plt.xlim([start, end]) plt.ylim([0, 1]) return equalized_region_mid_points def plotCoordinates(chrom, start, end, color=\\\"k\\\", ax =\",\n \"of intervals, defaults to None. :type offset: int, optional :param merge: Number of\",\n \"= len(groups) for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions +=\",\n \"cmap: str, optional :param max_dev: Maximal deviation from ploidy to plot, defaults to\",\n \"= link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices\",\n \"y = max_y_pos-y_pos_dict[gene_name]+0.5 patch = Rectangle((start, y-.03), end-start, .06, color=color, capstyle='butt', linewidth=0) ax.add_patch(patch)\",\n \":type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype: None ''' ax\",\n \"[lefts[0]] lefts = [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset\",\n \"TX_end = TX_pos rect = Rectangle((TX_start, .4), TX_end-TX_start, .2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect)\",\n \"lefts = [] height_merged += [np.mean(heights)] heights = [] heights += [height[i]] lefts\",\n \"color_forward = color_plus color_reverse = color_minus max_y_pos = None if(not len(genes_in_region) == 0):\",\n \"matplotlib.patches import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches\",\n \"j] intensity_value = (intensity_value/vmax if intensity_value <= vmax else 1.) facecolor = colormap(intensity_value)\",\n \"[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point,\",\n \"plt.gca() max_signal = 0 left = [] height = [] for signal in\",\n \"ploidy=2, cnv_threshold=0.7, cmap=\\\"bwr\\\", max_dev=None, ax=None): '''Function for plotting CNV segments as heatmap :param\",\n \"a mapping between gene ids :param gene_name_mapping_file: Path to a tab separated file,\",\n \"genes_in_region: start = int(i[1]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name]\",\n \"will be shown, else the GENE SYMBOLs are hidden. :type plot_gene_names: bool. :return:\",\n \"True plot legend, False otherwise, defaults to False. :type plot_legend: bool, optional :param\",\n \"= \\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False) ax.spines[\\\"right\\\"].set_visible(False)\",\n \"equalized_region_size=(end-start)/n_segments equalized_region_mid_points = [] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)-\",\n \"def plotHiCContactMap(contact_map, start, end, segment_size, cmap=\\\"Greys\\\", vmin=None, vmax=None, location=\\\"top\\\", ax=None): '''Function that plots\",\n \"color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3,\",\n \"ax=None): '''Function that plots methylation values as dot plots. :param meth_calls: Iterator containing\",\n \"plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i in\",\n \"\\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for\",\n \"[value] left_merged = [] height_merged = [] if(not merge is None): heights =\",\n \"= [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list = [g1_id, g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions)))\",\n \"the segments for which contacts were called. :type segment_size: int :param cmap: Name\",\n \"= str(e[3]) gene_names += [gene_names_map[gene_name_ens]] gene_regions += [[int(e[1]), int(e[2])]] region_right_border = int(region_bed[0][2]) region_left_border\",\n \"end: End position of region to be plotted. :type end: int :param color:\",\n \"expression (e.g. tumor and normal) on a gene region scale retaining the position\",\n \"segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i in\",\n \"float, optional :param ax: Axis used for plotting, defaults to None. :type ax:\",\n \"str, optional :param direction: Direction of distance equalization (top_down | bottom_up), defaults to\",\n \"TF sited to be plotted. :type motifs_bed: :class:`pybedtools.BedTool` :param start: Start position of\",\n \"plotted. :type start: int :param end: End position of region to be plotted.\",\n \"ploidy=2, cnv_threshold=0.7, color_gain=\\\"g\\\", color_loss=\\\"r\\\", color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param cnvs_bed:\",\n \"genes to be plotted. :type genes_bed: :class:`pybedtools.BedTool` :param region_size: Size of region to\",\n \"int :param end: End position on chromosome. :type end: int :param ploidy: Assumed\",\n \"Chromosomal end positiont of the region to be plotted. :type end_r: int :param\",\n \"in gene_names_clean]))) for tick in ax.get_xticklabels(): tick.set_rotation(45) tick.set_size(5) for ytick in ax.get_yticklabels(): ytick.set_size(5)\",\n \"None else plt.gca() # Get gene names and regions genes_in_region_bed = genes_bed.intersect(region_bed, wa=True,\",\n \"linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end])\",\n \"plotting, defaults to None. Number of ids must be the same as the\",\n \"rectangle, representing the region to be plotted, defaults to 1. :type alpha: float,\",\n \"= [] height_merged += [np.mean(heights)] heights = [] offset = merge*offset left =\",\n \"tick_labels = [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \\\"up\\\"):\",\n \"m in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for\",\n \"Determine first tick position first_tick = start+(tick_size-start%tick_size) ticks = [] current_tick = first_tick\",\n \"color: Color of points representing methylation values, defaults to \\\"k\\\". :type color: str,\",\n \"the rectangles representing the regions to be plotted, defaults to \\\"#cbebc4\\\". :type color:\",\n \"= max([abs(float(i[3])) for i in cnvs_bed]) for interval in cnvs_bed: current_start = int(interval[1])\",\n \"0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None): '''Function that plots\",\n \":type color: str, optional :param offset: Length of intervals, defaults to None. :type\",\n \"contact_map_index1 = (start)/segment_size contact_map_index2 = ((end)/segment_size)+1 sliced_contact_map = contact_map.iloc[contact_map_index1:contact_map_index2, contact_map_index1:contact_map_index2] if(vmin is None):\",\n \"0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\", color=color, linewidth=1) if(revert_coordinates):\",\n \"genes. 2. patch_list is the list of patches drawn on the ax. 3.\",\n \"optional :param color_g2: Color used for plotting g2 samples expression, defaults to \\\"#80b1d3\\\".\",\n \"region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points += [int(e[1])+(int(e[2])-int(e[1]))/2] for i in range(len(region_mid_points)): region_mid_point =\",\n \"end = int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in\",\n \"1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\" \\\" for i\",\n \"plot_legend: If True, a legend describing plus or minus stranded genes is plotted,\",\n \"in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy) if(line[0] == \\\"#\\\" or\",\n \"ax if ax is not None else plt.gca() for motif in motifs_bed: motif_start\",\n \"= [] for i in range(len(left)): if(i % merge == 0 and not\",\n \"of region edge. If False, no edge is plotted, defaults to False. :type\",\n \"plotted. :type chrom: str :param start: Start position of region to be plotted.\",\n \"''' ax = ax if ax is not None else plt.gca() tick_size =\",\n \"= None for line in input_file: if(line[:7] == \\\"#ploidy\\\"): ploidy = float(line.rstrip().split(\\\":\\\")[1]) print(ploidy)\",\n \"= color) gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label\",\n \"Chromosome region2 5. Start region2 6. End region2 :type links_bed: iterator :param chrom_r:\",\n \"CNV segments as heatmap :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs with following entries:\",\n \"genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. :type exons_bed: :class:`pybedtools.BedTool`\",\n \"the region to be plotted, defaults to 1. :type alpha: float, optional. :param\",\n \"of original and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments\",\n \"None else plt.gca() max_signal = 0 left = [] height = [] for\",\n \"of the region to be plotted. :type chrom: str :param start: Start position\",\n \"to 2. :type ploidy: int, optional :param cnv_threshold: Minimal deviation from ploidy to\",\n \"import Rectangle from matplotlib.patches import Arrow from matplotlib.path import Path from matplotlib.patches import\",\n \"contact map, defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be\",\n \"1000. :type head_length: int, optional :param overhang: Fraction that the arrow is swept\",\n \"= y_max # Plot Exons for i in exons_in_region: start = int(i[1]) end\",\n \"defaults to \\\"bottom\\\". :type position_gene_names: str, optional :param log_transformed: If True use log\",\n \"Color of region edge. If False, no edge is plotted, defaults to False.\",\n \"Deviation from ploidy, 5. True Copy Number) :type cnvs_bed: :class:`pybedtools.BedTool` :param chromosome: Chromosome\",\n \"contact_map_index2-(contact_map_index2-i))) for j in y_range: # Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2.,\",\n \"ax = ax if ax is not None else plt.gca() region_bed = pybedtools.BedTool(\\\"\\\\t\\\".join([str(i)\",\n \"1 continue if(counter < len(gene_names)-1): right_border = gene_regions[counter+1][0] bplot_g1_pos = left_border + extension/4.\",\n \"plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [4, 4], color=color_threshold, linestyle=\\\"--\\\",\",\n \"= 1000000 else: digits_to_round = int(5-np.log10(tick_size)) divisor = 100000 tick_labels = [ str(round(i/float(divisor),\",\n \":type revert_coordinates: bool, optional :param rotation: Rotational angle of coordinate strings, defaults to\",\n \":type g2_id: str, optional :param plot_gene_names: If True, the HUGO GENE SYMBOLs will\",\n \"must be included: Chrom, Start, End, Methylated Cs, Unmethylated Cs. :type methylation_bed: :class:`pybedtools.BedTool`\",\n \"offset left += [start] height += [value] left_merged = [] height_merged = []\",\n \"color = color_minus dx = -1.*head_length plt.arrow(arrow_start, .5, dx, 0, head_width=head_width, head_length=head_length, overhang=overhang,\",\n \"for plotting CNVs, defaults to \\\"bwr\\\". :type cmap: str, optional :param max_dev: Maximal\",\n \"else plt.gca() colors = plt.cm.get_cmap(cmap) if(max_dev is None): max_dev = max([abs(float(i[3])) for i\",\n \"log_transformed=True, plot_points=False, alpha=.5): '''Function for plotting grouped gene expression (e.g. tumor and normal)\",\n \"of points representing methylation values, defaults to \\\"k\\\". :type color: str, optional :param\",\n \"int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"int(region_bed[0][1]) # Determine minimal extension of barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else:\",\n \"np.percentile(contact_map, 99.9) colormap = plt.get_cmap(cmap) for i in range(contact_map_index1, contact_map_index2): y_range = (range(contact_map_index1+(i-contact_map_index1),\",\n \"to be plotted. :type end: int :param bin_size: size of bin to average\",\n \"or position > end): continue n_meth = int(element[3]) n_unmeth = int(element[4]) current_bin =\",\n \"of the genomic region. :type end: int :param color: Color of lines equalizing\",\n \"were called. :type segment_size: int :param cmap: Name of the colormap to be\",\n \"int(i[2]) gene_name = str(i[3]) if(not blacklist is None and gene_map[gene_name] in blacklist): continue\",\n \"gene_name = str(i[3]) gene_name_label = gene_name if(not gene_map is None): gene_name_label = gene_map[gene_name]\",\n \"color=color, edgecolor='none', capstyle='butt', linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy\",\n \"1. Chromosome, 2. Start Position, 3. End Position, 4. Deviation from ploidy, 5.\",\n \"= [ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start,\",\n \"border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label = gene_name if(not\",\n \"axis for plotting ax = ax if ax is not None else plt.gca()\",\n \"= [] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices =\",\n \"position \\\\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx in\",\n \"[]) if(loc_coordinates == \\\"up\\\"): plt.ylim([-.1, .8]) else: plt.ylim([-1.5, .3]) plt.xticks([], []) ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False)\",\n \"= \\\"Mb\\\" else: scale=\\\"Kb\\\" digits_to_round = None divisor = None if(scale == \\\"Mb\\\"):\",\n \"vertices = [(region_mid_point, 1), (region_mid_point, .8), (equalized_region_mid_point, .2), (equalized_region_mid_point, 0)] else: codes =\",\n \"height, offset, color = color, edgecolor = color) plt.xlim(r_start, r_end) def plotMethylationProfileHeat(methylation_bed, chrom,\",\n \"cytosines Or 1. Chromsome 2. Start position 3. end position 4. Beta Value\",\n \".2, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) plt.xlim([start_r, end_r]) plt.ylim([0.3, 0.7]) def plotRegions(regions, start, end,\",\n \"defaults to None. Number of ids must be the same as the number\",\n \"to None :type vmin: float, optional :param vmax: Maximal value of intensity range\",\n \"the region that is plotted), defaults to 1000. :type head_length: int, optional :param\",\n \":param color: Color of the genomic scales elements, defaults to \\\"k\\\". :type color:\",\n \"Defines the number of stacked genes. 2. y_pos_dict: Dictionary with keys = gene\",\n \"the one region, for which the gene plot is created. :type region_bed: :class:`pybedtools.BedTool`\",\n \"None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param upper: If True, make less ticks, else\",\n \"range to be plotted, defaults to None :type vmin: float, optional :param vmax:\",\n \":type end: int :param head_width: Width of the arrow head as proportion of\",\n \"patch_saved = False for gene_name in gene_names: left_border = gene_regions[counter][0] right_border = region_right_border\",\n \"= int(region_bed[0][1]) # Determine minimal extension of barplot extension=None for i in range(len(gene_regions)):\",\n \"region to be plotted. :type start: int :param end: End position of the\",\n \"plotted, defaults to 1. :type alpha: float, optional. :param ax: Axis of plot,\",\n \"barplot extension=None if(len(gene_mid_points) <= 1): extension=region[2]-region[1] else: extension=gene_mid_points[1]-gene_mid_points[0] # Subtract a small percentage\",\n \"= float(split_line[5])-ploidy chrom = split_line[0] if(chrom == \\\"23\\\"): chrom=\\\"X\\\" elif(chrom == \\\"24\\\"): chrom\",\n \"offset is None): end = start + offset left += [start] height +=\",\n \"expression, defaults to \\\"#fb8072\\\". :type color_g1: str, optional :param color_g2: Color used for\",\n \"2], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [3, 3], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)],\",\n \":type color: str, optional :param ax: Axis of plot, defaults to None. :type\",\n \"optional :param plot_legend: If True legend is plotted, False otherwise, defaults to False.\",\n \"== 0 and not (i == 0)): left_merged += [lefts[0]] lefts = []\",\n \"segments_list: segment_start = int(segment[1]) segment_end = int(segment[2]) color = tuple([ float(i)/256. for i\",\n \"= open(gene_name_mapping_filename, \\\"r\\\") gene_map = {} for line in gene_name_mapping_file: split_line = line.rstrip().split(\\\"\\\\t\\\")\",\n \"= matplotlib.patches.PathPatch(path, facecolor=facecolor, edgecolor='none') ax.add_patch(patch) ax.set_xlim(start, end) if(location == \\\"top\\\"): ax.set_ylim(0, (end-start)/2.) else:\",\n \"containing TXstart, and TXend of genes. :type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object\",\n \"= color_forward if(strand == \\\"-\\\"): color = color_reverse border_distance_down = region_border_down-start if(start <\",\n \"str(motif[3]) arrow_start = motif_start arrow_end = motif_end color=color_plus dx = head_length if(strand ==\",\n \"expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids is None): g_id = ids[g]\",\n \"\\\"#\\\" or line[:5] == \\\"chrom\\\"): continue split_line = line.rstrip().split(\\\"\\\\t\\\") ploidy_dev = float(split_line[5])-ploidy chrom\",\n \"i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions, expression_values, \\\"k.\\\", markersize=3) g_id = None if(not ids\",\n \"int(e[1])+(int(e[2])-int(e[1]))/2 link_pos2 = int(e[4])+(int(e[5])-int(e[4]))/2 distance = abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance\",\n \"color_minus: str, optional :param ax: Axis on which to plot contact map, defaults\",\n \"gene_start = int(interval[1]) gene_end = int(interval[2]) for i in range(max_y_pos+1): if(i == 0\",\n \"0.2 :type head_width: float, optional :param head_length: Length of the arrow in bp\",\n \"max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos =\",\n \"be plotted. :type chrom_r: str :param start_r: Chromosomal start position of the region\",\n \"< cnv_threshold): tcn = ploidy color = color_neutral if(ploidy_dev >= cnv_threshold): color=color_gain elif(ploidy_dev\",\n \"\\\"lower right\\\", \\\"upper left\\\", \\\"upper right\\\", default is \\\"lower right\\\". :type legend_loc: str,\",\n \"in meth_calls ], [ float(m[3])/(float(m[3])+float(m[4])) if not(float(m[3])+float(m[4]) == 0.) else 0. for m\",\n \"bin to average methylation values, defaults to 1000. :type bin_size: int, optional :param\",\n \":rtype: :class:`matplotlib.axes._subplots.AxesSubplot` ''' standard_colors = [\\\"#66c2a5\\\", \\\"#fc8d62\\\", \\\"#8da0cb\\\", \\\"#ec87c2\\\", \\\"#a6d854\\\", \\\"#ffd92f\\\", \\\"#e5c494\\\", \\\"#bbbbbb\\\"]\",\n \"= None): '''Function that plots links between genomic regions as arcs. :param links_bed:\",\n \"of region to be plotted. :type start: int :param end: Chromosomal end position\",\n \"of groups, defaults to None. :type colors: str, optional :param ids: IDs used\",\n \"= equalized_region_mid_points[i] codes = [] vertices = [] if(direction == \\\"top_down\\\"): codes =\",\n \"= float(interval[4]) # Smooth tcn, if ploidy_dev is smaller than cnv_threshold if(abs(ploidy_dev) <\",\n \"str :return: :class:`pybedtools.BedTool` object containing CNVs from ACESeq :rtype: :class:`pybedtools.BedTool` ''' input_file =\",\n \"tumor and normal) on a gene region scale retaining the position of genes.\",\n \"plt.gca() patches_dict = {} for segment in segments_list: segment_start = int(segment[1]) segment_end =\",\n \"to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned. :rtype: None\",\n \"else: current_color = color rect = Rectangle([int(region[1]), -.75], int(region[2])-int(region[1]), 1.5, facecolor=current_color, edgecolor=edgecolor, alpha=alpha)\",\n \":param meth_calls: Iterator containing list-like elements with the following entries: 1. Chromsome 2.\",\n \"binned_meth_calls ] binned_average_meth_no_missing = [] n = len(binned_average_meth) for i in range(n): if(not\",\n \"distance), (link_pos2, 0)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] path = Path(vertices, codes) patch\",\n \"linewidth=0) ax.add_patch(rect) # Plot thresholds color_threshold=(189./255., 189./255., 189./255., 0.5) if(ploidy == 2): plt.plot([int(start),\",\n \"str, optional. :param ax: Axis where the plot is drawn, defaults to None.\",\n \"plotted in addition to the boxplot, no points are plotted otherwise, defaults to\",\n \"regions: iterator :param start: Start position of the region to be plotted. :type\",\n \"[ str(round(i/float(divisor), digits_to_round))+scale for i in ticks ] if(loc_coordinates == \\\"up\\\"): plt.plot([start, end],\",\n \"\\\"k\\\", \\\"linewidth\\\": .3} capprops={\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} patch_list = [] patch_description_list = []\",\n \"arrow is swept back (0 overhang means triangular shape). Can be negative or\",\n \"alpha=.5) elif(n_entries == 4): plt.plot([ (float(m[1])+float(m[2]))/2. for m in meth_calls ], [ float(m[4])\",\n \"ids will be included in the plot, False otherwise, default is True :type\",\n \"is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5, patch_list, patch_description_list, where\",\n \"matplotlib.patches import PathPatch import matplotlib.cm as cm import matplotlib import tabix import math\",\n \"\\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\"], size=6) elif(ploidy == 4): plt.ylim([0, 6.5]) plt.yticks([0, 2, 4,\",\n \"meth_calls ], [ float(m[4]) for m in m in meth_calls], color=color, marker=\\\".\\\", linestyle='None',\",\n \"= [ [0, 0] for i in range(int(((end-start)/bin_size)+1)) ] counter = 0 for\",\n \"ax if ax is not None else plt.gca() patches_dict = {} for segment\",\n \"= region_border_up border_distance_down = region_border_down-start if(not(float(border_distance_down)/float(region_size) < distance_ratio)): gene_name = str(i[3]) gene_name_label =\",\n \"def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None, distance_ratio=0.1, ax=None, plot_legend=False, legend_loc=\\\"lower\",\n \"= list(exp_values_g2) else: exp_values_g2 = list(exp_values_g2.iloc[0, :]) bplot_g1 = ax.boxplot([np.log2([i if i >=\",\n \"the regions to be plotted, defaults to \\\"#cbebc4\\\". :type color: str, optional :param\",\n \":type start: int :param end: Chromosomal end position of region to be plotted.\",\n \"If True use log transformed values for plotting, non-transformed values otherwise. :type log_transformed:\",\n \"(region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)] path = Path(vertices, codes) path_patch = PathPatch(path,\",\n \"defaults to None. :type vmax: float, optional :param location: Either of \\\"top\\\" |\",\n \"+= [\\\"forward strand\\\"] met_forward = True elif(strand == \\\"-\\\" and not(met_reverse)): patch_list +=\",\n \"if(plot_points): x_positions = [ (bplot_pos+ (i-.5)* ((2*extension)/(float(n_groups)*3))) for i in list(np.random.rand(len(expression_values))) ] plt.plot(x_positions,\",\n \"the bar representing the translocation, defaults to \\\"k\\\". :type color: str, optional :param\",\n \"ax if ax is not None else plt.gca() # Calculate midpoints of original\",\n \"\\\"k\\\"} medianprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3} whiskerprops = {\\\"color\\\": \\\"k\\\", \\\"linewidth\\\": .3}\",\n \"genomic segments to equal distances. :param genomic_segments: List of segments for which distances\",\n \"[] lefts = [] for i in range(len(left)): if(i % merge == 0\",\n \"their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to\",\n \"of barplot extension=None for i in range(len(gene_regions)): if(not blacklist is None and gene_names[i]\",\n \"linewidth=1) plt.text(ticks[i], -.1, tick_labels[i], horizontalalignment=\\\"center\\\", fontsize=5, color=color, verticalalignment=\\\"top\\\", rotation=rotation) plt.xlim([start, end]) plt.yticks([], [])\",\n \"of the colormap to be used for plotting HiC intensities, defaults to \\\"Greys\\\".\",\n \"for plotting, non-transformed values otherwise. :type log_transformed: bool, optional :param plot_points: If True,\",\n \"[height[i]] lefts += [left[i]] if(not i % merge == 0): left_merged += [lefts[0]]\",\n \"region_mid_point = region_mid_points[i] equalized_region_mid_point = equalized_region_mid_points[i] codes = [] vertices = [] if(direction\",\n \"that creates a mapping between gene ids :param gene_name_mapping_file: Path to a tab\",\n \"the max_y_position + 1.5. max_y_pos defines the \\\\ number of stacked genes. 2.\",\n \"(chrom, start, end, name, score, strand, start, end, color). The color field is\",\n \":param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted :type region_bed: :class:`pybedtools.BedTool`\",\n \"int :param end: End position of region to be plotted. :type end: int\",\n \"values as dot plots. :param meth_calls: Iterator containing list-like elements with the following\",\n \"ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :param plot_legend: If True plot legend, False otherwise, defaults to\",\n \"color_neutral=\\\"k\\\", ax=None): '''Function for plotting CNV segments :param cnvs_bed: :class:`pybedtools.BedTool` object containing CNVs\",\n \"stranded genes, default is \\\"#fb8072\\\". :type color_minus: str, optional. :return: Tuple of max_y_pos+1.5,\",\n \"regions, defaults to \\\"k\\\". :type color_neutral: str, optional :param ax: Axis used for\",\n \"0, head_width=head_width, head_length=head_length, overhang=overhang, head_starts_at_zero=False, edgecolor=\\\"none\\\", facecolor=color, length_includes_head=True) plt.xlim([start, end]) plt.ylim([0.4, 0.6]) def\",\n \"[<chrom>, <start>, <end>]) :type genomic_segments: list :param start: Start position of the genomic\",\n \"be considered as a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param\",\n \"part of the genome that is translocated. :param chrom_r: Chromosome of the region\",\n \"[] for e in genomic_segments: if(int(e[1]) < start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) >\",\n \"ax=None, color=\\\"b\\\", offset=None, merge=None): '''Function that plots bedGraph like iterators. :param chip_signals: Iterator\",\n \"(columns: sample ids; index: gene ids). :type expression_df: class:`pandas.DataFrame` :param blacklist: Set containing\",\n \"not None else plt.gca() TX_start = TX_pos TX_end = end_r if(direction == \\\"left\\\"):\",\n \"max_signal = 0 left = [] height = [] for signal in chip_signals:\",\n \"unmethylated cytosines Or 1. Chromsome 2. Start position 3. end position 4. Beta\",\n \"average methylation values per bin # Define Colormap cmap = cm.bwr norm =\",\n \"blacklist: List of gene names, for genes that should not be shown on\",\n \"whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]] patch_description_list\",\n \"ids; index: gene ids) :type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression\",\n \"gene ids and values = y position \\\\ of gene. :rtype: tuple '''\",\n \"fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i], ticks[i]], [.3, .0], linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], -.1,\",\n \"Chromosomal start position of the region to be plotted. :type start_r: int :param\",\n \"+ offset left += [start] height += [value] left_merged = [] height_merged =\",\n \"Start position of the region to be plotted. :type start: str :param end:\",\n \":type genes_bed: :class:`pybedtools.BedTool` :param region_bed: :class:`pybedtools.BedTool` object containing the region to be plotted\",\n \"# Plot Exons for i in exons_in_region: start = int(i[1]) end = int(i[2])\",\n \"end: Chromosomal end position of region to be plotted. :type end: int :param\",\n \":param merge: Number of elements to be merged. If this value is not\",\n \":type expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples\",\n \"< start): region_mid_points += [start+(int(e[2])-start)/2] elif(int(e[2]) > end): region_mid_points += [int(e[1])+(end-int(e[1]))/2] else: region_mid_points\",\n \"[] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3]) gene_names +=\",\n \"linestyle=\\\"-\\\", color=color, linewidth=1) plt.text(ticks[i], .4, tick_labels[i], horizontalalignment=\\\"center\\\", verticalalignment=\\\"bottom\\\", fontsize=5, color=color, rotation=rotation) else: plt.plot([ticks[i],\",\n \"met_forward = False met_reverse = False # Plot Introns for i in introns_in_region:\",\n \"= Rectangle((start, y-.2), end-start, .4, color=color, capstyle='butt', linewidth=0) ax.add_patch(rect) patch_list = [] patch_description_list\",\n \"and distance equalized segments n_segments = len(genomic_segments) equalized_region_size = (end-start) if(n_segments > 0):\",\n \"Use given axis for plotting ax = ax if ax is not None\",\n \"g2_id] counter += 1 ax.set_xlim(region_left_border, region_right_border) ax.xaxis.set_major_locator(ticker.FixedLocator((tick_positions))) ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter( ticker.FixedFormatter(([ \\\"\",\n \"values, defaults to 1000. :type bin_size: int, optional :param ax: Axis to be\",\n \"background color of the boxplots boxes, defaults to 0.5. :type alpha: float, optional\",\n \"vmax: float, optional :param location: Either of \\\"top\\\" | \\\"bottom\\\". If location ==\",\n \"(end-start)/2.) else: ax.set_ylim(-1.*(end-start)/2., 0) def distanceEqualizer(genomic_segments, start, end, direction=\\\"top_down\\\", color=\\\"k\\\", ax = None):\",\n \"plotting max_y_pos, y_pos_dict = determineYPosGene(genes_in_region, (region_border_down- region_border_up), distance_ratio) if(not y_max is None): max_y_pos\",\n \"+= [gene_name] exp_values = expression_df.loc[gene_name, groups[g]] if(type(exp_values).__name__ == \\\"Series\\\"): exp_values = list(exp_values) else:\",\n \"not to be plotted, default to None. :type blacklist: set, optional :param ax:\",\n \"int(end)], [5, 5], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [6, 6], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5)\",\n \"int :param head_width: Width of the arrow head as proportion of the arrow,\",\n \":]) exp_values_g2 = expression_df_g2.loc[gene_name, :] if(type(exp_values_g2).__name__ == \\\"Series\\\"): exp_values_g2 = list(exp_values_g2) else: exp_values_g2\",\n \"medianprops=medianprops, whiskerprops=whiskerprops, capprops=capprops, showfliers=False) bplot_g1[\\\"boxes\\\"][0].set_facecolor(color_g1) bplot_g2[\\\"boxes\\\"][0].set_facecolor(color_g2) if(not patch_saved): patch_saved=True patch_list = [bplot_g1[\\\"boxes\\\"][0], bplot_g2[\\\"boxes\\\"][0]]\",\n \"= str(e[3]) if(not gene_names_map is None): gene_names += [gene_names_map[gene_name_ens]] else: gene_names += [gene_name_ens]\",\n \"y = max_y_pos-y_pos_dict[gene_name]+.8 plt.text(start, y, gene_name_label, size=5, color = color) gene_name = str(i[3])\",\n \"= PathPatch(path, facecolor = \\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False)\",\n \"y position \\\\ of gene. :rtype: tuple ''' sort_indices = [int(idx) for idx\",\n \"bool, optional :param rotation: Rotational angle of coordinate strings, defaults to 0. :type\",\n \"object containing the expression values of all samples (columns: sample ids; index: gene\",\n \"if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0), (mid_point,\",\n \":param plot_legend: If True, a legend describing plus or minus stranded genes is\",\n \"Alpha value of the rectangle, representing the region to be plotted, defaults to\",\n \"elif(gene_start > y_level_dict[i][-1][1] and float(gene_start-y_level_dict[i][-1][0])/float(region_size) > distance_ratio): y_pos_dict[gene_name] = i y_level_dict[i] += [[gene_start,\",\n \"= distance mid_point = link_pos1 + (link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2\",\n \"the arrow in bp (depends on the region that is plotted), defaults to\",\n \"the position of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing gene regions. :type genes_bed:\",\n \"start_r, end_r, lw=1, color=\\\"k\\\", ax = None): '''Function that plots links between genomic\",\n \"plots TF motifs as arrows, indicating their directionality. :param motifs_bed: :class:`pybedtools.BedTool` object containing\",\n \"# Define midpoint of rectangle midpoint = (i*segment_size+(j*segment_size-i*segment_size)/2., (j*segment_size-i*segment_size)/2.) vertices = [(midpoint[0]-segment_size/2., midpoint[1]),\",\n \"are plotted side by side. If this ratio is underwent, the genes will\",\n \"color: Color of the bar representing the translocation, defaults to \\\"k\\\". :type color:\",\n \"plt.plot([start, end], [0, 0], linestyle=\\\"-\\\", color=color, linewidth=1) else: plt.plot([start, end], [0.3, 0.3], linestyle=\\\"-\\\",\",\n \"[] if(direction == \\\"top_down\\\"): codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point,\",\n \"for g in range(n_groups): bplot_pos = left_border + (2*g+1)*extension/float((n_groups*2.)) tick_positions += [left_border +\",\n \"+ extension/2.] gene_names_clean += [gene_name] exp_values_g1 = expression_df_g1.loc[gene_name, :] if(type(exp_values_g1).__name__ == \\\"Series\\\"): exp_values_g1\",\n \"plt.yticks([], []) def readACESeqAsBed(input_filename): '''Function that reads CNVs from ACESeq (\\\"*most_important*\\\") files and\",\n \"if(not merge is None): heights = [] lefts = [] for i in\",\n \"expression_df_g1: :class:`pandas.DataFrame` :param expression_df_g2: :class:`pandas.Dataframe` containing the expression values of g2 samples (columns:\",\n \"[np.mean(heights)] heights = [] heights += [height[i]] lefts += [left[i]] if(not i %\",\n \"plotted :type region_bed: :class:`pybedtools.BedTool` :param expression_df_g1: :class:`pandas.Dataframe` containing the expression values of g1\",\n \"< len(gene_names)-1): right_border = gene_regions[i+1][0] else: right_border = region_right_border current_extension = right_border-left_border if(current_extension\",\n \"must be the same as the number of groups. :type ids: list, optional.\",\n \"facecolor = \\\"None\\\", edgecolor = color, lw = lw) ax.add_patch(patch) #ax.spines[\\\"bottom\\\"].set_visible(False) ax.spines[\\\"top\\\"].set_visible(False) ax.spines[\\\"left\\\"].set_visible(False)\",\n \"plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r, start_r, end_r, TX_pos, direction=\\\"right\\\", color=\\\"k\\\",\",\n \"otherwise, defaults to False. :type plot_legend: bool, optional :param colors: List of colors\",\n \"the region to be plotted. :type end: int :param color: Color of the\",\n \"motifs_bed: :class:`pybedtools.BedTool` object containing regions of the TF sited to be plotted. :type\",\n \"for plotting samples expression. The number of colors must be the same as\",\n \"rotation: int, optional :return: Nothing to be returned. :rtype: None ''' ax =\",\n \"(link_pos2-link_pos1)/2 if(link_pos2 < link_pos2): mid_point = link_pos2 + (link_pos1-link_pos2)/2 vertices = [(link_pos1, 0),\",\n \"the gene id mapping. :rtype: dictionary ''' gene_name_mapping_file = open(gene_name_mapping_filename, \\\"r\\\") gene_map =\",\n \"ax.xaxis.set_major_formatter(ticker.FixedFormatter((gene_names_clean))) if(not plot_gene_names): ax.xaxis.set_major_formatter(ticker.FixedFormatter( ([ \\\" \\\" for i in gene_names_clean]))) for tick\",\n \"to False. :type edge_color: str, optional :param alpha: Alpha value of the rectangle,\",\n \"position for gene plotting via function plotGenes. :param genes_bed: :class:`pybedtools.BedTool` object containing genes\",\n \"start: int :param end: End position of the region to be plotted. :type\",\n \"in addition to the boxplot, no points are plotted otherwise, defaults to False.\",\n \"if(strand == \\\"-\\\"): arrow_start = motif_end arrow_end = motif_start color = color_minus dx\",\n \"gene_names = [] gene_regions = [] for e in genes_in_region_bed: gene_name_ens = str(e[3])\",\n \"region. :type end: int :param color: Color of lines equalizing distances, defaults to\",\n \"(1,1,1,1) rect = Rectangle((segment_start, 0), segment_end-segment_start, 1, color=color) ax.add_patch(rect) patches_dict[segment_type] = rect plt.xlim(int(start),\",\n \"int :param cmap: Name of the colormap to be used for plotting HiC\",\n \"(float(i[0])+float(i[1])) > 0 else \\\"NA\\\" for i in binned_meth_calls ] binned_average_meth_no_missing = []\",\n \"None if(not ids is None): g_id = ids[g] else: g_id = \\\"group \\\"+str(g)\",\n \"[] for i in range(1, n_segments+1): equalized_region_mid_points += [((start+ i*equalized_region_size)- equalized_region_size/2)] region_mid_points =\",\n \"Path.LINETO, Path.LINETO, Path.LINETO] vertices = [(region_mid_point, 0), (region_mid_point, .2), (equalized_region_mid_point, .8), (equalized_region_mid_point, 1)]\",\n \"= abs(link_pos2-link_pos1) if(distance > max_dist): max_dist = distance mid_point = link_pos1 + (link_pos2-link_pos1)/2\",\n \"None and gene_name in blacklist): counter += 1 continue if(counter < len(gene_names)-1): right_border\",\n \"numpy as np import tabix import matplotlib.ticker as ticker from matplotlib.patches import Rectangle\",\n \"direction: Direction of the genomic part that is translocated. Either of \\\"left\\\" (upstream),\",\n \"int, optional :param overhang: Fraction that the arrow is swept back (0 overhang\",\n \"(top_down | bottom_up), defaults to \\\"top_down\\\". :type direction: str, optional. :param ax: Axis\",\n \"Color of the arc, defaults to \\\"k\\\". :type color: str, optional. :param ax:\",\n \"is translocated. Either of \\\"left\\\" (upstream), or \\\"right\\\" (downstream), defaults to \\\"left\\\". :type\",\n \"ratio of ax width, such that two genes are plotted side by side.\",\n \"1)] path = Path(vertices, codes) path_patch = PathPatch(path, facecolor=\\\"none\\\", edgecolor=color, linewidth=.5) ax.add_patch(path_patch) ax.axis(\\\"off\\\")\",\n \"0.7. :type cnv_threshold: float, optional :param cmap: Colormap used for plotting CNVs, defaults\",\n \"for ytick in ax.get_yticklabels(): ytick.set_size(6) if(plot_legend): ax.legend(patch_list, patch_description_list, fontsize=5, loc='lower left') return ax\",\n \"if(not upper): tick_size = 10**int((np.log10((end-start)/10))) # Determine first tick position first_tick = start+(tick_size-start%tick_size)\",\n \"a CNV, defaults to 0.7. :type cnv_threshold: float, optional :param color_gain: Plot color\",\n \"motif in motifs_bed: motif_start = int(motif[1]) motif_end = int(motif[2]) strand = str(motif[3]) arrow_start\",\n \"elif(tcn > max_dev): tcn = max_dev color = colors((ploidy_dev+max_dev)/(2*max_dev)) if(abs(ploidy_dev) < cnv_threshold): color=colors(.5)\",\n \"color=color, marker=\\\".\\\", linestyle='None', markersize=1, alpha=.5) plt.ylim([0, 1]) plt.xticks([], []) plt.xlim([start, end]) def plotTX(chrom_r,\",\n \"links between genomic regions as arcs. :param links_bed: Iterator, that contains bed-like structured\",\n \"== 2): plt.plot([int(start), int(end)], [1, 1], color=color_threshold, linestyle=\\\"--\\\", linewidth=.5) plt.plot([int(start), int(end)], [2, 2],\"\n]","isConversational":false}}},{"rowIdx":4094,"cells":{"ngram":{"kind":"list like","value":["\"state set.\"), message_plural=(\"Accept states {} are not members of the fsa's \" \"state","current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state","operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•',","an epsilon); there's no other way to match, for instance, {'', '0'} with","{ _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) }","element of B. Note that this `+` operation is not commutative. \"\"\" new_self,","the range of the transition \" \"function are not in the fsa's state","nfa that recognizes A concat B -- i.e., the language consisting of the","= str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol],","the input NFAs having different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state","states/symbols are the source of the problem. \"\"\" def __or__(self, other: \"DFA\") ->","def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function,","processed[-1] not in {'(', '|'} else '' ) if char not in alphabet","-- i.e., the set of all strings formed by concatenating any number of","message_plural=(\"Accept states {} are not members of the fsa's \" \"state set.\") )","set of states inferred from the transition function; * the range of the","new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs =","None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {}","= NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self,","\"some string\", and `False` otherwise. Will raise a ValueError exception is the string","concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other:","language recognised by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e.,","Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State -","transition function' keys implicitly define the nfa's state-set and alphabet; the first elements","and most common punctuation and white space. Actually, that's not quite right --","nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state,","] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State]","This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set]","with large numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) ->","in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states",") combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) ->","arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where","{} in the range of the transition \" \"function is not a set.\"),","( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states","standard ASCII letters and digits, and most common punctuation and white space. Actually,","State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols","set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that is not in \" \"alphabet","= NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states )","( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str","message_singular=(\"State {} in the range of the transition \" \"function is not in","2. the input regex string contains a character not in the alphabet, and","an accepted regex character.\" ) if char in OPERATORS and processed[-1] in {'|',","contain a binary operator followed by an operator, or 4. the input regex","= error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] =","recognises the set of all concatenations of strings in A with strings in","def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised","other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\")","bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a","following are true: 1. the start state is not a member of the","one-character strings) as input; returns an NFA that recognises the language defined by","string in place of an alphabet symbol in the transition function. Note that","+= 1 if char == ')': paren_count -= 1 if paren_count < 0:","the language recognised by dfa1, and B be the language recognized by dfa2.","in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1)","expression and an alphabet (i.e., a set of one-character strings) as input; returns","self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} |","DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1:","self.states, message_singular=(\"State {} in the range of the transition \" \"function is not","large numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA:","= _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet:","} _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of the transition \" \"function","{gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)]","self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return","* the range of the transition function is not a subset of the","accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the","for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return","alphabet inferred from the transition function is not a one-character string; 4. a","extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair]","work as you would expect). The class will raise a ValueError exception on","{}.\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set()","nfa2` returns an nfa that recognizes A union B. The cardinality of the","Regex: \"\"\" Let A be the language accepted by dfa. `dfa.encode()` returns a","\"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {}","new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf:","new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state}","-> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot start","Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1',","self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states -","very against English chauvinism, but your letter is so very close to the","dfa2. There is no problem with the input DFAs having different alphabets. \"\"\"","not a subset of the set of states inferred from the transition function;","a ValueError exception is the string contains symbols that aren't in the DFA's","mac keyboard) means concatenation. You can leave concatentation implicit, as is usual; no","alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that is not in","{ prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept =","Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\" Determines whether","& self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes","<filename>toc/fsa/fsa.py<gh_stars>0 \"\"\" File containing DFA and NFA public classes \"\"\" import collections.abc from","`my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some string\", and `False` otherwise. Will","you'd expect them to mean if you are familiar with regular expressions. '•'","a member of the transition function's range is not a set; 5. the","a set of one-character strings) as input; returns an NFA that recognises the","the syntax of the regular expressions that this method takes as input is","other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in","union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2,","char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char","returns an NFA instance. \"\"\" nd_transition_function = { key: {value} for key, value","required to specify that the successor set for a given state-symbol pair is","regex1 == \"Ø\": return regex2 if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\"","dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union B. The","ValueError exception on instantiation if any of th following are true: * the","\"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF","self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states","= self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] =","= {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts )","empty string in normal python regex syntax either, though it can be done;","NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)]","')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def","in regex: if char in alphabet or char == '(': if len(processed) >","+ regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \"Ø\":","function is missing a case -- i.e., it is not the case that","key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states","in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains binary operator","State = str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State,","things triggered the exception, and which states/symbols are the source of the problem.","ValueError exception is the string contains symbols that aren't in the nfa's alphabet.","def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2","{gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return","NFAs having different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states","new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\")","in the range of the transition \" \"function is not in the fsa's","self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1","function is missing cases -- i.e., it is not the case that every","NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] =","in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA(","None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states","+= '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept","against English chauvinism, but your letter is so very close to the empty-set","internally. '€' (option-shift-2) is used to match the empty string (because it kind","len(processed) > 0: processed += ( '•' if processed[-1] not in {'(', '|'}","== '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 =","for symbols in the alphabet except for '(', '')', '|', '*', '•', '€'","Scandinavian languages for the last one; I am very against English chauvinism, but","- {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state,","alphabet. The class will raise a ValueError exception on instantiation if any of","self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def","the current syntax. (Quick challenge: it's not totally obvious how to match the","DFA operator is parasitic on the NFA operator; it converts the input DFAs","if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container)","the last one; I am very against English chauvinism, but your letter is","empty set is a valid value; in fact, you are required to specify","`False` otherwise. Will raise a ValueError exception is the string contains symbols that","= transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range of the","AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State]","collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter","rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4","(state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set,","transition function; 6. the transition function is missing cases -- i.e., it is","states inferred from the transition function; * a member of the alphabet inferred","generates A. That regex string is liable to be much more complicated than","an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return","fact, you are required to specify that the successor set for a given","of the transition function is not a subset of the power set of","and the tilde, for reasons that I will explain presently. As of now,","cannot be symbols in the alphabet of the NFA. (My apologies to speakers","and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else:","\"\"\" A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State,","= self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states","so very close to the empty-set symbol. If, by some miracle, there is","-- i.e., the language consisting of the set of strings of the form","start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states","power-set of the nfa's state set --- i.e., the values of the transition","collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of the transition \"","language accepted by dfa. `dfa.encode()` returns a regex string that generates A. That","_stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return","nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction:","+ other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) |","cases -- i.e., it is not the case that every pair of a","- `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The","a valid value; in fact, you are required to specify that the successor","product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if","transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states","related reasons, the time complexity of this method is exponential in the number","now, the syntax of the regular expressions that this method takes as input","don't want to. But it gets used internally. '€' (option-shift-2) is used to","parenthesis occurs in regex withour matching \" \"left parenthesis.\" ) processed += char","is not a member of the fsa's \" \"state set.\"), message_plural=(\"Accept states {}","def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while","= _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol","'|': if paren_count == 0: return True return False def regex_star(regex: Regex) ->","bool: \"\"\" Determines whether nfa accepts input string. Will raise a ValueError exception","char == '|': if paren_count == 0: return True return False def regex_star(regex:","return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method that takes a","the empty string in place of an alphabet symbol in the transition function.","isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of the transition","if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in","expresssions. All characters are intepreted as literals for symbols in the alphabet except","nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states","\" \"operator; not cool.\" ) if char == '(': paren_count += 1 if","gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)]","self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state}","NFAs, uses the NFA '+', then converts the result back to a DFA.","self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states =","x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x","new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] =","recognised by nfa1, and B be the language recognized by nfa2. `nfa1 +","alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return","Symbols are one-char strings). The keys of the `transition_function` implicitly define the dfa's","Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet,","str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State,","For reaons related to the above, the characters '(', ')', '|', '*', '•',","DFAs into NFAs, uses the NFA '+', then converts the result back to","But it gets used internally. '€' (option-shift-2) is used to match the empty","{} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for","having different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states |","return True return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES:","+ EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if","parenthesis occurs in regex without matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction","NFA that recognises the language defined by that regular expression and that alphabet.","(state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA(","(option-8 on a mac keyboard) means concatenation. You can leave concatentation implicit, as","'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2","x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in","0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0","state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] =","bad_range = { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) }","and `False` otherwise. Will raise a ValueError exception is the string contains symbols","if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 +","All characters are intepreted as literals for symbols in the alphabet except for","self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self)","algorith. For that reason, I recommend you don't `+` dfas with large numbers","MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state:","if any of the following are true: 1. the start state is not","regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You can leave","_good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states","= _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states:","number of states of the NFA. Don't determinize big NFAs. \"\"\" # powerset","# (minor modification to make the return a set of frozensets). def powerset(iterable:","it a go.) 'Ø' (option-shift-o) represents the empty set; you can match to","of Scandinavian languages for the last one; I am very against English chauvinism,","NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\"","for reasons that I will explain presently. As of now, the syntax of","alphabet symbol in the transition function. Note that the empty string will not","\"\"\" Determines whether nfa accepts input string. Will raise a ValueError exception is","This DFA operator is parasitic on the NFA operator; it converts the input","state is not a member of the set of states inferred from the","apologies to speakers of Scandinavian languages for the last one; I am very","the regex and build the NFA. The method will raise a ValueError exception","which states/symbols are the source of the problem. \"\"\" def __or__(self, other: \"DFA\")","much simpler than the standard python regular expresssions. All characters are intepreted as","The class will raise a ValueError exception on instantiation if any of the","NFA. Don't determinize big NFAs. \"\"\" # powerset code an itertools recipe, from","determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def","not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char ==","dfa1, and B be the language recognized by dfa2. `dfa1 | dfa2` returns","for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if","star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in","{self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent","operations is: `*`, then `•`, then `|`. This method uses a version of","{'', '0'} with the current syntax. (Quick challenge: it's not totally obvious how","transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in","'*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS +","new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet |","problem with the input DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet |","= self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not current_states","nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states}","method uses a version of Dijkstra's shunting yard algorithm to parse the regex","on instantiation if any of the following are true: 1. the start state","while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours,","NFA instance. WARNING: The set of DFA states has the cardinality of the","inferred from the transition function; 6. the transition function is missing cases --","subset of the set of states inferred from the transition function; * a","regex and build the NFA. The method will raise a ValueError exception if","where a is an element of A and b is an element of","are ordered pairs of states from dfa1 and dfa2. There is no problem","\"\"\" File containing DFA and NFA public classes \"\"\" import collections.abc from itertools","-> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet -","paren_count = 0 for char in regex: if char == '(': paren_count +=","= transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states =","r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state,","state set --- i.e., the values of the transition function dictionary should be","`*`, then `•`, then `|`. This method uses a version of Dijkstra's shunting","\"function is not in the fsa's state set.\"), message_plural=(\"States {} in the range","1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(',","set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states )","the set of states inferred from the transition function; * the set of","= list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r","char == '(': paren_count += 1 if char == ')': paren_count -= 1","raise ValueError( \"Right parenthesis occurs in regex withour matching \" \"left parenthesis.\" )","processed += ( '•' if processed[-1] not in {'(', '|'} else '' )","of the problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A","is the string contains symbols that aren't in the nfa's alphabet. \"\"\" _check_input(string=string,","for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA(","a member of the alphabet (and hence the checks below will work as","public classes \"\"\" import collections.abc from itertools import product, chain, combinations from string","characters {}.\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair:","def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function","time complexity of this method is exponential in the number of states of","other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for","of th following are true: * the start state is not a member","(or frozensets). The empty set is a valid value; in fact, you are","state-set and alphabet. The class will raise a ValueError exception on instantiation if","is so very close to the empty-set symbol. If, by some miracle, there","if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char ==","cool.\" ) if char == '(': paren_count += 1 if char == ')':","'€' and 'Ø' cannot be symbols in the alphabet of the NFA. (My","but, sadly, computationally expensive algorith. For that reason, I recommend you don't `+`","the set of accept states is not a subset of the set of","= {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range","== '€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1","That regex string is liable to be much more complicated than necessary; maybe",") new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function)","is not a one-character string; 4. a member of the transition function's range","\" \"alphabet and not an accepted regex character.\" ) if char in OPERATORS","set is a valid value; in fact, you are required to specify that","\" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\"","self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) ->","would expect). The class will raise a ValueError exception on instantiation if any","For related reasons, the time complexity of this method is exponential in the","gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for","Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes three keyword","the fsa's state set.\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol","r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state,","the following are true: 1. the start state is not a member of","start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other = copy(other)","the transition function; * the range of the transition function is not a","for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for","def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while new_state","is liable to be much more complicated than necessary; maybe I'll figure out","Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]]","the second elements are the symbols in the alphabet. The domain of the","recognizes A concat B -- i.e., the language consisting of the set of","it's default value is string.printable -- i.e., the set of \"printable\" characters, which","extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states","Don't determinize big NFAs. \"\"\" # powerset code an itertools recipe, from #","cannot contain characters {}.\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction =","accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the","raise a ValueError exception if any of the following conditions hold: 1. the","A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol],","dfa2. `dfa1 + dfa2` returns a DFA that recognises the set of all","paren_count += 1 elif char == ')': paren_count -= 1 elif char ==","} _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain","state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) ->","not a set.\"), message_plural=(\"Values {} in the range of the transition \" \"function","operator, or 4. the input regex does not have properly matching parentheses. \"\"\"","in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol","cardinality of the power-set of the set of NFA states. For related reasons,","this, I will change the symbol for empty-set.) In the absence of parentheses,","recognizes A union B. The cardinality of the state-set of nfa1 | nfa2","{} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set)","the set of states inferred from the transition function; * the range of","nfa that recognizes A* -- i.e., the set of all strings formed by","= dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state","sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return","set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the range of","Optional, Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message,","'€' for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ):","state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')]","@staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')',","new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return","are not in the fsa's state set.\") ) def _get_successors( self, *, state_set:","match to the empty language with it. For reaons related to the above,","used internally. '€' (option-shift-2) is used to match the empty string (because it","FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self,","the absence of parentheses, the order of operations is: `*`, then `•`, then","\"left parenthesis.\" ) processed += char if paren_count > 0: raise ValueError( \"Left","nfa1, and B be the language recognized by nfa2. `nfa1 | nfa2` returns","{ frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) )","regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex) ==","{self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}:","bar and star mean what you'd expect them to mean if you are","but your letter is so very close to the empty-set symbol. If, by","self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\"","accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) ->","is the string contains symbols that aren't in the DFA's alphabet. \"\"\" _check_input(string=string,","regex string that generates A. That regex string is liable to be much","union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item in (","be the language recognised by dfa1, and B be the language recognized by","the cardinality of the state-set of nfa2 plus 1. There is no problem","3. the input regex contain a binary operator followed by an operator, or","f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1","new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states)","return regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex:","symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol)","char == ')': paren_count -= 1 if paren_count < 0: raise ValueError( \"Right","function; * a member of the alphabet inferred from the transition function is","): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys()","Note that this `+` operation is not commutative. \"\"\" new_self, new_other, concat_tf =","set.\"), message_plural=(\"Values {} in the range of the transition \" \"function are not","reaons related to the above, the characters '(', ')', '|', '*', '•', '€'","method that takes a DFA instance and returns an NFA instance. \"\"\" nd_transition_function","transition \" \"function are not in the fsa's state set.\") ) def accepts(self,","members of A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] =","if first_char in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed = ''","recognizes the same same language as the NFA instance. WARNING: The set of","OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed = '' paren_count = 0","Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø':","exception if any of the following conditions hold: 1. the alphabet contains any","= regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed","else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate()","of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF","ValueError( \"Right parenthesis occurs in regex withour matching \" \"left parenthesis.\" ) processed","new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs","is optional; it's default value is string.printable -- i.e., the set of \"printable\"","regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2","message_plural=(\"States {} in the range of the transition \" \"function are not in","of the alphabet (and hence the checks below will work as you would","= Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes","source of the problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let","conditions hold: 1. the alphabet contains any of the verboten characters -- i.e.,`(`","of Dijkstra's shunting yard algorithm to parse the regex and build the NFA.","the transition \" \"function is not a set.\"), message_plural=(\"Values {} in the range","in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states)","char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else:","your letter is so very close to the empty-set symbol. If, by some","return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for","returns an nfa that recognizes A concat B -- i.e., the language consisting","epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol=''","an nfa that recognizes A concat B -- i.e., the language consisting of","elements are the symbols in the alphabet. The domain of the transition function","the problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be","1. the alphabet contains any of the verboten characters -- i.e.,`(` , `)`,","sadly, computationally expensive algorith. For that reason, I recommend you don't `+` dfas","NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols:","parentheses, the order of operations is: `*`, then `•`, then `|`. This method","state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set,","product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1","for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def","white space. Actually, that's not quite right -- the default value is string.printable","self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]:","set of states inferred from the transition function; 2. the set of accept","'€' (option-shift-2) is used to match the empty string (because it kind of","other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised by dfa1,","expressions. '•' (option-8 on a mac keyboard) means concatenation. You can leave concatentation","member of the set of states inferred from the transition function; 2. the","self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return","alphabet contains any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`,","> 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <=","the nfa's state-set and alphabet; the first elements of the tuples represent the","for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept","if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2:","= { key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function,","set of one-character strings) as input; returns an NFA that recognises the language","set of \"printable\" characters, which includes the standard ASCII letters and digits, and","state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs,","epsilon); there's no other way to match, for instance, {'', '0'} with the","s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for","in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not","= 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range =","transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let A be the","can match to the empty language with it. For reaons related to the","inferred from the transition function; 3. a member of the alphabet inferred from",") def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def","to specify that the successor set for a given state-symbol pair is the","char == '(': if len(processed) > 0: processed += ( '•' if processed[-1]","give it a go.) 'Ø' (option-shift-o) represents the empty set; you can match","elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif","else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start})","'*'} ) ) -> \"NFA\": \"\"\" Takes a regular expression and an alphabet","-> Regex: if regex in EMPTIES: return '€' if len(regex) == 1: return","be a member of the alphabet (and hence the checks below will work","`*`, `•`, `€` and `Ø`, 2. the input regex string contains a character","of the fsa's \" \"state set.\"), message_plural=(\"Accept states {} are not members of","not a member of the fsa's \" \"state set.\"), message_plural=(\"Accept states {} are","alphabet. The domain of the transition function is the power-set of the nfa's","does not have properly matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•':",") def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State,","regex2 if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2","== '(': paren_count += 1 elif char == ')': paren_count -= 1 elif","by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the set","a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def","State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state,","Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes three","{prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap =","any number of members of A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function","In the absence of parentheses, the order of operations is: `*`, then `•`,","state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in","accept states is not a subset of the set of states inferred from","represents the empty set; you can match to the empty language with it.","tilde, for reasons that I will explain presently. As of now, the syntax","MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if","inferred to be a member of the alphabet (and hence the checks below","that recognizes A concat B -- i.e., the language consisting of the set","transition function is not a subset of the power set of states inferred","to speakers of Scandinavian languages for the last one; I am very against","> 0: raise ValueError( \"Left parenthesis occurs in regex without matching right \"","the transition function. Note that the empty string will not be inferred to","of B. Note that this `+` operation is not commutative. \"\"\" new_self, new_other,","ordered pairs of states from dfa1 and dfa2. There is no problem with","State: counter = 1 new_state = 'new_state1' while new_state in state_set: counter +=","return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA(","domain of the transition function. The exception message will specify which of these","not in the fsa's state set.\"), message_plural=(\"States {} in the range of the","range of the transition \" \"function are not in the fsa's state set.\")","error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state","_GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1,","regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states -","the alphabet of the NFA. (My apologies to speakers of Scandinavian languages for","fsa's state set.\") ) def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")`","expressions that this method takes as input is very simple -- much simpler","self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug.","NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This","def accepts(self, string: str) -> bool: \"\"\" Determines whether nfa accepts input string.","union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state,","member of the transition function's range is not a set; 5. the range","the language recognised by nfa1, and B be the language recognized by nfa2.",".base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol","set; 5. the range of the transition function is not a subset of","characters are intepreted as literals for symbols in the alphabet except for '(',","a ValueError exception if any of the following conditions hold: 1. the alphabet","much more complicated than necessary; maybe I'll figure out how to improve on","is not a member of the set of states inferred from the transition","the transition function is not a one-character string; 4. a member of the","current_states & self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that","OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if char in","dfa2 are ordered pairs of states from dfa1 and dfa2. There is no","that recognizes A union B. The cardinality of the state-set of nfa1 |","(i.e., a set of one-character strings) as input; returns an NFA that recognises","the language accepted by dfa. `dfa.encode()` returns a regex string that generates A.","it is. You can define epsilon-moves by using the empty string in place","in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else {'q1'}","state in it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for","def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys():","value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State])","as the NFA instance. WARNING: The set of DFA states has the cardinality","def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function","{} in the range of the transition \" \"function are not sets.\") )","`dfa1 | dfa2` returns a dfa that recognizes A union B. The states","= accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) ->","_get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in","= f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1:","is usual; no need to write '•'' explicitly if you don't want to.","the nfa's state set --- i.e., the values of the transition function dictionary","self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function","from itertools import product, chain, combinations from string import printable from typing import","+ state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state =","a one-character string; 4. a member of the transition function's range is not","if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf,","def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort()","\"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language","self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] =","to the empty language with it. For reaons related to the above, the","The exception message will specify which of these above conditions things triggered the","true: * the start state is not a member of the set of","- state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' )","the set of states inferred from the transition function; * a member of","hold: 1. the alphabet contains any of the verboten characters -- i.e.,`(` ,","you are familiar with regular expressions. '•' (option-8 on a mac keyboard) means","- dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for","symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states,","\"printable\" characters, which includes the standard ASCII letters and digits, and most common","machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator:",") NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA):","simple -- much simpler than the standard python regular expresssions. All characters are","'|', '*'} ) ) -> \"NFA\": \"\"\" Takes a regular expression and an","operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ):","fsa's state set.\"), message_plural=(\"States {} in the range of the transition \" \"function","\"NFA\": \"\"\" Let A be the language recognised by nfa. `nfa.self()` returns an","right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1])","letter is so very close to the empty-set symbol. If, by some miracle,","start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def","the empty set; you can match to the empty language with it. For","PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char)","and dfa2. There is no problem with the input DFAs having different alphabets.","A union B. The states of dfa1 | dfa2 are ordered pairs of","be done; give it a go.) 'Ø' (option-shift-o) represents the empty set; you","powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in","\"function are not in the fsa's state set.\") ) def accepts(self, string: str)","the power-set of the nfa's state set --- i.e., the values of the","Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from","deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State]","and Symbols are one-char strings). The keys of the `transition_function` implicitly define the","following conditions hold: 1. the alphabet contains any of the verboten characters --","string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some","very simple -- much simpler than the standard python regular expresssions. All characters","set of NFA states. For related reasons, the time complexity of this method","other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states,","AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton","set.\"), message_plural=(\"Accept states {} are not members of the fsa's \" \"state set.\")","- `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings). The","state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states)","else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA:","| {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in","implicitly define the dfa's state-set and alphabet. The class will raise a ValueError","This method uses a version of Dijkstra's shunting yard algorithm to parse the","== 1: return regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex)","star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for","symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)]","returns an nfa that recognizes A union B. The cardinality of the state-set","if regex1 == '€': return regex2 if regex2 == '€': return regex1 if","cannot start with '{first_char}'.\") processed = '' paren_count = 0 for char in","reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1,","for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA(","except for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical","dictionary should be sets (or frozensets). The empty set is a valid value;",") machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) )","(where States are strings and Symbols are one-char strings). The keys of the","English chauvinism, but your letter is so very close to the empty-set symbol.","File containing DFA and NFA public classes \"\"\" import collections.abc from itertools import","states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter =","-> bool: \"\"\" Determines whether nfa accepts input string. Will raise a ValueError","DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states","the input regex does not have properly matching parentheses. \"\"\" operator_to_operation = {","strings in B. This DFA operator is parasitic on the NFA operator; it","NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols =","regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if","it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in",") regex = _pre_process(regex, alphabet) for char in regex: if char in EMPTIES:","( '•' if processed[-1] not in {'(', '|'} else '' ) if char","None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand )","string contains symbols that aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state","FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set =","-> Regex: if regex1 == \"Ø\": return regex2 if regex2 == \"Ø\": return","cardinality of the state-set of nfa1 | nfa2 is the cardinality of the","then `•`, then `|`. This method uses a version of Dijkstra's shunting yard","_get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start,","for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours =","transition \" \"function are not in the fsa's state set.\") ) def _get_successors(","totally obvious how to match the empty string in normal python regex syntax","= nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states,","): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(':","nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union B. The","regex2: Regex) -> Regex: if regex1 == \"Ø\": return regex2 if regex2 ==","{star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet:","+ other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol","{'(', ')', '|', '*'} ) ) -> \"NFA\": \"\"\" Takes a regular expression","Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction,","source of the problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let","and not one of the above veboten characters, 3. the input regex contain","are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range -","\"\"\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification","self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states,","epsilon-moves by using the empty string in place of an alphabet symbol in","= '€' for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept}","which of these six conditions things triggered the exception, and which states/symbols are","for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = (","the order of operations is: `*`, then `•`, then `|`. This method uses","return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self)","& alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" )","class DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes three keyword arguments: -","current_state in self.accept_states def encode(self) -> Regex: \"\"\" Let A be the language","EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char","} for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states =","if char == '(': paren_count += 1 if char == ')': paren_count -=","defined by that regular expression and that alphabet. The alphabet parameter is optional;","combinations from string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable,","Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes three keyword","recognizes A union B. The states of dfa1 | dfa2 are ordered pairs","of NFA states. For related reasons, the time complexity of this method is","empty-set.) In the absence of parentheses, the order of operations is: `*`, then","'|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and star mean","\"Ø\": return regex2 if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip =","= { pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)]","the symbols in the alphabet. The domain of the transition function is the","members of the fsa's \" \"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction","-> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states =","a set; 5. the range of the transition function is not a subset","of all strings formed by concatenating any number of members of A. \"\"\"","input; returns an NFA that recognises the language defined by that regular expression","are intepreted as literals for symbols in the alphabet except for '(', '')',","of the tuples represent the nfa's states, and the second elements are the","the alphabet contains any of the verboten characters -- i.e.,`(` , `)`, `|`,","| {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø'","recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set","language recognised by dfa1, B be the language recognised by dfa2. `dfa1 +","DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state","if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states),","operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator)","in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set,","-> \"DFA\": \"\"\"Returns a DFA that recognizes the same same language as the","a version of Dijkstra's shunting yard algorithm to parse the regex and build","binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|',","matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class","_add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours","matching \" \"left parenthesis.\" ) processed += char if paren_count > 0: raise","< 0: raise ValueError( \"Right parenthesis occurs in regex withour matching \" \"left","NFA public classes \"\"\" import collections.abc from itertools import product, chain, combinations from","state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept)","NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) } accept_states =","GNFA equivalent to `self` with one less state in it. \"\"\" def union_main_scope(regex:","Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states)","the alphabet (and hence the checks below will work as you would expect).","A be the language recognised by nfa. `nfa.self()` returns an nfa that recognizes","by that regular expression and that alphabet. The alphabet parameter is optional; it's","= accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self)","other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state + '`'","to the empty-set symbol. If, by some miracle, there is someone who cares","in fact, you are required to specify that the successor set for a","operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop()","union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for","return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None:","in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set()","> 0: processed += ( '•' if processed[-1] not in {'(', '|'} else","for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def","len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE","uses a version of Dijkstra's shunting yard algorithm to parse the regex and","an operator, or 4. the input regex does not have properly matching parentheses.","parse the regex and build the NFA. The method will raise a ValueError","kind of looks like an epsilon); there's no other way to match, for","above, the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be","new_state in state_set: counter += 1 new_state = \"new_state\" + str(counter) return new_state",") def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return","it's not totally obvious how to match the empty string in normal python","( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output","transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in","start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let A be the language","if regex in EMPTIES: return '€' if len(regex) == 1: return regex +","def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes the same same language","extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1,","the language recognised by nfa. `nfa.self()` returns an nfa that recognizes A* --","exponential in the number of states of the NFA. Don't determinize big NFAs.","regular expression and that alphabet. The alphabet parameter is optional; it's default value","member of the alphabet (and hence the checks below will work as you","f\"Regex contains character '{char}' that is not in \" \"alphabet and not an","the language recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes","regex: if char == '(': paren_count += 1 elif char == ')': paren_count","character '{char}' that is not in \" \"alphabet and not an accepted regex","{} is not a member of the fsa's \" \"state set.\"), message_plural=(\"Accept states","tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) } accept_states","'*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or","+ nfa2` returns an nfa that recognizes A concat B -- i.e., the","= ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state","'|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character","is not a set.\"), message_plural=(\"Values {} in the range of the transition \"","to be a member of the alphabet (and hence the checks below will","very close to the empty-set symbol. If, by some miracle, there is someone","determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self)","counter = 1 new_state = 'new_state1' while new_state in state_set: counter += 1","elements of the tuples represent the nfa's states, and the second elements are","= self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit(","uses the NFA '+', then converts the result back to a DFA. That","__init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state","new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states,","of the set of states inferred from the transition function; 2. the set","State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {}","symbol)] return current_state in self.accept_states def encode(self) -> Regex: \"\"\" Let A be","-> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some string\", and","set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other)","extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf","the transition \" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values()","regex1 == '€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1):","= Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *,","get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return","the string contains symbols that aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet)","): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf,","'+', then converts the result back to a DFA. That makes for a","bad_set=bad_range, message_singular=(\"State {} in the range of the transition \" \"function is not","{ new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] =","return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable)","AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The transition function'","other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised by nfa1,","by nfa1, and B be the language recognized by nfa2. `nfa1 | nfa2`","not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) ->","states from dfa1 and dfa2. There is no problem with the input DFAs","if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains","paren_count += 1 if char == ')': paren_count -= 1 if paren_count <","returns an nfa that recognizes A* -- i.e., the set of all strings","== ')': paren_count -= 1 if paren_count < 0: raise ValueError( \"Right parenthesis","transition function is the power-set of the nfa's state set --- i.e., the","symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf","== 0: return True return False def regex_star(regex: Regex) -> Regex: if regex","self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]:","by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union B.","Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the","state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex","if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 ==","= dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states |","new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols:","self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states","= new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction =","simpler than the standard python regular expresssions. All characters are intepreted as literals","-> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for","{error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols:","sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State","- self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range of the transition \"","= '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2","elif char == '|': if paren_count == 0: return True return False def","parenthesis.\" ) processed += char if paren_count > 0: raise ValueError( \"Left parenthesis","<= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1]","regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def","key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states:","A union B. The cardinality of the state-set of nfa1 | nfa2 is","\" \"function is not in the fsa's state set.\"), message_plural=(\"States {} in the","string contains symbols that aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states","is not in \" \"alphabet and not an accepted regex character.\" ) if","that this method takes as input is very simple -- much simpler than","symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function","= '' paren_count = 0 for char in regex: if char in alphabet","of states from dfa1 and dfa2. There is no problem with the input","['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES","body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states","} copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA(","_GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ):","start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state:",") def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language","an nfa that recognizes A* -- i.e., the set of all strings formed","def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message(","function is not a one-character string; 4. a member of the transition function's","recognizes A* -- i.e., the set of all strings formed by concatenating any","'Ø' (option-shift-o) represents the empty set; you can match to the empty language","new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = {","@property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return","union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states))","0: return True return False def regex_star(regex: Regex) -> Regex: if regex in","i.e., the set of \"printable\" characters, which includes the standard ASCII letters and","GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states =","NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states,","new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state,","in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self)","\"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state + '`' def","properly matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message(","epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol:","> 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE =","AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot","a dfa that recognizes A union B. The states of dfa1 | dfa2","copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x)","of the state-set of nfa2 plus 1. There is no problem with the","converts the result back to a DFA. That makes for a relatively simple","set.\"), message_plural=(\"States {} in the range of the transition \" \"function are not","return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø'","not totally obvious how to match the empty string in normal python regex","you are required to specify that the successor set for a given state-symbol","is exponential in the number of states of the NFA. Don't determinize big","on a mac keyboard) means concatenation. You can leave concatentation implicit, as is","'|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept =","set.\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]:","The alphabet parameter is optional; it's default value is string.printable -- i.e., the","OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char","in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self,","union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet:","the transition \" \"function is not in the fsa's state set.\"), message_plural=(\"States {}","symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours","and B be the language recognized by nfa2. `nfa1 | nfa2` returns an","new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self =","-> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for","Let A be the language recognised by nfa1, and B be the language","how to improve on average simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states)","for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states","empty set, if it is. You can define epsilon-moves by using the empty","will change the symbol for empty-set.) In the absence of parentheses, the order","extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet:","start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() ->","for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 =","contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty: Regex) ->","| nfa2 is the cardinality of the state set of nfa1 plus the","transition function is not a one-character string; 4. a member of the transition","empty string (because it kind of looks like an epsilon); there's no other","overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) ->","not in the fsa's state set.\") ) def accepts(self, string: str) -> bool:","by dfa. `dfa.encode()` returns a regex string that generates A. That regex string","gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states","* a member of the alphabet inferred from the transition function is not","= { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) )","paren_count = 0 for char in regex: if char in alphabet or char","= {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in","the source of the problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\"","self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other","return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\",","+= ( '•' if processed[-1] not in {'(', '|'} else '' ) if","keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State]","`nfa1 + nfa2` returns an nfa that recognizes A concat B -- i.e.,","= 0 for char in regex: if char in alphabet or char ==","of all concatenations of strings in A with strings in B. This DFA","the set of states inferred from the transition function; 2. the set of","in the alphabet, and not one of the above veboten characters, 3. the","no problem with the input DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet","def star(self) -> \"NFA\": \"\"\" Let A be the language recognised by nfa.","it. For reaons related to the above, the characters '(', ')', '|', '*',","related to the above, the characters '(', ')', '|', '*', '•', '€' and","- nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return","nfa2` returns an nfa that recognizes A concat B -- i.e., the language","nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the set of","language consisting of the set of strings of the form a concat b,","accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]:","self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set()","r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)),","= self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in","explain presently. As of now, the syntax of the regular expressions that this","symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\" Determines","(option-shift-o) represents the empty set; you can match to the empty language with","not an accepted regex character.\" ) if char in OPERATORS and processed[-1] in","and B be the language recognized by nfa2. `nfa1 + nfa2` returns an","= self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}:","right -- the default value is string.printable *minus* parentheses, the vertical bar, the","Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and","not a member of the set of states inferred from the transition function;","_get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] =","are familiar with regular expressions. '•' (option-8 on a mac keyboard) means concatenation.","other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other =","super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states -","= powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets,","DFA instance and returns an NFA instance. \"\"\" nd_transition_function = { key: {value}","Let A be the language recognised by nfa. `nfa.self()` returns an nfa that","} tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA]","symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states |","nfa's state-set and alphabet; the first elements of the tuples represent the nfa's","in {'(', '|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS):","Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex) == 1:","in the alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'.","f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if","alphabet. The alphabet parameter is optional; it's default value is string.printable -- i.e.,","self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else:","return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy","message_singular=(\"Value {} in the range of the transition \" \"function is not a","r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept =","self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set)","version of Dijkstra's shunting yard algorithm to parse the regex and build the","that reason, I recommend you don't `+` dfas with large numbers of states.","gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start","state set.\") ) def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns","x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states &","the range of the transition function is not a subset of the power","Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø'","arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where","order of operations is: `*`, then `•`, then `|`. This method uses a","new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return","set of states inferred from the transition function; 3. a member of the","symbol in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states ==","NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let","symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\" Determines whether nfa accepts input","following are true: * the start state is not a member of the","checks below will work as you would expect). The class will raise a","\" \"function is not a set.\"), message_plural=(\"Values {} in the range of the","miracle, there is someone who cares about this, I will change the symbol","by an \" \"operator; not cool.\" ) if char == '(': paren_count +=","is used to match the empty string (because it kind of looks like","{gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€'","= gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method that","\"NFA\": \"\"\" Let A be the language recognised by nfa1, and B be","star mean what you'd expect them to mean if you are familiar with","`dfa.encode()` returns a regex string that generates A. That regex string is liable","state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors(",") reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state","symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1,","self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours","the values of the transition function dictionary should be sets (or frozensets). The","self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states -","union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex: if char","in the fsa's state set.\"), message_plural=(\"States {} in the range of the transition","states inferred from the transition function; 6. the transition function is missing cases","-> \"DFA\": \"\"\" Let A be the language recognised by dfa1, B be","= {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1,","alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def","other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2:","about this, I will change the symbol for empty-set.) In the absence of","the state set of nfa1 plus the cardinality of the state-set of nfa2","regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 ==","'•', '€' and 'Ø' cannot be symbols in the alphabet of the NFA.","The keys of the `transition_function` implicitly define the dfa's state-set and alphabet. The","== set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes the same","= [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand","= _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)]","start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states,","if paren_count < 0: raise ValueError( \"Right parenthesis occurs in regex withour matching","= ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine","Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base):","def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str)","the result back to a DFA. That makes for a relatively simple but,","'') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf,","of an alphabet symbol in the transition function. Note that the empty string","language recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A","= ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex,","case that every pair of a state and a symbol is in the","(option-shift-2) is used to match the empty string (because it kind of looks","__or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised by","union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) ->","and white space. Actually, that's not quite right -- the default value is","instance, {'', '0'} with the current syntax. (Quick challenge: it's not totally obvious","to `self` with one less state in it. \"\"\" def union_main_scope(regex: Regex) ->","state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours","ValueError exception on instantiation if any of the following are true: 1. the","1. There is no problem with the input NFAs having different alphabets. \"\"\"","message will specify which of these above conditions things triggered the exception, and","that regular expression and that alphabet. The alphabet parameter is optional; it's default","a given state-symbol pair is the empty set, if it is. You can","using the empty string in place of an alphabet symbol in the transition",") overlap = self.states & other.states while overlap: other = copy(other) overlap =","not current_states & self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA","of the NFA. (My apologies to speakers of Scandinavian languages for the last","in the range of the transition \" \"function are not in the fsa's","alphabet or char == '(': if len(processed) > 0: processed += ( '•'","AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols","of the set of strings of the form a concat b, where a",") } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") ->","a member of the fsa's \" \"state set.\"), message_plural=(\"Accept states {} are not","in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] =","str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some string\",","def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA,","= ( set(printable) - {'(', ')', '|', '*'} ) ) -> \"NFA\": \"\"\"","input NFAs having different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state =","from the transition function is not a one-character string; * the transition function","| dfa2 are ordered pairs of states from dfa1 and dfa2. There is","to match, for instance, {'', '0'} with the current syntax. (Quick challenge: it's","self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex:","( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif","tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] =","-= 1 elif char == '|': if paren_count == 0: return True return","regex withour matching \" \"left parenthesis.\" ) processed += char if paren_count >","not a one-character string; 4. a member of the transition function's range is","and which states/symbols are the source of the problem. \"\"\" def __or__(self, other:","'•' (option-8 on a mac keyboard) means concatenation. You can leave concatentation implicit,","\"Right parenthesis occurs in regex withour matching \" \"left parenthesis.\" ) processed +=","instance. \"\"\" nd_transition_function = { key: {value} for key, value in self.transition_function.items() }","else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self,","by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat B","complicated than necessary; maybe I'll figure out how to improve on average simplicity,","_error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member of the fsa's \"","raise a ValueError exception on instantiation if any of th following are true:","= self.start_state + other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states,","no other way to match, for instance, {'', '0'} with the current syntax.","empty string in place of an alphabet symbol in the transition function. Note","transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate()","= ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\"","symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item","_extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def","the checks below will work as you would expect). The class will raise","the range of the transition \" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]]","verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2.","or 4. the input regex does not have properly matching parentheses. \"\"\" operator_to_operation","in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in","determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) &","modification to make the return a set of frozensets). def powerset(iterable: Iterable) ->","as input; returns an NFA that recognises the language defined by that regular","A and b is an element of B. Note that this `+` operation","for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf","dfas with large numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self)","== \"Ø\": return regex2 if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip","the transition function; 2. the set of accept states is not a subset","- `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States","in place of an alphabet symbol in the transition function. Note that the","the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string:","machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if","to the above, the characters '(', ')', '|', '*', '•', '€' and 'Ø'","= f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex:","NFA states. For related reasons, the time complexity of this method is exponential","in it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char","_stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state})","input regex does not have properly matching parentheses. \"\"\" operator_to_operation = { '|':","concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states","{rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol],","\"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language","shunting yard algorithm to parse the regex and build the NFA. The method","transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence):","ValueError( f\"Regex contains character '{char}' that is not in \" \"alphabet and not","import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union,","the alphabet. The domain of the transition function is the power-set of the","(My apologies to speakers of Scandinavian languages for the last one; I am","new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State,","Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings,","self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes the","that this `+` operation is not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other)","and `Ø`, 2. the input regex string contains a character not in the","1 new_state = 'new_state1' while new_state in state_set: counter += 1 new_state =","of A and b is an element of B. Note that this `+`","= dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states","b, where a is an element of A and b is an element","state-set of nfa2 plus 1. There is no problem with the input NFAs","accepts(self, string: str) -> bool: \"\"\" Determines whether nfa accepts input string. Will","Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable(","set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other:","tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet) }","Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r)","overlap = self.states & other.states while overlap: other = copy(other) overlap = self.states","\" \"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex]","self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not current_states &","else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex","alphabet, and not one of the above veboten characters, 3. the input regex","be the language recognized by dfa2. `dfa1 | dfa2` returns a dfa that","space. Actually, that's not quite right -- the default value is string.printable *minus*","mean what you'd expect them to mean if you are familiar with regular","Convenience method that takes a DFA instance and returns an NFA instance. \"\"\"","6. the transition function is missing cases -- i.e., it is not the","which includes the standard ASCII letters and digits, and most common punctuation and","recognised by dfa1, and B be the language recognized by dfa2. `dfa1 |","the transition function dictionary should be sets (or frozensets). The empty set is","_GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range =","by nfa1, and B be the language recognized by nfa2. `nfa1 + nfa2`","set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range","states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state =","average simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa =","NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) ->","number of members of A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start,","AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet(","regular expressions that this method takes as input is very simple -- much","the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s =","\"DFA\": \"\"\" Let A be the language recognised by dfa1, and B be","other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state,","epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set","raise a ValueError exception is the string contains symbols that aren't in the","DFA that recognizes the same same language as the NFA instance. WARNING: The","item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets =","dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state =","dfa2` returns a DFA that recognises the set of all concatenations of strings","+= char if paren_count > 0: raise ValueError( \"Left parenthesis occurs in regex","string.printable *minus* parentheses, the vertical bar, the star symbol, and the tilde, for","self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined()","no need to write '•'' explicitly if you don't want to. But it","languages for the last one; I am very against English chauvinism, but your","string that generates A. That regex string is liable to be much more","of DFA states has the cardinality of the power-set of the set of","accept_states = set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1',","and returns an NFA instance. \"\"\" nd_transition_function = { key: {value} for key,","contains symbols that aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states =","symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self)","} return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\":","{} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] =","the transition function is not a one-character string; * the transition function is","are one-char strings.) The transition function' keys implicitly define the nfa's state-set and","a go.) 'Ø' (option-shift-o) represents the empty set; you can match to the","returns a regex string that generates A. That regex string is liable to","-> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set","symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = []","from the transition function is not a one-character string; 4. a member of","the range of the transition \" \"function is not a set.\"), message_plural=(\"Values {}","in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(':","the transition function; 3. a member of the alphabet inferred from the transition","List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import (","start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {}","string in normal python regex syntax either, though it can be done; give","concatentation implicit, as is usual; no need to write '•'' explicitly if you","out how to improve on average simplicity, eventually. \"\"\" gnfa = self._gnfize() while","will explain presently. As of now, the syntax of the regular expressions that","any of the following conditions hold: 1. the alphabet contains any of the","raise ValueError( f\"Regex contains character '{char}' that is not in \" \"alphabet and","the domain of the transition function. The exception message will specify which of","should be sets (or frozensets). The empty set is a valid value; in","self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states","for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state,","transition function. The exception message will specify which of these above conditions things","by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union B.","function is not a subset of the set of states inferred from the","-> None: bad_range = { x for x in self.transition_function.values() if not isinstance(x,","Will raise a ValueError exception is the string contains symbols that aren't in","(because it kind of looks like an epsilon); there's no other way to","def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function )","| {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1,",") def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states)","the transition function. The exception message will specify which of these above conditions","takes a DFA instance and returns an NFA instance. \"\"\" nd_transition_function = {","set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range","0 for char in regex: if char in alphabet or char == '(':","= ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE +","state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2)","set(printable) - {'(', ')', '|', '*'} ) ) -> \"NFA\": \"\"\" Takes a","function's range is not a set; 5. the range of the transition function","1: return regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) ->","A concat B -- i.e., the language consisting of the set of strings",") ) -> \"NFA\": \"\"\" Takes a regular expression and an alphabet (i.e.,","= {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap","compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex,","Container) -> State: counter = 1 new_state = 'new_state1' while new_state in state_set:","__init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function =","of the regular expressions that this method takes as input is very simple","There is no problem with the input DFAs having different alphabets. \"\"\" union_alphabet","return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x for","is very simple -- much simpler than the standard python regular expresssions. All","add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet -","self.start_state + other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states))","list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state","def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex: if","== ')': paren_count -= 1 elif char == '|': if paren_count == 0:","concatenation. You can leave concatentation implicit, as is usual; no need to write","nfa2 is the cardinality of the state set of nfa1 plus the cardinality","return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\"","DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols =","char == ')': paren_count -= 1 elif char == '|': if paren_count ==","NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf","= self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set |","of the state set of nfa1 plus the cardinality of the state-set of","NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) ->","Let A be the language recognised by dfa1, and B be the language","= {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)]","raise a ValueError exception on instantiation if any of the following are true:","fsa's \" \"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State],","The states of dfa1 | dfa2 are ordered pairs of states from dfa1","_well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states","self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept )","AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]:","accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) -","message_plural=(\"Values {} in the range of the transition \" \"function are not sets.\")","states inferred from the transition function; 2. the set of accept states is","inferred from the transition function is not a one-character string; * the transition","A. That regex string is liable to be much more complicated than necessary;","of the transition function is not a subset of the set of states","def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised","star symbol, and the tilde, for reasons that I will explain presently. As","{} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range =","string.printable -- i.e., the set of \"printable\" characters, which includes the standard ASCII","`+` operation is not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for state","combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None:","symbol is in the domain of the transition function. The exception message will","of strings in A with strings in B. This DFA operator is parasitic","paren_count < 0: raise ValueError( \"Right parenthesis occurs in regex withour matching \"","Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction,","`accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings). The keys","operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS","the empty string in normal python regex syntax either, though it can be","usual; no need to write '•'' explicitly if you don't want to. But","'')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state,","exception on instantiation if any of the following are true: 1. the start",") transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {}","transition function; 3. a member of the alphabet inferred from the transition function","a DFA. That makes for a relatively simple but, sadly, computationally expensive algorith.","import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol =","-> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is","is in the domain of the transition function. The exception message will specify","the power-set of the set of NFA states. For related reasons, the time","is: `*`, then `•`, then `|`. This method uses a version of Dijkstra's","\"DFA\": \"\"\" Let A be the language recognised by dfa1, B be the","elif char == ')': paren_count -= 1 elif char == '|': if paren_count","'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol])","any of the following are true: 1. the start state is not a","the set of all strings formed by concatenating any number of members of","class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State","return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in","missing a case -- i.e., it is not the case that every pair","it gets used internally. '€' (option-shift-2) is used to match the empty string","self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states)",") while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors(","of looks like an epsilon); there's no other way to match, for instance,","Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA)","the number of states of the NFA. Don't determinize big NFAs. \"\"\" #","transition \" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() )","-- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input","binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand,","2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience","= 'new_state1' while new_state in state_set: counter += 1 new_state = \"new_state\" +","\"\"\"Returns a DFA that recognizes the same same language as the NFA instance.","Dijkstra's shunting yard algorithm to parse the regex and build the NFA. The","the time complexity of this method is exponential in the number of states","the empty set, if it is. You can define epsilon-moves by using the","current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not","Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) )","\"\"\" Let A be the language recognised by dfa1, and B be the","syntax. (Quick challenge: it's not totally obvious how to match the empty string","done; give it a go.) 'Ø' (option-shift-o) represents the empty set; you can","self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol","\"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states,","{ pair: set() for pair in product({'q1'}, alphabet) } accept_states = set() if","-> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty:","for a relatively simple but, sadly, computationally expensive algorith. For that reason, I","# This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in","states of dfa1 | dfa2 are ordered pairs of states from dfa1 and","else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return","of the NFA. Don't determinize big NFAs. \"\"\" # powerset code an itertools","function. The exception message will specify which of these above conditions things triggered","to mean if you are familiar with regular expressions. '•' (option-8 on a","represent the nfa's states, and the second elements are the symbols in the","paren_count -= 1 elif char == '|': if paren_count == 0: return True","def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex =","right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine)","all strings formed by concatenating any number of members of A. \"\"\" star_start","true: 1. the start state is not a member of the set of","one less state in it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count =","pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf,","string will not be inferred to be a member of the alphabet (and","nfa that recognizes A union B. The cardinality of the state-set of nfa1","automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`:","nfa1 | nfa2 is the cardinality of the state set of nfa1 plus","B be the language recognized by dfa2. `dfa1 | dfa2` returns a dfa","nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set()","avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] )","way to match, for instance, {'', '0'} with the current syntax. (Quick challenge:","`start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char","of a state and a symbol is in the domain of the transition","( set(printable) - {'(', ')', '|', '*'} ) ) -> \"NFA\": \"\"\" Takes","{}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty: Regex) -> NFA: tf:","in the number of states of the NFA. Don't determinize big NFAs. \"\"\"","\"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state,","are strings, and Symbols are one-char strings.) The transition function' keys implicitly define","set, if it is. You can define epsilon-moves by using the empty string","_error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters","as is usual; no need to write '•'' explicitly if you don't want","by dfa1, B be the language recognised by dfa2. `dfa1 + dfa2` returns","gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range -","set of strings of the form a concat b, where a is an","def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet","if processed[-1] not in {'(', '|'} else '' ) if char not in","not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of the","an NFA that recognises the language defined by that regular expression and that","Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function:","start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property","return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self)","MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes three","characters, which includes the standard ASCII letters and digits, and most common punctuation","nfa1, and B be the language recognized by nfa2. `nfa1 + nfa2` returns","The transition function' keys implicitly define the nfa's state-set and alphabet; the first","will specify which of these six conditions things triggered the exception, and which","recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat","MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states:","and star mean what you'd expect them to mean if you are familiar","rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in","| {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] =","paren_count == 0: return True return False def regex_star(regex: Regex) -> Regex: if","r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {}","states. For related reasons, the time complexity of this method is exponential in","= {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state =","ASCII letters and digits, and most common punctuation and white space. Actually, that's","for symbol in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states","input regex string contains a character not in the alphabet, and not one","'€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 =",") def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent to `self` with","dfa1 and dfa2. There is no problem with the input DFAs having different","aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol","= self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \"\"\" Let","below will work as you would expect). The class will raise a ValueError","Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input )","product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf,","['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine =","'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) ->","set of all strings formed by concatenating any number of members of A.","union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] )","WARNING: The set of DFA states has the cardinality of the power-set of","State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function:","a DFA that recognizes the same same language as the NFA instance. WARNING:","by an operator, or 4. the input regex does not have properly matching","for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify(","set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states )","def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in","= {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") ->","return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel']","= regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA(","specify that the successor set for a given state-symbol pair is the empty","in regex withour matching \" \"left parenthesis.\" ) processed += char if paren_count","{} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x","operator followed by an operator, or 4. the input regex does not have","chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf","_good_alphabet, _check_input ) State = str Symbol = str Regex = str FsaTransitionFunction","self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) )","the symbol for empty-set.) In the absence of parentheses, the order of operations","then converts the result back to a DFA. That makes for a relatively","implicitly define the nfa's state-set and alphabet; the first elements of the tuples","expression and that alphabet. The alphabet parameter is optional; it's default value is","alphabet parameter is optional; it's default value is string.printable -- i.e., the set","OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while (","gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method that takes a DFA","`transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are","a subset of the set of states inferred from the transition function; *",") self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) ->","powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet):","(Where States are strings, and Symbols are one-char strings.) The transition function' keys","can leave concatentation implicit, as is usual; no need to write '•'' explicitly","AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states:","is not a one-character string; * the transition function is missing a case","\" \"function are not in the fsa's state set.\") ) def accepts(self, string:","{ x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range,","though it can be done; give it a go.) 'Ø' (option-shift-o) represents the","with the current syntax. (Quick challenge: it's not totally obvious how to match","self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids","dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state}","= _pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif","rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1,","exception, and which states/symbols are the source of the problem. \"\"\" def __or__(self,","the NFA. (My apologies to speakers of Scandinavian languages for the last one;","the alphabet, and not one of the above veboten characters, 3. the input","new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\":","paren_count -= 1 if paren_count < 0: raise ValueError( \"Right parenthesis occurs in","recognised by dfa1, B be the language recognised by dfa2. `dfa1 + dfa2`","state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state,","PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE","'(': if len(processed) > 0: processed += ( '•' if processed[-1] not in","The cardinality of the state-set of nfa1 | nfa2 is the cardinality of","= body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states |","_good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {}","return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self,","def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()](","if you are familiar with regular expressions. '•' (option-8 on a mac keyboard)","- {'(', ')', '|', '*'} ) ) -> \"NFA\": \"\"\" Takes a regular","the form a concat b, where a is an element of A and","NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair","self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range","is not in the fsa's state set.\"), message_plural=(\"States {} in the range of","A be the language recognised by dfa1, B be the language recognised by","= state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def","states/symbols are the source of the problem. \"\"\" def __or__(self, other: \"NFA\") ->","Regex) -> bool: paren_count = 0 for char in regex: if char ==","start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair:","and alphabet; the first elements of the tuples represent the nfa's states, and","union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self,","frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string:","the set of states inferred from the transition function; 3. a member of","there is someone who cares about this, I will change the symbol for","regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed =","of accept states is not a subset of the set of states inferred","of the alphabet inferred from the transition function is not a one-character string;","either, though it can be done; give it a go.) 'Ø' (option-shift-o) represents","= _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state,","\"\"\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '')","strings, and Symbols are one-char strings.) The transition function' keys implicitly define the","-> \"NFA\": \"\"\" Let A be the language recognised by nfa. `nfa.self()` returns","= maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2),","for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start}","transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A","a state and a symbol is in the domain of the transition function.","instance and returns an NFA instance. \"\"\" nd_transition_function = { key: {value} for","exception on instantiation if any of th following are true: * the start","-> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet)","parasitic on the NFA operator; it converts the input DFAs into NFAs, uses","star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)]","of nfa1 plus the cardinality of the state-set of nfa2 plus 1. There","in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) )","the empty string (because it kind of looks like an epsilon); there's no","determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes the same same language as","same same language as the NFA instance. WARNING: The set of DFA states","state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)]","- {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State,","other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf =","\"_GNFA\": \"\"\" Output a GNFA equivalent to `self` with one less state in","state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1,","= set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states","strings in A with strings in B. This DFA operator is parasitic on","in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in","gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for","be inferred to be a member of the alphabet (and hence the checks","close to the empty-set symbol. If, by some miracle, there is someone who","symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours =","one of the above veboten characters, 3. the input regex contain a binary","not be inferred to be a member of the alphabet (and hence the","are true: * the start state is not a member of the set","a one-character string; * the transition function is missing a case -- i.e.,","self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self)","pair: set() for pair in product({'q1'}, alphabet) } accept_states = set() if empty","machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand =","str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__(","Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols","star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts","fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair in","for instance, {'', '0'} with the current syntax. (Quick challenge: it's not totally","algorithm to parse the regex and build the NFA. The method will raise","self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept","The domain of the transition function is the power-set of the nfa's state","state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return","any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€`","Output a GNFA equivalent to `self` with one less state in it. \"\"\"","not in {'(', '|'} else '' ) if char not in alphabet |","-> State: counter = 1 new_state = 'new_state1' while new_state in state_set: counter","= set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the range","'|', '*', '•', '€' and 'Ø' cannot be symbols in the alphabet of","NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet","__or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised by","state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self,","len(regex) == 1: return regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2:","from dfa1 and dfa2. There is no problem with the input DFAs having","non_determinize(self) -> NFA: \"\"\" Convenience method that takes a DFA instance and returns","'*', '•', '€' and 'Ø'. The parentheses, vertical bar and star mean what","\"\"\" A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State,","is no problem with the input DFAs having different alphabets. \"\"\" union_alphabet =","in normal python regex syntax either, though it can be done; give it","+ '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol","last one; I am very against English chauvinism, but your letter is so","gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) ->","powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make","with one less state in it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count","return current_state in self.accept_states def encode(self) -> Regex: \"\"\" Let A be the","strings formed by concatenating any number of members of A. \"\"\" star_start =","return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES","if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2):","* the set of accept states is not a subset of the set","transition function; * the range of the transition function is not a subset","set of nfa1 plus the cardinality of the state-set of nfa2 plus 1.","self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member of the fsa's","reasons that I will explain presently. As of now, the syntax of the","cares about this, I will change the symbol for empty-set.) In the absence","= set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states )","states inferred from the transition function; * the range of the transition function","NFA. (My apologies to speakers of Scandinavian languages for the last one; I","nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf","return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF","reasons, the time complexity of this method is exponential in the number of","transition \" \"function is not a set.\"), message_plural=(\"Values {} in the range of","There is no problem with the input NFAs having different alphabets. \"\"\" new_self,","States are strings and Symbols are one-char strings). The keys of the `transition_function`","| dfa2` returns a dfa that recognizes A union B. The states of","this method takes as input is very simple -- much simpler than the","nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while","self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)]","while overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1:","nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if","regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states","of states of the NFA. Don't determinize big NFAs. \"\"\" # powerset code","start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be","of the following conditions hold: 1. the alphabet contains any of the verboten","followed by an operator, or 4. the input regex does not have properly","\"\"\" Let A be the language accepted by dfa. `dfa.encode()` returns a regex","to be much more complicated than necessary; maybe I'll figure out how to","'' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains","regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 ==","`nfa.self()` returns an nfa that recognizes A* -- i.e., the set of all","a ValueError exception on instantiation if any of the following are true: 1.","`True` if my_dfa accepts \"some string\", and `False` otherwise. Will raise a ValueError","the input regex string contains a character not in the alphabet, and not","'(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1:","regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€':","string (because it kind of looks like an epsilon); there's no other way","a DFA that recognises the set of all concatenations of strings in A","language defined by that regular expression and that alphabet. The alphabet parameter is","nfa's state set --- i.e., the values of the transition function dictionary should","symbols that aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state})","that the empty string will not be inferred to be a member of","return state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for","pair of a state and a symbol is in the domain of the","in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states","string is liable to be much more complicated than necessary; maybe I'll figure","-> NFA: \"\"\" Convenience method that takes a DFA instance and returns an","exception message will specify which of these above conditions things triggered the exception,","nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat B --","of parentheses, the order of operations is: `*`, then `•`, then `|`. This","the range of the transition \" \"function is not in the fsa's state","processed = '' paren_count = 0 for char in regex: if char in","in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states |","Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state","the problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be","instantiation if any of th following are true: * the start state is","can define epsilon-moves by using the empty string in place of an alphabet","= machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) ->","itertools import product, chain, combinations from string import printable from typing import (","in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set()","for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for","states of the NFA. Don't determinize big NFAs. \"\"\" # powerset code an","{new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\",","presently. As of now, the syntax of the regular expressions that this method","a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return","right \" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA):","start state is not a member of the set of states inferred from","def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state:","PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE","= Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__(","set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return","transition function is not a one-character string; * the transition function is missing","elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while","'•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS","the transition function; * the set of accept states is not a subset","symbols in the alphabet except for '(', '')', '|', '*', '•', '€' and","alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS:","from the transition function; * a member of the alphabet inferred from the","-> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s,","_stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return","| {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent to","for char in regex: if char in alphabet or char == '(': if","for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state,","epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State],","every pair of a state and a symbol is in the domain of","of \"printable\" characters, which includes the standard ASCII letters and digits, and most","--- i.e., the values of the transition function dictionary should be sets (or","regular expresssions. All characters are intepreted as literals for symbols in the alphabet","alphabet (i.e., a set of one-character strings) as input; returns an NFA that","= new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other:","language recognised by nfa1, and B be the language recognized by nfa2. `nfa1","machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex","= self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member","star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start,","match the empty string in normal python regex syntax either, though it can","\" \"state set.\"), message_plural=(\"Accept states {} are not members of the fsa's \"","regex = _pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char))","dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols","string: str) -> bool: \"\"\" Determines whether nfa accepts input string. Will raise","who cares about this, I will change the symbol for empty-set.) In the","raise ValueError( \"Left parenthesis occurs in regex without matching right \" \"parenthesis.\" )","of A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state}","super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() )","def encode(self) -> Regex: \"\"\" Let A be the language accepted by dfa.","self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] =","# https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of frozensets).","union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state","str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class","the set of all concatenations of strings in A with strings in B.","determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state","regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip,","exception is the string contains symbols that aren't in the DFA's alphabet. \"\"\"","r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip},","set of DFA states has the cardinality of the power-set of the set","DFA and NFA public classes \"\"\" import collections.abc from itertools import product, chain,","\"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range","determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start","'Ø' if regex1 == '€': return regex2 if regex2 == '€': return regex1","-> NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'},","of the transition \" \"function is not in the fsa's state set.\"), message_plural=(\"States","valid value; in fact, you are required to specify that the successor set","operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet","NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot",") return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def","`transition_function` implicitly define the dfa's state-set and alphabet. The class will raise a","new_other, combination_tf def _good_range(self) -> None: bad_range = { x for x in","the fsa's state set.\"), message_plural=(\"States {} in the range of the transition \"","char == '(': paren_count += 1 elif char == ')': paren_count -= 1","keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State]","value is string.printable -- i.e., the set of \"printable\" characters, which includes the","three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`:","r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 =","classes \"\"\" import collections.abc from itertools import product, chain, combinations from string import","are not in the fsa's state set.\") ) def accepts(self, string: str) ->","str) -> bool: \"\"\" Determines whether nfa accepts input string. Will raise a","{ key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state,","`|`, `*`, `•`, `€` and `Ø`, 2. the input regex string contains a","transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined()","that aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for","obvious how to match the empty string in normal python regex syntax either,","| other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf","_transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) ->","== 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol)","method takes as input is very simple -- much simpler than the standard","class will raise a ValueError exception on instantiation if any of th following","i.e., the set of all strings formed by concatenating any number of members","this method is exponential in the number of states of the NFA. Don't","the following conditions hold: 1. the alphabet contains any of the verboten characters","self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1,","- `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are","error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other,","will raise a ValueError exception on instantiation if any of th following are","that recognizes A* -- i.e., the set of all strings formed by concatenating","maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol","= list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1","| set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self,","} state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol)","inferred from the transition function; 2. the set of accept states is not","first elements of the tuples represent the nfa's states, and the second elements","transition function is not a subset of the set of states inferred from","transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A","first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\")","\"alphabet and not an accepted regex character.\" ) if char in OPERATORS and",") } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set,","define epsilon-moves by using the empty string in place of an alphabet symbol","Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes three keyword arguments:","and processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains binary operator followed by","alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack:","an NFA instance. \"\"\" nd_transition_function = { key: {value} for key, value in","return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})'","= _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify(","different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states)","finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] -","A be the language recognised by dfa1, and B be the language recognized","& other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def","on average simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa","a subset of the set of states inferred from the transition function; 3.","Regex, regex2: Regex) -> Regex: if regex1 == \"Ø\": return regex2 if regex2","regex string contains a character not in the alphabet, and not one of","Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state:","of operations is: `*`, then `•`, then `|`. This method uses a version","not in \" \"alphabet and not an accepted regex character.\" ) if char","A be the language recognised by nfa1, and B be the language recognized","The class will raise a ValueError exception on instantiation if any of th","input string. Will raise a ValueError exception is the string contains symbols that","self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range of the transition \" \"function","in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif","in \" \"alphabet and not an accepted regex character.\" ) if char in","Let A be the language accepted by dfa. `dfa.encode()` returns a regex string","in the alphabet. The domain of the transition function is the power-set of","an alphabet symbol in the transition function. Note that the empty string will","a is an element of A and b is an element of B.","symbol) return not current_states & self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns","State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings).","returns a DFA that recognises the set of all concatenations of strings in","in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the","'Ø' cannot be symbols in the alphabet of the NFA. (My apologies to","in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state,","+ '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1","cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() #","bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range of","copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state),","simple but, sadly, computationally expensive algorith. For that reason, I recommend you don't","= {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack","EMPTIES: return '€' if len(regex) == 1: return regex + '*' return f\"({regex})*\"","union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2",") union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item in","1 elif char == '|': if paren_count == 0: return True return False","\"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf","alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states =","regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2","rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3","the empty string will not be inferred to be a member of the","that is not in \" \"alphabet and not an accepted regex character.\" )","= self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' +","symbol in the transition function. Note that the empty string will not be","bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\"","regex character.\" ) if char in OPERATORS and processed[-1] in {'|', '•'}: raise","in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in","OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€',","aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol","accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let A be the language recognised","transition function's range is not a set; 5. the range of the transition","set.\") ) def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True`","range of the transition \" \"function is not a set.\"), message_plural=(\"Values {} in","dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states =","the cardinality of the state set of nfa1 plus the cardinality of the","of states inferred from the transition function; * a member of the alphabet","__add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised by","symbol. If, by some miracle, there is someone who cares about this, I","body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state}","0: raise ValueError( \"Left parenthesis occurs in regex without matching right \" \"parenthesis.\"","= {} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)]","Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex cannot start with","like an epsilon); there's no other way to match, for instance, {'', '0'}",") def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language","set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states |","alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in","DFA that recognises the set of all concatenations of strings in A with","bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set:","not a subset of the power set of states inferred from the transition","are one-char strings). The keys of the `transition_function` implicitly define the dfa's state-set","language recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises the","three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`:","exception message will specify which of these six conditions things triggered the exception,","NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = {","_pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS:","self.states & other.states while overlap: other = copy(other) overlap = self.states & other.states","function is not a subset of the power set of states inferred from","0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] !=","= 0 for char in regex: if char == '(': paren_count += 1","be the language recognized by nfa2. `nfa1 + nfa2` returns an nfa that","if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}'","bool: paren_count = 0 for char in regex: if char == '(': paren_count","_stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state)","one; I am very against English chauvinism, but your letter is so very","symbol, and the tilde, for reasons that I will explain presently. As of","error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state,","gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start =","chauvinism, but your letter is so very close to the empty-set symbol. If,","for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2),","new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf =","combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x","= { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message(","-> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def","transition function dictionary should be sets (or frozensets). The empty set is a","want to. But it gets used internally. '€' (option-shift-2) is used to match","= set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the","the case that every pair of a state and a symbol is in","def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None:","\"\"\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex:","start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable)","len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA:","collections.abc from itertools import product, chain, combinations from string import printable from typing","| set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that is not in \"","nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]]","message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction","= _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property","'*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 ==","the input DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def","Symbols are one-char strings.) The transition function' keys implicitly define the nfa's state-set","state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state","if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return","the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`,","with '{first_char}'.\") processed = '' paren_count = 0 for char in regex: if","(self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1,","class will raise a ValueError exception on instantiation if any of the following","import collections.abc from itertools import product, chain, combinations from string import printable from","union B. The states of dfa1 | dfa2 are ordered pairs of states","[] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand =","to improve on average simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) >","state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 )","the empty-set symbol. If, by some miracle, there is someone who cares about","range of the transition \" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] =","fsa's \" \"state set.\"), message_plural=(\"Accept states {} are not members of the fsa's","not have properly matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__","self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \"\"\" Let A","= {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for","for the last one; I am very against English chauvinism, but your letter","NFAs. \"\"\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor","symbols in the alphabet of the NFA. (My apologies to speakers of Scandinavian","character not in the alphabet, and not one of the above veboten characters,","-- much simpler than the standard python regular expresssions. All characters are intepreted","string; 4. a member of the transition function's range is not a set;","You can leave concatentation implicit, as is usual; no need to write '•''","the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string:","the default value is string.printable *minus* parentheses, the vertical bar, the star symbol,","for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in","A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for","self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states,","accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function)","and digits, and most common punctuation and white space. Actually, that's not quite","language with it. For reaons related to the above, the characters '(', ')',","dfa1 | dfa2 are ordered pairs of states from dfa1 and dfa2. There","in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] =","transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = {","i.e., the values of the transition function dictionary should be sets (or frozensets).","reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF","of the transition function. The exception message will specify which of these six","keyboard) means concatenation. You can leave concatentation implicit, as is usual; no need","state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction","states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF =","returns `True` if my_dfa accepts \"some string\", and `False` otherwise. Will raise a","from the transition function; 3. a member of the alphabet inferred from the","start with '{first_char}'.\") processed = '' paren_count = 0 for char in regex:","+= 1 elif char == ')': paren_count -= 1 elif char == '|':","and build the NFA. The method will raise a ValueError exception if any","strings and Symbols are one-char strings). The keys of the `transition_function` implicitly define","inferred from the transition function; * the set of accept states is not","NFA operator; it converts the input DFAs into NFAs, uses the NFA '+',","new_state = 'new_state1' while new_state in state_set: counter += 1 new_state = \"new_state\"","2. the set of accept states is not a subset of the set","char in alphabet or char == '(': if len(processed) > 0: processed +=","operator is parasitic on the NFA operator; it converts the input DFAs into","self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]:","= operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return (","gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None:","bad_set=transition_range - self.states, message_singular=(\"State {} in the range of the transition \" \"function","parameter is optional; it's default value is string.printable -- i.e., the set of","DFA. That makes for a relatively simple but, sadly, computationally expensive algorith. For","not a set; 5. the range of the transition function is not a","def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys():",") return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a","transition function; * a member of the alphabet inferred from the transition function","Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet","'€' if len(regex) == 1: return regex + '*' return f\"({regex})*\" def regex_concat(regex1:","machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star()","be the language recognised by dfa2. `dfa1 + dfa2` returns a DFA that","accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None:","'•', '€' and 'Ø'. The parentheses, vertical bar and star mean what you'd","ValueError( \"Left parenthesis occurs in regex without matching right \" \"parenthesis.\" ) return","'•'}: raise ValueError( \"Regex contains binary operator followed by an \" \"operator; not","new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states","`dfa1 + dfa2` returns a DFA that recognises the set of all concatenations","figure out how to improve on average simplicity, eventually. \"\"\" gnfa = self._gnfize()","https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of frozensets). def","if len(processed) > 0: processed += ( '•' if processed[-1] not in {'(',","gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method that takes a DFA instance","nfa2 plus 1. There is no problem with the input NFAs having different","be the language accepted by dfa. `dfa.encode()` returns a regex string that generates","the NFA instance. WARNING: The set of DFA states has the cardinality of","set of all concatenations of strings in A with strings in B. This","*, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states","return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\"","return \"\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1'","for char in regex: if char == '(': paren_count += 1 elif char","one-character string; 4. a member of the transition function's range is not a","self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym:","empty set; you can match to the empty language with it. For reaons","above conditions things triggered the exception, and which states/symbols are the source of","for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets","be the language recognised by nfa. `nfa.self()` returns an nfa that recognizes A*","def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised","value; in fact, you are required to specify that the successor set for","(minor modification to make the return a set of frozensets). def powerset(iterable: Iterable)","_combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state +","accept_states=copy_accept ) overlap = self.states & other.states while overlap: other = copy(other) overlap","recognised by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the","is missing a case -- i.e., it is not the case that every","): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state","of the above veboten characters, 3. the input regex contain a binary operator","B be the language recognised by dfa2. `dfa1 + dfa2` returns a DFA","self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self) -> \"DFA\":","of the state-set of nfa1 | nfa2 is the cardinality of the state","the language recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes","4. the input regex does not have properly matching parentheses. \"\"\" operator_to_operation =","`€` and `Ø`, 2. the input regex string contains a character not in","more complicated than necessary; maybe I'll figure out how to improve on average","DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if","_pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char","return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf =","will raise a ValueError exception if any of the following conditions hold: 1.","regex in EMPTIES: return '€' if len(regex) == 1: return regex + '*'","def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair","cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty: Regex)","None: bad_range = { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set)","machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char)","regex: if char in alphabet or char == '(': if len(processed) > 0:","states, and the second elements are the symbols in the alphabet. The domain","subset of the set of states inferred from the transition function; 3. a","conditions things triggered the exception, and which states/symbols are the source of the","B be the language recognized by nfa2. `nfa1 | nfa2` returns an nfa","product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)]","-> NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet)","\"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol","`|`. This method uses a version of Dijkstra's shunting yard algorithm to parse","copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept )","and a symbol is in the domain of the transition function. The exception","of the `transition_function` implicitly define the dfa's state-set and alphabet. The class will","*[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]:","product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in","B. This DFA operator is parasitic on the NFA operator; it converts the","self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF =","= { pair: set() for pair in product({'q1'}, alphabet) } accept_states = set()","ValueError( \"Regex contains binary operator followed by an \" \"operator; not cool.\" )","vertical bar and star mean what you'd expect them to mean if you","+ dfa2` returns a DFA that recognises the set of all concatenations of","')', '|', '*', '•', '€' and 'Ø' cannot be symbols in the alphabet","range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for","of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item)","regex does not have properly matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__,","frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) }","if it is. You can define epsilon-moves by using the empty string in","char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char))","symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts =","B -- i.e., the language consisting of the set of strings of the","character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty: Regex) -> NFA:","frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state","_extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex =","message_singular=(\"Accept state {} is not a member of the fsa's \" \"state set.\"),","and alphabet. The class will raise a ValueError exception on instantiation if any","return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for","in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) ->","State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State,","{} in the range of the transition \" \"function is not in the","'')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and star","union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state,","\"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some string\", and `False` otherwise.","other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1:","str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set:","{'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf:","state-symbol pair is the empty set, if it is. You can define epsilon-moves","into NFAs, uses the NFA '+', then converts the result back to a","= self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol):","maybe I'll figure out how to improve on average simplicity, eventually. \"\"\" gnfa","return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self","class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ):","set() def determinize(self) -> \"DFA\": \"\"\"Returns a DFA that recognizes the same same","no problem with the input NFAs having different alphabets. \"\"\" new_self, new_other, union_tf","AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) ) -> \"NFA\":","transition function; * the set of accept states is not a subset of","default value is string.printable *minus* parentheses, the vertical bar, the star symbol, and","Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state:","looks like an epsilon); there's no other way to match, for instance, {'',","\"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A","has the cardinality of the power-set of the set of NFA states. For","symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state)","is an element of B. Note that this `+` operation is not commutative.","symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return","== \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)]","_GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]]","itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a","`self` with one less state in it. \"\"\" def union_main_scope(regex: Regex) -> bool:","fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'}","MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA:","return 'Ø' if regex1 == '€': return regex2 if regex2 == '€': return","= set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self,","can be done; give it a go.) 'Ø' (option-shift-o) represents the empty set;","regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} )","'*', '•', '€' and 'Ø' cannot be symbols in the alphabet of the","pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'}","\"\"\" Convenience method that takes a DFA instance and returns an NFA instance.","triggered the exception, and which states/symbols are the source of the problem. \"\"\"","function; 2. the set of accept states is not a subset of the","| new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") ->","for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar","from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set,","copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start =","paren_count > 0: raise ValueError( \"Left parenthesis occurs in regex without matching right","you can match to the empty language with it. For reaons related to","transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states =","State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state =","for a given state-symbol pair is the empty set, if it is. You","will work as you would expect). The class will raise a ValueError exception",") def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for","symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')]","of the set of states inferred from the transition function; * the set","successor set for a given state-symbol pair is the empty set, if it","\" \"left parenthesis.\" ) processed += char if paren_count > 0: raise ValueError(","accepts \"some string\", and `False` otherwise. Will raise a ValueError exception is the","the set of strings of the form a concat b, where a is","_FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__(","= frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for","prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf =","operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop()","with strings in B. This DFA operator is parasitic on the NFA operator;","add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf,","of this method is exponential in the number of states of the NFA.","and an alphabet (i.e., a set of one-character strings) as input; returns an","encode(self) -> Regex: \"\"\" Let A be the language accepted by dfa. `dfa.encode()`","in the transition function. Note that the empty string will not be inferred","problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the","@property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet,",") -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet","= str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ]","DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite automaton class.","\"NFA\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA) ->","of the power-set of the set of NFA states. For related reasons, the","Takes a regular expression and an alphabet (i.e., a set of one-character strings)","machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int:","+ PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char =","product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else {'q1'} return","the exception, and which states/symbols are the source of the problem. \"\"\" def","empty string will not be inferred to be a member of the alphabet","'')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def","function is not a one-character string; * the transition function is missing a","of the transition \" \"function are not in the fsa's state set.\") )","operator followed by an \" \"operator; not cool.\" ) if char == '(':","operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not","nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA)","char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in","is parasitic on the NFA operator; it converts the input DFAs into NFAs,","'{first_char}'.\") processed = '' paren_count = 0 for char in regex: if char","yard algorithm to parse the regex and build the NFA. The method will","= symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] =","char in regex: if char == '(': paren_count += 1 elif char ==","'')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\")","left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex)","- self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member of the","self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in","character.\" ) if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError(","`start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char","simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce()","string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set() def","includes the standard ASCII letters and digits, and most common punctuation and white","if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1,","the transition \" \"function are not in the fsa's state set.\") ) def","concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state,","domain of the transition function is the power-set of the nfa's state set","of the nfa's state set --- i.e., the values of the transition function","set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return {","gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method","'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} )","EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex:","transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf","the input regex contain a binary operator followed by an operator, or 4.","other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {}","I recommend you don't `+` dfas with large numbers of states. \"\"\" return","not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if","state-set and alphabet; the first elements of the tuples represent the nfa's states,","used to match the empty string (because it kind of looks like an","states inferred from the transition function; 3. a member of the alphabet inferred","operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate()","operator; it converts the input DFAs into NFAs, uses the NFA '+', then","self.accept_states def encode(self) -> Regex: \"\"\" Let A be the language accepted by","in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) ->","(Quick challenge: it's not totally obvious how to match the empty string in","some miracle, there is someone who cares about this, I will change the","not a one-character string; * the transition function is missing a case --","determinize big NFAs. \"\"\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes","an element of A and b is an element of B. Note that","of the fsa's \" \"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction =","processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite automaton","Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State,","return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1","You can define epsilon-moves by using the empty string in place of an","of the transition function is the power-set of the nfa's state set ---","# powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to","of states inferred from the transition function; * the range of the transition","takes as input is very simple -- much simpler than the standard python","return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return","return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic finite","- {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex =","function; 3. a member of the alphabet inferred from the transition function is","symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start =","symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€'","standard python regular expresssions. All characters are intepreted as literals for symbols in","_check_input ) State = str Symbol = str Regex = str FsaTransitionFunction =","new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states","__add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised by",") return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let","function' keys implicitly define the nfa's state-set and alphabet; the first elements of","symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state","relatively simple but, sadly, computationally expensive algorith. For that reason, I recommend you","regular expression and an alphabet (i.e., a set of one-character strings) as input;","the tilde, for reasons that I will explain presently. As of now, the","not quite right -- the default value is string.printable *minus* parentheses, the vertical","the alphabet inferred from the transition function is not a one-character string; 4.","| {self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA","= self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self) ->","\"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised by nfa1, and","cardinality of the state-set of nfa2 plus 1. There is no problem with","symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)]","from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of","union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex)","\"function are not in the fsa's state set.\") ) def _get_successors( self, *,","the transition function; * a member of the alphabet inferred from the transition","'`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in","an \" \"operator; not cool.\" ) if char == '(': paren_count += 1","machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return","necessary; maybe I'll figure out how to improve on average simplicity, eventually. \"\"\"","that every pair of a state and a symbol is in the domain","in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1',","mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self,","'•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet","not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states,","alphabet of the NFA. (My apologies to speakers of Scandinavian languages for the","of the set of NFA states. For related reasons, the time complexity of","state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf =","from string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List,","States are strings, and Symbols are one-char strings.) The transition function' keys implicitly","you don't want to. But it gets used internally. '€' (option-shift-2) is used","== '|': if paren_count == 0: return True return False def regex_star(regex: Regex)","punctuation and white space. Actually, that's not quite right -- the default value","fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair in","will not be inferred to be a member of the alphabet (and hence","value is string.printable *minus* parentheses, the vertical bar, the star symbol, and the","method is exponential in the number of states of the NFA. Don't determinize","matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS)","raise ValueError( \"Regex contains binary operator followed by an \" \"operator; not cool.\"","is missing cases -- i.e., it is not the case that every pair","a set.\"), message_plural=(\"Values {} in the range of the transition \" \"function are","( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast","int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char","= self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state)","alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) ) ->","range of the transition function is not a subset of the set of","left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) -","that the successor set for a given state-symbol pair is the empty set,","having different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA,","def non_determinize(self) -> NFA: \"\"\" Convenience method that takes a DFA instance and","if regex1 == \"Ø\": return regex2 if regex2 == \"Ø\": return regex1 return","add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf,","i.e., the language consisting of the set of strings of the form a","{ '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain","build the NFA. The method will raise a ValueError exception if any of","binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate()","'|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError(","by dfa2. `dfa1 + dfa2` returns a DFA that recognises the set of","states has the cardinality of the power-set of the set of NFA states.","contain characters {}.\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = {","you don't `+` dfas with large numbers of states. \"\"\" return (self.non_determinize() +","in the alphabet of the NFA. (My apologies to speakers of Scandinavian languages","'new_state1' while new_state in state_set: counter += 1 new_state = \"new_state\" + str(counter)","syntax of the regular expressions that this method takes as input is very","someone who cares about this, I will change the symbol for empty-set.) In","if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1]","\"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')]","= maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function","overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA,","-> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State","state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)]","NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other","regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf,","same language as the NFA instance. WARNING: The set of DFA states has","a member of the set of states inferred from the transition function; 2.","regex contain a binary operator followed by an operator, or 4. the input","That makes for a relatively simple but, sadly, computationally expensive algorith. For that","'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values())","range of the transition function is not a subset of the power set","followed by an \" \"operator; not cool.\" ) if char == '(': paren_count","= add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA(","and NFA public classes \"\"\" import collections.abc from itertools import product, chain, combinations","current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \"\"\"","def prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf","machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0:","-> \"NFA\": \"\"\" Let A be the language recognised by nfa1, and B","None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not","the successor set for a given state-symbol pair is the empty set, if","set for a given state-symbol pair is the empty set, if it is.","are true: 1. the start state is not a member of the set","= cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset()","I will explain presently. As of now, the syntax of the regular expressions","pair in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else","the NFA '+', then converts the result back to a DFA. That makes","regex string is liable to be much more complicated than necessary; maybe I'll","symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol))","it can be done; give it a go.) 'Ø' (option-shift-o) represents the empty","in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)]","converts the input DFAs into NFAs, uses the NFA '+', then converts the","dfa1, B be the language recognised by dfa2. `dfa1 + dfa2` returns a","accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state","in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] }","the string contains symbols that aren't in the nfa's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet)","case -- i.e., it is not the case that every pair of a","new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state","| new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet","return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range","specify which of these above conditions things triggered the exception, and which states/symbols","{} in the range of the transition \" \"function are not in the","-- i.e., the set of \"printable\" characters, which includes the standard ASCII letters","will specify which of these above conditions things triggered the exception, and which","NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet)","gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start}, self.states |","all concatenations of strings in A with strings in B. This DFA operator","Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'},","these six conditions things triggered the exception, and which states/symbols are the source","def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state","NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF =","return '€' if len(regex) == 1: return regex + '*' return f\"({regex})*\" def","the fsa's \" \"state set.\"), message_plural=(\"Accept states {} are not members of the","determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state,","return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol]","of the set of states inferred from the transition function; * the range","or char == '(': if len(processed) > 0: processed += ( '•' if","State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state","4. a member of the transition function's range is not a set; 5.","self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex","1 if char == ')': paren_count -= 1 if paren_count < 0: raise","chain, combinations from string import printable from typing import ( AbstractSet, Container, FrozenSet,","combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf =","an element of B. Note that this `+` operation is not commutative. \"\"\"","concat b, where a is an element of A and b is an","'')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state","if any of the following conditions hold: 1. the alphabet contains any of","place of an alphabet symbol in the transition function. Note that the empty","transition function. Note that the empty string will not be inferred to be","exception is the string contains symbols that aren't in the nfa's alphabet. \"\"\"","write '•'' explicitly if you don't want to. But it gets used internally.","state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol=''","expensive algorith. For that reason, I recommend you don't `+` dfas with large","_get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in","state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range","Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input","string. Will raise a ValueError exception is the string contains symbols that aren't","a regex string that generates A. That regex string is liable to be","accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept()","regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \"Ø\": return regex2 if",") if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character","veboten characters, 3. the input regex contain a binary operator followed by an","= start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state}","language as the NFA instance. WARNING: The set of DFA states has the","nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start","have properly matching parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ }","with the input DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet","_error_message( bad_set=bad_range, message_singular=(\"State {} in the range of the transition \" \"function is","'(': paren_count += 1 elif char == ')': paren_count -= 1 elif char","(state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1,","set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def","empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol:","\"Regex contains binary operator followed by an \" \"operator; not cool.\" ) if","containing DFA and NFA public classes \"\"\" import collections.abc from itertools import product,","def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \"Ø\": return regex2","transition function; 2. the set of accept states is not a subset of","isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \"\".join(states) def _get_new_state(state_set: Container) -> State:","> 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\"","the language recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes","is. You can define epsilon-moves by using the empty string in place of","these above conditions things triggered the exception, and which states/symbols are the source","'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2 == '€':","The empty set is a valid value; in fact, you are required to","first_char in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed = '' paren_count","def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair","I am very against English chauvinism, but your letter is so very close","concatenations of strings in A with strings in B. This DFA operator is","than necessary; maybe I'll figure out how to improve on average simplicity, eventually.","= self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State],","commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state,","\"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised by dfa1, and","_get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State,","self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\" Determines whether nfa accepts","in self.accept_states def encode(self) -> Regex: \"\"\" Let A be the language accepted","== '(': paren_count += 1 if char == ')': paren_count -= 1 if","digits, and most common punctuation and white space. Actually, that's not quite right","concat B -- i.e., the language consisting of the set of strings of","prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept","are required to specify that the successor set for a given state-symbol pair","['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS","numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf:","MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet,","strings) as input; returns an NFA that recognises the language defined by that","- `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are","class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State","_check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol)","'•' if processed[-1] not in {'(', '|'} else '' ) if char not","define the nfa's state-set and alphabet; the first elements of the tuples represent","def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item","NFA '+', then converts the result back to a DFA. That makes for","`•`, then `|`. This method uses a version of Dijkstra's shunting yard algorithm","while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) ->","accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet =","of members of A. \"\"\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')]","of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and","Determines whether nfa accepts input string. Will raise a ValueError exception is the","Note that the empty string will not be inferred to be a member","\"\"\" nd_transition_function = { key: {value} for key, value in self.transition_function.items() } return","be much more complicated than necessary; maybe I'll figure out how to improve","parentheses, vertical bar and star mean what you'd expect them to mean if",") _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the range of the transition","on the NFA operator; it converts the input DFAs into NFAs, uses the","strings.) The transition function' keys implicitly define the nfa's state-set and alphabet; the","FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states","set of states inferred from the transition function; * a member of the","other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 +","'€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in","\"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA )","regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \"Ø\": return","as literals for symbols in the alphabet except for '(', '')', '|', '*',","the transition function is the power-set of the nfa's state set --- i.e.,","State] class DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes three keyword arguments:","-> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] =","other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for","the fsa's state set.\") ) def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some","is not a set; 5. the range of the transition function is not","printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional,","then `|`. This method uses a version of Dijkstra's shunting yard algorithm to","NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot contain character {}.\",","symbols that aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state","familiar with regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You","= { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\"Alphabet cannot","to match the empty string in normal python regex syntax either, though it","processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains binary operator followed by an","the state-set of nfa2 plus 1. There is no problem with the input","-> \"NFA\": \"\"\" Takes a regular expression and an alphabet (i.e., a set","\"\"\" Let A be the language recognised by nfa. `nfa.self()` returns an nfa","*self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the range of the","the transition function is missing a case -- i.e., it is not the","of the transition \" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union(","NFA. The method will raise a ValueError exception if any of the following","nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols):","transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = (","symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = {","_good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range,","start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be","be the language recognized by nfa2. `nfa1 | nfa2` returns an nfa that","back to a DFA. That makes for a relatively simple but, sadly, computationally","= prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start,","mean if you are familiar with regular expressions. '•' (option-8 on a mac","for empty-set.) In the absence of parentheses, the order of operations is: `*`,","subset of the set of states inferred from the transition function; * the","in OPERATORS: raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed = '' paren_count =","second elements are the symbols in the alphabet. The domain of the transition","Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite","to a DFA. That makes for a relatively simple but, sadly, computationally expensive","not members of the fsa's \" \"state set.\") ) def _good_range(self): raise NotImplementedError","to make the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]:","\"\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while","bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member of the fsa's \" \"state","\" \"function are not in the fsa's state set.\") ) def _get_successors( self,","that recognises the language defined by that regular expression and that alphabet. The","the power set of states inferred from the transition function; 6. the transition","which states/symbols are the source of the problem. \"\"\" def __or__(self, other: \"NFA\")","as you would expect). The class will raise a ValueError exception on instantiation","symbols in the alphabet. The domain of the transition function is the power-set","cardinality of the state set of nfa1 plus the cardinality of the state-set","0: raise ValueError( \"Right parenthesis occurs in regex withour matching \" \"left parenthesis.\"","state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1,","Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings","maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states","while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char)","Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function =","return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1)","if char == '(': paren_count += 1 elif char == ')': paren_count -=","add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2:","= self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def","the NFA. The method will raise a ValueError exception if any of the","self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf:","new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf,","this `+` operation is not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for","`)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex string contains","to write '•'' explicitly if you don't want to. But it gets used","self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\":","it kind of looks like an epsilon); there's no other way to match,","member of the fsa's \" \"state set.\"), message_plural=(\"Accept states {} are not members","an alphabet (i.e., a set of one-character strings) as input; returns an NFA","as input is very simple -- much simpler than the standard python regular",") def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' )","of nfa2 plus 1. There is no problem with the input NFAs having","be symbols in the alphabet of the NFA. (My apologies to speakers of","the star symbol, and the tilde, for reasons that I will explain presently.","_Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex","r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1,","state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set)","name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message(","of states inferred from the transition function; 3. a member of the alphabet","the empty language with it. For reaons related to the above, the characters","= MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes","in alphabet or char == '(': if len(processed) > 0: processed += (","\"\"\" Let A be the language recognised by dfa1, B be the language","{'|', '•'}: raise ValueError( \"Regex contains binary operator followed by an \" \"operator;","means concatenation. You can leave concatentation implicit, as is usual; no need to","= self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)]","less state in it. \"\"\" def union_main_scope(regex: Regex) -> bool: paren_count = 0","\"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet,","combination_tf def _good_range(self) -> None: bad_range = { x for x in self.transition_function.values()","contains a character not in the alphabet, and not one of the above","recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union","without matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State]","False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if","dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other)","am very against English chauvinism, but your letter is so very close to","a binary operator followed by an operator, or 4. the input regex does","concatenating any number of members of A. \"\"\" star_start = _get_new_state(self.states) star_tf =","is no problem with the input NFAs having different alphabets. \"\"\" new_self, new_other,","transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other =","-- i.e., it is not the case that every pair of a state","of nfa1 | nfa2 is the cardinality of the state set of nfa1","power set of states inferred from the transition function; 6. the transition function","if paren_count > 0: raise ValueError( \"Left parenthesis occurs in regex without matching","self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class","characters, 3. the input regex contain a binary operator followed by an operator,","{self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod","state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states","of the following are true: 1. the start state is not a member","in regex without matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State,","from the transition function; * the set of accept states is not a","AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\") self._good_accept() self._good_domain(self.alphabet) def","alphabet; the first elements of the tuples represent the nfa's states, and the","letters and digits, and most common punctuation and white space. Actually, that's not","in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2,","string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex:","the language consisting of the set of strings of the form a concat",") return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \"\"\" A deterministic","match, for instance, {'', '0'} with the current syntax. (Quick challenge: it's not","the above veboten characters, 3. the input regex contain a binary operator followed","in the fsa's state set.\") ) def accepts(self, string: str) -> bool: \"\"\"","transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf =","syntax either, though it can be done; give it a go.) 'Ø' (option-shift-o)","occurs in regex withour matching \" \"left parenthesis.\" ) processed += char if","in {'|', '•'}: raise ValueError( \"Regex contains binary operator followed by an \"","a regular expression and an alphabet (i.e., a set of one-character strings) as","or regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2 if","start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} )","NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]:","the start state is not a member of the set of states inferred","keys of the `transition_function` implicitly define the dfa's state-set and alphabet. The class",") -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast(","frozensets). The empty set is a valid value; in fact, you are required","for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product(","\"\"\" import collections.abc from itertools import product, chain, combinations from string import printable","dfa. `dfa.encode()` returns a regex string that generates A. That regex string is","accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State):","empty language with it. For reaons related to the above, the characters '(',","state set.\"), message_plural=(\"States {} in the range of the transition \" \"function are","for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in","one-char strings). The keys of the `transition_function` implicitly define the dfa's state-set and","to match the empty string (because it kind of looks like an epsilon);","`nfa1 | nfa2` returns an nfa that recognizes A union B. The cardinality","return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€'","elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1]","(state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)]","parentheses. \"\"\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) &","self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output a","transition function is missing a case -- i.e., it is not the case","above veboten characters, 3. the input regex contain a binary operator followed by","python regex syntax either, though it can be done; give it a go.)","regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {}","strings). The keys of the `transition_function` implicitly define the dfa's state-set and alphabet.","the transition function is not a subset of the set of states inferred","optional; it's default value is string.printable -- i.e., the set of \"printable\" characters,","1 if paren_count < 0: raise ValueError( \"Right parenthesis occurs in regex withour","function. The exception message will specify which of these six conditions things triggered","of strings of the form a concat b, where a is an element","member of the set of states inferred from the transition function; * the","= error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf =","of the transition function dictionary should be sets (or frozensets). The empty set","return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap:","accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self)","state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours -","{} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2)","what you'd expect them to mean if you are familiar with regular expressions.","'0'} with the current syntax. (Quick challenge: it's not totally obvious how to","char in regex: if char in alphabet or char == '(': if len(processed)","in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in","a subset of the power set of states inferred from the transition function;","frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for","_check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)]","elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char)","== 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€': return","member of the alphabet inferred from the transition function is not a one-character","A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol],",") if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA(",", `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex string","string\", and `False` otherwise. Will raise a ValueError exception is the string contains","a case -- i.e., it is not the case that every pair of","contains symbols that aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state =","-> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction,","not cool.\" ) if char == '(': paren_count += 1 if char ==","the language recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises","inferred from the transition function; * the range of the transition function is","{ pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] =","( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex:",") processed += char if paren_count > 0: raise ValueError( \"Left parenthesis occurs","binary operator followed by an \" \"operator; not cool.\" ) if char ==","of the set of states inferred from the transition function; * a member","that recognises the set of all concatenations of strings in A with strings","the above, the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot","current syntax. (Quick challenge: it's not totally obvious how to match the empty","the set of \"printable\" characters, which includes the standard ASCII letters and digits,","Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise","an nfa that recognizes A union B. The cardinality of the state-set of","is string.printable *minus* parentheses, the vertical bar, the star symbol, and the tilde,","DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let","for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2","-> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if","if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of","characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols in","List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop()","return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\" Determines whether nfa","import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping,","the state-set of nfa1 | nfa2 is the cardinality of the state set","empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state,","-> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa:","= { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept","bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts \"some string\", and `False`","self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet","def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State],","== '(': if len(processed) > 0: processed += ( '•' if processed[-1] not","state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return","AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \"\"\"","recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union","any of th following are true: * the start state is not a","if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop() r2 =","inferred from the transition function; * a member of the alphabet inferred from","of one-character strings) as input; returns an NFA that recognises the language defined","the source of the problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\"","of the power set of states inferred from the transition function; 6. the","== '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) >","new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in","characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the","of states inferred from the transition function; 6. the transition function is missing","alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state,","gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start}, self.states","copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA )",") -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf =","of now, the syntax of the regular expressions that this method takes as","1 elif char == ')': paren_count -= 1 elif char == '|': if","of the set of states inferred from the transition function; 3. a member","vertical bar, the star symbol, and the tilde, for reasons that I will","you would expect). The class will raise a ValueError exception on instantiation if","state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state,","-> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 =","plus the cardinality of the state-set of nfa2 plus 1. There is no","For that reason, I recommend you don't `+` dfas with large numbers of","is the power-set of the nfa's state set --- i.e., the values of","'€' and 'Ø'. The parentheses, vertical bar and star mean what you'd expect","is not a subset of the set of states inferred from the transition","EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] =","are the source of the problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\":","gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \"\"\" Convenience method that takes","the set of NFA states. For related reasons, the time complexity of this","return regex2 if regex2 == \"Ø\": return regex1 return f\"{regex1}|{regex2}\" rip = self.body_states.pop()","with the input NFAs having different alphabets. \"\"\" new_self, new_other, union_tf = self._combine(other)","recognises the language defined by that regular expression and that alphabet. The alphabet","char if paren_count > 0: raise ValueError( \"Left parenthesis occurs in regex without","pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2,","dfa that recognizes A union B. The states of dfa1 | dfa2 are","will raise a ValueError exception on instantiation if any of the following are","of dfa1 | dfa2 are ordered pairs of states from dfa1 and dfa2.","are the source of the problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\":","{self.accept_state} ) def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent to `self`","are the symbols in the alphabet. The domain of the transition function is","the transition function. The exception message will specify which of these six conditions","'(': paren_count += 1 if char == ')': paren_count -= 1 if paren_count","not the case that every pair of a state and a symbol is","def _good_range(self) -> None: bad_range = { x for x in self.transition_function.values() if","need to write '•'' explicitly if you don't want to. But it gets","dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol","| nfa2` returns an nfa that recognizes A union B. The cardinality of",") machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand","-> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand","be sets (or frozensets). The empty set is a valid value; in fact,","state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys():","from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str","eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return","\"\"\" Let A be the language recognised by nfa1, and B be the","that alphabet. The alphabet parameter is optional; it's default value is string.printable --","that recognizes A union B. The states of dfa1 | dfa2 are ordered","from the transition function; 6. the transition function is missing cases -- i.e.,","{self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states:","\"\"\" Takes a regular expression and an alphabet (i.e., a set of one-character","transition function is missing cases -- i.e., it is not the case that","gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)]","self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf,","0 for char in regex: if char == '(': paren_count += 1 elif","= _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state","the language defined by that regular expression and that alphabet. The alphabet parameter","transition function. The exception message will specify which of these six conditions things","six conditions things triggered the exception, and which states/symbols are the source of","prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x)","raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class","reason, I recommend you don't `+` dfas with large numbers of states. \"\"\"","finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] -","function. Note that the empty string will not be inferred to be a","with regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You can","sets (or frozensets). The empty set is a valid value; in fact, you","new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x for x","be the language recognised by dfa1, B be the language recognised by dfa2.","star(self) -> \"NFA\": \"\"\" Let A be the language recognised by nfa. `nfa.self()`","start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state =","equivalent to `self` with one less state in it. \"\"\" def union_main_scope(regex: Regex)","string contains a character not in the alphabet, and not one of the","gets used internally. '€' (option-shift-2) is used to match the empty string (because",") State = str Symbol = str Regex = str FsaTransitionFunction = Mapping[","are strings and Symbols are one-char strings). The keys of the `transition_function` implicitly","the same same language as the NFA instance. WARNING: The set of DFA","(and hence the checks below will work as you would expect). The class","fsa's state set.\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol )",") if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \"Regex","')': paren_count -= 1 if paren_count < 0: raise ValueError( \"Right parenthesis occurs","`transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are","product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'}","the dfa's state-set and alphabet. The class will raise a ValueError exception on","compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while","')', '|', '*'} ) ) -> \"NFA\": \"\"\" Takes a regular expression and","`Ø`, 2. the input regex string contains a character not in the alphabet,","parentheses, the vertical bar, the star symbol, and the tilde, for reasons that","set of states inferred from the transition function; 6. the transition function is","from the transition function; * the range of the transition function is not","state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|'","= self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction,","def reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent to `self` with one","my_dfa accepts \"some string\", and `False` otherwise. Will raise a ValueError exception is","if any of th following are true: * the start state is not","The method will raise a ValueError exception if any of the following conditions","['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet:","nfa accepts input string. Will raise a ValueError exception is the string contains","transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\"State {} in the range of the transition","state and a symbol is in the domain of the transition function. The","and Symbols are one-char strings.) The transition function' keys implicitly define the nfa's","while new_state in state_set: counter += 1 new_state = \"new_state\" + str(counter) return","state {} is not a member of the fsa's \" \"state set.\"), message_plural=(\"Accept","= self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state","element of A and b is an element of B. Note that this","symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return","match the empty string (because it kind of looks like an epsilon); there's","dfa2` returns a dfa that recognizes A union B. The states of dfa1","returns an NFA that recognises the language defined by that regular expression and","in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)]","the input DFAs into NFAs, uses the NFA '+', then converts the result","message will specify which of these six conditions things triggered the exception, and","B. The cardinality of the state-set of nfa1 | nfa2 is the cardinality","'(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols in the","in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state,","-> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function","method will raise a ValueError exception if any of the following conditions hold:","from the transition function; 2. the set of accept states is not a","consisting of the set of strings of the form a concat b, where","return not current_states & self.accept_states == set() def determinize(self) -> \"DFA\": \"\"\"Returns a","by some miracle, there is someone who cares about this, I will change","transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states = (","return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol],","for pair in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø'","- self.states, message_singular=(\"State {} in the range of the transition \" \"function is","= machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) >","self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state )","Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset())","& self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept","a mac keyboard) means concatenation. You can leave concatentation implicit, as is usual;","_get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)]","the fsa's \" \"state set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State,","the regular expressions that this method takes as input is very simple --","return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let A","self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]]","a symbol is in the domain of the transition function. The exception message","function; 6. the transition function is missing cases -- i.e., it is not","if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return","{'(', '|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise","new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return","accepted regex character.\" ) if char in OPERATORS and processed[-1] in {'|', '•'}:","other.states while overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions(","occurs in regex without matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction =",") if char == '(': paren_count += 1 if char == ')': paren_count","subset of the power set of states inferred from the transition function; 6.","!= '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS =","input DFAs having different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state(","ValueError exception is the string contains symbols that aren't in the DFA's alphabet.","whether nfa accepts input string. Will raise a ValueError exception is the string","function dictionary should be sets (or frozensets). The empty set is a valid","problem with the input NFAs having different alphabets. \"\"\" new_self, new_other, union_tf =","- OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if char","strings of the form a concat b, where a is an element of","a character not in the alphabet, and not one of the above veboten","is an element of A and b is an element of B. Note","not one of the above veboten characters, 3. the input regex contain a","A with strings in B. This DFA operator is parasitic on the NFA",") from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State =","ValueError exception if any of the following conditions hold: 1. the alphabet contains","= self.states & other.states while overlap: other = copy(other) overlap = self.states &","a GNFA equivalent to `self` with one less state in it. \"\"\" def","'(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and","the NFA operator; it converts the input DFAs into NFAs, uses the NFA","char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack)","states inferred from the transition function; * the set of accept states is","computationally expensive algorith. For that reason, I recommend you don't `+` dfas with","GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def","set.\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF =","'{char}' that is not in \" \"alphabet and not an accepted regex character.\"","the vertical bar, the star symbol, and the tilde, for reasons that I","absence of parentheses, the order of operations is: `*`, then `•`, then `|`.","set of states inferred from the transition function; * the set of accept","-> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self)","given state-symbol pair is the empty set, if it is. You can define","result back to a DFA. That makes for a relatively simple but, sadly,","def __add__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A be the language recognised","gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start,","alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state =","the alphabet inferred from the transition function is not a one-character string; *","pairs of states from dfa1 and dfa2. There is no problem with the","= Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A","instantiation if any of the following are true: 1. the start state is","for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states","item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function,","state-set of nfa1 | nfa2 is the cardinality of the state set of","* the start state is not a member of the set of states","| epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set:","B. Note that this `+` operation is not commutative. \"\"\" new_self, new_other, concat_tf","not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def","frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union(","in the fsa's state set.\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol:","set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] =","Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet)","define the dfa's state-set and alphabet. The class will raise a ValueError exception","self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start},","is the cardinality of the state set of nfa1 plus the cardinality of","or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and","go.) 'Ø' (option-shift-o) represents the empty set; you can match to the empty","copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf,","set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start,","message_singular=\"Alphabet cannot contain character {}.\", message_plural=\"Alphabet cannot contain characters {}.\" ) def fit_empty(empty:","the standard ASCII letters and digits, and most common punctuation and white space.","if len(regex) == 1: return regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex,","alphabet inferred from the transition function is not a one-character string; * the","= product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet):","a member of the set of states inferred from the transition function; *","alphabet) } accept_states = set() if empty == 'Ø' else {'q1'} return NFA(","don't `+` dfas with large numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize()","nfa's states, and the second elements are the symbols in the alphabet. The","the alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'. The","regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) ->","one-char strings.) The transition function' keys implicitly define the nfa's state-set and alphabet;","ValueError(f\"Regex cannot start with '{first_char}'.\") processed = '' paren_count = 0 for char","other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1,","the transition function; 6. the transition function is missing cases -- i.e., it","and not an accepted regex character.\" ) if char in OPERATORS and processed[-1]","quite right -- the default value is string.printable *minus* parentheses, the vertical bar,","= { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)]","MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1',","be the language recognised by nfa1, and B be the language recognized by","self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] +=","')': paren_count -= 1 elif char == '|': if paren_count == 0: return","\"\"\" Output a GNFA equivalent to `self` with one less state in it.","accepts input string. Will raise a ValueError exception is the string contains symbols","import product, chain, combinations from string import printable from typing import ( AbstractSet,","char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that","gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states:","The parentheses, vertical bar and star mean what you'd expect them to mean","= self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip,","of states inferred from the transition function; * the set of accept states","most common punctuation and white space. Actually, that's not quite right -- the","alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\"Regex","formed by concatenating any number of members of A. \"\"\" star_start = _get_new_state(self.states)","python regular expresssions. All characters are intepreted as literals for symbols in the","returns a dfa that recognizes A union B. The states of dfa1 |","set() for pair in product({'q1'}, alphabet) } accept_states = set() if empty ==","Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic","string\")` returns `True` if my_dfa accepts \"some string\", and `False` otherwise. Will raise","-> Regex: \"\"\" Let A be the language accepted by dfa. `dfa.encode()` returns","to. But it gets used internally. '€' (option-shift-2) is used to match the","is not the case that every pair of a state and a symbol","input is very simple -- much simpler than the standard python regular expresssions.","= set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)]","class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State","the transition function's range is not a set; 5. the range of the","common punctuation and white space. Actually, that's not quite right -- the default","for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet:","PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0]","\"Left parenthesis occurs in regex without matching right \" \"parenthesis.\" ) return processed","_good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex]","with it. For reaons related to the above, the characters '(', ')', '|',","MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \"\"\" A nondeterministic finite automaton class.","-> \"DFA\": \"\"\" Let A be the language recognised by dfa1, and B","symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state +","the first elements of the tuples represent the nfa's states, and the second","default value is string.printable -- i.e., the set of \"printable\" characters, which includes","-= 1 if paren_count < 0: raise ValueError( \"Right parenthesis occurs in regex","instance. WARNING: The set of DFA states has the cardinality of the power-set","'')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts","the tuples represent the nfa's states, and the second elements are the symbols","missing cases -- i.e., it is not the case that every pair of","The set of DFA states has the cardinality of the power-set of the","of the transition function's range is not a set; 5. the range of","union B. The cardinality of the state-set of nfa1 | nfa2 is the","'(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel',","implicit, as is usual; no need to write '•'' explicitly if you don't","Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and","in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that is not","DFA(_FSA): \"\"\" A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`:","(state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start,","that aren't in the DFA's alphabet. \"\"\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for","{value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states )","set of accept states is not a subset of the set of states","+ symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states","range is not a set; 5. the range of the transition function is","1. the start state is not a member of the set of states","current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self)","-- the default value is string.printable *minus* parentheses, the vertical bar, the star","the transition function is not a subset of the power set of states","union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states:","and b is an element of B. Note that this `+` operation is","recognised by nfa1, and B be the language recognized by nfa2. `nfa1 |","nfa1 plus the cardinality of the state-set of nfa2 plus 1. There is","Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State -","regex syntax either, though it can be done; give it a go.) 'Ø'","return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State])","are not members of the fsa's \" \"state set.\") ) def _good_range(self): raise","while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop()",") def star(self) -> \"NFA\": \"\"\" Let A be the language recognised by","input DFAs into NFAs, uses the NFA '+', then converts the result back","If, by some miracle, there is someone who cares about this, I will","transition \" \"function is not in the fsa's state set.\"), message_plural=(\"States {} in","contains character '{char}' that is not in \" \"alphabet and not an accepted","of the transition function. The exception message will specify which of these above","accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states =","make the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s","I'll figure out how to improve on average simplicity, eventually. \"\"\" gnfa =","how to match the empty string in normal python regex syntax either, though","_good_range(self) -> None: bad_range = { x for x in self.transition_function.values() if not","contains any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`,","automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`:","for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value {}","a concat b, where a is an element of A and b is","= set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states","expect them to mean if you are familiar with regular expressions. '•' (option-8","language recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A","state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in","alphabet (and hence the checks below will work as you would expect). The","is not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states:","recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises the set","the cardinality of the power-set of the set of NFA states. For related","which of these above conditions things triggered the exception, and which states/symbols are","State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.)","empty-set symbol. If, by some miracle, there is someone who cares about this,","alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def","improve on average simplicity, eventually. \"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2:","f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or","recommend you don't `+` dfas with large numbers of states. \"\"\" return (self.non_determinize()","State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet","A* -- i.e., the set of all strings formed by concatenating any number","of the problem. \"\"\" def __or__(self, other: \"NFA\") -> \"NFA\": \"\"\" Let A","NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) }","_error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex = str","self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] =","there's no other way to match, for instance, {'', '0'} with the current","is string.printable -- i.e., the set of \"printable\" characters, which includes the standard","operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char","= str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def","different alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2:","| {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex,","def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|',","As of now, the syntax of the regular expressions that this method takes","not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\"Regex contains character '{char}' that is",") def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for","otherwise. Will raise a ValueError exception is the string contains symbols that aren't","set; you can match to the empty language with it. For reaons related","a member of the alphabet inferred from the transition function is not a","'|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS =","if paren_count == 0: return True return False def regex_star(regex: Regex) -> Regex:","leave concatentation implicit, as is usual; no need to write '•'' explicitly if","normal python regex syntax either, though it can be done; give it a","and B be the language recognized by dfa2. `dfa1 | dfa2` returns a","in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in","liable to be much more complicated than necessary; maybe I'll figure out how","Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1","= self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3),","( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states","in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while","\"DFA\": \"\"\"Returns a DFA that recognizes the same same language as the NFA","th following are true: * the start state is not a member of","plus 1. There is no problem with the input NFAs having different alphabets.","withour matching \" \"left parenthesis.\" ) processed += char if paren_count > 0:","machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES =","bar, the star symbol, and the tilde, for reasons that I will explain","symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] +","NFA instance. \"\"\" nd_transition_function = { key: {value} for key, value in self.transition_function.items()","inferred from the transition function is not a one-character string; 4. a member","the NFA. Don't determinize big NFAs. \"\"\" # powerset code an itertools recipe,","Regex) -> Regex: if regex1 == \"Ø\": return regex2 if regex2 == \"Ø\":","A be the language accepted by dfa. `dfa.encode()` returns a regex string that","symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states |","AbstractSet[State] (where States are strings and Symbols are one-char strings). The keys of","binary operator followed by an operator, or 4. the input regex does not","the language recognised by dfa1, B be the language recognised by dfa2. `dfa1","b is an element of B. Note that this `+` operation is not","char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains binary","in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1]","= ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø']","-> bool: paren_count = 0 for char in regex: if char == '(':","typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple,","{'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack =","bad_set=bad_range, message_singular=(\"Value {} in the range of the transition \" \"function is not","string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping,","binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')']","is the empty set, if it is. You can define epsilon-moves by using","alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses,","new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set()","of these above conditions things triggered the exception, and which states/symbols are the","that recognizes the same same language as the NFA instance. WARNING: The set","symbol for empty-set.) In the absence of parentheses, the order of operations is:","self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state }","Regex: if regex in EMPTIES: return '€' if len(regex) == 1: return regex","reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state )","Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import ( _Base,","concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states )","NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other,","reduce(self) -> \"_GNFA\": \"\"\" Output a GNFA equivalent to `self` with one less","if you don't want to. But it gets used internally. '€' (option-shift-2) is","than the standard python regular expresssions. All characters are intepreted as literals for","values of the transition function dictionary should be sets (or frozensets). The empty","if char == ')': paren_count -= 1 if paren_count < 0: raise ValueError(","pair is the empty set, if it is. You can define epsilon-moves by","*minus* parentheses, the vertical bar, the star symbol, and the tilde, for reasons","the `transition_function` implicitly define the dfa's state-set and alphabet. The class will raise","3. a member of the alphabet inferred from the transition function is not","function; * the range of the transition function is not a subset of",") self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def","*, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol)","= machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def","in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states","= _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) ->","in B. This DFA operator is parasitic on the NFA operator; it converts","B be the language recognized by nfa2. `nfa1 + nfa2` returns an nfa","not in the fsa's state set.\") ) def _get_successors( self, *, state_set: AbstractSet[State],","self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self)","\"function is not a set.\"), message_plural=(\"Values {} in the range of the transition","if char in alphabet or char == '(': if len(processed) > 0: processed","of these six conditions things triggered the exception, and which states/symbols are the","them to mean if you are familiar with regular expressions. '•' (option-8 on","in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf","NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`:","i.e., it is not the case that every pair of a state and","keys implicitly define the nfa's state-set and alphabet; the first elements of the","B. The states of dfa1 | dfa2 are ordered pairs of states from","speakers of Scandinavian languages for the last one; I am very against English","self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str:","and 'Ø' cannot be symbols in the alphabet of the NFA. (My apologies","and that alphabet. The alphabet parameter is optional; it's default value is string.printable","on instantiation if any of th following are true: * the start state","accepted by dfa. `dfa.encode()` returns a regex string that generates A. That regex","= nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] =","states {} are not members of the fsa's \" \"state set.\") ) def","processed += char if paren_count > 0: raise ValueError( \"Left parenthesis occurs in","raise ValueError(f\"Regex cannot start with '{first_char}'.\") processed = '' paren_count = 0 for","specify which of these six conditions things triggered the exception, and which states/symbols","DFA states has the cardinality of the power-set of the set of NFA","5. the range of the transition function is not a subset of the","DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\": \"\"\" Let A be","Actually, that's not quite right -- the default value is string.printable *minus* parentheses,","The exception message will specify which of these six conditions things triggered the","= copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA","regex + '*' return f\"({regex})*\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if","transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \"NFA\") -> Tuple[\"NFA\", \"NFA\", MutableNfaTF]: def","= MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State],","of states inferred from the transition function; 2. the set of accept states","x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\"Value","the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols","0: processed += ( '•' if processed[-1] not in {'(', '|'} else ''","def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states","expect). The class will raise a ValueError exception on instantiation if any of","makes for a relatively simple but, sadly, computationally expensive algorith. For that reason,","NFA: \"\"\" Convenience method that takes a DFA instance and returns an NFA","def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\"alphabet\")","function; * the set of accept states is not a subset of the","is a valid value; in fact, you are required to specify that the","is not a subset of the power set of states inferred from the","that takes a DFA instance and returns an NFA instance. \"\"\" nd_transition_function =","} accept_states = set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf,","contains binary operator followed by an \" \"operator; not cool.\" ) if char","* the transition function is missing a case -- i.e., it is not","accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa accepts","sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym),","compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char)","in the domain of the transition function. The exception message will specify which","alphabets. \"\"\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA","by using the empty string in place of an alphabet symbol in the","that generates A. That regex string is liable to be much more complicated","problem. \"\"\" def __or__(self, other: \"DFA\") -> \"DFA\": \"\"\" Let A be the","the standard python regular expresssions. All characters are intepreted as literals for symbols","new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in",") -> \"NFA\": \"\"\" Takes a regular expression and an alphabet (i.e., a","set --- i.e., the values of the transition function dictionary should be sets","NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def","big NFAs. \"\"\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes #","Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self,","string; * the transition function is missing a case -- i.e., it is","= 1 new_state = 'new_state1' while new_state in state_set: counter += 1 new_state","| new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA(","NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf","intepreted as literals for symbols in the alphabet except for '(', '')', '|',","symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else:","== '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE","hence the checks below will work as you would expect). The class will","AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set:","add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states","literals for symbols in the alphabet except for '(', '')', '|', '*', '•',","if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def","one-character string; * the transition function is missing a case -- i.e., it","in A with strings in B. This DFA operator is parasitic on the","def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if my_dfa","True return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return","in regex: if char == '(': paren_count += 1 elif char == ')':","while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*']","= OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex:","class NFA(_FSA): \"\"\" A nondeterministic finite automaton class. Takes three keyword arguments: -","nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states )","rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1 =","_stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \"NFA\":","`+` dfas with large numbers of states. \"\"\" return (self.non_determinize() + other.non_determinize()).determinize() def","_gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2","if my_dfa accepts \"some string\", and `False` otherwise. Will raise a ValueError exception","other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def","FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol))","a DFA instance and returns an NFA instance. \"\"\" nd_transition_function = { key:","\" \"function are not sets.\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message(","in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in","\"NFA\": \"\"\" Takes a regular expression and an alphabet (i.e., a set of","range of the transition \" \"function is not in the fsa's state set.\"),","`accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The transition","of the form a concat b, where a is an element of A","nd_transition_function = { key: {value} for key, value in self.transition_function.items() } return NFA(","star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def","cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State","to parse the regex and build the NFA. The method will raise a","a relatively simple but, sadly, computationally expensive algorith. For that reason, I recommend","is someone who cares about this, I will change the symbol for empty-set.)","f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex,","it converts the input DFAs into NFAs, uses the NFA '+', then converts","dfa's state-set and alphabet. The class will raise a ValueError exception on instantiation","the range of the transition function is not a subset of the set","& other.states while overlap: other = copy(other) overlap = self.states & other.states def","function is the power-set of the nfa's state set --- i.e., the values","FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function","form a concat b, where a is an element of A and b","\"\"\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state,","extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states,","explicitly if you don't want to. But it gets used internally. '€' (option-shift-2)","FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base","Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import","and the second elements are the symbols in the alphabet. The domain of","OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \"Regex contains binary operator followed","dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states =","dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol","regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1","that I will explain presently. As of now, the syntax of the regular","NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA:","== 'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2 ==","the nfa's states, and the second elements are the symbols in the alphabet.","def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex)","regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char","tuples represent the nfa's states, and the second elements are the symbols in","it is not the case that every pair of a state and a","complexity of this method is exponential in the number of states of the","{} are not members of the fsa's \" \"state set.\") ) def _good_range(self):","by dfa1, and B be the language recognized by dfa2. `dfa1 | dfa2`","I will change the symbol for empty-set.) In the absence of parentheses, the","def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state","state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool:","state set.\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) ->","power-set of the set of NFA states. For related reasons, the time complexity","by concatenating any number of members of A. \"\"\" star_start = _get_new_state(self.states) star_tf","empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) ->","code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the","\"DFA\") -> \"DFA\": \"\"\" Let A be the language recognised by dfa1, B","that's not quite right -- the default value is string.printable *minus* parentheses, the","a ValueError exception on instantiation if any of th following are true: *","\"operator; not cool.\" ) if char == '(': paren_count += 1 if char","_get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while new_state in","symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states","i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex","-> \"_GNFA\": \"\"\" Output a GNFA equivalent to `self` with one less state","in the range of the transition \" \"function are not sets.\") ) transition_range:","self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4","product, chain, combinations from string import printable from typing import ( AbstractSet, Container,","language recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A","change the symbol for empty-set.) In the absence of parentheses, the order of","'' paren_count = 0 for char in regex: if char in alphabet or","regex without matching right \" \"parenthesis.\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol],","alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return","the transition function is missing cases -- i.e., it is not the case","challenge: it's not totally obvious how to match the empty string in normal","Let A be the language recognised by dfa1, B be the language recognised","return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not","list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in","'€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})'","states is not a subset of the set of states inferred from the","language recognised by dfa1, and B be the language recognized by dfa2. `dfa1","- `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States","'•'' explicitly if you don't want to. But it gets used internally. '€'","maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function =","of the transition \" \"function is not a set.\"), message_plural=(\"Values {} in the","AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast )","other way to match, for instance, {'', '0'} with the current syntax. (Quick",") def accepts(self, string: str) -> bool: \"\"\" `my_dfa.accepts(\"some string\")` returns `True` if","OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if","accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set()","a ValueError exception is the string contains symbols that aren't in the nfa's","in EMPTIES: return '€' if len(regex) == 1: return regex + '*' return","_error_message( bad_set=bad_range, message_singular=(\"Value {} in the range of the transition \" \"function is","not in the alphabet, and not one of the above veboten characters, 3.","combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = {","in the range of the transition \" \"function is not a set.\"), message_plural=(\"Values","FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) ->","self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\"Accept state {} is not a member of","regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return","and 'Ø'. The parentheses, vertical bar and star mean what you'd expect them","= dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for","input regex contain a binary operator followed by an operator, or 4. the","{self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union(","Regex: if regex1 == \"Ø\": return regex2 if regex2 == \"Ø\": return regex1","char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*':","} return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if","state set of nfa1 plus the cardinality of the state-set of nfa2 plus","operation is not commutative. \"\"\" new_self, new_other, concat_tf = self._combine(other) for state in","'Ø'. The parentheses, vertical bar and star mean what you'd expect them to","+ other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item)","_error_message( bad_set=transition_range - self.states, message_singular=(\"State {} in the range of the transition \"","`•`, `€` and `Ø`, 2. the input regex string contains a character not",") @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(',"],"string":"[\n \"\\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members of the fsa's \\\" \\\"state\",\n \"current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state\",\n \"operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•',\",\n \"an epsilon); there's no other way to match, for instance, {'', '0'} with\",\n \"{ _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) }\",\n \"element of B. Note that this `+` operation is not commutative. \\\"\\\"\\\" new_self,\",\n \"the range of the transition \\\" \\\"function are not in the fsa's state\",\n \"nfa that recognizes A concat B -- i.e., the language consisting of the\",\n \"= str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol],\",\n \"the input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state\",\n \"states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") ->\",\n \"def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function,\",\n \"processed[-1] not in {'(', '|'} else '' ) if char not in alphabet\",\n \"-- i.e., the set of all strings formed by concatenating any number of\",\n \"message_plural=(\\\"Accept states {} are not members of the fsa's \\\" \\\"state set.\\\") )\",\n \"set of states inferred from the transition function; * the range of the\",\n \"new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs =\",\n \"None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {}\",\n \"= NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self,\",\n \"\\\"some string\\\", and `False` otherwise. Will raise a ValueError exception is the string\",\n \"concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other:\",\n \"language recognised by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e.,\",\n \"Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State -\",\n \"transition function' keys implicitly define the nfa's state-set and alphabet; the first elements\",\n \"and most common punctuation and white space. Actually, that's not quite right --\",\n \"nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state,\",\n \"] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State]\",\n \"This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set]\",\n \"with large numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) ->\",\n \"in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states\",\n \") combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) ->\",\n \"arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where\",\n \"{} in the range of the transition \\\" \\\"function is not a set.\\\"),\",\n \"( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states\",\n \"standard ASCII letters and digits, and most common punctuation and white space. Actually,\",\n \"State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols\",\n \"set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that is not in \\\" \\\"alphabet\",\n \"= NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states )\",\n \"( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str\",\n \"message_singular=(\\\"State {} in the range of the transition \\\" \\\"function is not in\",\n \"2. the input regex string contains a character not in the alphabet, and\",\n \"an accepted regex character.\\\" ) if char in OPERATORS and processed[-1] in {'|',\",\n \"contain a binary operator followed by an operator, or 4. the input regex\",\n \"= error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] =\",\n \"recognises the set of all concatenations of strings in A with strings in\",\n \"def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised\",\n \"other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\")\",\n \"bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a\",\n \"following are true: 1. the start state is not a member of the\",\n \"one-character strings) as input; returns an NFA that recognises the language defined by\",\n \"string in place of an alphabet symbol in the transition function. Note that\",\n \"+= 1 if char == ')': paren_count -= 1 if paren_count < 0:\",\n \"the language recognised by dfa1, and B be the language recognized by dfa2.\",\n \"in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1)\",\n \"expression and an alphabet (i.e., a set of one-character strings) as input; returns\",\n \"self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} |\",\n \"DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1:\",\n \"self.states, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function is not\",\n \"large numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA:\",\n \"= _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet:\",\n \"} _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of the transition \\\" \\\"function\",\n \"{gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)]\",\n \"self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return\",\n \"* the range of the transition function is not a subset of the\",\n \"accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the\",\n \"for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return\",\n \"alphabet inferred from the transition function is not a one-character string; 4. a\",\n \"extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair]\",\n \"work as you would expect). The class will raise a ValueError exception on\",\n \"{}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set()\",\n \"nfa2` returns an nfa that recognizes A union B. The cardinality of the\",\n \"Regex: \\\"\\\"\\\" Let A be the language accepted by dfa. `dfa.encode()` returns a\",\n \"\\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {}\",\n \"new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf:\",\n \"new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state}\",\n \"-> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start\",\n \"Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1',\",\n \"self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states -\",\n \"very against English chauvinism, but your letter is so very close to the\",\n \"dfa2. There is no problem with the input DFAs having different alphabets. \\\"\\\"\\\"\",\n \"not a subset of the set of states inferred from the transition function;\",\n \"a ValueError exception is the string contains symbols that aren't in the DFA's\",\n \"mac keyboard) means concatenation. You can leave concatentation implicit, as is usual; no\",\n \"alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that is not in\",\n \"{ prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept =\",\n \"Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether\",\n \"& self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes\",\n \"<filename>toc/fsa/fsa.py<gh_stars>0 \\\"\\\"\\\" File containing DFA and NFA public classes \\\"\\\"\\\" import collections.abc from\",\n \"`my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some string\\\", and `False` otherwise. Will\",\n \"you'd expect them to mean if you are familiar with regular expressions. '•'\",\n \"a member of the transition function's range is not a set; 5. the\",\n \"a set of one-character strings) as input; returns an NFA that recognises the\",\n \"the syntax of the regular expressions that this method takes as input is\",\n \"other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in\",\n \"union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2,\",\n \"char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char\",\n \"returns an NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for key, value\",\n \"required to specify that the successor set for a given state-symbol pair is\",\n \"regex1 == \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\"\",\n \"dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union B. The\",\n \"ValueError exception on instantiation if any of th following are true: * the\",\n \"\\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF\",\n \"self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states\",\n \"= self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] =\",\n \"= {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts )\",\n \"empty string in normal python regex syntax either, though it can be done;\",\n \"NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)]\",\n \"')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def\",\n \"in regex: if char in alphabet or char == '(': if len(processed) >\",\n \"+ regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\":\",\n \"function is missing a case -- i.e., it is not the case that\",\n \"key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states\",\n \"in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains binary operator\",\n \"State = str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State,\",\n \"things triggered the exception, and which states/symbols are the source of the problem.\",\n \"ValueError exception is the string contains symbols that aren't in the nfa's alphabet.\",\n \"def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2\",\n \"{gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return\",\n \"NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states\",\n \"new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\")\",\n \"in the range of the transition \\\" \\\"function is not in the fsa's\",\n \"self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1\",\n \"function is missing cases -- i.e., it is not the case that every\",\n \"NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] =\",\n \"in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA(\",\n \"None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states\",\n \"+= '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept\",\n \"against English chauvinism, but your letter is so very close to the empty-set\",\n \"internally. '€' (option-shift-2) is used to match the empty string (because it kind\",\n \"len(processed) > 0: processed += ( '•' if processed[-1] not in {'(', '|'}\",\n \"== '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 =\",\n \"for symbols in the alphabet except for '(', '')', '|', '*', '•', '€'\",\n \"Scandinavian languages for the last one; I am very against English chauvinism, but\",\n \"- {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state,\",\n \"alphabet. The class will raise a ValueError exception on instantiation if any of\",\n \"self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def\",\n \"the current syntax. (Quick challenge: it's not totally obvious how to match the\",\n \"DFA operator is parasitic on the NFA operator; it converts the input DFAs\",\n \"if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container)\",\n \"the last one; I am very against English chauvinism, but your letter is\",\n \"empty set is a valid value; in fact, you are required to specify\",\n \"`False` otherwise. Will raise a ValueError exception is the string contains symbols that\",\n \"= transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of the\",\n \"AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State]\",\n \"collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter\",\n \"rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4\",\n \"(state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set,\",\n \"transition function; 6. the transition function is missing cases -- i.e., it is\",\n \"states inferred from the transition function; * a member of the alphabet inferred\",\n \"generates A. That regex string is liable to be much more complicated than\",\n \"an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return\",\n \"fact, you are required to specify that the successor set for a given\",\n \"of the transition function is not a subset of the power set of\",\n \"and the tilde, for reasons that I will explain presently. As of now,\",\n \"cannot be symbols in the alphabet of the NFA. (My apologies to speakers\",\n \"and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else:\",\n \"\\\"\\\"\\\" A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State,\",\n \"= self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states\",\n \"so very close to the empty-set symbol. If, by some miracle, there is\",\n \"-- i.e., the language consisting of the set of strings of the form\",\n \"start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states\",\n \"power-set of the nfa's state set --- i.e., the values of the transition\",\n \"collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of the transition \\\"\",\n \"language accepted by dfa. `dfa.encode()` returns a regex string that generates A. That\",\n \"_stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return\",\n \"nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction:\",\n \"+ other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) |\",\n \"cases -- i.e., it is not the case that every pair of a\",\n \"- `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The\",\n \"a valid value; in fact, you are required to specify that the successor\",\n \"product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if\",\n \"transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states\",\n \"related reasons, the time complexity of this method is exponential in the number\",\n \"now, the syntax of the regular expressions that this method takes as input\",\n \"don't want to. But it gets used internally. '€' (option-shift-2) is used to\",\n \"parenthesis occurs in regex withour matching \\\" \\\"left parenthesis.\\\" ) processed += char\",\n \"is not a member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {}\",\n \"def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while\",\n \"= _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol\",\n \"'|': if paren_count == 0: return True return False def regex_star(regex: Regex) ->\",\n \"bool: \\\"\\\"\\\" Determines whether nfa accepts input string. Will raise a ValueError exception\",\n \"char == '|': if paren_count == 0: return True return False def regex_star(regex:\",\n \"return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes a\",\n \"the empty string in place of an alphabet symbol in the transition function.\",\n \"isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of the transition\",\n \"if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in\",\n \"expresssions. All characters are intepreted as literals for symbols in the alphabet except\",\n \"nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states\",\n \"\\\" \\\"operator; not cool.\\\" ) if char == '(': paren_count += 1 if\",\n \"gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)]\",\n \"self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state}\",\n \"NFAs, uses the NFA '+', then converts the result back to a DFA.\",\n \"self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states =\",\n \"x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x\",\n \"new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] =\",\n \"recognised by nfa1, and B be the language recognized by nfa2. `nfa1 +\",\n \"alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return\",\n \"Symbols are one-char strings). The keys of the `transition_function` implicitly define the dfa's\",\n \"Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet,\",\n \"str Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State,\",\n \"For reaons related to the above, the characters '(', ')', '|', '*', '•',\",\n \"DFAs into NFAs, uses the NFA '+', then converts the result back to\",\n \"But it gets used internally. '€' (option-shift-2) is used to match the empty\",\n \"{} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for\",\n \"having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states |\",\n \"return True return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES:\",\n \"+ EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if\",\n \"parenthesis occurs in regex without matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction\",\n \"NFA that recognises the language defined by that regular expression and that alphabet.\",\n \"(state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA(\",\n \"(option-8 on a mac keyboard) means concatenation. You can leave concatentation implicit, as\",\n \"'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2\",\n \"x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in\",\n \"0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0\",\n \"state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] =\",\n \"bad_range = { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) }\",\n \"and `False` otherwise. Will raise a ValueError exception is the string contains symbols\",\n \"if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 +\",\n \"All characters are intepreted as literals for symbols in the alphabet except for\",\n \"self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self)\",\n \"algorith. For that reason, I recommend you don't `+` dfas with large numbers\",\n \"MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state:\",\n \"if any of the following are true: 1. the start state is not\",\n \"regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You can leave\",\n \"_good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states\",\n \"= _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states:\",\n \"number of states of the NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset\",\n \"# (minor modification to make the return a set of frozensets). def powerset(iterable:\",\n \"it a go.) 'Ø' (option-shift-o) represents the empty set; you can match to\",\n \"of Scandinavian languages for the last one; I am very against English chauvinism,\",\n \"NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\"\",\n \"for reasons that I will explain presently. As of now, the syntax of\",\n \"alphabet symbol in the transition function. Note that the empty string will not\",\n \"\\\"\\\"\\\" Determines whether nfa accepts input string. Will raise a ValueError exception is\",\n \"This DFA operator is parasitic on the NFA operator; it converts the input\",\n \"state is not a member of the set of states inferred from the\",\n \"apologies to speakers of Scandinavian languages for the last one; I am very\",\n \"the regex and build the NFA. The method will raise a ValueError exception\",\n \"which states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\")\",\n \"much simpler than the standard python regular expresssions. All characters are intepreted as\",\n \"The class will raise a ValueError exception on instantiation if any of the\",\n \"NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset code an itertools recipe, from\",\n \"determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def\",\n \"not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char ==\",\n \"dfa1, and B be the language recognized by dfa2. `dfa1 | dfa2` returns\",\n \"for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if\",\n \"star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in\",\n \"{self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent\",\n \"operations is: `*`, then `•`, then `|`. This method uses a version of\",\n \"{'', '0'} with the current syntax. (Quick challenge: it's not totally obvious how\",\n \"transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in\",\n \"'*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS +\",\n \"new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet |\",\n \"problem with the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet |\",\n \"= self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not current_states\",\n \"nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states}\",\n \"method uses a version of Dijkstra's shunting yard algorithm to parse the regex\",\n \"on instantiation if any of the following are true: 1. the start state\",\n \"while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours,\",\n \"NFA instance. WARNING: The set of DFA states has the cardinality of the\",\n \"inferred from the transition function; 6. the transition function is missing cases --\",\n \"subset of the set of states inferred from the transition function; * a\",\n \"regex and build the NFA. The method will raise a ValueError exception if\",\n \"where a is an element of A and b is an element of\",\n \"are ordered pairs of states from dfa1 and dfa2. There is no problem\",\n \"\\\"\\\"\\\" File containing DFA and NFA public classes \\\"\\\"\\\" import collections.abc from itertools\",\n \"-> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet -\",\n \"paren_count = 0 for char in regex: if char == '(': paren_count +=\",\n \"= transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states =\",\n \"r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state,\",\n \"state set --- i.e., the values of the transition function dictionary should be\",\n \"`*`, then `•`, then `|`. This method uses a version of Dijkstra's shunting\",\n \"\\\"function is not in the fsa's state set.\\\"), message_plural=(\\\"States {} in the range\",\n \"1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(',\",\n \"set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states )\",\n \"the set of states inferred from the transition function; * the set of\",\n \"= list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r\",\n \"char == '(': paren_count += 1 if char == ')': paren_count -= 1\",\n \"raise ValueError( \\\"Right parenthesis occurs in regex withour matching \\\" \\\"left parenthesis.\\\" )\",\n \"processed += ( '•' if processed[-1] not in {'(', '|'} else '' )\",\n \"of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A\",\n \"is the string contains symbols that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string,\",\n \"for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA(\",\n \"a member of the alphabet (and hence the checks below will work as\",\n \"public classes \\\"\\\"\\\" import collections.abc from itertools import product, chain, combinations from string\",\n \"characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair:\",\n \"def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function\",\n \"time complexity of this method is exponential in the number of states of\",\n \"other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for\",\n \"of th following are true: * the start state is not a member\",\n \"(or frozensets). The empty set is a valid value; in fact, you are\",\n \"state-set and alphabet. The class will raise a ValueError exception on instantiation if\",\n \"is so very close to the empty-set symbol. If, by some miracle, there\",\n \"if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char ==\",\n \"cool.\\\" ) if char == '(': paren_count += 1 if char == ')':\",\n \"'€' and 'Ø' cannot be symbols in the alphabet of the NFA. (My\",\n \"but, sadly, computationally expensive algorith. For that reason, I recommend you don't `+`\",\n \"the set of accept states is not a subset of the set of\",\n \"= {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range\",\n \"== '€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1\",\n \"That regex string is liable to be much more complicated than necessary; maybe\",\n \") new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function)\",\n \"is not a one-character string; 4. a member of the transition function's range\",\n \"\\\" \\\"alphabet and not an accepted regex character.\\\" ) if char in OPERATORS\",\n \"set is a valid value; in fact, you are required to specify that\",\n \"\\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\"\",\n \"self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) ->\",\n \"would expect). The class will raise a ValueError exception on instantiation if any\",\n \"For related reasons, the time complexity of this method is exponential in the\",\n \"gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for\",\n \"Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes three keyword\",\n \"the fsa's state set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol\",\n \"r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state,\",\n \"the following are true: 1. the start state is not a member of\",\n \"start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other = copy(other)\",\n \"the transition function; * the range of the transition function is not a\",\n \"for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for\",\n \"def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while new_state\",\n \"is liable to be much more complicated than necessary; maybe I'll figure out\",\n \"Symbol = str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]]\",\n \"the second elements are the symbols in the alphabet. The domain of the\",\n \"recognizes A concat B -- i.e., the language consisting of the set of\",\n \"it's default value is string.printable -- i.e., the set of \\\"printable\\\" characters, which\",\n \"extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states\",\n \"Don't determinize big NFAs. \\\"\\\"\\\" # powerset code an itertools recipe, from #\",\n \"cannot contain characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction =\",\n \"accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the\",\n \"raise a ValueError exception if any of the following conditions hold: 1. the\",\n \"A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol],\",\n \"dfa2. `dfa1 + dfa2` returns a DFA that recognises the set of all\",\n \"paren_count += 1 elif char == ')': paren_count -= 1 elif char ==\",\n \"} _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain\",\n \"state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) ->\",\n \"not a set.\\\"), message_plural=(\\\"Values {} in the range of the transition \\\" \\\"function\",\n \"operator, or 4. the input regex does not have properly matching parentheses. \\\"\\\"\\\"\",\n \"in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol\",\n \"cardinality of the power-set of the set of NFA states. For related reasons,\",\n \"this, I will change the symbol for empty-set.) In the absence of parentheses,\",\n \"recognizes A union B. The cardinality of the state-set of nfa1 | nfa2\",\n \"{} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set)\",\n \"the set of states inferred from the transition function; * the range of\",\n \"nfa that recognizes A* -- i.e., the set of all strings formed by\",\n \"= dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state\",\n \"sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return\",\n \"set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range of\",\n \"Optional, Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message,\",\n \"'€' for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ):\",\n \"state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')]\",\n \"@staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')',\",\n \"new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return\",\n \"are not in the fsa's state set.\\\") ) def _get_successors( self, *, state_set:\",\n \"match to the empty language with it. For reaons related to the above,\",\n \"used internally. '€' (option-shift-2) is used to match the empty string (because it\",\n \"FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self,\",\n \"the absence of parentheses, the order of operations is: `*`, then `•`, then\",\n \"\\\"left parenthesis.\\\" ) processed += char if paren_count > 0: raise ValueError( \\\"Left\",\n \"nfa1, and B be the language recognized by nfa2. `nfa1 | nfa2` returns\",\n \"{ frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) )\",\n \"regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex) ==\",\n \"{self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}:\",\n \"bar and star mean what you'd expect them to mean if you are\",\n \"but your letter is so very close to the empty-set symbol. If, by\",\n \"self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\"\",\n \"accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) ->\",\n \"is the string contains symbols that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string,\",\n \"regex string that generates A. That regex string is liable to be much\",\n \"union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item in (\",\n \"be the language recognised by dfa1, and B be the language recognized by\",\n \"the cardinality of the state-set of nfa2 plus 1. There is no problem\",\n \"3. the input regex contain a binary operator followed by an operator, or\",\n \"f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1\",\n \"new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states)\",\n \"return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex:\",\n \"symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol)\",\n \"char == ')': paren_count -= 1 if paren_count < 0: raise ValueError( \\\"Right\",\n \"function; * a member of the alphabet inferred from the transition function is\",\n \"): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys()\",\n \"Note that this `+` operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf =\",\n \"set.\\\"), message_plural=(\\\"Values {} in the range of the transition \\\" \\\"function are not\",\n \"reaons related to the above, the characters '(', ')', '|', '*', '•', '€'\",\n \"method that takes a DFA instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function\",\n \"transition \\\" \\\"function are not in the fsa's state set.\\\") ) def accepts(self,\",\n \"members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] =\",\n \"if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = ''\",\n \"recognizes the same same language as the NFA instance. WARNING: The set of\",\n \"OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = '' paren_count = 0\",\n \"Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø':\",\n \"exception if any of the following conditions hold: 1. the alphabet contains any\",\n \"= regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed\",\n \"else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate()\",\n \"of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF\",\n \"ValueError( \\\"Right parenthesis occurs in regex withour matching \\\" \\\"left parenthesis.\\\" ) processed\",\n \"new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs\",\n \"is optional; it's default value is string.printable -- i.e., the set of \\\"printable\\\"\",\n \"regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2\",\n \"message_plural=(\\\"States {} in the range of the transition \\\" \\\"function are not in\",\n \"of the alphabet (and hence the checks below will work as you would\",\n \"= Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes\",\n \"source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let\",\n \"conditions hold: 1. the alphabet contains any of the verboten characters -- i.e.,`(`\",\n \"of Dijkstra's shunting yard algorithm to parse the regex and build the NFA.\",\n \"the transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {} in the range\",\n \"in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states)\",\n \"char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else:\",\n \"your letter is so very close to the empty-set symbol. If, by some\",\n \"return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for\",\n \"returns an nfa that recognizes A concat B -- i.e., the language consisting\",\n \"epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol=''\",\n \"an nfa that recognizes A concat B -- i.e., the language consisting of\",\n \"elements are the symbols in the alphabet. The domain of the transition function\",\n \"the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be\",\n \"1. the alphabet contains any of the verboten characters -- i.e.,`(` , `)`,\",\n \"sadly, computationally expensive algorith. For that reason, I recommend you don't `+` dfas\",\n \"NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols:\",\n \"parentheses, the order of operations is: `*`, then `•`, then `|`. This method\",\n \"state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set,\",\n \"product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1\",\n \"for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def\",\n \"white space. Actually, that's not quite right -- the default value is string.printable\",\n \"self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]:\",\n \"set of states inferred from the transition function; 2. the set of accept\",\n \"'€' (option-shift-2) is used to match the empty string (because it kind of\",\n \"other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1,\",\n \"expressions. '•' (option-8 on a mac keyboard) means concatenation. You can leave concatentation\",\n \"member of the set of states inferred from the transition function; 2. the\",\n \"self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return\",\n \"alphabet contains any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`,\",\n \"> 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <=\",\n \"the nfa's state-set and alphabet; the first elements of the tuples represent the\",\n \"for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept\",\n \"if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2:\",\n \"= { key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function,\",\n \"set of one-character strings) as input; returns an NFA that recognises the language\",\n \"set of \\\"printable\\\" characters, which includes the standard ASCII letters and digits, and\",\n \"state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs,\",\n \"epsilon); there's no other way to match, for instance, {'', '0'} with the\",\n \"s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for\",\n \"in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not\",\n \"= 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range =\",\n \"transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the\",\n \"can match to the empty language with it. For reaons related to the\",\n \"inferred from the transition function; 3. a member of the alphabet inferred from\",\n \") def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def\",\n \"to specify that the successor set for a given state-symbol pair is the\",\n \"char == '(': if len(processed) > 0: processed += ( '•' if processed[-1]\",\n \"give it a go.) 'Ø' (option-shift-o) represents the empty set; you can match\",\n \"elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif\",\n \"else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start})\",\n \"'*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an alphabet\",\n \"-> Regex: if regex in EMPTIES: return '€' if len(regex) == 1: return\",\n \"be a member of the alphabet (and hence the checks below will work\",\n \"`*`, `•`, `€` and `Ø`, 2. the input regex string contains a character\",\n \"of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members of\",\n \"not a member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are\",\n \"alphabet. The domain of the transition function is the power-set of the nfa's\",\n \"does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•':\",\n \") def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State,\",\n \"regex2 if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2\",\n \"== '(': paren_count += 1 elif char == ')': paren_count -= 1 elif\",\n \"by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the set\",\n \"a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def\",\n \"State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state,\",\n \"Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes three\",\n \"{prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap =\",\n \"any number of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function\",\n \"In the absence of parentheses, the order of operations is: `*`, then `•`,\",\n \"state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in\",\n \"accept states is not a subset of the set of states inferred from\",\n \"represents the empty set; you can match to the empty language with it.\",\n \"tilde, for reasons that I will explain presently. As of now, the syntax\",\n \"MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if\",\n \"inferred to be a member of the alphabet (and hence the checks below\",\n \"that recognizes A concat B -- i.e., the language consisting of the set\",\n \"transition function is not a subset of the power set of states inferred\",\n \"to speakers of Scandinavian languages for the last one; I am very against\",\n \"> 0: raise ValueError( \\\"Left parenthesis occurs in regex without matching right \\\"\",\n \"the transition function. Note that the empty string will not be inferred to\",\n \"of B. Note that this `+` operation is not commutative. \\\"\\\"\\\" new_self, new_other,\",\n \"ordered pairs of states from dfa1 and dfa2. There is no problem with\",\n \"State: counter = 1 new_state = 'new_state1' while new_state in state_set: counter +=\",\n \"return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA(\",\n \"domain of the transition function. The exception message will specify which of these\",\n \"not in the fsa's state set.\\\"), message_plural=(\\\"States {} in the range of the\",\n \"range of the transition \\\" \\\"function are not in the fsa's state set.\\\")\",\n \"error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state\",\n \"_GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1,\",\n \"regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states -\",\n \"the alphabet of the NFA. (My apologies to speakers of Scandinavian languages for\",\n \"fsa's state set.\\\") ) def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")`\",\n \"expressions that this method takes as input is very simple -- much simpler\",\n \"self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug.\",\n \"NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This\",\n \"def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts input string.\",\n \"union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state,\",\n \"member of the transition function's range is not a set; 5. the range\",\n \"the language recognised by nfa1, and B be the language recognized by nfa2.\",\n \".base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol\",\n \"set; 5. the range of the transition function is not a subset of\",\n \"characters are intepreted as literals for symbols in the alphabet except for '(',\",\n \"a ValueError exception if any of the following conditions hold: 1. the alphabet\",\n \"much more complicated than necessary; maybe I'll figure out how to improve on\",\n \"is not a member of the set of states inferred from the transition\",\n \"the transition function is not a one-character string; 4. a member of the\",\n \"current_states & self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that\",\n \"OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if char in\",\n \"dfa2 are ordered pairs of states from dfa1 and dfa2. There is no\",\n \"that recognizes A union B. The cardinality of the state-set of nfa1 |\",\n \"(i.e., a set of one-character strings) as input; returns an NFA that recognises\",\n \"the language accepted by dfa. `dfa.encode()` returns a regex string that generates A.\",\n \"it is. You can define epsilon-moves by using the empty string in place\",\n \"in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else {'q1'}\",\n \"state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for\",\n \"def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys():\",\n \"value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State])\",\n \"as the NFA instance. WARNING: The set of DFA states has the cardinality\",\n \"def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function\",\n \"{} in the range of the transition \\\" \\\"function are not sets.\\\") )\",\n \"`dfa1 | dfa2` returns a dfa that recognizes A union B. The states\",\n \"= accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) ->\",\n \"_get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in\",\n \"= f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1:\",\n \"is usual; no need to write '•'' explicitly if you don't want to.\",\n \"the nfa's state set --- i.e., the values of the transition function dictionary\",\n \"self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function\",\n \"from itertools import product, chain, combinations from string import printable from typing import\",\n \"+ state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state =\",\n \"a one-character string; 4. a member of the transition function's range is not\",\n \"if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf,\",\n \"def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort()\",\n \"\\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language\",\n \"self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] =\",\n \"to the empty language with it. For reaons related to the above, the\",\n \"The exception message will specify which of these above conditions things triggered the\",\n \"true: * the start state is not a member of the set of\",\n \"- state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' )\",\n \"the set of states inferred from the transition function; * a member of\",\n \"hold: 1. the alphabet contains any of the verboten characters -- i.e.,`(` ,\",\n \"you are familiar with regular expressions. '•' (option-8 on a mac keyboard) means\",\n \"- dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for\",\n \"symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states,\",\n \"\\\"printable\\\" characters, which includes the standard ASCII letters and digits, and most common\",\n \"machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator:\",\n \") NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA):\",\n \"simple -- much simpler than the standard python regular expresssions. All characters are\",\n \"'|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an\",\n \"operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ):\",\n \"fsa's state set.\\\"), message_plural=(\\\"States {} in the range of the transition \\\" \\\"function\",\n \"\\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa. `nfa.self()` returns an\",\n \"right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1])\",\n \"letter is so very close to the empty-set symbol. If, by some miracle,\",\n \"start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def\",\n \"the empty set; you can match to the empty language with it. For\",\n \"PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char)\",\n \"and dfa2. There is no problem with the input DFAs having different alphabets.\",\n \"A union B. The states of dfa1 | dfa2 are ordered pairs of\",\n \"be done; give it a go.) 'Ø' (option-shift-o) represents the empty set; you\",\n \"powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in\",\n \"\\\"function are not in the fsa's state set.\\\") ) def accepts(self, string: str)\",\n \"the power-set of the nfa's state set --- i.e., the values of the\",\n \"Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from\",\n \"deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State]\",\n \"and Symbols are one-char strings). The keys of the `transition_function` implicitly define the\",\n \"following conditions hold: 1. the alphabet contains any of the verboten characters --\",\n \"string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some\",\n \"very simple -- much simpler than the standard python regular expresssions. All characters\",\n \"set of NFA states. For related reasons, the time complexity of this method\",\n \"other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states,\",\n \"AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton\",\n \"set.\\\"), message_plural=(\\\"Accept states {} are not members of the fsa's \\\" \\\"state set.\\\")\",\n \"- `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings). The\",\n \"state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states)\",\n \"else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA:\",\n \"| {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in\",\n \"implicitly define the dfa's state-set and alphabet. The class will raise a ValueError\",\n \"This method uses a version of Dijkstra's shunting yard algorithm to parse the\",\n \"== 1: return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex)\",\n \"star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for\",\n \"symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)]\",\n \"returns an nfa that recognizes A union B. The cardinality of the state-set\",\n \"if regex1 == '€': return regex2 if regex2 == '€': return regex1 if\",\n \"cannot start with '{first_char}'.\\\") processed = '' paren_count = 0 for char in\",\n \"reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1,\",\n \"for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA(\",\n \"except for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical\",\n \"dictionary should be sets (or frozensets). The empty set is a valid value;\",\n \") machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) )\",\n \"(where States are strings and Symbols are one-char strings). The keys of the\",\n \"English chauvinism, but your letter is so very close to the empty-set symbol.\",\n \"File containing DFA and NFA public classes \\\"\\\"\\\" import collections.abc from itertools import\",\n \"states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter =\",\n \"-> bool: \\\"\\\"\\\" Determines whether nfa accepts input string. Will raise a ValueError\",\n \"DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states\",\n \"the input regex does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = {\",\n \"strings in B. This DFA operator is parasitic on the NFA operator; it\",\n \"NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols =\",\n \"regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if\",\n \"it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in\",\n \") regex = _pre_process(regex, alphabet) for char in regex: if char in EMPTIES:\",\n \"( '•' if processed[-1] not in {'(', '|'} else '' ) if char\",\n \"None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand )\",\n \"string contains symbols that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state\",\n \"FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set =\",\n \"-> Regex: if regex1 == \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return\",\n \"cardinality of the state-set of nfa1 | nfa2 is the cardinality of the\",\n \"then `•`, then `|`. This method uses a version of Dijkstra's shunting yard\",\n \"_get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start,\",\n \"for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours =\",\n \"transition \\\" \\\"function are not in the fsa's state set.\\\") ) def _get_successors(\",\n \"totally obvious how to match the empty string in normal python regex syntax\",\n \"= nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states,\",\n \"): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(':\",\n \"nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union B. The\",\n \"regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\": return regex2 if regex2 ==\",\n \"{star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet:\",\n \"+ other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol\",\n \"{'(', ')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression\",\n \"Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction,\",\n \"source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let\",\n \"and not one of the above veboten characters, 3. the input regex contain\",\n \"are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range -\",\n \"\\\"\\\"\\\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification\",\n \"self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states,\",\n \"epsilon-moves by using the empty string in place of an alphabet symbol in\",\n \"= '€' for state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept}\",\n \"which of these six conditions things triggered the exception, and which states/symbols are\",\n \"for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = (\",\n \"the order of operations is: `*`, then `•`, then `|`. This method uses\",\n \"return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self)\",\n \"& alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" )\",\n \"class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes three keyword arguments: -\",\n \"current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A be the language\",\n \"EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char\",\n \"} for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states =\",\n \"if char == '(': paren_count += 1 if char == ')': paren_count -=\",\n \"defined by that regular expression and that alphabet. The alphabet parameter is optional;\",\n \"combinations from string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable,\",\n \"Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes three keyword\",\n \"recognizes A union B. The states of dfa1 | dfa2 are ordered pairs\",\n \"of NFA states. For related reasons, the time complexity of this method is\",\n \"empty-set.) In the absence of parentheses, the order of operations is: `*`, then\",\n \"'|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and star mean\",\n \"\\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip =\",\n \"= { pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)]\",\n \"the symbols in the alphabet. The domain of the transition function is the\",\n \"members of the fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction\",\n \"-> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states =\",\n \"a set; 5. the range of the transition function is not a subset\",\n \"of all strings formed by concatenating any number of members of A. \\\"\\\"\\\"\",\n \"input; returns an NFA that recognises the language defined by that regular expression\",\n \"are intepreted as literals for symbols in the alphabet except for '(', '')',\",\n \"of the tuples represent the nfa's states, and the second elements are the\",\n \"the alphabet contains any of the verboten characters -- i.e.,`(` , `)`, `|`,\",\n \"| {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø'\",\n \"recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set\",\n \"language recognised by dfa1, B be the language recognised by dfa2. `dfa1 +\",\n \"DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state\",\n \"if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states),\",\n \"operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator)\",\n \"in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set,\",\n \"-> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same same language as the\",\n \"a version of Dijkstra's shunting yard algorithm to parse the regex and build\",\n \"binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|',\",\n \"matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class\",\n \"_add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours\",\n \"matching \\\" \\\"left parenthesis.\\\" ) processed += char if paren_count > 0: raise\",\n \"< 0: raise ValueError( \\\"Right parenthesis occurs in regex withour matching \\\" \\\"left\",\n \"NFA public classes \\\"\\\"\\\" import collections.abc from itertools import product, chain, combinations from\",\n \"state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept)\",\n \"NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) } accept_states =\",\n \"GNFA equivalent to `self` with one less state in it. \\\"\\\"\\\" def union_main_scope(regex:\",\n \"Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states)\",\n \"the alphabet (and hence the checks below will work as you would expect).\",\n \"A be the language recognised by nfa. `nfa.self()` returns an nfa that recognizes\",\n \"by that regular expression and that alphabet. The alphabet parameter is optional; it's\",\n \"= accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self)\",\n \"other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state + '`'\",\n \"to the empty-set symbol. If, by some miracle, there is someone who cares\",\n \"in fact, you are required to specify that the successor set for a\",\n \"operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop()\",\n \"union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for\",\n \"return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None:\",\n \"in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set()\",\n \"> 0: processed += ( '•' if processed[-1] not in {'(', '|'} else\",\n \"for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def\",\n \"len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE\",\n \"uses a version of Dijkstra's shunting yard algorithm to parse the regex and\",\n \"an operator, or 4. the input regex does not have properly matching parentheses.\",\n \"parse the regex and build the NFA. The method will raise a ValueError\",\n \"kind of looks like an epsilon); there's no other way to match, for\",\n \"above, the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be\",\n \"new_state in state_set: counter += 1 new_state = \\\"new_state\\\" + str(counter) return new_state\",\n \") def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return\",\n \"it's not totally obvious how to match the empty string in normal python\",\n \"( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output\",\n \"transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in\",\n \"start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language\",\n \"if regex in EMPTIES: return '€' if len(regex) == 1: return regex +\",\n \"def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same same language\",\n \"extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1,\",\n \"the language recognised by nfa. `nfa.self()` returns an nfa that recognizes A* --\",\n \"exponential in the number of states of the NFA. Don't determinize big NFAs.\",\n \"regular expression and that alphabet. The alphabet parameter is optional; it's default value\",\n \"member of the alphabet (and hence the checks below will work as you\",\n \"f\\\"Regex contains character '{char}' that is not in \\\" \\\"alphabet and not an\",\n \"the language recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes\",\n \"regex: if char == '(': paren_count += 1 elif char == ')': paren_count\",\n \"character '{char}' that is not in \\\" \\\"alphabet and not an accepted regex\",\n \"{} is not a member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states\",\n \"tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) } accept_states\",\n \"'*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or\",\n \"+ nfa2` returns an nfa that recognizes A concat B -- i.e., the\",\n \"= ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state\",\n \"'|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character\",\n \"is not a set.\\\"), message_plural=(\\\"Values {} in the range of the transition \\\"\",\n \"to be a member of the alphabet (and hence the checks below will\",\n \"very close to the empty-set symbol. If, by some miracle, there is someone\",\n \"determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self)\",\n \"counter = 1 new_state = 'new_state1' while new_state in state_set: counter += 1\",\n \"elements of the tuples represent the nfa's states, and the second elements are\",\n \"= self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit(\",\n \"uses the NFA '+', then converts the result back to a DFA. That\",\n \"__init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state\",\n \"new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states,\",\n \"of the set of states inferred from the transition function; 2. the set\",\n \"State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {}\",\n \"symbol)] return current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A be\",\n \"-> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some string\\\", and\",\n \"set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other)\",\n \"extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf\",\n \"the transition \\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values()\",\n \"regex1 == '€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1):\",\n \"= Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *,\",\n \"get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return\",\n \"the string contains symbols that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet)\",\n \"): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf,\",\n \"'+', then converts the result back to a DFA. That makes for a\",\n \"bad_set=bad_range, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function is not\",\n \"{ new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] =\",\n \"return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable)\",\n \"AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The transition function'\",\n \"other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa1,\",\n \"by nfa1, and B be the language recognized by nfa2. `nfa1 | nfa2`\",\n \"not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) ->\",\n \"states from dfa1 and dfa2. There is no problem with the input DFAs\",\n \"if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains\",\n \"paren_count += 1 if char == ')': paren_count -= 1 if paren_count <\",\n \"returns an nfa that recognizes A* -- i.e., the set of all strings\",\n \"== ')': paren_count -= 1 if paren_count < 0: raise ValueError( \\\"Right parenthesis\",\n \"transition function is the power-set of the nfa's state set --- i.e., the\",\n \"symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf\",\n \"== 0: return True return False def regex_star(regex: Regex) -> Regex: if regex\",\n \"self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]:\",\n \"by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union B.\",\n \"Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the\",\n \"state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex\",\n \"if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 ==\",\n \"= dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states |\",\n \"new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols:\",\n \"self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states\",\n \"= new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction =\",\n \"simpler than the standard python regular expresssions. All characters are intepreted as literals\",\n \"-> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for\",\n \"{error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols:\",\n \"sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State\",\n \"- self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of the transition \\\"\",\n \"= '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2\",\n \"elif char == '|': if paren_count == 0: return True return False def\",\n \"parenthesis.\\\" ) processed += char if paren_count > 0: raise ValueError( \\\"Left parenthesis\",\n \"<= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1]\",\n \"regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def\",\n \"key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states:\",\n \"A union B. The cardinality of the state-set of nfa1 | nfa2 is\",\n \"\\\" \\\"function is not in the fsa's state set.\\\"), message_plural=(\\\"States {} in the\",\n \"string contains symbols that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states\",\n \"is not in \\\" \\\"alphabet and not an accepted regex character.\\\" ) if\",\n \"that this method takes as input is very simple -- much simpler than\",\n \"symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function\",\n \"= '' paren_count = 0 for char in regex: if char in alphabet\",\n \"of states from dfa1 and dfa2. There is no problem with the input\",\n \"['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES\",\n \"body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states\",\n \"} copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA(\",\n \"_GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ):\",\n \"start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state:\",\n \") def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language\",\n \"an nfa that recognizes A* -- i.e., the set of all strings formed\",\n \"def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message(\",\n \"function is not a one-character string; 4. a member of the transition function's\",\n \"recognizes A* -- i.e., the set of all strings formed by concatenating any\",\n \"'Ø' (option-shift-o) represents the empty set; you can match to the empty language\",\n \"new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = {\",\n \"@property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return\",\n \"union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states))\",\n \"0: return True return False def regex_star(regex: Regex) -> Regex: if regex in\",\n \"i.e., the set of \\\"printable\\\" characters, which includes the standard ASCII letters and\",\n \"GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states =\",\n \"NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states,\",\n \"new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states: new_tf[(state,\",\n \"in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self)\",\n \"\\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state + '`' def\",\n \"properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message(\",\n \"epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol:\",\n \"> 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE =\",\n \"AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot\",\n \"a dfa that recognizes A union B. The states of dfa1 | dfa2\",\n \"copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x)\",\n \"of the state-set of nfa2 plus 1. There is no problem with the\",\n \"converts the result back to a DFA. That makes for a relatively simple\",\n \"set.\\\"), message_plural=(\\\"States {} in the range of the transition \\\" \\\"function are not\",\n \"return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø'\",\n \"not totally obvious how to match the empty string in normal python regex\",\n \"you are required to specify that the successor set for a given state-symbol\",\n \"is exponential in the number of states of the NFA. Don't determinize big\",\n \"on a mac keyboard) means concatenation. You can leave concatentation implicit, as is\",\n \"'|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept =\",\n \"set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]:\",\n \"The alphabet parameter is optional; it's default value is string.printable -- i.e., the\",\n \"OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char\",\n \"in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self,\",\n \"union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet:\",\n \"the transition \\\" \\\"function is not in the fsa's state set.\\\"), message_plural=(\\\"States {}\",\n \"symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours\",\n \"and B be the language recognized by nfa2. `nfa1 | nfa2` returns an\",\n \"new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self =\",\n \"-> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for\",\n \"Let A be the language recognised by nfa1, and B be the language\",\n \"how to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states)\",\n \"for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states\",\n \"empty set, if it is. You can define epsilon-moves by using the empty\",\n \"will change the symbol for empty-set.) In the absence of parentheses, the order\",\n \"extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet:\",\n \"start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() ->\",\n \"for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 =\",\n \"contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty: Regex) ->\",\n \"| nfa2 is the cardinality of the state set of nfa1 plus the\",\n \"transition function is not a one-character string; 4. a member of the transition\",\n \"empty string (because it kind of looks like an epsilon); there's no other\",\n \"overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) ->\",\n \"not in the fsa's state set.\\\") ) def accepts(self, string: str) -> bool:\",\n \"by dfa. `dfa.encode()` returns a regex string that generates A. That regex string\",\n \"gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states\",\n \"* a member of the alphabet inferred from the transition function is not\",\n \"= { _stringify(item) for item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) )\",\n \"paren_count = 0 for char in regex: if char in alphabet or char\",\n \"= {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in\",\n \"the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\"\",\n \"self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other\",\n \"return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\",\",\n \"+= ( '•' if processed[-1] not in {'(', '|'} else '' ) if\",\n \"keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State]\",\n \"`nfa1 + nfa2` returns an nfa that recognizes A concat B -- i.e.,\",\n \"= 0 for char in regex: if char in alphabet or char ==\",\n \"of all concatenations of strings in A with strings in B. This DFA\",\n \"the set of states inferred from the transition function; 2. the set of\",\n \"in the alphabet, and not one of the above veboten characters, 3. the\",\n \"no problem with the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet\",\n \"def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa.\",\n \"it. For reaons related to the above, the characters '(', ')', '|', '*',\",\n \"related to the above, the characters '(', ')', '|', '*', '•', '€' and\",\n \"- nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return\",\n \"nfa2` returns an nfa that recognizes A concat B -- i.e., the language\",\n \"nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the set of\",\n \"language consisting of the set of strings of the form a concat b,\",\n \"accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]:\",\n \"self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set()\",\n \"r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)),\",\n \"= self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in\",\n \"explain presently. As of now, the syntax of the regular expressions that this\",\n \"symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines\",\n \"(option-shift-o) represents the empty set; you can match to the empty language with\",\n \"not an accepted regex character.\\\" ) if char in OPERATORS and processed[-1] in\",\n \"and B be the language recognized by nfa2. `nfa1 + nfa2` returns an\",\n \"= self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}:\",\n \"right -- the default value is string.printable *minus* parentheses, the vertical bar, the\",\n \"Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and\",\n \"not a member of the set of states inferred from the transition function;\",\n \"_get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] =\",\n \"are familiar with regular expressions. '•' (option-8 on a mac keyboard) means concatenation.\",\n \"other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other =\",\n \"super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states -\",\n \"= powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets,\",\n \"DFA instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value}\",\n \"Let A be the language recognised by nfa. `nfa.self()` returns an nfa that\",\n \"} tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA]\",\n \"symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states |\",\n \"nfa's state-set and alphabet; the first elements of the tuples represent the nfa's\",\n \"in {'(', '|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS):\",\n \"Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex) == 1:\",\n \"in the alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'.\",\n \"f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if\",\n \"alphabet. The alphabet parameter is optional; it's default value is string.printable -- i.e.,\",\n \"self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else:\",\n \"return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy\",\n \"message_singular=(\\\"Value {} in the range of the transition \\\" \\\"function is not a\",\n \"r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept =\",\n \"self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set)\",\n \"version of Dijkstra's shunting yard algorithm to parse the regex and build the\",\n \"that reason, I recommend you don't `+` dfas with large numbers of states.\",\n \"gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start\",\n \"state set.\\\") ) def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns\",\n \"x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states &\",\n \"the range of the transition function is not a subset of the power\",\n \"Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø'\",\n \"arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where\",\n \"order of operations is: `*`, then `•`, then `|`. This method uses a\",\n \"new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return\",\n \"set of states inferred from the transition function; 3. a member of the\",\n \"symbol in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states ==\",\n \"NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let\",\n \"symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts input\",\n \"following are true: * the start state is not a member of the\",\n \"checks below will work as you would expect). The class will raise a\",\n \"\\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {} in the range of the\",\n \"miracle, there is someone who cares about this, I will change the symbol\",\n \"by an \\\" \\\"operator; not cool.\\\" ) if char == '(': paren_count +=\",\n \"is used to match the empty string (because it kind of looks like\",\n \"{gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€'\",\n \"= gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that\",\n \"\\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa1, and B be\",\n \"star mean what you'd expect them to mean if you are familiar with\",\n \"`dfa.encode()` returns a regex string that generates A. That regex string is liable\",\n \"state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors(\",\n \") reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state\",\n \"symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1,\",\n \"self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours\",\n \"the values of the transition function dictionary should be sets (or frozensets). The\",\n \"self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in self.states -\",\n \"union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex: if char\",\n \"in the fsa's state set.\\\"), message_plural=(\\\"States {} in the range of the transition\",\n \"states inferred from the transition function; 6. the transition function is missing cases\",\n \"-> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, B be\",\n \"= {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1,\",\n \"alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def\",\n \"other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2:\",\n \"about this, I will change the symbol for empty-set.) In the absence of\",\n \"the state set of nfa1 plus the cardinality of the state-set of nfa2\",\n \"regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 ==\",\n \"'•', '€' and 'Ø' cannot be symbols in the alphabet of the NFA.\",\n \"The keys of the `transition_function` implicitly define the dfa's state-set and alphabet. The\",\n \"== set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same\",\n \"= [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand\",\n \"= _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)]\",\n \"start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states,\",\n \"if paren_count < 0: raise ValueError( \\\"Right parenthesis occurs in regex withour matching\",\n \"= ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine\",\n \"Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base):\",\n \"def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str)\",\n \"the result back to a DFA. That makes for a relatively simple but,\",\n \"'') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf,\",\n \"of an alphabet symbol in the transition function. Note that the empty string\",\n \"language recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A\",\n \"= ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex,\",\n \"case that every pair of a state and a symbol is in the\",\n \"(option-shift-2) is used to match the empty string (because it kind of looks\",\n \"__or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by\",\n \"union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) ->\",\n \"and white space. Actually, that's not quite right -- the default value is\",\n \"instance, {'', '0'} with the current syntax. (Quick challenge: it's not totally obvious\",\n \"to `self` with one less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) ->\",\n \"state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours\",\n \"ValueError exception on instantiation if any of the following are true: 1. the\",\n \"1. There is no problem with the input NFAs having different alphabets. \\\"\\\"\\\"\",\n \"message will specify which of these above conditions things triggered the exception, and\",\n \"that regular expression and that alphabet. The alphabet parameter is optional; it's default\",\n \"a given state-symbol pair is the empty set, if it is. You can\",\n \"using the empty string in place of an alphabet symbol in the transition\",\n \") overlap = self.states & other.states while overlap: other = copy(other) overlap =\",\n \"not current_states & self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA\",\n \"of the NFA. (My apologies to speakers of Scandinavian languages for the last\",\n \"in the range of the transition \\\" \\\"function are not in the fsa's\",\n \"alphabet or char == '(': if len(processed) > 0: processed += ( '•'\",\n \"AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols\",\n \"of the set of strings of the form a concat b, where a\",\n \") } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") ->\",\n \"a member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not\",\n \"in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] =\",\n \"str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some string\\\",\",\n \"def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA,\",\n \"= ( set(printable) - {'(', ')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\"\",\n \"input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state =\",\n \"from the transition function is not a one-character string; * the transition function\",\n \"| dfa2 are ordered pairs of states from dfa1 and dfa2. There is\",\n \"to match, for instance, {'', '0'} with the current syntax. (Quick challenge: it's\",\n \"self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex:\",\n \"( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif\",\n \"tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] =\",\n \"-= 1 elif char == '|': if paren_count == 0: return True return\",\n \"regex withour matching \\\" \\\"left parenthesis.\\\" ) processed += char if paren_count >\",\n \"not a one-character string; 4. a member of the transition function's range is\",\n \"and which states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self, other:\",\n \"'•' (option-8 on a mac keyboard) means concatenation. You can leave concatentation implicit,\",\n \"\\\"Right parenthesis occurs in regex withour matching \\\" \\\"left parenthesis.\\\" ) processed +=\",\n \"instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for key, value in self.transition_function.items() }\",\n \"else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self,\",\n \"by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat B\",\n \"complicated than necessary; maybe I'll figure out how to improve on average simplicity,\",\n \"_error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member of the fsa's \\\"\",\n \"raise a ValueError exception on instantiation if any of th following are true:\",\n \"= self.start_state + other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states,\",\n \"no other way to match, for instance, {'', '0'} with the current syntax.\",\n \"empty string in place of an alphabet symbol in the transition function. Note\",\n \"transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate()\",\n \"= ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\"\",\n \"symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item\",\n \"_extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def\",\n \"the checks below will work as you would expect). The class will raise\",\n \"the range of the transition \\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]]\",\n \"verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2.\",\n \"or 4. the input regex does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation\",\n \"in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in\",\n \"determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) &\",\n \"modification to make the return a set of frozensets). def powerset(iterable: Iterable) ->\",\n \"as input; returns an NFA that recognises the language defined by that regular\",\n \"A and b is an element of B. Note that this `+` operation\",\n \"for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf\",\n \"dfas with large numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self)\",\n \"== \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip\",\n \"the transition function; 2. the set of accept states is not a subset\",\n \"- `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States\",\n \"in place of an alphabet symbol in the transition function. Note that the\",\n \"the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string:\",\n \"machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if\",\n \"to the above, the characters '(', ')', '|', '*', '•', '€' and 'Ø'\",\n \"= f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex:\",\n \"NFA states. For related reasons, the time complexity of this method is exponential\",\n \"in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char\",\n \"_stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state})\",\n \"input regex does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|':\",\n \"concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states\",\n \"{rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol],\",\n \"\\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language\",\n \"shunting yard algorithm to parse the regex and build the NFA. The method\",\n \"transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence):\",\n \"ValueError( f\\\"Regex contains character '{char}' that is not in \\\" \\\"alphabet and not\",\n \"import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union,\",\n \"the alphabet. The domain of the transition function is the power-set of the\",\n \"(My apologies to speakers of Scandinavian languages for the last one; I am\",\n \"new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State,\",\n \"Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings,\",\n \"self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the\",\n \"that this `+` operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other)\",\n \"and `Ø`, 2. the input regex string contains a character not in the\",\n \"1 new_state = 'new_state1' while new_state in state_set: counter += 1 new_state =\",\n \"of A and b is an element of B. Note that this `+`\",\n \"= dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states\",\n \"b, where a is an element of A and b is an element\",\n \"state-set of nfa2 plus 1. There is no problem with the input NFAs\",\n \"accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts input string. Will\",\n \"Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable(\",\n \"set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other:\",\n \"tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet) }\",\n \"Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r)\",\n \"overlap = self.states & other.states while overlap: other = copy(other) overlap = self.states\",\n \"\\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex]\",\n \"self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not current_states &\",\n \"else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex\",\n \"alphabet, and not one of the above veboten characters, 3. the input regex\",\n \"be the language recognized by dfa2. `dfa1 | dfa2` returns a dfa that\",\n \"space. Actually, that's not quite right -- the default value is string.printable *minus*\",\n \"mean what you'd expect them to mean if you are familiar with regular\",\n \"Convenience method that takes a DFA instance and returns an NFA instance. \\\"\\\"\\\"\",\n \"6. the transition function is missing cases -- i.e., it is not the\",\n \"which includes the standard ASCII letters and digits, and most common punctuation and\",\n \"recognised by dfa1, and B be the language recognized by dfa2. `dfa1 |\",\n \"the transition function dictionary should be sets (or frozensets). The empty set is\",\n \"_GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range =\",\n \"by nfa1, and B be the language recognized by nfa2. `nfa1 + nfa2`\",\n \"set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range\",\n \"states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state =\",\n \"average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa =\",\n \"NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) ->\",\n \"number of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start,\",\n \"AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet(\",\n \"regular expressions that this method takes as input is very simple -- much\",\n \"the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s =\",\n \"\\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, and B be\",\n \"other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state,\",\n \"epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set\",\n \"raise a ValueError exception is the string contains symbols that aren't in the\",\n \"DFA that recognizes the same same language as the NFA instance. WARNING: The\",\n \"item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets =\",\n \"dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state =\",\n \"dfa2` returns a DFA that recognises the set of all concatenations of strings\",\n \"+= char if paren_count > 0: raise ValueError( \\\"Left parenthesis occurs in regex\",\n \"string.printable *minus* parentheses, the vertical bar, the star symbol, and the tilde, for\",\n \"self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined()\",\n \"no need to write '•'' explicitly if you don't want to. But it\",\n \"languages for the last one; I am very against English chauvinism, but your\",\n \"string that generates A. That regex string is liable to be much more\",\n \"of DFA states has the cardinality of the power-set of the set of\",\n \"accept_states = set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1',\",\n \"and returns an NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for key,\",\n \"contains symbols that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states =\",\n \"symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self)\",\n \"} return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\":\",\n \"{} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] =\",\n \"the transition function is not a one-character string; * the transition function is\",\n \"are one-char strings.) The transition function' keys implicitly define the nfa's state-set and\",\n \"a go.) 'Ø' (option-shift-o) represents the empty set; you can match to the\",\n \"returns a regex string that generates A. That regex string is liable to\",\n \"-> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set\",\n \"symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = []\",\n \"from the transition function is not a one-character string; 4. a member of\",\n \"the range of the transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {}\",\n \"in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(':\",\n \"the transition function; 3. a member of the alphabet inferred from the transition\",\n \"List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import (\",\n \"start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {}\",\n \"string in normal python regex syntax either, though it can be done; give\",\n \"concatentation implicit, as is usual; no need to write '•'' explicitly if you\",\n \"out how to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while\",\n \"will explain presently. As of now, the syntax of the regular expressions that\",\n \"any of the following conditions hold: 1. the alphabet contains any of the\",\n \"raise ValueError( f\\\"Regex contains character '{char}' that is not in \\\" \\\"alphabet and\",\n \"the domain of the transition function. The exception message will specify which of\",\n \"should be sets (or frozensets). The empty set is a valid value; in\",\n \"self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states\",\n \"for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state,\",\n \"transition function. The exception message will specify which of these above conditions things\",\n \"by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union B.\",\n \"function is not a subset of the set of states inferred from the\",\n \"-> None: bad_range = { x for x in self.transition_function.values() if not isinstance(x,\",\n \"Will raise a ValueError exception is the string contains symbols that aren't in\",\n \"(because it kind of looks like an epsilon); there's no other way to\",\n \"def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function )\",\n \"| {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf: gnfa_tf[(state1,\",\n \") def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states)\",\n \"the transition function. The exception message will specify which of these above conditions\",\n \"takes a DFA instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function = {\",\n \"set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range\",\n \"0 for char in regex: if char in alphabet or char == '(':\",\n \"= ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE +\",\n \"state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2)\",\n \"set(printable) - {'(', ')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a\",\n \"function's range is not a set; 5. the range of the transition function\",\n \"1: return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) ->\",\n \"A concat B -- i.e., the language consisting of the set of strings\",\n \") ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an alphabet (i.e.,\",\n \"= {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap\",\n \"compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex,\",\n \"Container) -> State: counter = 1 new_state = 'new_state1' while new_state in state_set:\",\n \"__init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State ): self.transition_function =\",\n \"of the regular expressions that this method takes as input is very simple\",\n \"There is no problem with the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet\",\n \"return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x for\",\n \"is very simple -- much simpler than the standard python regular expresssions. All\",\n \"add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet -\",\n \"self.start_state + other.start_state union_accept_states = { _stringify(item) for item in ( set(product(self.accept_states, other_states))\",\n \"list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state\",\n \"def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex: if\",\n \"== ')': paren_count -= 1 elif char == '|': if paren_count == 0:\",\n \"concatenation. You can leave concatentation implicit, as is usual; no need to write\",\n \"nfa2 is the cardinality of the state set of nfa1 plus the cardinality\",\n \"return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\"\",\n \"DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols =\",\n \"char == ')': paren_count -= 1 elif char == '|': if paren_count ==\",\n \"NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf\",\n \"= self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set: state_set = state_set |\",\n \"of the state set of nfa1 plus the cardinality of the state-set of\",\n \"NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) ->\",\n \"Let A be the language recognised by dfa1, and B be the language\",\n \"= {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)]\",\n \"raise a ValueError exception on instantiation if any of the following are true:\",\n \"fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State],\",\n \"The states of dfa1 | dfa2 are ordered pairs of states from dfa1\",\n \"_well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states\",\n \"self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept )\",\n \"AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]:\",\n \"accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) -\",\n \"message_plural=(\\\"Values {} in the range of the transition \\\" \\\"function are not sets.\\\")\",\n \"states inferred from the transition function; 2. the set of accept states is\",\n \"inferred from the transition function is not a one-character string; * the transition\",\n \"A. That regex string is liable to be much more complicated than necessary;\",\n \"of the transition function is not a subset of the set of states\",\n \"def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised\",\n \"star symbol, and the tilde, for reasons that I will explain presently. As\",\n \"{} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range =\",\n \"string.printable -- i.e., the set of \\\"printable\\\" characters, which includes the standard ASCII\",\n \"`+` operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state\",\n \"combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None:\",\n \"symbol is in the domain of the transition function. The exception message will\",\n \"of strings in A with strings in B. This DFA operator is parasitic\",\n \"paren_count < 0: raise ValueError( \\\"Right parenthesis occurs in regex withour matching \\\"\",\n \"Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction,\",\n \"`accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings). The keys\",\n \"operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS\",\n \"the empty string in normal python regex syntax either, though it can be\",\n \"usual; no need to write '•'' explicitly if you don't want to. But\",\n \"'')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state,\",\n \"exception on instantiation if any of the following are true: 1. the start\",\n \") transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {}\",\n \"transition function; 3. a member of the alphabet inferred from the transition function\",\n \"a DFA. That makes for a relatively simple but, sadly, computationally expensive algorith.\",\n \"import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol =\",\n \"-> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is\",\n \"is in the domain of the transition function. The exception message will specify\",\n \"the power-set of the set of NFA states. For related reasons, the time\",\n \"is: `*`, then `•`, then `|`. This method uses a version of Dijkstra's\",\n \"\\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, B be the\",\n \"elif char == ')': paren_count -= 1 elif char == '|': if paren_count\",\n \"'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol])\",\n \"any of the following are true: 1. the start state is not a\",\n \"the set of all strings formed by concatenating any number of members of\",\n \"class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State\",\n \"return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in\",\n \"missing a case -- i.e., it is not the case that every pair\",\n \"it gets used internally. '€' (option-shift-2) is used to match the empty string\",\n \"self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states)\",\n \") while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors(\",\n \"of looks like an epsilon); there's no other way to match, for instance,\",\n \"Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA)\",\n \"the number of states of the NFA. Don't determinize big NFAs. \\\"\\\"\\\" #\",\n \"transition \\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() )\",\n \"-- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input\",\n \"binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand,\",\n \"2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience\",\n \"= 'new_state1' while new_state in state_set: counter += 1 new_state = \\\"new_state\\\" +\",\n \"\\\"\\\"\\\"Returns a DFA that recognizes the same same language as the NFA instance.\",\n \"Dijkstra's shunting yard algorithm to parse the regex and build the NFA. The\",\n \"the time complexity of this method is exponential in the number of states\",\n \"the empty set, if it is. You can define epsilon-moves by using the\",\n \"current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol) return not\",\n \"Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) )\",\n \"\\\"\\\"\\\" Let A be the language recognised by dfa1, and B be the\",\n \"syntax. (Quick challenge: it's not totally obvious how to match the empty string\",\n \"done; give it a go.) 'Ø' (option-shift-o) represents the empty set; you can\",\n \"self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol\",\n \"\\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states,\",\n \"{ pair: set() for pair in product({'q1'}, alphabet) } accept_states = set() if\",\n \"-> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty:\",\n \"for a relatively simple but, sadly, computationally expensive algorith. For that reason, I\",\n \"# This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in\",\n \"states of dfa1 | dfa2 are ordered pairs of states from dfa1 and\",\n \"else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return\",\n \"of the NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset code an itertools\",\n \"function. The exception message will specify which of these above conditions things triggered\",\n \"to mean if you are familiar with regular expressions. '•' (option-8 on a\",\n \"represent the nfa's states, and the second elements are the symbols in the\",\n \"paren_count -= 1 elif char == '|': if paren_count == 0: return True\",\n \"def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex =\",\n \"right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine)\",\n \"all strings formed by concatenating any number of members of A. \\\"\\\"\\\" star_start\",\n \"true: 1. the start state is not a member of the set of\",\n \"one less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count =\",\n \"pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf,\",\n \"string will not be inferred to be a member of the alphabet (and\",\n \"nfa that recognizes A union B. The cardinality of the state-set of nfa1\",\n \"automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`:\",\n \"nfa1 | nfa2 is the cardinality of the state set of nfa1 plus\",\n \"B be the language recognized by dfa2. `dfa1 | dfa2` returns a dfa\",\n \"nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set()\",\n \"avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] )\",\n \"way to match, for instance, {'', '0'} with the current syntax. (Quick challenge:\",\n \"`start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char\",\n \"of a state and a symbol is in the domain of the transition\",\n \"( set(printable) - {'(', ')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes\",\n \"{}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf:\",\n \"in the number of states of the NFA. Don't determinize big NFAs. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state,\",\n \"are strings, and Symbols are one-char strings.) The transition function' keys implicitly define\",\n \"set, if it is. You can define epsilon-moves by using the empty string\",\n \"_error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters\",\n \"as is usual; no need to write '•'' explicitly if you don't want\",\n \"by dfa1, B be the language recognised by dfa2. `dfa1 + dfa2` returns\",\n \"gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range -\",\n \"set of strings of the form a concat b, where a is an\",\n \"def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet\",\n \"if processed[-1] not in {'(', '|'} else '' ) if char not in\",\n \"not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of the\",\n \"an NFA that recognises the language defined by that regular expression and that\",\n \"Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function:\",\n \"start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property\",\n \"return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self)\",\n \"MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes three\",\n \"characters, which includes the standard ASCII letters and digits, and most common punctuation\",\n \"nfa1, and B be the language recognized by nfa2. `nfa1 + nfa2` returns\",\n \"The transition function' keys implicitly define the nfa's state-set and alphabet; the first\",\n \"will specify which of these six conditions things triggered the exception, and which\",\n \"recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat\",\n \"MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states:\",\n \"and star mean what you'd expect them to mean if you are familiar\",\n \"rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for state1 in\",\n \"| {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] =\",\n \"paren_count == 0: return True return False def regex_star(regex: Regex) -> Regex: if\",\n \"r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {}\",\n \"states. For related reasons, the time complexity of this method is exponential in\",\n \"= {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state =\",\n \"ASCII letters and digits, and most common punctuation and white space. Actually, that's\",\n \"for symbol in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states\",\n \"input regex string contains a character not in the alphabet, and not one\",\n \"'€': return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 =\",\n \") def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self` with\",\n \"dfa1 and dfa2. There is no problem with the input DFAs having different\",\n \"aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol\",\n \"= self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let\",\n \"below will work as you would expect). The class will raise a ValueError\",\n \"Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input )\",\n \"product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf,\",\n \"['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine =\",\n \"'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) ->\",\n \"set of all strings formed by concatenating any number of members of A.\",\n \"union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] )\",\n \"WARNING: The set of DFA states has the cardinality of the power-set of\",\n \"State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function:\",\n \"a DFA that recognizes the same same language as the NFA instance. WARNING:\",\n \"by an operator, or 4. the input regex does not have properly matching\",\n \"for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify(\",\n \"set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states )\",\n \"def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in\",\n \"= {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") ->\",\n \"return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel']\",\n \"= regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA(\",\n \"specify that the successor set for a given state-symbol pair is the empty\",\n \"in regex withour matching \\\" \\\"left parenthesis.\\\" ) processed += char if paren_count\",\n \"{} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x\",\n \"operator followed by an operator, or 4. the input regex does not have\",\n \"chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf\",\n \"_good_alphabet, _check_input ) State = str Symbol = str Regex = str FsaTransitionFunction\",\n \"self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) )\",\n \"the symbol for empty-set.) In the absence of parentheses, the order of operations\",\n \"then converts the result back to a DFA. That makes for a relatively\",\n \"implicitly define the nfa's state-set and alphabet; the first elements of the tuples\",\n \"expression and that alphabet. The alphabet parameter is optional; it's default value is\",\n \"alphabet parameter is optional; it's default value is string.printable -- i.e., the set\",\n \"OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while (\",\n \"gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes a DFA\",\n \"`transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are\",\n \"a subset of the set of states inferred from the transition function; *\",\n \") self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) ->\",\n \"powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet):\",\n \"(Where States are strings, and Symbols are one-char strings.) The transition function' keys\",\n \"can leave concatentation implicit, as is usual; no need to write '•'' explicitly\",\n \"AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states:\",\n \"is not a one-character string; * the transition function is missing a case\",\n \"\\\" \\\"function are not in the fsa's state set.\\\") ) def accepts(self, string:\",\n \"{ x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range,\",\n \"though it can be done; give it a go.) 'Ø' (option-shift-o) represents the\",\n \"with the current syntax. (Quick challenge: it's not totally obvious how to match\",\n \"self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids\",\n \"dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state}\",\n \"= _pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif\",\n \"rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1,\",\n \"exception, and which states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self,\",\n \"the NFA. (My apologies to speakers of Scandinavian languages for the last one;\",\n \"the alphabet, and not one of the above veboten characters, 3. the input\",\n \"new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\":\",\n \"paren_count -= 1 if paren_count < 0: raise ValueError( \\\"Right parenthesis occurs in\",\n \"recognised by dfa1, B be the language recognised by dfa2. `dfa1 + dfa2`\",\n \"state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state,\",\n \"PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE\",\n \"'(': if len(processed) > 0: processed += ( '•' if processed[-1] not in\",\n \"The cardinality of the state-set of nfa1 | nfa2 is the cardinality of\",\n \"= body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states |\",\n \"_good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {}\",\n \"return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self,\",\n \"def binary_operate() -> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()](\",\n \"if you are familiar with regular expressions. '•' (option-8 on a mac keyboard)\",\n \"- {'(', ')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular\",\n \"the form a concat b, where a is an element of A and\",\n \"NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair\",\n \"self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range\",\n \"is not in the fsa's state set.\\\"), message_plural=(\\\"States {} in the range of\",\n \"A be the language recognised by dfa1, B be the language recognised by\",\n \"= state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def\",\n \"states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") ->\",\n \"Regex) -> bool: paren_count = 0 for char in regex: if char ==\",\n \"start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair:\",\n \"and alphabet; the first elements of the tuples represent the nfa's states, and\",\n \"union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self,\",\n \"frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string:\",\n \"the set of states inferred from the transition function; 3. a member of\",\n \"there is someone who cares about this, I will change the symbol for\",\n \"regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed =\",\n \"of accept states is not a subset of the set of states inferred\",\n \"of the alphabet inferred from the transition function is not a one-character string;\",\n \"either, though it can be done; give it a go.) 'Ø' (option-shift-o) represents\",\n \"= _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state,\",\n \"\\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '')\",\n \"strings, and Symbols are one-char strings.) The transition function' keys implicitly define the\",\n \"-> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa. `nfa.self()` returns\",\n \"= maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2),\",\n \"for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start}\",\n \"transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A\",\n \"a state and a symbol is in the domain of the transition function.\",\n \"instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for\",\n \"exception on instantiation if any of th following are true: * the start\",\n \"-> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet)\",\n \"parasitic on the NFA operator; it converts the input DFAs into NFAs, uses\",\n \"star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)]\",\n \"of nfa1 plus the cardinality of the state-set of nfa2 plus 1. There\",\n \"in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) )\",\n \"the empty string (because it kind of looks like an epsilon); there's no\",\n \"determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same same language as\",\n \"same same language as the NFA instance. WARNING: The set of DFA states\",\n \"state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)]\",\n \"- {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State,\",\n \"other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf =\",\n \"\\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self` with one less state in\",\n \"state1, state2 in product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1,\",\n \"= set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states\",\n \"strings in A with strings in B. This DFA operator is parasitic on\",\n \"in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in\",\n \"gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for\",\n \"be inferred to be a member of the alphabet (and hence the checks\",\n \"close to the empty-set symbol. If, by some miracle, there is someone who\",\n \"symbol='' ) while epsilon_neighbours - state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours =\",\n \"one of the above veboten characters, 3. the input regex contain a binary\",\n \"not be inferred to be a member of the alphabet (and hence the\",\n \"are true: * the start state is not a member of the set\",\n \"a one-character string; * the transition function is missing a case -- i.e.,\",\n \"self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self)\",\n \"pair: set() for pair in product({'q1'}, alphabet) } accept_states = set() if empty\",\n \"machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand =\",\n \"str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__(\",\n \"Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols\",\n \"star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts\",\n \"fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair in\",\n \"for instance, {'', '0'} with the current syntax. (Quick challenge: it's not totally\",\n \"algorithm to parse the regex and build the NFA. The method will raise\",\n \"self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept\",\n \"The domain of the transition function is the power-set of the nfa's state\",\n \"state_set: state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return\",\n \"any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€`\",\n \"Output a GNFA equivalent to `self` with one less state in it. \\\"\\\"\\\"\",\n \"not in {'(', '|'} else '' ) if char not in alphabet |\",\n \"-> State: counter = 1 new_state = 'new_state1' while new_state in state_set: counter\",\n \"= set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range\",\n \"'|', '*', '•', '€' and 'Ø' cannot be symbols in the alphabet of\",\n \"NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet\",\n \"__or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by\",\n \"state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self,\",\n \"len(regex) == 1: return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2:\",\n \"from dfa1 and dfa2. There is no problem with the input DFAs having\",\n \"non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes a DFA instance and returns\",\n \"'*', '•', '€' and 'Ø'. The parentheses, vertical bar and star mean what\",\n \"\\\"\\\"\\\" A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State,\",\n \"is no problem with the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet =\",\n \"in normal python regex syntax either, though it can be done; give it\",\n \"+ '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol\",\n \"last one; I am very against English chauvinism, but your letter is so\",\n \"gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) ->\",\n \"powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make\",\n \"with one less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count\",\n \"return current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A be the\",\n \"strings formed by concatenating any number of members of A. \\\"\\\"\\\" star_start =\",\n \"return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES\",\n \"if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2):\",\n \"* the set of accept states is not a subset of the set\",\n \"transition function; * the range of the transition function is not a subset\",\n \"set of nfa1 plus the cardinality of the state-set of nfa2 plus 1.\",\n \"self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member of the fsa's\",\n \"reasons that I will explain presently. As of now, the syntax of the\",\n \"cares about this, I will change the symbol for empty-set.) In the absence\",\n \"= set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states )\",\n \"states inferred from the transition function; * the range of the transition function\",\n \"NFA. (My apologies to speakers of Scandinavian languages for the last one; I\",\n \"nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf\",\n \"return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF\",\n \"reasons, the time complexity of this method is exponential in the number of\",\n \"transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {} in the range of\",\n \"There is no problem with the input NFAs having different alphabets. \\\"\\\"\\\" new_self,\",\n \"States are strings and Symbols are one-char strings). The keys of the `transition_function`\",\n \"| dfa2` returns a dfa that recognizes A union B. The states of\",\n \"this method takes as input is very simple -- much simpler than the\",\n \"nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while\",\n \"self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)]\",\n \"while overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1:\",\n \"nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if\",\n \"regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states\",\n \"of states of the NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset code\",\n \"start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be\",\n \"of the following conditions hold: 1. the alphabet contains any of the verboten\",\n \"followed by an operator, or 4. the input regex does not have properly\",\n \"\\\"\\\"\\\" Let A be the language accepted by dfa. `dfa.encode()` returns a regex\",\n \"to be much more complicated than necessary; maybe I'll figure out how to\",\n \"'' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains\",\n \"regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 ==\",\n \"`nfa.self()` returns an nfa that recognizes A* -- i.e., the set of all\",\n \"a ValueError exception on instantiation if any of the following are true: 1.\",\n \"`True` if my_dfa accepts \\\"some string\\\", and `False` otherwise. Will raise a ValueError\",\n \"the input regex string contains a character not in the alphabet, and not\",\n \"'(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1:\",\n \"regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€':\",\n \"string (because it kind of looks like an epsilon); there's no other way\",\n \"a DFA that recognises the set of all concatenations of strings in A\",\n \"language defined by that regular expression and that alphabet. The alphabet parameter is\",\n \"nfa's state set --- i.e., the values of the transition function dictionary should\",\n \"symbols that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state})\",\n \"that the empty string will not be inferred to be a member of\",\n \"return state + '`' def copy(nfa: NFA) -> NFA: copy_tf = {} for\",\n \"pair of a state and a symbol is in the domain of the\",\n \"in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states\",\n \"string is liable to be much more complicated than necessary; maybe I'll figure\",\n \"-> NFA: \\\"\\\"\\\" Convenience method that takes a DFA instance and returns an\",\n \"exception message will specify which of these above conditions things triggered the exception,\",\n \"nfa2. `nfa1 + nfa2` returns an nfa that recognizes A concat B --\",\n \"of parentheses, the order of operations is: `*`, then `•`, then `|`. This\",\n \"the range of the transition \\\" \\\"function is not in the fsa's state\",\n \"processed = '' paren_count = 0 for char in regex: if char in\",\n \"in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states |\",\n \"Set[State], start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state\",\n \"the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be\",\n \"instantiation if any of th following are true: * the start state is\",\n \"can define epsilon-moves by using the empty string in place of an alphabet\",\n \"= machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) ->\",\n \"itertools import product, chain, combinations from string import printable from typing import (\",\n \"in string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set()\",\n \"for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for\",\n \"states of the NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset code an\",\n \"{new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\",\",\n \"presently. As of now, the syntax of the regular expressions that this method\",\n \"a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return\",\n \"right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA):\",\n \"start state is not a member of the set of states inferred from\",\n \"def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state:\",\n \"PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE\",\n \"= Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__(\",\n \"set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return\",\n \"transition function is not a one-character string; * the transition function is missing\",\n \"elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while\",\n \"'•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS\",\n \"the transition function; * the set of accept states is not a subset\",\n \"symbols in the alphabet except for '(', '')', '|', '*', '•', '€' and\",\n \"alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS:\",\n \"from the transition function; * a member of the alphabet inferred from the\",\n \"-> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s,\",\n \"_stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return\",\n \"| {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to\",\n \"for char in regex: if char in alphabet or char == '(': if\",\n \"for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state,\",\n \"epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State],\",\n \"every pair of a state and a symbol is in the domain of\",\n \"of \\\"printable\\\" characters, which includes the standard ASCII letters and digits, and most\",\n \"--- i.e., the values of the transition function dictionary should be sets (or\",\n \"regular expresssions. All characters are intepreted as literals for symbols in the alphabet\",\n \"alphabet (i.e., a set of one-character strings) as input; returns an NFA that\",\n \"= new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other:\",\n \"language recognised by nfa1, and B be the language recognized by nfa2. `nfa1\",\n \"machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex\",\n \"= self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member\",\n \"star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start,\",\n \"match the empty string in normal python regex syntax either, though it can\",\n \"\\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members of the fsa's \\\"\",\n \"regex = _pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char))\",\n \"dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols\",\n \"string: str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts input string. Will raise\",\n \"who cares about this, I will change the symbol for empty-set.) In the\",\n \"raise ValueError( \\\"Left parenthesis occurs in regex without matching right \\\" \\\"parenthesis.\\\" )\",\n \"of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state}\",\n \"super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() )\",\n \"def encode(self) -> Regex: \\\"\\\"\\\" Let A be the language accepted by dfa.\",\n \"self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] =\",\n \"# https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of frozensets).\",\n \"union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state\",\n \"str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class\",\n \"the set of all concatenations of strings in A with strings in B.\",\n \"determinized_tf = {} determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state\",\n \"regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip,\",\n \"exception is the string contains symbols that aren't in the DFA's alphabet. \\\"\\\"\\\"\",\n \"r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip},\",\n \"set of DFA states has the cardinality of the power-set of the set\",\n \"DFA and NFA public classes \\\"\\\"\\\" import collections.abc from itertools import product, chain,\",\n \"\\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range\",\n \"determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start\",\n \"'Ø' if regex1 == '€': return regex2 if regex2 == '€': return regex1\",\n \"-> NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'},\",\n \"of the transition \\\" \\\"function is not in the fsa's state set.\\\"), message_plural=(\\\"States\",\n \"valid value; in fact, you are required to specify that the successor set\",\n \"operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet\",\n \"NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot\",\n \") return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def\",\n \"`transition_function` implicitly define the dfa's state-set and alphabet. The class will raise a\",\n \"new_other, combination_tf def _good_range(self) -> None: bad_range = { x for x in\",\n \"the fsa's state set.\\\"), message_plural=(\\\"States {} in the range of the transition \\\"\",\n \"char == '(': paren_count += 1 elif char == ')': paren_count -= 1\",\n \"keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State]\",\n \"value is string.printable -- i.e., the set of \\\"printable\\\" characters, which includes the\",\n \"three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`:\",\n \"r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 =\",\n \"classes \\\"\\\"\\\" import collections.abc from itertools import product, chain, combinations from string import\",\n \"are not in the fsa's state set.\\\") ) def accepts(self, string: str) ->\",\n \"str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts input string. Will raise a\",\n \"{ key: {value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state,\",\n \"`|`, `*`, `•`, `€` and `Ø`, 2. the input regex string contains a\",\n \"transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined()\",\n \"that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for\",\n \"obvious how to match the empty string in normal python regex syntax either,\",\n \"| other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf\",\n \"_transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) ->\",\n \"== 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol)\",\n \"method takes as input is very simple -- much simpler than the standard\",\n \"class will raise a ValueError exception on instantiation if any of th following\",\n \"i.e., the set of all strings formed by concatenating any number of members\",\n \"this method is exponential in the number of states of the NFA. Don't\",\n \"the following conditions hold: 1. the alphabet contains any of the verboten characters\",\n \"self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1,\",\n \"- `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are\",\n \"error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other,\",\n \"will raise a ValueError exception on instantiation if any of th following are\",\n \"that recognizes A* -- i.e., the set of all strings formed by concatenating\",\n \"maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol\",\n \"= list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1\",\n \"| set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self,\",\n \"} state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set, symbol)\",\n \"inferred from the transition function; 2. the set of accept states is not\",\n \"first elements of the tuples represent the nfa's states, and the second elements\",\n \"transition function is not a subset of the set of states inferred from\",\n \"transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A\",\n \"first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\")\",\n \"\\\"alphabet and not an accepted regex character.\\\" ) if char in OPERATORS and\",\n \") } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for (state_set,\",\n \"define epsilon-moves by using the empty string in place of an alphabet symbol\",\n \"Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes three keyword arguments:\",\n \"and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains binary operator followed by\",\n \"alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack:\",\n \"an NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for key, value in\",\n \"return regex2 if regex2 == '€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})'\",\n \"= _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify(\",\n \"different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states)\",\n \"finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] -\",\n \"A be the language recognised by dfa1, and B be the language recognized\",\n \"& other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def\",\n \"on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa\",\n \"a subset of the set of states inferred from the transition function; 3.\",\n \"Regex, regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\": return regex2 if regex2\",\n \"regex string contains a character not in the alphabet, and not one of\",\n \"Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state:\",\n \"of operations is: `*`, then `•`, then `|`. This method uses a version\",\n \"not in \\\" \\\"alphabet and not an accepted regex character.\\\" ) if char\",\n \"A be the language recognised by nfa1, and B be the language recognized\",\n \"The class will raise a ValueError exception on instantiation if any of th\",\n \"input string. Will raise a ValueError exception is the string contains symbols that\",\n \"self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function\",\n \"in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif\",\n \"in \\\" \\\"alphabet and not an accepted regex character.\\\" ) if char in\",\n \"Let A be the language accepted by dfa. `dfa.encode()` returns a regex string\",\n \"in the alphabet. The domain of the transition function is the power-set of\",\n \"an alphabet symbol in the transition function. Note that the empty string will\",\n \"a is an element of A and b is an element of B.\",\n \"symbol) return not current_states & self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns\",\n \"State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are one-char strings).\",\n \"returns a DFA that recognises the set of all concatenations of strings in\",\n \"in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the\",\n \"'Ø' cannot be symbols in the alphabet of the NFA. (My apologies to\",\n \"in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state,\",\n \"+ '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1\",\n \"cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() #\",\n \"bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of\",\n \"copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state),\",\n \"simple but, sadly, computationally expensive algorith. For that reason, I recommend you don't\",\n \"= {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack\",\n \"EMPTIES: return '€' if len(regex) == 1: return regex + '*' return f\\\"({regex})*\\\"\",\n \"union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2\",\n \") union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item) for item in\",\n \"1 elif char == '|': if paren_count == 0: return True return False\",\n \"\\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf\",\n \"alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states =\",\n \"regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2\",\n \"rip}: r1 = self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3\",\n \"the empty string will not be inferred to be a member of the\",\n \"that is not in \\\" \\\"alphabet and not an accepted regex character.\\\" )\",\n \"= self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' +\",\n \"symbol in the transition function. Note that the empty string will not be\",\n \"bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\"\",\n \"regex character.\\\" ) if char in OPERATORS and processed[-1] in {'|', '•'}: raise\",\n \"in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in\",\n \"OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€',\",\n \"aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol\",\n \"accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised\",\n \"transition function's range is not a set; 5. the range of the transition\",\n \"set.\\\") ) def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True`\",\n \"range of the transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {} in\",\n \"dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states =\",\n \"the cardinality of the state set of nfa1 plus the cardinality of the\",\n \"of states inferred from the transition function; * a member of the alphabet\",\n \"__add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by\",\n \"symbol. If, by some miracle, there is someone who cares about this, I\",\n \"body_states self.start_state = start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state}\",\n \"0: raise ValueError( \\\"Left parenthesis occurs in regex without matching right \\\" \\\"parenthesis.\\\"\",\n \"= {} for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)]\",\n \"Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with\",\n \"like an epsilon); there's no other way to match, for instance, {'', '0'}\",\n \") def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language\",\n \"set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states |\",\n \"alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in\",\n \"DFA that recognises the set of all concatenations of strings in A with\",\n \"bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set:\",\n \"not a subset of the power set of states inferred from the transition\",\n \"are one-char strings). The keys of the `transition_function` implicitly define the dfa's state-set\",\n \"language recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises the\",\n \"three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`:\",\n \"exception message will specify which of these six conditions things triggered the exception,\",\n \"NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = {\",\n \"_pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS:\",\n \"self.states & other.states while overlap: other = copy(other) overlap = self.states & other.states\",\n \"function is not a subset of the power set of states inferred from\",\n \"0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] !=\",\n \"= 0 for char in regex: if char == '(': paren_count += 1\",\n \"be the language recognized by nfa2. `nfa1 + nfa2` returns an nfa that\",\n \"if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}'\",\n \"bool: paren_count = 0 for char in regex: if char == '(': paren_count\",\n \"_stringify(state_set) determinized_tf[(determinzed_state, symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state)\",\n \"one; I am very against English chauvinism, but your letter is so very\",\n \"symbol, and the tilde, for reasons that I will explain presently. As of\",\n \"error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state,\",\n \"gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start =\",\n \"chauvinism, but your letter is so very close to the empty-set symbol. If,\",\n \"for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2),\",\n \"new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf =\",\n \"combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x\",\n \"= { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message(\",\n \"-> str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def\",\n \"transition function dictionary should be sets (or frozensets). The empty set is a\",\n \"want to. But it gets used internally. '€' (option-shift-2) is used to match\",\n \"= set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the\",\n \"the case that every pair of a state and a symbol is in\",\n \"def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None:\",\n \"\\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count = 0 for char in regex:\",\n \"start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable)\",\n \"len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA:\",\n \"collections.abc from itertools import product, chain, combinations from string import printable from typing\",\n \"| set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that is not in \\\"\",\n \"nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]]\",\n \"message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction\",\n \"= _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property\",\n \"'*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 ==\",\n \"the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def\",\n \"Symbols are one-char strings.) The transition function' keys implicitly define the nfa's state-set\",\n \"state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state\",\n \"if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return\",\n \"the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`,\",\n \"with '{first_char}'.\\\") processed = '' paren_count = 0 for char in regex: if\",\n \"(self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1,\",\n \"class will raise a ValueError exception on instantiation if any of the following\",\n \"import collections.abc from itertools import product, chain, combinations from string import printable from\",\n \"union B. The states of dfa1 | dfa2 are ordered pairs of states\",\n \"[] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop() left_operand =\",\n \"to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) >\",\n \"state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 )\",\n \"the empty-set symbol. If, by some miracle, there is someone who cares about\",\n \"range of the transition \\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] =\",\n \"fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members of the fsa's\",\n \"not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__\",\n \"self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A\",\n \"= {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for\",\n \"for the last one; I am very against English chauvinism, but your letter\",\n \"NFAs. \\\"\\\"\\\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor\",\n \"symbols in the alphabet of the NFA. (My apologies to speakers of Scandinavian\",\n \"character not in the alphabet, and not one of the above veboten characters,\",\n \"-- much simpler than the standard python regular expresssions. All characters are intepreted\",\n \"string; 4. a member of the transition function's range is not a set;\",\n \"You can leave concatentation implicit, as is usual; no need to write '•''\",\n \"the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string:\",\n \"the default value is string.printable *minus* parentheses, the vertical bar, the star symbol,\",\n \"for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in\",\n \"A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for\",\n \"self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states,\",\n \"accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function)\",\n \"and digits, and most common punctuation and white space. Actually, that's not quite\",\n \"language with it. For reaons related to the above, the characters '(', ')',\",\n \"dfa1 | dfa2 are ordered pairs of states from dfa1 and dfa2. There\",\n \"in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] =\",\n \"transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = {\",\n \"i.e., the values of the transition function dictionary should be sets (or frozensets).\",\n \"reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF\",\n \"of the transition function. The exception message will specify which of these six\",\n \"keyboard) means concatenation. You can leave concatentation implicit, as is usual; no need\",\n \"state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction\",\n \"states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF =\",\n \"returns `True` if my_dfa accepts \\\"some string\\\", and `False` otherwise. Will raise a\",\n \"from the transition function; 3. a member of the alphabet inferred from the\",\n \"start with '{first_char}'.\\\") processed = '' paren_count = 0 for char in regex:\",\n \"+= 1 elif char == ')': paren_count -= 1 elif char == '|':\",\n \"and build the NFA. The method will raise a ValueError exception if any\",\n \"strings and Symbols are one-char strings). The keys of the `transition_function` implicitly define\",\n \"inferred from the transition function; * the set of accept states is not\",\n \"NFA operator; it converts the input DFAs into NFAs, uses the NFA '+',\",\n \"new_state = 'new_state1' while new_state in state_set: counter += 1 new_state = \\\"new_state\\\"\",\n \"2. the set of accept states is not a subset of the set\",\n \"char in alphabet or char == '(': if len(processed) > 0: processed +=\",\n \"operator is parasitic on the NFA operator; it converts the input DFAs into\",\n \"self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]:\",\n \"= operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return (\",\n \"gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None:\",\n \"bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function\",\n \"parameter is optional; it's default value is string.printable -- i.e., the set of\",\n \"DFA. That makes for a relatively simple but, sadly, computationally expensive algorith. For\",\n \"not a set; 5. the range of the transition function is not a\",\n \"def _gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys():\",\n \") return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a\",\n \"transition function; * a member of the alphabet inferred from the transition function\",\n \"Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet\",\n \"'€' if len(regex) == 1: return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1:\",\n \"machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] = machine_stack[-1].star()\",\n \"be the language recognised by dfa2. `dfa1 + dfa2` returns a DFA that\",\n \"accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None:\",\n \"'•', '€' and 'Ø'. The parentheses, vertical bar and star mean what you'd\",\n \"ValueError( \\\"Left parenthesis occurs in regex without matching right \\\" \\\"parenthesis.\\\" ) return\",\n \"'•'}: raise ValueError( \\\"Regex contains binary operator followed by an \\\" \\\"operator; not\",\n \"new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states\",\n \"`dfa1 + dfa2` returns a DFA that recognises the set of all concatenations\",\n \"figure out how to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize()\",\n \"https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of frozensets). def\",\n \"if len(processed) > 0: processed += ( '•' if processed[-1] not in {'(',\",\n \"gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes a DFA instance\",\n \"nfa2 plus 1. There is no problem with the input NFAs having different\",\n \"be the language accepted by dfa. `dfa.encode()` returns a regex string that generates\",\n \"the NFA instance. WARNING: The set of DFA states has the cardinality of\",\n \"set of all concatenations of strings in A with strings in B. This\",\n \"*, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states\",\n \"return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\"\",\n \"return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1'\",\n \"for char in regex: if char == '(': paren_count += 1 elif char\",\n \"one-character string; 4. a member of the transition function's range is not a\",\n \"self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym:\",\n \"empty set; you can match to the empty language with it. For reaons\",\n \"above conditions things triggered the exception, and which states/symbols are the source of\",\n \"for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) } state_sets\",\n \"be the language recognised by nfa. `nfa.self()` returns an nfa that recognizes A*\",\n \"def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised\",\n \"value; in fact, you are required to specify that the successor set for\",\n \"(minor modification to make the return a set of frozensets). def powerset(iterable: Iterable)\",\n \"_combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state +\",\n \"accept_states=copy_accept ) overlap = self.states & other.states while overlap: other = copy(other) overlap\",\n \"recognised by nfa. `nfa.self()` returns an nfa that recognizes A* -- i.e., the\",\n \"is missing a case -- i.e., it is not the case that every\",\n \"): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state\",\n \"of the above veboten characters, 3. the input regex contain a binary operator\",\n \"B be the language recognised by dfa2. `dfa1 + dfa2` returns a DFA\",\n \"self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self) -> \\\"DFA\\\":\",\n \"of the state-set of nfa1 | nfa2 is the cardinality of the state\",\n \"the language recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes\",\n \"4. the input regex does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation =\",\n \"`€` and `Ø`, 2. the input regex string contains a character not in\",\n \"more complicated than necessary; maybe I'll figure out how to improve on average\",\n \"DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if\",\n \"_pre_process(regex, alphabet) for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char\",\n \"return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf =\",\n \"will raise a ValueError exception if any of the following conditions hold: 1.\",\n \"regex in EMPTIES: return '€' if len(regex) == 1: return regex + '*'\",\n \"def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair\",\n \"cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty: Regex)\",\n \"None: bad_range = { x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set)\",\n \"machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char)\",\n \"regex: if char in alphabet or char == '(': if len(processed) > 0:\",\n \"states, and the second elements are the symbols in the alphabet. The domain\",\n \"subset of the set of states inferred from the transition function; 3. a\",\n \"conditions things triggered the exception, and which states/symbols are the source of the\",\n \"B be the language recognized by nfa2. `nfa1 | nfa2` returns an nfa\",\n \"product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)]\",\n \"-> NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet)\",\n \"\\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')] = {self.start_state} for symbol\",\n \"`|`. This method uses a version of Dijkstra's shunting yard algorithm to parse\",\n \"copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept )\",\n \"and a symbol is in the domain of the transition function. The exception\",\n \"of the `transition_function` implicitly define the dfa's state-set and alphabet. The class will\",\n \"*[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]:\",\n \"product( self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in\",\n \"B. This DFA operator is parasitic on the NFA operator; it converts the\",\n \"self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF =\",\n \"= { pair: set() for pair in product({'q1'}, alphabet) } accept_states = set()\",\n \"ValueError( \\\"Regex contains binary operator followed by an \\\" \\\"operator; not cool.\\\" )\",\n \"vertical bar and star mean what you'd expect them to mean if you\",\n \"+ dfa2` returns a DFA that recognises the set of all concatenations of\",\n \"')', '|', '*', '•', '€' and 'Ø' cannot be symbols in the alphabet\",\n \"range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf = {} determinized_accept = set() for\",\n \"of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item)\",\n \"regex does not have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__,\",\n \"frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1) ) }\",\n \"if it is. You can define epsilon-moves by using the empty string in\",\n \"char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char))\",\n \"symbol)] = set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts =\",\n \"B -- i.e., the language consisting of the set of strings of the\",\n \"character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA:\",\n \"frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state\",\n \"_extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex =\",\n \"message_singular=(\\\"Accept state {} is not a member of the fsa's \\\" \\\"state set.\\\"),\",\n \"and alphabet. The class will raise a ValueError exception on instantiation if any\",\n \"return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {} for\",\n \"in string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) ->\",\n \"State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State,\",\n \"{} in the range of the transition \\\" \\\"function is not in the\",\n \"'')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and star\",\n \"union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state,\",\n \"\\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some string\\\", and `False` otherwise.\",\n \"other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1:\",\n \"str: if not isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set:\",\n \"{'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf:\",\n \"state-symbol pair is the empty set, if it is. You can define epsilon-moves\",\n \"into NFAs, uses the NFA '+', then converts the result back to a\",\n \"= self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol):\",\n \"maybe I'll figure out how to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa\",\n \"return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self\",\n \"class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ):\",\n \"set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same same\",\n \"no problem with the input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf\",\n \"AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) ) -> \\\"NFA\\\":\",\n \"transition function; * the set of accept states is not a subset of\",\n \"default value is string.printable *minus* parentheses, the vertical bar, the star symbol, and\",\n \"Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state:\",\n \"looks like an epsilon); there's no other way to match, for instance, {'',\",\n \"\\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A\",\n \"has the cardinality of the power-set of the set of NFA states. For\",\n \"symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state)\",\n \"is an element of B. Note that this `+` operation is not commutative.\",\n \"symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return\",\n \"== \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)]\",\n \"_GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]]\",\n \"itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a\",\n \"`self` with one less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool:\",\n \"fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'}\",\n \"MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA:\",\n \"return 'Ø' if regex1 == '€': return regex2 if regex2 == '€': return\",\n \"= set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self,\",\n \"can be done; give it a go.) 'Ø' (option-shift-o) represents the empty set;\",\n \"regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} )\",\n \"'*', '•', '€' and 'Ø' cannot be symbols in the alphabet of the\",\n \"pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'}\",\n \"\\\"\\\"\\\" Convenience method that takes a DFA instance and returns an NFA instance.\",\n \"triggered the exception, and which states/symbols are the source of the problem. \\\"\\\"\\\"\",\n \"function; 2. the set of accept states is not a subset of the\",\n \"| new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") ->\",\n \"for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar\",\n \"from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set,\",\n \"copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start =\",\n \"paren_count > 0: raise ValueError( \\\"Left parenthesis occurs in regex without matching right\",\n \"you can match to the empty language with it. For reaons related to\",\n \"transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states =\",\n \"State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state =\",\n \"for a given state-symbol pair is the empty set, if it is. You\",\n \"will work as you would expect). The class will raise a ValueError exception\",\n \") def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for\",\n \"symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states: star_tf[(state, '')]\",\n \"of the set of states inferred from the transition function; * the set\",\n \"successor set for a given state-symbol pair is the empty set, if it\",\n \"\\\" \\\"left parenthesis.\\\" ) processed += char if paren_count > 0: raise ValueError(\",\n \"accepts \\\"some string\\\", and `False` otherwise. Will raise a ValueError exception is the\",\n \"the set of strings of the form a concat b, where a is\",\n \"_FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__(\",\n \"= frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for\",\n \"prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf =\",\n \"operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop()\",\n \"with strings in B. This DFA operator is parasitic on the NFA operator;\",\n \"add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf,\",\n \"of this method is exponential in the number of states of the NFA.\",\n \"and an alphabet (i.e., a set of one-character strings) as input; returns an\",\n \"encode(self) -> Regex: \\\"\\\"\\\" Let A be the language accepted by dfa. `dfa.encode()`\",\n \"in the transition function. Note that the empty string will not be inferred\",\n \"problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the\",\n \"@property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet,\",\n \") -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states = dfa1.states extra_symbols = dfa2.alphabet\",\n \"= str Regex = str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ]\",\n \"DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class.\",\n \"\\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa: NFA) ->\",\n \"of the power-set of the set of NFA states. For related reasons, the\",\n \"Takes a regular expression and an alphabet (i.e., a set of one-character strings)\",\n \"machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int:\",\n \"+ PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char =\",\n \"product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else {'q1'} return\",\n \"the exception, and which states/symbols are the source of the problem. \\\"\\\"\\\" def\",\n \"empty string will not be inferred to be a member of the alphabet\",\n \"'')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def\",\n \"function is not a one-character string; * the transition function is missing a\",\n \"of the transition \\\" \\\"function are not in the fsa's state set.\\\") )\",\n \"operator followed by an \\\" \\\"operator; not cool.\\\" ) if char == '(':\",\n \"operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not\",\n \"nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA)\",\n \"char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in\",\n \"is parasitic on the NFA operator; it converts the input DFAs into NFAs,\",\n \"'{first_char}'.\\\") processed = '' paren_count = 0 for char in regex: if char\",\n \"yard algorithm to parse the regex and build the NFA. The method will\",\n \"= symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] =\",\n \"char in regex: if char == '(': paren_count += 1 elif char ==\",\n \"'')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\")\",\n \"left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex)\",\n \"- self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member of the\",\n \"self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in\",\n \"character.\\\" ) if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError(\",\n \"`start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char\",\n \"simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce()\",\n \"string: current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set() def\",\n \"includes the standard ASCII letters and digits, and most common punctuation and white\",\n \"if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1,\",\n \"the transition \\\" \\\"function are not in the fsa's state set.\\\") ) def\",\n \"concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state,\",\n \"domain of the transition function is the power-set of the nfa's state set\",\n \"of the nfa's state set --- i.e., the values of the transition function\",\n \"set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return {\",\n \"gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method\",\n \"'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} )\",\n \"EMPTIES = ['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex:\",\n \"transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf\",\n \"the input regex contain a binary operator followed by an operator, or 4.\",\n \"other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {}\",\n \"I recommend you don't `+` dfas with large numbers of states. \\\"\\\"\\\" return\",\n \"not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if\",\n \"state-set and alphabet; the first elements of the tuples represent the nfa's states,\",\n \"used to match the empty string (because it kind of looks like an\",\n \"states inferred from the transition function; 3. a member of the alphabet inferred\",\n \"operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate()\",\n \"operator; it converts the input DFAs into NFAs, uses the NFA '+', then\",\n \"self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A be the language accepted by\",\n \"in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) ->\",\n \"(Quick challenge: it's not totally obvious how to match the empty string in\",\n \"some miracle, there is someone who cares about this, I will change the\",\n \"not a one-character string; * the transition function is missing a case --\",\n \"determinize big NFAs. \\\"\\\"\\\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes\",\n \"an element of A and b is an element of B. Note that\",\n \"of the fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction =\",\n \"processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton\",\n \"Union[State, AbstractSet[State]] ] class _FSA(_Base): def __init__( self, *, transition_function: FsaTransitionFunction, start_state: State,\",\n \"return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1\",\n \"You can define epsilon-moves by using the empty string in place of an\",\n \"of the transition function is the power-set of the nfa's state set ---\",\n \"# powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to\",\n \"of states inferred from the transition function; * the range of the transition\",\n \"takes as input is very simple -- much simpler than the standard python\",\n \"return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return\",\n \"return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite\",\n \"- {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex =\",\n \"function; 3. a member of the alphabet inferred from the transition function is\",\n \"symbol)] = _stringify( self._transition(state_set, symbol) ) if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start =\",\n \"symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€'\",\n \"standard python regular expresssions. All characters are intepreted as literals for symbols in\",\n \"_check_input ) State = str Symbol = str Regex = str FsaTransitionFunction =\",\n \"new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states\",\n \"__add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by\",\n \") return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let\",\n \"function' keys implicitly define the nfa's state-set and alphabet; the first elements of\",\n \"symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state\",\n \"relatively simple but, sadly, computationally expensive algorith. For that reason, I recommend you\",\n \"regular expression and an alphabet (i.e., a set of one-character strings) as input;\",\n \"the tilde, for reasons that I will explain presently. As of now, the\",\n \"not quite right -- the default value is string.printable *minus* parentheses, the vertical\",\n \"the alphabet inferred from the transition function is not a one-character string; 4.\",\n \"| {self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA\",\n \"= self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self) ->\",\n \"\\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa1, and\",\n \"cardinality of the state-set of nfa2 plus 1. There is no problem with\",\n \"symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)]\",\n \"from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the return a set of\",\n \"union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex)\",\n \"\\\"function are not in the fsa's state set.\\\") ) def _get_successors( self, *,\",\n \"the transition function; * a member of the alphabet inferred from the transition\",\n \"'`' def copy(nfa: NFA) -> NFA: copy_tf = {} for state, symbol in\",\n \"an \\\" \\\"operator; not cool.\\\" ) if char == '(': paren_count += 1\",\n \"machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return\",\n \"necessary; maybe I'll figure out how to improve on average simplicity, eventually. \\\"\\\"\\\"\",\n \"that every pair of a state and a symbol is in the domain\",\n \"in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1',\",\n \"mypy bug. return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self,\",\n \"'•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet\",\n \"not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message( bad_set=transition_range - self.states,\",\n \"alphabet of the NFA. (My apologies to speakers of Scandinavian languages for the\",\n \"of the set of NFA states. For related reasons, the time complexity of\",\n \"state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf =\",\n \"from string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List,\",\n \"States are strings, and Symbols are one-char strings.) The transition function' keys implicitly\",\n \"you don't want to. But it gets used internally. '€' (option-shift-2) is used\",\n \"== '|': if paren_count == 0: return True return False def regex_star(regex: Regex)\",\n \"punctuation and white space. Actually, that's not quite right -- the default value\",\n \"fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair in\",\n \"will not be inferred to be a member of the alphabet (and hence\",\n \"value is string.printable *minus* parentheses, the vertical bar, the star symbol, and the\",\n \"method is exponential in the number of states of the NFA. Don't determinize\",\n \"matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS)\",\n \"raise ValueError( \\\"Regex contains binary operator followed by an \\\" \\\"operator; not cool.\\\"\",\n \"is missing cases -- i.e., it is not the case that every pair\",\n \"a set.\\\"), message_plural=(\\\"Values {} in the range of the transition \\\" \\\"function are\",\n \"( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast\",\n \"int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char\",\n \"= self._combine(other) for state in new_self.accept_states: if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state)\",\n \"alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|', '*'} ) ) ->\",\n \"range of the transition function is not a subset of the set of\",\n \"left_operand, right_operand ) machine_stack.append(machine) def compare(operator: Regex) -> int: return ( OPERATORS.index(operator) -\",\n \"that the successor set for a given state-symbol pair is the empty set,\",\n \"having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA,\",\n \"def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes a DFA instance and\",\n \"if regex1 == \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return regex1 return\",\n \"add_one_way(nfa1, nfa2), add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf,\",\n \"i.e., the language consisting of the set of strings of the form a\",\n \"{ '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain\",\n \"build the NFA. The method will raise a ValueError exception if any of\",\n \"binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate()\",\n \"'|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise ValueError(\",\n \"by dfa2. `dfa1 + dfa2` returns a DFA that recognises the set of\",\n \"states has the cardinality of the power-set of the set of NFA states.\",\n \"contain characters {}.\\\" ) def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = {\",\n \"you don't `+` dfas with large numbers of states. \\\"\\\"\\\" return (self.non_determinize() +\",\n \"in the alphabet of the NFA. (My apologies to speakers of Scandinavian languages\",\n \"'new_state1' while new_state in state_set: counter += 1 new_state = \\\"new_state\\\" + str(counter)\",\n \"syntax of the regular expressions that this method takes as input is very\",\n \"someone who cares about this, I will change the symbol for empty-set.) In\",\n \"if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1]\",\n \"\\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')]\",\n \"= maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function\",\n \"overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA,\",\n \"-> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State\",\n \"state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)]\",\n \"NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other\",\n \"regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf,\",\n \"same language as the NFA instance. WARNING: The set of DFA states has\",\n \"a member of the set of states inferred from the transition function; 2.\",\n \"regex contain a binary operator followed by an operator, or 4. the input\",\n \"That makes for a relatively simple but, sadly, computationally expensive algorith. For that\",\n \"'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values())\",\n \"range of the transition function is not a subset of the power set\",\n \"followed by an \\\" \\\"operator; not cool.\\\" ) if char == '(': paren_count\",\n \"= add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA(\",\n \"and NFA public classes \\\"\\\"\\\" import collections.abc from itertools import product, chain, combinations\",\n \"current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\"\",\n \"def prime(state: State): return state + '`' def copy(nfa: NFA) -> NFA: copy_tf\",\n \"machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0:\",\n \"-> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa1, and B\",\n \"None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not\",\n \"the successor set for a given state-symbol pair is the empty set, if\",\n \"set for a given state-symbol pair is the empty set, if it is.\",\n \"are true: 1. the start state is not a member of the set\",\n \"= cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] = frozenset()\",\n \"I will explain presently. As of now, the syntax of the regular expressions\",\n \"pair in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø' else\",\n \"the NFA '+', then converts the result back to a DFA. That makes\",\n \"regex string is liable to be much more complicated than necessary; maybe I'll\",\n \"symbol='' ) return frozenset(state_set) def _transition(self, state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol))\",\n \"it can be done; give it a go.) 'Ø' (option-shift-o) represents the empty\",\n \"in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)]\",\n \"converts the input DFAs into NFAs, uses the NFA '+', then converts the\",\n \"dfa1, B be the language recognised by dfa2. `dfa1 + dfa2` returns a\",\n \"accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state self.accept_state\",\n \"in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] }\",\n \"the string contains symbols that aren't in the nfa's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet)\",\n \"case -- i.e., it is not the case that every pair of a\",\n \"new_states = dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state\",\n \"| new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet\",\n \"return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range\",\n \"specify which of these above conditions things triggered the exception, and which states/symbols\",\n \"{} in the range of the transition \\\" \\\"function are not in the\",\n \"-- i.e., the set of \\\"printable\\\" characters, which includes the standard ASCII letters\",\n \"will specify which of these above conditions things triggered the exception, and which\",\n \"NFA: tf: MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet)\",\n \"gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start}, self.states |\",\n \"all concatenations of strings in A with strings in B. This DFA operator\",\n \"Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'},\",\n \"these six conditions things triggered the exception, and which states/symbols are the source\",\n \"def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state\",\n \"NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF =\",\n \"return '€' if len(regex) == 1: return regex + '*' return f\\\"({regex})*\\\" def\",\n \"the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members of the\",\n \"determinized_accept = set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state,\",\n \"return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol]\",\n \"of the set of states inferred from the transition function; * the range\",\n \"or char == '(': if len(processed) > 0: processed += ( '•' if\",\n \"State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state = start_state\",\n \"4. a member of the transition function's range is not a set; 5.\",\n \"self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex\",\n \"1 if char == ')': paren_count -= 1 if paren_count < 0: raise\",\n \"chain, combinations from string import printable from typing import ( AbstractSet, Container, FrozenSet,\",\n \"combinations(s, r) for r in range(len(s)+1) ) } state_sets = powerset(self.states) determinized_tf =\",\n \"an element of B. Note that this `+` operation is not commutative. \\\"\\\"\\\"\",\n \"concat b, where a is an element of A and b is an\",\n \"'')] = {self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state\",\n \"if any of the following conditions hold: 1. the alphabet contains any of\",\n \"place of an alphabet symbol in the transition function. Note that the empty\",\n \"transition function. Note that the empty string will not be inferred to be\",\n \"exception is the string contains symbols that aren't in the nfa's alphabet. \\\"\\\"\\\"\",\n \"write '•'' explicitly if you don't want to. But it gets used internally.\",\n \"state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol=''\",\n \"expensive algorith. For that reason, I recommend you don't `+` dfas with large\",\n \"_get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state } for symbol in\",\n \"state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def _good_range(self) -> None: transition_range\",\n \"Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input\",\n \"string. Will raise a ValueError exception is the string contains symbols that aren't\",\n \"a regex string that generates A. That regex string is liable to be\",\n \"accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept()\",\n \"regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\": return regex2 if\",\n \") if char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character\",\n \"veboten characters, 3. the input regex contain a binary operator followed by an\",\n \"= start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state}\",\n \"language as the NFA instance. WARNING: The set of DFA states has the\",\n \"nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start\",\n \"have properly matching parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ }\",\n \"with the input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet\",\n \"_error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function is\",\n \"'(': paren_count += 1 elif char == ')': paren_count -= 1 elif char\",\n \"(state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1,\",\n \"set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states ) def\",\n \"empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def fit_symbol(symbol:\",\n \"\\\"Regex contains binary operator followed by an \\\" \\\"operator; not cool.\\\" ) if\",\n \"containing DFA and NFA public classes \\\"\\\"\\\" import collections.abc from itertools import product,\",\n \"def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\": return regex2\",\n \"transition function; 2. the set of accept states is not a subset of\",\n \"isinstance(states, collections.abc.Sequence): states = list(states) states.sort() return \\\"\\\".join(states) def _get_new_state(state_set: Container) -> State:\",\n \"> 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\"\",\n \"the language recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes\",\n \"is. You can define epsilon-moves by using the empty string in place of\",\n \"these above conditions things triggered the exception, and which states/symbols are the source\",\n \"'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2 == '€':\",\n \"The empty set is a valid value; in fact, you are required to\",\n \"first_char in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = '' paren_count\",\n \"def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set() for pair\",\n \"I am very against English chauvinism, but your letter is so very close\",\n \"concatenations of strings in A with strings in B. This DFA operator is\",\n \"than necessary; maybe I'll figure out how to improve on average simplicity, eventually.\",\n \"= self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State],\",\n \"commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state,\",\n \"\\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, and\",\n \"_get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State,\",\n \"self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether nfa accepts\",\n \"in self.accept_states def encode(self) -> Regex: \\\"\\\"\\\" Let A be the language accepted\",\n \"== '(': paren_count += 1 if char == ')': paren_count -= 1 if\",\n \"digits, and most common punctuation and white space. Actually, that's not quite right\",\n \"concat B -- i.e., the language consisting of the set of strings of\",\n \"prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept\",\n \"are required to specify that the successor set for a given state-symbol pair\",\n \"['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS\",\n \"numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def _gnfize(self) -> _GNFA: gnfa_tf:\",\n \"MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import ( _Base, _extract_states_alphabet,\",\n \"strings) as input; returns an NFA that recognises the language defined by that\",\n \"- `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings and Symbols are\",\n \"class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State\",\n \"_check_input(string=string, alphabet=self.alphabet) current_states = self._add_epsilons({self.start_state}) for symbol in string: current_states = self._transition(current_states, symbol)\",\n \"'•' if processed[-1] not in {'(', '|'} else '' ) if char not\",\n \"define the nfa's state-set and alphabet; the first elements of the tuples represent\",\n \"def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for item\",\n \"NFA '+', then converts the result back to a DFA. That makes for\",\n \"`•`, then `|`. This method uses a version of Dijkstra's shunting yard algorithm\",\n \"while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) ->\",\n \"accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet =\",\n \"of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf = self.transition_function star_tf[(star_start, '')]\",\n \"of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and\",\n \"Determines whether nfa accepts input string. Will raise a ValueError exception is the\",\n \"Note that the empty string will not be inferred to be a member\",\n \"\\\"\\\"\\\" nd_transition_function = { key: {value} for key, value in self.transition_function.items() } return\",\n \"be much more complicated than necessary; maybe I'll figure out how to improve\",\n \"parentheses, vertical bar and star mean what you'd expect them to mean if\",\n \") _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range of the transition\",\n \"on the NFA operator; it converts the input DFAs into NFAs, uses the\",\n \"strings.) The transition function' keys implicitly define the nfa's state-set and alphabet; the\",\n \"FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states\",\n \"set of states inferred from the transition function; * a member of the\",\n \"other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 +\",\n \"'€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in\",\n \"\\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA )\",\n \"regex2 def regex_union(regex1: Regex, regex2: Regex) -> Regex: if regex1 == \\\"Ø\\\": return\",\n \"as literals for symbols in the alphabet except for '(', '')', '|', '*',\",\n \"the transition function is the power-set of the nfa's state set --- i.e.,\",\n \"State] class DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes three keyword arguments:\",\n \"-> NFA: copy_tf = {} for state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] =\",\n \"other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for\",\n \"the fsa's state set.\\\") ) def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some\",\n \"is not a set; 5. the range of the transition function is not\",\n \"printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional,\",\n \"then `|`. This method uses a version of Dijkstra's shunting yard algorithm to\",\n \"NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot contain character {}.\\\",\",\n \"symbols that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state\",\n \"familiar with regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You\",\n \"= { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet, message_singular=\\\"Alphabet cannot\",\n \"to match the empty string in normal python regex syntax either, though it\",\n \"processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains binary operator followed by an\",\n \"the state-set of nfa2 plus 1. There is no problem with the input\",\n \"-> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an alphabet (i.e., a set\",\n \"\\\"\\\"\\\" Let A be the language recognised by nfa. `nfa.self()` returns an nfa\",\n \"*self.transition_function.values() ) _error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range of the\",\n \"the transition function is missing a case -- i.e., it is not the\",\n \"of the transition \\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union(\",\n \"NFA. The method will raise a ValueError exception if any of the following\",\n \"nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols):\",\n \"transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = (\",\n \"symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = {\",\n \"_good_range(self) -> None: transition_range = set(self.transition_function.values()) bad_range = transition_range - self.states _error_message( bad_set=bad_range,\",\n \"start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be\",\n \"be the language recognized by nfa2. `nfa1 | nfa2` returns an nfa that\",\n \"back to a DFA. That makes for a relatively simple but, sadly, computationally\",\n \"= prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start,\",\n \"mean if you are familiar with regular expressions. '•' (option-8 on a mac\",\n \"for empty-set.) In the absence of parentheses, the order of operations is: `*`,\",\n \"subset of the set of states inferred from the transition function; * the\",\n \"in OPERATORS: raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = '' paren_count =\",\n \"second elements are the symbols in the alphabet. The domain of the transition\",\n \"Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite\",\n \"to a DFA. That makes for a relatively simple but, sadly, computationally expensive\",\n \"not members of the fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError\",\n \"to make the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]:\",\n \"\\\"\\\".join(states) def _get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while\",\n \"bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member of the fsa's \\\" \\\"state\",\n \"\\\" \\\"function are not in the fsa's state set.\\\") ) def _get_successors( self,\",\n \"that recognises the language defined by that regular expression and that alphabet. The\",\n \"the power set of states inferred from the transition function; 6. the transition\",\n \"which states/symbols are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\")\",\n \"as you would expect). The class will raise a ValueError exception on instantiation\",\n \"symbols in the alphabet. The domain of the transition function is the power-set\",\n \"cardinality of the state set of nfa1 plus the cardinality of the state-set\",\n \"0: raise ValueError( \\\"Right parenthesis occurs in regex withour matching \\\" \\\"left parenthesis.\\\"\",\n \"state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1,\",\n \"Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are strings\",\n \"maybe_add_state( dfa1: DFA, dfa2: DFA ) -> Tuple[FrozenSet[State], DfaTransitionFunction]: new_tf = dfa1.transition_function new_states\",\n \"while ( operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char)\",\n \"Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function =\",\n \"return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in range(len(s)+1)\",\n \"if char == '(': paren_count += 1 elif char == ')': paren_count -=\",\n \"add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2:\",\n \"= self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def\",\n \"the NFA. The method will raise a ValueError exception if any of the\",\n \"self.states | {gnfa_start}, self.states | {gnfa_accept} ): if (state1, state2) not in gnfa_tf:\",\n \"new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf,\",\n \"this `+` operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for\",\n \"`)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex string contains\",\n \"to write '•'' explicitly if you don't want to. But it gets used\",\n \"self.states = ( self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\":\",\n \"it kind of looks like an epsilon); there's no other way to match,\",\n \"member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept states {} are not members\",\n \"an alphabet (i.e., a set of one-character strings) as input; returns an NFA\",\n \"as input is very simple -- much simpler than the standard python regular\",\n \") def _add_epsilons(self, state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' )\",\n \"of nfa2 plus 1. There is no problem with the input NFAs having\",\n \"be symbols in the alphabet of the NFA. (My apologies to speakers of\",\n \"the star symbol, and the tilde, for reasons that I will explain presently.\",\n \"_Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex\",\n \"r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1,\",\n \"state_set = state_set | epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set)\",\n \"name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message(\",\n \"of states inferred from the transition function; 3. a member of the alphabet\",\n \"the empty language with it. For reaons related to the above, the characters\",\n \"= MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes\",\n \"in alphabet or char == '(': if len(processed) > 0: processed += (\",\n \"\\\"\\\"\\\" Let A be the language recognised by dfa1, B be the language\",\n \"{'|', '•'}: raise ValueError( \\\"Regex contains binary operator followed by an \\\" \\\"operator;\",\n \"means concatenation. You can leave concatentation implicit, as is usual; no need to\",\n \"= self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4 ) reduced_tf[(state1, state2)]\",\n \"less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex) -> bool: paren_count = 0\",\n \"\\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) & alphabet,\",\n \"combination_tf def _good_range(self) -> None: bad_range = { x for x in self.transition_function.values()\",\n \"contains a character not in the alphabet, and not one of the above\",\n \"recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A union\",\n \"without matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State]\",\n \"False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if\",\n \"dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other)\",\n \"am very against English chauvinism, but your letter is so very close to\",\n \"a binary operator followed by an operator, or 4. the input regex does\",\n \"concatenating any number of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf =\",\n \"is no problem with the input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other,\",\n \"transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap: other =\",\n \"-- i.e., it is not the case that every pair of a state\",\n \"of nfa1 | nfa2 is the cardinality of the state set of nfa1\",\n \"power set of states inferred from the transition function; 6. the transition function\",\n \"if paren_count > 0: raise ValueError( \\\"Left parenthesis occurs in regex without matching\",\n \"self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class\",\n \"characters, 3. the input regex contain a binary operator followed by an operator,\",\n \"{self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod\",\n \"state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states\",\n \"of the following are true: 1. the start state is not a member\",\n \"in regex without matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State,\",\n \"from the transition function; * the set of accept states is not a\",\n \"AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\") self._good_accept() self._good_domain(self.alphabet) def\",\n \"alphabet; the first elements of the tuples represent the nfa's states, and the\",\n \"letters and digits, and most common punctuation and white space. Actually, that's not\",\n \"in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2,\",\n \"string: current_state = self.transition_function[(current_state, symbol)] return current_state in self.accept_states def encode(self) -> Regex:\",\n \"the language consisting of the set of strings of the form a concat\",\n \") return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol], State] class DFA(_FSA): \\\"\\\"\\\" A deterministic\",\n \"match, for instance, {'', '0'} with the current syntax. (Quick challenge: it's not\",\n \"the above veboten characters, 3. the input regex contain a binary operator followed\",\n \"in the fsa's state set.\\\") ) def accepts(self, string: str) -> bool: \\\"\\\"\\\"\",\n \"transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf =\",\n \"syntax either, though it can be done; give it a go.) 'Ø' (option-shift-o)\",\n \"occurs in regex withour matching \\\" \\\"left parenthesis.\\\" ) processed += char if\",\n \"in {'|', '•'}: raise ValueError( \\\"Regex contains binary operator followed by an \\\"\",\n \"a regular expression and an alphabet (i.e., a set of one-character strings) as\",\n \"or regex2 == 'Ø': return 'Ø' if regex1 == '€': return regex2 if\",\n \"start_state self.accept_state = accept_state self.states = ( self.body_states | {self.start_state} | {self.accept_state} )\",\n \"NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]:\",\n \"the start state is not a member of the set of states inferred\",\n \"keys of the `transition_function` implicitly define the dfa's state-set and alphabet. The class\",\n \") -> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast(\",\n \"frozensets). The empty set is a valid value; in fact, you are required\",\n \"for state in self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product(\",\n \"\\\"\\\"\\\" import collections.abc from itertools import product, chain, combinations from string import printable\",\n \"dfa. `dfa.encode()` returns a regex string that generates A. That regex string is\",\n \"accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State):\",\n \"empty language with it. For reaons related to the above, the characters '(',\",\n \"state set.\\\"), message_plural=(\\\"States {} in the range of the transition \\\" \\\"function are\",\n \"for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in\",\n \"one-char strings). The keys of the `transition_function` implicitly define the dfa's state-set and\",\n \"to match the empty string (because it kind of looks like an epsilon);\",\n \"`nfa1 | nfa2` returns an nfa that recognizes A union B. The cardinality\",\n \"return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€'\",\n \"elif char == '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1]\",\n \"(state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else: gnfa_tf[(state1, state2)]\",\n \"parentheses. \\\"\\\"\\\" operator_to_operation = { '|': NFA.__or__, '•': NFA.__add__ } _error_message( bad_set=set(NOT_SYMBOLS) &\",\n \"self.body_states | {self.start_state} | {self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a\",\n \"transition function is missing a case -- i.e., it is not the case\",\n \"above veboten characters, 3. the input regex contain a binary operator followed by\",\n \"python regex syntax either, though it can be done; give it a go.)\",\n \"regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 = self.transition_function[(rip, rip)] reduced_tf = {}\",\n \"strings). The keys of the `transition_function` implicitly define the dfa's state-set and alphabet.\",\n \"the transition function is not a subset of the set of states inferred\",\n \"optional; it's default value is string.printable -- i.e., the set of \\\"printable\\\" characters,\",\n \"1 if paren_count < 0: raise ValueError( \\\"Right parenthesis occurs in regex withour\",\n \"function. The exception message will specify which of these six conditions things triggered\",\n \"of strings of the form a concat b, where a is an element\",\n \"member of the set of states inferred from the transition function; * the\",\n \"= error_state return new_states, new_tf self_states, self_tf = maybe_add_state(self, other) other_states, other_tf =\",\n \"of the transition function dictionary should be sets (or frozensets). The empty set\",\n \"return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states while overlap:\",\n \"accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self)\",\n \"state_set: AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours -\",\n \"{} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2)\",\n \"what you'd expect them to mean if you are familiar with regular expressions.\",\n \"'0'} with the current syntax. (Quick challenge: it's not totally obvious how to\",\n \"char in regex: if char in alphabet or char == '(': if len(processed)\",\n \"in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in\",\n \"a subset of the power set of states inferred from the transition function;\",\n \"frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s = list(iterable) return { frozenset(item) for\",\n \"_check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)]\",\n \"elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char)\",\n \"== 'Ø' or regex2 == 'Ø': return 'Ø' if regex1 == '€': return\",\n \"member of the alphabet inferred from the transition function is not a one-character\",\n \"A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol],\",\n \") if set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA(\",\n \", `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex string\",\n \"string\\\", and `False` otherwise. Will raise a ValueError exception is the string contains\",\n \"a case -- i.e., it is not the case that every pair of\",\n \"contains symbols that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state =\",\n \"-> FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction,\",\n \"not cool.\\\" ) if char == '(': paren_count += 1 if char ==\",\n \"the language recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises\",\n \"inferred from the transition function; * the range of the transition function is\",\n \"{ pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] =\",\n \"( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex:\",\n \") processed += char if paren_count > 0: raise ValueError( \\\"Left parenthesis occurs\",\n \"binary operator followed by an \\\" \\\"operator; not cool.\\\" ) if char ==\",\n \"of the set of states inferred from the transition function; * a member\",\n \"that recognises the set of all concatenations of strings in A with strings\",\n \"the above, the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot\",\n \"current syntax. (Quick challenge: it's not totally obvious how to match the empty\",\n \"the set of \\\"printable\\\" characters, which includes the standard ASCII letters and digits,\",\n \"Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise\",\n \"an nfa that recognizes A union B. The cardinality of the state-set of\",\n \"is string.printable *minus* parentheses, the vertical bar, the star symbol, and the tilde,\",\n \"DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def __add__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let\",\n \"for state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2\",\n \"-> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if\",\n \"if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of\",\n \"characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols in\",\n \"List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand = machine_stack.pop()\",\n \"return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\" Determines whether nfa\",\n \"import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping,\",\n \"the state-set of nfa1 | nfa2 is the cardinality of the state set\",\n \"empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state,\",\n \"-> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def prime(state: State): return state + '`' def copy(nfa:\",\n \"= { prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept\",\n \"bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts \\\"some string\\\", and `False`\",\n \"self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet\",\n \"def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State],\",\n \"== '(': if len(processed) > 0: processed += ( '•' if processed[-1] not\",\n \"state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return\",\n \"AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool: \\\"\\\"\\\"\",\n \"recognized by dfa2. `dfa1 | dfa2` returns a dfa that recognizes A union\",\n \"any of th following are true: * the start state is not a\",\n \"if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop() r2 =\",\n \"inferred from the transition function; * a member of the alphabet inferred from\",\n \"of one-character strings) as input; returns an NFA that recognises the language defined\",\n \"the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\"\",\n \"of the power set of states inferred from the transition function; 6. the\",\n \"== '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) >\",\n \"new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states: if (state, '') in\",\n \"characters -- i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the\",\n \"of states inferred from the transition function; 6. the transition function is missing\",\n \"alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state,\",\n \"gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start}, self.states\",\n \"copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA )\",\n \") -> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf =\",\n \"of now, the syntax of the regular expressions that this method takes as\",\n \"1 elif char == ')': paren_count -= 1 elif char == '|': if\",\n \"of the set of states inferred from the transition function; 3. a member\",\n \"vertical bar, the star symbol, and the tilde, for reasons that I will\",\n \"you would expect). The class will raise a ValueError exception on instantiation if\",\n \"state, symbol in nfa.transition_function.keys(): copy_tf[(prime(state), symbol)] = { prime(x) for x in nfa.transition_function[(state,\",\n \"-> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 =\",\n \"plus the cardinality of the state-set of nfa2 plus 1. There is no\",\n \"For that reason, I recommend you don't `+` dfas with large numbers of\",\n \"is the power-set of the nfa's state set --- i.e., the values of\",\n \"'€' and 'Ø'. The parentheses, vertical bar and star mean what you'd expect\",\n \"is not a subset of the set of states inferred from the transition\",\n \"EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1] =\",\n \"are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\":\",\n \"gnfa.reduce() return gnfa.transition_function[(gnfa.start_state, gnfa.accept_state)] def non_determinize(self) -> NFA: \\\"\\\"\\\" Convenience method that takes\",\n \"the set of NFA states. For related reasons, the time complexity of this\",\n \"return regex2 if regex2 == \\\"Ø\\\": return regex1 return f\\\"{regex1}|{regex2}\\\" rip = self.body_states.pop()\",\n \"with the input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf = self._combine(other)\",\n \"recognises the language defined by that regular expression and that alphabet. The alphabet\",\n \"char if paren_count > 0: raise ValueError( \\\"Left parenthesis occurs in regex without\",\n \"pair in product(nfa1.states, extra_symbols): new_tf[pair] = set() return new_tf return add_one_way(nfa1, nfa2), add_one_way(nfa2,\",\n \"dfa that recognizes A union B. The states of dfa1 | dfa2 are\",\n \"will raise a ValueError exception on instantiation if any of the following are\",\n \"of dfa1 | dfa2 are ordered pairs of states from dfa1 and dfa2.\",\n \"are the source of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\":\",\n \"{self.accept_state} ) def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self`\",\n \"are the symbols in the alphabet. The domain of the transition function is\",\n \"the transition function. The exception message will specify which of these six conditions\",\n \"'(': paren_count += 1 if char == ')': paren_count -= 1 if paren_count\",\n \"not the case that every pair of a state and a symbol is\",\n \"def _good_range(self) -> None: bad_range = { x for x in self.transition_function.values() if\",\n \"need to write '•'' explicitly if you don't want to. But it gets\",\n \"dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol\",\n \"| nfa2` returns an nfa that recognizes A union B. The cardinality of\",\n \") machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def binary_operate() -> None: right_operand\",\n \"-> None: right_operand = machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand\",\n \"be sets (or frozensets). The empty set is a valid value; in fact,\",\n \"state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys():\",\n \"from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State = str\",\n \"eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return\",\n \"\\\"\\\"\\\" Let A be the language recognised by nfa1, and B be the\",\n \"that alphabet. The alphabet parameter is optional; it's default value is string.printable --\",\n \"that recognizes A union B. The states of dfa1 | dfa2 are ordered\",\n \"from the transition function; 6. the transition function is missing cases -- i.e.,\",\n \"{self.start_state} for symbol in self.alphabet: star_tf[(star_start, symbol)] = set() for state in self.accept_states:\",\n \"\\\"\\\"\\\" Takes a regular expression and an alphabet (i.e., a set of one-character\",\n \"transition function is missing cases -- i.e., it is not the case that\",\n \"gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)]\",\n \"self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf,\",\n \"0 for char in regex: if char == '(': paren_count += 1 elif\",\n \"= _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state\",\n \"the language defined by that regular expression and that alphabet. The alphabet parameter\",\n \"transition function. The exception message will specify which of these six conditions things\",\n \"six conditions things triggered the exception, and which states/symbols are the source of\",\n \"prime(x) for x in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x)\",\n \"raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class\",\n \"reason, I recommend you don't `+` dfas with large numbers of states. \\\"\\\"\\\"\",\n \"finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] -\",\n \"function. Note that the empty string will not be inferred to be a\",\n \"with regular expressions. '•' (option-8 on a mac keyboard) means concatenation. You can\",\n \"sets (or frozensets). The empty set is a valid value; in fact, you\",\n \"new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = { x for x\",\n \"be the language recognised by dfa1, B be the language recognised by dfa2.\",\n \"star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by nfa. `nfa.self()`\",\n \"start_state: State, accept_state: State ): self.transition_function = transition_function self.body_states = body_states self.start_state =\",\n \"equivalent to `self` with one less state in it. \\\"\\\"\\\" def union_main_scope(regex: Regex)\",\n \"string contains a character not in the alphabet, and not one of the\",\n \"gets used internally. '€' (option-shift-2) is used to match the empty string (because\",\n \") State = str Symbol = str Regex = str FsaTransitionFunction = Mapping[\",\n \"are strings and Symbols are one-char strings). The keys of the `transition_function` implicitly\",\n \"the same same language as the NFA instance. WARNING: The set of DFA\",\n \"(and hence the checks below will work as you would expect). The class\",\n \"fsa's state set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol )\",\n \") if char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex\",\n \"')': paren_count -= 1 if paren_count < 0: raise ValueError( \\\"Right parenthesis occurs\",\n \"`transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States are\",\n \"product({'q1', 'q2'}, alphabet) } tf[('q1', symbol)] = {'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'}\",\n \"the dfa's state-set and alphabet. The class will raise a ValueError exception on\",\n \"compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char == '(': operator_stack.append(char) else: while\",\n \"')', '|', '*'} ) ) -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and\",\n \"`Ø`, 2. the input regex string contains a character not in the alphabet,\",\n \"parentheses, the vertical bar, the star symbol, and the tilde, for reasons that\",\n \"set of states inferred from the transition function; 6. the transition function is\",\n \"from the transition function; * the range of the transition function is not\",\n \"state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|'\",\n \"= self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA ) -> Tuple[NfaTransitionFunction,\",\n \"def reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self` with one\",\n \"my_dfa accepts \\\"some string\\\", and `False` otherwise. Will raise a ValueError exception is\",\n \"if any of th following are true: * the start state is not\",\n \"The method will raise a ValueError exception if any of the following conditions\",\n \"['€', 'Ø'] NOT_SYMBOLS = OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet:\",\n \"nfa accepts input string. Will raise a ValueError exception is the string contains\",\n \"transition_range - self.states _error_message( bad_set=bad_range, message_singular=(\\\"State {} in the range of the transition\",\n \"state and a symbol is in the domain of the transition function. The\",\n \"and Symbols are one-char strings.) The transition function' keys implicitly define the nfa's\",\n \"while new_state in state_set: counter += 1 new_state = \\\"new_state\\\" + str(counter) return\",\n \"state {} is not a member of the fsa's \\\" \\\"state set.\\\"), message_plural=(\\\"Accept\",\n \"= self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state\",\n \"element of A and b is an element of B. Note that this\",\n \"symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return\",\n \"match the empty string (because it kind of looks like an epsilon); there's\",\n \"dfa2` returns a dfa that recognizes A union B. The states of dfa1\",\n \"returns an NFA that recognises the language defined by that regular expression and\",\n \"in self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)]\",\n \"the input DFAs into NFAs, uses the NFA '+', then converts the result\",\n \"message will specify which of these six conditions things triggered the exception, and\",\n \"B. The cardinality of the state-set of nfa1 | nfa2 is the cardinality\",\n \"'(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols in the\",\n \"in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state,\",\n \"-> Tuple[NfaTransitionFunction, NfaTransitionFunction]: def add_one_way(nfa1: NFA, nfa2: NFA) -> NfaTransitionFunction: new_tf = nfa1.transition_function\",\n \"method will raise a ValueError exception if any of the following conditions hold:\",\n \"from the transition function; 2. the set of accept states is not a\",\n \"consisting of the set of strings of the form a concat b, where\",\n \"return not current_states & self.accept_states == set() def determinize(self) -> \\\"DFA\\\": \\\"\\\"\\\"Returns a\",\n \"by some miracle, there is someone who cares about this, I will change\",\n \"transition_function self.body_states = body_states self.start_state = start_state self.accept_state = accept_state self.states = (\",\n \"return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol],\",\n \"for pair in product({'q1'}, alphabet) } accept_states = set() if empty == 'Ø'\",\n \"- self.states, message_singular=(\\\"State {} in the range of the transition \\\" \\\"function is\",\n \"= machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) >\",\n \"self, *, transition_function: FsaTransitionFunction, start_state: State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state )\",\n \"Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset())\",\n \"& self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept\",\n \"a mac keyboard) means concatenation. You can leave concatentation implicit, as is usual;\",\n \"_get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states: gnfa_tf[(state, gnfa_accept)]\",\n \"the fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State,\",\n \"the regular expressions that this method takes as input is very simple --\",\n \"return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A\",\n \"self.start_state, self.accept_state ) NfaTransitionFunction = Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]]\",\n \"a symbol is in the domain of the transition function. The exception message\",\n \"function; 6. the transition function is missing cases -- i.e., it is not\",\n \"if (state, '') in concat_tf: concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return\",\n \"{'(', '|'} else '' ) if char not in alphabet | set(NOT_SYMBOLS): raise\",\n \"new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return\",\n \"accepted regex character.\\\" ) if char in OPERATORS and processed[-1] in {'|', '•'}:\",\n \"other.states while overlap: other = copy(other) overlap = self.states & other.states def add_empty_transitions(\",\n \"occurs in regex without matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction =\",\n \") if char == '(': paren_count += 1 if char == ')': paren_count\",\n \"subset of the power set of states inferred from the transition function; 6.\",\n \"!= '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS =\",\n \"input DFAs having different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state(\",\n \"ValueError exception is the string contains symbols that aren't in the DFA's alphabet.\",\n \"whether nfa accepts input string. Will raise a ValueError exception is the string\",\n \"function dictionary should be sets (or frozensets). The empty set is a valid\",\n \"problem with the input NFAs having different alphabets. \\\"\\\"\\\" new_self, new_other, union_tf =\",\n \"- OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if char\",\n \"strings of the form a concat b, where a is an element of\",\n \"a character not in the alphabet, and not one of the above veboten\",\n \"is an element of A and b is an element of B. Note\",\n \"not one of the above veboten characters, 3. the input regex contain a\",\n \"A with strings in B. This DFA operator is parasitic on the NFA\",\n \") from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State =\",\n \"ValueError exception if any of the following conditions hold: 1. the alphabet contains\",\n \"= self.states & other.states while overlap: other = copy(other) overlap = self.states &\",\n \"a GNFA equivalent to `self` with one less state in it. \\\"\\\"\\\" def\",\n \"'(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses, vertical bar and\",\n \"the NFA operator; it converts the input DFAs into NFAs, uses the NFA\",\n \"char == '(': operator_stack.append(char) else: while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack)\",\n \"states inferred from the transition function; * the set of accept states is\",\n \"computationally expensive algorith. For that reason, I recommend you don't `+` dfas with\",\n \"GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def\",\n \"set.\\\") ) def _good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF =\",\n \"'{char}' that is not in \\\" \\\"alphabet and not an accepted regex character.\\\"\",\n \"the vertical bar, the star symbol, and the tilde, for reasons that I\",\n \"absence of parentheses, the order of operations is: `*`, then `•`, then `|`.\",\n \"set of states inferred from the transition function; * the set of accept\",\n \"-> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self)\",\n \"given state-symbol pair is the empty set, if it is. You can define\",\n \"result back to a DFA. That makes for a relatively simple but, sadly,\",\n \"def __add__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised\",\n \"gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start,\",\n \"alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state =\",\n \"the alphabet inferred from the transition function is not a one-character string; *\",\n \"pairs of states from dfa1 and dfa2. There is no problem with the\",\n \"= Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A\",\n \"instantiation if any of the following are true: 1. the start state is\",\n \"for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states\",\n \"item in ( set(product(self.accept_states, other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function,\",\n \"state-set of nfa1 | nfa2 is the cardinality of the state set of\",\n \"* the start state is not a member of the set of states\",\n \"| epsilon_neighbours epsilon_neighbours = self._get_successors( state_set=epsilon_neighbours, symbol='' ) return frozenset(state_set) def _transition(self, state_set:\",\n \"B. Note that this `+` operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf\",\n \"not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start, gnfa_accept) def\",\n \"frozenset()) empty: FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union(\",\n \"in the fsa's state set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol:\",\n \"set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)] =\",\n \"Regex) -> int: return ( OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet)\",\n \"define the dfa's state-set and alphabet. The class will raise a ValueError exception\",\n \"self.accept_states: gnfa_tf[(state, gnfa_accept)] = '€' for state1, state2 in product( self.states | {gnfa_start},\",\n \"is the cardinality of the state set of nfa1 plus the cardinality of\",\n \"or compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and\",\n \"go.) 'Ø' (option-shift-o) represents the empty set; you can match to the empty\",\n \"copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in nfa.accept_states} return NFA( transition_function=copy_tf,\",\n \"set(state_set) & self.accept_states: determinized_accept.add(determinzed_state) determinized_start = _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start,\",\n \"message_singular=\\\"Alphabet cannot contain character {}.\\\", message_plural=\\\"Alphabet cannot contain characters {}.\\\" ) def fit_empty(empty:\",\n \"the standard ASCII letters and digits, and most common punctuation and white space.\",\n \"if len(regex) == 1: return regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex,\",\n \"alphabet inferred from the transition function is not a one-character string; * the\",\n \"= product(self_states, other_states) union_transition_function = {} for (state1, state2), symbol in product(state_pairs, union_alphabet):\",\n \"a member of the set of states inferred from the transition function; *\",\n \"alphabet) } accept_states = set() if empty == 'Ø' else {'q1'} return NFA(\",\n \"don't `+` dfas with large numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize()\",\n \"nfa's states, and the second elements are the symbols in the alphabet. The\",\n \"the alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'. The\",\n \"regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex, regex2: Regex) ->\",\n \"one-char strings.) The transition function' keys implicitly define the nfa's state-set and alphabet;\",\n \"ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = '' paren_count = 0 for char\",\n \"other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function = {} for (state1,\",\n \"the transition function; 6. the transition function is missing cases -- i.e., it\",\n \"and not an accepted regex character.\\\" ) if char in OPERATORS and processed[-1]\",\n \"quite right -- the default value is string.printable *minus* parentheses, the vertical bar,\",\n \"= { new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)]\",\n \"MutableNfaTF = { pair: set() for pair in product({'q1', 'q2'}, alphabet) } tf[('q1',\",\n \"be the language recognised by nfa1, and B be the language recognized by\",\n \"self.transition_function.keys(): state2 = self.transition_function[(state1, symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] +=\",\n \"')': paren_count -= 1 elif char == '|': if paren_count == 0: return\",\n \"\\\"\\\"\\\" Output a GNFA equivalent to `self` with one less state in it.\",\n \"accepts input string. Will raise a ValueError exception is the string contains symbols\",\n \"import product, chain, combinations from string import printable from typing import ( AbstractSet,\",\n \"char not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that\",\n \"gnfa_accept = _get_new_state(self.states | {gnfa_start}) gnfa_tf[(gnfa_start, self.start_state)] = '€' for state in self.accept_states:\",\n \"The parentheses, vertical bar and star mean what you'd expect them to mean\",\n \"= self.transition_function[(state1, rip)] for state2 in self.states - {self.start_state, rip}: r3 = self.transition_function[(rip,\",\n \"of states inferred from the transition function; * the set of accept states\",\n \"most common punctuation and white space. Actually, that's not quite right -- the\",\n \"alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0] if first_char in OPERATORS: raise ValueError(f\\\"Regex\",\n \"formed by concatenating any number of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states)\",\n \"python regular expresssions. All characters are intepreted as literals for symbols in the\",\n \"returns a dfa that recognizes A union B. The states of dfa1 |\",\n \"set() for pair in product({'q1'}, alphabet) } accept_states = set() if empty ==\",\n \"Mapping[Tuple[State, Symbol], AbstractSet[State]] MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic\",\n \"string\\\")` returns `True` if my_dfa accepts \\\"some string\\\", and `False` otherwise. Will raise\",\n \"-> Regex: \\\"\\\"\\\" Let A be the language accepted by dfa. `dfa.encode()` returns\",\n \"to. But it gets used internally. '€' (option-shift-2) is used to match the\",\n \"is not the case that every pair of a state and a symbol\",\n \"input is very simple -- much simpler than the standard python regular expresssions.\",\n \"= set() for (state_set, symbol) in product(state_sets, self._alphabet): determinzed_state = _stringify(state_set) determinized_tf[(determinzed_state, symbol)]\",\n \"class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State], start_state: State, accept_state: State\",\n \"the transition function's range is not a set; 5. the range of the\",\n \"common punctuation and white space. Actually, that's not quite right -- the default\",\n \"for char in regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet:\",\n \"PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex: first_char = regex[0]\",\n \"\\\"Left parenthesis occurs in regex without matching right \\\" \\\"parenthesis.\\\" ) return processed\",\n \"_good_range(self): raise NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex]\",\n \"with it. For reaons related to the above, the characters '(', ')', '|',\",\n \"MutableNfaTF = MutableMapping[Tuple[State, Symbol], Set[State]] class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class.\",\n \"-> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, and B\",\n \"symbol)] = ( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state +\",\n \"the first elements of the tuples represent the nfa's states, and the second\",\n \"default value is string.printable -- i.e., the set of \\\"printable\\\" characters, which includes\",\n \"-= 1 if paren_count < 0: raise ValueError( \\\"Right parenthesis occurs in regex\",\n \"instance. WARNING: The set of DFA states has the cardinality of the power-set\",\n \"'')] = {self.start_state} star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts\",\n \"the tuples represent the nfa's states, and the second elements are the symbols\",\n \"missing cases -- i.e., it is not the case that every pair of\",\n \"The set of DFA states has the cardinality of the power-set of the\",\n \"of the transition function's range is not a set; 5. the range of\",\n \"union B. The cardinality of the state-set of nfa1 | nfa2 is the\",\n \"'(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel',\",\n \"implicit, as is usual; no need to write '•'' explicitly if you don't\",\n \"Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractSet[State] (Where States are strings, and\",\n \"in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that is not\",\n \"DFA(_FSA): \\\"\\\"\\\" A deterministic finite automaton class. Takes three keyword arguments: - `transition_function`:\",\n \"(state1, state2) not in gnfa_tf: gnfa_tf[(state1, state2)] = 'Ø' return _GNFA(gnfa_tf, set(self.states), gnfa_start,\",\n \"that aren't in the DFA's alphabet. \\\"\\\"\\\" _check_input(string=string, alphabet=self.alphabet) current_state = self.start_state for\",\n \"{value} for key, value in self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states )\",\n \"set of accept states is not a subset of the set of states\",\n \"+ symbol else: gnfa_tf[(state1, state2)] = symbol gnfa_start = _get_new_state(self.states) gnfa_accept = _get_new_state(self.states\",\n \"range is not a set; 5. the range of the transition function is\",\n \"1. the start state is not a member of the set of states\",\n \"current_states = self._transition(current_states, symbol) return not current_states & self.accept_states == set() def determinize(self)\",\n \"-- the default value is string.printable *minus* parentheses, the vertical bar, the star\",\n \"the transition function is not a subset of the power set of states\",\n \"union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in dfa1.states:\",\n \"and b is an element of B. Note that this `+` operation is\",\n \"recognised by nfa1, and B be the language recognized by nfa2. `nfa1 |\",\n \"nfa1 plus the cardinality of the state-set of nfa2 plus 1. There is\",\n \"Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State -\",\n \"regex syntax either, though it can be done; give it a go.) 'Ø'\",\n \"return empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State])\",\n \"are not members of the fsa's \\\" \\\"state set.\\\") ) def _good_range(self): raise\",\n \"while operator_stack[-1] != '(': binary_operate() operator_stack.pop() while len(operator_stack) > 1: binary_operate() return machine_stack.pop()\",\n \") def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be the language recognised by\",\n \"input DFAs into NFAs, uses the NFA '+', then converts the result back\",\n \"If, by some miracle, there is someone who cares about this, I will\",\n \"transition \\\" \\\"function is not in the fsa's state set.\\\"), message_plural=(\\\"States {} in\",\n \"contains character '{char}' that is not in \\\" \\\"alphabet and not an accepted\",\n \"of the transition function. The exception message will specify which of these above\",\n \"accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not isinstance(states, collections.abc.Sequence): states =\",\n \"make the return a set of frozensets). def powerset(iterable: Iterable) -> Set[FrozenSet]: s\",\n \"I'll figure out how to improve on average simplicity, eventually. \\\"\\\"\\\" gnfa =\",\n \"how to match the empty string in normal python regex syntax either, though\",\n \"_good_range(self) -> None: bad_range = { x for x in self.transition_function.values() if not\",\n \"contains any of the verboten characters -- i.e.,`(` , `)`, `|`, `*`, `•`,\",\n \"automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`:\",\n \"for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value {}\",\n \"a concat b, where a is an element of A and b is\",\n \"= set() for state in self.accept_states: star_tf[(state, '')] = {self.start_state} star_accepts = self.accept_states\",\n \"expect them to mean if you are familiar with regular expressions. '•' (option-8\",\n \"language recognized by nfa2. `nfa1 + nfa2` returns an nfa that recognizes A\",\n \"state1 in self.states - {self.accept_state, rip}: r1 = self.transition_function[(state1, rip)] for state2 in\",\n \"alphabet (and hence the checks below will work as you would expect). The\",\n \"is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state in new_self.accept_states:\",\n \"recognised by dfa2. `dfa1 + dfa2` returns a DFA that recognises the set\",\n \"the cardinality of the power-set of the set of NFA states. For related\",\n \"which of these above conditions things triggered the exception, and which states/symbols are\",\n \"State - `accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.)\",\n \"empty-set symbol. If, by some miracle, there is someone who cares about this,\",\n \"alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def\",\n \"improve on average simplicity, eventually. \\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2:\",\n \"f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if regex1 == 'Ø' or\",\n \"recommend you don't `+` dfas with large numbers of states. \\\"\\\"\\\" return (self.non_determinize()\",\n \"State, accept_states: AbstractSet[State] ): super().__init__( transition_function=transition_function, start_state=start_state ) self._accept_states = accept_states self._states, self._alphabet\",\n \"A* -- i.e., the set of all strings formed by concatenating any number\",\n \"of the problem. \\\"\\\"\\\" def __or__(self, other: \\\"NFA\\\") -> \\\"NFA\\\": \\\"\\\"\\\" Let A\",\n \"NFA: tf: NfaTransitionFunction = { pair: set() for pair in product({'q1'}, alphabet) }\",\n \"_error_message, _good_alphabet, _check_input ) State = str Symbol = str Regex = str\",\n \"self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym), frozenset()) empty: FrozenSet[State] =\",\n \"there's no other way to match, for instance, {'', '0'} with the current\",\n \"is string.printable -- i.e., the set of \\\"printable\\\" characters, which includes the standard\",\n \"operator_stack[-1] not in PARENTHE and compare(char) <= 0 ): binary_operate() operator_stack.append(char) elif char\",\n \"= str FsaTransitionFunction = Mapping[ Tuple[State, Symbol], Union[State, AbstractSet[State]] ] class _FSA(_Base): def\",\n \"different alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2:\",\n \"| {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def fit( regex: Regex,\",\n \"def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(', ')', '|',\",\n \"As of now, the syntax of the regular expressions that this method takes\",\n \"not in alphabet | set(NOT_SYMBOLS): raise ValueError( f\\\"Regex contains character '{char}' that is\",\n \") def fit_empty(empty: Regex) -> NFA: tf: NfaTransitionFunction = { pair: set() for\",\n \"otherwise. Will raise a ValueError exception is the string contains symbols that aren't\",\n \"set; you can match to the empty language with it. For reaons related\",\n \"a member of the alphabet inferred from the transition function is not a\",\n \"'|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø'] NOT_SYMBOLS =\",\n \"if paren_count == 0: return True return False def regex_star(regex: Regex) -> Regex:\",\n \"leave concatentation implicit, as is usual; no need to write '•'' explicitly if\",\n \"normal python regex syntax either, though it can be done; give it a\",\n \"and B be the language recognized by dfa2. `dfa1 | dfa2` returns a\",\n \"in union_alphabet: new_tf[(error_state, symbol)] = error_state for symbol in extra_symbols: for state in\",\n \"liable to be much more complicated than necessary; maybe I'll figure out how\",\n \"Regex: if regex1 == 'Ø' or regex2 == 'Ø': return 'Ø' if regex1\",\n \"= self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3),\",\n \"( self_tf[(state1, symbol)] + other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states\",\n \"in OPERATORS: if operator_stack[-1] in PARENTHE or compare(char) > 0: operator_stack.append(char) else: while\",\n \"\\\"DFA\\\": \\\"\\\"\\\"Returns a DFA that recognizes the same same language as the NFA\",\n \"th following are true: * the start state is not a member of\",\n \"plus 1. There is no problem with the input NFAs having different alphabets.\",\n \"withour matching \\\" \\\"left parenthesis.\\\" ) processed += char if paren_count > 0:\",\n \"machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES =\",\n \"bar, the star symbol, and the tilde, for reasons that I will explain\",\n \"symbol in product(state_pairs, union_alphabet): union_transition_function[(state1 + state2, symbol)] = ( self_tf[(state1, symbol)] +\",\n \"NFA instance. \\\"\\\"\\\" nd_transition_function = { key: {value} for key, value in self.transition_function.items()\",\n \"inferred from the transition function is not a one-character string; 4. a member\",\n \"the NFA. Don't determinize big NFAs. \\\"\\\"\\\" # powerset code an itertools recipe,\",\n \"Regex) -> Regex: if regex1 == \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\":\",\n \"A be the language accepted by dfa. `dfa.encode()` returns a regex string that\",\n \"symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states |\",\n \"AbstractSet[State] (where States are strings and Symbols are one-char strings). The keys of\",\n \"binary operator followed by an operator, or 4. the input regex does not\",\n \"the language recognised by dfa1, B be the language recognised by dfa2. `dfa1\",\n \"b is an element of B. Note that this `+` operation is not\",\n \"char in OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains binary\",\n \"in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*': machine_stack[-1]\",\n \"= ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')'] EMPTIES = ['€', 'Ø']\",\n \"-> bool: paren_count = 0 for char in regex: if char == '(':\",\n \"typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple,\",\n \"{'q2'} return NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack =\",\n \"bad_set=bad_range, message_singular=(\\\"Value {} in the range of the transition \\\" \\\"function is not\",\n \"string import printable from typing import ( AbstractSet, Container, FrozenSet, Iterable, List, Mapping,\",\n \"binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*'] PARENTHE = ['(', ')']\",\n \"is the empty set, if it is. You can define epsilon-moves by using\",\n \"alphabet except for '(', '')', '|', '*', '•', '€' and 'Ø'. The parentheses,\",\n \"new_self.start_state, new_other.start_state } for symbol in new_self.alphabet | new_other.alphabet: union_tf[(union_start_state, symbol)] = set()\",\n \"of these above conditions things triggered the exception, and which states/symbols are the\",\n \"that recognizes the same same language as the NFA instance. WARNING: The set\",\n \"symbol for empty-set.) In the absence of parentheses, the order of operations is:\",\n \"self._combine(other) union_start_state = _get_new_state(new_self.states | new_other.states) union_tf[(union_start_state, '')] = { new_self.start_state, new_other.start_state }\",\n \"Regex: if regex in EMPTIES: return '€' if len(regex) == 1: return regex\",\n \"reduced_tf[(state1, state2)] = new_regex return _GNFA( reduced_tf, self.body_states - {rip}, self.start_state, self.accept_state )\",\n \"Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import ( _Base,\",\n \"concat_tf[(state, '')].add(new_other.start_state) else: concat_tf[(state, '')] = {new_other.start_state} return NFA( transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states )\",\n \"NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf = {} combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other,\",\n \"reduce(self) -> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self` with one less\",\n \"if you don't want to. But it gets used internally. '€' (option-shift-2) is\",\n \"than the standard python regular expresssions. All characters are intepreted as literals for\",\n \"values of the transition function dictionary should be sets (or frozensets). The empty\",\n \"if char == ')': paren_count -= 1 if paren_count < 0: raise ValueError(\",\n \"pair is the empty set, if it is. You can define epsilon-moves by\",\n \"*minus* parentheses, the vertical bar, the star symbol, and the tilde, for reasons\",\n \"the `transition_function` implicitly define the dfa's state-set and alphabet. The class will raise\",\n \"3. a member of the alphabet inferred from the transition function is not\",\n \"function; * the range of the transition function is not a subset of\",\n \") self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def\",\n \"*, state_set: AbstractSet[State], symbol: Symbol ) -> FrozenSet[State]: def get_successor(state: State, sym: Symbol)\",\n \"= machine_stack.pop() left_operand = machine_stack.pop() machine = operator_to_operation[operator_stack.pop()]( left_operand, right_operand ) machine_stack.append(machine) def\",\n \"in nfa.accept_states} return NFA( transition_function=copy_tf, start_state=copy_start, accept_states=copy_accept ) overlap = self.states & other.states\",\n \"= _stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) ->\",\n \"in B. This DFA operator is parasitic on the NFA operator; it converts\",\n \"B be the language recognized by nfa2. `nfa1 + nfa2` returns an nfa\",\n \"not in the fsa's state set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State],\",\n \"self._accept_states = accept_states self._states, self._alphabet = _extract_states_alphabet( self._transition_function.keys() ) self._well_defined() @property def alphabet(self)\",\n \"\\\"function is not a set.\\\"), message_plural=(\\\"Values {} in the range of the transition\",\n \"if char in alphabet or char == '(': if len(processed) > 0: processed\",\n \"of these six conditions things triggered the exception, and which states/symbols are the\",\n \"them to mean if you are familiar with regular expressions. '•' (option-8 on\",\n \"in extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf\",\n \"NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`:\",\n \"i.e., it is not the case that every pair of a state and\",\n \"keys implicitly define the nfa's state-set and alphabet; the first elements of the\",\n \"B. The states of dfa1 | dfa2 are ordered pairs of states from\",\n \"speakers of Scandinavian languages for the last one; I am very against English\",\n \"self.transition_function.items() } return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str:\",\n \"and 'Ø' cannot be symbols in the alphabet of the NFA. (My apologies\",\n \"and that alphabet. The alphabet parameter is optional; it's default value is string.printable\",\n \"on instantiation if any of th following are true: * the start state\",\n \"accepted by dfa. `dfa.encode()` returns a regex string that generates A. That regex\",\n \"= nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in product(nfa1.states, extra_symbols): new_tf[pair] =\",\n \"states {} are not members of the fsa's \\\" \\\"state set.\\\") ) def\",\n \"processed += char if paren_count > 0: raise ValueError( \\\"Left parenthesis occurs in\",\n \"raise ValueError(f\\\"Regex cannot start with '{first_char}'.\\\") processed = '' paren_count = 0 for\",\n \"specify which of these six conditions things triggered the exception, and which states/symbols\",\n \"DFA states has the cardinality of the power-set of the set of NFA\",\n \"5. the range of the transition function is not a subset of the\",\n \"DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\": \\\"\\\"\\\" Let A be\",\n \"Actually, that's not quite right -- the default value is string.printable *minus* parentheses,\",\n \"The exception message will specify which of these six conditions things triggered the\",\n \"= copy(other) overlap = self.states & other.states def add_empty_transitions( nfa1: NFA, nfa2: NFA\",\n \"regex + '*' return f\\\"({regex})*\\\" def regex_concat(regex1: Regex, regex2: Regex) -> Regex: if\",\n \"transition_function=concat_tf, start_state=new_self.start_state, accept_states=new_other.accept_states ) def _combine(self, other: \\\"NFA\\\") -> Tuple[\\\"NFA\\\", \\\"NFA\\\", MutableNfaTF]: def\",\n \"= MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self, transition_function: GnfaTransitionFunction, body_states: Set[State],\",\n \"of states inferred from the transition function; 2. the set of accept states\",\n \"x for x in self.transition_function.values() if not isinstance(x, collections.abc.Set) } _error_message( bad_set=bad_range, message_singular=(\\\"Value\",\n \"the characters '(', ')', '|', '*', '•', '€' and 'Ø' cannot be symbols\",\n \"0: processed += ( '•' if processed[-1] not in {'(', '|'} else ''\",\n \"def alphabet(self) -> FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states\",\n \"expect). The class will raise a ValueError exception on instantiation if any of\",\n \"makes for a relatively simple but, sadly, computationally expensive algorith. For that reason,\",\n \"NFA: \\\"\\\"\\\" Convenience method that takes a DFA instance and returns an NFA\",\n \"def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) -> None: super()._well_defined() _good_alphabet(alphabet=self.alphabet, name=\\\"alphabet\\\")\",\n \"function; * the set of accept states is not a subset of the\",\n \"is a valid value; in fact, you are required to specify that the\",\n \"is not a subset of the power set of states inferred from the\",\n \"that takes a DFA instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function =\",\n \"} accept_states = set() if empty == 'Ø' else {'q1'} return NFA( transition_function=tf,\",\n \"contains binary operator followed by an \\\" \\\"operator; not cool.\\\" ) if char\",\n \"* the transition function is missing a case -- i.e., it is not\",\n \"accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa accepts\",\n \"sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function ) return self._transition_function.get((state, sym),\",\n \"compare(char) > 0: operator_stack.append(char) else: while ( operator_stack[-1] not in PARENTHE and compare(char)\",\n \"in the domain of the transition function. The exception message will specify which\",\n \"alphabets. \\\"\\\"\\\" union_alphabet = self.alphabet | other.alphabet def maybe_add_state( dfa1: DFA, dfa2: DFA\",\n \"by using the empty string in place of an alphabet symbol in the\",\n \"that generates A. That regex string is liable to be much more complicated\",\n \"problem. \\\"\\\"\\\" def __or__(self, other: \\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the\",\n \"the standard python regular expresssions. All characters are intepreted as literals for symbols\",\n \"new_tf = nfa1.transition_function extra_symbols = nfa2.alphabet - nfa1.alphabet if extra_symbols: for pair in\",\n \") -> \\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an alphabet (i.e., a\",\n \"set --- i.e., the values of the transition function dictionary should be sets\",\n \"NFA( transition_function=tf, start_state='q1', accept_states={'q2'} ) machine_stack: List[NFA] = [] operator_stack = ['sentinel'] def\",\n \"big NFAs. \\\"\\\"\\\" # powerset code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes #\",\n \"Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA: def __init__( self,\",\n \"string; * the transition function is missing a case -- i.e., it is\",\n \"= 1 new_state = 'new_state1' while new_state in state_set: counter += 1 new_state\",\n \"| new_other.alphabet: union_tf[(union_start_state, symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA(\",\n \"NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states ) new_other = NFA( transition_function=other_tf, start_state=other.start_state, accept_states=other.accept_states ) combination_tf\",\n \"intepreted as literals for symbols in the alphabet except for '(', '')', '|',\",\n \"symbol)] if (state1, state2) in gnfa_tf.keys(): gnfa_tf[(state1, state2)] += '|' + symbol else:\",\n \"== '*': machine_stack[-1] = machine_stack[-1].star() elif char in OPERATORS: if operator_stack[-1] in PARENTHE\",\n \"hence the checks below will work as you would expect). The class will\",\n \"AbstractSet[State]) -> FrozenSet[State]: epsilon_neighbours = self._get_successors( state_set=state_set, symbol='' ) while epsilon_neighbours - state_set:\",\n \"add_one_way(nfa2, nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states\",\n \"literals for symbols in the alphabet except for '(', '')', '|', '*', '•',\",\n \"if empty == 'Ø' else {'q1'} return NFA( transition_function=tf, start_state='q1', accept_states=accept_states ) def\",\n \"one-character string; * the transition function is missing a case -- i.e., it\",\n \"in A with strings in B. This DFA operator is parasitic on the\",\n \"def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if my_dfa\",\n \"True return False def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return\",\n \"in regex: if char == '(': paren_count += 1 elif char == ')':\",\n \"while len(operator_stack) > 1: binary_operate() return machine_stack.pop() OPERATORS = ['sentinel', '|', '•', '*']\",\n \"= OPERATORS + PARENTHE + EMPTIES def _pre_process(regex: Regex, alphabet: AbstractSet[Symbol]) -> Regex:\",\n \"class NFA(_FSA): \\\"\\\"\\\" A nondeterministic finite automaton class. Takes three keyword arguments: -\",\n \"nfa1) self_tf, other_tf = add_empty_transitions(self, other) new_self = NFA( transition_function=self_tf, start_state=self.start_state, accept_states=self.accept_states )\",\n \"rip)] reduced_tf = {} for state1 in self.states - {self.accept_state, rip}: r1 =\",\n \"_stringify( self._add_epsilons({self._start_state}) ) return DFA( transition_function=determinized_tf, start_state=determinized_start, accept_states=determinized_accept ) def star(self) -> \\\"NFA\\\":\",\n \"`+` dfas with large numbers of states. \\\"\\\"\\\" return (self.non_determinize() + other.non_determinize()).determinize() def\",\n \"_gnfize(self) -> _GNFA: gnfa_tf: MutableGnfaTF = {} for state1, symbol in self.transition_function.keys(): state2\",\n \"if my_dfa accepts \\\"some string\\\", and `False` otherwise. Will raise a ValueError exception\",\n \"other_states)) | set(product(self_states, other.accept_states)) ) } return DFA( transition_function=union_transition_function, start_state=union_start_state, accept_states=union_accept_states ) def\",\n \"FrozenSet[State] = frozenset() # This avoids a mypy bug. return empty.union( *[frozenset(get_successor(state, symbol))\",\n \"a DFA instance and returns an NFA instance. \\\"\\\"\\\" nd_transition_function = { key:\",\n \"\\\" \\\"function are not sets.\\\") ) transition_range: Set[Optional[AbstractSet[State]]] = set.union( *self.transition_function.values() ) _error_message(\",\n \"in nfa.transition_function[(state, symbol)] } copy_start = prime(nfa.start_state) copy_accept = {prime(x) for x in\",\n \"\\\"NFA\\\": \\\"\\\"\\\" Takes a regular expression and an alphabet (i.e., a set of\",\n \"range of the transition \\\" \\\"function is not in the fsa's state set.\\\"),\",\n \"`accept_states`: AbstractSet[State] (Where States are strings, and Symbols are one-char strings.) The transition\",\n \"of the form a concat b, where a is an element of A\",\n \"nd_transition_function = { key: {value} for key, value in self.transition_function.items() } return NFA(\",\n \"star_accepts = self.accept_states | {star_start} return NFA( transition_function=star_tf, start_state=star_start, accept_states=star_accepts ) @staticmethod def\",\n \"cast ) from .base import ( _Base, _extract_states_alphabet, _error_message, _good_alphabet, _check_input ) State\",\n \"to parse the regex and build the NFA. The method will raise a\",\n \"a relatively simple but, sadly, computationally expensive algorith. For that reason, I recommend\",\n \"is someone who cares about this, I will change the symbol for empty-set.)\",\n \"f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1 + regex2 def regex_union(regex1: Regex,\",\n \"it converts the input DFAs into NFAs, uses the NFA '+', then converts\",\n \"dfa's state-set and alphabet. The class will raise a ValueError exception on instantiation\",\n \"the range of the transition function is not a subset of the set\",\n \"& other.states while overlap: other = copy(other) overlap = self.states & other.states def\",\n \"function is the power-set of the nfa's state set --- i.e., the values\",\n \"FrozenSet[State]: def get_successor(state: State, sym: Symbol) -> AbstractSet[State]: self._transition_function = cast( NfaTransitionFunction, self._transition_function\",\n \"form a concat b, where a is an element of A and b\",\n \"\\\"\\\"\\\" gnfa = self._gnfize() while len(gnfa.states) > 2: gnfa = gnfa.reduce() return gnfa.transition_function[(gnfa.start_state,\",\n \"extra_symbols: for state in dfa1.states: new_tf[(state, symbol)] = error_state return new_states, new_tf self_states,\",\n \"explicitly if you don't want to. But it gets used internally. '€' (option-shift-2)\",\n \"FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base\",\n \"Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast ) from .base import\",\n \"and the second elements are the symbols in the alphabet. The domain of\",\n \"OPERATORS and processed[-1] in {'|', '•'}: raise ValueError( \\\"Regex contains binary operator followed\",\n \"dfa1.states extra_symbols = dfa2.alphabet - dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states =\",\n \"dfa1.alphabet if extra_symbols: error_state = _get_new_state(dfa1.states) new_states = dfa1.states | {error_state} for symbol\",\n \"regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})' return regex1\",\n \"that I will explain presently. As of now, the syntax of the regular\",\n \"NotImplementedError GnfaTransitionFunction = Mapping[Tuple[State, State], Regex] MutableGnfaTF = MutableMapping[Tuple[State, State], Regex] class _GNFA:\",\n \"== 'Ø': return 'Ø' if regex1 == '€': return regex2 if regex2 ==\",\n \"the nfa's states, and the second elements are the symbols in the alphabet.\",\n \"def regex_star(regex: Regex) -> Regex: if regex in EMPTIES: return '€' if len(regex)\",\n \"regex: if char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char\",\n \"tuples represent the nfa's states, and the second elements are the symbols in\",\n \"it is not the case that every pair of a state and a\",\n \"complexity of this method is exponential in the number of states of the\",\n \"{} are not members of the fsa's \\\" \\\"state set.\\\") ) def _good_range(self):\",\n \"by dfa1, and B be the language recognized by dfa2. `dfa1 | dfa2`\",\n \"I will change the symbol for empty-set.) In the absence of parentheses, the\",\n \"def _good_accept(self) -> None: bad_accept_states = self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state\",\n \"state_set: AbstractSet[State], symbol: Symbol): return self._add_epsilons(self._get_successors(state_set=state_set, symbol=symbol)) def accepts(self, string: str) -> bool:\",\n \"state set.\\\") ) def _get_successors( self, *, state_set: AbstractSet[State], symbol: Symbol ) ->\",\n \"power-set of the set of NFA states. For related reasons, the time complexity\",\n \"by concatenating any number of members of A. \\\"\\\"\\\" star_start = _get_new_state(self.states) star_tf\",\n \"empty.union( *[frozenset(get_successor(state, symbol)) for state in state_set] ) def _add_epsilons(self, state_set: AbstractSet[State]) ->\",\n \"code an itertools recipe, from # https://docs.python.org/3/library/itertools.html#recipes # (minor modification to make the\",\n \"\\\"DFA\\\") -> \\\"DFA\\\": \\\"\\\"\\\" Let A be the language recognised by dfa1, B\",\n \"that's not quite right -- the default value is string.printable *minus* parentheses, the\",\n \"a ValueError exception on instantiation if any of th following are true: *\",\n \"\\\"operator; not cool.\\\" ) if char == '(': paren_count += 1 if char\",\n \"_get_new_state(state_set: Container) -> State: counter = 1 new_state = 'new_state1' while new_state in\",\n \"symbol)] = set() union_accept_states = new_self.accept_states | new_other.accept_states return NFA( transition_function=union_tf, start_state=union_start_state, accept_states=union_accept_states\",\n \"i.e.,`(` , `)`, `|`, `*`, `•`, `€` and `Ø`, 2. the input regex\",\n \"-> \\\"_GNFA\\\": \\\"\\\"\\\" Output a GNFA equivalent to `self` with one less state\",\n \"in the range of the transition \\\" \\\"function are not sets.\\\") ) transition_range:\",\n \"self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union( regex_concat(regex_concat(r1, regex_star(r2)), r3), r4\",\n \"product, chain, combinations from string import printable from typing import ( AbstractSet, Container,\",\n \"language recognized by nfa2. `nfa1 | nfa2` returns an nfa that recognizes A\",\n \"change the symbol for empty-set.) In the absence of parentheses, the order of\",\n \"'' paren_count = 0 for char in regex: if char in alphabet or\",\n \"regex without matching right \\\" \\\"parenthesis.\\\" ) return processed DfaTransitionFunction = Mapping[Tuple[State, Symbol],\",\n \"alphabet=self.alphabet) current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return\",\n \"the transition function is missing cases -- i.e., it is not the case\",\n \"challenge: it's not totally obvious how to match the empty string in normal\",\n \"Let A be the language recognised by dfa1, B be the language recognised\",\n \"return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if not\",\n \"list(iterable) return { frozenset(item) for item in chain.from_iterable( combinations(s, r) for r in\",\n \"'€': return regex1 if union_main_scope(regex1): regex1 = f'({regex1})' if union_main_scope(regex2): regex2 = f'({regex2})'\",\n \"states is not a subset of the set of states inferred from the\",\n \"language recognised by dfa1, and B be the language recognized by dfa2. `dfa1\",\n \"- `transition_function`: Mapping[Tuple[State, Symbol], State] - `start_state`: State - `accept_state`: AbstractSet[State] (where States\",\n \"'•'' explicitly if you don't want to. But it gets used internally. '€'\",\n \"maybe_add_state(self, other) other_states, other_tf = maybe_add_state(other, self) state_pairs = product(self_states, other_states) union_transition_function =\",\n \"of the transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values {} in the\",\n \"AbstractSet, Container, FrozenSet, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, Union, cast )\",\n \"other way to match, for instance, {'', '0'} with the current syntax. (Quick\",\n \") def accepts(self, string: str) -> bool: \\\"\\\"\\\" `my_dfa.accepts(\\\"some string\\\")` returns `True` if\",\n \"OPERATORS.index(operator) - OPERATORS.index(operator_stack[-1]) ) regex = _pre_process(regex, alphabet) for char in regex: if\",\n \"accept_states=accept_states ) def fit_symbol(symbol: Symbol) -> NFA: tf: MutableNfaTF = { pair: set()\",\n \"a ValueError exception is the string contains symbols that aren't in the nfa's\",\n \"in EMPTIES: return '€' if len(regex) == 1: return regex + '*' return\",\n \"_error_message( bad_set=bad_range, message_singular=(\\\"Value {} in the range of the transition \\\" \\\"function is\",\n \"not in the alphabet, and not one of the above veboten characters, 3.\",\n \"combination_tf.update(new_self.transition_function) combination_tf.update(new_other.transition_function) return new_self, new_other, combination_tf def _good_range(self) -> None: bad_range = {\",\n \"in the range of the transition \\\" \\\"function is not a set.\\\"), message_plural=(\\\"Values\",\n \"FrozenSet[Symbol]: return self._alphabet @property def accept_states(self) -> AbstractSet[State]: return self._accept_states def _well_defined(self) ->\",\n \"self.accept_states - self.states _error_message( bad_set=bad_accept_states, message_singular=(\\\"Accept state {} is not a member of\",\n \"regex2: Regex) -> Regex: if regex1 == 'Ø' or regex2 == 'Ø': return\",\n \"and 'Ø'. The parentheses, vertical bar and star mean what you'd expect them\",\n \"= dfa1.states | {error_state} for symbol in union_alphabet: new_tf[(error_state, symbol)] = error_state for\",\n \"input regex contain a binary operator followed by an operator, or 4. the\",\n \"{self.start_state, rip}: r3 = self.transition_function[(rip, state2)] r4 = self.transition_function[(state1, state2)] new_regex = regex_union(\",\n \"Regex: if regex1 == \\\"Ø\\\": return regex2 if regex2 == \\\"Ø\\\": return regex1\",\n \"char in EMPTIES: machine_stack.append(fit_empty(char)) elif char in alphabet: machine_stack.append(fit_symbol(char)) elif char == '*':\",\n \"} return NFA( transition_function=nd_transition_function, start_state=self.start_state, accept_states=self.accept_states ) def _stringify(states: Iterable[State]) -> str: if\",\n \"state set of nfa1 plus the cardinality of the state-set of nfa2 plus\",\n \"operation is not commutative. \\\"\\\"\\\" new_self, new_other, concat_tf = self._combine(other) for state in\",\n \"'Ø'. The parentheses, vertical bar and star mean what you'd expect them to\",\n \"+ other_tf[(state2, symbol)] ) union_start_state = self.start_state + other.start_state union_accept_states = { _stringify(item)\",\n \"_error_message( bad_set=transition_range - self.states, message_singular=(\\\"State {} in the range of the transition \\\"\",\n \"`•`, `€` and `Ø`, 2. the input regex string contains a character not\",\n \") @staticmethod def fit( regex: Regex, alphabet: AbstractSet[Symbol] = ( set(printable) - {'(',\"\n]","isConversational":false}}},{"rowIdx":4095,"cells":{"ngram":{"kind":"list like","value":["_start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if __name__ ==","#!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError,","Number Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def","Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if","def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if __name__","arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if __name__ == \"__main__\": unittest.main()","<reponame>fossabot/IdeaBag2-Solutions<filename>Numbers/Roman Number Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase):","roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12),","arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20),","import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\")","class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\")","self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if __name__ == \"__main__\":","import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000)","from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\")","Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self):","unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4),","python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman,"],"string":"[\n \"_start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\") if __name__ ==\",\n \"#!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError,\",\n \"Number Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def\",\n \"Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\") if\",\n \"def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\") if __name__\",\n \"arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\") if __name__ == \\\"__main__\\\": unittest.main()\",\n \"<reponame>fossabot/IdeaBag2-Solutions<filename>Numbers/Roman Number Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase):\",\n \"roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12),\",\n \"arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20),\",\n \"import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\")\",\n \"class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\")\",\n \"self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\") self.assertEqual(arabic_to_roman(12), \\\"XII\\\") self.assertEqual(arabic_to_roman(20), \\\"XX\\\") if __name__ == \\\"__main__\\\":\",\n \"import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000)\",\n \"from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \\\"IV\\\")\",\n \"Generator/tests.py #!/usr/bin/env python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self):\",\n \"unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4),\",\n \"python3 import unittest from roman_number_generator import arabic_to_roman class Test(unittest.TestCase): def _start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman,\"\n]","isConversational":false}}},{"rowIdx":4096,"cells":{"ngram":{"kind":"list like","value":["import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name,","infrastructure.satnogClient import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir","os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path,","ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation): raise","import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def","class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation):","from infrastructure.satnogClient import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir =","SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self,","def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation): raise NotImplementedError()"],"string":"[\n \"import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name,\",\n \"infrastructure.satnogClient import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir\",\n \"os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path,\",\n \"ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation): raise\",\n \"import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def\",\n \"class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation):\",\n \"from infrastructure.satnogClient import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir =\",\n \"SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self,\",\n \"def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name, full_path, observation): raise NotImplementedError()\"\n]","isConversational":false}}},{"rowIdx":4097,"cells":{"ngram":{"kind":"list like","value":["back to the # verifier, there is no privacy leak by the ommission.","proxy such as nginx for that purpose. \"\"\" import secrets import sys import","= [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() #","even restart / end # without sharing components. skip_signatures = False if len(all_components)","True: covert_server.reset() # Clean up dead clients self.clients = [c for c in","except ValidationError as e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame","self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys","if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We","More than one blame per proof is malicious. Boot client # immediately since","check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values -- round numbers that","proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results)","Since nothing after this point can report back to the # verifier, there","client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for client","client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof","was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str):","(sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list]","s in signatures if s is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs}","else: self.queue.append(client) return can_pool def remove(self, client): # make sure to call try_move_from_queue()","almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 =","pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() #","submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message","# Permission is hereby granted, free of charge, to any person # obtaining","moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to","the # signatures. This makes it slightly harder for one of the players","the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg,","7.5, 8.2, 9.1] # TODO - make these configurable class Params: num_components =","client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx =","self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results","covert components phase, call start_components. - To signal the end of covert components","= round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes","submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError:","try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes","time.sleep(remtime) # Upload the full commitment list; we're a bit generous with the","scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars))","self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments,","Upload the full commitment list; we're a bit generous with the timeout but","sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results)","e: self.print_error(f\"Ended with error: {e}\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr)","else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert","timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers)","same tag\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t]","= start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags","self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return","src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob,","5: client.error(\"Too many tags\") # Event for signalling us that a pool started.","\"\"\" This controls the Fusion rounds running from server side. \"\"\" def __init__(self,","None tier_best = None size_best = 0 for t, pool in self.waiting_pools.items(): ft","make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool","self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime:","= time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay","continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame,","while if many # checks against blockchain need to be done, perhaps even","reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret,","proofs\") if any(len(p) > 200 for p in proofs): client.error(\"too-long proof\") # they","as an 'internal server error'. raise else: self.print_error(\"broadcast was successful!\") # Give our","FusionServer(GenericServer): \"\"\"Server for clients waiting to start a fusion. New clients get a","# as an 'internal server error'. raise else: self.print_error(\"broadcast was successful!\") # Give","self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices","= defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in","def add(self, client): can_pool = True for t in client.tags: ts = self.tags[t]","for c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped","end # without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures","in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note, the","in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() #","Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list","# checks against blockchain need to be done, perhaps even still # running","largest possible component (uncompressed p2pkh input), how many could we take until the","for client in self.clients: client.addjob(client_start, collector) # Record the time that we sent","super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate):","+ 5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components >=","self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except","= None # when did pool exceed fill_threshold self.tag_max = tag_max # how","Does not natively offer SSL support, however a server admin may run an","component (uncompressed p2pkh input), how many could we take until the result would","are allowed to stay open without activity. # note this needs to consider","uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2,","= [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0,","for them, and they are put into the waiting pools. Once a Fusion","same tag (in pool and queue) def check_add(self, client): for t in client.tags:","varint for either inputs or outputs lists overhead = 60 else: # the","last fuse happened; as a placeholder, set this to startup time. self.reset_timer() def","pool.fill_time is not None: # a non-favoured pool will start eventually remtime =","unlimited access client.tags = [] else: # Default tag: this IP cannot be","not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests","many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point","'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients =","\"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be","immediately self.start_fuse(t) return # we have added to pools, which may have changed","clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0:","both inputs and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients","(unless hit max time or pool is full). start_time_spacing = 120 # But","the waiting pools. Once a Fusion thread is started, the ClientThreads are passed","1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0,","the time that we sent 'startround' message to players; this # will form","self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_","elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses))","AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER","removed; on stop we don't care.) with self.lock: for t, pool in mytierpools.items():","collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment","except ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time","in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars","commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic()","timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise","TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE","self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector =","schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from .","particular tier with self.lock: if self.stopping: return # add this client to waiting","indicated bad input but checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated","[10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How many clients","between messages: # - how long from first connection to last possible Tor","for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT)","'startround' message to players; this # will form the basis of our covert","len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way as client","= 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers","pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left","until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic()","Ergon client # CashFusion - an advanced coin anonymizer # # Copyright (C)","privacy with what we have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert","players\") raise FusionError(\"too few remaining live players\") def run (self, ): self.print_error(f'Starting fusion","1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components","some 'ban score' to the player. # we aren't collecting any results, rather","whether to print a lot of logs noisy = False # How long","of {component: contrib}, where contrib is (+- amount - fee). - Before start","pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg):","process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who","try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as","# Do some preliminary checks to see whether we should just skip the","bad input: ' + reason) continue except Exception as e: self.print_error(f\"player indicated bad","if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and","over to a FusionController to run the rounds.\"\"\" def __init__(self, config, network, bindhost,","all clients submitted largest possible component (uncompressed p2pkh input), how many could we","= announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in","i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete()","def reset_timer(self, ): \"\"\" Scan pools for the favoured fuse: - Out of","if self.tier_best in mytierpools: # we left from best pool, so it might","src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex,","= (overhead + min_safe_clients - 1) // min_safe_clients # How many clients can","in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time","covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash","upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT):","= ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b)","import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import","time.monotonic() def add(self, client): can_pool = True for t in client.tags: ts =","continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one of the inputs is","start of covert components phase, call start_components. - To signal the end of","tnow = time.monotonic() # scan through tiers and collect statuses, also check start","= [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list):","False try: self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool)","Await commitment messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS)","# If the blamed client is already dead, don't waste more time. #","= commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components,","20: client.error(\"Tag id too long\") if not (0 < tag.limit < 6): client.error(\"Tag","status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove","float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic()","# way third parties can't abuse us to find out the # timing","which returns a list of signatures (which will have None at positions of","Copyright (C) 2020 <NAME> # # Permission is hereby granted, free of charge,","fee based on dividing the overhead amongst players, in the smallest fusion #","_add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1","contrib) in component_master_list] del component_master_list # Do some preliminary checks to see whether","self.results del self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except","if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with error: {e}\")","bit before uploading commitments, as clients are doing this. remtime = covert_T0 +","many players can they represent in the same fuse? ip_max_simul_fuse = 3 #","software and associated documentation files # (the \"Software\"), to deal in the Software","@property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self):","continue if client.got_submit: # We got a second submission before a new phase","input: {reason}\") src_client.kill('you provided a bad input: ' + reason) continue except Exception","existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: #","round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate","{t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the","assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try:","could have aborted earlier but this # way third parties can't abuse us","take until the result would exceed 100 kB standard tx size limitation? max_clients","in the Software without restriction, # including without limitation the rights to use,","enhanced bytes object to represent a pool tag \"\"\" __slots__ = () def","covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier","msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment","SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A","!= sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash","made for them, and they are put into the waiting pools. Once a","the most number of players, - Choose the pool with the earliest fill","LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,","[[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj]","sharing the # signatures. This makes it slightly harder for one of the","{msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions. How it works: - Launch","but remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for","require 3-byte varint for both inputs and outputs lists overhead = 62 elif","done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place","ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if the pool","'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret)","in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx,","submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail = True):","placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear()","be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources,","= session_hash)) # Sleep until end of covert signatures phase remtime = covert_T0","== 0: # cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self):","Sleep a bit before uploading commitments, as clients are doing this. remtime =","src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}')","bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig)","_ in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx","if filling more than one pool, we don't have bias towards any particular","len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few components submitted\") if","in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes","_ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More","+ ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class","msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to","slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if","collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def","and ts.all_ >= self.tag_max: return \"too many clients with same tag\" def _add_pool(self,","run the rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost","self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\" Scan pools for the favoured","if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello(","src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector","happen, we had plenty of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures =","are checked, we # can start next round right away. collector.add(None) for idx,","input), how many could we take until the result would exceed 100 kB","- Before start of covert components phase, call start_components. - To signal the","del results, collector # Sleep a bit before uploading commitments, as clients are","is started, the ClientThreads are passed over to a FusionController to run the","{len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results)","return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError:","traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) +","pool is full). start_time_min = 400 # whether to print a lot of","out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit,","from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components,","start_ev.wait(2) except: # Remove client from waiting pools on failure (on success, we","for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue","False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few components",".stop(). \"\"\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp","time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input]","AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig","round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted","size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best +","client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig =","of a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try:","try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping:","return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components =","5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class","for pool in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res)","commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for","to an ephemeral port. - Before start of covert components phase, call start_components.","the originator, however # blockchain checks are somewhat subjective. It would be #","= len([s for s in signatures if s is None]) ### self.print_error(f\"ending covert","hash) ) if min_safe_clients * num_components >= 2 * 0xfc: # the smallest","# to have a good connection to the EC server. Report this back","self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t","self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *,","in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1:","spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for","component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used","60 else: # the smallest fusion will use 1-byte varint for both inputs","proof\") # they should only be 129 bytes long. # generate the possible","= msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer):","= self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at","in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in","# we left from best pool, so it might not be best anymore.","self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if","dividing the overhead amongst players, in the smallest fusion # (these overhead numbers","= covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport =","with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed","a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input]","validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad","len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest of this","been above min_clients for a short time (unless pool is full). start_time_min =","in self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround' message","don't start a fusion if it has only been above min_clients for a","minimum excess fee based on dividing the overhead amongst players, in the smallest","time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients list, so","bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None) # further,","# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN","shall be # included in all copies or substantial portions of the Software.","try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii')","clients leave ... How many clients do we need as an absolute minimum","None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >=","no favoured fuse. (since fill time is a float, this will almost always","'' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate =","thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage,","baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub","c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except","proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob =","return client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the","\"\"\" a waiting pool for a specific tier \"\"\" def __init__(self, fill_threshold, tag_max):","connection # per phase. client.error('multiple submission in same phase') if mtype == 'component':","= client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please","any fusion, wait this long before starting the next one (unless hit max","possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex]","with self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys):","= None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not","times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool in","pools are filled then there is no favoured fuse. (since fill time is","them slide...\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address =","// min_safe_clients # How many clients can share same tag on a given","self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect","start at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime","there is no privacy leak by the ommission. continue assert ret, 'expecting input","ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures","'' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t]","clientjob_goodbye(client, text): # a gentler goodbye than killing if text is not None:","are filled then there is no favoured fuse. (since fill time is a","in commitment_master_list) # Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv,","in signatures if s is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing","False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make","this point we could blame the originator, however # blockchain checks are somewhat","else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def","client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\") # Event for signalling","if len(tag.id) > 20: client.error(\"Tag id too long\") if not (0 < tag.limit","= self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except","self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers","if the pool has stayed at or above min_clients for this long. start_time_max","remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) #","THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT.","client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout","we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments),","op_return script size of 1 + 5 (lokad) + 33 (session hash) )","if ft is None: continue size = len(pool.pool) if size >= size_best: if","a distinct valid signature. This is not # allowed and we break the","either inputs or outputs lists overhead = 60 else: # the smallest fusion","we # can start next round right away. collector.add(None) for idx, (client, proofs)","annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier,","tag_max): self.pool = set() # clients who will be put into fusion round","# Remove client from waiting pools on failure (on success, we are already","smallest fusion could require 3-byte varint for both inputs and outputs lists overhead","will start. for c in chosen_clients: c.start_ev.set() # Remove those clients from all","= self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b","if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) > 200 for","mytierpools: # we left from best pool, so it might not be best","# # Oregano - a lightweight Ergon client # CashFusion - an advanced","break the connection as a result. # Note that we could have aborted","for this long. start_time_max = 1200 # Inter-fusion delay -- after starting any","starting the next one (unless hit max time or pool is full). start_time_spacing","inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) ==","for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c","= covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as:","commitment list; we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments","the favoured fuse: - Out of the pool(s) with the most number of","to permit persons to whom the Software is furnished to do so, #","msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time that we","3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO","can_pool = True for t in client.tags: ts = self.tags[t] ts.all_ += 1","msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}')","in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used tags","permission notice shall be # included in all copies or substantial portions of","to last possible Tor component submission? # - how long from one round's","ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the","= len(pool.pool) if size >= size_best: if time_best is None or ft <","destinations, same way as client did. relays = [] for i, proof in","c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\"too few remaining live","# how many clients can share same tag (in pool and queue) def","= CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None","from first connection to last possible Tor component submission? # - how long","if len(msg.tags) > 5: client.error(\"Too many tags\") # Event for signalling us that","*expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def","separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in","check_client_count(self,): live = [c for c in self.clients if not c.dead] if len(live)","CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components =","= dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if","MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL","_ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\")","we sent 'startround' message to players; this # will form the basis of","6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6,","of covert signatures phase, owner calls start_signatures. - To signal the end of","timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is","the favoured pool, can start at a special time remtime = min(remtime, self.tier_best_starttime","how many could we take until the result would exceed 100 kB standard","bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason))","def start_fuse(self, tier): \"\"\" Immediately launch Fusion at the selected tier. \"\"\" with","len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof')","on a given tier (if more try to join, reject) max_tier_client_tags = 100","to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic","in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime =","in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() +","is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy","an ephemeral port. - Before start of covert components phase, call start_components. -","list and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for","= True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send,","out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs","# further, search for duplicated inputs (through matching the prevout and claimed pubkey).","proofs_to_relay = [list() for _ in self.clients] for src_client, relays in results: for","Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove","len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few components submitted\") if total_excess_fees !=","tiers = Params.tiers, donation_address = donation_address )) # We allow a long timeout","one pool, we don't have bias towards any particular tier with self.lock: if","and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i)","put into the waiting pools. Once a Fusion thread is started, the ClientThreads","not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different","done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock","'checking finished' so that if all blames are checked, we # can start","pool.fill_time - tfill_thresh if t == self.tier_best: # this is the favoured pool,","len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one of","dead, don't waste more time. # Since nothing after this point can report","return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,):","phase, owner calls start_signatures. - To signal the end of covert signatures phase,","to the maximum size, so start immediately self.start_fuse(t) return # we have added","remtime > 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert","def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures =","unique) \"\"\" with self.lock: time_best = None tier_best = None size_best = 0","component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s)","number of proofs\") if any(len(p) > 200 for p in proofs): client.error(\"too-long proof\")","for commit, ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations)","from waiting pools on failure (on success, we are already removed; on stop","True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components:","of logs noisy = False # How long covert connections are allowed to","Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY","ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass","client, we'll let them slide...\") if self.stopping: return donation_address = '' if isinstance(self.donation_address,","# signed, then it's malicious behaviour! if sum((signatures[i] is not None) for i","{reason}\") src_client.kill('you provided a bad input: ' + reason) continue except Exception as","not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\"too few remaining","(100000 - 12) // (num_components * 173) # Every round, clients leave ...","clients who didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients","from one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT =","if all blames are checked, we # can start next round right away.","they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to","- Out of the pool(s) with the most number of players, - Choose","__enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not","the rest of this function might run for a while if many #","start next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)):","import time import traceback from collections import defaultdict import oregano.schnorr as schnorr from","no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients","component_master_list] del component_master_list # Do some preliminary checks to see whether we should","hash declared by client, we'll let them slide...\") if self.stopping: return donation_address =","which returns a dict of {component: contrib}, where contrib is (+- amount -","Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment","network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network =","rest of this function might run for a while if many # checks","all_components) #TODO : Check the inputs and outputs to see if we even","self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue","remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings set","= self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input","Out of the pool(s) with the most number of players, - Choose the","will use 1-byte varint for both inputs and outputs lists overhead = 58","round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 #","pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not","should only be 129 bytes long. # generate the possible destinations list (all","for _ in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx,","# not reference self.<variables> that may change. for blame in msg.blames: try: encproof,","one of the inputs is signed, we don't punish him # because he's","None and ts.all_ >= self.tag_max: return \"too many clients with same tag\" def","commitment idx removes ordering correlations about which client sent which proof proofs.sort(key =","None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey,","with self.lock: ret = self.signatures del self.signatures return ret def reset(self): try: del","False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for","needs to consider the maximum interval between messages: # - how long from","class WaitingPool: \"\"\" a waiting pool for a specific tier \"\"\" def __init__(self,","= all_components, session_hash = session_hash)) # Sleep until end of covert signatures phase","'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We","the Software is furnished to do so, # subject to the following conditions:","us to find out the # timing of a given input's signature submission.","= [c for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients:","client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in self.clients if","client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure","self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout","# Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0])","self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client):","network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp","self.fill_threshold = fill_threshold # minimum number of pool clients to trigger setting fill_time","isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}):","src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx,","indicated bad input but it was fine ({outpoint})\") # At this point we","tier self.queue = list() # clients who are waiting due to tags being","clients from queue into pool moved = [] for client in self.queue: for","are e.g. on testnet # and we are mainnet, etc. client.error(\"This server is","class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds running from server side.","be a resubmission after ack failed delivery, # but we don't allow it","# How long covert connections are allowed to stay open without activity. #","dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector)","None for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool:","len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\")","submitted largest possible component (uncompressed p2pkh input), how many could we take until","in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as a","if we even have reasonable # privacy with what we have. bad_components =","leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for","self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class","a new round, call .reset(); to kill all connections, call .stop(). \"\"\" def","= False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number","self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses","has stayed at or above min_clients for this long. start_time_max = 1200 #","check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts is not","we tolerate it # missing. However, if the client declares the genesis_hash, we","what we have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\")","in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector","covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b,","checks against blockchain need to be done, perhaps even still # running after","THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\"","if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy","can_pool def remove(self, client): # make sure to call try_move_from_queue() after calling this","ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed","represent a pool tag \"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul):","which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for","# the smallest fusion could require 3-byte varint for both inputs and outputs","return # add this client to waiting pools for pool in mytierpools.values(): res","this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc","many clients do we need as an absolute minimum (for privacy)? min_safe_clients =","if len(live) < Params.min_safe_clients: for c in live: c.kill(\"too few remaining live players\")","len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) > 200 for p","range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message to players;","done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points =","for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep","clients who will be put into fusion round if started at this tier","Fusion rounds running from server side. \"\"\" def __init__(self, network, tier, clients, bindhost,","clients to trigger setting fill_time self.fill_time = None # when did pool exceed","clients can share same tag (in pool and queue) def check_add(self, client): for","c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in","signal the end of covert components phase, owner calls end_components, which returns a","full). start_time_min = 400 # whether to print a lot of logs noisy","wrong') # It might be we already have this signature. This is fine","the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()};","[1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24","is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ()))","clients # as an 'internal server error'. raise else: self.print_error(\"broadcast was successful!\") #","component submission? # - how long from one round's component submission to the","src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients:","= network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags)","else: self.print_error(\"broadcast was successful!\") # Give our transaction a small head start in","f{prevout} detected\") # If exactly one of the inputs is signed, we don't","WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT","to blame, or maybe even restart / end # without sharing components. skip_signatures","at wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if","to # not reference self.<variables> that may change. for blame in msg.blames: try:","declared by client, we'll let them slide...\") if self.stopping: return donation_address = ''","[None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit =","doesn't even make sense with one player. for c in self.clients: c.kill('blame yourself!')","time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret def","while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() #","# we aren't collecting any results, rather just marking that # 'checking finished'","msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions.","= lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in","TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0","sense with one player. for c in self.clients: c.kill('blame yourself!') return # scan","size >= size_best: if time_best is None or ft < time_best or size","for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else:","proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False)","all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError","fusion. New clients get a ClientThread made for them, and they are put","= True for t in client.tags: ts = self.tags[t] ts.all_ += 1 if","Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We","self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from queue into","client.error(\"Too many tags\") # Event for signalling us that a pool started. start_ev","self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of covert signatures","E12] # How many clients do we want before starting a fusion? min_clients","need as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the","input but it was fine ({outpoint})\") # At this point we could blame","EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM,","num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for","ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES","= {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when","_, _ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count -","< 2: # Sanity check for testing -- the proof sharing thing doesn't","clients list, so that the fusion can continue independently of waiting server. #","but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj)","class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ =","tiers and collect statuses, also check start times. statuses = dict() tfill_thresh =","goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread):","# Event for signalling us that a pool started. start_ev = threading.Event() client.start_ev","fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) >","\"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for","many could we take until the result would exceed 100 kB standard tx","+ str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})'","t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we","and all the other components were # just imposters who didn't have private","would be # appropriate to add some 'ban score' to the player. #","commitment list, but remember exactly where each commitment originated. commitment_master_list = [(commit, ci,","signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\") # mark all missing-signature","< self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_","inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until","msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) > 200","accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in","is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse +","this signature. This is fine # since it might be a resubmission after","with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time')","we are mainnet, etc. client.error(\"This server is on a different chain, please switch","import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import","up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") #","as e: self.print_error(f\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\")","({outpoint})\") else: self.print_error(f\"player indicated bad input but it was fine ({outpoint})\") # At","b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results,","so it might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect","basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting","just imposters who didn't have private key. If more than one # signed,","class ClientTag(bytes): \"\"\" enhanced bytes object to represent a pool tag \"\"\" __slots__","# Sleep until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS","start of covert signatures phase, owner calls start_signatures. - To signal the end","def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected","add(self, client): can_pool = True for t in client.tags: ts = self.tags[t] ts.all_","return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None","last possible Tor component submission? # - how long from one round's component","GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash,","allow unlimited access client.tags = [] else: # Default tag: this IP cannot","can report back to the # verifier, there is no privacy leak by","for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages =","# whether to print a lot of logs noisy = False # How","for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError:","submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT)","admin may run an SSL server proxy such as nginx for that purpose.","assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires","sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct","owner calls end_signatures, which returns a list of signatures (which will have None","# We received a distinct valid signature. This is not # allowed and","str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}]","and they are put into the waiting pools. Once a Fusion thread is","to: Remove from spawned clients list, so that the fusion can continue independently","mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return","if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) #","if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e:","time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components","to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of","ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls,","= self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at","components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds","with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate","this client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if","self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else:","= True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey,","server at any time. By default, will bind to an ephemeral port. -","with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong","list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key,","range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash","calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError:","self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason =","record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients:","= 8 # If all clients submitted largest possible component (uncompressed p2pkh input),","owner calls end_components, which returns a dict of {component: contrib}, where contrib is","chosen_clients: c.start_ev.set() # Remove those clients from all pools for t, pool in","error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] #","self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time))","len(msg.tags) > 5: client.error(\"Too many tags\") # Event for signalling us that a","send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message =","5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable","elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline):","# Clean up dead clients self.clients = [c for c in self.clients if","WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO","start_components. - To signal the end of covert components phase, owner calls end_components,","time (unless pool is full). start_time_min = 400 # whether to print a","self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion at the selected","already removed; on stop we don't care.) with self.lock: for t, pool in","self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return","collector) # Record the time that we sent 'startround' message to players; this","the smallest fusion will use 1-byte varint for both inputs and outputs lists","the EC server. Report this back to clients # as an 'internal server","passed over to a FusionController to run the rounds.\"\"\" def __init__(self, config, network,","if sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders) if","did not match commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number","class CovertServer(GenericServer): \"\"\" Server for covert submissions. How it works: - Launch the","self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with a deadline.","client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients:",">= 2 * 0xfc: # the smallest fusion could require 3-byte varint for","TODO - make these configurable class Params: num_components = 23 component_feerate = 1000","else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients","### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True))","pool is full). start_time_spacing = 120 # But don't start a fusion if","WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last","if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size, so","them, and they are put into the waiting pools. Once a Fusion thread","+ len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This controls","False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients)","oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms","a server admin may run an SSL server proxy such as nginx for","many clients do we want before starting a fusion? min_clients = 8 #","self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break","= [] self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type,","':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]}","= sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit = True","a good connection to the EC server. Report this back to clients #","1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1,","documentation files # (the \"Software\"), to deal in the Software without restriction, #","> 5: client.error(\"Too many tags\") # Event for signalling us that a pool","self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None)","timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if text is","ft < time_best or size > size_best: time_best = ft tier_best = t","smallest fusion could require 3-byte varint for either inputs or outputs lists overhead","# For now, msg.genesis_hash is optional and we tolerate it # missing. However,","remtime = inftime if pool.fill_time is not None: # a non-favoured pool will","ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_","e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests","# note this needs to consider the maximum interval between messages: # -","as client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx","- a lightweight Ergon client # CashFusion - an advanced coin anonymizer #","session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys","than one # signed, then it's malicious behaviour! if sum((signatures[i] is not None)","(since fill time is a float, this will almost always be unique) \"\"\"","absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess fee","network, bindhost, port, upnp = None, announcehost = None, donation_address = None): assert","Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): #","blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad","continue size = len(pool.pool) if size >= size_best: if time_best is None or","= deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results","be 129 bytes long. # generate the possible destinations list (all commitments, but","def try_move_from_queue(self): # attempt to move clients from queue into pool moved =","try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results:","the earliest fill time; - If no pools are filled then there is","__exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set()","granted, free of charge, to any person # obtaining a copy of this","self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello')","0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status","tag_max # how many clients can share same tag (in pool and queue)","self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components","How long covert connections are allowed to stay open without activity. # note","amount - fee). - Before start of covert signatures phase, owner calls start_signatures.","= round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock:","with self.lock: time_best = None tier_best = None size_best = 0 for t,","set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes)","for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout}","e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\")","False def end_components(self): with self.lock: ret = self.components del self.components return ret def","except TimeoutException: self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\") # This probably","min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion could require","If exactly one of the inputs is signed, we don't punish him #","= feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def","False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread,","Remove client from waiting pools on failure (on success, we are already removed;","have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components =","client.got_submit: # We got a second submission before a new phase started. As","str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}')","= False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if","waiting to start a fusion. New clients get a ClientThread made for them,","[] self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value,","True class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds running from server","None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign()","not match commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of","but we don't allow it to consume our CPU power. if sig !=","Filter clients who didn't manage to give a good commitment. prev_client_count = len(self.clients)","but this # way third parties can't abuse us to find out the","# sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000]","c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments, as","+ tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__","collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did","client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1 if","AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype =","support, however a server admin may run an SSL server proxy such as","# since it might be a resubmission after ack failed delivery, # but","at or above min_clients for this long. start_time_max = 1200 # Inter-fusion delay","side. \"\"\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost =","remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del","txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering","at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component]","uploading commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS -","time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names,","up to the maximum size, so start immediately self.start_fuse(t) return # we have","covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good","or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp)","server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly","try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit =","len(live) < Params.min_safe_clients: for c in live: c.kill(\"too few remaining live players\") raise","COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got a second","we should just skip the # signing phase and go directly to blame,","favoured fuse. (since fill time is a float, this will almost always be","0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance.","that we will start. for c in chosen_clients: c.start_ev.set() # Remove those clients","proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z,","generate the possible destinations list (all commitments, but leaving out the originating client's","if pool.fill_time is not None: # a non-favoured pool will start eventually remtime","if started at this tier self.queue = list() # clients who are waiting","t size_best = size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime","chain, please switch servers\") else: client.print_error(\"👀 No genesis hash declared by client, we'll","signatures. This makes it slightly harder for one of the players to #","are the various tags self.fill_threshold = fill_threshold # minimum number of pool clients","IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF","servers\") else: client.print_error(\"👀 No genesis hash declared by client, we'll let them slide...\")","SSL support, however a server admin may run an SSL server proxy such","fuse happened; as a placeholder, set this to startup time. self.reset_timer() def run(self):","any particular tier with self.lock: if self.stopping: return # add this client to","= begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run","None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv,","time. By default, will bind to an ephemeral port. - Before start of","started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted","= bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self,","the commitment list and note where each client's commitments ended up client_commit_indexes =","client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the","ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN #","client): # make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except","pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout)","2: # Sanity check for testing -- the proof sharing thing doesn't even","+ reason) continue except Exception as e: self.print_error(f\"player indicated bad input but checking","time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return","from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f in","E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7,","# Give our transaction a small head start in relaying, before sharing the","expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record","= True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags:","= tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion at the selected tier.","time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\") # Await commitment messages then","= possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not","favoured pool, can start at a special time remtime = min(remtime, self.tier_best_starttime -","def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp)","out while trying to broadcast transaction! misconfigured?\") # This probably indicates misconfiguration since","transaction a small head start in relaying, before sharing the # signatures. This","spawned clients list, so that the fusion can continue independently of waiting server.","client # CashFusion - an advanced coin anonymizer # # Copyright (C) 2020","to whom the Software is furnished to do so, # subject to the","True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock()","dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector =","inputs and outputs to see if we even have reasonable # privacy with","the pool(s) with the most number of players, - Choose the pool with","if t == self.tier_best: # this is the favoured pool, can start at","or maybe even restart / end # without sharing components. skip_signatures = False","3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1]","outputs lists overhead = 60 else: # the smallest fusion will use 1-byte","in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for t","to consider the maximum interval between messages: # - how long from first","tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes","for client in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool",".util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit,","missing. However, if the client declares the genesis_hash, we # do indeed disallow","in mytierpools: # we left from best pool, so it might not be","i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components =","pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit","client.error(\"too-long proof\") # they should only be 129 bytes long. # generate the","= False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT)","None size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if","rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost =","server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier,","the # timing of a given input's signature submission. raise ValidationError('conflicting valid signature')","= self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip =","DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR","class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype","= [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit","self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround' message to","remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this is the","N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way","#TODO : Check the inputs and outputs to see if we even have","2020 <NAME> # # Permission is hereby granted, free of charge, to any","= client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash:","class ResultsCollector: # Collect submissions from different sources, with a deadline. def __init__(self,","provided a bad input: ' + reason) continue except Exception as e: self.print_error(f\"player","# It might be we already have this signature. This is fine #","fusion will use 1-byte varint for both inputs and outputs lists overhead =","timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than","self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold #","baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub,","the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':'","return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad","more time. # Since nothing after this point can report back to the","one # signed, then it's malicious behaviour! if sum((signatures[i] is not None) for","fuse. (since fill time is a float, this will almost always be unique)",")) # We allow a long timeout for clients to choose their pool.","if mtype == 'ping': continue if client.got_submit: # We got a second submission","included in all copies or substantial portions of the Software. # # THE","whom the Software is furnished to do so, # subject to the following","= False # How long covert connections are allowed to stay open without","self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def","is malicious. Boot client # immediately since client may be trying to DoS","3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] #","to the EC server. Report this back to clients # as an 'internal","in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now,","break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with error: {e}\") except Exception","num_components >= 2 * 0xfc: # the smallest fusion could require 3-byte varint","maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool =","(all commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations","but it was fine ({outpoint})\") # At this point we could blame the","which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x,","source commitment idx removes ordering correlations about which client sent which proof proofs.sort(key","dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT +","against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame","results) # Generate scrambled commitment list, but remember exactly where each commitment originated.","for _,_,f in results) # Generate scrambled commitment list, but remember exactly where","for c in live: c.kill(\"too few remaining live players\") raise FusionError(\"too few remaining","t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing",".protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position)","__init__(self, config, network, bindhost, port, upnp = None, announcehost = None, donation_address =","# self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier,","client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong","self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names,","the various tags self.fill_threshold = fill_threshold # minimum number of pool clients to","of 1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients *","only been above min_clients for a short time (unless pool is full). start_time_min","= f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a bad input:","in chosen_clients: c.start_ev.set() # Remove those clients from all pools for t, pool","perhaps even still # running after run_round has exited. For this reason we","mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for t in","fusion? min_clients = 8 # If all clients submitted largest possible component (uncompressed","the proof sharing thing doesn't even make sense with one player. for c","raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature","len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment list,","pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit","{e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad proof (for","p in proofs): client.error(\"too-long proof\") # they should only be 129 bytes long.","fusion # (these overhead numbers assume op_return script size of 1 + 5","be # included in all copies or substantial portions of the Software. #","= status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools","best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with","so, # subject to the following conditions: # # The above copyright notice","the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail =","= set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes)","was successful!\") # Give our transaction a small head start in relaying, before","a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost","c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time","us by # making us check many inputs against blockchain. if len(msg.blames) >","match commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\")","ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False","1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6,","covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey,","only be 129 bytes long. # generate the possible destinations list (all commitments,","all_commitments, all_components) #TODO : Check the inputs and outputs to see if we","we try to # not reference self.<variables> that may change. for blame in","3-byte varint for both inputs and outputs lists overhead = 62 elif min_safe_clients","before sharing the # signatures. This makes it slightly harder for one of","fee). - Before start of covert signatures phase, owner calls start_signatures. - To","if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min,","there is no favoured fuse. (since fill time is a float, this will","maximum interval between messages: # - how long from first connection to last","= check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with","1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1,","# Filter clients who didn't manage to give a good commitment. prev_client_count =","+ Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype","a bad input: ' + reason) continue except Exception as e: self.print_error(f\"player indicated","for t, pool in mytierpools.items(): if client not in pool.pool: continue status =","# Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to:","including without limitation the rights to use, copy, modify, merge, # publish, distribute,",">= size_best: if time_best is None or ft < time_best or size >","exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif","self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size = len(pool.pool) if","else: # the smallest fusion will use 1-byte varint for both inputs and","with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through","msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest","behaviour! if sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders)","of proofs\") if any(len(p) > 200 for p in proofs): client.error(\"too-long proof\") #","self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with","self.queue = list() # clients who are waiting due to tags being full","testing -- the proof sharing thing doesn't even make sense with one player.","many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof","harder for one of the players to # broadcast a malleated version by","reject) max_tier_client_tags = 100 # For a given IP, how many players can","in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message to",": Check the inputs and outputs to see if we even have reasonable","b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How","a long timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120)","'41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \" + txid) try:","and we are mainnet, etc. client.error(\"This server is on a different chain, please","ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False)","[ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout","if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def","good connection to the EC server. Report this back to clients # as","if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\")","_) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj","del self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError:","round numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3,","one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True","where contrib is (+- amount - fee). - Before start of covert signatures","relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline =","self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_ -=","continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if","on stop we don't care.) with self.lock: for t, pool in mytierpools.items(): if","= (100000 - 12) // (num_components * 173) # Every round, clients leave","dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame; clamed reason","start_signatures. - To signal the end of covert signatures phase, owner calls end_signatures,","mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue","signalling us that a pool started. start_ev = threading.Event() client.start_ev = start_ev if","we had plenty of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures())","to add some 'ban score' to the player. # we aren't collecting any","num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats","server. Report this back to clients # as an 'internal server error'. raise","= ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: #","clients do we need as an absolute minimum (for privacy)? min_safe_clients = 6","allow a long timeout for clients to choose their pool. msg = client.recv('joinpools',","be trying to DoS us by # making us check many inputs against","after ack failed delivery, # but we don't allow it to consume our","signatures if s is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'('","= recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection,","self.lock: ret = self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey","self.print_error(f\"startround sent at {time.time()}; accepting covert components\") # Await commitment messages then process","# shuffle the adding order so that if filling more than one pool,","are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime >","index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]]","time.monotonic() if remtime < 0: # really shouldn't happen, we had plenty of","== len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c","self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails","consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash):","len(sig) != 64: raise ValidationError('signature length is wrong') # It might be we","signature. This is fine # since it might be a resubmission after ack","OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A basic server implementation for CashFusion.","limit out of range\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id,","= msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes):","recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse,","self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e:","timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of","limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or","pool with the earliest fill time; - If no pools are filled then","is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else","they are put into the waiting pools. Once a Fusion thread is started,","None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results:","same way as client did. relays = [] for i, proof in enumerate(proofs):","super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost","upnp = upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address","{src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is already dead,","i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] =","- To reset the server for a new round, call .reset(); to kill","for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) #","by source commitment idx removes ordering correlations about which client sent which proof","will have None at positions of missing signatures). - To reset the server","and collect statuses, also check start times. statuses = dict() tfill_thresh = tnow","covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\") # Await commitment","are put into the waiting pools. Once a Fusion thread is started, the","give a good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _,","Before start of covert signatures phase, owner calls start_signatures. - To signal the","# verifier, there is no privacy leak by the ommission. continue assert ret,","lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the smallest","self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs ==","per connection # per phase. client.error('multiple submission in same phase') if mtype ==","{annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport,","different chain, please switch servers\") else: client.print_error(\"👀 No genesis hash declared by client,","USE OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A basic server implementation","= Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers =","FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE","x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg","OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR","each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for","if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ =","bias towards any particular tier with self.lock: if self.stopping: return # add this","collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound())","BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN #","i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i]","time that we sent 'startround' message to players; this # will form the","\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args","Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee =","= [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if","raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions. How it","earliest fill time; - If no pools are filled then there is no","genesis hash declared by client, we'll let them slide...\") if self.stopping: return donation_address","= COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got a","1000000, 10000000, 100000000] for s in E12] # How many clients do we","self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients:","submission in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate","Software is furnished to do so, # subject to the following conditions: #","8 # If all clients submitted largest possible component (uncompressed p2pkh input), how","overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion","= pool.fill_time if ft is None: continue size = len(pool.pool) if size >=","pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the","signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong","proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _","that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8]","commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients: scalars =","client.error('component submitted at wrong time') else: assert mtype == 'signature' try: sighash =","prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']}","to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False","= None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best =","for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients),","result. # Note that we could have aborted earlier but this # way","checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but it","rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def","round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret","submission? # - how long from one round's component submission to the next","all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime = covert_T0","from best pool, so it might not be best anymore. self.reset_timer() raise class","bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(),","for a new round, call .reset(); to kill all connections, call .stop(). \"\"\"","inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow","collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _","return tnow = time.monotonic() # scan through tiers and collect statuses, also check","done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed =","msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers =","remtime < 0: # really shouldn't happen, we had plenty of time raise","self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False","A basic server implementation for CashFusion. Does not natively offer SSL support, however","time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True: covert_server.reset() #","let them slide...\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address","live = [c for c in self.clients if not c.dead] if len(live) <","{time.time()}; accepting covert components\") # Await commitment messages then process results results =","subject to the following conditions: # # The above copyright notice and this","selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will","# # Permission is hereby granted, free of charge, to any person #","enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list)","start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool","relaying, before sharing the # signatures. This makes it slightly harder for one","be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag","True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan","PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import send_pb,","that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick","calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or","the selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we","self.print_error(f\"player indicated bad input but it was fine ({outpoint})\") # At this point","list() # clients who are waiting due to tags being full self.tags =","that a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): #","full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold","a specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients","end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if","few remaining live players\") raise FusionError(\"too few remaining live players\") def run (self,","we already have this signature. This is fine # since it might be","before a new phase started. As # an anti-spam measure we only allow","independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion","probably indicates misconfiguration since fusion server ought # to have a good connection","covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\") #","c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep","already have this signature. This is fine # since it might be a","blames are checked, we # can start next round right away. collector.add(None) for","prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\")","fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if the","except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self,","no privacy leak by the ommission. continue assert ret, 'expecting input component' outpoint","time is a float, this will almost always be unique) \"\"\" with self.lock:","activity. # note this needs to consider the maximum interval between messages: #","too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is","= time.monotonic() # scan through tiers and collect statuses, also check start times.","could we take until the result would exceed 100 kB standard tx size","sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def","many clients can share same tag (in pool and queue) def check_add(self, client):","client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y,","TimeoutException: self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\") # This probably indicates","i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs)","to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean","a short time (unless pool is full). start_time_min = 400 # whether to","try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if","failure (on success, we are already removed; on stop we don't care.) with","cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list)","print a lot of logs noisy = False # How long covert connections","f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a bad input: '","not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime","for b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] #","we want before starting a fusion? min_clients = 8 # If all clients","clients do we want before starting a fusion? min_clients = 8 # If","numbers assume op_return script size of 1 + 5 (lokad) + 33 (session","(overhead + min_safe_clients - 1) // min_safe_clients # How many clients can share","mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players","= all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime =","\"\"\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp =","{t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier","Remove from spawned clients list, so that the fusion can continue independently of","add this client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client)","in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size = len(pool.pool)","_,_,f in results) # Generate scrambled commitment list, but remember exactly where each","commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit,","# SOFTWARE. \"\"\" A basic server implementation for CashFusion. Does not natively offer","in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments)","Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot client #","reset the server for a new round, call .reset(); to kill all connections,","super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\" Scan pools for","(num_components * 173) # Every round, clients leave ... How many clients do","start a fusion if it has only been above min_clients for a short","without restriction, # including without limitation the rights to use, copy, modify, merge,","a given tier (if more try to join, reject) max_tier_client_tags = 100 #","in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse","players; record the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients),","== 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one of the","skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash,","*expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def","RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network","exceed 100 kB standard tx size limitation? max_clients = (100000 - 12) //","waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self.","at {time.time()}; accepting covert components\") # Await commitment messages then process results results","time; - If no pools are filled then there is no favoured fuse.","if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for","would exceed 100 kB standard tx size limitation? max_clients = (100000 - 12)","self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with","cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for","bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components,","proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic()","client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version,","results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't","special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t)","this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime)","blame the originator, however # blockchain checks are somewhat subjective. It would be","have changed the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock:","# scan through tiers and collect statuses, also check start times. statuses =","an in-place sort by source commitment idx removes ordering correlations about which client","not # allowed and we break the connection as a result. # Note","clean def reset_timer(self, ): \"\"\" Scan pools for the favoured fuse: - Out","signal the end of covert signatures phase, owner calls end_signatures, which returns a","new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched","[ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag id","Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good commitment. prev_client_count","= scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before","p2pkh input), how many could we take until the result would exceed 100","# sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b in","self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting","the smallest fusion # (these overhead numbers assume op_return script size of 1","self.queue.append(client) return can_pool def remove(self, client): # make sure to call try_move_from_queue() after","LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION","connection to last possible Tor component submission? # - how long from one","not natively offer SSL support, however a server admin may run an SSL","calls end_signatures, which returns a list of signatures (which will have None at","for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in","start_time_spacing = 120 # But don't start a fusion if it has only","+ Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion at","send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client:","happened; as a placeholder, set this to startup time. self.reset_timer() def run(self): try:","it # missing. However, if the client declares the genesis_hash, we # do","tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ =","def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail:","= inftime if pool.fill_time is not None: # a non-favoured pool will start","remaining live players\") raise FusionError(\"too few remaining live players\") def run (self, ):","\"\"\" enhanced bytes object to represent a pool tag \"\"\" __slots__ = ()","pools. Once a Fusion thread is started, the ClientThreads are passed over to","find out the # timing of a given input's signature submission. raise ValidationError('conflicting","3-byte varint for either inputs or outputs lists overhead = 60 else: #","for i,sig in enumerate(signatures) if sig is None) # further, search for duplicated","many clients with same tag\" def _add_pool(self, client): self.pool.add(client) for t in client.tags:","self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components del self.components","FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to represent a pool","players, - Choose the pool with the earliest fill time; - If no","t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server","(sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks to see","call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread,","defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError,","then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib)","self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures","collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time =","in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) !=","received.\") # Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda","However, if the client declares the genesis_hash, we # do indeed disallow them","self.print_error(f\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player","that we sent 'startround' message to players; this # will form the basis","server error'. raise else: self.print_error(\"broadcast was successful!\") # Give our transaction a small","timing of a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock:","proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2)","client): can_pool = True for t in client.tags: ts = self.tags[t] ts.all_ +=","ValidationError, check_input_electrumx) # Resistor \"E series\" values -- round numbers that are almost","fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost","set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub =","merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and","raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s","with collector: # an in-place sort by source commitment idx removes ordering correlations","mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5)","as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None)","from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import","if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames))","without activity. # note this needs to consider the maximum interval between messages:","src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof,","len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size, so start","time.monotonic() assert remtime > 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items())","anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with a","m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes)","(through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp","s in E12] # How many clients do we want before starting a","smallest fusion # (these overhead numbers assume op_return script size of 1 +","exited. For this reason we try to # not reference self.<variables> that may","by the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() +","None # when did pool exceed fill_threshold self.tag_max = tag_max # how many","client.tags = [] else: # Default tag: this IP cannot be present in","pool exceed fill_threshold self.tag_max = tag_max # how many clients can share same","statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure (on success,","except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\")","timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg,","# This probably indicates misconfiguration since fusion server ought # to have a","1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b","True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ###","= client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per","False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def","# Collect submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail","Clean up dead clients self.clients = [c for c in self.clients if not","OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING","this is the favoured pool, can start at a special time remtime =","waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res is not","while trying to broadcast transaction! misconfigured?\") # This probably indicates misconfiguration since fusion","cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N ==","True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype ==","don't punish him # because he's the honest guy and all the other","= ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool:","ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used tags del","that we could have aborted earlier but this # way third parties can't","copy of this software and associated documentation files # (the \"Software\"), to deal","in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail","in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How many","relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail =","(if more try to join, reject) max_tier_client_tags = 100 # For a given","fill time is a float, this will almost always be unique) \"\"\" with","varint for both inputs and outputs lists overhead = 58 min_excess_fee = (overhead","Permission is hereby granted, free of charge, to any person # obtaining a","repeatedly run rounds until successful or exception while True: covert_server.reset() # Clean up","round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey","+ Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot client","power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction",")) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash","in proofs): client.error(\"too-long proof\") # they should only be 129 bytes long. #","# mark all missing-signature components as bad. bad_inputs = set(i for i,sig in","msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame","tolerate it # missing. However, if the client declares the genesis_hash, we #","exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails)","players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host","CashFusion. Does not natively offer SSL support, however a server admin may run","= () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul,","(session hash) ) if min_safe_clients * num_components >= 2 * 0xfc: # the","len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) !=","= [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit","len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not","makes it slightly harder for one of the players to # broadcast a","generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep","acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True))))","port. - Before start of covert components phase, call start_components. - To signal","5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0,","enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector):","= False try: self.queue.remove(client) except ValueError: return False else: in_pool = True if","we left from best pool, so it might not be best anymore. self.reset_timer()","than one pool, we don't have bias towards any particular tier with self.lock:","if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash","long from one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT","!= inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except:","seen_salthashes = set() # Send start message to players; record the time we","commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len:","it to consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey,","prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend","< 0: # really shouldn't happen, we had plenty of time raise FusionError(\"way","can't abuse us to find out the # timing of a given input's","non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout =","23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers =","= pool.fill_time - tfill_thresh if t == self.tier_best: # this is the favoured","c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub #","IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT","except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate)","del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate","from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from","continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index)","dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate)","# CashFusion - an advanced coin anonymizer # # Copyright (C) 2020 <NAME>","= self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components","= None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients","src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client","= covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings set up","it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list]","malicious. Boot client # immediately since client may be trying to DoS us","msg.tags: if len(tag.id) > 20: client.error(\"Tag id too long\") if not (0 <","bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None) #","by # making us check many inputs against blockchain. if len(msg.blames) > len(proofs):","short time (unless pool is full). start_time_min = 400 # whether to print","self.lock: time_best = None tier_best = None size_best = 0 for t, pool","calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx)","We received a distinct valid signature. This is not # allowed and we","[] # gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for","msg, timeout) def check_client_count(self,): live = [c for c in self.clients if not","= time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self,","- fee). - Before start of covert signatures phase, owner calls start_signatures. -","the minimum excess fee based on dividing the overhead amongst players, in the","port, ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost =","= msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) >","12) // (num_components * 173) # Every round, clients leave ... How many","b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def","before starting a fusion? min_clients = 8 # If all clients submitted largest","sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash =","c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components","signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is","server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out","if self.stopping: return # add this client to waiting pools for pool in","= 0 self.all_ = 0 class WaitingPool: \"\"\" a waiting pool for a","dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client,","clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier","# just imposters who didn't have private key. If more than one #","Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype =","round, call .reset(); to kill all connections, call .stop(). \"\"\" def __init__(self, bindhost,","= False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): #","# Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for","donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components,","run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\" Scan","to run the rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp = None,","CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #","mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up","the Software, # and to permit persons to whom the Software is furnished","upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict()","res is not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client)","del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from queue","with same tag\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts =","announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in","del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass","allowed and we break the connection as a result. # Note that we","clients waiting to start a fusion. New clients get a ClientThread made for","donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic()","As # an anti-spam measure we only allow one submission per connection #","annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing","all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c","OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER","tag (in pool and queue) def check_add(self, client): for t in client.tags: ts","[b.get_R() for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages","else ')'}\") # mark all missing-signature components as bad. bad_inputs = set(i for","self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci !=","encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame;","of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert","(which will have None at positions of missing signatures). - To reset the","wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] =","return # scan the commitment list and note where each client's commitments ended","access client.tags = [] else: # Default tag: this IP cannot be present","when the last fuse happened; as a placeholder, set this to startup time.","for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: #","AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True","not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for","covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen,","dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if","a waiting pool for a specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool","later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector)","self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed:","to the player. # we aren't collecting any results, rather just marking that","Remove those clients from all pools for t, pool in self.waiting_pools.items(): for c","server proxy such as nginx for that purpose. \"\"\" import secrets import sys","in self.clients: c.kill('blame yourself!') return # scan the commitment list and note where","switch servers\") else: client.print_error(\"👀 No genesis hash declared by client, we'll let them","nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)]","= covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload","!= client.random_number_commitment: client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs if len(proofs) !=","pool in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players =","purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT):","_ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib =","not (0 < tag.limit < 6): client.error(\"Tag limit out of range\") ip =","restriction, # including without limitation the rights to use, copy, modify, merge, #","1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one of the inputs","signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the","sig is None) # further, search for duplicated inputs (through matching the prevout","for a short time (unless pool is full). start_time_min = 400 # whether","config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t:","input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i","else: self.print_error(f\"player indicated bad input but it was fine ({outpoint})\") # At this","= None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey","python3 # # Oregano - a lightweight Ergon client # CashFusion - an","Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len =","it was fine ({outpoint})\") # At this point we could blame the originator,","t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client)","put into fusion round if started at this tier self.queue = list() #","round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from","commit, ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert","incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort","<= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] =","for t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >=","c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer()","(check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values -- round numbers","({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a bad input: ' +","publish, distribute, sublicense, and/or sell copies of the Software, # and to permit","= [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2]","= bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self):","defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items():","chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment","selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so","of f{prevout} detected\") # If exactly one of the inputs is signed, we","bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return","# scan the commitment list and note where each client's commitments ended up","covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime >","round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c","inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction!","!= 64: raise ValidationError('signature length is wrong') # It might be we already","testnet # and we are mainnet, etc. client.error(\"This server is on a different","Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if","time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self,","if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per","= defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold # minimum","session_hash = session_hash)) # Sleep until end of covert signatures phase remtime =","= list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients: c.start_ev.set()","the full commitment list; we're a bit generous with the timeout but that's","self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon","\"E series\" values -- round numbers that are almost geometrically uniform E6 =","text): # a gentler goodbye than killing if text is not None: client.send_error(text)","could require 3-byte varint for both inputs and outputs lists overhead = 62","since it might be a resubmission after ack failed delivery, # but we","self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients","cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to","lock = threading.Lock() seen_salthashes = set() # Send start message to players; record","share same tag on a given tier (if more try to join, reject)","tags self.fill_threshold = fill_threshold # minimum number of pool clients to trigger setting","threading.Lock() seen_salthashes = set() # Send start message to players; record the time","newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) +","Check the inputs and outputs to see if we even have reasonable #","100000, 1000000, 10000000, 100000000] for s in E12] # How many clients do","for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') #","a dict of {component: contrib}, where contrib is (+- amount - fee). -","port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None","# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE","components: {bad_components}\") if len(self.clients) < 2: # Sanity check for testing -- the","a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True,","-- round numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2,","from queue into pool moved = [] for client in self.queue: for t","had plenty of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs","def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) +","a fusion. New clients get a ClientThread made for them, and they are","Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay = [list() for","a fusion if it has only been above min_clients for a short time","generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co","this needs to consider the maximum interval between messages: # - how long","client in self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround'","recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype","msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for","{blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try:","of the players to # broadcast a malleated version by re-signing one of","bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})')","= 62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could","# How many clients can share same tag on a given tier (if","components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee","tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost","ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client","None: continue size = len(pool.pool) if size >= size_best: if time_best is None","to players; record the time we did this round_time = round(time.time()) collector =","= msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record","favoured fuse: - Out of the pool(s) with the most number of players,","self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return \"too many","self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best","players; this # will form the basis of our covert timeline. covert_T0 =","- time.monotonic() assert remtime > 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list =","{nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\") # This","= sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \"","client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if","pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool, so","HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN","or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect statuses,","self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments from","tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def","report back to the # verifier, there is no privacy leak by the","random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout)","finished' so that if all blames are checked, we # can start next","ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else:","for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened;","up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj)","are passed over to a FusionController to run the rounds.\"\"\" def __init__(self, config,","blame phase. bad components: {bad_components}\") if len(self.clients) < 2: # Sanity check for","and we break the connection as a result. # Note that we could","due to tags being full self.tags = defaultdict(TagStatus) # how are the various","start_fuse(self, tier): \"\"\" Immediately launch Fusion at the selected tier. \"\"\" with self.lock:","fusion could require 3-byte varint for either inputs or outputs lists overhead =","[comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key,","of covert components phase, owner calls end_components, which returns a dict of {component:","self.all_ = 0 class WaitingPool: \"\"\" a waiting pool for a specific tier","+ 33 (session hash) ) if min_safe_clients * num_components >= 2 * 0xfc:","self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp)","lot of logs noisy = False # How long covert connections are allowed","how many clients can share same tag (in pool and queue) def check_add(self,",">= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion","a bit before uploading commitments, as clients are doing this. remtime = covert_T0","round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx,","consider the maximum interval between messages: # - how long from first connection","{len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost =","many # checks against blockchain need to be done, perhaps even still #","return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False","def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected","try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client)","submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT):","LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND","import threading import time import traceback from collections import defaultdict import oregano.schnorr as","False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for","+= 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool","len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline","with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until","None or ft < time_best or size > size_best: time_best = ft tier_best","('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \"\"\"","components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp","announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients = list(clients)","dict of {component: contrib}, where contrib is (+- amount - fee). - Before","ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client):","the last fuse happened; as a placeholder, set this to startup time. self.reset_timer()","def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes","tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order","stop we don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client):","N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly","submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(),","this tier self.queue = list() # clients who are waiting due to tags","self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to start a fusion. New clients","we don't punish him # because he's the honest guy and all the","sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') #","Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 #","an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess","rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies","call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False try:","int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = []","ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property","# an anti-spam measure we only allow one submission per connection # per","way third parties can't abuse us to find out the # timing of","first connection to last possible Tor component submission? # - how long from","assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx,","ResultsCollector: # Collect submissions from different sources, with a deadline. def __init__(self, num_results,","in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from","ephemeral port. - Before start of covert components phase, call start_components. - To","try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component","covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces","commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests","else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError):","msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye","signed, then it's malicious behaviour! if sum((signatures[i] is not None) for i in","== 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except","def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message =","def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def","Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation","commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages)","def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if","broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to broadcast transaction!","300000 # sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000,","= None size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time","len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t]","bad input but checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad","ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time =","self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients:","correlations about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs =","have bias towards any particular tier with self.lock: if self.stopping: return # add","Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately launch","so that if filling more than one pool, we don't have bias towards","live players\") raise FusionError(\"too few remaining live players\") def run (self, ): self.print_error(f'Starting","1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3,","end_signatures, which returns a list of signatures (which will have None at positions","point can report back to the # verifier, there is no privacy leak","values -- round numbers that are almost geometrically uniform E6 = [1.0, 1.5,","False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) #","try to # not reference self.<variables> that may change. for blame in msg.blames:","search for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders =","ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e:","= round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time ))","commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations =","into fusion round if started at this tier self.queue = list() # clients","is furnished to do so, # subject to the following conditions: # #","waste more time. # Since nothing after this point can report back to","# blockchain checks are somewhat subjective. It would be # appropriate to add","declares the genesis_hash, we # do indeed disallow them connecting if they are","OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE","bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier =","= covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't","except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype","maximum size, so start immediately self.start_fuse(t) return # we have added to pools,","too few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected:","for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic()","goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def","blockchain need to be done, perhaps even still # running after run_round has","100000000] for s in E12] # How many clients do we want before","upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip","mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input]","idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME *","no pools are filled then there is no favoured fuse. (since fill time","False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if","self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients,","be done, perhaps even still # running after run_round has exited. For this","client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions. How it works: -","= dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit","self.config = config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools","txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg =","back to clients # as an 'internal server error'. raise else: self.print_error(\"broadcast was","e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error')","len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for","Event for signalling us that a pool started. start_ev = threading.Event() client.start_ev =","def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed)","remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci,","client.addjob(client_start, collector) # Record the time that we sent 'startround' message to players;","component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng","self.lock: if self.stopping: return # add this client to waiting pools for pool","start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect statuses, also","if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in","the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()):","is already dead, don't waste more time. # Since nothing after this point","t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul:","* 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection,","can share same tag (in pool and queue) def check_add(self, client): for t","end_components(self): with self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes,","self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client","phase, owner calls end_components, which returns a dict of {component: contrib}, where contrib","run_round has exited. For this reason we try to # not reference self.<variables>","= len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector):","= None for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if","super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost = bindhost","client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access","from different sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results","client # immediately since client may be trying to DoS us by #","pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol","# - how long from one round's component submission to the next round's","if not (0 < tag.limit < 6): client.error(\"Tag limit out of range\") ip","# Sleep a bit before uploading commitments, as clients are doing this. remtime","= e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException:","with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail","tag\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool","new phase started. As # an anti-spam measure we only allow one submission","remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're a bit","= [list() for _ in self.clients] for src_client, relays in results: for proof,","But don't start a fusion if it has only been above min_clients for","self.print_error(f\"Ended with error: {e}\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for","as e: self.print_error(f\"Ended with error: {e}\") except Exception as e: self.print_error('Failed with exception!')","broadcast transaction! misconfigured?\") # This probably indicates misconfiguration since fusion server ought #","time_best = ft tier_best = t size_best = size if time_best is None:","# will form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround","def new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature',","1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7,","msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we","timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self,","same proof') # Note, the rest of this function might run for a","inputs or outputs lists overhead = 60 else: # the smallest fusion will","start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to","many clients can share same tag on a given tier (if more try","self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with error: {e}\") except","reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a bad","# Note that we could have aborted earlier but this # way third","ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus:","this will almost always be unique) \"\"\" with self.lock: time_best = None tier_best","to pools, which may have changed the favoured tier self.reset_timer() inftime = float('inf')","players\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server","messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter","in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex()","the inputs and outputs to see if we even have reasonable # privacy","client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For","excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO","verifier, there is no privacy leak by the ommission. continue assert ret, 'expecting","= int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results =","finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc","if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts =","# Default tag: this IP cannot be present in too many fuses. client.tags","up dead clients self.clients = [c for c in self.clients if not c.dead]","amongst players, in the smallest fusion # (these overhead numbers assume op_return script","tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if","check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock:","that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components","annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port}","covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is","the ClientThreads are passed over to a FusionController to run the rounds.\"\"\" def","Notify that we will start. for c in chosen_clients: c.start_ev.set() # Remove those","\"\"\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None):","upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost","def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey","inputs and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients -","in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee,","TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND #","# per phase. client.error('multiple submission in same phase') if mtype == 'component': try:","How many clients do we need as an absolute minimum (for privacy)? min_safe_clients","= '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t:","1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client)","long covert connections are allowed to stay open without activity. # note this","input: ' + reason) continue except Exception as e: self.print_error(f\"player indicated bad input","hereby granted, free of charge, to any person # obtaining a copy of","pool, so it might not be best anymore. self.reset_timer() raise class ResultsCollector: #","= statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure (on","each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _)","is signed, we don't punish him # because he's the honest guy and","possible component (uncompressed p2pkh input), how many could we take until the result","= threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self,","<NAME> # # Permission is hereby granted, free of charge, to any person","status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time","For this reason we try to # not reference self.<variables> that may change.","almost always be unique) \"\"\" with self.lock: time_best = None tier_best = None","True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time,","It would be # appropriate to add some 'ban score' to the player.","and we tolerate it # missing. However, if the client declares the genesis_hash,",">= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def","# Choose the minimum excess fee based on dividing the overhead amongst players,","For a given IP, how many players can they represent in the same","pool(s) with the most number of players, - Choose the pool with the","inputs is signed, we don't punish him # because he's the honest guy","optional and we tolerate it # missing. However, if the client declares the","# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, #","did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed,","NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE","in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else:","server for a new round, call .reset(); to kill all connections, call .stop().","!= existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig:","for tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag id too long\") if","fill_threshold # minimum number of pool clients to trigger setting fill_time self.fill_time =","msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if","with self.lock: ret = self.results del self.results return ret def add(self, result): with","PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS","randomly chosen destinations, same way as client did. relays = [] for i,","timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise","not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to broadcast","Inter-fusion delay -- after starting any fusion, wait this long before starting the","OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS","success, we are already removed; on stop we don't care.) with self.lock: for","129 bytes long. # generate the possible destinations list (all commitments, but leaving","- To signal the end of covert components phase, owner calls end_components, which","FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return","try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for","E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5,","\"Software\"), to deal in the Software without restriction, # including without limitation the","return # we have added to pools, which may have changed the favoured","of the pool(s) with the most number of players, - Choose the pool","Params.num_components # calculate the randomly chosen destinations, same way as client did. relays","is optional and we tolerate it # missing. However, if the client declares","def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout)","time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse","in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind","scan the commitment list and note where each client's commitments ended up client_commit_indexes","self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b =","Note, the rest of this function might run for a while if many","represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch","= Params.tiers, donation_address = donation_address )) # We allow a long timeout for","self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort the components & contribs list,","(for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is already","# when the last fuse happened; as a placeholder, set this to startup","== self.tier_best: # this is the favoured pool, can start at a special","# clients who will be put into fusion round if started at this","upnp = None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address,","- time.monotonic() if remtime < 0: # really shouldn't happen, we had plenty","*, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret =","indicates misconfiguration since fusion server ought # to have a good connection to","in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than","{len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret =","self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for","= calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs","use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in","msg.genesis_hash is optional and we tolerate it # missing. However, if the client","works: - Launch the server at any time. By default, will bind to","overhead amongst players, in the smallest fusion # (these overhead numbers assume op_return","component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list #","8.2, 9.1] # TODO - make these configurable class Params: num_components = 23","as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum","client.tags: ts = self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max:","ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs =","c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in","Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad","this software and associated documentation files # (the \"Software\"), to deal in the","same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if","# Oregano - a lightweight Ergon client # CashFusion - an advanced coin","feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at","our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise","network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients)","network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for","sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients) < 2: #","client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers}","submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for","for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: #","in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector","may be trying to DoS us by # making us check many inputs","it might be a resubmission after ack failed delivery, # but we don't","msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index","({outpoint})\") # At this point we could blame the originator, however # blockchain","above min_clients for this long. start_time_max = 1200 # Inter-fusion delay -- after","- To signal the end of covert signatures phase, owner calls end_signatures, which","self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert","want before starting a fusion? min_clients = 8 # If all clients submitted","it works: - Launch the server at any time. By default, will bind","of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s","in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del","in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey =","Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) > 200 for p in proofs):","if ts is not None and ts.all_ >= self.tag_max: return \"too many clients","fusion could require 3-byte varint for both inputs and outputs lists overhead =","Sleep until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES -","dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue","feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in","natively offer SSL support, however a server admin may run an SSL server","src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\")","len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys =","did pool exceed fill_threshold self.tag_max = tag_max # how many clients can share","Once a Fusion thread is started, the ClientThreads are passed over to a","self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >=","Record the time that we sent 'startround' message to players; this # will","the next one (unless hit max time or pool is full). start_time_spacing =","results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage","EC server. Report this back to clients # as an 'internal server error'.","if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few components submitted\")","= client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if","def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies:","if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same","component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs =","= annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport,","= FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start()","sharing thing doesn't even make sense with one player. for c in self.clients:","= tag_max # how many clients can share same tag (in pool and","relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for client in","= msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') # It","end_components, which returns a dict of {component: contrib}, where contrib is (+- amount","62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could require","= 0 class WaitingPool: \"\"\" a waiting pool for a specific tier \"\"\"","given tier (if more try to join, reject) max_tier_client_tags = 100 # For","{bad_components}\") if len(self.clients) < 2: # Sanity check for testing -- the proof","directly to blame, or maybe even restart / end # without sharing components.","@ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain =","aren't collecting any results, rather just marking that # 'checking finished' so that","script size of 1 + 5 (lokad) + 33 (session hash) ) if","= e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while","client.print_error(\"👀 No genesis hash declared by client, we'll let them slide...\") if self.stopping:","logs noisy = False # How long covert connections are allowed to stay","# privacy with what we have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping","+ txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg","measure we only allow one submission per connection # per phase. client.error('multiple submission","chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients:","assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig =","protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now,","blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad","collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds],","few remaining live players\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players","us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames')","aborted earlier but this # way third parties can't abuse us to find","a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES)","thread is started, the ClientThreads are passed over to a FusionController to run","= self.results del self.results return ret def add(self, result): with self.lock: try: self.results.append(result)","timeout) def check_client_count(self,): live = [c for c in self.clients if not c.dead]","didn't have private key. If more than one # signed, then it's malicious","except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye,","def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg","and associated documentation files # (the \"Software\"), to deal in the Software without","self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds running","= sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients) < 2:","class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout)","off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp =","msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) >","{blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad","size_best: if time_best is None or ft < time_best or size > size_best:","copyright notice and this permission notice shall be # included in all copies","get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it # missing.","commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) -","# appropriate to add some 'ban score' to the player. # we aren't","in client.tags: ts = self.tags.get(t) if ts is not None and ts.all_ >=","to do so, # subject to the following conditions: # # The above","in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client)","please switch servers\") else: client.print_error(\"👀 No genesis hash declared by client, we'll let","self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one of the inputs is signed,","recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def","= collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread):","len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with","announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg","* num_components >= 0xfc: # the smallest fusion could require 3-byte varint for","sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \" +","rounds until successful or exception while True: covert_server.reset() # Clean up dead clients","# immediately since client may be trying to DoS us by # making","collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util","x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib","timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\") # Await","missing_sigs else ')'}\") # mark all missing-signature components as bad. bad_inputs = set(i","in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool),","if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return","-- the proof sharing thing doesn't even make sense with one player. for","input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except","network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion","if remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're a","our transaction a small head start in relaying, before sharing the # signatures.","= threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return self","collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment:","function might run for a while if many # checks against blockchain need","client.error(\"Tag id too long\") if not (0 < tag.limit < 6): client.error(\"Tag limit","leak by the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex()","earlier but this # way third parties can't abuse us to find out","to deal in the Software without restriction, # including without limitation the rights","sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys =","= Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing","lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component","client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with","with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time","self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with","we could have aborted earlier but this # way third parties can't abuse","= [b.get_R() for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit')","tag: this IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip,","for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks","acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\") # mark all missing-signature components","inftime if pool.fill_time is not None: # a non-favoured pool will start eventually","If all clients submitted largest possible component (uncompressed p2pkh input), how many could","proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one","to the # verifier, there is no privacy leak by the ommission. continue","don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if","= t size_best = size if time_best is None: self.tier_best_starttime = None else:","= False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort","component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError","= set() # clients who will be put into fusion round if started","= float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow =","+ min_safe_clients - 1) // min_safe_clients # How many clients can share same","and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _","share same tag (in pool and queue) def check_add(self, client): for t in","covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port","= signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame","client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients)","upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address = donation_address","time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're","# Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost,","many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id)","')'}\") # mark all missing-signature components as bad. bad_inputs = set(i for i,sig","# a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if","isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\")","*expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout)","'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError:","len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime","now, msg.genesis_hash is optional and we tolerate it # missing. However, if the","Fusion thread is started, the ClientThreads are passed over to a FusionController to","\"\"\" Scan pools for the favoured fuse: - Out of the pool(s) with","the following to: Remove from spawned clients list, so that the fusion can","For now, msg.genesis_hash is optional and we tolerate it # missing. However, if","() def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)])","server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components)","client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock:","covert components\") # Await commitment messages then process results results = collector.gather(deadline =","default, will bind to an ephemeral port. - Before start of covert components","the randomly chosen destinations, same way as client did. relays = [] for","so start immediately self.start_fuse(t) return # we have added to pools, which may",">= 0xfc: # the smallest fusion could require 3-byte varint for either inputs","can they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time","for either inputs or outputs lists overhead = 60 else: # the smallest","with self.lock: if self.stopping: return # add this client to waiting pools for","msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}')","statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items():","delay -- after starting any fusion, wait this long before starting the next","chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self,","per proof is malicious. Boot client # immediately since client may be trying","3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9,","some preliminary checks to see whether we should just skip the # signing","this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector):","has exited. For this reason we try to # not reference self.<variables> that","chosen destinations, same way as client did. relays = [] for i, proof","fusion server ought # to have a good connection to the EC server.","self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set()","proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT +","1 if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for","all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame; clamed reason was:","way as client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx,","not None and ts.all_ >= self.tag_max: return \"too many clients with same tag\"","bindhost, port, upnp = None, announcehost = None, donation_address = None): assert network","len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version !=","def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \"\"\" a waiting","time_best or size > size_best: time_best = ft tier_best = t size_best =","msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the","full commitment list; we're a bit generous with the timeout but that's OK.","disallow them connecting if they are e.g. on testnet # and we are","200 for p in proofs): client.error(\"too-long proof\") # they should only be 129","for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for","the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp,","AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS #","annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False,","that # 'checking finished' so that if all blames are checked, we #","timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def","self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c in","see whether we should just skip the # signing phase and go directly","this IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'',","message to players; this # will form the basis of our covert timeline.","signatures phase, owner calls end_signatures, which returns a list of signatures (which will","Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool: continue status","and outputs to see if we even have reasonable # privacy with what","the genesis_hash, we # do indeed disallow them connecting if they are e.g.","covert component acceptance. {len(component_master_list)} received.\") # Sort the components & contribs list, then","Collect submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail =","pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash =","gentler goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect class","threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited","tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s","self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f","inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i,","nginx for that purpose. \"\"\" import secrets import sys import threading import time","all copies or substantial portions of the Software. # # THE SOFTWARE IS","filling more than one pool, we don't have bias towards any particular tier","= self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in","None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best","True for t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool","# Send start message to players; record the time we did this round_time","commitment list and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components","end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert","ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic()","skip the # signing phase and go directly to blame, or maybe even","too long\") if not (0 < tag.limit < 6): client.error(\"Tag limit out of","the # verifier, there is no privacy leak by the ommission. continue assert","in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees","if s is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if","not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended","more than one # signed, then it's malicious behaviour! if sum((signatures[i] is not","1200 # Inter-fusion delay -- after starting any fusion, wait this long before","If no pools are filled then there is no favoured fuse. (since fill","status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools on","controls the Fusion rounds running from server side. \"\"\" def __init__(self, network, tier,","time raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for","len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\") #","self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\") # This probably indicates misconfiguration","(+- amount - fee). - Before start of covert signatures phase, owner calls","accepting covert components\") # Await commitment messages then process results results = collector.gather(deadline","= Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) #","= ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist')","len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations,","component acceptance. {len(component_master_list)} received.\") # Sort the components & contribs list, then separate","pool will start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best:","min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None: # a","any(len(p) > 200 for p in proofs): client.error(\"too-long proof\") # they should only","= self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee","tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E","before starting the next one (unless hit max time or pool is full).","= {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid","CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None else","# More than one blame per proof is malicious. Boot client # immediately","min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address ))","sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for","done, perhaps even still # running after run_round has exited. For this reason","= tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost =","we could blame the originator, however # blockchain checks are somewhat subjective. It","gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret","for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if","try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof}","try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg","2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8,","to # broadcast a malleated version by re-signing one of their inputs. time.sleep(2)","self.fill_time = None # when did pool exceed fill_threshold self.tag_max = tag_max #","- Before start of covert signatures phase, owner calls start_signatures. - To signal","following conditions: # # The above copyright notice and this permission notice shall","len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This","covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert components","in enumerate(signatures) if sig is None) # further, search for duplicated inputs (through","the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci,","0xfc: # the smallest fusion could require 3-byte varint for both inputs and","don't have bias towards any particular tier with self.lock: if self.stopping: return #","through tiers and collect statuses, also check start times. statuses = dict() tfill_thresh","True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes =","> 20: client.error(\"Tag id too long\") if not (0 < tag.limit < 6):","in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components,","start a fusion. New clients get a ClientThread made for them, and they","AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR","FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions. How it works:","long. start_time_max = 1200 # Inter-fusion delay -- after starting any fusion, wait","is on a different chain, please switch servers\") else: client.print_error(\"👀 No genesis hash","The above copyright notice and this permission notice shall be # included in","next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames,","(you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}):","at any time. By default, will bind to an ephemeral port. - Before","list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling more than","pool in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for","started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain","ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' +","# missing. However, if the client declares the genesis_hash, we # do indeed","prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly","else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41'","rng.shuffle(mytiers) # shuffle the adding order so that if filling more than one","Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal","self.print_error(\"broadcast was successful!\") # Give our transaction a small head start in relaying,","c.kill('blame yourself!') return # scan the commitment list and note where each client's","{missing_sigs} missing :{'(' if missing_sigs else ')'}\") # mark all missing-signature components as","IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED,","size limitation? max_clients = (100000 - 12) // (num_components * 173) # Every","pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout,","= client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match","have None at positions of missing signatures). - To reset the server for","self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey,","covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time)","# How many clients do we want before starting a fusion? min_clients =","we'll let them slide...\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address):","elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could require 3-byte","for both inputs and outputs lists overhead = 58 min_excess_fee = (overhead +","eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this is","start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for","originator, however # blockchain checks are somewhat subjective. It would be # appropriate","(unless pool is full). start_time_min = 400 # whether to print a lot","minimum number of pool clients to trigger setting fill_time self.fill_time = None #","but checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but","many tags\") # Event for signalling us that a pool started. start_ev =","= round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client","for t in client.tags: ts = self.tags.get(t) if ts is not None and","tag.limit < 6): client.error(\"Tag limit out of range\") ip = '' if tag.no_ip","tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining =","prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders","enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx,","self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while","= proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx,","trying to DoS us by # making us check many inputs against blockchain.","as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided","comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in","self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\"too","timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server","attempt to move clients from queue into pool moved = [] for client","to tags being full self.tags = defaultdict(TagStatus) # how are the various tags","proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof,","tier with self.lock: if self.stopping: return # add this client to waiting pools","thing doesn't even make sense with one player. for c in self.clients: c.kill('blame","reason) continue except Exception as e: self.print_error(f\"player indicated bad input but checking failed","self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib","in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client,","player. for c in self.clients: c.kill('blame yourself!') return # scan the commitment list","input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig","outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the","successfully!') except FusionError as e: self.print_error(f\"Ended with error: {e}\") except Exception as e:","fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol","secrets import sys import threading import time import traceback from collections import defaultdict","myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if","= 1200 # Inter-fusion delay -- after starting any fusion, wait this long","{covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b,","= [c for c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)}","submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try:","numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7,","from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from","9.1] # TODO - make these configurable class Params: num_components = 23 component_feerate","the pool has stayed at or above min_clients for this long. start_time_max =","favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or","is the favoured pool, can start at a special time remtime = min(remtime,","self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes =","[(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in","/ end # without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components:","class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000","then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients","missing :{'(' if missing_sigs else ')'}\") # mark all missing-signature components as bad.","rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): #","session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and","= upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout =","# timing of a given input's signature submission. raise ValidationError('conflicting valid signature') with","[] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx","Oregano - a lightweight Ergon client # CashFusion - an advanced coin anonymizer","after run_round has exited. For this reason we try to # not reference","in the smallest fusion # (these overhead numbers assume op_return script size of","= None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost","src_client.kill('you provided a bad input: ' + reason) continue except Exception as e:","self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools:","until the result would exceed 100 kB standard tx size limitation? max_clients =","OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS","waiting pools. Once a Fusion thread is started, the ClientThreads are passed over","= c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m","ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason","(on success, we are already removed; on stop we don't care.) with self.lock:","N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)):","object to represent a pool tag \"\"\" __slots__ = () def __new__(cls, ipstr,","tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag id too long\") if not","raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT):","if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread,","pools for pool in mytierpools.values(): res = pool.check_add(client) if res is not None:","{component: contrib}, where contrib is (+- amount - fee). - Before start of","an 'internal server error'. raise else: self.print_error(\"broadcast was successful!\") # Give our transaction","c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee,","wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig)","raise FusionError(\"too few remaining live players\") def run (self, ): self.print_error(f'Starting fusion with","for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock =","proof is malicious. Boot client # immediately since client may be trying to","non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if t ==","a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0:","waiting due to tags being full self.tags = defaultdict(TagStatus) # how are the","the honest guy and all the other components were # just imposters who","covert components phase, owner calls end_components, which returns a dict of {component: contrib},","should just skip the # signing phase and go directly to blame, or","our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\")","self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse","we have added to pools, which may have changed the favoured tier self.reset_timer()","deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results","if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure","deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail)","To signal the end of covert components phase, owner calls end_components, which returns","for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients =","have private key. If more than one # signed, then it's malicious behaviour!","enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed","self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components =","so that if all blames are checked, we # can start next round","ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost = announcehost","run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to","# localhost is whitelisted to allow unlimited access client.tags = [] else: #","else: self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i,","if not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\"too few","since client may be trying to DoS us by # making us check","> 200 for p in proofs): client.error(\"too-long proof\") # they should only be","sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs if len(proofs)","component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort the components","in E12] # How many clients do we want before starting a fusion?","= len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys","submission per connection # per phase. client.error('multiple submission in same phase') if mtype","True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg,","try_move_from_queue(self): # attempt to move clients from queue into pool moved = []","calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) #","possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client,","of range\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try:","len([s for s in signatures if s is None]) ### self.print_error(f\"ending covert signature","(the \"Software\"), to deal in the Software without restriction, # including without limitation","- len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment","= pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is","bytes object to represent a pool tag \"\"\" __slots__ = () def __new__(cls,","distinct valid signature. This is not # allowed and we break the connection","tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost","tier_best = t size_best = size if time_best is None: self.tier_best_starttime = None","# Sleep until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES","blockchain checks are somewhat subjective. It would be # appropriate to add some","# and to permit persons to whom the Software is furnished to do","txid = tx.txid() self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except","in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components del","in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye,","for testing -- the proof sharing thing doesn't even make sense with one","components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is","do so, # subject to the following conditions: # # The above copyright","except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return","= len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\"got","make sense with one player. for c in self.clients: c.kill('blame yourself!') return #","anti-spam measure we only allow one submission per connection # per phase. client.error('multiple","client may be trying to DoS us by # making us check many","client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool =","len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0]","!= myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate","one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40","we break the connection as a result. # Note that we could have","= [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s in","c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count","self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for","return can_pool def remove(self, client): # make sure to call try_move_from_queue() after calling","long timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if","Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c","+ Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen, we","live: c.kill(\"too few remaining live players\") raise FusionError(\"too few remaining live players\") def","submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\")","ts is not None and ts.all_ >= self.tag_max: return \"too many clients with","can start next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients,","blame per proof is malicious. Boot client # immediately since client may be","announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if","# signing phase and go directly to blame, or maybe even restart /","[schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send","inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for","Params.tiers, donation_address = donation_address )) # We allow a long timeout for clients","according to destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays","failed delivery, # but we don't allow it to consume our CPU power.","components phase, owner calls end_components, which returns a dict of {component: contrib}, where","= Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address =","FusionController to run the rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp =","connection to the EC server. Report this back to clients # as an","not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp","used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg,","of signatures (which will have None at positions of missing signatures). - To","covert_server.reset() # Clean up dead clients self.clients = [c for c in self.clients","covert signatures phase, owner calls start_signatures. - To signal the end of covert","might run for a while if many # checks against blockchain need to","= ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey,","# (these overhead numbers assume op_return script size of 1 + 5 (lokad)","src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT","next one (unless hit max time or pool is full). start_time_spacing = 120","without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense,","if remtime < 0: # really shouldn't happen, we had plenty of time","given IP, how many players can they represent in the same fuse? ip_max_simul_fuse","an SSL server proxy such as nginx for that purpose. \"\"\" import secrets","= None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not","collector # Sleep a bit before uploading commitments, as clients are doing this.","score' to the player. # we aren't collecting any results, rather just marking","None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\")","self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert","yourself!') return # scan the commitment list and note where each client's commitments","Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e","inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False,","covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept","proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if any(len(p)","mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components","- Choose the pool with the earliest fill time; - If no pools","ought # to have a good connection to the EC server. Report this","are already removed; on stop we don't care.) with self.lock: for t, pool","results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according","except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high')","KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF","in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in","pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime","self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41,","== self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for t","begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl","this permission notice shall be # included in all copies or substantial portions","{ret}') continue if src_client.dead: # If the blamed client is already dead, don't","[1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3,","CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT","Launch the server at any time. By default, will bind to an ephemeral","bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails = []","= 60 else: # the smallest fusion will use 1-byte varint for both","in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError","= self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key,","= [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames',","!= Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash():","0xfc: # the smallest fusion could require 3-byte varint for either inputs or","c in chosen_clients: c.start_ev.set() # Remove those clients from all pools for t,","< 6): client.error(\"Tag limit out of range\") ip = '' if tag.no_ip else","= self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return \"too","__init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev =","left from best pool, so it might not be best anymore. self.reset_timer() raise","def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server =","client.error(\"wrong number of proofs\") if any(len(p) > 200 for p in proofs): client.error(\"too-long","False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if","-= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup","else: client.print_error(\"👀 No genesis hash declared by client, we'll let them slide...\") if","= list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling more","port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components","if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If exactly one","of covert signatures phase, owner calls end_signatures, which returns a list of signatures","same tag on a given tier (if more try to join, reject) max_tier_client_tags","0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None:","announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers}","plenty of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs =","1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 =","= len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None:","of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime","any person # obtaining a copy of this software and associated documentation files","IN THE # SOFTWARE. \"\"\" A basic server implementation for CashFusion. Does not","2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2,","if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime)","fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the","self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong","# Since nothing after this point can report back to the # verifier,","self.network = network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp","rounds running from server side. \"\"\" def __init__(self, network, tier, clients, bindhost, upnp","to the following conditions: # # The above copyright notice and this permission","in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled","self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the","\"too many clients with same tag\" def _add_pool(self, client): self.pool.add(client) for t in","if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late","of the Software, # and to permit persons to whom the Software is","contrib}, where contrib is (+- amount - fee). - Before start of covert","max_tier_client_tags = 100 # For a given IP, how many players can they","get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair,","[b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds,","phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings","a list of signatures (which will have None at positions of missing signatures).","covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime <","(FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame,","the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end","server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = []","preliminary checks to see whether we should just skip the # signing phase","because he's the honest guy and all the other components were # just","KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers)","# Note, the rest of this function might run for a while if","client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to represent a pool tag","total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash =","CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return","= 6 # Choose the minimum excess fee based on dividing the overhead","text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected","run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost,","= sum(f for _,_,f in results) # Generate scrambled commitment list, but remember","Boot client # immediately since client may be trying to DoS us by","= sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def","def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try:","stayed at or above min_clients for this long. start_time_max = 1200 # Inter-fusion","msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it","Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion at the","ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools =","OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE,","if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self,","whether we should just skip the # signing phase and go directly to","= check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError:","of players, - Choose the pool with the earliest fill time; - If","rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost = None,","submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg):","# they should only be 129 bytes long. # generate the possible destinations","{src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the","None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients =","return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys)","more than one pool, we don't have bias towards any particular tier with","0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\") # Event for","results, collector # Sleep a bit before uploading commitments, as clients are doing","3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5,","self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return","'ping': continue if client.got_submit: # We got a second submission before a new","# how are the various tags self.fill_threshold = fill_threshold # minimum number of","= [(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex] N","client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by source commitment","they should only be 129 bytes long. # generate the possible destinations list","from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from . import","EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY,","their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok =","self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as","destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays in results:","THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR","get a ClientThread made for them, and they are put into the waiting","!= 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i,","1-byte varint for both inputs and outputs lists overhead = 58 min_excess_fee =","how are the various tags self.fill_threshold = fill_threshold # minimum number of pool","c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients","= mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the","running from server side. \"\"\" def __init__(self, network, tier, clients, bindhost, upnp =","the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes","per phase. client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey","still # running after run_round has exited. For this reason we try to","ret = self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del","WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED","= 1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for","standard tx size limitation? max_clients = (100000 - 12) // (num_components * 173)","dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs,","reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del","localhost is whitelisted to allow unlimited access client.tags = [] else: # Default","pool, can start at a special time remtime = min(remtime, self.tier_best_starttime - tnow)","live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs,","* 0xfc: # the smallest fusion could require 3-byte varint for both inputs","proof') # Note, the rest of this function might run for a while","{e}\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients:","this long before starting the next one (unless hit max time or pool","- Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool: continue","we are already removed; on stop we don't care.) with self.lock: for t,","PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING","self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved:","distribute, sublicense, and/or sell copies of the Software, # and to permit persons","players, in the smallest fusion # (these overhead numbers assume op_return script size","nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except","removes ordering correlations about which client sent which proof proofs.sort(key = lambda x:x[1])","kill all connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp = None):","return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value)","6 # Choose the minimum excess fee based on dividing the overhead amongst","round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with","collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give","for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib)","in all copies or substantial portions of the Software. # # THE SOFTWARE","self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier =","the end of covert components phase, owner calls end_components, which returns a dict","enumerate(signatures) if sig is None) # further, search for duplicated inputs (through matching","ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)]","end of covert components phase, owner calls end_components, which returns a dict of","will bind to an ephemeral port. - Before start of covert components phase,","covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of covert","timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self,","too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if","import sys import threading import time import traceback from collections import defaultdict import","also check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for","ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import send_pb, recv_pb,","on dividing the overhead amongst players, in the smallest fusion # (these overhead","relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx,","Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb","one (unless hit max time or pool is full). start_time_spacing = 120 #","to start a fusion. New clients get a ClientThread made for them, and","covert connections are allowed to stay open without activity. # note this needs","ClientThread made for them, and they are put into the waiting pools. Once","if isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for","commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f","# 'checking finished' so that if all blames are checked, we # can","time.monotonic() # scan through tiers and collect statuses, also check start times. statuses","for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments","client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding","annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport","+ Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately","exception while True: covert_server.reset() # Clean up dead clients self.clients = [c for","with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but it was fine","!= Params.num_components: client.error(\"wrong number of proofs\") if any(len(p) > 200 for p in","-- after starting any fusion, wait this long before starting the next one","upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def","continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients)","- If no pools are filled then there is no favoured fuse. (since","pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg),","if missing_sigs else ')'}\") # mark all missing-signature components as bad. bad_inputs =","if len(sig) != 64: raise ValidationError('signature length is wrong') # It might be","and queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if","# minimum number of pool clients to trigger setting fill_time self.fill_time = None","if not collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results","= 3 # Guaranteed time to launch a fusion if the pool has","\"\"\" import secrets import sys import threading import time import traceback from collections","both inputs and outputs lists overhead = 62 elif min_safe_clients * num_components >=","*expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self,","waiting pool for a specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool =","or outputs lists overhead = 60 else: # the smallest fusion will use","= fill_threshold # minimum number of pool clients to trigger setting fill_time self.fill_time","del component_master_list # Do some preliminary checks to see whether we should just","one submission per connection # per phase. client.error('multiple submission in same phase') if","transaction! misconfigured?\") # This probably indicates misconfiguration since fusion server ought # to","e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a","# But don't start a fusion if it has only been above min_clients","# clients who are waiting due to tags being full self.tags = defaultdict(TagStatus)","we don't have bias towards any particular tier with self.lock: if self.stopping: return","Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components","for p in proofs): client.error(\"too-long proof\") # they should only be 129 bytes","if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and","< time_best or size > size_best: time_best = ft tier_best = t size_best","we only allow one submission per connection # per phase. client.error('multiple submission in","len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments","for a specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set() #","the following conditions: # # The above copyright notice and this permission notice","def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\"","CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad","### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\") #","sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z)","It might be we already have this signature. This is fine # since","raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature","except AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature' try:","= recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage,","- how long from one round's component submission to the next round's component","and to permit persons to whom the Software is furnished to do so,","120 # But don't start a fusion if it has only been above","= self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if","# Now, repackage the proofs according to destination. proofs_to_relay = [list() for _","start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags =","may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof]","however # blockchain checks are somewhat subjective. It would be # appropriate to","threading import time import traceback from collections import defaultdict import oregano.schnorr as schnorr","[c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended","to be done, perhaps even still # running after run_round has exited. For","\"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b","= time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients list,","detected: excess fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components)","self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert submissions. How","self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self,","error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for covert","is fine # since it might be a resubmission after ack failed delivery,","players can they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed","input but checking failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input","= [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del","dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx))","is no favoured fuse. (since fill time is a float, this will almost","= ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e:","!= len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest of","continue if isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof","try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit","# the smallest fusion will use 1-byte varint for both inputs and outputs","random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text):","notice shall be # included in all copies or substantial portions of the","commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _ in results]","Note that we could have aborted earlier but this # way third parties","are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12","client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\")","ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components #","punish him # because he's the honest guy and all the other components","started at this tier self.queue = list() # clients who are waiting due","Give our transaction a small head start in relaying, before sharing the #","configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee =","= 40 # used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32))","about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [","# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN","self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate","long. # generate the possible destinations list (all commitments, but leaving out the","c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return","tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3)","self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage","= True self.print_error(\"problem detected: too few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures","ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype =","self.t_last_fuse = time.monotonic() # when the last fuse happened; as a placeholder, set","len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in","lightweight Ergon client # CashFusion - an advanced coin anonymizer # # Copyright","send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client:","in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid()","have added to pools, which may have changed the favoured tier self.reset_timer() inftime","for one of the players to # broadcast a malleated version by re-signing","# because he's the honest guy and all the other components were #","How it works: - Launch the server at any time. By default, will","sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool =","libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network = network","tag \"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode()","with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results)","# Uncomment the following to: Remove from spawned clients list, so that the","different sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results =","c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with","= [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)]","broadcast a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok =","an anti-spam measure we only allow one submission per connection # per phase.","### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients) < 2: # Sanity","id too long\") if not (0 < tag.limit < 6): client.error(\"Tag limit out","total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment list, but","reasonable # privacy with what we have. bad_components = set() ### if skip_signatures:","run rounds until successful or exception while True: covert_server.reset() # Clean up dead","covert submissions. How it works: - Launch the server at any time. By","all connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost,","requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network =","for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)]","skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash)","clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if","changed the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if","time_best = None tier_best = None size_best = 0 for t, pool in","if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players =","!= expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") #","size, so start immediately self.start_fuse(t) return # we have added to pools, which","max_clients = (100000 - 12) // (num_components * 173) # Every round, clients","the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell","self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with error:","indeed disallow them connecting if they are e.g. on testnet # and we","self.tier_best in mytierpools: # we left from best pool, so it might not","max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow","genesis_hash, we # do indeed disallow them connecting if they are e.g. on","send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self,","def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock:","mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check","further, search for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders","False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by","join, reject) max_tier_client_tags = 100 # For a given IP, how many players","tx size limitation? max_clients = (100000 - 12) // (num_components * 173) #","myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the","IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN","mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None):","exactly one of the inputs is signed, we don't punish him # because","comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks to","repackage the proofs according to destination. proofs_to_relay = [list() for _ in self.clients]","= len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen","self.clients: c.kill('blame yourself!') return # scan the commitment list and note where each","right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs,","as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import","in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1","= True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock =","return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class","def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a","self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback):","THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A basic server","mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum","self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret def add(self,","message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad proof","class FusionServer(GenericServer): \"\"\"Server for clients waiting to start a fusion. New clients get","import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from","def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be put","= set() # Send start message to players; record the time we did","how long from first connection to last possible Tor component submission? # -","will almost always be unique) \"\"\" with self.lock: time_best = None tier_best =","return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version","remtime)) with self.lock: ret = self.results del self.results return ret def add(self, result):","if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess fee mismatch\") self.last_hash","client in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >=","re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return","the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\")","all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components,","blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to","for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ =","IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise","+ Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay = [list()","submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg)","the blamed client is already dead, don't waste more time. # Since nothing","def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\")","until successful or exception while True: covert_server.reset() # Clean up dead clients self.clients","time to launch a fusion if the pool has stayed at or above","fill_threshold, tag_max): self.pool = set() # clients who will be put into fusion","isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee","- Launch the server at any time. By default, will bind to an","live players\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}')","in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason","being full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold =","c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send,","OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A basic","version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash","msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,):","= Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time =","without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True","in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") # If","100 kB standard tx size limitation? max_clients = (100000 - 12) // (num_components","returns a list of signatures (which will have None at positions of missing","bytes long. # generate the possible destinations list (all commitments, but leaving out","self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier selected:","INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A","collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination.","or exception while True: covert_server.reset() # Clean up dead clients self.clients = [c","FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS","# start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!)","blame, or maybe even restart / end # without sharing components. skip_signatures =","= True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes","del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False","a lightweight Ergon client # CashFusion - an advanced coin anonymizer # #","for s in E12] # How many clients do we want before starting","self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle","except FusionError as e: self.print_error(f\"Ended with error: {e}\") except Exception as e: self.print_error('Failed","already dead, don't waste more time. # Since nothing after this point can","ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted","3 # Guaranteed time to launch a fusion if the pool has stayed","# a gentler goodbye than killing if text is not None: client.send_error(text) raise","for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to start","hit max time or pool is full). start_time_spacing = 120 # But don't","statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting","it slightly harder for one of the players to # broadcast a malleated","# At this point we could blame the originator, however # blockchain checks","if existing_sig: # We received a distinct valid signature. This is not #","False # How long covert connections are allowed to stay open without activity.","= 100 # For a given IP, how many players can they represent","use 1-byte varint for both inputs and outputs lists overhead = 58 min_excess_fee","# gc clean def reset_timer(self, ): \"\"\" Scan pools for the favoured fuse:","To signal the end of covert signatures phase, owner calls end_signatures, which returns","len(pool.pool) if size >= size_best: if time_best is None or ft < time_best","tier): \"\"\" Immediately launch Fusion at the selected tier. \"\"\" with self.lock: chosen_clients","abuse us to find out the # timing of a given input's signature","setting fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max =","or above min_clients for this long. start_time_max = 1200 # Inter-fusion delay --","sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if","(client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline =","= [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp,","self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy =","messages: # - how long from first connection to last possible Tor component","for the favoured fuse: - Out of the pool(s) with the most number","_ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class","calls start_signatures. - To signal the end of covert signatures phase, owner calls","6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2,","to see if we even have reasonable # privacy with what we have.","= (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype","start. for c in chosen_clients: c.start_ev.set() # Remove those clients from all pools","to trigger setting fill_time self.fill_time = None # when did pool exceed fill_threshold","phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: #","he's the honest guy and all the other components were # just imposters","return True def try_move_from_queue(self): # attempt to move clients from queue into pool","client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic()","ts.all_ >= self.tag_max: return \"too many clients with same tag\" def _add_pool(self, client):","from . import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer,","- make these configurable class Params: num_components = 23 component_feerate = 1000 #","is not None and ts.all_ >= self.tag_max: return \"too many clients with same","Params.min_clients) remtime = inftime if pool.fill_time is not None: # a non-favoured pool","- Params.num_components # calculate the randomly chosen destinations, same way as client did.","src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got bad blame; clamed","None at positions of missing signatures). - To reset the server for a","call start_components. - To signal the end of covert components phase, owner calls","and/or sell copies of the Software, # and to permit persons to whom","missing_sigs = len([s for s in signatures if s is None]) ### self.print_error(f\"ending","except Exception as e: self.print_error(f\"player indicated bad input but checking failed with exception","nothing after this point can report back to the # verifier, there is","c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None):","= False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME)","use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the","in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof","check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad","player. # we aren't collecting any results, rather just marking that # 'checking","positions of missing signatures). - To reset the server for a new round,","limitation? max_clients = (100000 - 12) // (num_components * 173) # Every round,","change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except","sum(f for _,_,f in results) # Generate scrambled commitment list, but remember exactly","annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception","finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\" Scan pools for the","list, so that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients)","try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else:","minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess fee based","tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set():","6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class Params:","could blame the originator, however # blockchain checks are somewhat subjective. It would","start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this","= 400 # whether to print a lot of logs noisy = False","t in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try:","ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you","launch Fusion at the selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) #","these configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee","client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if","tfill_thresh if t == self.tier_best: # this is the favoured pool, can start","error: {e}\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in","might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from","for signalling us that a pool started. start_ev = threading.Event() client.start_ev = start_ev","in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool","self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time')","reset_timer(self, ): \"\"\" Scan pools for the favoured fuse: - Out of the","covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})')","threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self, ):","sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b in [10000,","# If all clients submitted largest possible component (uncompressed p2pkh input), how many","# do indeed disallow them connecting if they are e.g. on testnet #","clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler","time_best is None or ft < time_best or size > size_best: time_best =","donation_address = donation_address )) # We allow a long timeout for clients to","self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def","to join, reject) max_tier_client_tags = 100 # For a given IP, how many","self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ):","+ Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None):","pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following","shouldn't happen, we had plenty of time raise FusionError(\"way too slow\") time.sleep(remtime) signatures","# cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt","= config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools =","# used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client,","not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def","NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY","# and we are mainnet, etc. client.error(\"This server is on a different chain,","tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from","donation_address )) # We allow a long timeout for clients to choose their","connecting if they are e.g. on testnet # and we are mainnet, etc.","= [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag","self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\") # mark","Report this back to clients # as an 'internal server error'. raise else:","launch a fusion if the pool has stayed at or above min_clients for","e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying","even make sense with one player. for c in self.clients: c.kill('blame yourself!') return","wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey =","be we already have this signature. This is fine # since it might","from server side. \"\"\" def __init__(self, network, tier, clients, bindhost, upnp = None,","continue except Exception as e: self.print_error(f\"player indicated bad input but checking failed with","all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue()","send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg):","= Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious.","for a while if many # checks against blockchain need to be done,","clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results) #","if they are e.g. on testnet # and we are mainnet, etc. client.error(\"This","covert signatures phase, owner calls end_signatures, which returns a list of signatures (which","a lot of logs noisy = False # How long covert connections are","'internal server error'. raise else: self.print_error(\"broadcast was successful!\") # Give our transaction a","AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with","noisy = False # How long covert connections are allowed to stay open","BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR","with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost","connections are allowed to stay open without activity. # note this needs to","self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until","seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)):","this # will form the basis of our covert timeline. covert_T0 = time.monotonic()","stay open without activity. # note this needs to consider the maximum interval","ft tier_best = t size_best = size if time_best is None: self.tier_best_starttime =","= list() # clients who are waiting due to tags being full self.tags","acceptance. {len(component_master_list)} received.\") # Sort the components & contribs list, then separate it","range\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools","self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds","on a different chain, please switch servers\") else: client.print_error(\"👀 No genesis hash declared","for bad input: {reason}\") src_client.kill('you provided a bad input: ' + reason) continue","# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF #","persons to whom the Software is furnished to do so, # subject to","gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) #","[contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary","covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings set up incorrectly\"","rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\"","> 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component","a gentler goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect","= calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful","2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage,","import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values -- round","start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs","check_input_electrumx) # Resistor \"E series\" values -- round numbers that are almost geometrically","client): for t in client.tags: ts = self.tags.get(t) if ts is not None","# broadcast a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok","self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong","when did pool exceed fill_threshold self.tag_max = tag_max # how many clients can","# Await commitment messages then process results results = collector.gather(deadline = covert_T0 +","sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem","is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\"","return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0","remaining live players\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at","scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading","IP, how many players can they represent in the same fuse? ip_max_simul_fuse =","= None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self,","len(self.clients) < 2: # Sanity check for testing -- the proof sharing thing","max_excess_fee = 300000 # sats tiers = [round(b*s) for b in [10000, 100000,","client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME","the other components were # just imposters who didn't have private key. If","to print a lot of logs noisy = False # How long covert","missing signatures). - To reset the server for a new round, call .reset();","a resubmission after ack failed delivery, # but we don't allow it to","the overhead amongst players, in the smallest fusion # (these overhead numbers assume","self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion","privacy leak by the ommission. continue assert ret, 'expecting input component' outpoint =","server side. \"\"\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost","more try to join, reject) max_tier_client_tags = 100 # For a given IP,","commitment messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) #","= 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is","= round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl =","per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names,","charge, to any person # obtaining a copy of this software and associated","if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return True","check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t,","skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting","then there is no favoured fuse. (since fill time is a float, this","ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N","didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients = [c","for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof,","overhead = 60 else: # the smallest fusion will use 1-byte varint for","# - how long from first connection to last possible Tor component submission?","coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission is hereby","client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob","= announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client","collector): with collector: # an in-place sort by source commitment idx removes ordering","if it has only been above min_clients for a short time (unless pool","== len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way as","Generate scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list =","remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2)","form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent at","6): client.error(\"Tag limit out of range\") ip = '' if tag.no_ip else client_ip","WaitingPool: \"\"\" a waiting pool for a specific tier \"\"\" def __init__(self, fill_threshold,","self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_ -= 1","num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers","We got a second submission before a new phase started. As # an","E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8,","as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from","try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False","point we could blame the originator, however # blockchain checks are somewhat subjective.","new round, call .reset(); to kill all connections, call .stop(). \"\"\" def __init__(self,","misconfigured?\") # This probably indicates misconfiguration since fusion server ought # to have","rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for","msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not","sort by source commitment idx removes ordering correlations about which client sent which","for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\")","cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send","session_hash)) # Sleep until end of covert signatures phase remtime = covert_T0 +","possible destinations list (all commitments, but leaving out the originating client's commitments). myindex","allow one submission per connection # per phase. client.error('multiple submission in same phase')","- how long from first connection to last possible Tor component submission? #","import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from","valid signature. This is not # allowed and we break the connection as","blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in","AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client):","OR OTHER DEALINGS IN THE # SOFTWARE. \"\"\" A basic server implementation for","mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best","# including without limitation the rights to use, copy, modify, merge, # publish,","return \"too many clients with same tag\" def _add_pool(self, client): self.pool.add(client) for t","relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N,","def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def reset(self):","really shouldn't happen, we had plenty of time raise FusionError(\"way too slow\") time.sleep(remtime)","= tx.txid() self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError","FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds running from server side. \"\"\"","i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg","- time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment list;","statuses, also check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max","ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci,","do we want before starting a fusion? min_clients = 8 # If all","round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to see","timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not","[sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for","self.results = [] self.fails = [] def __enter__(self, ): return self def __exit__(self,","at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <=","number of pool clients to trigger setting fill_time self.fill_time = None # when","if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True","else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in","notice and this permission notice shall be # included in all copies or","def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start","c.error(\"late commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for","got a second submission before a new phase started. As # an anti-spam","be put into fusion round if started at this tier self.queue = list()","msg.version != Protocol.VERSION: client.error(\"Mismatched protocol version, please upgrade\") if msg.genesis_hash: if msg.genesis_hash !=","mainnet, etc. client.error(\"This server is on a different chain, please switch servers\") else:","# (the \"Software\"), to deal in the Software without restriction, # including without","# # The above copyright notice and this permission notice shall be #","transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, =","collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector:","modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, #","# the smallest fusion could require 3-byte varint for either inputs or outputs","OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase","1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7,","[c for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for","to allow unlimited access client.tags = [] else: # Default tag: this IP","def __init__(self, config, network, bindhost, port, upnp = None, announcehost = None, donation_address","ClientTag(bytes): \"\"\" enhanced bytes object to represent a pool tag \"\"\" __slots__ =","client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names,","to move clients from queue into pool moved = [] for client in","for commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients: scalars","self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee =","somewhat subjective. It would be # appropriate to add some 'ban score' to","dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit =","in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in","== 'ping': continue if client.got_submit: # We got a second submission before a","before uploading commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS","signatures (which will have None at positions of missing signatures). - To reset","waiting pools on failure (on success, we are already removed; on stop we","fusion, wait this long before starting the next one (unless hit max time","covert signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache","lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients","list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True","FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE","server admin may run an SSL server proxy such as nginx for that","clients get a ClientThread made for them, and they are put into the","failed with exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but it was","# can start next round right away. collector.add(None) for idx, (client, proofs) in","timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got","__init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network","few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem detected: excess","# included in all copies or substantial portions of the Software. # #","pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component',",">= Params.max_clients: # pool filled up to the maximum size, so start immediately","a bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if","self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for t in","choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\")","Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag id too long\")","exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_,","del self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except","client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags = [] else:","- 12) // (num_components * 173) # Every round, clients leave ... How","as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server","submitted at wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input]","def remove(self, client): # make sure to call try_move_from_queue() after calling this try:","claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for","zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector #","Sleep until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS -","Choose the minimum excess fee based on dividing the overhead amongst players, in","ret = self.results del self.results return ret def add(self, result): with self.lock: try:","trying to broadcast transaction! misconfigured?\") # This probably indicates misconfiguration since fusion server","of this function might run for a while if many # checks against","outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as","the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping","-= 1 if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t]","tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start.","the players to # broadcast a malleated version by re-signing one of their","proofs): client.error(\"too-long proof\") # they should only be 129 bytes long. # generate","components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected:","who will be put into fusion round if started at this tier self.queue","{prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled","+ '41' assert tx.is_complete() txid = tx.txid() self.print_error(\"completed the transaction! \" + txid)",".validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values --","def clientjob_goodbye(client, text): # a gentler goodbye than killing if text is not","try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \"\"\" Scan pools","self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients:","min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How many clients","signature') if existing_sig: # We received a distinct valid signature. This is not","message to players; record the time we did this round_time = round(time.time()) collector","for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start,","t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -=","= '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate","if the client declares the genesis_hash, we # do indeed disallow them connecting","'ban score' to the player. # we aren't collecting any results, rather just","#!/usr/bin/env python3 # # Oregano - a lightweight Ergon client # CashFusion -","copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED","at the selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that","is hereby granted, free of charge, to any person # obtaining a copy","at wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey","tags being full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold","covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def","set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\")","True self.print_error(\"problem detected: too few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures =","# We got a second submission before a new phase started. As #","set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures =","the server at any time. By default, will bind to an ephemeral port.","Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes)","not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >=",">= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer):","else: # Default tag: this IP cannot be present in too many fuses.","# The above copyright notice and this permission notice shall be # included","WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN","was fine ({outpoint})\") # At this point we could blame the originator, however","64: raise ValidationError('signature length is wrong') # It might be we already have","fill_threshold self.tag_max = tag_max # how many clients can share same tag (in","in msg.tags: if len(tag.id) > 20: client.error(\"Tag id too long\") if not (0","i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail","(_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit","is None) # further, search for duplicated inputs (through matching the prevout and","Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers,","permit persons to whom the Software is furnished to do so, # subject","Software without restriction, # including without limitation the rights to use, copy, modify,","__slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b =","for clients waiting to start a fusion. New clients get a ClientThread made","self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in self.clients","): \"\"\" Scan pools for the favoured fuse: - Out of the pool(s)","where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients]","del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments,","in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client))","res = pool.check_add(client) if res is not None: client.error(res) for t in mytiers:","t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as","timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def","def end_components(self): with self.lock: ret = self.components del self.components return ret def start_signatures(self,","if sig is None) # further, search for duplicated inputs (through matching the","if min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion could","the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off","c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a","manage to give a good commitment. prev_client_count = len(self.clients) self.clients = [c for","third parties can't abuse us to find out the # timing of a","To reset the server for a new round, call .reset(); to kill all","pool moved = [] for client in self.queue: for t in client.tags: ts","a ClientThread made for them, and they are put into the waiting pools.","THE # SOFTWARE. \"\"\" A basic server implementation for CashFusion. Does not natively","note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in","transaction signature') if existing_sig: # We received a distinct valid signature. This is","= bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails =","for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx =","msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping':","begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds","c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for","started, the ClientThreads are passed over to a FusionController to run the rounds.\"\"\"","else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig","timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from","WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE.","raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config","substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\",","encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} /","sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib)","= validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\"got","(self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp","* num_components >= 2 * 0xfc: # the smallest fusion could require 3-byte","the rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost =","self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies =","error'. raise else: self.print_error(\"broadcast was successful!\") # Give our transaction a small head","are somewhat subjective. It would be # appropriate to add some 'ban score'","We allow a long timeout for clients to choose their pool. msg =","blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in","Scan pools for the favoured fuse: - Out of the pool(s) with the","OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION","covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the","(announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port","' + reason) continue except Exception as e: self.print_error(f\"player indicated bad input but","// (num_components * 173) # Every round, clients leave ... How many clients","and outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: #","\"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection,","= ft tier_best = t size_best = size if time_best is None: self.tier_best_starttime","server implementation for CashFusion. Does not natively offer SSL support, however a server","def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg,","result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) +","= False def end_components(self): with self.lock: ret = self.components del self.components return ret","def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by source","how many players can they represent in the same fuse? ip_max_simul_fuse = 3","pool.fill_time if ft is None: continue size = len(pool.pool) if size >= size_best:","__init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \"\"\" a waiting pool","self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0]","the possible destinations list (all commitments, but leaving out the originating client's commitments).","results, rather just marking that # 'checking finished' so that if all blames","pools for the favoured fuse: - Out of the pool(s) with the most","commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client","have reasonable # privacy with what we have. bad_components = set() ### if","high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong')","for that purpose. \"\"\" import secrets import sys import threading import time import","in live: c.kill(\"too few remaining live players\") raise FusionError(\"too few remaining live players\")","len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\" This controls the","all the other components were # just imposters who didn't have private key.","malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures","num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes","sublicense, and/or sell copies of the Software, # and to permit persons to","def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts is","t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self,","blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue","# Resistor \"E series\" values -- round numbers that are almost geometrically uniform","as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if","bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)):","round, clients leave ... How many clients do we need as an absolute","Every round, clients leave ... How many clients do we need as an","list, but remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj)","+ Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full","msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\"","away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector)","If more than one # signed, then it's malicious behaviour! if sum((signatures[i] is","a copy of this software and associated documentation files # (the \"Software\"), to","of covert components phase, call start_components. - To signal the end of covert","is no privacy leak by the ommission. continue assert ret, 'expecting input component'","the adding order so that if filling more than one pool, we don't","call .reset(); to kill all connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0,","= Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live =","= all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except","self.tag_max = tag_max # how many clients can share same tag (in pool","def check_client_count(self,): live = [c for c in self.clients if not c.dead] if","= msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\") proofs =","see if we even have reasonable # privacy with what we have. bad_components","checks to see whether we should just skip the # signing phase and","do we need as an absolute minimum (for privacy)? min_safe_clients = 6 #","< Params.min_safe_clients: for c in live: c.kill(\"too few remaining live players\") raise FusionError(\"too","def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool +=","for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!')","prev_client_count = len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count()","as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port =","= [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0])","s is None]) ### self.print_error(f\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs","version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures =","msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\"","None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None)))","pool and queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t)","to players; this # will form the basis of our covert timeline. covert_T0","None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey =","'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline -","1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool =","doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0:","honest guy and all the other components were # just imposters who didn't","0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp","# make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError:","successful!\") # Give our transaction a small head start in relaying, before sharing","bad input but it was fine ({outpoint})\") # At this point we could","return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool',","the pool with the earliest fill time; - If no pools are filled","self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars =","players to # broadcast a malleated version by re-signing one of their inputs.","to kill all connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp =","__slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class","= upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address =","info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned","self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self,","send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self,","as a result. # Note that we could have aborted earlier but this","clients submitted largest possible component (uncompressed p2pkh input), how many could we take","5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components >= 2","min_safe_clients # How many clients can share same tag on a given tier","config, network, bindhost, port, upnp = None, announcehost = None, donation_address = None):","\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified","for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if","as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction!","outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) //","will start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: #","client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i,","dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients,","\"\"\" with self.lock: time_best = None tier_best = None size_best = 0 for","fine ({outpoint})\") # At this point we could blame the originator, however #","offer SSL support, however a server admin may run an SSL server proxy","the end of covert signatures phase, owner calls end_signatures, which returns a list","record the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail","= upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate","seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\") proofs","for both inputs and outputs lists overhead = 62 elif min_safe_clients * num_components","enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT","delivery, # but we don't allow it to consume our CPU power. if","Software, # and to permit persons to whom the Software is furnished to","client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) !=","reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self,","raise else: self.print_error(\"broadcast was successful!\") # Give our transaction a small head start","= 300000 # sats tiers = [round(b*s) for b in [10000, 100000, 1000000,","has only been above min_clients for a short time (unless pool is full).","(uncompressed p2pkh input), how many could we take until the result would exceed","self.lock: ret = self.results del self.results return ret def add(self, result): with self.lock:","Send start message to players; record the time we did this round_time =","covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert","= [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients)","= set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub","i, inp in enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders)","fine # since it might be a resubmission after ack failed delivery, #","owner calls start_signatures. - To signal the end of covert signatures phase, owner","# gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c","open without activity. # note this needs to consider the maximum interval between","self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs","ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client)","leave ... How many clients do we need as an absolute minimum (for","possible Tor component submission? # - how long from one round's component submission",") if min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion","queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts","if client.got_submit: # We got a second submission before a new phase started.","# add this client to waiting pools for pool in mytierpools.values(): res =","for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME)","# Record the time that we sent 'startround' message to players; this #","del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes del","enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures","import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import","[1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2,","100 # For a given IP, how many players can they represent in","and go directly to blame, or maybe even restart / end # without","# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT","annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started","ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash,","())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic()","if size >= size_best: if time_best is None or ft < time_best or","fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if","self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost","oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException","# Upload the full commitment list; we're a bit generous with the timeout","Server for covert submissions. How it works: - Launch the server at any","6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class Params: num_components","in msg.tiers} except KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers","to same proof') # Note, the rest of this function might run for","with the earliest fill time; - If no pools are filled then there","(these overhead numbers assume op_return script size of 1 + 5 (lokad) +","deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return","# # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,","wait this long before starting the next one (unless hit max time or","\"\"\" Immediately launch Fusion at the selected tier. \"\"\" with self.lock: chosen_clients =","client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too","client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20:","as e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e}","= list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort the components &","the maximum size, so start immediately self.start_fuse(t) return # we have added to","following to: Remove from spawned clients list, so that the fusion can continue","component_master_list # Do some preliminary checks to see whether we should just skip","(lokad) + 33 (session hash) ) if min_safe_clients * num_components >= 2 *","the player. # we aren't collecting any results, rather just marking that #","in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate =","Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as a placeholder,","except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could not broadcast","- tfill_thresh if t == self.tier_best: # this is the favoured pool, can","a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail =","claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a bad proof (for {src_commitment_idx}): {ret}\")","of this software and associated documentation files # (the \"Software\"), to deal in","Do some preliminary checks to see whether we should just skip the #","check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if","raise ValidationError('signature length is wrong') # It might be we already have this","after starting any fusion, wait this long before starting the next one (unless","= time.monotonic() self.print_error(f\"startround sent at {time.time()}; accepting covert components\") # Await commitment messages","c in self.clients: c.kill('blame yourself!') return # scan the commitment list and note","components phase, call start_components. - To signal the end of covert components phase,","is not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if","4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make","in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags:","proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() +","detected: too few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\"problem","try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature","\"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)}","self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self,","trigger setting fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max","time. # Since nothing after this point can report back to the #","min_clients = 8 # If all clients submitted largest possible component (uncompressed p2pkh","2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2,","= size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best","us that a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'):","proof sharing thing doesn't even make sense with one player. for c in","bad proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead:","one player. for c in self.clients: c.kill('blame yourself!') return # scan the commitment","an advanced coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission","client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except","c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments, as clients","max time or pool is full). start_time_spacing = 120 # But don't start","size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft","making us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many","any time. By default, will bind to an ephemeral port. - Before start","= time.monotonic() # when the last fuse happened; as a placeholder, set this","who didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients =","= False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few","the Fusion rounds running from server side. \"\"\" def __init__(self, network, tier, clients,","the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components =","ClientThreads are passed over to a FusionController to run the rounds.\"\"\" def __init__(self,","order so that if filling more than one pool, we don't have bias","+ len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c,","time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort the","This makes it slightly harder for one of the players to # broadcast","x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) #","signature acceptance\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache =","__init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be put into","blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note,","to destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays in","This is fine # since it might be a resubmission after ack failed","commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\"wrong number of proofs\") if","traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c","ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT,","msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment =","clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime","phase and go directly to blame, or maybe even restart / end #","# obtaining a copy of this software and associated documentation files # (the","for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret =","b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg,","e: self.print_error(f\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\") else:","commitment_master_list) # Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e)","min_safe_clients = 6 # Choose the minimum excess fee based on dividing the","need to be done, perhaps even still # running after run_round has exited.","if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate","collector: # an in-place sort by source commitment idx removes ordering correlations about","client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg =","a good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _","How many clients can share same tag on a given tier (if more","start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for","if any(len(p) > 200 for p in proofs): client.error(\"too-long proof\") # they should","False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True:","(for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If","than killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic","end of covert signatures phase, owner calls end_signatures, which returns a list of","remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif","ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0: #","pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg,","if src_client.dead: # If the blamed client is already dead, don't waste more","immediately since client may be trying to DoS us by # making us","a placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run() finally:","pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size,","could require 3-byte varint for either inputs or outputs lists overhead = 60","self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del","# but we don't allow it to consume our CPU power. if sig","existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise","size_best: time_best = ft tier_best = t size_best = size if time_best is","= annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash =","= False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R()","long before starting the next one (unless hit max time or pool is","# we have added to pools, which may have changed the favoured tier","enumerate(tx.inputs()): prevout_spenders[f\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue","0 self.all_ = 0 class WaitingPool: \"\"\" a waiting pool for a specific","DoS us by # making us check many inputs against blockchain. if len(msg.blames)","to DoS us by # making us check many inputs against blockchain. if","filled up to the maximum size, so start immediately self.start_fuse(t) return # we","point to same proof') # Note, the rest of this function might run",".comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util","pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info","self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError:","proofs, collector): with collector: # an in-place sort by source commitment idx removes","IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx]","Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address","= min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime","try: self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool) <","collect statuses, also check start times. statuses = dict() tfill_thresh = tnow -","pool.check_add(client) if res is not None: client.error(res) for t in mytiers: pool =","for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair()","t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size","dead clients self.clients = [c for c in self.clients if not c.dead] self.check_client_count()","sent 'startround' message to players; this # will form the basis of our","component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address",">= self.tag_max: return \"too many clients with same tag\" def _add_pool(self, client): self.pool.add(client)","for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start","conditions: # # The above copyright notice and this permission notice shall be","myindex, proofs, collector): with collector: # an in-place sort by source commitment idx","sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes","= self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg =","This probably indicates misconfiguration since fusion server ought # to have a good","__init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey","= round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector:","phase. bad components: {bad_components}\") if len(self.clients) < 2: # Sanity check for testing","i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late","(dropped {prev_client_count - len(self.clients)})\") total_excess_fees = sum(f for _,_,f in results) # Generate","At this point we could blame the originator, however # blockchain checks are","maybe even restart / end # without sharing components. skip_signatures = False if","in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list","this point can report back to the # verifier, there is no privacy","or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS","tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t","the proofs according to destination. proofs_to_relay = [list() for _ in self.clients] for","= None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server):","scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list = [(commit,","min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime !=","a fusion if the pool has stayed at or above min_clients for this","error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object","min_safe_clients - 1) // min_safe_clients # How many clients can share same tag","pool, we don't have bias towards any particular tier with self.lock: if self.stopping:","not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool)","'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: #","_ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i","most number of players, - Choose the pool with the earliest fill time;","c.kill(\"too few remaining live players\") raise FusionError(\"too few remaining live players\") def run","\"\"\"Server for clients waiting to start a fusion. New clients get a ClientThread","if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client)","one of the players to # broadcast a malleated version by re-signing one","[] else: # Default tag: this IP cannot be present in too many","skip_signatures = True self.print_error(\"problem detected: too few components submitted\") if total_excess_fees != sum(component_contribs):","basic server implementation for CashFusion. Does not natively offer SSL support, however a","of charge, to any person # obtaining a copy of this software and","is a float, this will almost always be unique) \"\"\" with self.lock: time_best","a while if many # checks against blockchain need to be done, perhaps","other components were # just imposters who didn't have private key. If more","ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: # an","this function might run for a while if many # checks against blockchain","clients with same tag\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts","= session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs","results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients","[bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) #","msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') # It might","to stay open without activity. # note this needs to consider the maximum","self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure to call","Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot","person # obtaining a copy of this software and associated documentation files #","assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys","that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client =","10000000, 100000000] for s in E12] # How many clients do we want","size_best = size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime =","FusionError(\"too few remaining live players\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)}","+ Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good commitment.","import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg,","for s in signatures if s is None]) ### self.print_error(f\"ending covert signature acceptance.","with one player. for c in self.clients: c.kill('blame yourself!') return # scan the","in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul:","have a good connection to the EC server. Report this back to clients","submissions. How it works: - Launch the server at any time. By default,","ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid signature. This","<reponame>MrNaif2018/Oregano #!/usr/bin/env python3 # # Oregano - a lightweight Ergon client # CashFusion","originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj","contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del","that purpose. \"\"\" import secrets import sys import threading import time import traceback","threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return self def","etc. client.error(\"This server is on a different chain, please switch servers\") else: client.print_error(\"👀","src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def","/ {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret","not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config","run for a while if many # checks against blockchain need to be","number of players, - Choose the pool with the earliest fill time; -","b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\"Tag id too","exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for","advanced coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission is","exceed fill_threshold self.tag_max = tag_max # how many clients can share same tag","contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except","signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except","it's malicious behaviour! if sum((signatures[i] is not None) for i in spenders) !=","components\") # Await commitment messages then process results results = collector.gather(deadline = covert_T0","we will start. for c in chosen_clients: c.start_ev.set() # Remove those clients from","the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF","IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)]","def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev","= gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate)","then it's malicious behaviour! if sum((signatures[i] is not None) for i in spenders)","one blame per proof is malicious. Boot client # immediately since client may","round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False,","if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature')","{inp['pubkeys'][0]}\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of","for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] =","tx.txid() self.print_error(\"completed the transaction! \" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as","so that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) #","dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout =","pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags)","after this point can report back to the # verifier, there is no","shuffle the adding order so that if filling more than one pool, we","Sanity check for testing -- the proof sharing thing doesn't even make sense","time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) !=","if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \"\"\"","0 class WaitingPool: \"\"\" a waiting pool for a specific tier \"\"\" def","self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def","with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c","new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping',","float, this will almost always be unique) \"\"\" with self.lock: time_best = None","del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs","running after run_round has exited. For this reason we try to # not","check for testing -- the proof sharing thing doesn't even make sense with","client.error(\"Tag limit out of range\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip,","1 if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return","+ Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \"timings set up incorrectly\" time.sleep(remtime)","c.start_ev.set() # Remove those clients from all pools for t, pool in self.waiting_pools.items():","- time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret","of missing signatures). - To reset the server for a new round, call","= 120 # But don't start a fusion if it has only been","c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients,","tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not","client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey","1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp,","# Copyright (C) 2020 <NAME> # # Permission is hereby granted, free of","except KeyError: if self.stopping: return client.error(f\"Invalid tier selected: {t}\") try: mytiers = list(mytierpools)","sell copies of the Software, # and to permit persons to whom the","fuse: - Out of the pool(s) with the most number of players, -","in relaying, before sharing the # signatures. This makes it slightly harder for","deal in the Software without restriction, # including without limitation the rights to","ValidationError('signature length is wrong') # It might be we already have this signature.","# really shouldn't happen, we had plenty of time raise FusionError(\"way too slow\")","commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components,","fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try:","of the inputs is signed, we don't punish him # because he's the","SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR","timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime = covert_T0 +","Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse","pools on failure (on success, we are already removed; on stop we don't","covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\") # mark all","{repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but it was fine ({outpoint})\") #","copies of the Software, # and to permit persons to whom the Software","# generate the possible destinations list (all commitments, but leaving out the originating","spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\"multi-spend of f{prevout} detected\") #","None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies","_co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message","or pool is full). start_time_spacing = 120 # But don't start a fusion","clients self.clients = [c for c in self.clients if not c.dead] self.check_client_count() if","submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5)","bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) #","such as nginx for that purpose. \"\"\" import secrets import sys import threading","IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR","): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None:","COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER","b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def","True def try_move_from_queue(self): # attempt to move clients from queue into pool moved","schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp =","anonymizer # # Copyright (C) 2020 <NAME> # # Permission is hereby granted,","into pool moved = [] for client in self.queue: for t in client.tags:","with error: {e}\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c","self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime","in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end","None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost =","submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg,","# # Copyright (C) 2020 <NAME> # # Permission is hereby granted, free","the Software without restriction, # including without limitation the rights to use, copy,","of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY","Before start of covert components phase, call start_components. - To signal the end","pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to","= self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time =","def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b","> 0: time.sleep(remtime) # Upload the full commitment list; we're a bit generous","sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid =","1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6,","as a placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run()","always be unique) \"\"\" with self.lock: time_best = None tier_best = None size_best","component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do","raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid signature.","def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool","No genesis hash declared by client, we'll let them slide...\") if self.stopping: return","OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO","client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for","= [] for client in self.queue: for t in client.tags: ts = self.tags[t]","# repeatedly run rounds until successful or exception while True: covert_server.reset() # Clean","recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256,","FusionError(\"way too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in","too slow\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures","to clients # as an 'internal server error'. raise else: self.print_error(\"broadcast was successful!\")","components were # just imposters who didn't have private key. If more than","t, pool in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players","above min_clients for a short time (unless pool is full). start_time_min = 400","only allow one submission per connection # per phase. client.error('multiple submission in same","of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime","all missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if","series\" values -- round numbers that are almost geometrically uniform E6 = [1.0,","added to pools, which may have changed the favoured tier self.reset_timer() inftime =","{len(component_master_list)} received.\") # Sort the components & contribs list, then separate it out.","client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout =","check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component","import secrets import sys import threading import time import traceback from collections import","various tags self.fill_threshold = fill_threshold # minimum number of pool clients to trigger","mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling","= Params.min_clients) remtime = inftime if pool.fill_time is not None: # a non-favoured","self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too","specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who","for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update","client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time =","AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature' try: sighash","self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() #","go directly to blame, or maybe even restart / end # without sharing","don't allow it to consume our CPU power. if sig != existing_sig: if","this back to clients # as an 'internal server error'. raise else: self.print_error(\"broadcast","- tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining","for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool","self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool:","on failure (on success, we are already removed; on stop we don't care.)","pool for a specific tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set()","ft = pool.fill_time if ft is None: continue size = len(pool.pool) if size","client.random_number_commitment: client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components:","+ len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime =","self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse =","8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4,","a given IP, how many players can they represent in the same fuse?","list; we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments =","from spawned clients list, so that the fusion can continue independently of waiting","len(tag.id) > 20: client.error(\"Tag id too long\") if not (0 < tag.limit <","in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to start a fusion.","a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if t","acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\") tx,","oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2","a pool tag \"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb","for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert","3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO -","a fusion? min_clients = 8 # If all clients submitted largest possible component","client from waiting pools on failure (on success, we are already removed; on",". import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash","FusionError as e: self.print_error(f\"Ended with error: {e}\") except Exception as e: self.print_error('Failed with","is (+- amount - fee). - Before start of covert signatures phase, owner","Tor component submission? # - how long from one round's component submission to","raise class ResultsCollector: # Collect submissions from different sources, with a deadline. def","timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced","# an in-place sort by source commitment idx removes ordering correlations about which","(0 < tag.limit < 6): client.error(\"Tag limit out of range\") ip = ''","which may have changed the favoured tier self.reset_timer() inftime = float('inf') while True:","upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network self.tier = tier","full). start_time_spacing = 120 # But don't start a fusion if it has","maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return","time.monotonic() # when the last fuse happened; as a placeholder, set this to","import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError,","= donation_address )) # We allow a long timeout for clients to choose","): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp =","we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def","calculate the randomly chosen destinations, same way as client did. relays = []","blamed client is already dead, don't waste more time. # Since nothing after","self.print_error(f\"blaming[{src_commitment_idx}] for bad input: {reason}\") src_client.kill('you provided a bad input: ' + reason)","client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit:","not None: # a non-favoured pool will start eventually remtime = pool.fill_time -","ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length","IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR","starting a fusion? min_clients = 8 # If all clients submitted largest possible","if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags = []","Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time())","# For a given IP, how many players can they represent in the","\"\"\" A basic server implementation for CashFusion. Does not natively offer SSL support,","= i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector:","purpose. \"\"\" import secrets import sys import threading import time import traceback from","long from first connection to last possible Tor component submission? # - how","and this permission notice shall be # included in all copies or substantial","# Guaranteed time to launch a fusion if the pool has stayed at","min_clients for a short time (unless pool is full). start_time_min = 400 #","for t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) ==","tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\") # Event for signalling us","kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason)","that if filling more than one pool, we don't have bias towards any","set this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() #","[c for c, _, _ in results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients","for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in","overhead numbers assume op_return script size of 1 + 5 (lokad) + 33","how long from one round's component submission to the next round's component submission?","Params.min_safe_clients: for c in live: c.kill(\"too few remaining live players\") raise FusionError(\"too few","can share same tag on a given tier (if more try to join,","= len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if","= collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to","against blockchain need to be done, perhaps even still # running after run_round","= self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg,","self.tag_max: return \"too many clients with same tag\" def _add_pool(self, client): self.pool.add(client) for","round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time )) msg","if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later c.blind_sig_requests =","signatures). - To reset the server for a new round, call .reset(); to","= False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret","= random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client,","\"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT","self.fill_time = time.monotonic() def add(self, client): can_pool = True for t in client.tags:","to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No","return True class FusionController(threading.Thread, PrintError): \"\"\" This controls the Fusion rounds running from","\"\"\" Server for covert submissions. How it works: - Launch the server at","else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for t","start immediately self.start_fuse(t) return # we have added to pools, which may have","True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys)","present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in","- an advanced coin anonymizer # # Copyright (C) 2020 <NAME> # #","annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while","the result would exceed 100 kB standard tx size limitation? max_clients = (100000","if time_best is None or ft < time_best or size > size_best: time_best","self.tier_best: # this is the favoured pool, can start at a special time","self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class","proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is","send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg),","defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold # minimum number","of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion =","excess fee based on dividing the overhead amongst players, in the smallest fusion","given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] =","= annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @","to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c","# running after run_round has exited. For this reason we try to #","the # signing phase and go directly to blame, or maybe even restart","scan through tiers and collect statuses, also check start times. statuses = dict()","but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert","successful or exception while True: covert_server.reset() # Clean up dead clients self.clients =","have this signature. This is fine # since it might be a resubmission","signing phase and go directly to blame, or maybe even restart / end","the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion","commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list","A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR","size of 1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients","those clients from all pools for t, pool in self.waiting_pools.items(): for c in","= set(i for i,sig in enumerate(signatures) if sig is None) # further, search","don't waste more time. # Since nothing after this point can report back","= tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not in","None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing)","round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random","collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs","by client, we'll let them slide...\") if self.stopping: return donation_address = '' if","not reference self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx,","upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate =","except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64:","is None or ft < time_best or size > size_best: time_best = ft","appropriate to add some 'ban score' to the player. # we aren't collecting","however a server admin may run an SSL server proxy such as nginx","clients from all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients:","covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time","NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE","type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port,","even have reasonable # privacy with what we have. bad_components = set() ###","remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really","Guaranteed time to launch a fusion if the pool has stayed at or","this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return","a second submission before a new phase started. As # an anti-spam measure","by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures))","ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class","if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else:","server ought # to have a good connection to the EC server. Report","transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to broadcast transaction! misconfigured?\") #","4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these","Now, repackage the proofs according to destination. proofs_to_relay = [list() for _ in","# Inter-fusion delay -- after starting any fusion, wait this long before starting","self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool)","from all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c)","self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\"Ended with error: {e}\") except Exception as","ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments =","proof (for {src_commitment_idx}): {ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: #","400 # whether to print a lot of logs noisy = False #","OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE","and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1)","except: # Remove client from waiting pools on failure (on success, we are","time import traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address","is not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i]","src_client.dead: # If the blamed client is already dead, don't waste more time.","break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for","sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self):","= client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\"late proofs\") for","c.got_submit = False def end_components(self): with self.lock: ret = self.components del self.components return","Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen, we had","missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig","run an SSL server proxy such as nginx for that purpose. \"\"\" import","for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self,","donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee,","ordering correlations about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs","client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res","Params.max_clients: # pool filled up to the maximum size, so start immediately self.start_fuse(t)","second submission before a new phase started. As # an anti-spam measure we","after calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except","self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok()","bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg,","self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False","c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients:","implementation for CashFusion. Does not natively offer SSL support, however a server admin","= pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock:","if res is not None: client.error(res) for t in mytiers: pool = mytierpools[t]","fusion if the pool has stayed at or above min_clients for this long.","destinations list (all commitments, but leaving out the originating client's commitments). myindex =","== 0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many tags\") # Event","SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES","timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live","in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a","recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg,","= [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8,","at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if","# attempt to move clients from queue into pool moved = [] for","(commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail =","= self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes)","max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \"\"\"","signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0:","(in pool and queue) def check_add(self, client): for t in client.tags: ts =","adding order so that if filling more than one pool, we don't have","whitelisted to allow unlimited access client.tags = [] else: # Default tag: this","{t}\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that","covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature","covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients:","Resistor \"E series\" values -- round numbers that are almost geometrically uniform E6","care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best","< tag.limit < 6): client.error(\"Tag limit out of range\") ip = '' if","the client declares the genesis_hash, we # do indeed disallow them connecting if","raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to represent a","# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE","of pool clients to trigger setting fill_time self.fill_time = None # when did","into the waiting pools. Once a Fusion thread is started, the ClientThreads are","pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is","associated documentation files # (the \"Software\"), to deal in the Software without restriction,","if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used","feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self):","len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None: #","(for privacy)? min_safe_clients = 6 # Choose the minimum excess fee based on","is full). start_time_spacing = 120 # But don't start a fusion if it","self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients,","require 3-byte varint for either inputs or outputs lists overhead = 60 else:","fusion round if started at this tier self.queue = list() # clients who","= None tier_best = None size_best = 0 for t, pool in self.waiting_pools.items():","time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s","calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to","self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg)","'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \"\"\" a","OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR","if len(self.clients) < 2: # Sanity check for testing -- the proof sharing","are waiting due to tags being full self.tags = defaultdict(TagStatus) # how are","dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if client","originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results)","obtaining a copy of this software and associated documentation files # (the \"Software\"),","self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting covert","self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays:","port, upnp = None, announcehost = None, donation_address = None): assert network assert","started. As # an anti-spam measure we only allow one submission per connection","be unique) \"\"\" with self.lock: time_best = None tier_best = None size_best =","DEALINGS IN THE # SOFTWARE. \"\"\" A basic server implementation for CashFusion. Does","# Every round, clients leave ... How many clients do we need as","self.stopping: return # add this client to waiting pools for pool in mytierpools.values():","t == self.tier_best: # this is the favoured pool, can start at a","allowed to stay open without activity. # note this needs to consider the","SOFTWARE. \"\"\" A basic server implementation for CashFusion. Does not natively offer SSL","ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb +","traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address","# TODO - make these configurable class Params: num_components = 23 component_feerate =","tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of","e.args server_msg = e.server_msg self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed","is None: continue size = len(pool.pool) if size >= size_best: if time_best is","Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg,","tags\") # Event for signalling us that a pool started. start_ev = threading.Event()","may have changed the favoured tier self.reset_timer() inftime = float('inf') while True: with","How many clients do we want before starting a fusion? min_clients = 8","bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey =","round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes =","# generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for","to launch a fusion if the pool has stayed at or above min_clients","make these configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB","blames point to same proof') # Note, the rest of this function might","based on dividing the overhead amongst players, in the smallest fusion # (these","the connection as a result. # Note that we could have aborted earlier","we even have reasonable # privacy with what we have. bad_components = set()","the inputs is signed, we don't punish him # because he's the honest","idx removes ordering correlations about which client sent which proof proofs.sort(key = lambda","long\") if not (0 < tag.limit < 6): client.error(\"Tag limit out of range\")","interval between messages: # - how long from first connection to last possible","i,sig in enumerate(signatures) if sig is None) # further, search for duplicated inputs","= msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time that","size = len(pool.pool) if size >= size_best: if time_best is None or ft","= Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow a","None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start()","malicious behaviour! if sum((signatures[i] is not None) for i in spenders) != 1:","client in moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to start a","c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) #","can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client):","from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from","!= get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it #","signatures phase, owner calls start_signatures. - To signal the end of covert signatures","client.error('multiple blames point to same proof') # Note, the rest of this function","or ft < time_best or size > size_best: time_best = ft tier_best =","to see whether we should just skip the # signing phase and go","lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs]))","tier \"\"\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will","0: time.sleep(remtime) # Upload the full commitment list; we're a bit generous with","just skip the # signing phase and go directly to blame, or maybe","# subject to the following conditions: # # The above copyright notice and","tier (if more try to join, reject) max_tier_client_tags = 100 # For a","start message to players; record the time we did this round_time = round(time.time())","for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0]","# We allow a long timeout for clients to choose their pool. msg","server is on a different chain, please switch servers\") else: client.print_error(\"👀 No genesis","start in relaying, before sharing the # signatures. This makes it slightly harder","self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients) < 2: # Sanity check","# Sanity check for testing -- the proof sharing thing doesn't even make","client is already dead, don't waste more time. # Since nothing after this","all blames are checked, we # can start next round right away. collector.add(None)","CovertServer(GenericServer): \"\"\" Server for covert submissions. How it works: - Launch the server","ack failed delivery, # but we don't allow it to consume our CPU","it has only been above min_clients for a short time (unless pool is","start_time_max = 1200 # Inter-fusion delay -- after starting any fusion, wait this","(Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost,","sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs:","1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9,","restart / end # without sharing components. skip_signatures = False if len(all_components) !=","valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at","pool tag \"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb =","with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop()","None) # further, search for duplicated inputs (through matching the prevout and claimed","ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return True def","we have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components","send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError,","set() # clients who will be put into fusion round if started at","= pool.check_add(client) if res is not None: client.error(res) for t in mytiers: pool","if many # checks against blockchain need to be done, perhaps even still","if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs if","_ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob,","msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\") proofs = msg.encrypted_proofs","tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients:","continue if src_client.dead: # If the blamed client is already dead, don't waste","pool clients to trigger setting fill_time self.fill_time = None # when did pool","def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is","try to join, reject) max_tier_client_tags = 100 # For a given IP, how","This is not # allowed and we break the connection as a result.","KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool =","= time.monotonic() def add(self, client): can_pool = True for t in client.tags: ts","upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional","for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components","sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError,","c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey","def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \"\"\" Server for","do indeed disallow them connecting if they are e.g. on testnet # and","him # because he's the honest guy and all the other components were","tier_best = None size_best = 0 for t, pool in self.waiting_pools.items(): ft =","who didn't have private key. If more than one # signed, then it's","if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components","self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally:","idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline","Choose the pool with the earliest fill time; - If no pools are","skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too","did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c,","privacy)? min_safe_clients = 6 # Choose the minimum excess fee based on dividing","a different chain, please switch servers\") else: client.print_error(\"👀 No genesis hash declared by","time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False)","t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold:","a float, this will almost always be unique) \"\"\" with self.lock: time_best =","pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool, so it","validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values -- round numbers that are","with what we have. bad_components = set() ### if skip_signatures: self.print_error(\"skipping covert signature","at positions of missing signatures). - To reset the server for a new","covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds =","to find out the # timing of a given input's signature submission. raise","33 (session hash) ) if min_safe_clients * num_components >= 2 * 0xfc: #","client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) #","= client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) > 5:","pool filled up to the maximum size, so start immediately self.start_fuse(t) return #","add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails)","tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool:","clients who are waiting due to tags being full self.tags = defaultdict(TagStatus) #","= covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a","2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2,","client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in","to broadcast transaction! misconfigured?\") # This probably indicates misconfiguration since fusion server ought","assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise","mark all missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures)","kB standard tx size limitation? max_clients = (100000 - 12) // (num_components *","PrintError): \"\"\" This controls the Fusion rounds running from server side. \"\"\" def","= [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some","= donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse =","= tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in","add some 'ban score' to the player. # we aren't collecting any results,",".reset(); to kill all connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp","we don't allow it to consume our CPU power. if sig != existing_sig:","connection as a result. # Note that we could have aborted earlier but","for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret =","self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\")","[(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex] N =","{ret}\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed","= [] else: # Default tag: this IP cannot be present in too","set(i for i,sig in enumerate(signatures) if sig is None) # further, search for","marking that # 'checking finished' so that if all blames are checked, we","in-place sort by source commitment idx removes ordering correlations about which client sent","commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for","2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3,","a Fusion thread is started, the ClientThreads are passed over to a FusionController","is not # allowed and we break the connection as a result. #","matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in","even still # running after run_round has exited. For this reason we try","collecting any results, rather just marking that # 'checking finished' so that if","Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner()","list(covert_server.end_components().items()) self.print_error(f\"ending covert component acceptance. {len(component_master_list)} received.\") # Sort the components & contribs","slide...\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string()","timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if len(msg.tags) > 5: client.error(\"Too many","since fusion server ought # to have a good connection to the EC","import traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import","just marking that # 'checking finished' so that if all blames are checked,","schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a","ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE","c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time","starting any fusion, wait this long before starting the next one (unless hit","tag on a given tier (if more try to join, reject) max_tier_client_tags =","component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\"late commitment\") # record for later","killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread","ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool","# If exactly one of the inputs is signed, we don't punish him","len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments,","exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results',","while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype","existing_sig: # We received a distinct valid signature. This is not # allowed","remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t]","we take until the result would exceed 100 kB standard tx size limitation?","is not None: # a non-favoured pool will start eventually remtime = pool.fill_time","Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len","tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\"FusionController\") self.network = network","client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if text","all_components, session_hash = session_hash)) # Sleep until end of covert signatures phase remtime","this reason we try to # not reference self.<variables> that may change. for","ft is None: continue size = len(pool.pool) if size >= size_best: if time_best","moved: self.queue.remove(client) class FusionServer(GenericServer): \"\"\"Server for clients waiting to start a fusion. New","tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for","self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server","mtype == 'ping': continue if client.got_submit: # We got a second submission before","can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure to","True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts","client.error(\"This server is on a different chain, please switch servers\") else: client.print_error(\"👀 No","client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in","58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How many","self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0:","from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \"E series\" values","If the blamed client is already dead, don't waste more time. # Since","phase started. As # an anti-spam measure we only allow one submission per","good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _ in","blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time )) msg =","tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))]","phase, call start_components. - To signal the end of covert components phase, owner","self.print_error(\"problem detected: too few components submitted\") if total_excess_fees != sum(component_contribs): skip_signatures = True","can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion.","import Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as","time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay =","collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg =","= collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to","the smallest fusion could require 3-byte varint for both inputs and outputs lists","to give a good commitment. prev_client_count = len(self.clients) self.clients = [c for c,","out the # timing of a given input's signature submission. raise ValidationError('conflicting valid","(C) 2020 <NAME> # # Permission is hereby granted, free of charge, to","will be put into fusion round if started at this tier self.queue =","TimeoutException from . import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread,","this # way third parties can't abuse us to find out the #","... How many clients do we need as an absolute minimum (for privacy)?","tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError:","None: # a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh","reason we try to # not reference self.<variables> that may change. for blame","at this tier self.queue = list() # clients who are waiting due to","+= 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else:","startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def","min_safe_clients * num_components >= 0xfc: # the smallest fusion could require 3-byte varint","> len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames):","del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True:","* 173) # Every round, clients leave ... How many clients do we","feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time')","might be a resubmission after ack failed delivery, # but we don't allow","!= len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\"problem detected: too few components submitted\") if total_excess_fees","outputs to see if we even have reasonable # privacy with what we","result would exceed 100 kB standard tx size limitation? max_clients = (100000 -","pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes =","# when did pool exceed fill_threshold self.tag_max = tag_max # how many clients","is whitelisted to allow unlimited access client.tags = [] else: # Default tag:","guy and all the other components were # just imposters who didn't have","until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic()","round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to see if","1) // min_safe_clients # How many clients can share same tag on a","list (all commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client)","rather just marking that # 'checking finished' so that if all blames are","t in client.tags: ts = self.tags.get(t) if ts is not None and ts.all_","to a FusionController to run the rounds.\"\"\" def __init__(self, config, network, bindhost, port,","ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\"blaming[{src_commitment_idx}] for bad input:","gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in","list of signatures (which will have None at positions of missing signatures). -",">= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0.,","def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION:","FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF","= [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash))","assume op_return script size of 1 + 5 (lokad) + 33 (session hash)","= self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def","can start at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if","= self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ ==","expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment')","self.pool = 0 self.all_ = 0 class WaitingPool: \"\"\" a waiting pool for","who are waiting due to tags being full self.tags = defaultdict(TagStatus) # how","smallest fusion will use 1-byte varint for both inputs and outputs lists overhead","- 1) // min_safe_clients # How many clients can share same tag on","gc clean def reset_timer(self, ): \"\"\" Scan pools for the favoured fuse: -","signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase.","3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6,","len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple","Exception as e: self.print_error(f\"player indicated bad input but checking failed with exception {repr(e)}","sent at {time.time()}; accepting covert components\") # Await commitment messages then process results","best pool, so it might not be best anymore. self.reset_timer() raise class ResultsCollector:","is full). start_time_min = 400 # whether to print a lot of logs","to represent a pool tag \"\"\" __slots__ = () def __new__(cls, ipstr, tagbytes,","{msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to represent a pool tag \"\"\"","we # do indeed disallow them connecting if they are e.g. on testnet","clients can share same tag on a given tier (if more try to","pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size =","self.start_fuse(t) return # we have added to pools, which may have changed the","detected\") # If exactly one of the inputs is signed, we don't punish","queue into pool moved = [] for client in self.queue: for t in","resubmission after ack failed delivery, # but we don't allow it to consume","= threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow","fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost)","have aborted earlier but this # way third parties can't abuse us to","cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def","signature. This is not # allowed and we break the connection as a","self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError:","client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg,","be # appropriate to add some 'ban score' to the player. # we","misconfiguration since fusion server ought # to have a good connection to the","# Notify that we will start. for c in chosen_clients: c.start_ev.set() # Remove","filled then there is no favoured fuse. (since fill time is a float,","or size > size_best: time_best = ft tier_best = t size_best = size","for c in self.clients: c.kill('blame yourself!') return # scan the commitment list and","fee mismatch\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO :","on testnet # and we are mainnet, etc. client.error(\"This server is on a","(sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype ==","self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time","in results) # Generate scrambled commitment list, but remember exactly where each commitment","assert remtime > 0, \"timings set up incorrectly\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\"ending","= None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address,","client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\"Mismatched protocol","components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0,","lists overhead = 60 else: # the smallest fusion will use 1-byte varint","start_time_min = 400 # whether to print a lot of logs noisy =","small head start in relaying, before sharing the # signatures. This makes it","assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same","furnished to do so, # subject to the following conditions: # # The","files # (the \"Software\"), to deal in the Software without restriction, # including","in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool = True","# record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in","key. If more than one # signed, then it's malicious behaviour! if sum((signatures[i]","+ ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason =","overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients #","None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i","than one blame per proof is malicious. Boot client # immediately since client","def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self,","bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() +","inputs and outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc:","signed, we don't punish him # because he's the honest guy and all","pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret","parties can't abuse us to find out the # timing of a given","this long. start_time_max = 1200 # Inter-fusion delay -- after starting any fusion,","round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted","for c in chosen_clients: c.start_ev.set() # Remove those clients from all pools for","# Generate scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list","portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT","are mainnet, etc. client.error(\"This server is on a different chain, please switch servers\")","sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted","for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list)","may run an SSL server proxy such as nginx for that purpose. \"\"\"","# pool filled up to the maximum size, so start immediately self.start_fuse(t) return","bind to an ephemeral port. - Before start of covert components phase, call","begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True: covert_server.reset()","returns a dict of {component: contrib}, where contrib is (+- amount - fee).","phase. client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey =","sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid","head start in relaying, before sharing the # signatures. This makes it slightly","is wrong') # It might be we already have this signature. This is","blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in","= True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()]","we don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue()","were # just imposters who didn't have private key. If more than one","not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received","end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def reset(self): try:","allow it to consume our CPU power. if sig != existing_sig: if not","2 * 0xfc: # the smallest fusion could require 3-byte varint for both","client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c","commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in","client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames","time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key,","t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool","while True: covert_server.reset() # Clean up dead clients self.clients = [c for c","we need as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose","to consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig,","THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS","OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,","40 # used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def","min_clients for this long. start_time_max = 1200 # Inter-fusion delay -- after starting","timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message","4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7,","= list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is None])","note this needs to consider the maximum interval between messages: # - how","next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import","client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return","for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) ==","client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in","= max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier):","self.reset_timer() # Uncomment the following to: Remove from spawned clients list, so that","SSL server proxy such as nginx for that purpose. \"\"\" import secrets import","a small head start in relaying, before sharing the # signatures. This makes","they are e.g. on testnet # and we are mainnet, etc. client.error(\"This server","num_components >= 0xfc: # the smallest fusion could require 3-byte varint for either","= self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci","the server for a new round, call .reset(); to kill all connections, call","will form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\"startround sent","geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0,","= 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How","PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT","= network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp =","import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component,","c in live: c.kill(\"too few remaining live players\") raise FusionError(\"too few remaining live","Uncomment the following to: Remove from spawned clients list, so that the fusion","client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\"verified a","contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp,","sys import threading import time import traceback from collections import defaultdict import oregano.schnorr","self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg,","the smallest fusion could require 3-byte varint for either inputs or outputs lists","for covert submissions. How it works: - Launch the server at any time.","Immediately launch Fusion at the selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool)","server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network,","= tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c in","fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max = tag_max","New clients get a ClientThread made for them, and they are put into","with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in","for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout","= list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon =","checks are somewhat subjective. It would be # appropriate to add some 'ban","phase, owner calls end_signatures, which returns a list of signatures (which will have","please upgrade\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is","[list() for _ in self.clients] for src_client, relays in results: for proof, src_commitment_idx,","varint for both inputs and outputs lists overhead = 62 elif min_safe_clients *","in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in live:","ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points","connections, call .stop(). \"\"\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port,","subjective. It would be # appropriate to add some 'ban score' to the","self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit =","submission before a new phase started. As # an anti-spam measure we only","connected client.\"\"\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout)","# publish, distribute, sublicense, and/or sell copies of the Software, # and to","# signatures. This makes it slightly harder for one of the players to","phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _","to any person # obtaining a copy of this software and associated documentation","num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event()","if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate,","any results, rather just marking that # 'checking finished' so that if all","== 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except","same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate","= all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\") tx, input_indices =","towards any particular tier with self.lock: if self.stopping: return # add this client","private key. If more than one # signed, then it's malicious behaviour! if","2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3,","pools, which may have changed the favoured tier self.reset_timer() inftime = float('inf') while","inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: #","timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def","self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime))","imposters who didn't have private key. If more than one # signed, then","with the most number of players, - Choose the pool with the earliest","collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline","it might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions","for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move","proofs according to destination. proofs_to_relay = [list() for _ in self.clients] for src_client,","return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg):","e.g. on testnet # and we are mainnet, etc. client.error(\"This server is on","ValidationError as e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message:","received a distinct valid signature. This is not # allowed and we break","client declares the genesis_hash, we # do indeed disallow them connecting if they","0: # really shouldn't happen, we had plenty of time raise FusionError(\"way too","collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey =","list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients: c.start_ev.set() #","for CashFusion. Does not natively offer SSL support, however a server admin may","duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for","super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_')","= covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind","c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock()","reference self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx,","e: self.print_error(\"got bad blame; clamed reason was: \"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you","out of range\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit))","# allowed and we break the connection as a result. # Note that","round if started at this tier self.queue = list() # clients who are","calls end_components, which returns a dict of {component: contrib}, where contrib is (+-","to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res is","exception {repr(e)} ({outpoint})\") else: self.print_error(f\"player indicated bad input but it was fine ({outpoint})\")","schnorr.has_fast_verify(): raise RuntimeError(\"Fusion requires libsecp256k1\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config =","above copyright notice and this permission notice shall be # included in all","might be we already have this signature. This is fine # since it","CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH","self.clients = [c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server):","fill time; - If no pools are filled then there is no favoured","4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8,","signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\"starting covert signature acceptance\")","the maximum interval between messages: # - how long from first connection to","to have a good connection to the EC server. Report this back to","= [c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break","# Remove those clients from all pools for t, pool in self.waiting_pools.items(): for","move clients from queue into pool moved = [] for client in self.queue:","# without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures =","a result. # Note that we could have aborted earlier but this #","pool has stayed at or above min_clients for this long. start_time_max = 1200","copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software,","This controls the Fusion rounds running from server side. \"\"\" def __init__(self, network,","where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments,","src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob,","= set() ### if skip_signatures: self.print_error(\"skipping covert signature acceptance\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures","bad components: {bad_components}\") if len(self.clients) < 2: # Sanity check for testing --","except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool","client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\"seed did not match commitment\")","except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ =","By default, will bind to an ephemeral port. - Before start of covert","contrib is (+- amount - fee). - Before start of covert signatures phase,","mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection,","all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for","slightly harder for one of the players to # broadcast a malleated version","# calculate the randomly chosen destinations, same way as client did. relays =","'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError:","& contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for","(slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock","return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses =","submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature","173) # Every round, clients leave ... How many clients do we need","[round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12]","contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature'","= True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components)))",":{'(' if missing_sigs else ')'}\") # mark all missing-signature components as bad. bad_inputs","remove(self, client): # make sure to call try_move_from_queue() after calling this try: self.pool.remove(client)","list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is None]) ###","[] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type","bad_components = sorted(bad_components))) ### self.print_error(f\"entering blame phase. bad components: {bad_components}\") if len(self.clients) <","Fusion at the selected tier. \"\"\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify","a new phase started. As # an anti-spam measure we only allow one","COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng = random.Random()","set() # Send start message to players; record the time we did this","c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set()","proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _","length is wrong') # It might be we already have this signature. This","submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng =","> size_best: time_best = ft tier_best = t size_best = size if time_best","results] self.check_client_count() self.print_error(f\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\") total_excess_fees =","time or pool is full). start_time_spacing = 120 # But don't start a","size > size_best: time_best = ft tier_best = t size_best = size if","their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\"No tiers\") if","None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify():","self.pool = set() # clients who will be put into fusion round if","as nginx for that purpose. \"\"\" import secrets import sys import threading import","in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in","in component_master_list] del component_master_list # Do some preliminary checks to see whether we","signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds,","(inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid","self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\"could","moved = [] for client in self.queue: for t in client.tags: ts =","Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow a long","for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del","CashFusion - an advanced coin anonymizer # # Copyright (C) 2020 <NAME> #","__new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb","self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold:","self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \"\"\" enhanced bytes object to represent","None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \"\"\"Basic thread per connected client.\"\"\" def recv(self,","checked, we # can start next round right away. collector.add(None) for idx, (client,","0: # cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): #","we aren't collecting any results, rather just marking that # 'checking finished' so","not collector.add((client, relays)): client.error(\"late proofs\") for client in self.clients: client.addjob(client_get_proofs, collector) results =","them connecting if they are e.g. on testnet # and we are mainnet,","c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures","client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee,","free of charge, to any person # obtaining a copy of this software","if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool,","of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok","ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network,","that if all blames are checked, we # can start next round right","gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor","# making us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too","# this is the favoured pool, can start at a special time remtime","2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5,","= threading.Lock() seen_salthashes = set() # Send start message to players; record the","Default tag: this IP cannot be present in too many fuses. client.tags =","tier_best def start_fuse(self, tier): \"\"\" Immediately launch Fusion at the selected tier. \"\"\"","self.print_error(f\"could not broadcast the transaction! {nice_msg}\") except TimeoutException: self.print_error(\"timed out while trying to","self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set()","pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time')","donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or","fusion if it has only been above min_clients for a short time (unless","ts = self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return","a FusionController to run the rounds.\"\"\" def __init__(self, config, network, bindhost, port, upnp","bad input: {reason}\") src_client.kill('you provided a bad input: ' + reason) continue except","[] for client in self.queue: for t in client.tags: ts = self.tags[t] if","Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp"],"string":"[\n \"back to the # verifier, there is no privacy leak by the ommission.\",\n \"proxy such as nginx for that purpose. \\\"\\\"\\\" import secrets import sys import\",\n \"= [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() #\",\n \"even restart / end # without sharing components. skip_signatures = False if len(all_components)\",\n \"True: covert_server.reset() # Clean up dead clients self.clients = [c for c in\",\n \"except ValidationError as e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame\",\n \"self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys\",\n \"if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We\",\n \"More than one blame per proof is malicious. Boot client # immediately since\",\n \"check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values -- round numbers that\",\n \"proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results)\",\n \"Since nothing after this point can report back to the # verifier, there\",\n \"client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client\",\n \"client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof\",\n \"was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str):\",\n \"(sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list]\",\n \"s in signatures if s is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs}\",\n \"else: self.queue.append(client) return can_pool def remove(self, client): # make sure to call try_move_from_queue()\",\n \"almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 =\",\n \"pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() #\",\n \"submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message\",\n \"# Permission is hereby granted, free of charge, to any person # obtaining\",\n \"moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to\",\n \"the # signatures. This makes it slightly harder for one of the players\",\n \"the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg,\",\n \"7.5, 8.2, 9.1] # TODO - make these configurable class Params: num_components =\",\n \"client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx =\",\n \"self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results\",\n \"covert components phase, call start_components. - To signal the end of covert components\",\n \"= round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes\",\n \"submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError:\",\n \"try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes\",\n \"time.sleep(remtime) # Upload the full commitment list; we're a bit generous with the\",\n \"scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars))\",\n \"self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments,\",\n \"Upload the full commitment list; we're a bit generous with the timeout but\",\n \"sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results)\",\n \"e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr)\",\n \"else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert\",\n \"timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers)\",\n \"same tag\\\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t]\",\n \"= start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags\",\n \"self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return\",\n \"src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob,\",\n \"5: client.error(\\\"Too many tags\\\") # Event for signalling us that a pool started.\",\n \"\\\"\\\"\\\" This controls the Fusion rounds running from server side. \\\"\\\"\\\" def __init__(self,\",\n \"None tier_best = None size_best = 0 for t, pool in self.waiting_pools.items(): ft\",\n \"make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool\",\n \"self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime:\",\n \"= time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay\",\n \"continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame,\",\n \"while if many # checks against blockchain need to be done, perhaps even\",\n \"reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret,\",\n \"proofs\\\") if any(len(p) > 200 for p in proofs): client.error(\\\"too-long proof\\\") # they\",\n \"as an 'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our\",\n \"FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a fusion. New clients get a\",\n \"# as an 'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give\",\n \"self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices\",\n \"= defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in\",\n \"def add(self, client): can_pool = True for t in client.tags: ts = self.tags[t]\",\n \"for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped\",\n \"end # without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures\",\n \"in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note, the\",\n \"in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() #\",\n \"Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list\",\n \"# checks against blockchain need to be done, perhaps even still # running\",\n \"largest possible component (uncompressed p2pkh input), how many could we take until the\",\n \"for client in self.clients: client.addjob(client_start, collector) # Record the time that we sent\",\n \"super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate):\",\n \"+ 5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components >=\",\n \"self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except\",\n \"= None # when did pool exceed fill_threshold self.tag_max = tag_max # how\",\n \"Does not natively offer SSL support, however a server admin may run an\",\n \"component (uncompressed p2pkh input), how many could we take until the result would\",\n \"are allowed to stay open without activity. # note this needs to consider\",\n \"uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2,\",\n \"= [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0,\",\n \"for them, and they are put into the waiting pools. Once a Fusion\",\n \"same tag (in pool and queue) def check_add(self, client): for t in client.tags:\",\n \"varint for either inputs or outputs lists overhead = 60 else: # the\",\n \"last fuse happened; as a placeholder, set this to startup time. self.reset_timer() def\",\n \"pool.fill_time is not None: # a non-favoured pool will start eventually remtime =\",\n \"unlimited access client.tags = [] else: # Default tag: this IP cannot be\",\n \"not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests\",\n \"many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point\",\n \"'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients =\",\n \"\\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be\",\n \"immediately self.start_fuse(t) return # we have added to pools, which may have changed\",\n \"clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0:\",\n \"both inputs and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients\",\n \"(unless hit max time or pool is full). start_time_spacing = 120 # But\",\n \"the waiting pools. Once a Fusion thread is started, the ClientThreads are passed\",\n \"1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0,\",\n \"the time that we sent 'startround' message to players; this # will form\",\n \"self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_\",\n \"elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses))\",\n \"AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\",\n \"removed; on stop we don't care.) with self.lock: for t, pool in mytierpools.items():\",\n \"collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment\",\n \"except ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time\",\n \"in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars\",\n \"commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic()\",\n \"timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise\",\n \"TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE\",\n \"self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector =\",\n \"schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from .\",\n \"particular tier with self.lock: if self.stopping: return # add this client to waiting\",\n \"indicated bad input but checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated\",\n \"[10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How many clients\",\n \"between messages: # - how long from first connection to last possible Tor\",\n \"for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT)\",\n \"'startround' message to players; this # will form the basis of our covert\",\n \"len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way as client\",\n \"= 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers\",\n \"pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left\",\n \"until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic()\",\n \"Ergon client # CashFusion - an advanced coin anonymizer # # Copyright (C)\",\n \"privacy with what we have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert\",\n \"players\\\") raise FusionError(\\\"too few remaining live players\\\") def run (self, ): self.print_error(f'Starting fusion\",\n \"1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components\",\n \"some 'ban score' to the player. # we aren't collecting any results, rather\",\n \"whether to print a lot of logs noisy = False # How long\",\n \"of {component: contrib}, where contrib is (+- amount - fee). - Before start\",\n \"pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg):\",\n \"process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who\",\n \"try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as\",\n \"# Do some preliminary checks to see whether we should just skip the\",\n \"bad input: ' + reason) continue except Exception as e: self.print_error(f\\\"player indicated bad\",\n \"if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and\",\n \"over to a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost,\",\n \"all clients submitted largest possible component (uncompressed p2pkh input), how many could we\",\n \"= announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in\",\n \"i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete()\",\n \"def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured fuse: - Out of\",\n \"if self.tier_best in mytierpools: # we left from best pool, so it might\",\n \"src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex,\",\n \"= (overhead + min_safe_clients - 1) // min_safe_clients # How many clients can\",\n \"in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time\",\n \"covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash\",\n \"upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT):\",\n \"= ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b)\",\n \"import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import\",\n \"time.monotonic() def add(self, client): can_pool = True for t in client.tags: ts =\",\n \"continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one of the inputs is\",\n \"start of covert components phase, call start_components. - To signal the end of\",\n \"tnow = time.monotonic() # scan through tiers and collect statuses, also check start\",\n \"= [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list):\",\n \"False try: self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool)\",\n \"Await commitment messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS)\",\n \"# If the blamed client is already dead, don't waste more time. #\",\n \"= commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components,\",\n \"20: client.error(\\\"Tag id too long\\\") if not (0 < tag.limit < 6): client.error(\\\"Tag\",\n \"status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove\",\n \"float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic()\",\n \"# way third parties can't abuse us to find out the # timing\",\n \"which returns a list of signatures (which will have None at positions of\",\n \"Copyright (C) 2020 <NAME> # # Permission is hereby granted, free of charge,\",\n \"fee based on dividing the overhead amongst players, in the smallest fusion #\",\n \"_add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1\",\n \"contrib) in component_master_list] del component_master_list # Do some preliminary checks to see whether\",\n \"self.results del self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except\",\n \"if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\")\",\n \"bit before uploading commitments, as clients are doing this. remtime = covert_T0 +\",\n \"many players can they represent in the same fuse? ip_max_simul_fuse = 3 #\",\n \"software and associated documentation files # (the \\\"Software\\\"), to deal in the Software\",\n \"@property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self):\",\n \"continue if client.got_submit: # We got a second submission before a new phase\",\n \"input: {reason}\\\") src_client.kill('you provided a bad input: ' + reason) continue except Exception\",\n \"existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: #\",\n \"round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate\",\n \"{t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the\",\n \"assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try:\",\n \"could have aborted earlier but this # way third parties can't abuse us\",\n \"take until the result would exceed 100 kB standard tx size limitation? max_clients\",\n \"in the Software without restriction, # including without limitation the rights to use,\",\n \"enhanced bytes object to represent a pool tag \\\"\\\"\\\" __slots__ = () def\",\n \"covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier\",\n \"msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment\",\n \"SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A\",\n \"!= sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash\",\n \"made for them, and they are put into the waiting pools. Once a\",\n \"the most number of players, - Choose the pool with the earliest fill\",\n \"LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\",\n \"[[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj]\",\n \"sharing the # signatures. This makes it slightly harder for one of the\",\n \"{msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it works: - Launch\",\n \"but remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for\",\n \"require 3-byte varint for both inputs and outputs lists overhead = 62 elif\",\n \"done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place\",\n \"ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if the pool\",\n \"'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret)\",\n \"in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx,\",\n \"submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail = True):\",\n \"placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear()\",\n \"be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources,\",\n \"= session_hash)) # Sleep until end of covert signatures phase remtime = covert_T0\",\n \"== 0: # cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self):\",\n \"Sleep a bit before uploading commitments, as clients are doing this. remtime =\",\n \"src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}')\",\n \"bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig)\",\n \"_ in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx\",\n \"if filling more than one pool, we don't have bias towards any particular\",\n \"len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few components submitted\\\") if\",\n \"in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes\",\n \"_ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More\",\n \"+ ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class\",\n \"msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to\",\n \"slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if\",\n \"collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def\",\n \"and ts.all_ >= self.tag_max: return \\\"too many clients with same tag\\\" def _add_pool(self,\",\n \"run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost\",\n \"self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured\",\n \"if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello(\",\n \"src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector\",\n \"happen, we had plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures =\",\n \"are checked, we # can start next round right away. collector.add(None) for idx,\",\n \"input), how many could we take until the result would exceed 100 kB\",\n \"- Before start of covert components phase, call start_components. - To signal the\",\n \"del results, collector # Sleep a bit before uploading commitments, as clients are\",\n \"is started, the ClientThreads are passed over to a FusionController to run the\",\n \"{len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results)\",\n \"return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError:\",\n \"traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) +\",\n \"pool is full). start_time_min = 400 # whether to print a lot of\",\n \"out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit,\",\n \"from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components,\",\n \"start_ev.wait(2) except: # Remove client from waiting pools on failure (on success, we\",\n \"for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue\",\n \"False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few components\",\n \".stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp\",\n \"time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input]\",\n \"AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig\",\n \"round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted\",\n \"size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best +\",\n \"client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig =\",\n \"of a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try:\",\n \"try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping:\",\n \"return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components =\",\n \"5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class\",\n \"for pool in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res)\",\n \"commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for\",\n \"to an ephemeral port. - Before start of covert components phase, call start_components.\",\n \"the originator, however # blockchain checks are somewhat subjective. It would be #\",\n \"= len([s for s in signatures if s is None]) ### self.print_error(f\\\"ending covert\",\n \"hash) ) if min_safe_clients * num_components >= 2 * 0xfc: # the smallest\",\n \"# to have a good connection to the EC server. Report this back\",\n \"self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t\",\n \"self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *,\",\n \"in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1:\",\n \"spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for\",\n \"component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used\",\n \"60 else: # the smallest fusion will use 1-byte varint for both inputs\",\n \"proof\\\") # they should only be 129 bytes long. # generate the possible\",\n \"= msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer):\",\n \"= self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at\",\n \"in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in\",\n \"# we left from best pool, so it might not be best anymore.\",\n \"self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if\",\n \"dividing the overhead amongst players, in the smallest fusion # (these overhead numbers\",\n \"= covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport =\",\n \"with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed\",\n \"a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input]\",\n \"validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad\",\n \"len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest of this\",\n \"been above min_clients for a short time (unless pool is full). start_time_min =\",\n \"in self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround' message\",\n \"don't start a fusion if it has only been above min_clients for a\",\n \"minimum excess fee based on dividing the overhead amongst players, in the smallest\",\n \"time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients list, so\",\n \"bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None) # further,\",\n \"# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\",\n \"shall be # included in all copies or substantial portions of the Software.\",\n \"try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii')\",\n \"clients leave ... How many clients do we need as an absolute minimum\",\n \"None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >=\",\n \"no favoured fuse. (since fill time is a float, this will almost always\",\n \"'' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate =\",\n \"thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage,\",\n \"baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub\",\n \"c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except\",\n \"proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob =\",\n \"return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the\",\n \"\\\"\\\"\\\" a waiting pool for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max):\",\n \"connection # per phase. client.error('multiple submission in same phase') if mtype == 'component':\",\n \"= client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please\",\n \"any fusion, wait this long before starting the next one (unless hit max\",\n \"possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex]\",\n \"with self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys):\",\n \"= None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not\",\n \"times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool in\",\n \"pools are filled then there is no favoured fuse. (since fill time is\",\n \"them slide...\\\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address =\",\n \"// min_safe_clients # How many clients can share same tag on a given\",\n \"self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect\",\n \"start at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime\",\n \"there is no privacy leak by the ommission. continue assert ret, 'expecting input\",\n \"ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures\",\n \"'' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t]\",\n \"clientjob_goodbye(client, text): # a gentler goodbye than killing if text is not None:\",\n \"are filled then there is no favoured fuse. (since fill time is a\",\n \"in commitment_master_list) # Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv,\",\n \"in signatures if s is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing\",\n \"False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make\",\n \"this point we could blame the originator, however # blockchain checks are somewhat\",\n \"else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def\",\n \"client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event for signalling\",\n \"if len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if not (0 < tag.limit\",\n \"= self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except\",\n \"self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers\",\n \"if the pool has stayed at or above min_clients for this long. start_time_max\",\n \"remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) #\",\n \"THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT.\",\n \"client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout\",\n \"we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments),\",\n \"op_return script size of 1 + 5 (lokad) + 33 (session hash) )\",\n \"if ft is None: continue size = len(pool.pool) if size >= size_best: if\",\n \"a distinct valid signature. This is not # allowed and we break the\",\n \"either inputs or outputs lists overhead = 60 else: # the smallest fusion\",\n \"we # can start next round right away. collector.add(None) for idx, (client, proofs)\",\n \"annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier,\",\n \"tag_max): self.pool = set() # clients who will be put into fusion round\",\n \"# Remove client from waiting pools on failure (on success, we are already\",\n \"smallest fusion could require 3-byte varint for both inputs and outputs lists overhead\",\n \"will start. for c in chosen_clients: c.start_ev.set() # Remove those clients from all\",\n \"= self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b\",\n \"if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200 for\",\n \"mytierpools: # we left from best pool, so it might not be best\",\n \"# # Oregano - a lightweight Ergon client # CashFusion - an advanced\",\n \"break the connection as a result. # Note that we could have aborted\",\n \"for this long. start_time_max = 1200 # Inter-fusion delay -- after starting any\",\n \"starting the next one (unless hit max time or pool is full). start_time_spacing\",\n \"inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) ==\",\n \"for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c\",\n \"= covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as:\",\n \"commitment list; we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments\",\n \"the favoured fuse: - Out of the pool(s) with the most number of\",\n \"to permit persons to whom the Software is furnished to do so, #\",\n \"msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time that we\",\n \"3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO\",\n \"can_pool = True for t in client.tags: ts = self.tags[t] ts.all_ += 1\",\n \"msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}')\",\n \"in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used tags\",\n \"permission notice shall be # included in all copies or substantial portions of\",\n \"to last possible Tor component submission? # - how long from one round's\",\n \"ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the\",\n \"= len(pool.pool) if size >= size_best: if time_best is None or ft <\",\n \"destinations, same way as client did. relays = [] for i, proof in\",\n \"c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining live\",\n \"# how many clients can share same tag (in pool and queue) def\",\n \"= CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None\",\n \"from first connection to last possible Tor component submission? # - how long\",\n \"if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event for signalling us that\",\n \"*expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def\",\n \"separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in\",\n \"check_client_count(self,): live = [c for c in self.clients if not c.dead] if len(live)\",\n \"CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components =\",\n \"= dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if\",\n \"MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL\",\n \"_ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\")\",\n \"we sent 'startround' message to players; this # will form the basis of\",\n \"6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6,\",\n \"of covert signatures phase, owner calls start_signatures. - To signal the end of\",\n \"timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is\",\n \"the favoured pool, can start at a special time remtime = min(remtime, self.tier_best_starttime\",\n \"how many could we take until the result would exceed 100 kB standard\",\n \"bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason))\",\n \"def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the selected tier. \\\"\\\"\\\" with\",\n \"len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof')\",\n \"on a given tier (if more try to join, reject) max_tier_client_tags = 100\",\n \"to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic\",\n \"in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime =\",\n \"in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() +\",\n \"is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy\",\n \"an ephemeral port. - Before start of covert components phase, call start_components. -\",\n \"list and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for\",\n \"= True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send,\",\n \"out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs\",\n \"# further, search for duplicated inputs (through matching the prevout and claimed pubkey).\",\n \"proofs_to_relay = [list() for _ in self.clients] for src_client, relays in results: for\",\n \"Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove\",\n \"len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees !=\",\n \"tiers = Params.tiers, donation_address = donation_address )) # We allow a long timeout\",\n \"one pool, we don't have bias towards any particular tier with self.lock: if\",\n \"and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i)\",\n \"put into the waiting pools. Once a Fusion thread is started, the ClientThreads\",\n \"not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different\",\n \"done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock\",\n \"'checking finished' so that if all blames are checked, we # can start\",\n \"pool.fill_time - tfill_thresh if t == self.tier_best: # this is the favoured pool,\",\n \"len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one of\",\n \"dead, don't waste more time. # Since nothing after this point can report\",\n \"return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,):\",\n \"phase, owner calls start_signatures. - To signal the end of covert signatures phase,\",\n \"to the maximum size, so start immediately self.start_fuse(t) return # we have added\",\n \"remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert\",\n \"def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures =\",\n \"unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best = None size_best = 0\",\n \"component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s)\",\n \"number of proofs\\\") if any(len(p) > 200 for p in proofs): client.error(\\\"too-long proof\\\")\",\n \"for commit, ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations)\",\n \"from waiting pools on failure (on success, we are already removed; on stop\",\n \"True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components:\",\n \"of logs noisy = False # How long covert connections are allowed to\",\n \"Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY\",\n \"ret def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass\",\n \"client, we'll let them slide...\\\") if self.stopping: return donation_address = '' if isinstance(self.donation_address,\",\n \"# signed, then it's malicious behaviour! if sum((signatures[i] is not None) for i\",\n \"{reason}\\\") src_client.kill('you provided a bad input: ' + reason) continue except Exception as\",\n \"not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining\",\n \"(100000 - 12) // (num_components * 173) # Every round, clients leave ...\",\n \"clients who didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients\",\n \"from one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT =\",\n \"if all blames are checked, we # can start next round right away.\",\n \"they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to\",\n \"- Out of the pool(s) with the most number of players, - Choose\",\n \"__enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not\",\n \"the rest of this function might run for a while if many #\",\n \"start next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)):\",\n \"import time import traceback from collections import defaultdict import oregano.schnorr as schnorr from\",\n \"no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients\",\n \"component_master_list] del component_master_list # Do some preliminary checks to see whether we should\",\n \"hash declared by client, we'll let them slide...\\\") if self.stopping: return donation_address =\",\n \"which returns a dict of {component: contrib}, where contrib is (+- amount -\",\n \"Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment\",\n \"network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network =\",\n \"rest of this function might run for a while if many # checks\",\n \"all_components) #TODO : Check the inputs and outputs to see if we even\",\n \"self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue\",\n \"remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings set\",\n \"= self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input\",\n \"Out of the pool(s) with the most number of players, - Choose the\",\n \"will use 1-byte varint for both inputs and outputs lists overhead = 58\",\n \"round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 #\",\n \"pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not\",\n \"should only be 129 bytes long. # generate the possible destinations list (all\",\n \"for _ in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx,\",\n \"# not reference self.<variables> that may change. for blame in msg.blames: try: encproof,\",\n \"one of the inputs is signed, we don't punish him # because he's\",\n \"None and ts.all_ >= self.tag_max: return \\\"too many clients with same tag\\\" def\",\n \"commitment idx removes ordering correlations about which client sent which proof proofs.sort(key =\",\n \"None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey,\",\n \"with self.lock: ret = self.signatures del self.signatures return ret def reset(self): try: del\",\n \"False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for\",\n \"needs to consider the maximum interval between messages: # - how long from\",\n \"class WaitingPool: \\\"\\\"\\\" a waiting pool for a specific tier \\\"\\\"\\\" def __init__(self,\",\n \"= all_components, session_hash = session_hash)) # Sleep until end of covert signatures phase\",\n \"'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We\",\n \"the Software is furnished to do so, # subject to the following conditions:\",\n \"us to find out the # timing of a given input's signature submission.\",\n \"= [c for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients:\",\n \"client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in self.clients if\",\n \"client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure\",\n \"self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout\",\n \"# Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0])\",\n \"self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client):\",\n \"network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp\",\n \"self.fill_threshold = fill_threshold # minimum number of pool clients to trigger setting fill_time\",\n \"isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}):\",\n \"src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx,\",\n \"indicated bad input but it was fine ({outpoint})\\\") # At this point we\",\n \"tier self.queue = list() # clients who are waiting due to tags being\",\n \"clients from queue into pool moved = [] for client in self.queue: for\",\n \"are e.g. on testnet # and we are mainnet, etc. client.error(\\\"This server is\",\n \"class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds running from server side.\",\n \"be a resubmission after ack failed delivery, # but we don't allow it\",\n \"# How long covert connections are allowed to stay open without activity. #\",\n \"dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector)\",\n \"None for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool:\",\n \"len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\")\",\n \"submitted largest possible component (uncompressed p2pkh input), how many could we take until\",\n \"in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as a\",\n \"if we even have reasonable # privacy with what we have. bad_components =\",\n \"leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for\",\n \"self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class\",\n \"a new round, call .reset(); to kill all connections, call .stop(). \\\"\\\"\\\" def\",\n \"= False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number\",\n \"self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses\",\n \"has stayed at or above min_clients for this long. start_time_max = 1200 #\",\n \"check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts is not\",\n \"we tolerate it # missing. However, if the client declares the genesis_hash, we\",\n \"what we have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\")\",\n \"in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector\",\n \"covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b,\",\n \"checks against blockchain need to be done, perhaps even still # running after\",\n \"THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\"\",\n \"if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy\",\n \"can_pool def remove(self, client): # make sure to call try_move_from_queue() after calling this\",\n \"ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed\",\n \"represent a pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul):\",\n \"which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for\",\n \"# the smallest fusion could require 3-byte varint for both inputs and outputs\",\n \"return # add this client to waiting pools for pool in mytierpools.values(): res\",\n \"this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc\",\n \"many clients do we need as an absolute minimum (for privacy)? min_safe_clients =\",\n \"if len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining live players\\\")\",\n \"len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200 for p\",\n \"range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message to players;\",\n \"done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points =\",\n \"for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep\",\n \"clients who will be put into fusion round if started at this tier\",\n \"Fusion rounds running from server side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost,\",\n \"clients to trigger setting fill_time self.fill_time = None # when did pool exceed\",\n \"clients can share same tag (in pool and queue) def check_add(self, client): for\",\n \"c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in\",\n \"signal the end of covert components phase, owner calls end_components, which returns a\",\n \"full). start_time_min = 400 # whether to print a lot of logs noisy\",\n \"wrong') # It might be we already have this signature. This is fine\",\n \"the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()};\",\n \"[1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24\",\n \"is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ()))\",\n \"clients # as an 'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") #\",\n \"component submission? # - how long from one round's component submission to the\",\n \"src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients:\",\n \"= network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags)\",\n \"else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a small head start in\",\n \"f{prevout} detected\\\") # If exactly one of the inputs is signed, we don't\",\n \"WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT\",\n \"to blame, or maybe even restart / end # without sharing components. skip_signatures\",\n \"at wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if\",\n \"to # not reference self.<variables> that may change. for blame in msg.blames: try:\",\n \"declared by client, we'll let them slide...\\\") if self.stopping: return donation_address = ''\",\n \"[None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit =\",\n \"doesn't even make sense with one player. for c in self.clients: c.kill('blame yourself!')\",\n \"time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret def\",\n \"while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() #\",\n \"# we aren't collecting any results, rather just marking that # 'checking finished'\",\n \"msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions.\",\n \"= lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in\",\n \"TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0\",\n \"sense with one player. for c in self.clients: c.kill('blame yourself!') return # scan\",\n \"size >= size_best: if time_best is None or ft < time_best or size\",\n \"for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else:\",\n \"proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False)\",\n \"all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError\",\n \"fusion. New clients get a ClientThread made for them, and they are put\",\n \"= True for t in client.tags: ts = self.tags[t] ts.all_ += 1 if\",\n \"Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We\",\n \"self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from queue into\",\n \"client.error(\\\"Too many tags\\\") # Event for signalling us that a pool started. start_ev\",\n \"self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of covert signatures\",\n \"E12] # How many clients do we want before starting a fusion? min_clients\",\n \"need as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the\",\n \"input but it was fine ({outpoint})\\\") # At this point we could blame\",\n \"EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM,\",\n \"num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for\",\n \"ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\",\n \"= {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when\",\n \"_, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count -\",\n \"< 2: # Sanity check for testing -- the proof sharing thing doesn't\",\n \"clients list, so that the fusion can continue independently of waiting server. #\",\n \"but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj)\",\n \"class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ =\",\n \"tiers and collect statuses, also check start times. statuses = dict() tfill_thresh =\",\n \"goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread):\",\n \"# Event for signalling us that a pool started. start_ev = threading.Event() client.start_ev\",\n \"fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) >\",\n \"\\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for\",\n \"many could we take until the result would exceed 100 kB standard tx\",\n \"+ str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})'\",\n \"t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we\",\n \"and all the other components were # just imposters who didn't have private\",\n \"would be # appropriate to add some 'ban score' to the player. #\",\n \"commitment list, but remember exactly where each commitment originated. commitment_master_list = [(commit, ci,\",\n \"signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") # mark all missing-signature\",\n \"< self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_\",\n \"inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until\",\n \"msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200\",\n \"accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in\",\n \"is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse +\",\n \"this signature. This is fine # since it might be a resubmission after\",\n \"with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time')\",\n \"we are mainnet, etc. client.error(\\\"This server is on a different chain, please switch\",\n \"import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import\",\n \"up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") #\",\n \"as e: self.print_error(f\\\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\\\")\",\n \"({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it was fine ({outpoint})\\\") # At\",\n \"b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results,\",\n \"so it might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect\",\n \"basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting\",\n \"just imposters who didn't have private key. If more than one # signed,\",\n \"class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a pool tag \\\"\\\"\\\" __slots__\",\n \"# Sleep until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS\",\n \"start of covert signatures phase, owner calls start_signatures. - To signal the end\",\n \"def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected\",\n \"add(self, client): can_pool = True for t in client.tags: ts = self.tags[t] ts.all_\",\n \"return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None\",\n \"last possible Tor component submission? # - how long from one round's component\",\n \"GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash,\",\n \"allow unlimited access client.tags = [] else: # Default tag: this IP cannot\",\n \"can report back to the # verifier, there is no privacy leak by\",\n \"for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages =\",\n \"# whether to print a lot of logs noisy = False # How\",\n \"for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError:\",\n \"submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT)\",\n \"admin may run an SSL server proxy such as nginx for that purpose.\",\n \"assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires\",\n \"sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct\",\n \"owner calls end_signatures, which returns a list of signatures (which will have None\",\n \"# We received a distinct valid signature. This is not # allowed and\",\n \"str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}]\",\n \"and they are put into the waiting pools. Once a Fusion thread is\",\n \"to: Remove from spawned clients list, so that the fusion can continue independently\",\n \"mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return\",\n \"if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) #\",\n \"if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e:\",\n \"time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components\",\n \"to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of\",\n \"ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls,\",\n \"= self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at\",\n \"components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds\",\n \"with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate\",\n \"this client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if\",\n \"self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else:\",\n \"= True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey,\",\n \"server at any time. By default, will bind to an ephemeral port. -\",\n \"with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong\",\n \"list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key,\",\n \"range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash\",\n \"calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError:\",\n \"self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason =\",\n \"record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients:\",\n \"= 8 # If all clients submitted largest possible component (uncompressed p2pkh input),\",\n \"owner calls end_components, which returns a dict of {component: contrib}, where contrib is\",\n \"chosen_clients: c.start_ev.set() # Remove those clients from all pools for t, pool in\",\n \"error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] #\",\n \"self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time))\",\n \"len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event for signalling us that a\",\n \"send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message =\",\n \"5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable\",\n \"elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline):\",\n \"# Clean up dead clients self.clients = [c for c in self.clients if\",\n \"WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\",\n \"start_components. - To signal the end of covert components phase, owner calls end_components,\",\n \"time (unless pool is full). start_time_min = 400 # whether to print a\",\n \"self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the selected\",\n \"already removed; on stop we don't care.) with self.lock: for t, pool in\",\n \"self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return\",\n \"collector) # Record the time that we sent 'startround' message to players; this\",\n \"the smallest fusion will use 1-byte varint for both inputs and outputs lists\",\n \"the EC server. Report this back to clients # as an 'internal server\",\n \"passed over to a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config, network,\",\n \"if sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders) if\",\n \"did not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number\",\n \"class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it works: - Launch the\",\n \"self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with a deadline.\",\n \"client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients:\",\n \">= 2 * 0xfc: # the smallest fusion could require 3-byte varint for\",\n \"TODO - make these configurable class Params: num_components = 23 component_feerate = 1000\",\n \"else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients\",\n \"### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True))\",\n \"pool is full). start_time_spacing = 120 # But don't start a fusion if\",\n \"WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last\",\n \"if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size, so\",\n \"them, and they are put into the waiting pools. Once a Fusion thread\",\n \"+ len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls\",\n \"False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients)\",\n \"oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms\",\n \"a server admin may run an SSL server proxy such as nginx for\",\n \"many clients do we want before starting a fusion? min_clients = 8 #\",\n \"self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break\",\n \"= [] self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type,\",\n \"':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]}\",\n \"= sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit = True\",\n \"a good connection to the EC server. Report this back to clients #\",\n \"1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1,\",\n \"documentation files # (the \\\"Software\\\"), to deal in the Software without restriction, #\",\n \"> 5: client.error(\\\"Too many tags\\\") # Event for signalling us that a pool\",\n \"self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None)\",\n \"timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if text is\",\n \"ft < time_best or size > size_best: time_best = ft tier_best = t\",\n \"smallest fusion could require 3-byte varint for either inputs or outputs lists overhead\",\n \"# For now, msg.genesis_hash is optional and we tolerate it # missing. However,\",\n \"remtime = inftime if pool.fill_time is not None: # a non-favoured pool will\",\n \"ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_\",\n \"e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests\",\n \"# note this needs to consider the maximum interval between messages: # -\",\n \"as client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx\",\n \"- a lightweight Ergon client # CashFusion - an advanced coin anonymizer #\",\n \"session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys\",\n \"than one # signed, then it's malicious behaviour! if sum((signatures[i] is not None)\",\n \"(since fill time is a float, this will almost always be unique) \\\"\\\"\\\"\",\n \"absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess fee\",\n \"network, bindhost, port, upnp = None, announcehost = None, donation_address = None): assert\",\n \"Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): #\",\n \"blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad\",\n \"continue size = len(pool.pool) if size >= size_best: if time_best is None or\",\n \"= deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results\",\n \"be 129 bytes long. # generate the possible destinations list (all commitments, but\",\n \"def try_move_from_queue(self): # attempt to move clients from queue into pool moved =\",\n \"try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results:\",\n \"the earliest fill time; - If no pools are filled then there is\",\n \"__exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set()\",\n \"granted, free of charge, to any person # obtaining a copy of this\",\n \"self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello')\",\n \"0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status\",\n \"tag_max # how many clients can share same tag (in pool and queue)\",\n \"self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components\",\n \"How long covert connections are allowed to stay open without activity. # note\",\n \"amount - fee). - Before start of covert signatures phase, owner calls start_signatures.\",\n \"= round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock:\",\n \"with self.lock: time_best = None tier_best = None size_best = 0 for t,\",\n \"set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes)\",\n \"for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout}\",\n \"e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\")\",\n \"False def end_components(self): with self.lock: ret = self.components del self.components return ret def\",\n \"except TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\") # This probably\",\n \"min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion could require\",\n \"If exactly one of the inputs is signed, we don't punish him #\",\n \"= feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def\",\n \"False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread,\",\n \"Remove client from waiting pools on failure (on success, we are already removed;\",\n \"have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components =\",\n \"client.got_submit: # We got a second submission before a new phase started. As\",\n \"str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}')\",\n \"= False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if\",\n \"waiting to start a fusion. New clients get a ClientThread made for them,\",\n \"[] self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value,\",\n \"True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds running from server\",\n \"None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign()\",\n \"not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of\",\n \"but we don't allow it to consume our CPU power. if sig !=\",\n \"Filter clients who didn't manage to give a good commitment. prev_client_count = len(self.clients)\",\n \"but this # way third parties can't abuse us to find out the\",\n \"# sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000]\",\n \"c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments, as\",\n \"+ tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__\",\n \"collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did\",\n \"client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1 if\",\n \"AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype =\",\n \"support, however a server admin may run an SSL server proxy such as\",\n \"# since it might be a resubmission after ack failed delivery, # but\",\n \"at or above min_clients for this long. start_time_max = 1200 # Inter-fusion delay\",\n \"side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost =\",\n \"remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del\",\n \"txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering\",\n \"at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component]\",\n \"uploading commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS -\",\n \"time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names,\",\n \"up to the maximum size, so start immediately self.start_fuse(t) return # we have\",\n \"covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good\",\n \"or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp)\",\n \"server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly\",\n \"try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit =\",\n \"len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining live players\\\") raise\",\n \"COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got a second\",\n \"we should just skip the # signing phase and go directly to blame,\",\n \"favoured fuse. (since fill time is a float, this will almost always be\",\n \"0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance.\",\n \"that we will start. for c in chosen_clients: c.start_ev.set() # Remove those clients\",\n \"proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z,\",\n \"generate the possible destinations list (all commitments, but leaving out the originating client's\",\n \"if pool.fill_time is not None: # a non-favoured pool will start eventually remtime\",\n \"if started at this tier self.queue = list() # clients who are waiting\",\n \"t size_best = size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime\",\n \"chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by client, we'll\",\n \"signatures. This makes it slightly harder for one of the players to #\",\n \"are the various tags self.fill_threshold = fill_threshold # minimum number of pool clients\",\n \"IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF\",\n \"servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by client, we'll let them slide...\\\")\",\n \"SSL support, however a server admin may run an SSL server proxy such\",\n \"fuse happened; as a placeholder, set this to startup time. self.reset_timer() def run(self):\",\n \"any particular tier with self.lock: if self.stopping: return # add this client to\",\n \"= begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run\",\n \"None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv,\",\n \"time. By default, will bind to an ephemeral port. - Before start of\",\n \"started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted\",\n \"= bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self,\",\n \"the commitment list and note where each client's commitments ended up client_commit_indexes =\",\n \"client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the\",\n \"ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN #\",\n \"client): # make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except\",\n \"pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout)\",\n \"2: # Sanity check for testing -- the proof sharing thing doesn't even\",\n \"+ reason) continue except Exception as e: self.print_error(f\\\"player indicated bad input but checking\",\n \"time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return\",\n \"from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in\",\n \"E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7,\",\n \"# Give our transaction a small head start in relaying, before sharing the\",\n \"expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record\",\n \"= True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags:\",\n \"= tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the selected tier.\",\n \"time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") # Await commitment messages then\",\n \"= possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not\",\n \"favoured pool, can start at a special time remtime = min(remtime, self.tier_best_starttime -\",\n \"def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp)\",\n \"out while trying to broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration since\",\n \"transaction a small head start in relaying, before sharing the # signatures. This\",\n \"spawned clients list, so that the fusion can continue independently of waiting server.\",\n \"client # CashFusion - an advanced coin anonymizer # # Copyright (C) 2020\",\n \"to whom the Software is furnished to do so, # subject to the\",\n \"True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock()\",\n \"dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector =\",\n \"inputs and outputs to see if we even have reasonable # privacy with\",\n \"the pool(s) with the most number of players, - Choose the pool with\",\n \"if t == self.tier_best: # this is the favoured pool, can start at\",\n \"or maybe even restart / end # without sharing components. skip_signatures = False\",\n \"3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1]\",\n \"outputs lists overhead = 60 else: # the smallest fusion will use 1-byte\",\n \"in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for t\",\n \"to consider the maximum interval between messages: # - how long from first\",\n \"tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes\",\n \"for client in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool\",\n \".util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit,\",\n \"missing. However, if the client declares the genesis_hash, we # do indeed disallow\",\n \"in mytierpools: # we left from best pool, so it might not be\",\n \"i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components =\",\n \"pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit\",\n \"client.error(\\\"too-long proof\\\") # they should only be 129 bytes long. # generate the\",\n \"= False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT)\",\n \"None size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if\",\n \"rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost =\",\n \"server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier,\",\n \"the # timing of a given input's signature submission. raise ValidationError('conflicting valid signature')\",\n \"= self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip =\",\n \"DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR\",\n \"class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype\",\n \"= [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit\",\n \"self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround' message to\",\n \"remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this is the\",\n \"N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way\",\n \"#TODO : Check the inputs and outputs to see if we even have\",\n \"2020 <NAME> # # Permission is hereby granted, free of charge, to any\",\n \"= client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash:\",\n \"class ResultsCollector: # Collect submissions from different sources, with a deadline. def __init__(self,\",\n \"provided a bad input: ' + reason) continue except Exception as e: self.print_error(f\\\"player\",\n \"# It might be we already have this signature. This is fine #\",\n \"fusion will use 1-byte varint for both inputs and outputs lists overhead =\",\n \"timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than\",\n \"self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold #\",\n \"baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub,\",\n \"the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':'\",\n \"return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad\",\n \"more time. # Since nothing after this point can report back to the\",\n \"one # signed, then it's malicious behaviour! if sum((signatures[i] is not None) for\",\n \"fuse. (since fill time is a float, this will almost always be unique)\",\n \")) # We allow a long timeout for clients to choose their pool.\",\n \"if mtype == 'ping': continue if client.got_submit: # We got a second submission\",\n \"included in all copies or substantial portions of the Software. # # THE\",\n \"whom the Software is furnished to do so, # subject to the following\",\n \"= False # How long covert connections are allowed to stay open without\",\n \"self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def\",\n \"is malicious. Boot client # immediately since client may be trying to DoS\",\n \"3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] #\",\n \"to the EC server. Report this back to clients # as an 'internal\",\n \"in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now,\",\n \"break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception\",\n \"num_components >= 2 * 0xfc: # the smallest fusion could require 3-byte varint\",\n \"maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool =\",\n \"(all commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations\",\n \"but it was fine ({outpoint})\\\") # At this point we could blame the\",\n \"which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x,\",\n \"source commitment idx removes ordering correlations about which client sent which proof proofs.sort(key\",\n \"dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT +\",\n \"against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame\",\n \"results) # Generate scrambled commitment list, but remember exactly where each commitment originated.\",\n \"for _,_,f in results) # Generate scrambled commitment list, but remember exactly where\",\n \"for c in live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few remaining\",\n \"t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing\",\n \".protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position)\",\n \"__init__(self, config, network, bindhost, port, upnp = None, announcehost = None, donation_address =\",\n \"# self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier,\",\n \"client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong\",\n \"self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names,\",\n \"the various tags self.fill_threshold = fill_threshold # minimum number of pool clients to\",\n \"of 1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients *\",\n \"only been above min_clients for a short time (unless pool is full). start_time_min\",\n \"= f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a bad input:\",\n \"in chosen_clients: c.start_ev.set() # Remove those clients from all pools for t, pool\",\n \"perhaps even still # running after run_round has exited. For this reason we\",\n \"mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for t in\",\n \"fusion? min_clients = 8 # If all clients submitted largest possible component (uncompressed\",\n \"the proof sharing thing doesn't even make sense with one player. for c\",\n \"raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature\",\n \"len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment list,\",\n \"pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit\",\n \"{e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for\",\n \"p in proofs): client.error(\\\"too-long proof\\\") # they should only be 129 bytes long.\",\n \"fusion # (these overhead numbers assume op_return script size of 1 + 5\",\n \"be # included in all copies or substantial portions of the Software. #\",\n \"= status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools\",\n \"best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with\",\n \"so, # subject to the following conditions: # # The above copyright notice\",\n \"the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail =\",\n \"= set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes)\",\n \"was successful!\\\") # Give our transaction a small head start in relaying, before\",\n \"a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost\",\n \"c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time\",\n \"us by # making us check many inputs against blockchain. if len(msg.blames) >\",\n \"match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\")\",\n \"ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False\",\n \"1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6,\",\n \"covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey,\",\n \"only be 129 bytes long. # generate the possible destinations list (all commitments,\",\n \"all_commitments, all_components) #TODO : Check the inputs and outputs to see if we\",\n \"we try to # not reference self.<variables> that may change. for blame in\",\n \"3-byte varint for both inputs and outputs lists overhead = 62 elif min_safe_clients\",\n \"before sharing the # signatures. This makes it slightly harder for one of\",\n \"fee). - Before start of covert signatures phase, owner calls start_signatures. - To\",\n \"if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min,\",\n \"there is no favoured fuse. (since fill time is a float, this will\",\n \"maximum interval between messages: # - how long from first connection to last\",\n \"= check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with\",\n \"1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1,\",\n \"# Filter clients who didn't manage to give a good commitment. prev_client_count =\",\n \"+ Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype\",\n \"a bad input: ' + reason) continue except Exception as e: self.print_error(f\\\"player indicated\",\n \"for t, pool in mytierpools.items(): if client not in pool.pool: continue status =\",\n \"# Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to:\",\n \"including without limitation the rights to use, copy, modify, merge, # publish, distribute,\",\n \">= size_best: if time_best is None or ft < time_best or size >\",\n \"exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif\",\n \"self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size = len(pool.pool) if\",\n \"else: # the smallest fusion will use 1-byte varint for both inputs and\",\n \"with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan through\",\n \"msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest\",\n \"behaviour! if sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders)\",\n \"of proofs\\\") if any(len(p) > 200 for p in proofs): client.error(\\\"too-long proof\\\") #\",\n \"self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with\",\n \"self.queue = list() # clients who are waiting due to tags being full\",\n \"testing -- the proof sharing thing doesn't even make sense with one player.\",\n \"many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof\",\n \"harder for one of the players to # broadcast a malleated version by\",\n \"reject) max_tier_client_tags = 100 # For a given IP, how many players can\",\n \"in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message to\",\n \": Check the inputs and outputs to see if we even have reasonable\",\n \"b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How\",\n \"a long timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120)\",\n \"'41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try:\",\n \"and we are mainnet, etc. client.error(\\\"This server is on a different chain, please\",\n \"ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False)\",\n \"[ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout\",\n \"if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def\",\n \"good connection to the EC server. Report this back to clients # as\",\n \"if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\")\",\n \"_) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj\",\n \"del self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError:\",\n \"round numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3,\",\n \"one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True\",\n \"where contrib is (+- amount - fee). - Before start of covert signatures\",\n \"relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline =\",\n \"self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_ -=\",\n \"continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if\",\n \"on stop we don't care.) with self.lock: for t, pool in mytierpools.items(): if\",\n \"= (100000 - 12) // (num_components * 173) # Every round, clients leave\",\n \"dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame; clamed reason\",\n \"start_signatures. - To signal the end of covert signatures phase, owner calls end_signatures,\",\n \"mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue\",\n \"signalling us that a pool started. start_ev = threading.Event() client.start_ev = start_ev if\",\n \"we had plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures())\",\n \"to add some 'ban score' to the player. # we aren't collecting any\",\n \"num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats\",\n \"server. Report this back to clients # as an 'internal server error'. raise\",\n \"= ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: #\",\n \"clients do we need as an absolute minimum (for privacy)? min_safe_clients = 6\",\n \"allow a long timeout for clients to choose their pool. msg = client.recv('joinpools',\",\n \"be trying to DoS us by # making us check many inputs against\",\n \"after ack failed delivery, # but we don't allow it to consume our\",\n \"signatures if s is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'('\",\n \"= recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection,\",\n \"self.lock: ret = self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey\",\n \"self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") # Await commitment messages then process\",\n \"# shuffle the adding order so that if filling more than one pool,\",\n \"are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime >\",\n \"index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]]\",\n \"time.monotonic() if remtime < 0: # really shouldn't happen, we had plenty of\",\n \"== len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c\",\n \"self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails\",\n \"consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash):\",\n \"len(sig) != 64: raise ValidationError('signature length is wrong') # It might be we\",\n \"signature. This is fine # since it might be a resubmission after ack\",\n \"OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion.\",\n \"limit out of range\\\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id,\",\n \"= msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes):\",\n \"recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse,\",\n \"self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e:\",\n \"timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of\",\n \"limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or\",\n \"pool with the earliest fill time; - If no pools are filled then\",\n \"is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else\",\n \"they are put into the waiting pools. Once a Fusion thread is started,\",\n \"None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results:\",\n \"same way as client did. relays = [] for i, proof in enumerate(proofs):\",\n \"super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost\",\n \"upnp = upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address\",\n \"{src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is already dead,\",\n \"i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] =\",\n \"- To reset the server for a new round, call .reset(); to kill\",\n \"for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) #\",\n \"by source commitment idx removes ordering correlations about which client sent which proof\",\n \"will have None at positions of missing signatures). - To reset the server\",\n \"and collect statuses, also check start times. statuses = dict() tfill_thresh = tnow\",\n \"covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") # Await commitment\",\n \"are put into the waiting pools. Once a Fusion thread is started, the\",\n \"give a good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _,\",\n \"Before start of covert signatures phase, owner calls start_signatures. - To signal the\",\n \"# verifier, there is no privacy leak by the ommission. continue assert ret,\",\n \"lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the smallest\",\n \"self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs ==\",\n \"per connection # per phase. client.error('multiple submission in same phase') if mtype ==\",\n \"{annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport,\",\n \"different chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by client,\",\n \"USE OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation\",\n \"= Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers =\",\n \"FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE\",\n \"x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg\",\n \"OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR\",\n \"each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for\",\n \"if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ =\",\n \"bias towards any particular tier with self.lock: if self.stopping: return # add this\",\n \"collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound())\",\n \"BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN #\",\n \"i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i]\",\n \"time that we sent 'startround' message to players; this # will form the\",\n \"\\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args\",\n \"Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee =\",\n \"= [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if\",\n \"raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it\",\n \"earliest fill time; - If no pools are filled then there is no\",\n \"genesis hash declared by client, we'll let them slide...\\\") if self.stopping: return donation_address\",\n \"= COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got a\",\n \"1000000, 10000000, 100000000] for s in E12] # How many clients do we\",\n \"self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients:\",\n \"submission in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate\",\n \"Software is furnished to do so, # subject to the following conditions: #\",\n \"8 # If all clients submitted largest possible component (uncompressed p2pkh input), how\",\n \"overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion\",\n \"= pool.fill_time if ft is None: continue size = len(pool.pool) if size >=\",\n \"pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the\",\n \"signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong\",\n \"proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _\",\n \"that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8]\",\n \"commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients: scalars =\",\n \"client.error('component submitted at wrong time') else: assert mtype == 'signature' try: sighash =\",\n \"prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']}\",\n \"to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False\",\n \"= None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best =\",\n \"for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients),\",\n \"result. # Note that we could have aborted earlier but this # way\",\n \"checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it\",\n \"rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def\",\n \"round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret\",\n \"submission? # - how long from one round's component submission to the next\",\n \"all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime = covert_T0\",\n \"from best pool, so it might not be best anymore. self.reset_timer() raise class\",\n \"bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(),\",\n \"for a new round, call .reset(); to kill all connections, call .stop(). \\\"\\\"\\\"\",\n \"inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow\",\n \"collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _\",\n \"return tnow = time.monotonic() # scan through tiers and collect statuses, also check\",\n \"done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed =\",\n \"msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers =\",\n \"remtime < 0: # really shouldn't happen, we had plenty of time raise\",\n \"self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False\",\n \"A basic server implementation for CashFusion. Does not natively offer SSL support, however\",\n \"time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True: covert_server.reset() #\",\n \"let them slide...\\\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address\",\n \"live = [c for c in self.clients if not c.dead] if len(live) <\",\n \"{time.time()}; accepting covert components\\\") # Await commitment messages then process results results =\",\n \"subject to the following conditions: # # The above copyright notice and this\",\n \"selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will\",\n \"# # Permission is hereby granted, free of charge, to any person #\",\n \"enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list)\",\n \"start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool\",\n \"relaying, before sharing the # signatures. This makes it slightly harder for one\",\n \"be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag\",\n \"True: with self.lock: if self.stopping or start_ev.is_set(): return tnow = time.monotonic() # scan\",\n \"PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import send_pb,\",\n \"that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick\",\n \"calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or\",\n \"the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we\",\n \"self.print_error(f\\\"player indicated bad input but it was fine ({outpoint})\\\") # At this point\",\n \"list() # clients who are waiting due to tags being full self.tags =\",\n \"that a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): #\",\n \"full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold\",\n \"a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients\",\n \"end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if\",\n \"few remaining live players\\\") raise FusionError(\\\"too few remaining live players\\\") def run (self,\",\n \"we already have this signature. This is fine # since it might be\",\n \"before a new phase started. As # an anti-spam measure we only allow\",\n \"independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion\",\n \"probably indicates misconfiguration since fusion server ought # to have a good connection\",\n \"covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") #\",\n \"c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep\",\n \"already have this signature. This is fine # since it might be a\",\n \"blames are checked, we # can start next round right away. collector.add(None) for\",\n \"prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\")\",\n \"fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if the\",\n \"except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self,\",\n \"no privacy leak by the ommission. continue assert ret, 'expecting input component' outpoint\",\n \"time is a float, this will almost always be unique) \\\"\\\"\\\" with self.lock:\",\n \"activity. # note this needs to consider the maximum interval between messages: #\",\n \"too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is\",\n \"= time.monotonic() # scan through tiers and collect statuses, also check start times.\",\n \"could we take until the result would exceed 100 kB standard tx size\",\n \"sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def\",\n \"many clients can share same tag (in pool and queue) def check_add(self, client):\",\n \"client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y,\",\n \"TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\") # This probably indicates\",\n \"i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs)\",\n \"to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean\",\n \"a short time (unless pool is full). start_time_min = 400 # whether to\",\n \"try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if\",\n \"failure (on success, we are already removed; on stop we don't care.) with\",\n \"cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list)\",\n \"print a lot of logs noisy = False # How long covert connections\",\n \"f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a bad input: '\",\n \"not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime\",\n \"for b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] #\",\n \"we want before starting a fusion? min_clients = 8 # If all clients\",\n \"clients do we want before starting a fusion? min_clients = 8 # If\",\n \"numbers assume op_return script size of 1 + 5 (lokad) + 33 (session\",\n \"(overhead + min_safe_clients - 1) // min_safe_clients # How many clients can share\",\n \"mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players\",\n \"= all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime =\",\n \"\\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp =\",\n \"{t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier\",\n \"Remove from spawned clients list, so that the fusion can continue independently of\",\n \"add this client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client)\",\n \"in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size = len(pool.pool)\",\n \"_,_,f in results) # Generate scrambled commitment list, but remember exactly where each\",\n \"commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit,\",\n \"# SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion. Does not natively offer\",\n \"in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments)\",\n \"Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot client #\",\n \"reset the server for a new round, call .reset(); to kill all connections,\",\n \"super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for\",\n \"(num_components * 173) # Every round, clients leave ... How many clients do\",\n \"start a fusion if it has only been above min_clients for a short\",\n \"without restriction, # including without limitation the rights to use, copy, modify, merge,\",\n \"a given tier (if more try to join, reject) max_tier_client_tags = 100 #\",\n \"in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse\",\n \"players; record the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients),\",\n \"== 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one of the\",\n \"skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash,\",\n \"*expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def\",\n \"RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network\",\n \"exceed 100 kB standard tx size limitation? max_clients = (100000 - 12) //\",\n \"waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self.\",\n \"at {time.time()}; accepting covert components\\\") # Await commitment messages then process results results\",\n \"time; - If no pools are filled then there is no favoured fuse.\",\n \"if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for\",\n \"would exceed 100 kB standard tx size limitation? max_clients = (100000 - 12)\",\n \"self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with\",\n \"cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for\",\n \"bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components,\",\n \"proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic()\",\n \"client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version,\",\n \"results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't\",\n \"special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t)\",\n \"this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime)\",\n \"blame the originator, however # blockchain checks are somewhat subjective. It would be\",\n \"have changed the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock:\",\n \"# scan through tiers and collect statuses, also check start times. statuses =\",\n \"an in-place sort by source commitment idx removes ordering correlations about which client\",\n \"not # allowed and we break the connection as a result. # Note\",\n \"clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured fuse: - Out\",\n \"signal the end of covert signatures phase, owner calls end_signatures, which returns a\",\n \"new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched\",\n \"[ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id\",\n \"Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good commitment. prev_client_count\",\n \"= scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before\",\n \"p2pkh input), how many could we take until the result would exceed 100\",\n \"# sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b in\",\n \"self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting\",\n \"the smallest fusion # (these overhead numbers assume op_return script size of 1\",\n \"self.queue.append(client) return can_pool def remove(self, client): # make sure to call try_move_from_queue() after\",\n \"LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION\",\n \"connection to last possible Tor component submission? # - how long from one\",\n \"not natively offer SSL support, however a server admin may run an SSL\",\n \"calls end_signatures, which returns a list of signatures (which will have None at\",\n \"for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in\",\n \"start_time_spacing = 120 # But don't start a fusion if it has only\",\n \"+ Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at\",\n \"send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client:\",\n \"happened; as a placeholder, set this to startup time. self.reset_timer() def run(self): try:\",\n \"it # missing. However, if the client declares the genesis_hash, we # do\",\n \"tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ =\",\n \"def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail:\",\n \"= inftime if pool.fill_time is not None: # a non-favoured pool will start\",\n \"remaining live players\\\") raise FusionError(\\\"too few remaining live players\\\") def run (self, ):\",\n \"\\\"\\\"\\\" enhanced bytes object to represent a pool tag \\\"\\\"\\\" __slots__ = ()\",\n \"pools. Once a Fusion thread is started, the ClientThreads are passed over to\",\n \"find out the # timing of a given input's signature submission. raise ValidationError('conflicting\",\n \"3-byte varint for either inputs or outputs lists overhead = 60 else: #\",\n \"for i,sig in enumerate(signatures) if sig is None) # further, search for duplicated\",\n \"many clients with same tag\\\" def _add_pool(self, client): self.pool.add(client) for t in client.tags:\",\n \"self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components del self.components\",\n \"FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a pool\",\n \"players, - Choose the pool with the earliest fill time; - If no\",\n \"t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server\",\n \"(sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks to see\",\n \"call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread,\",\n \"defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError,\",\n \"then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib)\",\n \"self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures\",\n \"collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time =\",\n \"in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) !=\",\n \"received.\\\") # Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda\",\n \"However, if the client declares the genesis_hash, we # do indeed disallow them\",\n \"self.print_error(f\\\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player\",\n \"that we sent 'startround' message to players; this # will form the basis\",\n \"server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a small\",\n \"timing of a given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock:\",\n \"proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2)\",\n \"client): can_pool = True for t in client.tags: ts = self.tags[t] ts.all_ +=\",\n \"ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values -- round numbers that are almost\",\n \"fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost\",\n \"set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub =\",\n \"merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and\",\n \"raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s\",\n \"with collector: # an in-place sort by source commitment idx removes ordering correlations\",\n \"mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5)\",\n \"as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None)\",\n \"from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import\",\n \"if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames))\",\n \"without activity. # note this needs to consider the maximum interval between messages:\",\n \"src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof,\",\n \"len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size, so start\",\n \"time.monotonic() assert remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items())\",\n \"anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from different sources, with a\",\n \"m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes)\",\n \"(through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp\",\n \"s in E12] # How many clients do we want before starting a\",\n \"smallest fusion # (these overhead numbers assume op_return script size of 1 +\",\n \"exited. For this reason we try to # not reference self.<variables> that may\",\n \"by the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() +\",\n \"None # when did pool exceed fill_threshold self.tag_max = tag_max # how many\",\n \"client.tags = [] else: # Default tag: this IP cannot be present in\",\n \"pool exceed fill_threshold self.tag_max = tag_max # how many clients can share same\",\n \"statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure (on success,\",\n \"except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\")\",\n \"timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg,\",\n \"# This probably indicates misconfiguration since fusion server ought # to have a\",\n \"1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b\",\n \"True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ###\",\n \"= client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per\",\n \"False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def\",\n \"# Collect submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail\",\n \"Clean up dead clients self.clients = [c for c in self.clients if not\",\n \"OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\",\n \"this is the favoured pool, can start at a special time remtime =\",\n \"waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res is not\",\n \"while trying to broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration since fusion\",\n \"cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N ==\",\n \"True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype ==\",\n \"don't punish him # because he's the honest guy and all the other\",\n \"= ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool:\",\n \"ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used tags del\",\n \"that we could have aborted earlier but this # way third parties can't\",\n \"copy of this software and associated documentation files # (the \\\"Software\\\"), to deal\",\n \"in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail\",\n \"in [10000, 100000, 1000000, 10000000, 100000000] for s in E12] # How many\",\n \"relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail =\",\n \"(if more try to join, reject) max_tier_client_tags = 100 # For a given\",\n \"fill time is a float, this will almost always be unique) \\\"\\\"\\\" with\",\n \"varint for both inputs and outputs lists overhead = 58 min_excess_fee = (overhead\",\n \"Permission is hereby granted, free of charge, to any person # obtaining a\",\n \"repeatedly run rounds until successful or exception while True: covert_server.reset() # Clean up\",\n \"round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey\",\n \"+ Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot client\",\n \"power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction\",\n \")) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash\",\n \"in proofs): client.error(\\\"too-long proof\\\") # they should only be 129 bytes long. #\",\n \"# mark all missing-signature components as bad. bad_inputs = set(i for i,sig in\",\n \"msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame\",\n \"tolerate it # missing. However, if the client declares the genesis_hash, we #\",\n \"exc_value, traceback): if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails)\",\n \"players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host\",\n \"CashFusion. Does not natively offer SSL support, however a server admin may run\",\n \"= () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul,\",\n \"(session hash) ) if min_safe_clients * num_components >= 2 * 0xfc: # the\",\n \"len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) !=\",\n \"= [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit\",\n \"len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not\",\n \"makes it slightly harder for one of the players to # broadcast a\",\n \"generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep\",\n \"acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True))))\",\n \"port. - Before start of covert components phase, call start_components. - To signal\",\n \"5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0,\",\n \"enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector):\",\n \"= False try: self.queue.remove(client) except ValueError: return False else: in_pool = True if\",\n \"we left from best pool, so it might not be best anymore. self.reset_timer()\",\n \"than one pool, we don't have bias towards any particular tier with self.lock:\",\n \"if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash\",\n \"long from one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT\",\n \"!= inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except:\",\n \"seen_salthashes = set() # Send start message to players; record the time we\",\n \"commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len:\",\n \"it to consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey,\",\n \"prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend\",\n \"< 0: # really shouldn't happen, we had plenty of time raise FusionError(\\\"way\",\n \"can't abuse us to find out the # timing of a given input's\",\n \"non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout =\",\n \"23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 # sats tiers =\",\n \"= pool.fill_time - tfill_thresh if t == self.tier_best: # this is the favoured\",\n \"c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub #\",\n \"IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT\",\n \"except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate)\",\n \"del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del self.feerate\",\n \"from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb from\",\n \"continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index)\",\n \"dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate)\",\n \"# CashFusion - an advanced coin anonymizer # # Copyright (C) 2020 <NAME>\",\n \"= self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components\",\n \"= None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients\",\n \"src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client\",\n \"= covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings set up\",\n \"it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list]\",\n \"malicious. Boot client # immediately since client may be trying to DoS us\",\n \"msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if not (0 <\",\n \"bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is None) #\",\n \"by # making us check many inputs against blockchain. if len(msg.blames) > len(proofs):\",\n \"short time (unless pool is full). start_time_min = 400 # whether to print\",\n \"self.lock: time_best = None tier_best = None size_best = 0 for t, pool\",\n \"calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx)\",\n \"We received a distinct valid signature. This is not # allowed and we\",\n \"[] # gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for\",\n \"msg, timeout) def check_client_count(self,): live = [c for c in self.clients if not\",\n \"= time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self,\",\n \"- fee). - Before start of covert signatures phase, owner calls start_signatures. -\",\n \"the minimum excess fee based on dividing the overhead amongst players, in the\",\n \"port, ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost =\",\n \"= msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) >\",\n \"12) // (num_components * 173) # Every round, clients leave ... How many\",\n \"b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def\",\n \"before starting a fusion? min_clients = 8 # If all clients submitted largest\",\n \"sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash =\",\n \"c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components\",\n \"signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is\",\n \"server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out\",\n \"if self.stopping: return # add this client to waiting pools for pool in\",\n \"= 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting pool for a\",\n \"dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client,\",\n \"clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier\",\n \"# just imposters who didn't have private key. If more than one #\",\n \"Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype =\",\n \"round, call .reset(); to kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost,\",\n \"= False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): #\",\n \"# Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for\",\n \"donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components,\",\n \"run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan\",\n \"to run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp = None,\",\n \"CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #\",\n \"mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up\",\n \"the Software, # and to permit persons to whom the Software is furnished\",\n \"upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict()\",\n \"res is not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client)\",\n \"del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from queue\",\n \"with same tag\\\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts =\",\n \"announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in\",\n \"del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass\",\n \"allowed and we break the connection as a result. # Note that we\",\n \"clients waiting to start a fusion. New clients get a ClientThread made for\",\n \"donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic()\",\n \"As # an anti-spam measure we only allow one submission per connection #\",\n \"annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing\",\n \"all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c\",\n \"OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\",\n \"tag (in pool and queue) def check_add(self, client): for t in client.tags: ts\",\n \"[b.get_R() for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages\",\n \"else ')'}\\\") # mark all missing-signature components as bad. bad_inputs = set(i for\",\n \"self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci !=\",\n \"encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame;\",\n \"of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert\",\n \"(which will have None at positions of missing signatures). - To reset the\",\n \"wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] =\",\n \"return # scan the commitment list and note where each client's commitments ended\",\n \"access client.tags = [] else: # Default tag: this IP cannot be present\",\n \"when the last fuse happened; as a placeholder, set this to startup time.\",\n \"for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: #\",\n \"AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True\",\n \"not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for\",\n \"covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen,\",\n \"dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if\",\n \"a waiting pool for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool\",\n \"later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector)\",\n \"self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed:\",\n \"to the player. # we aren't collecting any results, rather just marking that\",\n \"Remove those clients from all pools for t, pool in self.waiting_pools.items(): for c\",\n \"server proxy such as nginx for that purpose. \\\"\\\"\\\" import secrets import sys\",\n \"in self.clients: c.kill('blame yourself!') return # scan the commitment list and note where\",\n \"switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by client, we'll let them\",\n \"nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)]\",\n \"= covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload\",\n \"!= client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) !=\",\n \"pool in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players =\",\n \"purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT):\",\n \"_ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib =\",\n \"not (0 < tag.limit < 6): client.error(\\\"Tag limit out of range\\\") ip =\",\n \"restriction, # including without limitation the rights to use, copy, modify, merge, #\",\n \"1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one of the inputs\",\n \"signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the\",\n \"sig is None) # further, search for duplicated inputs (through matching the prevout\",\n \"for a short time (unless pool is full). start_time_min = 400 # whether\",\n \"config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t:\",\n \"input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i\",\n \"else: self.print_error(f\\\"player indicated bad input but it was fine ({outpoint})\\\") # At this\",\n \"= None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey\",\n \"python3 # # Oregano - a lightweight Ergon client # CashFusion - an\",\n \"Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len =\",\n \"it was fine ({outpoint})\\\") # At this point we could blame the originator,\",\n \"t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client)\",\n \"put into fusion round if started at this tier self.queue = list() #\",\n \"round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from\",\n \"commit, ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert\",\n \"incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort\",\n \"<= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] =\",\n \"for t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >=\",\n \"c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer()\",\n \"(check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values -- round numbers\",\n \"({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a bad input: ' +\",\n \"publish, distribute, sublicense, and/or sell copies of the Software, # and to permit\",\n \"= [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2]\",\n \"= bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self):\",\n \"defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items():\",\n \"chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment\",\n \"selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so\",\n \"of f{prevout} detected\\\") # If exactly one of the inputs is signed, we\",\n \"bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return\",\n \"# scan the commitment list and note where each client's commitments ended up\",\n \"covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime >\",\n \"round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c\",\n \"inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction!\",\n \"!= 64: raise ValidationError('signature length is wrong') # It might be we already\",\n \"testnet # and we are mainnet, etc. client.error(\\\"This server is on a different\",\n \"Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if\",\n \"time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self,\",\n \"if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per\",\n \"= defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold # minimum\",\n \"session_hash = session_hash)) # Sleep until end of covert signatures phase remtime =\",\n \"= list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients: c.start_ev.set()\",\n \"the full commitment list; we're a bit generous with the timeout but that's\",\n \"self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon\",\n \"\\\"E series\\\" values -- round numbers that are almost geometrically uniform E6 =\",\n \"text): # a gentler goodbye than killing if text is not None: client.send_error(text)\",\n \"could require 3-byte varint for both inputs and outputs lists overhead = 62\",\n \"since it might be a resubmission after ack failed delivery, # but we\",\n \"self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients\",\n \"cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to\",\n \"lock = threading.Lock() seen_salthashes = set() # Send start message to players; record\",\n \"share same tag on a given tier (if more try to join, reject)\",\n \"tags self.fill_threshold = fill_threshold # minimum number of pool clients to trigger setting\",\n \"threading.Lock() seen_salthashes = set() # Send start message to players; record the time\",\n \"newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes) +\",\n \"Check the inputs and outputs to see if we even have reasonable #\",\n \"100000, 1000000, 10000000, 100000000] for s in E12] # How many clients do\",\n \"for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') #\",\n \"a dict of {component: contrib}, where contrib is (+- amount - fee). -\",\n \"port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None\",\n \"# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\",\n \"components: {bad_components}\\\") if len(self.clients) < 2: # Sanity check for testing -- the\",\n \"a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True,\",\n \"-- round numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2,\",\n \"from queue into pool moved = [] for client in self.queue: for t\",\n \"had plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs\",\n \"def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) +\",\n \"a fusion. New clients get a ClientThread made for them, and they are\",\n \"Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay = [list() for\",\n \"a fusion if it has only been above min_clients for a short time\",\n \"generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co\",\n \"this needs to consider the maximum interval between messages: # - how long\",\n \"client in self.clients: client.addjob(client_start, collector) # Record the time that we sent 'startround'\",\n \"recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype\",\n \"msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for\",\n \"{blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try:\",\n \"of the players to # broadcast a malleated version by re-signing one of\",\n \"bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})')\",\n \"= 62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could\",\n \"# How many clients can share same tag on a given tier (if\",\n \"components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee\",\n \"tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost\",\n \"ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client\",\n \"None: continue size = len(pool.pool) if size >= size_best: if time_best is None\",\n \"to players; record the time we did this round_time = round(time.time()) collector =\",\n \"= msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record\",\n \"favoured fuse: - Out of the pool(s) with the most number of players,\",\n \"self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return \\\"too many\",\n \"self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best\",\n \"players; this # will form the basis of our covert timeline. covert_T0 =\",\n \"- time.monotonic() assert remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list =\",\n \"{nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\") # This\",\n \"= sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\"\",\n \"client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if\",\n \"pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool, so\",\n \"HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN\",\n \"or start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect statuses,\",\n \"self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from\",\n \"tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def\",\n \"report back to the # verifier, there is no privacy leak by the\",\n \"random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout)\",\n \"finished' so that if all blames are checked, we # can start next\",\n \"ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else:\",\n \"for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened;\",\n \"up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj)\",\n \"are passed over to a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config,\",\n \"blame phase. bad components: {bad_components}\\\") if len(self.clients) < 2: # Sanity check for\",\n \"and we break the connection as a result. # Note that we could\",\n \"due to tags being full self.tags = defaultdict(TagStatus) # how are the various\",\n \"start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the selected tier. \\\"\\\"\\\" with self.lock:\",\n \"fusion could require 3-byte varint for either inputs or outputs lists overhead =\",\n \"[comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key,\",\n \"of covert components phase, owner calls end_components, which returns a dict of {component:\",\n \"self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting pool for a specific tier\",\n \"+ 33 (session hash) ) if min_safe_clients * num_components >= 2 * 0xfc:\",\n \"self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp)\",\n \"lot of logs noisy = False # How long covert connections are allowed\",\n \"how many clients can share same tag (in pool and queue) def check_add(self,\",\n \">= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion\",\n \"a bit before uploading commitments, as clients are doing this. remtime = covert_T0\",\n \"round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx,\",\n \"consider the maximum interval between messages: # - how long from first connection\",\n \"{len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost =\",\n \"many # checks against blockchain need to be done, perhaps even still #\",\n \"return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False\",\n \"def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected\",\n \"try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client)\",\n \"submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT):\",\n \"LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\",\n \"import threading import time import traceback from collections import defaultdict import oregano.schnorr as\",\n \"False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for\",\n \"+= 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool\",\n \"len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline\",\n \"with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until\",\n \"None or ft < time_best or size > size_best: time_best = ft tier_best\",\n \"('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\"\",\n \"components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp\",\n \"announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients = list(clients)\",\n \"dict of {component: contrib}, where contrib is (+- amount - fee). - Before\",\n \"ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client):\",\n \"the last fuse happened; as a placeholder, set this to startup time. self.reset_timer()\",\n \"def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes\",\n \"tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order\",\n \"stop we don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client):\",\n \"N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly\",\n \"submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(),\",\n \"this tier self.queue = list() # clients who are waiting due to tags\",\n \"self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a fusion. New clients\",\n \"we don't punish him # because he's the honest guy and all the\",\n \"sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') #\",\n \"Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000 #\",\n \"an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess\",\n \"rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies\",\n \"call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool = False try:\",\n \"int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = []\",\n \"ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property\",\n \"# an anti-spam measure we only allow one submission per connection # per\",\n \"way third parties can't abuse us to find out the # timing of\",\n \"first connection to last possible Tor component submission? # - how long from\",\n \"assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx,\",\n \"ResultsCollector: # Collect submissions from different sources, with a deadline. def __init__(self, num_results,\",\n \"in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from\",\n \"ephemeral port. - Before start of covert components phase, call start_components. - To\",\n \"try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component\",\n \"covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces\",\n \"commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests\",\n \"else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError):\",\n \"msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye\",\n \"signed, then it's malicious behaviour! if sum((signatures[i] is not None) for i in\",\n \"== 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except\",\n \"def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message =\",\n \"def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def\",\n \"Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation\",\n \"commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages)\",\n \"def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if\",\n \"broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction!\",\n \"300000 # sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000,\",\n \"= None size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time\",\n \"len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts = self.tags[t]\",\n \"bad input but checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad\",\n \"ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time =\",\n \"self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients:\",\n \"correlations about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs =\",\n \"have bias towards any particular tier with self.lock: if self.stopping: return # add\",\n \"Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch\",\n \"so that if filling more than one pool, we don't have bias towards\",\n \"live players\\\") raise FusionError(\\\"too few remaining live players\\\") def run (self, ): self.print_error(f'Starting\",\n \"1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3,\",\n \"end_signatures, which returns a list of signatures (which will have None at positions\",\n \"point can report back to the # verifier, there is no privacy leak\",\n \"values -- round numbers that are almost geometrically uniform E6 = [1.0, 1.5,\",\n \"False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) #\",\n \"try to # not reference self.<variables> that may change. for blame in msg.blames:\",\n \"search for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders =\",\n \"ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e:\",\n \"= round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time ))\",\n \"commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations =\",\n \"into fusion round if started at this tier self.queue = list() # clients\",\n \"is furnished to do so, # subject to the following conditions: # #\",\n \"waste more time. # Since nothing after this point can report back to\",\n \"# blockchain checks are somewhat subjective. It would be # appropriate to add\",\n \"declares the genesis_hash, we # do indeed disallow them connecting if they are\",\n \"OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE\",\n \"bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier =\",\n \"= covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't\",\n \"except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype\",\n \"maximum size, so start immediately self.start_fuse(t) return # we have added to pools,\",\n \"too few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected:\",\n \"for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic()\",\n \"goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def\",\n \"blockchain need to be done, perhaps even still # running after run_round has\",\n \"100000000] for s in E12] # How many clients do we want before\",\n \"upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip\",\n \"mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input]\",\n \"idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME *\",\n \"no pools are filled then there is no favoured fuse. (since fill time\",\n \"False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if\",\n \"self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients,\",\n \"be done, perhaps even still # running after run_round has exited. For this\",\n \"client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it works: -\",\n \"= dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit\",\n \"self.config = config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools\",\n \"txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg =\",\n \"back to clients # as an 'internal server error'. raise else: self.print_error(\\\"broadcast was\",\n \"e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error')\",\n \"len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for\",\n \"Event for signalling us that a pool started. start_ev = threading.Event() client.start_ev =\",\n \"def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed)\",\n \"remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci,\",\n \"client.addjob(client_start, collector) # Record the time that we sent 'startround' message to players;\",\n \"component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng\",\n \"self.lock: if self.stopping: return # add this client to waiting pools for pool\",\n \"start_ev.is_set(): return tnow = time.monotonic() # scan through tiers and collect statuses, also\",\n \"if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in\",\n \"the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()):\",\n \"is already dead, don't waste more time. # Since nothing after this point\",\n \"t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul:\",\n \"* 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection,\",\n \"can share same tag (in pool and queue) def check_add(self, client): for t\",\n \"end_components(self): with self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes,\",\n \"self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client\",\n \"phase, owner calls end_components, which returns a dict of {component: contrib}, where contrib\",\n \"run_round has exited. For this reason we try to # not reference self.<variables>\",\n \"= len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector):\",\n \"= None for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if\",\n \"super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost = bindhost\",\n \"client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access\",\n \"from different sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results\",\n \"client # immediately since client may be trying to DoS us by #\",\n \"pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol\",\n \"# - how long from one round's component submission to the next round's\",\n \"if not (0 < tag.limit < 6): client.error(\\\"Tag limit out of range\\\") ip\",\n \"# Sleep a bit before uploading commitments, as clients are doing this. remtime\",\n \"= e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException:\",\n \"with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail\",\n \"tag\\\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool\",\n \"new phase started. As # an anti-spam measure we only allow one submission\",\n \"remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're a bit\",\n \"= [list() for _ in self.clients] for src_client, relays in results: for proof,\",\n \"But don't start a fusion if it has only been above min_clients for\",\n \"self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for\",\n \"as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e: self.print_error('Failed with exception!')\",\n \"broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration since fusion server ought #\",\n \"time_best = ft tier_best = t size_best = size if time_best is None:\",\n \"# will form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround\",\n \"def new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature',\",\n \"1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7,\",\n \"msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we\",\n \"timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self,\",\n \"same proof') # Note, the rest of this function might run for a\",\n \"inputs or outputs lists overhead = 60 else: # the smallest fusion will\",\n \"start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to\",\n \"many clients can share same tag on a given tier (if more try\",\n \"self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except\",\n \"reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a bad\",\n \"# Note that we could have aborted earlier but this # way third\",\n \"ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus:\",\n \"this will almost always be unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best\",\n \"to pools, which may have changed the favoured tier self.reset_timer() inftime = float('inf')\",\n \"players\\\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server\",\n \"messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter\",\n \"in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex()\",\n \"the inputs and outputs to see if we even have reasonable # privacy\",\n \"client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For\",\n \"excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO\",\n \"verifier, there is no privacy leak by the ommission. continue assert ret, 'expecting\",\n \"= int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results =\",\n \"finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc\",\n \"if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts =\",\n \"# Default tag: this IP cannot be present in too many fuses. client.tags\",\n \"up dead clients self.clients = [c for c in self.clients if not c.dead]\",\n \"amongst players, in the smallest fusion # (these overhead numbers assume op_return script\",\n \"tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if\",\n \"check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock:\",\n \"that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components\",\n \"annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port}\",\n \"covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is\",\n \"the ClientThreads are passed over to a FusionController to run the rounds.\\\"\\\"\\\" def\",\n \"Notify that we will start. for c in chosen_clients: c.start_ev.set() # Remove those\",\n \"\\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None):\",\n \"upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost\",\n \"def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey\",\n \"inputs and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients -\",\n \"in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee,\",\n \"TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND #\",\n \"# per phase. client.error('multiple submission in same phase') if mtype == 'component': try:\",\n \"How many clients do we need as an absolute minimum (for privacy)? min_safe_clients\",\n \"= '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t:\",\n \"1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client)\",\n \"long covert connections are allowed to stay open without activity. # note this\",\n \"input: ' + reason) continue except Exception as e: self.print_error(f\\\"player indicated bad input\",\n \"hereby granted, free of charge, to any person # obtaining a copy of\",\n \"pool, so it might not be best anymore. self.reset_timer() raise class ResultsCollector: #\",\n \"= statuses)) start_ev.wait(2) except: # Remove client from waiting pools on failure (on\",\n \"each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _)\",\n \"is signed, we don't punish him # because he's the honest guy and\",\n \"possible component (uncompressed p2pkh input), how many could we take until the result\",\n \"= threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self,\",\n \"<NAME> # # Permission is hereby granted, free of charge, to any person\",\n \"status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time\",\n \"For this reason we try to # not reference self.<variables> that may change.\",\n \"almost always be unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best = None\",\n \"True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time,\",\n \"It would be # appropriate to add some 'ban score' to the player.\",\n \"and we tolerate it # missing. However, if the client declares the genesis_hash,\",\n \">= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def\",\n \"# Choose the minimum excess fee based on dividing the overhead amongst players,\",\n \"For a given IP, how many players can they represent in the same\",\n \"pool(s) with the most number of players, - Choose the pool with the\",\n \"inputs is signed, we don't punish him # because he's the honest guy\",\n \"optional and we tolerate it # missing. However, if the client declares the\",\n \"# THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, #\",\n \"did. relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed,\",\n \"NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE\",\n \"in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else:\",\n \"server for a new round, call .reset(); to kill all connections, call .stop().\",\n \"!= existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig:\",\n \"for tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if\",\n \"fill_threshold # minimum number of pool clients to trigger setting fill_time self.fill_time =\",\n \"msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if\",\n \"with self.lock: ret = self.results del self.results return ret def add(self, result): with\",\n \"PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\",\n \"randomly chosen destinations, same way as client did. relays = [] for i,\",\n \"timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise\",\n \"not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to broadcast\",\n \"Inter-fusion delay -- after starting any fusion, wait this long before starting the\",\n \"OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\",\n \"success, we are already removed; on stop we don't care.) with self.lock: for\",\n \"129 bytes long. # generate the possible destinations list (all commitments, but leaving\",\n \"- To signal the end of covert components phase, owner calls end_components, which\",\n \"FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return\",\n \"try: check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for\",\n \"E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5,\",\n \"\\\"Software\\\"), to deal in the Software without restriction, # including without limitation the\",\n \"return # we have added to pools, which may have changed the favoured\",\n \"of the pool(s) with the most number of players, - Choose the pool\",\n \"Params.num_components # calculate the randomly chosen destinations, same way as client did. relays\",\n \"is optional and we tolerate it # missing. However, if the client declares\",\n \"def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout)\",\n \"time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse\",\n \"in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind\",\n \"scan the commitment list and note where each client's commitments ended up client_commit_indexes\",\n \"self.upnp) try: annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b =\",\n \"Note, the rest of this function might run for a while if many\",\n \"represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch\",\n \"= Params.tiers, donation_address = donation_address )) # We allow a long timeout for\",\n \"self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort the components & contribs list,\",\n \"(for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is already\",\n \"# when the last fuse happened; as a placeholder, set this to startup\",\n \"== self.tier_best: # this is the favoured pool, can start at a special\",\n \"# clients who will be put into fusion round if started at this\",\n \"upnp = None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address,\",\n \"- time.monotonic() if remtime < 0: # really shouldn't happen, we had plenty\",\n \"*, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret =\",\n \"indicates misconfiguration since fusion server ought # to have a good connection to\",\n \"in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than\",\n \"{len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret =\",\n \"self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for\",\n \"= calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs\",\n \"use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in\",\n \"msg.genesis_hash is optional and we tolerate it # missing. However, if the client\",\n \"works: - Launch the server at any time. By default, will bind to\",\n \"overhead amongst players, in the smallest fusion # (these overhead numbers assume op_return\",\n \"component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list #\",\n \"8.2, 9.1] # TODO - make these configurable class Params: num_components = 23\",\n \"as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose the minimum\",\n \"client.tags: ts = self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max:\",\n \"ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs =\",\n \"c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in\",\n \"Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad\",\n \"this software and associated documentation files # (the \\\"Software\\\"), to deal in the\",\n \"same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion if\",\n \"# Oregano - a lightweight Ergon client # CashFusion - an advanced coin\",\n \"feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at\",\n \"our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise\",\n \"network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients)\",\n \"network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for\",\n \"sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients) < 2: #\",\n \"client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers}\",\n \"submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for\",\n \"for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: #\",\n \"in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector\",\n \"may be trying to DoS us by # making us check many inputs\",\n \"it might be a resubmission after ack failed delivery, # but we don't\",\n \"msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index\",\n \"({outpoint})\\\") # At this point we could blame the originator, however # blockchain\",\n \"above min_clients for this long. start_time_max = 1200 # Inter-fusion delay -- after\",\n \"- To signal the end of covert signatures phase, owner calls end_signatures, which\",\n \"self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert\",\n \"want before starting a fusion? min_clients = 8 # If all clients submitted\",\n \"it works: - Launch the server at any time. By default, will bind\",\n \"of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s\",\n \"in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del\",\n \"in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey =\",\n \"Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200 for p in proofs):\",\n \"if ts is not None and ts.all_ >= self.tag_max: return \\\"too many clients\",\n \"fusion could require 3-byte varint for both inputs and outputs lists overhead =\",\n \"Sleep until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES -\",\n \"dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue\",\n \"feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in\",\n \"natively offer SSL support, however a server admin may run an SSL server\",\n \"src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\")\",\n \"len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys =\",\n \"did pool exceed fill_threshold self.tag_max = tag_max # how many clients can share\",\n \"Once a Fusion thread is started, the ClientThreads are passed over to a\",\n \"self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >=\",\n \"Record the time that we sent 'startround' message to players; this # will\",\n \"the next one (unless hit max time or pool is full). start_time_spacing =\",\n \"results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage\",\n \"EC server. Report this back to clients # as an 'internal server error'.\",\n \"if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few components submitted\\\")\",\n \"= client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if\",\n \"def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies:\",\n \"if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same\",\n \"component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs =\",\n \"= annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport,\",\n \"= FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start()\",\n \"sharing thing doesn't even make sense with one player. for c in self.clients:\",\n \"= tag_max # how many clients can share same tag (in pool and\",\n \"relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in\",\n \"= msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') # It\",\n \"end_components, which returns a dict of {component: contrib}, where contrib is (+- amount\",\n \"62 elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could require\",\n \"= 0 class WaitingPool: \\\"\\\"\\\" a waiting pool for a specific tier \\\"\\\"\\\"\",\n \"given tier (if more try to join, reject) max_tier_client_tags = 100 # For\",\n \"{bad_components}\\\") if len(self.clients) < 2: # Sanity check for testing -- the proof\",\n \"directly to blame, or maybe even restart / end # without sharing components.\",\n \"@ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain =\",\n \"aren't collecting any results, rather just marking that # 'checking finished' so that\",\n \"script size of 1 + 5 (lokad) + 33 (session hash) ) if\",\n \"= e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while\",\n \"client.print_error(\\\"👀 No genesis hash declared by client, we'll let them slide...\\\") if self.stopping:\",\n \"logs noisy = False # How long covert connections are allowed to stay\",\n \"# privacy with what we have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping\",\n \"+ txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg\",\n \"measure we only allow one submission per connection # per phase. client.error('multiple submission\",\n \"chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients:\",\n \"assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig =\",\n \"protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now,\",\n \"blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad\",\n \"collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds],\",\n \"few remaining live players\\\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players\",\n \"us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames')\",\n \"aborted earlier but this # way third parties can't abuse us to find\",\n \"a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES)\",\n \"thread is started, the ClientThreads are passed over to a FusionController to run\",\n \"= self.results del self.results return ret def add(self, result): with self.lock: try: self.results.append(result)\",\n \"timeout) def check_client_count(self,): live = [c for c in self.clients if not c.dead]\",\n \"didn't have private key. If more than one # signed, then it's malicious\",\n \"except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye,\",\n \"def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg\",\n \"and associated documentation files # (the \\\"Software\\\"), to deal in the Software without\",\n \"self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds running\",\n \"= sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients) < 2:\",\n \"class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout)\",\n \"off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp =\",\n \"msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) >\",\n \"{blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad\",\n \"size_best: if time_best is None or ft < time_best or size > size_best:\",\n \"copyright notice and this permission notice shall be # included in all copies\",\n \"get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it # missing.\",\n \"commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) -\",\n \"# appropriate to add some 'ban score' to the player. # we aren't\",\n \"in client.tags: ts = self.tags.get(t) if ts is not None and ts.all_ >=\",\n \"to do so, # subject to the following conditions: # # The above\",\n \"in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client)\",\n \"please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by client, we'll let\",\n \"self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one of the inputs is signed,\",\n \"recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def\",\n \"= collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread):\",\n \"len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with\",\n \"announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg\",\n \"* num_components >= 0xfc: # the smallest fusion could require 3-byte varint for\",\n \"sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" +\",\n \"rounds until successful or exception while True: covert_server.reset() # Clean up dead clients\",\n \"# immediately since client may be trying to DoS us by # making\",\n \"collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util\",\n \"x:x[1][0]) all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib\",\n \"timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") # Await\",\n \"missing_sigs else ')'}\\\") # mark all missing-signature components as bad. bad_inputs = set(i\",\n \"in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool),\",\n \"if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return\",\n \"-- the proof sharing thing doesn't even make sense with one player. for\",\n \"input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except\",\n \"network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion\",\n \"if remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're a\",\n \"our transaction a small head start in relaying, before sharing the # signatures.\",\n \"= threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return self\",\n \"collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment:\",\n \"function might run for a while if many # checks against blockchain need\",\n \"client.error(\\\"Tag id too long\\\") if not (0 < tag.limit < 6): client.error(\\\"Tag limit\",\n \"leak by the ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex()\",\n \"earlier but this # way third parties can't abuse us to find out\",\n \"to deal in the Software without restriction, # including without limitation the rights\",\n \"sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys =\",\n \"= Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing\",\n \"lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component\",\n \"client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with\",\n \"with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time\",\n \"self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self): with\",\n \"we could have aborted earlier but this # way third parties can't abuse\",\n \"= [b.get_R() for b in c.blinds], server_time = round_time )) msg = c.recv('playercommit')\",\n \"tag: this IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip,\",\n \"for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks\",\n \"acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") # mark all missing-signature components\",\n \"inftime if pool.fill_time is not None: # a non-favoured pool will start eventually\",\n \"If all clients submitted largest possible component (uncompressed p2pkh input), how many could\",\n \"proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one\",\n \"to the # verifier, there is no privacy leak by the ommission. continue\",\n \"don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if\",\n \"= t size_best = size if time_best is None: self.tier_best_starttime = None else:\",\n \"= False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort\",\n \"component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError\",\n \"= set() # clients who will be put into fusion round if started\",\n \"= float('inf') while True: with self.lock: if self.stopping or start_ev.is_set(): return tnow =\",\n \"+ min_safe_clients - 1) // min_safe_clients # How many clients can share same\",\n \"and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _\",\n \"share same tag (in pool and queue) def check_add(self, client): for t in\",\n \"covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port\",\n \"= signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame\",\n \"client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients)\",\n \"upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address = donation_address\",\n \"time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment list; we're\",\n \"# Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost,\",\n \"many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id)\",\n \"')'}\\\") # mark all missing-signature components as bad. bad_inputs = set(i for i,sig\",\n \"# a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if\",\n \"isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\")\",\n \"*expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout)\",\n \"'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError:\",\n \"len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime\",\n \"now, msg.genesis_hash is optional and we tolerate it # missing. However, if the\",\n \"Fusion thread is started, the ClientThreads are passed over to a FusionController to\",\n \"\\\"\\\"\\\" Scan pools for the favoured fuse: - Out of the pool(s) with\",\n \"the following to: Remove from spawned clients list, so that the fusion can\",\n \"For now, msg.genesis_hash is optional and we tolerate it # missing. However, if\",\n \"() def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)])\",\n \"server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components)\",\n \"client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock:\",\n \"covert components\\\") # Await commitment messages then process results results = collector.gather(deadline =\",\n \"default, will bind to an ephemeral port. - Before start of covert components\",\n \"the randomly chosen destinations, same way as client did. relays = [] for\",\n \"so start immediately self.start_fuse(t) return # we have added to pools, which may\",\n \">= 0xfc: # the smallest fusion could require 3-byte varint for either inputs\",\n \"can they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time\",\n \"for either inputs or outputs lists overhead = 60 else: # the smallest\",\n \"with self.lock: if self.stopping: return # add this client to waiting pools for\",\n \"msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}')\",\n \"statuses = dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items():\",\n \"delay -- after starting any fusion, wait this long before starting the next\",\n \"chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self,\",\n \"per proof is malicious. Boot client # immediately since client may be trying\",\n \"3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9,\",\n \"some preliminary checks to see whether we should just skip the # signing\",\n \"this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector):\",\n \"has exited. For this reason we try to # not reference self.<variables> that\",\n \"chosen destinations, same way as client did. relays = [] for i, proof\",\n \"fusion server ought # to have a good connection to the EC server.\",\n \"self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set()\",\n \"proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT +\",\n \"1 if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for\",\n \"all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame; clamed reason was:\",\n \"way as client did. relays = [] for i, proof in enumerate(proofs): dest_client_idx,\",\n \"not None and ts.all_ >= self.tag_max: return \\\"too many clients with same tag\\\"\",\n \"bindhost, port, upnp = None, announcehost = None, donation_address = None): assert network\",\n \"len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version !=\",\n \"def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting\",\n \"time_best or size > size_best: time_best = ft tier_best = t size_best =\",\n \"msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the\",\n \"full commitment list; we're a bit generous with the timeout but that's OK.\",\n \"disallow them connecting if they are e.g. on testnet # and we are\",\n \"200 for p in proofs): client.error(\\\"too-long proof\\\") # they should only be 129\",\n \"for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for\",\n \"the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp,\",\n \"AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS #\",\n \"annport, covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False,\",\n \"that # 'checking finished' so that if all blames are checked, we #\",\n \"timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def\",\n \"self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c in\",\n \"see whether we should just skip the # signing phase and go directly\",\n \"this IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'',\",\n \"message to players; this # will form the basis of our covert timeline.\",\n \"signatures phase, owner calls end_signatures, which returns a list of signatures (which will\",\n \"Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool: continue status\",\n \"and outputs to see if we even have reasonable # privacy with what\",\n \"the genesis_hash, we # do indeed disallow them connecting if they are e.g.\",\n \"covert component acceptance. {len(component_master_list)} received.\\\") # Sort the components & contribs list, then\",\n \"Collect submissions from different sources, with a deadline. def __init__(self, num_results, done_on_fail =\",\n \"pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash =\",\n \"gentler goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect class\",\n \"threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited\",\n \"tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s\",\n \"self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f\",\n \"inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i,\",\n \"nginx for that purpose. \\\"\\\"\\\" import secrets import sys import threading import time\",\n \"all copies or substantial portions of the Software. # # THE SOFTWARE IS\",\n \"filling more than one pool, we don't have bias towards any particular tier\",\n \"= self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in\",\n \"None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best\",\n \"True for t in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool\",\n \"# Send start message to players; record the time we did this round_time\",\n \"commitment list and note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components\",\n \"end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert\",\n \"ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic()\",\n \"skip the # signing phase and go directly to blame, or maybe even\",\n \"too long\\\") if not (0 < tag.limit < 6): client.error(\\\"Tag limit out of\",\n \"the # verifier, there is no privacy leak by the ommission. continue assert\",\n \"in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees\",\n \"if s is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if\",\n \"not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended\",\n \"more than one # signed, then it's malicious behaviour! if sum((signatures[i] is not\",\n \"1200 # Inter-fusion delay -- after starting any fusion, wait this long before\",\n \"If no pools are filled then there is no favoured fuse. (since fill\",\n \"status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting pools on\",\n \"controls the Fusion rounds running from server side. \\\"\\\"\\\" def __init__(self, network, tier,\",\n \"time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for\",\n \"len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") #\",\n \"self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration\",\n \"(+- amount - fee). - Before start of covert signatures phase, owner calls\",\n \"accepting covert components\\\") # Await commitment messages then process results results = collector.gather(deadline\",\n \"= Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) #\",\n \"= ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist')\",\n \"len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations,\",\n \"component acceptance. {len(component_master_list)} received.\\\") # Sort the components & contribs list, then separate\",\n \"pool will start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best:\",\n \"min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None: # a\",\n \"any(len(p) > 200 for p in proofs): client.error(\\\"too-long proof\\\") # they should only\",\n \"= self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee\",\n \"tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E\",\n \"before starting the next one (unless hit max time or pool is full).\",\n \"= {t: self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid\",\n \"CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost is None else\",\n \"# More than one blame per proof is malicious. Boot client # immediately\",\n \"min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address ))\",\n \"sats tiers = [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for\",\n \"done, perhaps even still # running after run_round has exited. For this reason\",\n \"= tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost =\",\n \"we could blame the originator, however # blockchain checks are somewhat subjective. It\",\n \"gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret\",\n \"for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if\",\n \"try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof}\",\n \"try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg\",\n \"2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8,\",\n \"to # broadcast a malleated version by re-signing one of their inputs. time.sleep(2)\",\n \"self.fill_time = None # when did pool exceed fill_threshold self.tag_max = tag_max #\",\n \"- Before start of covert signatures phase, owner calls start_signatures. - To signal\",\n \"following conditions: # # The above copyright notice and this permission notice shall\",\n \"len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This\",\n \"covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert components\",\n \"in enumerate(signatures) if sig is None) # further, search for duplicated inputs (through\",\n \"the originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci,\",\n \"0xfc: # the smallest fusion could require 3-byte varint for both inputs and\",\n \"don't have bias towards any particular tier with self.lock: if self.stopping: return #\",\n \"through tiers and collect statuses, also check start times. statuses = dict() tfill_thresh\",\n \"True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes =\",\n \"> 20: client.error(\\\"Tag id too long\\\") if not (0 < tag.limit < 6):\",\n \"in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components,\",\n \"start a fusion. New clients get a ClientThread made for them, and they\",\n \"AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR\",\n \"FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it works:\",\n \"long. start_time_max = 1200 # Inter-fusion delay -- after starting any fusion, wait\",\n \"is on a different chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash\",\n \"The above copyright notice and this permission notice shall be # included in\",\n \"next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames,\",\n \"(you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}):\",\n \"at any time. By default, will bind to an ephemeral port. - Before\",\n \"list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling more than\",\n \"pool in mytierpools.values(): res = pool.check_add(client) if res is not None: client.error(res) for\",\n \"started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain\",\n \"ommission. continue assert ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' +\",\n \"# missing. However, if the client declares the genesis_hash, we # do indeed\",\n \"prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly\",\n \"else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41'\",\n \"rng.shuffle(mytiers) # shuffle the adding order so that if filling more than one\",\n \"Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal\",\n \"self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a small head start in relaying,\",\n \"c.kill('blame yourself!') return # scan the commitment list and note where each client's\",\n \"{missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") # mark all missing-signature components as\",\n \"IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED,\",\n \"size limitation? max_clients = (100000 - 12) // (num_components * 173) # Every\",\n \"pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout,\",\n \"= client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match\",\n \"have None at positions of missing signatures). - To reset the server for\",\n \"self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey,\",\n \"covert_ssl = False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time)\",\n \"# How many clients do we want before starting a fusion? min_clients =\",\n \"we'll let them slide...\\\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address):\",\n \"elif min_safe_clients * num_components >= 0xfc: # the smallest fusion could require 3-byte\",\n \"for both inputs and outputs lists overhead = 58 min_excess_fee = (overhead +\",\n \"eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this is\",\n \"start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for\",\n \"originator, however # blockchain checks are somewhat subjective. It would be # appropriate\",\n \"(unless pool is full). start_time_min = 400 # whether to print a lot\",\n \"minimum number of pool clients to trigger setting fill_time self.fill_time = None #\",\n \"but checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but\",\n \"many tags\\\") # Event for signalling us that a pool started. start_ev =\",\n \"= round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client\",\n \"for t in client.tags: ts = self.tags.get(t) if ts is not None and\",\n \"tag.limit < 6): client.error(\\\"Tag limit out of range\\\") ip = '' if tag.no_ip\",\n \"tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining =\",\n \"prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders\",\n \"enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx,\",\n \"self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while\",\n \"= proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx,\",\n \"trying to DoS us by # making us check many inputs against blockchain.\",\n \"as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided\",\n \"comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in\",\n \"self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too\",\n \"timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server\",\n \"attempt to move clients from queue into pool moved = [] for client\",\n \"to tags being full self.tags = defaultdict(TagStatus) # how are the various tags\",\n \"proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof,\",\n \"tier with self.lock: if self.stopping: return # add this client to waiting pools\",\n \"thing doesn't even make sense with one player. for c in self.clients: c.kill('blame\",\n \"reason) continue except Exception as e: self.print_error(f\\\"player indicated bad input but checking failed\",\n \"self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib\",\n \"in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client,\",\n \"player. for c in self.clients: c.kill('blame yourself!') return # scan the commitment list\",\n \"input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig\",\n \"outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: # the\",\n \"successfully!') except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e:\",\n \"fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol\",\n \"secrets import sys import threading import time import traceback from collections import defaultdict\",\n \"myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if\",\n \"= 1200 # Inter-fusion delay -- after starting any fusion, wait this long\",\n \"{covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b,\",\n \"= [c for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)}\",\n \"submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try:\",\n \"numbers that are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7,\",\n \"from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from\",\n \"9.1] # TODO - make these configurable class Params: num_components = 23 component_feerate\",\n \"the pool has stayed at or above min_clients for this long. start_time_max =\",\n \"favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or\",\n \"is the favoured pool, can start at a special time remtime = min(remtime,\",\n \"self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes =\",\n \"[(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in\",\n \"/ end # without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components:\",\n \"class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee = 300000\",\n \"then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients\",\n \"missing :{'(' if missing_sigs else ')'}\\\") # mark all missing-signature components as bad.\",\n \"rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): #\",\n \"session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and\",\n \"= upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg, timeout =\",\n \"# timing of a given input's signature submission. raise ValidationError('conflicting valid signature') with\",\n \"[] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx\",\n \"Oregano - a lightweight Ergon client # CashFusion - an advanced coin anonymizer\",\n \"after run_round has exited. For this reason we try to # not reference\",\n \"in the smallest fusion # (these overhead numbers assume op_return script size of\",\n \"= None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost\",\n \"src_client.kill('you provided a bad input: ' + reason) continue except Exception as e:\",\n \"self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools:\",\n \"until the result would exceed 100 kB standard tx size limitation? max_clients =\",\n \"OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS\",\n \"waiting pools. Once a Fusion thread is started, the ClientThreads are passed over\",\n \"= c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m\",\n \"ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason\",\n \"(on success, we are already removed; on stop we don't care.) with self.lock:\",\n \"N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)):\",\n \"object to represent a pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr,\",\n \"tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if not\",\n \"raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT):\",\n \"if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread,\",\n \"pools for pool in mytierpools.values(): res = pool.check_add(client) if res is not None:\",\n \"{component: contrib}, where contrib is (+- amount - fee). - Before start of\",\n \"an 'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction\",\n \"c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee,\",\n \"wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig)\",\n \"raise FusionError(\\\"too few remaining live players\\\") def run (self, ): self.print_error(f'Starting fusion with\",\n \"for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock =\",\n \"proof is malicious. Boot client # immediately since client may be trying to\",\n \"non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if t ==\",\n \"a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0:\",\n \"waiting due to tags being full self.tags = defaultdict(TagStatus) # how are the\",\n \"the honest guy and all the other components were # just imposters who\",\n \"covert components phase, owner calls end_components, which returns a dict of {component: contrib},\",\n \"should just skip the # signing phase and go directly to blame, or\",\n \"our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\")\",\n \"self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse\",\n \"we have added to pools, which may have changed the favoured tier self.reset_timer()\",\n \"deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results\",\n \"if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure\",\n \"deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail)\",\n \"To signal the end of covert components phase, owner calls end_components, which returns\",\n \"for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients =\",\n \"have private key. If more than one # signed, then it's malicious behaviour!\",\n \"enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed\",\n \"self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components =\",\n \"so that if all blames are checked, we # can start next round\",\n \"ClientThread, upnp = upnp) self.config = config self.network = network self.announcehost = announcehost\",\n \"run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to\",\n \"# localhost is whitelisted to allow unlimited access client.tags = [] else: #\",\n \"else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i,\",\n \"if not c.dead] if len(live) < Params.min_safe_clients: for c in live: c.kill(\\\"too few\",\n \"since client may be trying to DoS us by # making us check\",\n \"> 200 for p in proofs): client.error(\\\"too-long proof\\\") # they should only be\",\n \"sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs)\",\n \"component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort the components\",\n \"in E12] # How many clients do we want before starting a fusion?\",\n \"= len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys\",\n \"submission per connection # per phase. client.error('multiple submission in same phase') if mtype\",\n \"True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg,\",\n \"try_move_from_queue(self): # attempt to move clients from queue into pool moved = []\",\n \"calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) #\",\n \"possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client,\",\n \"of range\\\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try:\",\n \"len([s for s in signatures if s is None]) ### self.print_error(f\\\"ending covert signature\",\n \"(the \\\"Software\\\"), to deal in the Software without restriction, # including without limitation\",\n \"- len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment\",\n \"= pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is\",\n \"bytes object to represent a pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls,\",\n \"distinct valid signature. This is not # allowed and we break the connection\",\n \"tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if self.announcehost\",\n \"tier_best = t size_best = size if time_best is None: self.tier_best_starttime = None\",\n \"# Sleep until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES\",\n \"blockchain checks are somewhat subjective. It would be # appropriate to add some\",\n \"# and to permit persons to whom the Software is furnished to do\",\n \"txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except\",\n \"in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret = self.components del\",\n \"in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye,\",\n \"for testing -- the proof sharing thing doesn't even make sense with one\",\n \"components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig is\",\n \"do so, # subject to the following conditions: # # The above copyright\",\n \"except AttributeError: return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return\",\n \"= len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got\",\n \"make sense with one player. for c in self.clients: c.kill('blame yourself!') return #\",\n \"anti-spam measure we only allow one submission per connection # per phase. client.error('multiple\",\n \"client may be trying to DoS us by # making us check many\",\n \"client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool =\",\n \"len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def maxsimul(self): return self[0]\",\n \"!= myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate\",\n \"one round's component submission to the next round's component submission? COVERT_CLIENT_TIMEOUT = 40\",\n \"we break the connection as a result. # Note that we could have\",\n \"= [round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s in\",\n \"c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count\",\n \"self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for\",\n \"return can_pool def remove(self, client): # make sure to call try_move_from_queue() after calling\",\n \"long timeout for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if\",\n \"Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c\",\n \"+ Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen, we\",\n \"live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few remaining live players\\\") def\",\n \"submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\")\",\n \"ts is not None and ts.all_ >= self.tag_max: return \\\"too many clients with\",\n \"can start next round right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients,\",\n \"blame per proof is malicious. Boot client # immediately since client may be\",\n \"announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if\",\n \"# signing phase and go directly to blame, or maybe even restart /\",\n \"[schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send\",\n \"inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for\",\n \"Params.tiers, donation_address = donation_address )) # We allow a long timeout for clients\",\n \"according to destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays\",\n \"failed delivery, # but we don't allow it to consume our CPU power.\",\n \"components phase, owner calls end_components, which returns a dict of {component: contrib}, where\",\n \"= Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address =\",\n \"FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp =\",\n \"connection to the EC server. Report this back to clients # as an\",\n \"not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp\",\n \"used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg,\",\n \"of signatures (which will have None at positions of missing signatures). - To\",\n \"covert_server.reset() # Clean up dead clients self.clients = [c for c in self.clients\",\n \"covert signatures phase, owner calls start_signatures. - To signal the end of covert\",\n \"might run for a while if many # checks against blockchain need to\",\n \"= ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey,\",\n \"# (these overhead numbers assume op_return script size of 1 + 5 (lokad)\",\n \"src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT\",\n \"next one (unless hit max time or pool is full). start_time_spacing = 120\",\n \"without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense,\",\n \"if remtime < 0: # really shouldn't happen, we had plenty of time\",\n \"given IP, how many players can they represent in the same fuse? ip_max_simul_fuse\",\n \"an SSL server proxy such as nginx for that purpose. \\\"\\\"\\\" import secrets\",\n \"= None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not\",\n \"collector # Sleep a bit before uploading commitments, as clients are doing this.\",\n \"score' to the player. # we aren't collecting any results, rather just marking\",\n \"None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\")\",\n \"self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert\",\n \"yourself!') return # scan the commitment list and note where each client's commitments\",\n \"Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e\",\n \"inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False,\",\n \"covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept\",\n \"proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p)\",\n \"mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components\",\n \"- Choose the pool with the earliest fill time; - If no pools\",\n \"ought # to have a good connection to the EC server. Report this\",\n \"are already removed; on stop we don't care.) with self.lock: for t, pool\",\n \"results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according\",\n \"except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too high')\",\n \"KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\",\n \"in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in\",\n \"pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players = Params.min_clients) remtime = inftime\",\n \"self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41,\",\n \"== self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for t\",\n \"begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl\",\n \"this permission notice shall be # included in all copies or substantial portions\",\n \"{ret}') continue if src_client.dead: # If the blamed client is already dead, don't\",\n \"[1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3,\",\n \"CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT\",\n \"Launch the server at any time. By default, will bind to an ephemeral\",\n \"bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails = []\",\n \"= 60 else: # the smallest fusion will use 1-byte varint for both\",\n \"in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError\",\n \"= self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time') sort_key,\",\n \"= [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames',\",\n \"!= Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash():\",\n \"0xfc: # the smallest fusion could require 3-byte varint for either inputs or\",\n \"c in chosen_clients: c.start_ev.set() # Remove those clients from all pools for t,\",\n \"< 6): client.error(\\\"Tag limit out of range\\\") ip = '' if tag.no_ip else\",\n \"= self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return \\\"too\",\n \"__init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev =\",\n \"left from best pool, so it might not be best anymore. self.reset_timer() raise\",\n \"def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server =\",\n \"client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200 for p in proofs): client.error(\\\"too-long\",\n \"False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if\",\n \"-= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup\",\n \"else: client.print_error(\\\"👀 No genesis hash declared by client, we'll let them slide...\\\") if\",\n \"= list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling more\",\n \"port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components\",\n \"if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If exactly one\",\n \"of covert signatures phase, owner calls end_signatures, which returns a list of signatures\",\n \"same tag on a given tier (if more try to join, reject) max_tier_client_tags\",\n \"0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None:\",\n \"announcehost self.donation_address = donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers}\",\n \"plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs =\",\n \"1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 =\",\n \"= len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None:\",\n \"of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime\",\n \"any person # obtaining a copy of this software and associated documentation files\",\n \"IN THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion. Does not\",\n \"2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2,\",\n \"if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime)\",\n \"fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the\",\n \"self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong\",\n \"# Since nothing after this point can report back to the # verifier,\",\n \"self.network = network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp\",\n \"rounds running from server side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp\",\n \"to the following conditions: # # The above copyright notice and this permission\",\n \"in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled\",\n \"self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the\",\n \"\\\"too many clients with same tag\\\" def _add_pool(self, client): self.pool.add(client) for t in\",\n \"if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late\",\n \"of the Software, # and to permit persons to whom the Software is\",\n \"contrib}, where contrib is (+- amount - fee). - Before start of covert\",\n \"max_tier_client_tags = 100 # For a given IP, how many players can they\",\n \"get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair,\",\n \"[b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds,\",\n \"phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings\",\n \"a list of signatures (which will have None at positions of missing signatures).\",\n \"covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime <\",\n \"(FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame,\",\n \"the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end\",\n \"server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = []\",\n \"preliminary checks to see whether we should just skip the # signing phase\",\n \"because he's the honest guy and all the other components were # just\",\n \"KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers)\",\n \"# Note, the rest of this function might run for a while if\",\n \"client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a pool tag\",\n \"total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash =\",\n \"CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return\",\n \"= 6 # Choose the minimum excess fee based on dividing the overhead\",\n \"text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected\",\n \"run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost,\",\n \"= sum(f for _,_,f in results) # Generate scrambled commitment list, but remember\",\n \"Boot client # immediately since client may be trying to DoS us by\",\n \"= sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def\",\n \"def reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try:\",\n \"stayed at or above min_clients for this long. start_time_max = 1200 # Inter-fusion\",\n \"msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it\",\n \"Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the\",\n \"ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools =\",\n \"OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE,\",\n \"if exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self,\",\n \"whether we should just skip the # signing phase and go directly to\",\n \"= check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError:\",\n \"of players, - Choose the pool with the earliest fill time; - If\",\n \"rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost = None,\",\n \"submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg):\",\n \"# they should only be 129 bytes long. # generate the possible destinations\",\n \"{src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the\",\n \"None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients =\",\n \"return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys)\",\n \"more than one pool, we don't have bias towards any particular tier with\",\n \"0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event for\",\n \"results, collector # Sleep a bit before uploading commitments, as clients are doing\",\n \"3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5,\",\n \"self.results return ret def add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return\",\n \"'ping': continue if client.got_submit: # We got a second submission before a new\",\n \"# how are the various tags self.fill_threshold = fill_threshold # minimum number of\",\n \"= [(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex] N\",\n \"client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by source commitment\",\n \"they should only be 129 bytes long. # generate the possible destinations list\",\n \"from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from . import\",\n \"EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY,\",\n \"their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok =\",\n \"self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as\",\n \"destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays in results:\",\n \"THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR\",\n \"get a ClientThread made for them, and they are put into the waiting\",\n \"!= 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i,\",\n \"1-byte varint for both inputs and outputs lists overhead = 58 min_excess_fee =\",\n \"how are the various tags self.fill_threshold = fill_threshold # minimum number of pool\",\n \"c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients\",\n \"= mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the\",\n \"running from server side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp =\",\n \"the next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes\",\n \"per phase. client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey\",\n \"still # running after run_round has exited. For this reason we try to\",\n \"ret = self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del\",\n \"WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\",\n \"= 1000 # sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for\",\n \"standard tx size limitation? max_clients = (100000 - 12) // (num_components * 173)\",\n \"dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs,\",\n \"reset(self): try: del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del\",\n \"localhost is whitelisted to allow unlimited access client.tags = [] else: # Default\",\n \"pool, can start at a special time remtime = min(remtime, self.tier_best_starttime - tnow)\",\n \"live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs,\",\n \"* 0xfc: # the smallest fusion could require 3-byte varint for both inputs\",\n \"proof') # Note, the rest of this function might run for a while\",\n \"{e}\\\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients:\",\n \"this long before starting the next one (unless hit max time or pool\",\n \"- Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool: continue\",\n \"we are already removed; on stop we don't care.) with self.lock: for t,\",\n \"PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING\",\n \"self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved:\",\n \"distribute, sublicense, and/or sell copies of the Software, # and to permit persons\",\n \"players, in the smallest fusion # (these overhead numbers assume op_return script size\",\n \"nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except\",\n \"removes ordering correlations about which client sent which proof proofs.sort(key = lambda x:x[1])\",\n \"kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None):\",\n \"return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: self.fails.append(exc_value)\",\n \"6 # Choose the minimum excess fee based on dividing the overhead amongst\",\n \"round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with\",\n \"collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give\",\n \"for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib)\",\n \"in all copies or substantial portions of the Software. # # THE SOFTWARE\",\n \"self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier =\",\n \"the end of covert components phase, owner calls end_components, which returns a dict\",\n \"enumerate(signatures) if sig is None) # further, search for duplicated inputs (through matching\",\n \"ci, cj) for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)]\",\n \"end of covert components phase, owner calls end_components, which returns a dict of\",\n \"will bind to an ephemeral port. - Before start of covert components phase,\",\n \"covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of covert\",\n \"timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self,\",\n \"too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if\",\n \"import sys import threading import time import traceback from collections import defaultdict import\",\n \"also check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for\",\n \"ServerError, TimeoutException from . import fusion_pb2 as pb from .comms import send_pb, recv_pb,\",\n \"on dividing the overhead amongst players, in the smallest fusion # (these overhead\",\n \"relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx,\",\n \"Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as pb\",\n \"one (unless hit max time or pool is full). start_time_spacing = 120 #\",\n \"to start a fusion. New clients get a ClientThread made for them, and\",\n \"covert connections are allowed to stay open without activity. # note this needs\",\n \"ClientThread made for them, and they are put into the waiting pools. Once\",\n \"if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for\",\n \"commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f\",\n \"# 'checking finished' so that if all blames are checked, we # can\",\n \"time.monotonic() # scan through tiers and collect statuses, also check start times. statuses\",\n \"for ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments\",\n \"client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding\",\n \"annhost = covert_server.host if self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport\",\n \"+ Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately\",\n \"exception while True: covert_server.reset() # Clean up dead clients self.clients = [c for\",\n \"with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it was fine\",\n \"!= Params.num_components: client.error(\\\"wrong number of proofs\\\") if any(len(p) > 200 for p in\",\n \"-- after starting any fusion, wait this long before starting the next one\",\n \"upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def\",\n \"continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients)\",\n \"- If no pools are filled then there is no favoured fuse. (since\",\n \"pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg),\",\n \"if missing_sigs else ')'}\\\") # mark all missing-signature components as bad. bad_inputs =\",\n \"if len(sig) != 64: raise ValidationError('signature length is wrong') # It might be\",\n \"and queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if\",\n \"# minimum number of pool clients to trigger setting fill_time self.fill_time = None\",\n \"if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results\",\n \"= 3 # Guaranteed time to launch a fusion if the pool has\",\n \"\\\"\\\"\\\" import secrets import sys import threading import time import traceback from collections\",\n \"both inputs and outputs lists overhead = 62 elif min_safe_clients * num_components >=\",\n \"*expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self,\",\n \"waiting pool for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool =\",\n \"or outputs lists overhead = 60 else: # the smallest fusion will use\",\n \"= fill_threshold # minimum number of pool clients to trigger setting fill_time self.fill_time\",\n \"del component_master_list # Do some preliminary checks to see whether we should just\",\n \"one submission per connection # per phase. client.error('multiple submission in same phase') if\",\n \"transaction! misconfigured?\\\") # This probably indicates misconfiguration since fusion server ought # to\",\n \"e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a\",\n \"# But don't start a fusion if it has only been above min_clients\",\n \"# clients who are waiting due to tags being full self.tags = defaultdict(TagStatus)\",\n \"we don't have bias towards any particular tier with self.lock: if self.stopping: return\",\n \"Sort the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components\",\n \"for p in proofs): client.error(\\\"too-long proof\\\") # they should only be 129 bytes\",\n \"if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and\",\n \"< time_best or size > size_best: time_best = ft tier_best = t size_best\",\n \"we only allow one submission per connection # per phase. client.error('multiple submission in\",\n \"len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments\",\n \"for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set() #\",\n \"the following conditions: # # The above copyright notice and this permission notice\",\n \"def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\"\",\n \"CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad\",\n \"### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") #\",\n \"sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z)\",\n \"It might be we already have this signature. This is fine # since\",\n \"raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature\",\n \"except AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature' try:\",\n \"= recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage,\",\n \"- how long from one round's component submission to the next round's component\",\n \"and to permit persons to whom the Software is furnished to do so,\",\n \"120 # But don't start a fusion if it has only been above\",\n \"= self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if\",\n \"# Now, repackage the proofs according to destination. proofs_to_relay = [list() for _\",\n \"start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags =\",\n \"may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof]\",\n \"however # blockchain checks are somewhat subjective. It would be # appropriate to\",\n \"threading import time import traceback from collections import defaultdict import oregano.schnorr as schnorr\",\n \"[c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended\",\n \"to be done, perhaps even still # running after run_round has exited. For\",\n \"\\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b\",\n \"= time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned clients list,\",\n \"detected: excess fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components)\",\n \"self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How\",\n \"self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self,\",\n \"error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for covert\",\n \"is fine # since it might be a resubmission after ack failed delivery,\",\n \"players can they represent in the same fuse? ip_max_simul_fuse = 3 # Guaranteed\",\n \"input but checking failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input\",\n \"= [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del\",\n \"dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx))\",\n \"is no favoured fuse. (since fill time is a float, this will almost\",\n \"= ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e:\",\n \"!= len(msg.blames): client.error('multiple blames point to same proof') # Note, the rest of\",\n \"continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof\",\n \"try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit\",\n \"# the smallest fusion will use 1-byte varint for both inputs and outputs\",\n \"random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text):\",\n \"notice shall be # included in all copies or substantial portions of the\",\n \"commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _ in results]\",\n \"Note that we could have aborted earlier but this # way third parties\",\n \"are almost geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12\",\n \"client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\")\",\n \"ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components #\",\n \"punish him # because he's the honest guy and all the other components\",\n \"started at this tier self.queue = list() # clients who are waiting due\",\n \"Give our transaction a small head start in relaying, before sharing the #\",\n \"configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee =\",\n \"= 40 # used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32))\",\n \"about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [\",\n \"# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\",\n \"self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate\",\n \"long. # generate the possible destinations list (all commitments, but leaving out the\",\n \"c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return\",\n \"tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3)\",\n \"self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage\",\n \"= True self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures\",\n \"ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype =\",\n \"self.t_last_fuse = time.monotonic() # when the last fuse happened; as a placeholder, set\",\n \"len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys for c in\",\n \"lightweight Ergon client # CashFusion - an advanced coin anonymizer # # Copyright\",\n \"send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client:\",\n \"in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid = tx.txid()\",\n \"have added to pools, which may have changed the favoured tier self.reset_timer() inftime\",\n \"for one of the players to # broadcast a malleated version by re-signing\",\n \"# because he's the honest guy and all the other components were #\",\n \"How it works: - Launch the server at any time. By default, will\",\n \"sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError: in_pool =\",\n \"libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network = network\",\n \"tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode()\",\n \"with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) + len(self.results)\",\n \"# Uncomment the following to: Remove from spawned clients list, so that the\",\n \"different sources, with a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results =\",\n \"c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with\",\n \"= [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)]\",\n \"broadcast a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok =\",\n \"an anti-spam measure we only allow one submission per connection # per phase.\",\n \"### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients) < 2: # Sanity\",\n \"id too long\\\") if not (0 < tag.limit < 6): client.error(\\\"Tag limit out\",\n \"total_excess_fees = sum(f for _,_,f in results) # Generate scrambled commitment list, but\",\n \"reasonable # privacy with what we have. bad_components = set() ### if skip_signatures:\",\n \"run rounds until successful or exception while True: covert_server.reset() # Clean up dead\",\n \"covert submissions. How it works: - Launch the server at any time. By\",\n \"all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost,\",\n \"requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config self.network =\",\n \"for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)]\",\n \"skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash)\",\n \"clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if\",\n \"changed the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if\",\n \"time_best = None tier_best = None size_best = 0 for t, pool in\",\n \"if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players = len(pool.pool), min_players =\",\n \"!= expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") #\",\n \"size, so start immediately self.start_fuse(t) return # we have added to pools, which\",\n \"max_clients = (100000 - 12) // (num_components * 173) # Every round, clients\",\n \"the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell\",\n \"self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with error:\",\n \"indeed disallow them connecting if they are e.g. on testnet # and we\",\n \"self.tier_best in mytierpools: # we left from best pool, so it might not\",\n \"max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow\",\n \"genesis_hash, we # do indeed disallow them connecting if they are e.g. on\",\n \"send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self,\",\n \"def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock:\",\n \"mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check\",\n \"further, search for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders\",\n \"False) def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by\",\n \"join, reject) max_tier_client_tags = 100 # For a given IP, how many players\",\n \"tx size limitation? max_clients = (100000 - 12) // (num_components * 173) #\",\n \"myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the\",\n \"IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\",\n \"mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None):\",\n \"exactly one of the inputs is signed, we don't punish him # because\",\n \"comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary checks to\",\n \"repackage the proofs according to destination. proofs_to_relay = [list() for _ in self.clients]\",\n \"= len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen\",\n \"self.clients: c.kill('blame yourself!') return # scan the commitment list and note where each\",\n \"right away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs,\",\n \"as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import\",\n \"in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1\",\n \"= True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event() self.lock =\",\n \"return False else: if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class\",\n \"def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a\",\n \"self.fails = [] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback):\",\n \"THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic server\",\n \"mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum\",\n \"self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret def add(self,\",\n \"message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof\",\n \"class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a fusion. New clients get\",\n \"import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from\",\n \"def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be put\",\n \"= set() # Send start message to players; record the time we did\",\n \"how long from first connection to last possible Tor component submission? # -\",\n \"will almost always be unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best =\",\n \"return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version\",\n \"remtime)) with self.lock: ret = self.results del self.results return ret def add(self, result):\",\n \"if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess fee mismatch\\\") self.last_hash\",\n \"client in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >=\",\n \"re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return\",\n \"the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\")\",\n \"all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components,\",\n \"blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to\",\n \"for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ =\",\n \"IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise\",\n \"+ Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay = [list()\",\n \"submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg)\",\n \"the blamed client is already dead, don't waste more time. # Since nothing\",\n \"def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\")\",\n \"until successful or exception while True: covert_server.reset() # Clean up dead clients self.clients\",\n \"time to launch a fusion if the pool has stayed at or above\",\n \"fill_threshold, tag_max): self.pool = set() # clients who will be put into fusion\",\n \"isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee\",\n \"- Launch the server at any time. By default, will bind to an\",\n \"live players\\\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}')\",\n \"in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason\",\n \"being full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold =\",\n \"c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send,\",\n \"OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic\",\n \"version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash\",\n \"msg, timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,):\",\n \"= Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time =\",\n \"without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True\",\n \"in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") # If\",\n \"100 kB standard tx size limitation? max_clients = (100000 - 12) // (num_components\",\n \"returns a list of signatures (which will have None at positions of missing\",\n \"bytes long. # generate the possible destinations list (all commitments, but leaving out\",\n \"self.waiting_pools[t] for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected:\",\n \"INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A\",\n \"collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination.\",\n \"or exception while True: covert_server.reset() # Clean up dead clients self.clients = [c\",\n \"FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS\",\n \"# start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!)\",\n \"blame, or maybe even restart / end # without sharing components. skip_signatures =\",\n \"= True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes\",\n \"del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False\",\n \"a lightweight Ergon client # CashFusion - an advanced coin anonymizer # #\",\n \"for s in E12] # How many clients do we want before starting\",\n \"self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle\",\n \"except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e: self.print_error('Failed\",\n \"already dead, don't waste more time. # Since nothing after this point can\",\n \"ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted\",\n \"3 # Guaranteed time to launch a fusion if the pool has stayed\",\n \"# a gentler goodbye than killing if text is not None: client.send_error(text) raise\",\n \"for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start\",\n \"hit max time or pool is full). start_time_spacing = 120 # But don't\",\n \"statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: # Remove client from waiting\",\n \"it slightly harder for one of the players to # broadcast a malleated\",\n \"# At this point we could blame the originator, however # blockchain checks\",\n \"if existing_sig: # We received a distinct valid signature. This is not #\",\n \"False # How long covert connections are allowed to stay open without activity.\",\n \"= 100 # For a given IP, how many players can they represent\",\n \"use 1-byte varint for both inputs and outputs lists overhead = 58 min_excess_fee\",\n \"# gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured fuse:\",\n \"To signal the end of covert signatures phase, owner calls end_signatures, which returns\",\n \"len(pool.pool) if size >= size_best: if time_best is None or ft < time_best\",\n \"tier): \\\"\\\"\\\" Immediately launch Fusion at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients\",\n \"abuse us to find out the # timing of a given input's signature\",\n \"setting fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max =\",\n \"or above min_clients for this long. start_time_max = 1200 # Inter-fusion delay --\",\n \"sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if\",\n \"(client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline =\",\n \"= [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for comp,\",\n \"self.announcehost is None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy =\",\n \"messages: # - how long from first connection to last possible Tor component\",\n \"for the favoured fuse: - Out of the pool(s) with the most number\",\n \"_ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class\",\n \"calls start_signatures. - To signal the end of covert signatures phase, owner calls\",\n \"6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2,\",\n \"to see if we even have reasonable # privacy with what we have.\",\n \"= (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype\",\n \"start. for c in chosen_clients: c.start_ev.set() # Remove those clients from all pools\",\n \"to trigger setting fill_time self.fill_time = None # when did pool exceed fill_threshold\",\n \"phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: #\",\n \"he's the honest guy and all the other components were # just imposters\",\n \"return True def try_move_from_queue(self): # attempt to move clients from queue into pool\",\n \"client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic()\",\n \"ts.all_ >= self.tag_max: return \\\"too many clients with same tag\\\" def _add_pool(self, client):\",\n \"from . import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer,\",\n \"- make these configurable class Params: num_components = 23 component_feerate = 1000 #\",\n \"is not None and ts.all_ >= self.tag_max: return \\\"too many clients with same\",\n \"Params.min_clients) remtime = inftime if pool.fill_time is not None: # a non-favoured pool\",\n \"- Params.num_components # calculate the randomly chosen destinations, same way as client did.\",\n \"src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got bad blame; clamed\",\n \"None at positions of missing signatures). - To reset the server for a\",\n \"call start_components. - To signal the end of covert components phase, owner calls\",\n \"and/or sell copies of the Software, # and to permit persons to whom\",\n \"missing_sigs = len([s for s in signatures if s is None]) ### self.print_error(f\\\"ending\",\n \"except Exception as e: self.print_error(f\\\"player indicated bad input but checking failed with exception\",\n \"nothing after this point can report back to the # verifier, there is\",\n \"c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None):\",\n \"= False, server_time = begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME)\",\n \"use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the\",\n \"in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof\",\n \"check_input_electrumx(self.network, ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad\",\n \"player. # we aren't collecting any results, rather just marking that # 'checking\",\n \"positions of missing signatures). - To reset the server for a new round,\",\n \"limitation? max_clients = (100000 - 12) // (num_components * 173) # Every round,\",\n \"change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except\",\n \"sum(f for _,_,f in results) # Generate scrambled commitment list, but remember exactly\",\n \"annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception\",\n \"finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the\",\n \"list, so that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients)\",\n \"try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else:\",\n \"minimum (for privacy)? min_safe_clients = 6 # Choose the minimum excess fee based\",\n \"tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping or start_ev.is_set():\",\n \"6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class Params:\",\n \"could blame the originator, however # blockchain checks are somewhat subjective. It would\",\n \"start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: # this\",\n \"= 400 # whether to print a lot of logs noisy = False\",\n \"t in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try:\",\n \"ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you\",\n \"launch Fusion at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) #\",\n \"these configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB max_excess_fee\",\n \"client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if\",\n \"tfill_thresh if t == self.tier_best: # this is the favoured pool, can start\",\n \"error: {e}\\\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in\",\n \"might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions from\",\n \"for signalling us that a pool started. start_ev = threading.Event() client.start_ev = start_ev\",\n \"in client.tags: ts = self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool\",\n \"self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted at wrong time')\",\n \"reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured fuse: - Out of the\",\n \"covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})')\",\n \"threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def __enter__(self, ):\",\n \"sats/kB max_excess_fee = 300000 # sats tiers = [round(b*s) for b in [10000,\",\n \"# If all clients submitted largest possible component (uncompressed p2pkh input), how many\",\n \"# do indeed disallow them connecting if they are e.g. on testnet #\",\n \"clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler\",\n \"time_best is None or ft < time_best or size > size_best: time_best =\",\n \"donation_address = donation_address )) # We allow a long timeout for clients to\",\n \"self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails = [] def\",\n \"to join, reject) max_tier_client_tags = 100 # For a given IP, how many\",\n \"self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ):\",\n \"+ Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None):\",\n \"pool.try_move_from_queue() # Update timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following\",\n \"shouldn't happen, we had plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures\",\n \"# cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt\",\n \"= config self.network = network self.announcehost = announcehost self.donation_address = donation_address self.waiting_pools =\",\n \"# used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client,\",\n \"not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def\",\n \"NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY\",\n \"# and we are mainnet, etc. client.error(\\\"This server is on a different chain,\",\n \"tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move clients from\",\n \"donation_address )) # We allow a long timeout for clients to choose their\",\n \"connecting if they are e.g. on testnet # and we are mainnet, etc.\",\n \"= [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag\",\n \"self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") # mark\",\n \"Report this back to clients # as an 'internal server error'. raise else:\",\n \"launch a fusion if the pool has stayed at or above min_clients for\",\n \"e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying\",\n \"even make sense with one player. for c in self.clients: c.kill('blame yourself!') return\",\n \"wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey =\",\n \"be we already have this signature. This is fine # since it might\",\n \"from server side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp = None,\",\n \"continue except Exception as e: self.print_error(f\\\"player indicated bad input but checking failed with\",\n \"all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue()\",\n \"send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg):\",\n \"= Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious.\",\n \"for a while if many # checks against blockchain need to be done,\",\n \"clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results) #\",\n \"if they are e.g. on testnet # and we are mainnet, etc. client.error(\\\"This\",\n \"covert signatures phase, owner calls end_signatures, which returns a list of signatures (which\",\n \"a lot of logs noisy = False # How long covert connections are\",\n \"'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a\",\n \"AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with\",\n \"noisy = False # How long covert connections are allowed to stay open\",\n \"BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR\",\n \"with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost\",\n \"connections are allowed to stay open without activity. # note this needs to\",\n \"self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until\",\n \"seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)):\",\n \"this # will form the basis of our covert timeline. covert_T0 = time.monotonic()\",\n \"stay open without activity. # note this needs to consider the maximum interval\",\n \"ft tier_best = t size_best = size if time_best is None: self.tier_best_starttime =\",\n \"= list() # clients who are waiting due to tags being full self.tags\",\n \"acceptance. {len(component_master_list)} received.\\\") # Sort the components & contribs list, then separate it\",\n \"range\\\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools\",\n \"self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds\",\n \"on a different chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared\",\n \"for bad input: {reason}\\\") src_client.kill('you provided a bad input: ' + reason) continue\",\n \"# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF #\",\n \"persons to whom the Software is furnished to do so, # subject to\",\n \"gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) #\",\n \"[contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some preliminary\",\n \"covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings set up incorrectly\\\"\",\n \"rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\"\",\n \"> 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component\",\n \"a gentler goodbye than killing if text is not None: client.send_error(text) raise client.Disconnect\",\n \"= calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful\",\n \"2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage,\",\n \"import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values -- round\",\n \"start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs\",\n \"check_input_electrumx) # Resistor \\\"E series\\\" values -- round numbers that are almost geometrically\",\n \"client): for t in client.tags: ts = self.tags.get(t) if ts is not None\",\n \"# broadcast a malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok\",\n \"self.round_pubkey feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong\",\n \"when did pool exceed fill_threshold self.tag_max = tag_max # how many clients can\",\n \"# Await commitment messages then process results results = collector.gather(deadline = covert_T0 +\",\n \"sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem\",\n \"is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\"\",\n \"return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool = 0\",\n \"remaining live players\\\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at\",\n \"scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading\",\n \"IP, how many players can they represent in the same fuse? ip_max_simul_fuse =\",\n \"= None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey = None def start_components(self,\",\n \"len(self.clients) < 2: # Sanity check for testing -- the proof sharing thing\",\n \"max_excess_fee = 300000 # sats tiers = [round(b*s) for b in [10000, 100000,\",\n \"client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME\",\n \"the other components were # just imposters who didn't have private key. If\",\n \"to print a lot of logs noisy = False # How long covert\",\n \"missing signatures). - To reset the server for a new round, call .reset();\",\n \"a resubmission after ack failed delivery, # but we don't allow it to\",\n \"the overhead amongst players, in the smallest fusion # (these overhead numbers assume\",\n \"self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion\",\n \"privacy leak by the ommission. continue assert ret, 'expecting input component' outpoint =\",\n \"server side. \\\"\\\"\\\" def __init__(self, network, tier, clients, bindhost, upnp = None, announcehost\",\n \"more try to join, reject) max_tier_client_tags = 100 # For a given IP,\",\n \"commitment messages then process results results = collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) #\",\n \"= 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is\",\n \"= round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl =\",\n \"per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names,\",\n \"charge, to any person # obtaining a copy of this software and associated\",\n \"if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return True\",\n \"check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max for t,\",\n \"skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting\",\n \"then there is no favoured fuse. (since fill time is a float, this\",\n \"ci, cj in commitment_master_list if ci != myindex] N = len(possible_commitment_destinations) assert N\",\n \"didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients = [c\",\n \"for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof,\",\n \"overhead = 60 else: # the smallest fusion will use 1-byte varint for\",\n \"# - how long from first connection to last possible Tor component submission?\",\n \"coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission is hereby\",\n \"client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob\",\n \"= announcehost self.daemon = True def sendall(self, msg, timeout = Protocol.STANDARD_TIMEOUT): for client\",\n \"collector): with collector: # an in-place sort by source commitment idx removes ordering\",\n \"if it has only been above min_clients for a short time (unless pool\",\n \"== len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same way as\",\n \"Generate scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list =\",\n \"remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2)\",\n \"form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent at\",\n \"6): client.error(\\\"Tag limit out of range\\\") ip = '' if tag.no_ip else client_ip\",\n \"WaitingPool: \\\"\\\"\\\" a waiting pool for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold,\",\n \"self.fill_time = None for t in client.tags: ts = self.tags[t] ts.all_ -= 1\",\n \"num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers\",\n \"We got a second submission before a new phase started. As # an\",\n \"E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8,\",\n \"as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from\",\n \"try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False\",\n \"point we could blame the originator, however # blockchain checks are somewhat subjective.\",\n \"new round, call .reset(); to kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self,\",\n \"misconfigured?\\\") # This probably indicates misconfiguration since fusion server ought # to have\",\n \"rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for\",\n \"msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not\",\n \"sort by source commitment idx removes ordering correlations about which client sent which\",\n \"for t in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\")\",\n \"cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send\",\n \"session_hash)) # Sleep until end of covert signatures phase remtime = covert_T0 +\",\n \"possible destinations list (all commitments, but leaving out the originating client's commitments). myindex\",\n \"allow one submission per connection # per phase. client.error('multiple submission in same phase')\",\n \"- how long from first connection to last possible Tor component submission? #\",\n \"import Protocol from .util import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from\",\n \"valid signature. This is not # allowed and we break the connection as\",\n \"blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in\",\n \"AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def new_client_job(self, client):\",\n \"OR OTHER DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation for\",\n \"mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best\",\n \"# including without limitation the rights to use, copy, modify, merge, # publish,\",\n \"return \\\"too many clients with same tag\\\" def _add_pool(self, client): self.pool.add(client) for t\",\n \"relays = [] for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N,\",\n \"def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def reset(self):\",\n \"really shouldn't happen, we had plenty of time raise FusionError(\\\"way too slow\\\") time.sleep(remtime)\",\n \"= tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError\",\n \"FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds running from server side. \\\"\\\"\\\"\",\n \"i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg\",\n \"- time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full commitment list;\",\n \"statuses, also check start times. statuses = dict() tfill_thresh = tnow - Params.start_time_max\",\n \"ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci,\",\n \"do we want before starting a fusion? min_clients = 8 # If all\",\n \"round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to see\",\n \"timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not\",\n \"[sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for\",\n \"self.results = [] self.fails = [] def __enter__(self, ): return self def __exit__(self,\",\n \"at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <=\",\n \"number of pool clients to trigger setting fill_time self.fill_time = None # when\",\n \"if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True\",\n \"else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in\",\n \"notice and this permission notice shall be # included in all copies or\",\n \"def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start\",\n \"c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for\",\n \"got a second submission before a new phase started. As # an anti-spam\",\n \"be put into fusion round if started at this tier self.queue = list()\",\n \"msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol version, please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash !=\",\n \"mainnet, etc. client.error(\\\"This server is on a different chain, please switch servers\\\") else:\",\n \"# (the \\\"Software\\\"), to deal in the Software without restriction, # including without\",\n \"# # The above copyright notice and this permission notice shall be #\",\n \"transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, =\",\n \"collector = ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector:\",\n \"modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, #\",\n \"# the smallest fusion could require 3-byte varint for either inputs or outputs\",\n \"OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase\",\n \"1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7,\",\n \"[c for c in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for\",\n \"to allow unlimited access client.tags = [] else: # Default tag: this IP\",\n \"def __init__(self, config, network, bindhost, port, upnp = None, announcehost = None, donation_address\",\n \"ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a pool tag \\\"\\\"\\\" __slots__ =\",\n \"client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names,\",\n \"to move clients from queue into pool moved = [] for client in\",\n \"for commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients: scalars\",\n \"self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee =\",\n \"somewhat subjective. It would be # appropriate to add some 'ban score' to\",\n \"dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit =\",\n \"in self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in\",\n \"== 'ping': continue if client.got_submit: # We got a second submission before a\",\n \"before uploading commitments, as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS\",\n \"signatures (which will have None at positions of missing signatures). - To reset\",\n \"waiting pools on failure (on success, we are already removed; on stop we\",\n \"fusion, wait this long before starting the next one (unless hit max time\",\n \"covert signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache\",\n \"lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients\",\n \"list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True\",\n \"FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE\",\n \"server admin may run an SSL server proxy such as nginx for that\",\n \"clients get a ClientThread made for them, and they are put into the\",\n \"failed with exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it was\",\n \"# can start next round right away. collector.add(None) for idx, (client, proofs) in\",\n \"timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: # We got\",\n \"__init__(self, network, tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network\",\n \"few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem detected: excess\",\n \"# included in all copies or substantial portions of the Software. # #\",\n \"pass def new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component',\",\n \">= Params.max_clients: # pool filled up to the maximum size, so start immediately\",\n \"a bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if\",\n \"self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool = True for t in\",\n \"choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\")\",\n \"Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too long\\\")\",\n \"exactly where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_,\",\n \"del self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except\",\n \"client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags = [] else:\",\n \"- 12) // (num_components * 173) # Every round, clients leave ... How\",\n \"as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server\",\n \"submitted at wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input]\",\n \"def remove(self, client): # make sure to call try_move_from_queue() after calling this try:\",\n \"claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for\",\n \"zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector #\",\n \"Sleep until end of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS -\",\n \"Choose the minimum excess fee based on dividing the overhead amongst players, in\",\n \"ret = self.results del self.results return ret def add(self, result): with self.lock: try:\",\n \"trying to broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration since fusion server\",\n \"of this function might run for a while if many # checks against\",\n \"outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as\",\n \"the favoured tier self.reset_timer() inftime = float('inf') while True: with self.lock: if self.stopping\",\n \"-= 1 if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t]\",\n \"tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start.\",\n \"the players to # broadcast a malleated version by re-signing one of their\",\n \"proofs): client.error(\\\"too-long proof\\\") # they should only be 129 bytes long. # generate\",\n \"components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected:\",\n \"who will be put into fusion round if started at this tier self.queue\",\n \"{prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results) # Generate scrambled\",\n \"+ '41' assert tx.is_complete() txid = tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid)\",\n \".validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values --\",\n \"def clientjob_goodbye(client, text): # a gentler goodbye than killing if text is not\",\n \"try: super().run() finally: self.waiting_pools.clear() # gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools\",\n \"self.components = dict() self.feerate = feerate self.round_pubkey = round_pubkey for c in self.spawned_clients:\",\n \"min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How many clients\",\n \"signature') if existing_sig: # We received a distinct valid signature. This is not\",\n \"message to players; record the time we did this round_time = round(time.time()) collector\",\n \"for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start,\",\n \"t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -=\",\n \"= '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate\",\n \"if the client declares the genesis_hash, we # do indeed disallow them connecting\",\n \"'ban score' to the player. # we aren't collecting any results, rather just\",\n \"#!/usr/bin/env python3 # # Oregano - a lightweight Ergon client # CashFusion -\",\n \"copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED\",\n \"at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that\",\n \"is hereby granted, free of charge, to any person # obtaining a copy\",\n \"at wrong time') else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey\",\n \"tags being full self.tags = defaultdict(TagStatus) # how are the various tags self.fill_threshold\",\n \"covert_server.stop() for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def\",\n \"set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\")\",\n \"True self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures =\",\n \"# We got a second submission before a new phase started. As #\",\n \"set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures =\",\n \"the server at any time. By default, will bind to an ephemeral port.\",\n \"Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len = len(seen_salthashes)\",\n \"not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >=\",\n \">= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer):\",\n \"else: # Default tag: this IP cannot be present in too many fuses.\",\n \"# The above copyright notice and this permission notice shall be # included\",\n \"WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN\",\n \"was fine ({outpoint})\\\") # At this point we could blame the originator, however\",\n \"64: raise ValidationError('signature length is wrong') # It might be we already have\",\n \"fill_threshold self.tag_max = tag_max # how many clients can share same tag (in\",\n \"in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if not (0\",\n \"i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail\",\n \"(_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit\",\n \"is None) # further, search for duplicated inputs (through matching the prevout and\",\n \"Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers,\",\n \"permit persons to whom the Software is furnished to do so, # subject\",\n \"Software without restriction, # including without limitation the rights to use, copy, modify,\",\n \"__slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b =\",\n \"for clients waiting to start a fusion. New clients get a ClientThread made\",\n \"self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c in self.clients\",\n \"): \\\"\\\"\\\" Scan pools for the favoured fuse: - Out of the pool(s)\",\n \"where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients]\",\n \"del c.blinds, c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments,\",\n \"in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client))\",\n \"res = pool.check_add(client) if res is not None: client.error(res) for t in mytiers:\",\n \"t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as\",\n \"timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def\",\n \"def end_components(self): with self.lock: ret = self.components del self.components return ret def start_signatures(self,\",\n \"if sig is None) # further, search for duplicated inputs (through matching the\",\n \"if min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion could\",\n \"the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off\",\n \"c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del results, collector # Sleep a\",\n \"manage to give a good commitment. prev_client_count = len(self.clients) self.clients = [c for\",\n \"third parties can't abuse us to find out the # timing of a\",\n \"To reset the server for a new round, call .reset(); to kill all\",\n \"pool moved = [] for client in self.queue: for t in client.tags: ts\",\n \"a ClientThread made for them, and they are put into the waiting pools.\",\n \"THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion. Does not natively\",\n \"note where each client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in\",\n \"transaction signature') if existing_sig: # We received a distinct valid signature. This is\",\n \"= bool(done_on_fail) self.done_ev = threading.Event() self.lock = threading.Lock() self.results = [] self.fails =\",\n \"for i, proof in enumerate(proofs): dest_client_idx, dest_key_idx = possible_commitment_destinations[rand_position(seed, N, i)] src_commitment_idx =\",\n \"msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping':\",\n \"begin_time)) self.last_hash = calc_initial_hash(self.tier, annhost_b, annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds\",\n \"c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for\",\n \"started, the ClientThreads are passed over to a FusionController to run the rounds.\\\"\\\"\\\"\",\n \"else: assert mtype == 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig\",\n \"timing info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from\",\n \"WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE.\",\n \"raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config = config\",\n \"substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\",\",\n \"encproof, src_commitment_idx, dest_key_idx, src_client = proofs[blame.which_proof] except IndexError: client.kill(f'bad proof index {blame.which_proof} /\",\n \"sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib)\",\n \"= validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except ValidationError as e: self.print_error(\\\"got\",\n \"(self, ): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp\",\n \"* num_components >= 2 * 0xfc: # the smallest fusion could require 3-byte\",\n \"the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp = None, announcehost =\",\n \"self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies =\",\n \"error'. raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a small head\",\n \"are somewhat subjective. It would be # appropriate to add some 'ban score'\",\n \"We allow a long timeout for clients to choose their pool. msg =\",\n \"blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in\",\n \"Scan pools for the favoured fuse: - Out of the pool(s) with the\",\n \"OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION\",\n \"covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the\",\n \"(announcing as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port\",\n \"' + reason) continue except Exception as e: self.print_error(f\\\"player indicated bad input but\",\n \"// (num_components * 173) # Every round, clients leave ... How many clients\",\n \"and outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc: #\",\n \"\\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection,\",\n \"= ft tier_best = t size_best = size if time_best is None: self.tier_best_starttime\",\n \"server implementation for CashFusion. Does not natively offer SSL support, however a server\",\n \"def recv(self, *expected_msg_names, timeout=None): submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg,\",\n \"result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails) +\",\n \"= False def end_components(self): with self.lock: ret = self.components del self.components return ret\",\n \"def client_get_blames(client, myindex, proofs, collector): with collector: # an in-place sort by source\",\n \"how many players can they represent in the same fuse? ip_max_simul_fuse = 3\",\n \"pool.fill_time if ft is None: continue size = len(pool.pool) if size >= size_best:\",\n \"__init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting pool\",\n \"self.upnp, announcehost = self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0]\",\n \"the possible destinations list (all commitments, but leaving out the originating client's commitments).\",\n \"results, rather just marking that # 'checking finished' so that if all blames\",\n \"pools for the favoured fuse: - Out of the pool(s) with the most\",\n \"commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client\",\n \"have reasonable # privacy with what we have. bad_components = set() ### if\",\n \"high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong')\",\n \"for that purpose. \\\"\\\"\\\" import secrets import sys import threading import time import\",\n \"in live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few remaining live players\\\")\",\n \"len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the\",\n \"all the other components were # just imposters who didn't have private key.\",\n \"malleated version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures\",\n \"num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes\",\n \"sublicense, and/or sell copies of the Software, # and to permit persons to\",\n \"def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts is\",\n \"t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self,\",\n \"blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue\",\n \"# Resistor \\\"E series\\\" values -- round numbers that are almost geometrically uniform\",\n \"as clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if\",\n \"bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)):\",\n \"round, clients leave ... How many clients do we need as an absolute\",\n \"Every round, clients leave ... How many clients do we need as an\",\n \"list, but remember exactly where each commitment originated. commitment_master_list = [(commit, ci, cj)\",\n \"+ Protocol.T_START_COMPS - time.monotonic() if remtime > 0: time.sleep(remtime) # Upload the full\",\n \"msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\"\",\n \"away. collector.add(None) for idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector)\",\n \"If more than one # signed, then it's malicious behaviour! if sum((signatures[i] is\",\n \"a copy of this software and associated documentation files # (the \\\"Software\\\"), to\",\n \"of covert components phase, call start_components. - To signal the end of covert\",\n \"is no privacy leak by the ommission. continue assert ret, 'expecting input component'\",\n \"the adding order so that if filling more than one pool, we don't\",\n \"call .reset(); to kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0,\",\n \"= Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live =\",\n \"= all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components, Params.component_feerate) except\",\n \"self.tag_max = tag_max # how many clients can share same tag (in pool\",\n \"def check_client_count(self,): live = [c for c in self.clients if not c.dead] if\",\n \"= msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs =\",\n \"see if we even have reasonable # privacy with what we have. bad_components\",\n \"checks to see whether we should just skip the # signing phase and\",\n \"do we need as an absolute minimum (for privacy)? min_safe_clients = 6 #\",\n \"< Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too\",\n \"def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool +=\",\n \"for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break self.print_error('Ended successfully!')\",\n \"prev_client_count = len(self.clients) self.clients = [c for c, _, _ in results] self.check_client_count()\",\n \"as: {annhost_b}:{annport})') begin_time = round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port =\",\n \"= [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0])\",\n \"s is None]) ### self.print_error(f\\\"ending covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs\",\n \"version by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures =\",\n \"msg), timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\"\",\n \"None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address, type(None)))\",\n \"pool and queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t)\",\n \"to players; this # will form the basis of our covert timeline. covert_T0\",\n \"None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate = feerate self.round_pubkey =\",\n \"'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline -\",\n \"1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self, client): can_pool =\",\n \"doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime > 0:\",\n \"honest guy and all the other components were # just imposters who didn't\",\n \"0x41, use_cache = True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp\",\n \"# make sure to call try_move_from_queue() after calling this try: self.pool.remove(client) except KeyError:\",\n \"successful!\\\") # Give our transaction a small head start in relaying, before sharing\",\n \"bad input but it was fine ({outpoint})\\\") # At this point we could\",\n \"return super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool',\",\n \"the pool with the earliest fill time; - If no pools are filled\",\n \"self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars =\",\n \"players to # broadcast a malleated version by re-signing one of their inputs.\",\n \"to kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp =\",\n \"__slots__ = ('pool', 'all_') def __init__(self): self.pool = 0 self.all_ = 0 class\",\n \"= upnp) self.config = config self.network = network self.announcehost = announcehost self.donation_address =\",\n \"info self.t_last_fuse = time.monotonic() self.reset_timer() # Uncomment the following to: Remove from spawned\",\n \"self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self,\",\n \"send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self,\",\n \"as a result. # Note that we could have aborted earlier but this\",\n \"clients submitted largest possible component (uncompressed p2pkh input), how many could we take\",\n \"5 (lokad) + 33 (session hash) ) if min_safe_clients * num_components >= 2\",\n \"min_safe_clients # How many clients can share same tag on a given tier\",\n \"config, network, bindhost, port, upnp = None, announcehost = None, donation_address = None):\",\n \"\\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified\",\n \"for m in commit_messages) with lock: expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if\",\n \"as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction!\",\n \"outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) //\",\n \"will start eventually remtime = pool.fill_time - tfill_thresh if t == self.tier_best: #\",\n \"client's commitments ended up client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i,\",\n \"dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients,\",\n \"\\\"\\\"\\\" with self.lock: time_best = None tier_best = None size_best = 0 for\",\n \"fine ({outpoint})\\\") # At this point we could blame the originator, however #\",\n \"offer SSL support, however a server admin may run an SSL server proxy\",\n \"the end of covert signatures phase, owner calls end_signatures, which returns a list\",\n \"record the time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail\",\n \"= upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate\",\n \"seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs\",\n \"for both inputs and outputs lists overhead = 62 elif min_safe_clients * num_components\",\n \"enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT\",\n \"delivery, # but we don't allow it to consume our CPU power. if\",\n \"Software, # and to permit persons to whom the Software is furnished to\",\n \"client_get_proofs(client, collector): with collector: msg = client.recv('myproofslist') seed = msg.random_number if sha256(seed) !=\",\n \"reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self,\",\n \"raise else: self.print_error(\\\"broadcast was successful!\\\") # Give our transaction a small head start\",\n \"= 300000 # sats tiers = [round(b*s) for b in [10000, 100000, 1000000,\",\n \"has only been above min_clients for a short time (unless pool is full).\",\n \"(uncompressed p2pkh input), how many could we take until the result would exceed\",\n \"self.lock: ret = self.results del self.results return ret def add(self, result): with self.lock:\",\n \"Send start message to players; record the time we did this round_time =\",\n \"covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey = covert_Cpub # start to accept covert\",\n \"= [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients)\",\n \"= set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub\",\n \"i, inp in enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders)\",\n \"fine # since it might be a resubmission after ack failed delivery, #\",\n \"owner calls start_signatures. - To signal the end of covert signatures phase, owner\",\n \"# gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c\",\n \"open without activity. # note this needs to consider the maximum interval between\",\n \"self.lock: ret = self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs\",\n \"ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client)\",\n \"leave ... How many clients do we need as an absolute minimum (for\",\n \"possible Tor component submission? # - how long from one round's component submission\",\n \") if min_safe_clients * num_components >= 2 * 0xfc: # the smallest fusion\",\n \"queue) def check_add(self, client): for t in client.tags: ts = self.tags.get(t) if ts\",\n \"if client.got_submit: # We got a second submission before a new phase started.\",\n \"# add this client to waiting pools for pool in mytierpools.values(): res =\",\n \"for x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME)\",\n \"# Record the time that we sent 'startround' message to players; this #\",\n \"del self.round_pubkey del self.components del self.feerate except AttributeError: pass try: del self.sighashes del\",\n \"enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures\",\n \"import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import\",\n \"[1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2,\",\n \"100 # For a given IP, how many players can they represent in\",\n \"and go directly to blame, or maybe even restart / end # without\",\n \"# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT\",\n \"annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started\",\n \"ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256, calc_initial_hash,\",\n \"())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic()\",\n \"if size >= size_best: if time_best is None or ft < time_best or\",\n \"fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if\",\n \"self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost\",\n \"oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException\",\n \"# Upload the full commitment list; we're a bit generous with the timeout\",\n \"Server for covert submissions. How it works: - Launch the server at any\",\n \"6.8, 7.5, 8.2, 9.1] # TODO - make these configurable class Params: num_components\",\n \"in msg.tiers} except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers\",\n \"to same proof') # Note, the rest of this function might run for\",\n \"with the earliest fill time; - If no pools are filled then there\",\n \"(these overhead numbers assume op_return script size of 1 + 5 (lokad) +\",\n \"deadline - time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return\",\n \"# # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND,\",\n \"wait this long before starting the next one (unless hit max time or\",\n \"\\\"\\\"\\\" Immediately launch Fusion at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients =\",\n \"client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too\",\n \"client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20:\",\n \"as e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e}\",\n \"= list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort the components &\",\n \"the maximum size, so start immediately self.start_fuse(t) return # we have added to\",\n \"following to: Remove from spawned clients list, so that the fusion can continue\",\n \"component_master_list # Do some preliminary checks to see whether we should just skip\",\n \"(lokad) + 33 (session hash) ) if min_safe_clients * num_components >= 2 *\",\n \"the player. # we aren't collecting any results, rather just marking that #\",\n \"in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate =\",\n \"Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse happened; as a placeholder,\",\n \"except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast\",\n \"- tfill_thresh if t == self.tier_best: # this is the favoured pool, can\",\n \"a deadline. def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail =\",\n \"claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a bad proof (for {src_commitment_idx}): {ret}\\\")\",\n \"of this software and associated documentation files # (the \\\"Software\\\"), to deal in\",\n \"Do some preliminary checks to see whether we should just skip the #\",\n \"check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many blames') if\",\n \"raise ValidationError('signature length is wrong') # It might be we already have this\",\n \"after starting any fusion, wait this long before starting the next one (unless\",\n \"= time.monotonic() self.print_error(f\\\"startround sent at {time.time()}; accepting covert components\\\") # Await commitment messages\",\n \"c in self.clients: c.kill('blame yourself!') return # scan the commitment list and note\",\n \"components phase, call start_components. - To signal the end of covert components phase,\",\n \"is not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if\",\n \"4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make\",\n \"in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in msg.tags:\",\n \"proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() +\",\n \"detected: too few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True self.print_error(\\\"problem\",\n \"try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature\",\n \"\\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)}\",\n \"self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def add(self,\",\n \"trigger setting fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max\",\n \"time. # Since nothing after this point can report back to the #\",\n \"min_clients = 8 # If all clients submitted largest possible component (uncompressed p2pkh\",\n \"2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2,\",\n \"= size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best\",\n \"us that a pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'):\",\n \"proof sharing thing doesn't even make sense with one player. for c in\",\n \"bad proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead:\",\n \"one player. for c in self.clients: c.kill('blame yourself!') return # scan the commitment\",\n \"an advanced coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission\",\n \"client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except\",\n \"c.blind_sig_requests del results, collector # Sleep a bit before uploading commitments, as clients\",\n \"max time or pool is full). start_time_spacing = 120 # But don't start\",\n \"size_best = 0 for t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft\",\n \"making us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too many\",\n \"any time. By default, will bind to an ephemeral port. - Before start\",\n \"= time.monotonic() # when the last fuse happened; as a placeholder, set this\",\n \"who didn't manage to give a good commitment. prev_client_count = len(self.clients) self.clients =\",\n \"= False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few\",\n \"the Fusion rounds running from server side. \\\"\\\"\\\" def __init__(self, network, tier, clients,\",\n \"the components & contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components =\",\n \"ClientThreads are passed over to a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self,\",\n \"order so that if filling more than one pool, we don't have bias\",\n \"+ len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c,\",\n \"time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort the\",\n \"This makes it slightly harder for one of the players to # broadcast\",\n \"x,y,z, _ in proofs])) msg = client.recv('blames', timeout = Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) #\",\n \"signature acceptance\\\") tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache =\",\n \"__init__(self, fill_threshold, tag_max): self.pool = set() # clients who will be put into\",\n \"blame in msg.blames)) != len(msg.blames): client.error('multiple blames point to same proof') # Note,\",\n \"to destination. proofs_to_relay = [list() for _ in self.clients] for src_client, relays in\",\n \"This is fine # since it might be a resubmission after ack failed\",\n \"commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components: client.error(\\\"wrong number of proofs\\\") if\",\n \"traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c\",\n \"ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT,\",\n \"msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment =\",\n \"clients are doing this. remtime = covert_T0 + Protocol.T_START_COMPS - time.monotonic() if remtime\",\n \"phase and go directly to blame, or maybe even restart / end #\",\n \"# obtaining a copy of this software and associated documentation files # (the\",\n \"for c in self.spawned_clients: c.got_submit = False def end_components(self): with self.lock: ret =\",\n \"b in c.blinds], server_time = round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg,\",\n \"e: self.print_error(f\\\"player indicated bad input but checking failed with exception {repr(e)} ({outpoint})\\\") else:\",\n \"commitment_master_list) # Send blind signatures for c in self.clients: scalars = [b.sign(covert_priv, e)\",\n \"min_safe_clients = 6 # Choose the minimum excess fee based on dividing the\",\n \"need to be done, perhaps even still # running after run_round has exited.\",\n \"if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate\",\n \"collector: # an in-place sort by source commitment idx removes ordering correlations about\",\n \"client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg =\",\n \"a good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _\",\n \"How many clients can share same tag on a given tier (if more\",\n \"start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for\",\n \"if any(len(p) > 200 for p in proofs): client.error(\\\"too-long proof\\\") # they should\",\n \"False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True:\",\n \"(for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If\",\n \"than killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic\",\n \"end of covert signatures phase, owner calls end_signatures, which returns a list of\",\n \"remtime = min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif\",\n \"ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0: #\",\n \"pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg, timeout=None): send_pb(self.connection, pb.CovertResponse, submsg,\",\n \"if src_client.dead: # If the blamed client is already dead, don't waste more\",\n \"immediately since client may be trying to DoS us by # making us\",\n \"a placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run() finally:\",\n \"pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to the maximum size,\",\n \"could require 3-byte varint for either inputs or outputs lists overhead = 60\",\n \"self.signatures del self.signatures return ret def reset(self): try: del self.round_pubkey del self.components del\",\n \"# but we don't allow it to consume our CPU power. if sig\",\n \"existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise\",\n \"size_best: time_best = ft tier_best = t size_best = size if time_best is\",\n \"= annhost_b, covert_port = annport, covert_ssl = False, server_time = begin_time)) self.last_hash =\",\n \"= False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R()\",\n \"long before starting the next one (unless hit max time or pool is\",\n \"# we have added to pools, which may have changed the favoured tier\",\n \"enumerate(tx.inputs()): prevout_spenders[f\\\"{inp['prevout_hash']}:{inp['prevout_n']} {inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue\",\n \"0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting pool for a specific\",\n \"DoS us by # making us check many inputs against blockchain. if len(msg.blames)\",\n \"to DoS us by # making us check many inputs against blockchain. if\",\n \"filled up to the maximum size, so start immediately self.start_fuse(t) return # we\",\n \"point to same proof') # Note, the rest of this function might run\",\n \".comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util\",\n \"pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info\",\n \"self.components del self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError:\",\n \"proofs, collector): with collector: # an in-place sort by source commitment idx removes\",\n \"IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx]\",\n \"Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address\",\n \"= min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime\",\n \"try: self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool) <\",\n \"collect statuses, also check start times. statuses = dict() tfill_thresh = tnow -\",\n \"pool.check_add(client) if res is not None: client.error(res) for t in mytiers: pool =\",\n \"for c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair()\",\n \"t, pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size\",\n \"dead clients self.clients = [c for c in self.clients if not c.dead] self.check_client_count()\",\n \"sent 'startround' message to players; this # will form the basis of our\",\n \"component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee, tiers = Params.tiers, donation_address\",\n \">= self.tag_max: return \\\"too many clients with same tag\\\" def _add_pool(self, client): self.pool.add(client)\",\n \"for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start\",\n \"conditions: # # The above copyright notice and this permission notice shall be\",\n \"myindex, proofs, collector): with collector: # an in-place sort by source commitment idx\",\n \"sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes\",\n \"= self.announcehost) fusion.start() return len(chosen_clients) def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg =\",\n \"This probably indicates misconfiguration since fusion server ought # to have a good\",\n \"__init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey\",\n \"= round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector:\",\n \"phase. bad components: {bad_components}\\\") if len(self.clients) < 2: # Sanity check for testing\",\n \"i)] src_commitment_idx = client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late\",\n \"(dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees = sum(f for _,_,f in results) # Generate\",\n \"At this point we could blame the originator, however # blockchain checks are\",\n \"maybe even restart / end # without sharing components. skip_signatures = False if\",\n \"in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list\",\n \"this point can report back to the # verifier, there is no privacy\",\n \"or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS\",\n \"tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t\",\n \"the proofs according to destination. proofs_to_relay = [list() for _ in self.clients] for\",\n \"= None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason) def run_round(self, covert_server):\",\n \"scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list = [(commit,\",\n \"min(remtime, self.tier_best_starttime - tnow) if remtime <= 0: self.start_fuse(t) return elif remtime !=\",\n \"a fusion if the pool has stayed at or above min_clients for this\",\n \"error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object\",\n \"min_safe_clients - 1) // min_safe_clients # How many clients can share same tag\",\n \"pool, we don't have bias towards any particular tier with self.lock: if self.stopping:\",\n \"not None: client.error(res) for t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool)\",\n \"'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit: #\",\n \"_ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i\",\n \"most number of players, - Choose the pool with the earliest fill time;\",\n \"c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few remaining live players\\\") def run\",\n \"\\\"\\\"\\\"Server for clients waiting to start a fusion. New clients get a ClientThread\",\n \"if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client)\",\n \"one of the players to # broadcast a malleated version by re-signing one\",\n \"[] else: # Default tag: this IP cannot be present in too many\",\n \"skip_signatures = True self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees != sum(component_contribs):\",\n \"basic server implementation for CashFusion. Does not natively offer SSL support, however a\",\n \"of charge, to any person # obtaining a copy of this software and\",\n \"is a float, this will almost always be unique) \\\"\\\"\\\" with self.lock: time_best\",\n \"a while if many # checks against blockchain need to be done, perhaps\",\n \"other components were # just imposters who didn't have private key. If more\",\n \"ResultsCollector(live_clients, done_on_fail = False) def client_get_blames(client, myindex, proofs, collector): with collector: # an\",\n \"this function might run for a while if many # checks against blockchain\",\n \"clients with same tag\\\" def _add_pool(self, client): self.pool.add(client) for t in client.tags: ts\",\n \"= session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs\",\n \"results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays: proofs_to_relay[dest_client_idx].append((proof, src_commitment_idx, dest_key_idx, src_client)) live_clients\",\n \"[bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) #\",\n \"msg.txsignature if len(sig) != 64: raise ValidationError('signature length is wrong') # It might\",\n \"to stay open without activity. # note this needs to consider the maximum\",\n \"self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure to call\",\n \"Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME) # More than one blame per proof is malicious. Boot\",\n \"person # obtaining a copy of this software and associated documentation files #\",\n \"assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes = sighashes self.pubkeys = pubkeys\",\n \"that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx, src_client =\",\n \"10000000, 100000000] for s in E12] # How many clients do we want\",\n \"size_best = size if time_best is None: self.tier_best_starttime = None else: self.tier_best_starttime =\",\n \"FusionError(\\\"too few remaining live players\\\") def run (self, ): self.print_error(f'Starting fusion with {len(self.clients)}\",\n \"+ Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a good commitment.\",\n \"import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg,\",\n \"for s in signatures if s is None]) ### self.print_error(f\\\"ending covert signature acceptance.\",\n \"with one player. for c in self.clients: c.kill('blame yourself!') return # scan the\",\n \"in self.queue: for t in client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul:\",\n \"have a good connection to the EC server. Report this back to clients\",\n \"submissions. How it works: - Launch the server at any time. By default,\",\n \"ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid signature. This\",\n \"<reponame>MrNaif2018/Oregano #!/usr/bin/env python3 # # Oregano - a lightweight Ergon client # CashFusion\",\n \"originating client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj\",\n \"contrib) in component_master_list] component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del\",\n \"that purpose. \\\"\\\"\\\" import secrets import sys import threading import time import traceback\",\n \"threading.Lock() self.results = [] self.fails = [] def __enter__(self, ): return self def\",\n \"etc. client.error(\\\"This server is on a different chain, please switch servers\\\") else: client.print_error(\\\"👀\",\n \"src_commitment_idx, dest_key_idx, src_client)) live_clients = len(results) collector = ResultsCollector(live_clients, done_on_fail = False) def\",\n \"/ {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret\",\n \"not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config\",\n \"run for a while if many # checks against blockchain need to be\",\n \"number of players, - Choose the pool with the earliest fill time; -\",\n \"b'', Params.ip_max_simul_fuse)] for tag in msg.tags: if len(tag.id) > 20: client.error(\\\"Tag id too\",\n \"exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for\",\n \"advanced coin anonymizer # # Copyright (C) 2020 <NAME> # # Permission is\",\n \"exceed fill_threshold self.tag_max = tag_max # how many clients can share same tag\",\n \"contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except\",\n \"signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except\",\n \"it's malicious behaviour! if sum((signatures[i] is not None) for i in spenders) !=\",\n \"components\\\") # Await commitment messages then process results results = collector.gather(deadline = covert_T0\",\n \"we will start. for c in chosen_clients: c.start_ev.set() # Remove those clients from\",\n \"the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF\",\n \"IP cannot be present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)]\",\n \"def __init__(self, num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev\",\n \"= gen_keypair() round_pubkey = covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate)\",\n \"then it's malicious behaviour! if sum((signatures[i] is not None) for i in spenders)\",\n \"one blame per proof is malicious. Boot client # immediately since client may\",\n \"round(time.time()) self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False,\",\n \"if sig != existing_sig: if not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature')\",\n \"{inp['pubkeys'][0]}\\\"].append(i) for prevout, spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of\",\n \"for _ in self.clients] for i, (commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] =\",\n \"tx.txid() self.print_error(\\\"completed the transaction! \\\" + txid) try: self.network.broadcast_transaction2(tx, timeout=3) except ServerError as\",\n \"so that the fusion can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) #\",\n \"dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs])) msg = client.recv('blames', timeout =\",\n \"pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags)\",\n \"after this point can report back to the # verifier, there is no\",\n \"shuffle the adding order so that if filling more than one pool, we\",\n \"Sanity check for testing -- the proof sharing thing doesn't even make sense\",\n \"time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) !=\",\n \"if len(self.fails) + len(self.results) >= self.num_results: self.done_ev.set() return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\"\",\n \"0 class WaitingPool: \\\"\\\"\\\" a waiting pool for a specific tier \\\"\\\"\\\" def\",\n \"self.feerate except AttributeError: pass try: del self.sighashes del self.pubkeys except AttributeError: pass def\",\n \"with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify that we will start. for c\",\n \"new_client_job(self, client): client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping',\",\n \"float, this will almost always be unique) \\\"\\\"\\\" with self.lock: time_best = None\",\n \"del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes) assert num_inputs\",\n \"running after run_round has exited. For this reason we try to # not\",\n \"check for testing -- the proof sharing thing doesn't even make sense with\",\n \"client.error(\\\"Tag limit out of range\\\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip,\",\n \"1 if ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return\",\n \"+ Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime)\",\n \"c.start_ev.set() # Remove those clients from all pools for t, pool in self.waiting_pools.items():\",\n \"- time.monotonic() self.done_ev.wait(max(0., remtime)) with self.lock: ret = self.results del self.results return ret\",\n \"of missing signatures). - To reset the server for a new round, call\",\n \"= 120 # But don't start a fusion if it has only been\",\n \"c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self, goodclients,\",\n \"tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not\",\n \"client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey\",\n \"1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i in bad_inputs) else: for i, (inp,\",\n \"# Copyright (C) 2020 <NAME> # # Permission is hereby granted, free of\",\n \"except KeyError: if self.stopping: return client.error(f\\\"Invalid tier selected: {t}\\\") try: mytiers = list(mytierpools)\",\n \"sell copies of the Software, # and to permit persons to whom the\",\n \"fuse: - Out of the pool(s) with the most number of players, -\",\n \"in relaying, before sharing the # signatures. This makes it slightly harder for\",\n \"deal in the Software without restriction, # including without limitation the rights to\",\n \"ValidationError('signature length is wrong') # It might be we already have this signature.\",\n \"# really shouldn't happen, we had plenty of time raise FusionError(\\\"way too slow\\\")\",\n \"commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob, all_components, bad_components,\",\n \"fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try:\",\n \"of the inputs is signed, we don't punish him # because he's the\",\n \"SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR\",\n \"timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime = covert_T0 +\",\n \"Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() # when the last fuse\",\n \"pools on failure (on success, we are already removed; on stop we don't\",\n \"covert signature acceptance. {missing_sigs} missing :{'(' if missing_sigs else ')'}\\\") # mark all\",\n \"{repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it was fine ({outpoint})\\\") #\",\n \"copies of the Software, # and to permit persons to whom the Software\",\n \"# generate the possible destinations list (all commitments, but leaving out the originating\",\n \"spenders in prevout_spenders.items(): if len(spenders) == 1: continue self.print_error(f\\\"multi-spend of f{prevout} detected\\\") #\",\n \"None) self.clients = [] # gc def kick_missing_clients(self, goodclients, reason = None): baddies\",\n \"_co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() # Send start message\",\n \"or pool is full). start_time_spacing = 120 # But don't start a fusion\",\n \"clients self.clients = [c for c in self.clients if not c.dead] self.check_client_count() if\",\n \"submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5)\",\n \"bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) #\",\n \"such as nginx for that purpose. \\\"\\\"\\\" import secrets import sys import threading\",\n \"IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR\",\n \"): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None:\",\n \"COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\",\n \"b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return super().__new__(cls, b) @property def\",\n \"True def try_move_from_queue(self): # attempt to move clients from queue into pool moved\",\n \"schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp =\",\n \"anonymizer # # Copyright (C) 2020 <NAME> # # Permission is hereby granted,\",\n \"into pool moved = [] for client in self.queue: for t in client.tags:\",\n \"with error: {e}\\\") except Exception as e: self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c\",\n \"self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert components phase remtime\",\n \"in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end\",\n \"None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier self.clients = list(clients) self.bindhost =\",\n \"submsg, mtype = recv_pb(self.connection, pb.CovertMessage, *expected_msg_names, timeout=timeout) return submsg, mtype def send(self, submsg,\",\n \"# # Copyright (C) 2020 <NAME> # # Permission is hereby granted, free\",\n \"the Software without restriction, # including without limitation the rights to use, copy,\",\n \"of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY\",\n \"Before start of covert components phase, call start_components. - To signal the end\",\n \"pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool filled up to\",\n \"= self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time =\",\n \"def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b\",\n \"> 0: time.sleep(remtime) # Upload the full commitment list; we're a bit generous\",\n \"sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid =\",\n \"1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6,\",\n \"as a placeholder, set this to startup time. self.reset_timer() def run(self): try: super().run()\",\n \"always be unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best = None size_best\",\n \"component_contribs = [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do\",\n \"raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid signature.\",\n \"def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_') def __init__(self): self.pool\",\n \"No genesis hash declared by client, we'll let them slide...\\\") if self.stopping: return\",\n \"OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO\",\n \"client.tags: ts = self.tags[t] if ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for\",\n \"= [] for client in self.queue: for t in client.tags: ts = self.tags[t]\",\n \"# repeatedly run rounds until successful or exception while True: covert_server.reset() # Clean\",\n \"recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError, sha256,\",\n \"FusionError(\\\"way too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in\",\n \"too slow\\\") time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures\",\n \"to clients # as an 'internal server error'. raise else: self.print_error(\\\"broadcast was successful!\\\")\",\n \"components were # just imposters who didn't have private key. If more than\",\n \"t, pool in mytierpools.items(): if client not in pool.pool: continue status = pb.TierStatusUpdate.TierStatus(players\",\n \"above min_clients for a short time (unless pool is full). start_time_min = 400\",\n \"only allow one submission per connection # per phase. client.error('multiple submission in same\",\n \"of covert components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime\",\n \"all missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if\",\n \"series\\\" values -- round numbers that are almost geometrically uniform E6 = [1.0,\",\n \"added to pools, which may have changed the favoured tier self.reset_timer() inftime =\",\n \"{len(component_master_list)} received.\\\") # Sort the components & contribs list, then separate it out.\",\n \"client.got_submit = False while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout =\",\n \"check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component\",\n \"import secrets import sys import threading import time import traceback from collections import\",\n \"various tags self.fill_threshold = fill_threshold # minimum number of pool clients to trigger\",\n \"mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that if filling\",\n \"= Params.min_clients) remtime = inftime if pool.fill_time is not None: # a non-favoured\",\n \"self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError: raise ValidationError('which_input too\",\n \"specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who\",\n \"for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update\",\n \"client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time =\",\n \"AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature' try: sighash\",\n \"self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse = time.monotonic() #\",\n \"go directly to blame, or maybe even restart / end # without sharing\",\n \"don't allow it to consume our CPU power. if sig != existing_sig: if\",\n \"this back to clients # as an 'internal server error'. raise else: self.print_error(\\\"broadcast\",\n \"- tnow) if remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining\",\n \"for t in client.tags: ts = self.tags[t] ts.all_ -= 1 if in_pool: ts.pool\",\n \"self.tags[t] ts.all_ += 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool:\",\n \"on failure (on success, we are already removed; on stop we don't care.)\",\n \"pool for a specific tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set()\",\n \"ft = pool.fill_time if ft is None: continue size = len(pool.pool) if size\",\n \"client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs if len(proofs) != Params.num_components:\",\n \"+ len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime =\",\n \"self.waiting_pools.items(): for c in chosen_clients: pool.remove(c) pool.try_move_from_queue() # Update timing info self.t_last_fuse =\",\n \"8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4,\",\n \"a given IP, how many players can they represent in the same fuse?\",\n \"list; we're a bit generous with the timeout but that's OK. self.sendall(pb.AllCommitments(initial_commitments =\",\n \"from spawned clients list, so that the fusion can continue independently of waiting\",\n \"len(tag.id) > 20: client.error(\\\"Tag id too long\\\") if not (0 < tag.limit <\",\n \"in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a fusion.\",\n \"a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh if t\",\n \"acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx,\",\n \"oregano.address import Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2\",\n \"a pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb\",\n \"for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert\",\n \"3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO -\",\n \"a fusion? min_clients = 8 # If all clients submitted largest possible component\",\n \"client from waiting pools on failure (on success, we are already removed; on\",\n \". import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash\",\n \"FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as e: self.print_error('Failed with\",\n \"is (+- amount - fee). - Before start of covert signatures phase, owner\",\n \"Tor component submission? # - how long from one round's component submission to\",\n \"raise class ResultsCollector: # Collect submissions from different sources, with a deadline. def\",\n \"timeout=Protocol.STANDARD_TIMEOUT) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced\",\n \"# an in-place sort by source commitment idx removes ordering correlations about which\",\n \"(0 < tag.limit < 6): client.error(\\\"Tag limit out of range\\\") ip = ''\",\n \"which may have changed the favoured tier self.reset_timer() inftime = float('inf') while True:\",\n \"upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network self.tier = tier\",\n \"full). start_time_spacing = 120 # But don't start a fusion if it has\",\n \"maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb + tagbytes return\",\n \"time.monotonic() # when the last fuse happened; as a placeholder, set this to\",\n \"import oregano.schnorr as schnorr from oregano.address import Address from oregano.util import PrintError, ServerError,\",\n \"= donation_address )) # We allow a long timeout for clients to choose\",\n \"): self.print_error(f'Starting fusion with {len(self.clients)} players at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp =\",\n \"we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def\",\n \"calculate the randomly chosen destinations, same way as client did. relays = []\",\n \"blamed client is already dead, don't waste more time. # Since nothing after\",\n \"self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input: {reason}\\\") src_client.kill('you provided a bad input: ' + reason)\",\n \"client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype == 'ping': continue if client.got_submit:\",\n \"not None: # a non-favoured pool will start eventually remtime = pool.fill_time -\",\n \"ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64: raise ValidationError('signature length\",\n \"IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR\",\n \"starting a fusion? min_clients = 8 # If all clients submitted largest possible\",\n \"if client_ip.startswith('127.'): # localhost is whitelisted to allow unlimited access client.tags = []\",\n \"Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time = round(time.time())\",\n \"# For a given IP, how many players can they represent in the\",\n \"\\\"\\\"\\\" A basic server implementation for CashFusion. Does not natively offer SSL support,\",\n \"= i collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector:\",\n \"purpose. \\\"\\\"\\\" import secrets import sys import threading import time import traceback from\",\n \"long from first connection to last possible Tor component submission? # - how\",\n \"and this permission notice shall be # included in all copies or substantial\",\n \"# Guaranteed time to launch a fusion if the pool has stayed at\",\n \"min_clients for a short time (unless pool is full). start_time_min = 400 #\",\n \"for t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) ==\",\n \"tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event for signalling us\",\n \"kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in baddies: c.kill(reason)\",\n \"that if filling more than one pool, we don't have bias towards any\",\n \"set this to startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() #\",\n \"[c for c, _, _ in results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients\",\n \"for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in\",\n \"overhead numbers assume op_return script size of 1 + 5 (lokad) + 33\",\n \"how long from one round's component submission to the next round's component submission?\",\n \"Params.min_safe_clients: for c in live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few\",\n \"can share same tag on a given tier (if more try to join,\",\n \"= len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if\",\n \"= collector.gather(deadline = covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to\",\n \"against blockchain need to be done, perhaps even still # running after run_round\",\n \"= self.components except AttributeError: client.error('component submitted at wrong time') sort_key, contrib = check_covert_component(msg,\",\n \"self.tag_max: return \\\"too many clients with same tag\\\" def _add_pool(self, client): self.pool.add(client) for\",\n \"round_pubkey, blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time )) msg\",\n \"if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later c.blind_sig_requests =\",\n \"signatures). - To reset the server for a new round, call .reset(); to\",\n \"= False def end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret\",\n \"= random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout = Protocol.STANDARD_TIMEOUT): client.send(msg, timeout=timeout) def clientjob_goodbye(client,\",\n \"\\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT\",\n \"self.fill_time = time.monotonic() def add(self, client): can_pool = True for t in client.tags:\",\n \"to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No\",\n \"return True class FusionController(threading.Thread, PrintError): \\\"\\\"\\\" This controls the Fusion rounds running from\",\n \"\\\"\\\"\\\" Server for covert submissions. How it works: - Launch the server at\",\n \"else: in_pool = True if len(self.pool) < self.fill_threshold: self.fill_time = None for t\",\n \"start immediately self.start_fuse(t) return # we have added to pools, which may have\",\n \"True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys)\",\n \"present in too many fuses. client.tags = [ClientTag(client_ip, b'', Params.ip_max_simul_fuse)] for tag in\",\n \"- an advanced coin anonymizer # # Copyright (C) 2020 <NAME> # #\",\n \"annport, False, begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while\",\n \"the result would exceed 100 kB standard tx size limitation? max_clients = (100000\",\n \"if time_best is None or ft < time_best or size > size_best: time_best\",\n \"self.tier_best: # this is the favoured pool, can start at a special time\",\n \"self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class\",\n \"proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed client is\",\n \"send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg),\",\n \"defaultdict(TagStatus) # how are the various tags self.fill_threshold = fill_threshold # minimum number\",\n \"of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion =\",\n \"excess fee based on dividing the overhead amongst players, in the smallest fusion\",\n \"given input's signature submission. raise ValidationError('conflicting valid signature') with self.lock: try: self.signatures[msg.which_input] =\",\n \"= annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @\",\n \"to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c\",\n \"# running after run_round has exited. For this reason we try to #\",\n \"the # signing phase and go directly to blame, or maybe even restart\",\n \"scan through tiers and collect statuses, also check start times. statuses = dict()\",\n \"but that's OK. self.sendall(pb.AllCommitments(initial_commitments = all_commitments), timeout=Protocol.TS_EXPECTING_COVERT_SIGNATURES) # Sleep until end of covert\",\n \"successful or exception while True: covert_server.reset() # Clean up dead clients self.clients =\",\n \"have this signature. This is fine # since it might be a resubmission\",\n \"signing phase and go directly to blame, or maybe even restart / end\",\n \"the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a fusion\",\n \"commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list\",\n \"A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\",\n \"size of 1 + 5 (lokad) + 33 (session hash) ) if min_safe_clients\",\n \"those clients from all pools for t, pool in self.waiting_pools.items(): for c in\",\n \"= set(i for i,sig in enumerate(signatures) if sig is None) # further, search\",\n \"don't waste more time. # Since nothing after this point can report back\",\n \"= tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not in\",\n \"None: self.tier_best_starttime = None else: self.tier_best_starttime = max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing)\",\n \"round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random\",\n \"collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs\",\n \"by client, we'll let them slide...\\\") if self.stopping: return donation_address = '' if\",\n \"not reference self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx,\",\n \"upnp) self.round_pubkey = None def start_components(self, round_pubkey, feerate): self.components = dict() self.feerate =\",\n \"except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature if len(sig) != 64:\",\n \"is None or ft < time_best or size > size_best: time_best = ft\",\n \"appropriate to add some 'ban score' to the player. # we aren't collecting\",\n \"however a server admin may run an SSL server proxy such as nginx\",\n \"clients from all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients:\",\n \"covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server started @ {covert_server.host}:{covert_server.port} (announcing as: {annhost_b}:{annport})') begin_time\",\n \"NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE\",\n \"type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port,\",\n \"even have reasonable # privacy with what we have. bad_components = set() ###\",\n \"remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really\",\n \"Guaranteed time to launch a fusion if the pool has stayed at or\",\n \"this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return\",\n \"a second submission before a new phase started. As # an anti-spam measure\",\n \"by re-signing one of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures))\",\n \"ts.pool >= t.maxsimul: break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class\",\n \"if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else:\",\n \"server ought # to have a good connection to the EC server. Report\",\n \"transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to broadcast transaction! misconfigured?\\\") #\",\n \"4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1] # TODO - make these\",\n \"Now, repackage the proofs according to destination. proofs_to_relay = [list() for _ in\",\n \"# Inter-fusion delay -- after starting any fusion, wait this long before starting\",\n \"self.pool.add(client) for t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool)\",\n \"from all pools for t, pool in self.waiting_pools.items(): for c in chosen_clients: pool.remove(c)\",\n \"self.print_error('Ended successfully!') except FusionError as e: self.print_error(f\\\"Ended with error: {e}\\\") except Exception as\",\n \"ci, (_, commitments, _) in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments =\",\n \"proof (for {src_commitment_idx}): {ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: #\",\n \"400 # whether to print a lot of logs noisy = False #\",\n \"OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE\",\n \"and outputs lists overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1)\",\n \"except: # Remove client from waiting pools on failure (on success, we are\",\n \"time import traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address\",\n \"is not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i]\",\n \"src_client.dead: # If the blamed client is already dead, don't waste more time.\",\n \"break else: self._add_pool(client) moved.append(client) for client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for\",\n \"sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self):\",\n \"= client_commit_indexes[myindex][i] relays.append((proof, src_commitment_idx, dest_client_idx, dest_key_idx)) if not collector.add((client, relays)): client.error(\\\"late proofs\\\") for\",\n \"c.got_submit = False def end_components(self): with self.lock: ret = self.components del self.components return\",\n \"Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0: # really shouldn't happen, we had\",\n \"missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures) if sig\",\n \"run an SSL server proxy such as nginx for that purpose. \\\"\\\"\\\" import\",\n \"for c in self.clients: c.addjob(clientjob_goodbye, None) self.clients = [] # gc def kick_missing_clients(self,\",\n \"donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee,\",\n \"ordering correlations about which client sent which proof proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs\",\n \"client to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res\",\n \"Params.max_clients: # pool filled up to the maximum size, so start immediately self.start_fuse(t)\",\n \"second submission before a new phase started. As # an anti-spam measure we\",\n \"after calling this try: self.pool.remove(client) except KeyError: in_pool = False try: self.queue.remove(client) except\",\n \"self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok()\",\n \"bindhost self.upnp = upnp self.announcehost = announcehost self.daemon = True def sendall(self, msg,\",\n \"self.sighashes = sighashes self.pubkeys = pubkeys for c in self.spawned_clients: c.got_submit = False\",\n \"c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop() for c in self.clients:\",\n \"implementation for CashFusion. Does not natively offer SSL support, however a server admin\",\n \"= pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock:\",\n \"if res is not None: client.error(res) for t in mytiers: pool = mytierpools[t]\",\n \"fusion if the pool has stayed at or above min_clients for this long.\",\n \"destinations list (all commitments, but leaving out the originating client's commitments). myindex =\",\n \"== 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many tags\\\") # Event\",\n \"SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES\",\n \"timeout = Protocol.STANDARD_TIMEOUT): for client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live\",\n \"in the same fuse? ip_max_simul_fuse = 3 # Guaranteed time to launch a\",\n \"recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg,\",\n \"= [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8,\",\n \"at tier={self.tier}') covert_server = CovertServer(self.bindhost, upnp = self.upnp) try: annhost = covert_server.host if\",\n \"# attempt to move clients from queue into pool moved = [] for\",\n \"(commit, ci, cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail =\",\n \"= self.components del self.components return ret def start_signatures(self, sighashes, pubkeys): num_inputs = len(sighashes)\",\n \"max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier): \\\"\\\"\\\"\",\n \"signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic() if remtime < 0:\",\n \"(in pool and queue) def check_add(self, client): for t in client.tags: ts =\",\n \"adding order so that if filling more than one pool, we don't have\",\n \"whitelisted to allow unlimited access client.tags = [] else: # Default tag: this\",\n \"{t}\\\") try: mytiers = list(mytierpools) rng.shuffle(mytiers) # shuffle the adding order so that\",\n \"covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature\",\n \"covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients:\",\n \"Resistor \\\"E series\\\" values -- round numbers that are almost geometrically uniform E6\",\n \"care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best\",\n \"< tag.limit < 6): client.error(\\\"Tag limit out of range\\\") ip = '' if\",\n \"the client declares the genesis_hash, we # do indeed disallow them connecting if\",\n \"raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a\",\n \"# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE\",\n \"of pool clients to trigger setting fill_time self.fill_time = None # when did\",\n \"into the waiting pools. Once a Fusion thread is started, the ClientThreads are\",\n \"pool started. start_ev = threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is\",\n \"associated documentation files # (the \\\"Software\\\"), to deal in the Software without restriction,\",\n \"if in_pool: ts.pool -= 1 if ts.all_ == 0: # cleanup for no-longer-used\",\n \"feerate self.round_pubkey = round_pubkey for c in self.spawned_clients: c.got_submit = False def end_components(self):\",\n \"len(pool.pool), min_players = Params.min_clients) remtime = inftime if pool.fill_time is not None: #\",\n \"(for privacy)? min_safe_clients = 6 # Choose the minimum excess fee based on\",\n \"is full). start_time_spacing = 120 # But don't start a fusion if it\",\n \"self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients,\",\n \"require 3-byte varint for either inputs or outputs lists overhead = 60 else:\",\n \"fusion round if started at this tier self.queue = list() # clients who\",\n \"= None tier_best = None size_best = 0 for t, pool in self.waiting_pools.items():\",\n \"time.sleep(remtime) signatures = list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s\",\n \"calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to\",\n \"self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self, msg): self.send_error(msg)\",\n \"'all_') def __init__(self): self.pool = 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a\",\n \"OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR\",\n \"if len(self.clients) < 2: # Sanity check for testing -- the proof sharing\",\n \"are waiting due to tags being full self.tags = defaultdict(TagStatus) # how are\",\n \"dict() tfill_thresh = tnow - Params.start_time_max for t, pool in mytierpools.items(): if client\",\n \"originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results)\",\n \"obtaining a copy of this software and associated documentation files # (the \\\"Software\\\"),\",\n \"self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert\",\n \"self.clients] for src_client, relays in results: for proof, src_commitment_idx, dest_client_idx, dest_key_idx in relays:\",\n \"port, upnp = None, announcehost = None, donation_address = None): assert network assert\",\n \"started. As # an anti-spam measure we only allow one submission per connection\",\n \"be unique) \\\"\\\"\\\" with self.lock: time_best = None tier_best = None size_best =\",\n \"DEALINGS IN THE # SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion. Does\",\n \"# Every round, clients leave ... How many clients do we need as\",\n \"self.stopping: return # add this client to waiting pools for pool in mytierpools.values():\",\n \"t == self.tier_best: # this is the favoured pool, can start at a\",\n \"allowed to stay open without activity. # note this needs to consider the\",\n \"SOFTWARE. \\\"\\\"\\\" A basic server implementation for CashFusion. Does not natively offer SSL\",\n \"ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb +\",\n \"traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address\",\n \"# TODO - make these configurable class Params: num_components = 23 component_feerate =\",\n \"tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash)) # Sleep until end of\",\n \"e.args server_msg = e.server_msg self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed\",\n \"is None: continue size = len(pool.pool) if size >= size_best: if time_best is\",\n \"Protocol.STANDARD_TIMEOUT + Protocol.BLAME_VERIFY_TIME * 2) self.sendall(pb.RestartRound()) class CovertClientThread(ClientHandlerThread): def recv(self, *expected_msg_names, timeout=None): submsg,\",\n \"tags\\\") # Event for signalling us that a pool started. start_ev = threading.Event()\",\n \"may have changed the favoured tier self.reset_timer() inftime = float('inf') while True: with\",\n \"How many clients do we want before starting a fusion? min_clients = 8\",\n \"bindhost, port=0, upnp = None): super().__init__(bindhost, port, CovertClientThread, upnp = upnp) self.round_pubkey =\",\n \"round_time )) msg = c.recv('playercommit') commit_messages = check_playercommit(msg, Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes =\",\n \"# generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for\",\n \"to launch a fusion if the pool has stayed at or above min_clients\",\n \"make these configurable class Params: num_components = 23 component_feerate = 1000 # sats/kB\",\n \"blames point to same proof') # Note, the rest of this function might\",\n \"based on dividing the overhead amongst players, in the smallest fusion # (these\",\n \"the connection as a result. # Note that we could have aborted earlier\",\n \"we even have reasonable # privacy with what we have. bad_components = set()\",\n \"the inputs is signed, we don't punish him # because he's the honest\",\n \"idx removes ordering correlations about which client sent which proof proofs.sort(key = lambda\",\n \"long\\\") if not (0 < tag.limit < 6): client.error(\\\"Tag limit out of range\\\")\",\n \"interval between messages: # - how long from first connection to last possible\",\n \"i,sig in enumerate(signatures) if sig is None) # further, search for duplicated inputs\",\n \"= msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time that\",\n \"size = len(pool.pool) if size >= size_best: if time_best is None or ft\",\n \"= Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow a\",\n \"None else self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start()\",\n \"malicious behaviour! if sum((signatures[i] is not None) for i in spenders) != 1:\",\n \"client in moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a\",\n \"c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) #\",\n \"can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client):\",\n \"from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import Address from\",\n \"!= get_current_genesis_hash(): # For now, msg.genesis_hash is optional and we tolerate it #\",\n \"signatures phase, owner calls start_signatures. - To signal the end of covert signatures\",\n \"client.error('multiple blames point to same proof') # Note, the rest of this function\",\n \"or ft < time_best or size > size_best: time_best = ft tier_best =\",\n \"to see whether we should just skip the # signing phase and go\",\n \"lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _ in proofs]))\",\n \"tier \\\"\\\"\\\" def __init__(self, fill_threshold, tag_max): self.pool = set() # clients who will\",\n \"0: time.sleep(remtime) # Upload the full commitment list; we're a bit generous with\",\n \"just skip the # signing phase and go directly to blame, or maybe\",\n \"# subject to the following conditions: # # The above copyright notice and\",\n \"tier (if more try to join, reject) max_tier_client_tags = 100 # For a\",\n \"start message to players; record the time we did this round_time = round(time.time())\",\n \"for i in bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0]\",\n \"# We allow a long timeout for clients to choose their pool. msg\",\n \"server is on a different chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis\",\n \"start in relaying, before sharing the # signatures. This makes it slightly harder\",\n \"self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients) < 2: # Sanity check\",\n \"# Sanity check for testing -- the proof sharing thing doesn't even make\",\n \"client is already dead, don't waste more time. # Since nothing after this\",\n \"all blames are checked, we # can start next round right away. collector.add(None)\",\n \"CovertServer(GenericServer): \\\"\\\"\\\" Server for covert submissions. How it works: - Launch the server\",\n \"ack failed delivery, # but we don't allow it to consume our CPU\",\n \"it has only been above min_clients for a short time (unless pool is\",\n \"start_time_max = 1200 # Inter-fusion delay -- after starting any fusion, wait this\",\n \"(Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost,\",\n \"sum((signatures[i] is not None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs:\",\n \"1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9,\",\n \"restart / end # without sharing components. skip_signatures = False if len(all_components) !=\",\n \"valid signature') with self.lock: try: self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at\",\n \"pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes, maxsimul): ipb =\",\n \"with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally: covert_server.stop()\",\n \"None) # further, search for duplicated inputs (through matching the prevout and claimed\",\n \"ts.all_ == 0: # cleanup for no-longer-used tags del self.tags[t] return True def\",\n \"we have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components\",\n \"send_pb, recv_pb, ClientHandlerThread, GenericServer, get_current_genesis_hash from .protocol import Protocol from .util import (FusionError,\",\n \"set() # clients who will be put into fusion round if started at\",\n \"= pool.check_add(client) if res is not None: client.error(res) for t in mytiers: pool\",\n \"if many # checks against blockchain need to be done, perhaps even still\",\n \"if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs if\",\n \"_ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob, dest_commit_blob,\",\n \"msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\") proofs = msg.encrypted_proofs\",\n \"tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c in self.clients:\",\n \"continue if src_client.dead: # If the blamed client is already dead, don't waste\",\n \"pool clients to trigger setting fill_time self.fill_time = None # when did pool\",\n \"def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is\",\n \"try to join, reject) max_tier_client_tags = 100 # For a given IP, how\",\n \"This is not # allowed and we break the connection as a result.\",\n \"KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool =\",\n \"= time.monotonic() def add(self, client): can_pool = True for t in client.tags: ts\",\n \"upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is optional\",\n \"for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components\",\n \"sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError,\",\n \"c in baddies: c.kill(reason) def run_round(self, covert_server): covert_priv, covert_Upub, covert_Cpub = gen_keypair() round_pubkey\",\n \"def error(self, msg): self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class CovertServer(GenericServer): \\\"\\\"\\\" Server for\",\n \"do indeed disallow them connecting if they are e.g. on testnet # and\",\n \"him # because he's the honest guy and all the other components were\",\n \"tier_best = None size_best = 0 for t, pool in self.waiting_pools.items(): ft =\",\n \"who didn't have private key. If more than one # signed, then it's\",\n \"if ci != myindex] N = len(possible_commitment_destinations) assert N == len(all_commitments) - Params.num_components\",\n \"self.print_error('Failed with exception!') traceback.print_exc(file=sys.stderr) for c in self.clients: c.addjob(clientjob_goodbye, 'internal server error') finally:\",\n \"idx, (client, proofs) in enumerate(zip(self.clients, proofs_to_relay)): client.addjob(client_get_blames, idx, proofs, collector) _ = collector.gather(deadline\",\n \"Choose the pool with the earliest fill time; - If no pools are\",\n \"skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too\",\n \"did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c,\",\n \"privacy)? min_safe_clients = 6 # Choose the minimum excess fee based on dividing\",\n \"a different chain, please switch servers\\\") else: client.print_error(\\\"👀 No genesis hash declared by\",\n \"time we did this round_time = round(time.time()) collector = ResultsCollector(len(self.clients), done_on_fail = False)\",\n \"t in client.tags: ts = self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold:\",\n \"a float, this will almost always be unique) \\\"\\\"\\\" with self.lock: time_best =\",\n \"pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool, so it\",\n \"validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values -- round numbers that are\",\n \"with what we have. bad_components = set() ### if skip_signatures: self.print_error(\\\"skipping covert signature\",\n \"at positions of missing signatures). - To reset the server for a new\",\n \"covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds =\",\n \"to find out the # timing of a given input's signature submission. raise\",\n \"33 (session hash) ) if min_safe_clients * num_components >= 2 * 0xfc: #\",\n \"client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) #\",\n \"= client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5:\",\n \"pool filled up to the maximum size, so start immediately self.start_fuse(t) return #\",\n \"add(self, result): with self.lock: try: self.results.append(result) except AttributeError: return False else: if len(self.fails)\",\n \"tnow - Params.start_time_max for t, pool in mytierpools.items(): if client not in pool.pool:\",\n \"clients who are waiting due to tags being full self.tags = defaultdict(TagStatus) #\",\n \"= covert_T0 + Protocol.TS_EXPECTING_COMMITMENTS) # Filter clients who didn't manage to give a\",\n \"2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2,\",\n \"client's commitments). myindex = self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in\",\n \"to broadcast transaction! misconfigured?\\\") # This probably indicates misconfiguration since fusion server ought\",\n \"assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify(): raise\",\n \"mark all missing-signature components as bad. bad_inputs = set(i for i,sig in enumerate(signatures)\",\n \"kB standard tx size limitation? max_clients = (100000 - 12) // (num_components *\",\n \"PrintError): \\\"\\\"\\\" This controls the Fusion rounds running from server side. \\\"\\\"\\\" def\",\n \"= [contrib for comp, (sort_key, contrib) in component_master_list] del component_master_list # Do some\",\n \"= donation_address self.waiting_pools = {t: WaitingPool(Params.min_clients, Params.max_tier_client_tags) for t in Params.tiers} self.t_last_fuse =\",\n \"= tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in\",\n \"add some 'ban score' to the player. # we aren't collecting any results,\",\n \".reset(); to kill all connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp\",\n \"we don't allow it to consume our CPU power. if sig != existing_sig:\",\n \"connection as a result. # Note that we could have aborted earlier but\",\n \"for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret =\",\n \"self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\")\",\n \"[(ci,cj) for commit, ci, cj in commitment_master_list if ci != myindex] N =\",\n \"{ret}\\\") src_client.kill(f'bad proof (for {src_commitment_idx}): {ret}') continue if src_client.dead: # If the blamed\",\n \"= [] else: # Default tag: this IP cannot be present in too\",\n \"set(i for i,sig in enumerate(signatures) if sig is None) # further, search for\",\n \"marking that # 'checking finished' so that if all blames are checked, we\",\n \"in-place sort by source commitment idx removes ordering correlations about which client sent\",\n \"commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in enumerate(results) for\",\n \"2.2, 3.3, 4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3,\",\n \"a Fusion thread is started, the ClientThreads are passed over to a FusionController\",\n \"is not # allowed and we break the connection as a result. #\",\n \"matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for i, inp in\",\n \"even still # running after run_round has exited. For this reason we try\",\n \"collecting any results, rather just marking that # 'checking finished' so that if\",\n \"Params.component_feerate) # generate blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner()\",\n \"list(covert_server.end_components().items()) self.print_error(f\\\"ending covert component acceptance. {len(component_master_list)} received.\\\") # Sort the components & contribs\",\n \"slide...\\\") if self.stopping: return donation_address = '' if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string()\",\n \"timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if len(msg.tags) > 5: client.error(\\\"Too many\",\n \"since fusion server ought # to have a good connection to the EC\",\n \"import traceback from collections import defaultdict import oregano.schnorr as schnorr from oregano.address import\",\n \"just marking that # 'checking finished' so that if all blames are checked,\",\n \"schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a\",\n \"ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE\",\n \"c.random_number_commitment = msg.random_number_commitment for client in self.clients: client.addjob(client_start, collector) # Record the time\",\n \"starting any fusion, wait this long before starting the next one (unless hit\",\n \"tag on a given tier (if more try to join, reject) max_tier_client_tags =\",\n \"component commitment') if not collector.add((c, msg.initial_commitments, msg.excess_fee)): c.error(\\\"late commitment\\\") # record for later\",\n \"killing if text is not None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread\",\n \"ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool\",\n \"# If exactly one of the inputs is signed, we don't punish him\",\n \"len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment') if not collector.add((c, msg.initial_commitments,\",\n \"exc_type is not None: self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results',\",\n \"while True: msg, mtype = client.recv('component', 'signature', 'ping', timeout = COVERT_CLIENT_TIMEOUT) if mtype\",\n \"existing_sig: # We received a distinct valid signature. This is not # allowed\",\n \"remtime <= 0: self.start_fuse(t) return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t]\",\n \"we take until the result would exceed 100 kB standard tx size limitation?\",\n \"is not None: # a non-favoured pool will start eventually remtime = pool.fill_time\",\n \"Params.min_excess_fee, Params.max_excess_fee, Params.num_components) newhashes = set(m.salted_component_hash for m in commit_messages) with lock: expected_len\",\n \"tier, clients, bindhost, upnp = None, announcehost = None): super().__init__(name=\\\"FusionController\\\") self.network = network\",\n \"client.send(msg, timeout=timeout) def clientjob_goodbye(client, text): # a gentler goodbye than killing if text\",\n \"all_components, session_hash = session_hash)) # Sleep until end of covert signatures phase remtime\",\n \"this reason we try to # not reference self.<variables> that may change. for\",\n \"ft is None: continue size = len(pool.pool) if size >= size_best: if time_best\",\n \"moved: self.queue.remove(client) class FusionServer(GenericServer): \\\"\\\"\\\"Server for clients waiting to start a fusion. New\",\n \"tx, input_indices = tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for\",\n \"self.announcehost annhost_b = annhost.encode('ascii') annport = covert_server.port covert_server.noisy = Params.noisy covert_server.start() self.print_error(f'Covert server\",\n \"mtype == 'ping': continue if client.got_submit: # We got a second submission before\",\n \"can_pool: self._add_pool(client) else: self.queue.append(client) return can_pool def remove(self, client): # make sure to\",\n \"True if len(self.pool) < self.fill_threshold: self.fill_time = None for t in client.tags: ts\",\n \"client.error(\\\"This server is on a different chain, please switch servers\\\") else: client.print_error(\\\"👀 No\",\n \"client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points = [b.get_R() for b in\",\n \"58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How many\",\n \"self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ == 0:\",\n \"from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor \\\"E series\\\" values\",\n \"If the blamed client is already dead, don't waste more time. # Since\",\n \"phase started. As # an anti-spam measure we only allow one submission per\",\n \"good commitment. prev_client_count = len(self.clients) self.clients = [c for c, _, _ in\",\n \"blind_nonce_points = [b.get_R() for b in c.blinds], server_time = round_time )) msg =\",\n \"tx_from_components(all_components, session_hash) sighashes = [sha256(sha256(bytes.fromhex(tx.serialize_preimage(i, 0x41, use_cache = True)))) for i in range(len(tx.inputs()))]\",\n \"phase, call start_components. - To signal the end of covert components phase, owner\",\n \"self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees != sum(component_contribs): skip_signatures = True\",\n \"can continue independently of waiting server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion.\",\n \"import Address from oregano.util import PrintError, ServerError, TimeoutException from . import fusion_pb2 as\",\n \"time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to destination. proofs_to_relay =\",\n \"collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_get_proofs(client, collector): with collector: msg =\",\n \"= collector.gather(deadline = time.monotonic() + Protocol.STANDARD_TIMEOUT) # Now, repackage the proofs according to\",\n \"the smallest fusion could require 3-byte varint for both inputs and outputs lists\",\n \"to give a good commitment. prev_client_count = len(self.clients) self.clients = [c for c,\",\n \"out the # timing of a given input's signature submission. raise ValidationError('conflicting valid\",\n \"(C) 2020 <NAME> # # Permission is hereby granted, free of charge, to\",\n \"will be put into fusion round if started at this tier self.queue =\",\n \"TimeoutException from . import fusion_pb2 as pb from .comms import send_pb, recv_pb, ClientHandlerThread,\",\n \"this # way third parties can't abuse us to find out the #\",\n \"... How many clients do we need as an absolute minimum (for privacy)?\",\n \"tag.id, tag.limit)) try: mytierpools = {t: self.waiting_pools[t] for t in msg.tiers} except KeyError:\",\n \"None: # a non-favoured pool will start eventually remtime = pool.fill_time - tfill_thresh\",\n \"reason we try to # not reference self.<variables> that may change. for blame\",\n \"at this tier self.queue = list() # clients who are waiting due to\",\n \"+= 1 if ts.pool >= t.maxsimul: can_pool = False if can_pool: self._add_pool(client) else:\",\n \"startup time. self.reset_timer() def run(self): try: super().run() finally: self.waiting_pools.clear() # gc clean def\",\n \"min_safe_clients * num_components >= 0xfc: # the smallest fusion could require 3-byte varint\",\n \"> len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames):\",\n \"del self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True:\",\n \"* 173) # Every round, clients leave ... How many clients do we\",\n \"feerate = self.feerate _ = self.components except AttributeError: client.error('component submitted at wrong time')\",\n \"might be a resubmission after ack failed delivery, # but we don't allow\",\n \"!= len(self.clients)*Params.num_components: skip_signatures = True self.print_error(\\\"problem detected: too few components submitted\\\") if total_excess_fees\",\n \"outputs to see if we even have reasonable # privacy with what we\",\n \"result would exceed 100 kB standard tx size limitation? max_clients = (100000 -\",\n \"pubkeys): num_inputs = len(sighashes) assert num_inputs == len(pubkeys) self.signatures = [None]*num_inputs self.sighashes =\",\n \"# when did pool exceed fill_threshold self.tag_max = tag_max # how many clients\",\n \"is whitelisted to allow unlimited access client.tags = [] else: # Default tag:\",\n \"guy and all the other components were # just imposters who didn't have\",\n \"until end of covert signatures phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_SIGNATURES - time.monotonic()\",\n \"round_time, all_commitments, all_components) #TODO : Check the inputs and outputs to see if\",\n \"1) // min_safe_clients # How many clients can share same tag on a\",\n \"list (all commitments, but leaving out the originating client's commitments). myindex = self.clients.index(client)\",\n \"rather just marking that # 'checking finished' so that if all blames are\",\n \"t in client.tags: ts = self.tags.get(t) if ts is not None and ts.all_\",\n \"to a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port,\",\n \"ret) except ValidationError as e: reason = f'{e.args[0]} ({outpoint})' self.print_error(f\\\"blaming[{src_commitment_idx}] for bad input:\",\n \"gc def kick_missing_clients(self, goodclients, reason = None): baddies = set(self.clients).difference(goodclients) for c in\",\n \"list of signatures (which will have None at positions of missing signatures). -\",\n \">= self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0.,\",\n \"def new_client_job(self, client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION:\",\n \"FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF\",\n \"= [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()] covert_server.start_signatures(sighashes,pubkeys) self.sendall(pb.ShareCovertComponents(components = all_components, session_hash = session_hash))\",\n \"assume op_return script size of 1 + 5 (lokad) + 33 (session hash)\",\n \"= self.tags[t] ts.pool += 1 if len(self.pool) == self.fill_threshold: self.fill_time = time.monotonic() def\",\n \"can start at a special time remtime = min(remtime, self.tier_best_starttime - tnow) if\",\n \"= self.tags[t] ts.all_ -= 1 if in_pool: ts.pool -= 1 if ts.all_ ==\",\n \"expected_len = len(seen_salthashes) + len(newhashes) seen_salthashes.update(newhashes) if len(seen_salthashes) != expected_len: c.error('duplicate component commitment')\",\n \"self.pool = 0 self.all_ = 0 class WaitingPool: \\\"\\\"\\\" a waiting pool for\",\n \"who are waiting due to tags being full self.tags = defaultdict(TagStatus) # how\",\n \"smallest fusion will use 1-byte varint for both inputs and outputs lists overhead\",\n \"- 1) // min_safe_clients # How many clients can share same tag on\",\n \"gc clean def reset_timer(self, ): \\\"\\\"\\\" Scan pools for the favoured fuse: -\",\n \"signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase.\",\n \"3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6,\",\n \"len(proofs): client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple\",\n \"Exception as e: self.print_error(f\\\"player indicated bad input but checking failed with exception {repr(e)}\",\n \"sent at {time.time()}; accepting covert components\\\") # Await commitment messages then process results\",\n \"best pool, so it might not be best anymore. self.reset_timer() raise class ResultsCollector:\",\n \"is full). start_time_min = 400 # whether to print a lot of logs\",\n \"to represent a pool tag \\\"\\\"\\\" __slots__ = () def __new__(cls, ipstr, tagbytes,\",\n \"{msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent a pool tag \\\"\\\"\\\"\",\n \"we # do indeed disallow them connecting if they are e.g. on testnet\",\n \"clients can share same tag on a given tier (if more try to\",\n \"pool in self.waiting_pools.items(): ft = pool.fill_time if ft is None: continue size =\",\n \"self.start_fuse(t) return # we have added to pools, which may have changed the\",\n \"detected\\\") # If exactly one of the inputs is signed, we don't punish\",\n \"queue into pool moved = [] for client in self.queue: for t in\",\n \"resubmission after ack failed delivery, # but we don't allow it to consume\",\n \"= threading.Event() client.start_ev = start_ev if client_ip.startswith('127.'): # localhost is whitelisted to allow\",\n \"fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp = self.upnp, announcehost = self.announcehost)\",\n \"have aborted earlier but this # way third parties can't abuse us to\",\n \"cj) in enumerate(commitment_master_list): client_commit_indexes[ci][cj] = i collector = ResultsCollector(len(self.clients), done_on_fail = False) def\",\n \"signature. This is not # allowed and we break the connection as a\",\n \"self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time') except IndexError:\",\n \"client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg,\",\n \"be # appropriate to add some 'ban score' to the player. # we\",\n \"misconfiguration since fusion server ought # to have a good connection to the\",\n \"# Notify that we will start. for c in chosen_clients: c.start_ev.set() # Remove\",\n \"filled then there is no favoured fuse. (since fill time is a float,\",\n \"or size > size_best: time_best = ft tier_best = t size_best = size\",\n \"for c in self.clients: c.kill('blame yourself!') return # scan the commitment list and\",\n \"fee mismatch\\\") self.last_hash = session_hash = calc_round_hash(self.last_hash, round_pubkey, round_time, all_commitments, all_components) #TODO :\",\n \"on testnet # and we are mainnet, etc. client.error(\\\"This server is on a\",\n \"(sort_key, contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype ==\",\n \"self.sendall(pb.FusionBegin(tier = self.tier, covert_domain = annhost_b, covert_port = annport, covert_ssl = False, server_time\",\n \"in results) # Generate scrambled commitment list, but remember exactly where each commitment\",\n \"assert remtime > 0, \\\"timings set up incorrectly\\\" time.sleep(remtime) component_master_list = list(covert_server.end_components().items()) self.print_error(f\\\"ending\",\n \"= None, announcehost = None, donation_address = None): assert network assert isinstance(donation_address, (Address,\",\n \"client): client_ip = client.connection.socket.getpeername()[0] msg = client.recv('clienthello') if msg.version != Protocol.VERSION: client.error(\\\"Mismatched protocol\",\n \"components phase remtime = covert_T0 + Protocol.TS_EXPECTING_COVERT_COMPONENTS - time.monotonic() assert remtime > 0,\",\n \"lists overhead = 60 else: # the smallest fusion will use 1-byte varint\",\n \"start_time_min = 400 # whether to print a lot of logs noisy =\",\n \"small head start in relaying, before sharing the # signatures. This makes it\",\n \"assert N == len(all_commitments) - Params.num_components # calculate the randomly chosen destinations, same\",\n \"furnished to do so, # subject to the following conditions: # # The\",\n \"files # (the \\\"Software\\\"), to deal in the Software without restriction, # including\",\n \"in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool = True\",\n \"# record for later c.blind_sig_requests = msg.blind_sig_requests c.random_number_commitment = msg.random_number_commitment for client in\",\n \"key. If more than one # signed, then it's malicious behaviour! if sum((signatures[i]\",\n \"+ ':' + str(ret.prev_index) try: check_input_electrumx(self.network, ret) except ValidationError as e: reason =\",\n \"overhead = 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients #\",\n \"None) for i in spenders) != 1: bad_inputs.update(spenders) if bad_inputs: bad_components.update(input_indices[i] for i\",\n \"than one blame per proof is malicious. Boot client # immediately since client\",\n \"def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=5) def error(self,\",\n \"bad_inputs) else: for i, (inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() +\",\n \"inputs and outputs lists overhead = 62 elif min_safe_clients * num_components >= 0xfc:\",\n \"signed, we don't punish him # because he's the honest guy and all\",\n \"pubkeys for c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret\",\n \"parties can't abuse us to find out the # timing of a given\",\n \"this long. start_time_max = 1200 # Inter-fusion delay -- after starting any fusion,\",\n \"round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key, contrib) except AttributeError: client.error('component submitted\",\n \"for c in chosen_clients: c.start_ev.set() # Remove those clients from all pools for\",\n \"# Generate scrambled commitment list, but remember exactly where each commitment originated. commitment_master_list\",\n \"portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT\",\n \"are mainnet, etc. client.error(\\\"This server is on a different chain, please switch servers\\\")\",\n \"sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted\",\n \"for duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list)\",\n \"may run an SSL server proxy such as nginx for that purpose. \\\"\\\"\\\"\",\n \"# pool filled up to the maximum size, so start immediately self.start_fuse(t) return\",\n \"bind to an ephemeral port. - Before start of covert components phase, call\",\n \"begin_time) time.sleep(Protocol.WARMUP_TIME) # repeatedly run rounds until successful or exception while True: covert_server.reset()\",\n \"returns a dict of {component: contrib}, where contrib is (+- amount - fee).\",\n \"phase. client.error('multiple submission in same phase') if mtype == 'component': try: round_pubkey =\",\n \"sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received a distinct valid\",\n \"head start in relaying, before sharing the # signatures. This makes it slightly\",\n \"is wrong') # It might be we already have this signature. This is\",\n \"blind nonces (slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in\",\n \"= True)))) for i in range(len(tx.inputs()))] pubkeys = [bytes.fromhex(inp['pubkeys'][0]) for inp in tx.inputs()]\",\n \"we don't care.) with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue()\",\n \"were # just imposters who didn't have private key. If more than one\",\n \"not schnorr.verify(pubkey, sig, sighash): raise ValidationError('bad transaction signature') if existing_sig: # We received\",\n \"end_signatures(self): with self.lock: ret = self.signatures del self.signatures return ret def reset(self): try:\",\n \"allow it to consume our CPU power. if sig != existing_sig: if not\",\n \"2 * 0xfc: # the smallest fusion could require 3-byte varint for both\",\n \"client in self.clients: client.addjob(clientjob_send, msg, timeout) def check_client_count(self,): live = [c for c\",\n \"commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments, _) in\",\n \"client.error('too many blames') if len(set(blame.which_proof for blame in msg.blames)) != len(msg.blames): client.error('multiple blames\",\n \"time') sort_key, contrib = check_covert_component(msg, round_pubkey, feerate) with self.lock: try: self.components[msg.component] = (sort_key,\",\n \"t in mytiers: pool = mytierpools[t] pool.add(client) if len(pool.pool) >= Params.max_clients: # pool\",\n \"while True: covert_server.reset() # Clean up dead clients self.clients = [c for c\",\n \"we need as an absolute minimum (for privacy)? min_safe_clients = 6 # Choose\",\n \"to consume our CPU power. if sig != existing_sig: if not schnorr.verify(pubkey, sig,\",\n \"THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS\",\n \"OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\",\n \"40 # used for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def\",\n \"min_clients for this long. start_time_max = 1200 # Inter-fusion delay -- after starting\",\n \"timeout=None): send_pb(self.connection, pb.CovertResponse, submsg, timeout=timeout) def send_ok(self,): self.send(pb.OK(), timeout=5) def send_error(self, msg): self.send(pb.Error(message\",\n \"4.7, 6.8] E12 = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7,\",\n \"= list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is None])\",\n \"note this needs to consider the maximum interval between messages: # - how\",\n \"next round's component submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import\",\n \"client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return\",\n \"for clients to choose their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) ==\",\n \"client_commit_indexes = [[None]*Params.num_components for _ in self.clients] for i, (commit, ci, cj) in\",\n \"= max(time_best + Params.start_time_min, self.t_last_fuse + Params.start_time_spacing) self.tier_best = tier_best def start_fuse(self, tier):\",\n \"self.reset_timer() # Uncomment the following to: Remove from spawned clients list, so that\",\n \"SSL server proxy such as nginx for that purpose. \\\"\\\"\\\" import secrets import\",\n \"a small head start in relaying, before sharing the # signatures. This makes\",\n \"they are e.g. on testnet # and we are mainnet, etc. client.error(\\\"This server\",\n \"num_components >= 0xfc: # the smallest fusion could require 3-byte varint for either\",\n \"= self.clients.index(client) possible_commitment_destinations = [(ci,cj) for commit, ci, cj in commitment_master_list if ci\",\n \"the server for a new round, call .reset(); to kill all connections, call\",\n \"will form the basis of our covert timeline. covert_T0 = time.monotonic() self.print_error(f\\\"startround sent\",\n \"geometrically uniform E6 = [1.0, 1.5, 2.2, 3.3, 4.7, 6.8] E12 = [1.0,\",\n \"= 58 min_excess_fee = (overhead + min_safe_clients - 1) // min_safe_clients # How\",\n \"PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\",\n \"= network self.tier = tier self.clients = list(clients) self.bindhost = bindhost self.upnp =\",\n \"import (FusionError, sha256, calc_initial_hash, calc_round_hash, gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component,\",\n \"c in live: c.kill(\\\"too few remaining live players\\\") raise FusionError(\\\"too few remaining live\",\n \"Uncomment the following to: Remove from spawned clients list, so that the fusion\",\n \"client.kill(f'bad blame message: {e} (you claimed: {blame.blame_reason!r})') continue if isinstance(ret, str): self.print_error(f\\\"verified a\",\n \"contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for comp,\",\n \"sys import threading import time import traceback from collections import defaultdict import oregano.schnorr\",\n \"self.pubkeys except AttributeError: pass def new_client_job(self, client): client.got_submit = False while True: msg,\",\n \"the smallest fusion could require 3-byte varint for either inputs or outputs lists\",\n \"for covert submissions. How it works: - Launch the server at any time.\",\n \"Immediately launch Fusion at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool)\",\n \"server. # self.spawned_clients.difference_update(chosen_clients) # Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network,\",\n \"= tuple(commit for commit,ci,cj in commitment_master_list) # Send blind signatures for c in\",\n \"fill_time self.fill_time = None # when did pool exceed fill_threshold self.tag_max = tag_max\",\n \"New clients get a ClientThread made for them, and they are put into\",\n \"with self.lock: for t, pool in mytierpools.items(): if pool.remove(client): pool.try_move_from_queue() if self.tier_best in\",\n \"for non-cryptographic purposes import random rng = random.Random() rng.seed(secrets.token_bytes(32)) def clientjob_send(client, msg, timeout\",\n \"= list(clients) self.bindhost = bindhost self.upnp = upnp self.announcehost = announcehost self.daemon =\",\n \"checks are somewhat subjective. It would be # appropriate to add some 'ban\",\n \"phase, owner calls end_signatures, which returns a list of signatures (which will have\",\n \"please upgrade\\\") if msg.genesis_hash: if msg.genesis_hash != get_current_genesis_hash(): # For now, msg.genesis_hash is\",\n \"[list() for _ in self.clients] for src_client, relays in results: for proof, src_commitment_idx,\",\n \"varint for both inputs and outputs lists overhead = 62 elif min_safe_clients *\",\n \"in self.clients if not c.dead] if len(live) < Params.min_safe_clients: for c in live:\",\n \"ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey = round_pubkey, blind_nonce_points\",\n \"connections, call .stop(). \\\"\\\"\\\" def __init__(self, bindhost, port=0, upnp = None): super().__init__(bindhost, port,\",\n \"subjective. It would be # appropriate to add some 'ban score' to the\",\n \"self.signatures[msg.which_input] = sig except AttributeError: client.error('signature submitted at wrong time') client.send_ok() client.got_submit =\",\n \"submission before a new phase started. As # an anti-spam measure we only\",\n \"connected client.\\\"\\\"\\\" def recv(self, *expected_msg_names, timeout=Protocol.STANDARD_TIMEOUT): submsg, mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout)\",\n \"# publish, distribute, sublicense, and/or sell copies of the Software, # and to\",\n \"# signatures. This makes it slightly harder for one of the players to\",\n \"phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _\",\n \"to any person # obtaining a copy of this software and associated documentation\",\n \"num_results, done_on_fail = True): self.num_results = int(num_results) self.done_on_fail = bool(done_on_fail) self.done_ev = threading.Event()\",\n \"if isinstance(self.donation_address, Address): donation_address = self.donation_address.to_full_ui_string() client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate,\",\n \"any results, rather just marking that # 'checking finished' so that if all\",\n \"== 'signature' try: sighash = self.sighashes[msg.which_input] pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except\",\n \"same phase') if mtype == 'component': try: round_pubkey = self.round_pubkey feerate = self.feerate\",\n \"= all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\") tx, input_indices =\",\n \"towards any particular tier with self.lock: if self.stopping: return # add this client\",\n \"private key. If more than one # signed, then it's malicious behaviour! if\",\n \"2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3,\",\n \"pools, which may have changed the favoured tier self.reset_timer() inftime = float('inf') while\",\n \"inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses = statuses)) start_ev.wait(2) except: #\",\n \"timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg): self.send(pb.Error(message = msg), timeout=Protocol.STANDARD_TIMEOUT) def\",\n \"self.num_results: self.done_ev.set() def gather(self, *, deadline): remtime = deadline - time.monotonic() self.done_ev.wait(max(0., remtime))\",\n \"imposters who didn't have private key. If more than one # signed, then\",\n \"with the most number of players, - Choose the pool with the earliest\",\n \"collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results = collector.gather(deadline\",\n \"it might not be best anymore. self.reset_timer() raise class ResultsCollector: # Collect submissions\",\n \"for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): # attempt to move\",\n \"proofs according to destination. proofs_to_relay = [list() for _ in self.clients] for src_client,\",\n \"return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection, pb.ServerMessage, submsg, timeout=timeout) def send_error(self, msg):\",\n \"e.g. on testnet # and we are mainnet, etc. client.error(\\\"This server is on\",\n \"ValidationError as e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message:\",\n \"received a distinct valid signature. This is not # allowed and we break\",\n \"client declares the genesis_hash, we # do indeed disallow them connecting if they\",\n \"0: # really shouldn't happen, we had plenty of time raise FusionError(\\\"way too\",\n \"collector = ResultsCollector(len(self.clients), done_on_fail = False) def client_start(c, collector): with collector: c.send(pb.StartRound(round_pubkey =\",\n \"list(self.waiting_pools[tier].pool) # Notify that we will start. for c in chosen_clients: c.start_ev.set() #\",\n \"for CashFusion. Does not natively offer SSL support, however a server admin may\",\n \"duplicated inputs (through matching the prevout and claimed pubkey). prevout_spenders = defaultdict(list) for\",\n \"super().__new__(cls, b) @property def maxsimul(self): return self[0] class TagStatus: __slots__ = ('pool', 'all_')\",\n \"= covert_Cpub # start to accept covert components covert_server.start_components(round_pubkey, Params.component_feerate) # generate blind\",\n \"c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock()\",\n \"reference self.<variables> that may change. for blame in msg.blames: try: encproof, src_commitment_idx, dest_key_idx,\",\n \"e: self.print_error(\\\"got bad blame; clamed reason was: \\\"+repr(blame.blame_reason)) client.kill(f'bad blame message: {e} (you\",\n \"out of range\\\") ip = '' if tag.no_ip else client_ip client.tags.append(ClientTag(ip, tag.id, tag.limit))\",\n \"# allowed and we break the connection as a result. # Note that\",\n \"round if started at this tier self.queue = list() # clients who are\",\n \"calls end_components, which returns a dict of {component: contrib}, where contrib is (+-\",\n \"to waiting pools for pool in mytierpools.values(): res = pool.check_add(client) if res is\",\n \"exception {repr(e)} ({outpoint})\\\") else: self.print_error(f\\\"player indicated bad input but it was fine ({outpoint})\\\")\",\n \"schnorr.has_fast_verify(): raise RuntimeError(\\\"Fusion requires libsecp256k1\\\") super().__init__(bindhost, port, ClientThread, upnp = upnp) self.config =\",\n \"above copyright notice and this permission notice shall be # included in all\",\n \"might be we already have this signature. This is fine # since it\",\n \"CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH\",\n \"self.clients = [c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server):\",\n \"fill time; - If no pools are filled then there is no favoured\",\n \"4.7, 5.6, 6.8, 8.2] E24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8,\",\n \"signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures = True)) else: self.print_error(\\\"starting covert signature acceptance\\\")\",\n \"the maximum interval between messages: # - how long from first connection to\",\n \"to have a good connection to the EC server. Report this back to\",\n \"= [c for c in self.clients if not c.dead] self.check_client_count() if self.run_round(covert_server): break\",\n \"# Remove those clients from all pools for t, pool in self.waiting_pools.items(): for\",\n \"move clients from queue into pool moved = [] for client in self.queue:\",\n \"# without sharing components. skip_signatures = False if len(all_components) != len(self.clients)*Params.num_components: skip_signatures =\",\n \"a result. # Note that we could have aborted earlier but this #\",\n \"pool has stayed at or above min_clients for this long. start_time_max = 1200\",\n \"copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software,\",\n \"This controls the Fusion rounds running from server side. \\\"\\\"\\\" def __init__(self, network,\",\n \"where each commitment originated. commitment_master_list = [(commit, ci, cj) for ci, (_, commitments,\",\n \"src_commit_client_idx, _ = commitment_master_list[src_commitment_idx] dest_commit_blob = all_commitments[client_commit_indexes[myindex][dest_key_idx]] try: ret = validate_blame(blame, encproof, src_commit_blob,\",\n \"= set() ### if skip_signatures: self.print_error(\\\"skipping covert signature acceptance\\\") self.sendall(pb.ShareCovertComponents(components = all_components, skip_signatures\",\n \"bad components: {bad_components}\\\") if len(self.clients) < 2: # Sanity check for testing --\",\n \"except KeyError: in_pool = False try: self.queue.remove(client) except ValueError: return False else: in_pool\",\n \"client.recv('myproofslist') seed = msg.random_number if sha256(seed) != client.random_number_commitment: client.error(\\\"seed did not match commitment\\\")\",\n \"except IndexError: client.kill(f'bad proof index {blame.which_proof} / {len(proofs)}') continue src_commit_blob, src_commit_client_idx, _ =\",\n \"By default, will bind to an ephemeral port. - Before start of covert\",\n \"contrib is (+- amount - fee). - Before start of covert signatures phase,\",\n \"mtype = recv_pb(self.connection, pb.ClientMessage, *expected_msg_names, timeout=timeout) return submsg def send(self, submsg, timeout=Protocol.STANDARD_TIMEOUT): send_pb(self.connection,\",\n \"all_components = [comp for comp, (sort_key, contrib) in component_master_list] component_contribs = [contrib for\",\n \"slightly harder for one of the players to # broadcast a malleated version\",\n \"# calculate the randomly chosen destinations, same way as client did. relays =\",\n \"'component': try: round_pubkey = self.round_pubkey feerate = self.feerate _ = self.components except AttributeError:\",\n \"& contribs list, then separate it out. component_master_list.sort(key=lambda x:x[1][0]) all_components = [comp for\",\n \"(slow!) for c in self.clients: c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock\",\n \"return elif remtime != inftime: status.time_remaining = round(remtime) statuses[t] = status client.send(pb.TierStatusUpdate(statuses =\",\n \"submitted at wrong time') except IndexError: raise ValidationError('which_input too high') sig = msg.txsignature\",\n \"173) # Every round, clients leave ... How many clients do we need\",\n \"[round(b*s) for b in [10000, 100000, 1000000, 10000000, 100000000] for s in E12]\",\n \"contrib) except AttributeError: client.error('component submitted at wrong time') else: assert mtype == 'signature'\",\n \"= True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok = False, bad_components = sorted(bad_components)))\",\n \":{'(' if missing_sigs else ')'}\\\") # mark all missing-signature components as bad. bad_inputs\",\n \"remove(self, client): # make sure to call try_move_from_queue() after calling this try: self.pool.remove(client)\",\n \"list(covert_server.end_signatures()) missing_sigs = len([s for s in signatures if s is None]) ###\",\n \"[] def __enter__(self, ): return self def __exit__(self, exc_type, exc_value, traceback): if exc_type\",\n \"bad_components = sorted(bad_components))) ### self.print_error(f\\\"entering blame phase. bad components: {bad_components}\\\") if len(self.clients) <\",\n \"Fusion at the selected tier. \\\"\\\"\\\" with self.lock: chosen_clients = list(self.waiting_pools[tier].pool) # Notify\",\n \"a new phase started. As # an anti-spam measure we only allow one\",\n \"COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng = random.Random()\",\n \"set() # Send start message to players; record the time we did this\",\n \"c.blinds = [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set()\",\n \"proofs.sort(key = lambda x:x[1]) client.send(pb.TheirProofsList(proofs = [ dict(encrypted_proof=x, src_commitment_idx=y, dst_key_idx=z) for x,y,z, _\",\n \"length is wrong') # It might be we already have this signature. This\",\n \"submission? COVERT_CLIENT_TIMEOUT = 40 # used for non-cryptographic purposes import random rng =\",\n \"> size_best: time_best = ft tier_best = t size_best = size if time_best\",\n \"results] self.check_client_count() self.print_error(f\\\"got commitments from {len(self.clients)} clients (dropped {prev_client_count - len(self.clients)})\\\") total_excess_fees =\",\n \"time or pool is full). start_time_spacing = 120 # But don't start a\",\n \"size > size_best: time_best = ft tier_best = t size_best = size if\",\n \"their pool. msg = client.recv('joinpools', timeout=120) if len(msg.tiers) == 0: client.error(\\\"No tiers\\\") if\",\n \"None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or not schnorr.has_fast_verify():\",\n \"self.pool = set() # clients who will be put into fusion round if\",\n \"as nginx for that purpose. \\\"\\\"\\\" import secrets import sys import threading import\",\n \"in enumerate(results) for cj,commit in enumerate(commitments)] rng.shuffle(commitment_master_list) all_commitments = tuple(commit for commit,ci,cj in\",\n \"in component_master_list] del component_master_list # Do some preliminary checks to see whether we\",\n \"signatures for c in self.clients: scalars = [b.sign(covert_priv, e) for b,e in zip(c.blinds,\",\n \"(inp, sig) in enumerate(zip(tx.inputs(), signatures)): inp['signatures'][0] = sig.hex() + '41' assert tx.is_complete() txid\",\n \"self.network.broadcast_transaction2(tx, timeout=3) except ServerError as e: nice_msg, = e.args server_msg = e.server_msg self.print_error(f\\\"could\",\n \"moved = [] for client in self.queue: for t in client.tags: ts =\",\n \"Params.max_excess_fee, tiers = Params.tiers, donation_address = donation_address )) # We allow a long\",\n \"for b,e in zip(c.blinds, c.blind_sig_requests)] c.addjob(clientjob_send, pb.BlindSigResponses(scalars = scalars)) del c.blinds, c.blind_sig_requests del\",\n \"CashFusion - an advanced coin anonymizer # # Copyright (C) 2020 <NAME> #\",\n \"__new__(cls, ipstr, tagbytes, maxsimul): ipb = ipstr.encode() b = bytes([maxsimul, len(ipb)]) + ipb\",\n \"self.queue.remove(client) except ValueError: return False else: in_pool = True if len(self.pool) < self.fill_threshold:\",\n \"self.send_error(msg) raise FusionError(f'Rejected client: {msg}') class ClientTag(bytes): \\\"\\\"\\\" enhanced bytes object to represent\",\n \"None: client.send_error(text) raise client.Disconnect class ClientThread(ClientHandlerThread): \\\"\\\"\\\"Basic thread per connected client.\\\"\\\"\\\" def recv(self,\",\n \"checked, we # can start next round right away. collector.add(None) for idx, (client,\",\n \"0: # cleanup for no-longer-used tags del self.tags[t] return True def try_move_from_queue(self): #\",\n \"we aren't collecting any results, rather just marking that # 'checking finished' so\",\n \"not collector.add((client, relays)): client.error(\\\"late proofs\\\") for client in self.clients: client.addjob(client_get_proofs, collector) results =\",\n \"them connecting if they are e.g. on testnet # and we are mainnet,\",\n \"c in self.spawned_clients: c.got_submit = False def end_signatures(self): with self.lock: ret = self.signatures\",\n \"client.send(pb.ServerHello( num_components = Params.num_components, component_feerate = Params.component_feerate, min_excess_fee = Params.min_excess_fee, max_excess_fee = Params.max_excess_fee,\",\n \"free of charge, to any person # obtaining a copy of this software\",\n \"if pool.remove(client): pool.try_move_from_queue() if self.tier_best in mytierpools: # we left from best pool,\",\n \"of their inputs. time.sleep(2) self.sendall(pb.FusionResult(ok = True, txsignatures = signatures)) return True self.sendall(pb.FusionResult(ok\",\n \"ret, 'expecting input component' outpoint = ret.prev_txid[::-1].hex() + ':' + str(ret.prev_index) try: check_input_electrumx(self.network,\",\n \"that if all blames are checked, we # can start next round right\",\n \"gen_keypair, tx_from_components, rand_position) from .validation import (check_playercommit, check_covert_component, validate_blame, ValidationError, check_input_electrumx) # Resistor\",\n \"# making us check many inputs against blockchain. if len(msg.blames) > len(proofs): client.error('too\",\n \"# this is the favoured pool, can start at a special time remtime\",\n \"2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5,\",\n \"= threading.Lock() seen_salthashes = set() # Send start message to players; record the\",\n \"Default tag: this IP cannot be present in too many fuses. client.tags =\",\n \"tier_best def start_fuse(self, tier): \\\"\\\"\\\" Immediately launch Fusion at the selected tier. \\\"\\\"\\\"\",\n \"self.print_error(f\\\"could not broadcast the transaction! {nice_msg}\\\") except TimeoutException: self.print_error(\\\"timed out while trying to\",\n \"self.fails.append(exc_value) if self.done_on_fail: self.done_ev.set() elif len(self.fails) + len(getattr(self, 'results', ())) >= self.num_results: self.done_ev.set()\",\n \"pubkey = self.pubkeys[msg.which_input] existing_sig = self.signatures[msg.which_input] except AttributeError: client.error('signature submitted at wrong time')\",\n \"donation_address = None): assert network assert isinstance(donation_address, (Address, type(None))) if not schnorr.has_fast_sign() or\",\n \"fusion if it has only been above min_clients for a short time (unless\",\n \"ts = self.tags.get(t) if ts is not None and ts.all_ >= self.tag_max: return\",\n \"a FusionController to run the rounds.\\\"\\\"\\\" def __init__(self, config, network, bindhost, port, upnp\",\n \"bad input: {reason}\\\") src_client.kill('you provided a bad input: ' + reason) continue except\",\n \"[] for client in self.queue: for t in client.tags: ts = self.tags[t] if\",\n \"Kick off the fusion. rng.shuffle(chosen_clients) fusion = FusionController(self. network, tier, chosen_clients, self.bindhost, upnp\"\n]","isConversational":false}}},{"rowIdx":4098,"cells":{"ngram":{"kind":"list like","value":["o no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim","print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de","case=False)].copy() # Afegim els valors en funció del samplesize a dues noves columnes","de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def","el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\",","plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe,","= df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal","dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a dues noves","Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100","grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el","= [label1, label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2]","def printar(df, subject): # Printem a la consola els valors print(\"Valors per subject","per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de","Printem a la consola els valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum()","= (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge","pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no per l'economia\") def filtrar(dataframe,","/ 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax,","# Filtrem i tractem el dataset economydf = filtrar(dataframe, \"economy\") # el printem","\"Satisfacció\", \"% de persones preocupades o no per infected\") def printar(df, subject): #","economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) # Filtrem ara per","grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre","valors per l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x,","Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0]","l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y","matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf","a la consola els valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot","pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) #","# Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538","tractem el dataset economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) #","valors en funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] /","df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per","100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery,","# el printem printar(economydf, subject) # Filtrem ara per subject infected i ho","a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] =","[valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x","en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica","(infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all:","i tractem el dataset economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf, subject)","# Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos valors","df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar","= filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum()","# Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) #","l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de","per subject infected i ho desem en un altre df infectedf = filtrar(dataframe,","en funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100","printem printar(economydf, subject) # Filtrem ara per subject infected i ho desem en","plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a","Finalment, grafiquem # Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very',","/ infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall))","not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones","/ 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df","= dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a dues","plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo)","Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda","label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem","\"% de persones preocupades o no per infected\") def printar(df, subject): # Printem","plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje","df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2,","ara per subject infected i ho desem en un altre df infectedf =","eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x","Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4","en un altre df infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery","valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday)","return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors","df infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall","df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem","plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0] print(df.groupby('538 Grade').size())","el printem printar(economydf, subject) # Filtrem ara per subject infected i ho desem","i ho desem en un altre df infectedf = filtrar(dataframe, \"infected\") # Calculem","a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones","eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio","{}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic","# Finalment, grafiquem # Cal tancar el grafic per a seguir amb l'execució","columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100","\"economy\") # el printem printar(economydf, subject) # Filtrem ara per subject infected i","per l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix y","grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x","label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x","Filtrem i tractem el dataset economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf,","ho desem en un altre df infectedf = filtrar(dataframe, \"infected\") # Calculem els","grafiquem # Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All',","l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no","infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall =","= (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge","df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 *","countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \"economy\") #","print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\",","subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize","els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 #","= [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix","def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix","Filtrem ara per subject infected i ho desem en un altre df infectedf","subject): # Printem a la consola els valors print(\"Valors per subject {}\".format(subject)) pplvery","consola els valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum()","leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1, label2]","\" % Persones\", \"Satisfacció\", \"% de persones preocupades o no per infected\") def","= df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment,","# Afegim els valors en funció del samplesize a dues noves columnes df['ppl_very']","df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return","l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) #","la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en","# Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) *","[label1, label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2] #","plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538","Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0] print(df.groupby('538","percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els","100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def","preocupades o no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() #","Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos valors per","Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) #","printar(economydf, subject) # Filtrem ara per subject infected i ho desem en un","la consola els valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot =","subject infected i ho desem en un altre df infectedf = filtrar(dataframe, \"infected\")","filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() /","(infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very:","noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] /","tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\",","# Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax)","100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo):","desem en un altre df infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes","x eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y =","very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\",","\"Satisfacció\", \"Nombre de persones preocupades o no per l'economia\") def filtrar(dataframe, subject1): df","% Persones\", \"Satisfacció\", \"% de persones preocupades o no per infected\") def printar(df,","valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery))","pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem #","= df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2,","un altre df infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery =","print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el","els valors en funció del samplesize a dues noves columnes df['ppl_very'] = df['very']","\"Nombre de persones preocupades o no per l'economia\") def filtrar(dataframe, subject1): df =","df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1,","at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a","\"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no per l'economia\") def filtrar(dataframe, subject1):","dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all']","* df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1,","valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1,","els valors print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very:","{}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones preocupades","# Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) #","per l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y)","printar(df, subject): # Printem a la consola els valors print(\"Valors per subject {}\".format(subject))","subject) # Filtrem ara per subject infected i ho desem en un altre","'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no per l'economia\")","persones preocupades o no per infected\") def printar(df, subject): # Printem a la","infected\") def printar(df, subject): # Printem a la consola els valors print(\"Valors per","printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" %","del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size']","percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones preocupades o no per infected\")","Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem","infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very',","'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones preocupades o no","<gh_stars>0 import matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el","* 100 # Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All',","* df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): #","Persones\", \"Satisfacció\", \"% de persones preocupades o no per infected\") def printar(df, subject):","pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no per l'economia\") def","dataset economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) # Filtrem ara","percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem","el dataset economydf = filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) # Filtrem","= filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) # Filtrem ara per subject","Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df):","percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\"percentatge very: {}%\".format(percentvery))","leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1, label2] #","infected i ho desem en un altre df infectedf = filtrar(dataframe, \"infected\") #","altre df infectedf = filtrar(dataframe, \"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100","# Filtrem ara per subject infected i ho desem en un altre df","amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o","df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos","x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica","{}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a seguir amb","subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \"economy\") # el","All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a seguir","{}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All: {}\".format(pplnot))","filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del","persones preocupades o no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy()","de persones preocupades o no per infected\") def printar(df, subject): # Printem a","de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol","titulo): # Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos","funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 *","preocupades o no per infected\") def printar(df, subject): # Printem a la consola","per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at","# Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery,","# Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem la","no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els","# Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall,","Afegim els valors en funció del samplesize a dues noves columnes df['ppl_very'] =","# Printem a la consola els valors print(\"Valors per subject {}\".format(subject)) pplvery =","{}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"%","\"infected\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum())","as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf =","el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show()","print(\"Not at All: {}\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per","eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de","subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not at All:","valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x =","samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all']","y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda","de persones preocupades o no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1,","grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades o no per","import matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset","per infected\") def printar(df, subject): # Printem a la consola els valors print(\"Valors","l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en","def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció","eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y = [valor1,","Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem la grafica","x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per","grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones preocupades o","# Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici","Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot,","print(\"Valors per subject {}\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\"Very: {}\".format(pplvery)) print(\"Not","percentvery, percentnotatall, \" % Persones\", \"Satisfacció\", \"% de persones preocupades o no per","df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday,","df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a","no per infected\") def printar(df, subject): # Printem a la consola els valors","filtrar(dataframe, \"economy\") # el printem printar(economydf, subject) # Filtrem ara per subject infected","def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \"economy\")","Els printem print(\"percentatge very: {}%\".format(percentvery)) print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \"","seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \"Persones\", \"Satisfacció\", \"Nombre de persones preocupades","= df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size']","per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors","valors per l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix","o no per infected\") def printar(df, subject): # Printem a la consola els"],"string":"[\n \"o no per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim\",\n \"print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de\",\n \"case=False)].copy() # Afegim els valors en funció del samplesize a dues noves columnes\",\n \"de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def\",\n \"el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\",\",\n \"plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe,\",\n \"= df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal\",\n \"dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a dues noves\",\n \"Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100\",\n \"grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el\",\n \"= [label1, label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2]\",\n \"def printar(df, subject): # Printem a la consola els valors print(\\\"Valors per subject\",\n \"per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de\",\n \"Printem a la consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum()\",\n \"= (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge\",\n \"pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per l'economia\\\") def filtrar(dataframe,\",\n \"/ 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax,\",\n \"# Filtrem i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem\",\n \"\\\"Satisfacció\\\", \\\"% de persones preocupades o no per infected\\\") def printar(df, subject): #\",\n \"economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) # Filtrem ara per\",\n \"grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre\",\n \"valors per l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x,\",\n \"Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0]\",\n \"l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y\",\n \"matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf\",\n \"a la consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot\",\n \"pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) #\",\n \"# Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538\",\n \"tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) #\",\n \"valors en funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] /\",\n \"df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per\",\n \"100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery,\",\n \"# el printem printar(economydf, subject) # Filtrem ara per subject infected i ho\",\n \"a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] =\",\n \"[valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x\",\n \"en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica\",\n \"(infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all:\",\n \"i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject)\",\n \"# Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos valors\",\n \"df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar\",\n \"= filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum()\",\n \"# Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) #\",\n \"l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de\",\n \"per subject infected i ho desem en un altre df infectedf = filtrar(dataframe,\",\n \"en funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100\",\n \"printem printar(economydf, subject) # Filtrem ara per subject infected i ho desem en\",\n \"plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a\",\n \"Finalment, grafiquem # Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very',\",\n \"/ infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall))\",\n \"not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones\",\n \"/ 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df\",\n \"= dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a dues\",\n \"plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo)\",\n \"Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda\",\n \"label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem\",\n \"\\\"% de persones preocupades o no per infected\\\") def printar(df, subject): # Printem\",\n \"plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje\",\n \"df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2,\",\n \"ara per subject infected i ho desem en un altre df infectedf =\",\n \"eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x\",\n \"Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4\",\n \"en un altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery\",\n \"valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday)\",\n \"return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors\",\n \"df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall\",\n \"df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem\",\n \"plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0] print(df.groupby('538 Grade').size())\",\n \"el printem printar(economydf, subject) # Filtrem ara per subject infected i ho desem\",\n \"i ho desem en un altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem\",\n \"a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones\",\n \"eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio\",\n \"{}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic\",\n \"# Finalment, grafiquem # Cal tancar el grafic per a seguir amb l'execució\",\n \"columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100\",\n \"\\\"economy\\\") # el printem printar(economydf, subject) # Filtrem ara per subject infected i\",\n \"per l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix y\",\n \"grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x\",\n \"label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x\",\n \"Filtrem i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf,\",\n \"ho desem en un altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els\",\n \"grafiquem # Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All',\",\n \"l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no\",\n \"infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall =\",\n \"= (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge\",\n \"df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 *\",\n \"countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") #\",\n \"print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\",\",\n \"subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize\",\n \"els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 #\",\n \"= [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix\",\n \"def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix\",\n \"Filtrem ara per subject infected i ho desem en un altre df infectedf\",\n \"subject): # Printem a la consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery\",\n \"consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum()\",\n \"leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1, label2]\",\n \"\\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no per infected\\\") def\",\n \"= df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment,\",\n \"# Afegim els valors en funció del samplesize a dues noves columnes df['ppl_very']\",\n \"df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return\",\n \"l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) #\",\n \"la grafica plt.bar(eje_x, eje_y) # Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en\",\n \"# Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) *\",\n \"[label1, label2] # Declaramos valors per l'eix y eje_y = [valor1, valor2] #\",\n \"plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538\",\n \"Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df): df['538 Grade']=df['538 Grade'].str[0] print(df.groupby('538\",\n \"percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els\",\n \"100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def\",\n \"preocupades o no per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() #\",\n \"Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos valors per\",\n \"Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) #\",\n \"printar(economydf, subject) # Filtrem ara per subject infected i ho desem en un\",\n \"la consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot =\",\n \"subject infected i ho desem en un altre df infectedf = filtrar(dataframe, \\\"infected\\\")\",\n \"filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() /\",\n \"(infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very:\",\n \"noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] /\",\n \"tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\",\",\n \"# Llegenda de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax)\",\n \"100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo):\",\n \"desem en un altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes\",\n \"x eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y =\",\n \"very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\",\",\n \"\\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per l'economia\\\") def filtrar(dataframe, subject1): df\",\n \"% Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no per infected\\\") def printar(df,\",\n \"valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery))\",\n \"pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem #\",\n \"= df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2,\",\n \"un altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery =\",\n \"print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el\",\n \"els valors en funció del samplesize a dues noves columnes df['ppl_very'] = df['very']\",\n \"\\\"Nombre de persones preocupades o no per l'economia\\\") def filtrar(dataframe, subject1): df =\",\n \"df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1,\",\n \"at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a\",\n \"\\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per l'economia\\\") def filtrar(dataframe, subject1):\",\n \"dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all']\",\n \"* df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1,\",\n \"valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1,\",\n \"els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very:\",\n \"{}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades\",\n \"# Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) #\",\n \"per l'eix y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y)\",\n \"printar(df, subject): # Printem a la consola els valors print(\\\"Valors per subject {}\\\".format(subject))\",\n \"subject) # Filtrem ara per subject infected i ho desem en un altre\",\n \"'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per l'economia\\\")\",\n \"persones preocupades o no per infected\\\") def printar(df, subject): # Printem a la\",\n \"infected\\\") def printar(df, subject): # Printem a la consola els valors print(\\\"Valors per\",\n \"printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" %\",\n \"del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size']\",\n \"percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no per infected\\\")\",\n \"Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem\",\n \"infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very',\",\n \"'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no\",\n \"<gh_stars>0 import matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el\",\n \"* 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All',\",\n \"* df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): #\",\n \"Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no per infected\\\") def printar(df, subject):\",\n \"pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per l'economia\\\") def\",\n \"dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) # Filtrem ara\",\n \"percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem\",\n \"el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) # Filtrem\",\n \"= filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) # Filtrem ara per subject\",\n \"Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici 4.4 def grades(df):\",\n \"percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum()) * 100 # Els printem print(\\\"percentatge very: {}%\\\".format(percentvery))\",\n \"leyendax, titulo): # Declaramos valors per l'eix x eje_x = [label1, label2] #\",\n \"infected i ho desem en un altre df infectedf = filtrar(dataframe, \\\"infected\\\") #\",\n \"altre df infectedf = filtrar(dataframe, \\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100\",\n \"# Filtrem ara per subject infected i ho desem en un altre df\",\n \"amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o\",\n \"df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday, leyendax, titulo): # Declaramos\",\n \"x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol de Grafica\",\n \"{}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a seguir amb\",\n \"subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\") # el\",\n \"All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per a seguir\",\n \"{}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All: {}\\\".format(pplnot))\",\n \"filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del\",\n \"persones preocupades o no per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy()\",\n \"de persones preocupades o no per infected\\\") def printar(df, subject): # Printem a\",\n \"de l'eix x plt.ylabel(leyenday) # Legenda en el eje x plt.xlabel(leyendax) # Títol\",\n \"titulo): # Declaramos valors per l'eix x eje_x = [label1, label2] # Declaramos\",\n \"funció del samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 *\",\n \"preocupades o no per infected\\\") def printar(df, subject): # Printem a la consola\",\n \"per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at\",\n \"# Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery,\",\n \"# Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem la\",\n \"no per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els\",\n \"# Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall,\",\n \"Afegim els valors en funció del samplesize a dues noves columnes df['ppl_very'] =\",\n \"# Printem a la consola els valors print(\\\"Valors per subject {}\\\".format(subject)) pplvery =\",\n \"{}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"%\",\n \"\\\"infected\\\") # Calculem els percentatjes percentvery = (infectedf['ppl_very'].sum()/infectedf['sample_size'].sum())*100 percentnotatall = (infectedf['ppl_not_at_all'].sum() / infectedf['sample_size'].sum())\",\n \"as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf =\",\n \"el eje x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show()\",\n \"print(\\\"Not at All: {}\\\".format(pplnot)) # Finalment, grafiquem # Cal tancar el grafic per\",\n \"eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda de\",\n \"subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not at All:\",\n \"valor1, valor2, leyenday, leyendax, titulo): # Declaramos valors per l'eix x eje_x =\",\n \"samplesize a dues noves columnes df['ppl_very'] = df['very'] / 100 * df['sample_size'] df['ppl_not_at_all']\",\n \"y eje_y = [valor1, valor2] # Fem la grafica plt.bar(eje_x, eje_y) # Llegenda\",\n \"de persones preocupades o no per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1,\",\n \"grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades o no per\",\n \"import matplotlib.pyplot as plt def countvalues(dataframe, subject): # Filtrem i tractem el dataset\",\n \"per infected\\\") def printar(df, subject): # Printem a la consola els valors print(\\\"Valors\",\n \"l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en\",\n \"def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció\",\n \"eje_x = [label1, label2] # Declaramos valors per l'eix y eje_y = [valor1,\",\n \"Declaramos valors per l'eix y eje_y = [valor1, valor2] # Fem la grafica\",\n \"x plt.xlabel(leyendax) # Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per\",\n \"grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o\",\n \"# Títol de Grafica plt.title(titulo) # Mostrem Grafica plt.show() #Funcio per a l'excercici\",\n \"Cal tancar el grafic per a seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot,\",\n \"print(\\\"Valors per subject {}\\\".format(subject)) pplvery = df['ppl_very'].sum() pplnot = df['ppl_not_at_all'].sum() print(\\\"Very: {}\\\".format(pplvery)) print(\\\"Not\",\n \"percentvery, percentnotatall, \\\" % Persones\\\", \\\"Satisfacció\\\", \\\"% de persones preocupades o no per\",\n \"df['not_at_all'] / 100 * df['sample_size'] return df def grafic4(label1, label2, valor1, valor2, leyenday,\",\n \"df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors en funció del samplesize a\",\n \"no per infected\\\") def printar(df, subject): # Printem a la consola els valors\",\n \"filtrar(dataframe, \\\"economy\\\") # el printem printar(economydf, subject) # Filtrem ara per subject infected\",\n \"def countvalues(dataframe, subject): # Filtrem i tractem el dataset economydf = filtrar(dataframe, \\\"economy\\\")\",\n \"Els printem print(\\\"percentatge very: {}%\\\".format(percentvery)) print(\\\"percentatge not_at_all: {}%\\\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \\\"\",\n \"seguir amb l'execució grafic4('People_Very', 'People_Not_At_All', pplvery, pplnot, \\\"Persones\\\", \\\"Satisfacció\\\", \\\"Nombre de persones preocupades\",\n \"= df['very'] / 100 * df['sample_size'] df['ppl_not_at_all'] = df['not_at_all'] / 100 * df['sample_size']\",\n \"per l'economia\\\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim els valors\",\n \"valors per l'eix x eje_x = [label1, label2] # Declaramos valors per l'eix\",\n \"o no per infected\\\") def printar(df, subject): # Printem a la consola els\"\n]","isConversational":false}}},{"rowIdx":4099,"cells":{"ngram":{"kind":"list like","value":["pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd,","ctx.label.name # steps # run htex on all *md, gives book.htex markdowns =",") outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf {archive} {dir})\".format(","outputs = [outdir, html_file], tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\","+ markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs =","quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the","its reference directory. # # Params: # script_path: (string) The full path to","doc = \"Generate an ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file =","captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe this is","file into png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png =","[outdir, html_file], tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r","= dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file =","epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in","\\ asy -render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd),","[out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG with","file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures","ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return","markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain","\"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on the resulting htex","-r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), )","}, doc = \"Generate an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path):","using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target","equation dir # Maybe this is not needed. tar_command = \"(cd {base} ;","for this book\", ), \"metadata_xml\": attr.label( allow_files = True, doc = \"The epub-metadata.xml","html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs = [htex_file],","markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation =","_copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures","markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run","script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message =","needed because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns)","script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures","directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting","targets should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True,","= rule( implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc = \"All","tar_command = \"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell(","_copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs","not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [","set of markdown files\", attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"], doc","= [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html","target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles =","+= [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with","\"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd,","ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message","inputs = inputs, tools = [script], outputs = [ebook_pdf], command = \"\"\"\\ {script}","_ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you need","_dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file","markdown source files\", ), \"deps\": attr.label_list( doc = \"The file to compile\", providers","= [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy","outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name),","# This is duplicated in _ebook_pdf_impl. # steps # run htex on all","title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir,","[out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\"","to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path),","[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png =","\"deps\": attr.label_list( doc = \"The dependencies, any targets should be allowed\", ), \"output\":","(provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep","base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs","\"The title.yaml file to use for this book\", ), \"metadata_xml\": attr.label( allow_files =","markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped =","htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf","= rule( implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc = \"All","dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures = []","to the directory dir_reference, not the # directory where the build happens! This","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz dot file into","script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub.","in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo]","ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools =","deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)","and its reference directory. # # Params: # script_path: (string) The full path","= figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation =","root of the repository. # Build rules for building ebooks. # This is","EPUB for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command","ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file],","dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def","script invocation command based on the # script path and its reference directory.","--epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference,","archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs =","[outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub. # This","= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation =","# Maybe this is not needed. tar_command = \"(cd {base} ; tar xvf","figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for:","doc = \"Transform a graphviz dot file into png using dot\", ) def","attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file to","There can be only one such file outputs = default_info.files.to_list() epub_file = outputs[0]","dir # Maybe this is not needed. tar_command = \"(cd {base} ; tar","default_info) = _ebook_epub_impl(ctx) # There can be only one such file outputs =","ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ]","executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in PDF format\" ) def","to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools","= target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles","{htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename))","out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider =","epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx,","= [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng -o","asymptote = rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"],","out # the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference):","ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles","<gh_stars>1-10 # Copyright (C) 2020 Google Inc. # # This file has been","htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference,","= \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd),","= [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def","target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep","= ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures","in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs =","[file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd","\"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The file to compile\", ), \"deps\":","src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs =","= provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation command based on the","return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png","\"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The file to compile\", ), \"deps\":","tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd =","generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz","= _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name))","progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools","for building ebooks. # This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use","attr.label_list( doc = \"The file to compile\", providers = [EbookInfo], ), }, )","a graphviz dot file into png using dot\", ) def _asymptote_impl(ctx): asycc =","_ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps #","provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] #","attr.label_list( allow_files = [\".md\"], doc = \"The markdown source files\", ), \"deps\": attr.label_list(","200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) )","command = \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path,","ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, )","docker_run script invocation command based on the # script path and its reference","[src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy =","= epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell(","the equation dir # Maybe this is not needed. tar_command = \"(cd {base}","[\".asy\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc = \"The","src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures +=","target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns +=","[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs =","at the root of the repository. # Build rules for building ebooks. #","title.yaml file to use for this book\", ), \"metadata_xml\": attr.label( allow_files = True,","def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference,","equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference","output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output],","ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs =","and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message","png using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for","for: {}\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command","\"\"\"\\ {script} \\ pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path,","\\ gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference,","reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script}","epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx,","= \"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message","= \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs =","= [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF for:","script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on the resulting htex to","outputs = [htex_file], tools = [script], command = \"\"\"\\ {script} \\ pandoc -s","outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml","happens! This is needed because we can not control # figure inclusion. markdowns_paths","\"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation command based","# This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata)","runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo(","[captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name =","ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs =","Google Inc. # # This file has been licensed under Apache 2.0 license.","= \"Generate an ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name))","inputs = [in_file], outputs = [out_file], tools = [docker_run], command = \"\"\"\\ {script}","cfg=\"host\"), }, doc = \"Generate an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir,","[out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\"","= ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file,","control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd =","dot file into png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png","command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename)","True, doc = \"The title.yaml file to use for this book\", ), \"metadata_xml\":","attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an","files\", attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The markdown","command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name","[ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf}","gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0]","PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools =","runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle =","\"The epub-metadata.xml file to use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True,","tools = [asycc], command = \"\"\"\\ {script} \\ asy -render 5 -f png","\"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for","dependencies, any targets should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label(","= ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs","= \"Transform a graphviz dot file into png using dot\", ) def _asymptote_impl(ctx):","= [script], command = \"\"\"\\ {script} \\ pandoc -s --gladtex -o {target} {sources}","for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns","-- relative to the directory dir_reference, not the # directory where the build","-o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for","can be only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir","name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run","-o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), ))","# run htex on all *md, gives book.htex markdowns = [] figures =","{mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for","This is needed because we can not control # figure inclusion. markdowns_paths =","title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata,","file to use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), },","ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote =",") ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list(","_copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path,","Fixed up paths -- relative to the directory dir_reference, not the # directory","provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path","= { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file to compile\",","{base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message =","progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command =","= \"The file to compile\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx):","), \"deps\": attr.label_list( doc = \"The file to compile\", providers = [EbookInfo], ),","doc = \"The epub-metadata.xml file to use for this book\", ), \"_script\": attr.label(","epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles","of markdown files\", attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"], doc =","\"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe this is not needed. tar_command","dir_reference = markdowns[0] # Fixed up paths -- relative to the directory dir_reference,","runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule(","in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ),","allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc","\".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to",") ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list(","# Params: # script_path: (string) The full path to the script to invoke","}, doc = \"Generate an ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file","doc = \"The file to compile\", providers = [EbookInfo], ), }, ) def","ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles =","file used for figuring out # the reference directories (build root and repo","doc = \"Generate an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return","cfg=\"host\"), }, doc = \"Generate an ebook in the Kindle's MOBI format\" )","= outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) #",")) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [","\"All the targets you need to make this book work.\", providers = [EbookInfo],","png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = []","}, doc = \"Transform a timing diagram file into png using drawtiming\", )","root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER,","dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]),","= ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in target.files.to_list():","= _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you","= \"The markdown source files\", ), \"deps\": attr.label_list( doc = \"The file to","epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools","_copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] +","_markdown_lib_impl, doc = \"Declares a set of markdown files\", attrs = { \"srcs\":","= ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename,","cfg=\"host\"), }, doc = \"Transform a timing diagram file into png using drawtiming\",","ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub,","in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles,","implementation = _markdown_lib_impl, doc = \"Declares a set of markdown files\", attrs =","return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list(","{script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd,",") # run htexepub to obtain book.epub. # This is gonna be fun!","title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell(","\"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in","= ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns)","out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell(","= \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command,","# script path and its reference directory. # # Params: # script_path: (string)","\"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in","_asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The file","= [] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns","an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path))","= [outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub. #","equation_outdir], tools = [script], outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\","= [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir}","markdowns[0] # Fixed up paths -- relative to the directory dir_reference, not the",") return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying","allow_files = [\".asy\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc","_drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures = [] for target in","> {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs =","_script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs","tools = [script], outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc","provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file","compile\", ), \"deps\": attr.label_list( doc = \"The dependencies, any targets should be allowed\",","tar_command, ) # run htexepub to obtain book.epub. # This is gonna be","continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]),","ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This","#CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation","# Returns the docker_run script invocation command based on the # script path","[script], command = \"\"\"\\ {script} \\ pandoc -s --gladtex -o {target} {sources} \\","outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar","dir_reference: (string) The path to a file used for figuring out # the","figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs = inputs, tools =","in _ebook_pdf_impl. # steps # run htex on all *md, gives book.htex markdowns","{ \"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The file to compile\", ),","src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in","allow_files = [\".t\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc","{}\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command =","\"Building EPUB for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub],","attr.label( allow_files = True, doc = \"The title.yaml file to use for this","cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs","the build happens! This is needed because we can not control # figure","runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib","allow_files = [\".dot\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc","= \"(cd {base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell(","= ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml)","\"markdowns\"]) # Returns the docker_run script invocation command based on the # script","figures += provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to","src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message =","\"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools =","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in EPUB format\" )","the repository. # Build rules for building ebooks. # This is the container","script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in the Kindle's MOBI","= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib =","src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures +=","[] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)]","dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns]","for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command =","file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message","# steps # run htex on all *md, gives book.htex markdowns = []","\"\"\"\\ {script} \\ asy -render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path,","the root of the repository. # Build rules for building ebooks. # This","duplicated in _ebook_pdf_impl. # steps # run htex on all *md, gives book.htex","{epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file])","--output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for","{}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) # run","if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in","neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs","cfg=\"host\"), }, doc = \"Transform an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx,","script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd","] asymptote = rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files =","-Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []","figuring out # the reference directories (build root and repo root). def _script_cmd(script_path,","script to invoke # dir_reference: (string) The path to a file used for","= ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png =","graphviz dot file into png using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script","outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs","= \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command,","cfg=\"host\"), }, doc = \"Transform a graphviz dot file into png using neato\",","ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files","tar_command = \"(cd {base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path)","command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference,","[_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures","[]) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [","ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc =","base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs","}, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl.","equation environments for: {}\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script],","dir_reference, not the # directory where the build happens! This is needed because","gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0]","= ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name),","script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs =","# # This file has been licensed under Apache 2.0 license. Please see","in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = []","path to the script to invoke # dir_reference: (string) The path to a","[script], outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\","Build rules for building ebooks. # This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\"","ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs = inputs, tools = [script],","deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps),","progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path,","ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf","= \"The file to compile\", ), \"deps\": attr.label_list( doc = \"The dependencies, any","\"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs =","\"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in","\"Generate an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\")","= [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue","{archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs =","), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in","markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths","epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf =","to make this book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files =","dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ]","neato_png = rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"],","= \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo","\\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run =","path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files]","outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir","= markdowns, outputs = [htex_file], tools = [script], command = \"\"\"\\ {script} \\","ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf =","steps # run htex on all *md, gives book.htex markdowns = [] figures","= \"Transform a timing diagram file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx,","progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command =","= ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata)","[ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl,","container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures =","a file used for figuring out # the reference directories (build root and","= \"Transform an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy =","+= ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return",") ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf {archive}","for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns","gives book.htex markdowns = [] figures = [] for dep in ctx.attr.deps: provider","relative to the directory dir_reference, not the # directory where the build happens!","= _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The","provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative","{}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message","EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl,","The path to a file used for figuring out # the reference directories","--cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), ))","ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run htex on","_drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The file","(build root and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container}","= { \"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The file to compile\",","{}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return","[outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub to obtain","This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml","EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation command based on","environments for: {}\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command","if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return","[out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}:","be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), },","provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can","_script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd),","= ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote file into png\", )","[ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs","[equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script =","def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures = [] for target","outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script","{ \"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The markdown source files\", ),","= [asycc], command = \"\"\"\\ {script} \\ asy -render 5 -f png -o","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in the","= ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo(","\"title_yaml\": attr.label( allow_files = True, doc = \"The title.yaml file to use for","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in PDF format\"","[in_file], outputs = [out_file], tools = [asycc], command = \"\"\"\\ {script} \\ asy","epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs =","}, doc = \"Transform a graphviz dot file into png using neato\", )","outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output","src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path)","--dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script","src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path)","file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote file","epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps:","runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl,","dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference","= tar_command, ) # run htexepub to obtain book.epub. # This is gonna","attrs = { \"deps\": attr.label_list( doc = \"All the targets you need to","to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command","allow_files = [\".md\"], doc = \"The markdown source files\", ), \"deps\": attr.label_list( doc","with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz dot file into png","outputs = [outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub.","= \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools =","in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file]","2020 Google Inc. # # This file has been licensed under Apache 2.0","ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub","= \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path,","\"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file to compile\", ), \"deps\":","drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target","command = tar_command, ) # run htexepub to obtain book.epub. # This is","Apache 2.0 license. Please see the LICENSE # file at the root of","rule( implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc = \"All the","is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml =","runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files","dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar],","the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs. #CONTAINER","ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI","file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex","DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list(","= \"Transform a graphviz dot file into png using neato\", ) def _dot_png_impl(ctx):","asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in","tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d","to invoke # dir_reference: (string) The path to a file used for figuring","figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path)","docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in","= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation","[out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs",") def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in","] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files =","Copyright (C) 2020 Google Inc. # # This file has been licensed under","ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe this is not needed.","\"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command =","), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \"Declares a set","generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a timing","rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc =","\"(cd {base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message","is not needed. tar_command = \"(cd {base} ; tar xvf {archive}) > {output}\".format(","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in EPUB","ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps)","\"Declares a set of markdown files\", attrs = { \"srcs\": attr.label_list( allow_files =","paths -- relative to the directory dir_reference, not the # directory where the","output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message =","{script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), )","{}\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command =","in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0])","inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command = \"\"\"\\","in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles","outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f","executable=True, cfg=\"host\"), }, doc = \"Transform a timing diagram file into png using","this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an","\\ -f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path),","not the # directory where the build happens! This is needed because we","file to compile\", ), \"deps\": attr.label_list( doc = \"The dependencies, any targets should","figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG:","progress_message = \"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs = [outdir, html_file],","ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles =","_strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for","title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata,","= outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation","= rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc","tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations:","outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs =","run htexepub to obtain book.epub. # This is gonna be fun! epub_metadata =","path and its reference directory. # # Params: # script_path: (string) The full","= ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message =","{archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs","[EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated","The full path to the script to invoke # dir_reference: (string) The path","This is duplicated in _ebook_pdf_impl. # steps # run htex on all *md,","-s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) #","target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures","a timing diagram file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run","{}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), )","{1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command","= [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format(","src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in","is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml =","= \"The epub-metadata.xml file to use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\",","ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns +","EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return","= _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is gonna","fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml =","return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path),","ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo(","= \"The title.yaml file to use for this book\", ), \"metadata_xml\": attr.label( allow_files","to use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc","such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar =","--cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference,","{script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file}","doc = \"The dependencies, any targets should be allowed\", ), \"output\": attr.output(doc=\"The generated","= ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run htex","[in_file], outputs = [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd}","{ \"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The file to compile\", ),","src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return","full path to the script to invoke # dir_reference: (string) The path to","= { \"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The markdown source files\",","files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs =","_ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you need","= [\".asy\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc =","+= provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths =","= _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs = [epub_file,","= inputs, tools = [script], outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference","+ markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs =","(provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)","command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3","= \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs","the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\","), \"deps\": attr.label_list( doc = \"The dependencies, any targets should be allowed\", ),","_ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you need","allow_files = True, doc = \"The epub-metadata.xml file to use for this book\",","attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a","not needed. tar_command = \"(cd {base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname,","cfg=\"host\"), }, doc = \"Transform a graphviz dot file into png using dot\",","title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB","return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation =","_strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name","_ebook_pdf_impl. # steps # run htex on all *md, gives book.htex markdowns =","this book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc","[epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building","runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation","Use this for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"])","in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ]","obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata =","deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider:","in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs =","= [script], outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata}","figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl,","the LICENSE # file at the root of the repository. # Build rules","untar the equation dir # Maybe this is not needed. tar_command = \"(cd","fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml =","\"drawtiming\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src","[script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file}","html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB for:","DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list(","= ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]),","the # script path and its reference directory. # # Params: # script_path:","[] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file","markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation",") def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {}","epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles =","directory where the build happens! This is needed because we can not control","{}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command = \"\"\"\\","def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list():","\\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =","because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script","\"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message =","doc = \"The title.yaml file to use for this book\", ), \"metadata_xml\": attr.label(","\"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file],","provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation command based on the #","progress_message = \"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools = [script],","DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such file","on the resulting htex to obtain html and output directory with figures. outdir","= outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe","for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, )","cfg=\"host\"), }, doc = \"Generate an ebook in PDF format\" ) def _ebook_kindle_impl(ctx):","inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns","\"The dependencies, any targets should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\":","for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures","to the script to invoke # dir_reference: (string) The path to a file","ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle","needed. tar_command = \"(cd {base} ; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename,","-o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target","ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs",") def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs:","{target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on","invocation command based on the # script path and its reference directory. #","htex to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file","= ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]),","= _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The","tools = [script], outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert","= _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path,","_generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for","we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script =","_dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs = { \"srcs\":","to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file =","{}\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command = \"\"\"\\","{script} \\ pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\"","epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs =","{}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command = \"\"\"\\","out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell(","# figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path,","_script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name), inputs = markdowns,","EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only","target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain","markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs = inputs,","in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files):","into png using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = []","script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs =","\"Transform a timing diagram file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd):","= rule( implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc = \"All","provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to the directory","resulting htex to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name))","ctx.actions.run_shell( progress_message = \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs =","Please see the LICENSE # file at the root of the repository. #","_script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs","ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles","be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml","is needed because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference,","DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc =","= _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar]","runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \"deps\":","= [captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name","(C) 2020 Google Inc. # # This file has been licensed under Apache","run gladtex on the resulting htex to obtain html and output directory with","book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx,","\\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\"","+= provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file","= \"\"\"\\ {script} \\ pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd,","book\", ), \"metadata_xml\": attr.label( allow_files = True, doc = \"The epub-metadata.xml file to","{cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =","dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles,","return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list(","markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns +=","html_file], tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200","\"deps\": attr.label_list( doc = \"All the targets you need to make this book","see the LICENSE # file at the root of the repository. # Build","= [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message =","= rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc","--container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run","for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles),","\"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp {}","{0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command = \"\"\"\\","htex on all *md, gives book.htex markdowns = [] figures = [] for","in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote","tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\"","and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format(","attr.label_list( allow_files = [\".asy\"], doc = \"The file to compile\", ), \"deps\": attr.label_list(","# the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return","for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command =","doc = \"Transform a graphviz dot file into png using neato\", ) def","\"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd),","script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if","to a file used for figuring out # the reference directories (build root","in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs","figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures","src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs","ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script","{ \"deps\": attr.label_list( doc = \"All the targets you need to make this","command = \"\"\"\\ {script} \\ asy -render 5 -f png -o \"{out_file}\" \"{in_file}\"","compile\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name #","return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl,","an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def","\"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider","\"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo =","rule( implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc = \"All the","runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation =","runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ]","attr.label_list( doc = \"The dependencies, any targets should be allowed\", ), \"output\": attr.output(doc=\"The","doc = \"The markdown source files\", ), \"deps\": attr.label_list( doc = \"The file","\\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path),","ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message =","\"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps:","html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)","\"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def","def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo.","executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz dot file into png using","), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in","been licensed under Apache 2.0 license. Please see the LICENSE # file at",") ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list(","= \"\"\"\\ {script} \\ asy -render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format(","outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe this","diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file],","= [script], outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file}","-t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference,","ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles =","asy -render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), )","sources=\" \".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain html","this book\", ), \"metadata_xml\": attr.label( allow_files = True, doc = \"The epub-metadata.xml file","\".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain html and","inputs, tools = [script], outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\","target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures =","--mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths),","ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ]","= dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed","\"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for","format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second","cmd = \"drawtiming\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs:","{script} \\ asy -render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path,","ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [","dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name))","pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference,","epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub =","diagram file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script","PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command =","MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file],","{dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir],","all *md, gives book.htex markdowns = [] figures = [] for dep in","command = \"\"\"\\ {script} \\ pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format(","progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src],","= _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The","[htex_file], tools = [script], command = \"\"\"\\ {script} \\ pandoc -s --gladtex -o","= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule(","+= [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG","rule( implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc = \"All the","= True, doc = \"The title.yaml file to use for this book\", ),","+= [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path),","= target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for","LICENSE # file at the root of the repository. # Build rules for","_ebook_epub_impl(ctx) # There can be only one such file outputs = default_info.files.to_list() epub_file","# This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata)","html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return","files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all","), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc =","file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps #","; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations:","\\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep","has been licensed under Apache 2.0 license. Please see the LICENSE # file","generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote","book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook","for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target","figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns","--epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path),","a graphviz dot file into png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx,","up paths -- relative to the directory dir_reference, not the # directory where","in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns","\"metadata_xml\": attr.label( allow_files = True, doc = \"The epub-metadata.xml file to use for","figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG","= [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script","into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures =","ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\")","= _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you","{base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving","ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name), inputs = markdowns, outputs =","using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for","_neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs = { \"srcs\":","is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be","{} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path,","{}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference =","ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or","for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd =","= src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path,","-f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference,","this is not needed. tar_command = \"(cd {base} ; tar xvf {archive}) >","return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule(","invoke # dir_reference: (string) The path to a file used for figuring out","= markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped","\"The markdown source files\", ), \"deps\": attr.label_list( doc = \"The file to compile\",","This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml","cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src","), \"title_yaml\": attr.label( allow_files = True, doc = \"The title.yaml file to use","# file at the root of the repository. # Build rules for building","ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir,","= \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o","= _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns +","second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one","equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) #","# First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) #","\"Generate an ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) #","implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets","the directory dir_reference, not the # directory where the build happens! This is","= [htex_file], tools = [script], command = \"\"\"\\ {script} \\ pandoc -s --gladtex","markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell(","epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path),","dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ]","= True, doc = \"The epub-metadata.xml file to use for this book\", ),","inputs = [in_file], outputs = [out_file], tools = [asycc], command = \"\"\"\\ {script}","continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles =","ebooks. # This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for","make this book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True,","attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The file to","This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local","to compile\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name",") return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for","dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation =","= [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \"\"\"\\ {script}","any targets should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\",","markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name), inputs = markdowns, outputs","--gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run","this for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) #","target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures)","inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src):","need to make this book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a timing diagram file into","htexepub to obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0]","outputs = [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} --output","rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"], doc =","[script], outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\","# # Params: # script_path: (string) The full path to the script to","markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\":","png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying","in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps:","runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo],","ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider","directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\","file has been licensed under Apache 2.0 license. Please see the LICENSE #","doc = \"Transform an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy","\\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps","the resulting htex to obtain html and output directory with figures. outdir =","DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs =","markdown files\", attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The","{ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles","doc = \"All the targets you need to make this book work.\", providers","captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = {","a set of markdown files\", attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"],","def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs:","ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles =","markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message","+ figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs = inputs, tools","runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule(","-o {ebook_pdf} {markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), ))","{cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []","runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png","[out_file], tools = [asycc], command = \"\"\"\\ {script} \\ asy -render 5 -f","runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub =","mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return","for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures","outputs = [out_file], tools = [asycc], command = \"\"\"\\ {script} \\ asy -render","for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir,","into png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation","inputs, tools = [script], outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\","progress_message = \"Building PDF for: {}\".format(name), inputs = inputs, tools = [script], outputs","_ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info,","\"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs =","attr.label( allow_files = True, doc = \"The epub-metadata.xml file to use for this","] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs =","build happens! This is needed because we can not control # figure inclusion.","First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There","only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1]","an ebook in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First","= [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures","epub-metadata.xml file to use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"),","\"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps:","= \"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs","doc = \"The file to compile\", ), \"deps\": attr.label_list( doc = \"The dependencies,","ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc =","epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps:","[ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name #","[src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = []","use for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc =","gladtex on the resulting htex to obtain html and output directory with figures.",") deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not","under Apache 2.0 license. Please see the LICENSE # file at the root","runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps,","implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets","title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs","= [htex_file], outputs = [outdir, html_file], tools = [script], command = \"\"\"\\ {script}","for this book\", ), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate","CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs. #CONTAINER = \"ebook-buildenv:local\"","= ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx):","= ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. #","runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \"deps\":","doc = \"Transform a timing diagram file into png using drawtiming\", ) def","markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)","Inc. # # This file has been licensed under Apache 2.0 license. Please","\"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), )","EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation","[ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation","ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message =","DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl,","targets you need to make this book work.\", providers = [EbookInfo], ), \"title_yaml\":","-d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar","\\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \"drawtiming\"","ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy],","runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script","_generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files","command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file} \\","runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a timing diagram file","_neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file","where the build happens! This is needed because we can not control #","markdowns + figures ctx.actions.run_shell( progress_message = \"Building EPUB for: {}\".format(name), inputs = inputs,","tools = [script], outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc","= _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets you","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in PDF format\" )","ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command","markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \"srcs\":","inputs = markdowns, outputs = [htex_file], tools = [script], command = \"\"\"\\ {script}","= [docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format(","def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz dot file","{markdowns} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles =","command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for","figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for","= ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src],","src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs","executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote file into png\", ) def","= [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\"","_asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src","container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs. #CONTAINER =","PDF for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command","# script_path: (string) The full path to the script to invoke # dir_reference:","ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file","= { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The file to compile\",","= figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx,","run htex on all *md, gives book.htex markdowns = [] figures = []","= [\".t\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc =","runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures,","\"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The markdown source files\", ), \"deps\":","runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \"Declares a","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in EPUB format\"","\\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub])","Params: # script_path: (string) The full path to the script to invoke #","asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell(","png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for","in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename +","ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file,","[script], outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file}","runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \"deps\":","\".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram","return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs","ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]),",") def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir,","_strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps","name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for:","\\ pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths))","xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path),","\\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on the resulting","[\".md\"], doc = \"The markdown source files\", ), \"deps\": attr.label_list( doc = \"The","# This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick","\".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to","runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files","file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a graphviz dot","\\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles","used for figuring out # the reference directories (build root and repo root).","EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = {","ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs =","file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a timing diagram","This file has been licensed under Apache 2.0 license. Please see the LICENSE","_script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building MOBI for: {}\".format(name), inputs = [epub_file, equation_outdir],",") # run gladtex on the resulting htex to obtain html and output","of the repository. # Build rules for building ebooks. # This is the","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in PDF","True, doc = \"The epub-metadata.xml file to use for this book\", ), \"_script\":","provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or [])","+= [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo]","\"Transform a graphviz dot file into png using dot\", ) def _asymptote_impl(ctx): asycc","+= (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles =","# run gladtex on the resulting htex to obtain html and output directory","\"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc],","[docker_run], command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path,","return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs","\"Building PDF for: {}\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf],","outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base}","on all *md, gives book.htex markdowns = [] figures = [] for dep","should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"),","or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return","= target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files =","= [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc = \"The title.yaml file","}, doc = \"Transform a graphviz dot file into png using dot\", )","ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell(","drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"],","using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl,","\"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), )","the targets you need to make this book work.\", providers = [EbookInfo], ),","[ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t","files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name =","= [\".md\"], doc = \"The markdown source files\", ), \"deps\": attr.label_list( doc =","use for this book\", ), \"metadata_xml\": attr.label( allow_files = True, doc = \"The","[ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation =","= [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is","executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in the Kindle's MOBI format\"","\\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format(","\"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\ \"\"\".format(",") dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path,","Returns the docker_run script invocation command based on the # script path and","= \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run script invocation command","_strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to","*md, gives book.htex markdowns = [] figures = [] for dep in ctx.attr.deps:","ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command","PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo,","[\".t\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc = \"The","src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs","def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx):","# Use this for quick local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\",","htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar","to obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata","= [outdir, html_file], tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex","tools = [script], command = \"\"\"\\ {script} \\ pandoc -s --gladtex -o {target}","\"dot\") dot_png = rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files =","= \"Generate an ebook in EPUB format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\",","[]) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps:","is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such","\"output\": attr.output(doc=\"The generated file\"), \"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform","in PDF format\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is","ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx)","src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs","# run htexepub to obtain book.epub. # This is gonna be fun! epub_metadata","_copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs","= _markdown_lib_impl, doc = \"Declares a set of markdown files\", attrs = {","command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path,","book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc =","--cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file} \\","markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc","# dir_reference: (string) The path to a file used for figuring out #","] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc","= \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path),","script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to PNG with {1}:","= [in_file], outputs = [out_file], tools = [asycc], command = \"\"\"\\ {script} \\","target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd","\"Archiving equations: {}\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, )","figures = [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file =","graphviz dot file into png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\")","files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \"Declares","file to compile\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name =","dot_png = rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"],","= [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src","= src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path,","outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\","= _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub","the docker_run script invocation command based on the # script path and its","progress_message = \"Building equation environments for: {}\".format(name), inputs = markdowns, outputs = [htex_file],","ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp","repository. # Build rules for building ebooks. # This is the container CONTAINER","= \"drawtiming\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for","figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing diagram to","return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src","outputs = [src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy","ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps,","figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\")","{html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles =","[] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns +=","= ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for:","script_path: (string) The full path to the script to invoke # dir_reference: (string)","{outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar =","the script to invoke # dir_reference: (string) The path to a file used","= ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs =","+= ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles),","provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\"{}.htex\".format(name)) markdowns_paths = [file.path for file in","+ \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"timing","tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format(","[asycc], command = \"\"\"\\ {script} \\ asy -render 5 -f png -o \"{out_file}\"","= ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns),","ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file],","= ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml,","attrs = { \"srcs\": attr.label_list( allow_files = [\".md\"], doc = \"The markdown source","archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs =","= \"Declares a set of markdown files\", attrs = { \"srcs\": attr.label_list( allow_files","html and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell(","inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub","ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs = [outdir,","not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps:","] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \"deps\": attr.label_list( doc","ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name),","= default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output =","into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message =","= ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) =","epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message","[\".dot\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc = \"The","= markdowns[0] # Fixed up paths -- relative to the directory dir_reference, not","is duplicated in _ebook_pdf_impl. # steps # run htex on all *md, gives","= \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns} \\","\"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, )","= \"The dependencies, any targets should be allowed\", ), \"output\": attr.output(doc=\"The generated file\"),","def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all *md,","in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on","script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name), inputs","_copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy],","{script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference,","= rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc","2.0 license. Please see the LICENSE # file at the root of the","title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns","[] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures +=","figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs = inputs, tools =","(ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such file outputs","ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles","), \"metadata_xml\": attr.label( allow_files = True, doc = \"The epub-metadata.xml file to use","providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc = \"The title.yaml","ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles,","return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name","ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \"deps\": attr.label_list( doc =","the # directory where the build happens! This is needed because we can","work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc = \"The","files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs","for: {}\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command","= [script], outputs = [ebook_epub], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata}","[src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx):","def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs =","to use for this book\", ), \"metadata_xml\": attr.label( allow_files = True, doc =","ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs = inputs, tools = [script],","markdowns, outputs = [htex_file], tools = [script], command = \"\"\"\\ {script} \\ pandoc","dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path)","executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in EPUB format\" ) def","outputs = [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng","= tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd","[docker_run], command = \"\"\"\\ {script} \\ {cmd} -Tpng -o \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd,","[EbookInfo], ), \"title_yaml\": attr.label( allow_files = True, doc = \"The title.yaml file to","default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform a timing diagram file into png","= _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY to PNG: {0}\".format(in_file.short_path), inputs = [in_file],","= [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script","cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider","in_file = src out_file = ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd =","ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building","on the # script path and its reference directory. # # Params: #","= _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building equation environments for: {}\".format(name), inputs =","\"The file to compile\", ), \"deps\": attr.label_list( doc = \"The dependencies, any targets","path to a file used for figuring out # the reference directories (build","= [outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub to","+ \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \"ASY","= \"\"\"\\ {script} --cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format(","{}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in","script path and its reference directory. # # Params: # script_path: (string) The","src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs:","= ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml]","path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file","= ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir # Maybe this is not","{output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving equations: {}\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar],","markdowns = [] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo]","tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \"Archiving equations: {}\".format(outdir_tar.short_path),","+ \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz","cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command = \"\"\"\\","file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2]","source files\", ), \"deps\": attr.label_list( doc = \"The file to compile\", providers =","DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs","to compile\", ), \"deps\": attr.label_list( doc = \"The dependencies, any targets should be","obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name))","in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf","timing diagram file into png using drawtiming\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run =","in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference =","{} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target","attr.label_list( allow_files = [\".dot\"], doc = \"The file to compile\", ), \"deps\": attr.label_list(","command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o {ebook_pdf} {markdowns}","inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) #","doc = \"Declares a set of markdown files\", attrs = { \"srcs\": attr.label_list(","ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename)","] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \"Declares a set of","or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in","figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns)","def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for","{sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex on the","file at the root of the repository. # Build rules for building ebooks.","equations for: {}\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script],","= _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd),","format\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [","{0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command =","\"neato\") neato_png = rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files =","for figuring out # the reference directories (build root and repo root). def","rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc =","return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation","attr.label_list( doc = \"All the targets you need to make this book work.\",","return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in","# There can be only one such file outputs = default_info.files.to_list() epub_file =","runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation","be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml","= [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns =","= [\".dot\"], doc = \"The file to compile\", ), \"deps\": attr.label_list( doc =","runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo(","_script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is gonna be","= _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name))","_script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, )","markdown_lib = rule( implementation = _markdown_lib_impl, doc = \"Declares a set of markdown","progress_message = \"timing diagram to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file],","def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\"/\", \"\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path)","script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path,","markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script =","epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in","png using neato\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation =","epub_metadata) ebook_epub = ctx.actions.declare_file(\"{}.epub\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns","script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is","not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd","you need to make this book work.\", providers = [EbookInfo], ), \"title_yaml\": attr.label(","local runs. #CONTAINER = \"ebook-buildenv:local\" EbookInfo = provider(fields=[\"figures\", \"markdowns\"]) # Returns the docker_run","gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path),","for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Generate an ebook in the Kindle's","ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0]","\"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target","files\", ), \"deps\": attr.label_list( doc = \"The file to compile\", providers = [EbookInfo],","-render 5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps","_markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns","licensed under Apache 2.0 license. Please see the LICENSE # file at the",")) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [","[src_copy], inputs = [src], command=\"cp {} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx,","can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script","equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the equation dir #","\"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference,","ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps),","; tar xvf {archive}) > {output}\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \"Unarchiving","\"_script\": attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote file into","\\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command","\"deps\": attr.label_list( doc = \"The file to compile\", providers = [EbookInfo], ), },","script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps:","= \"Building equation environments for: {}\".format(name), inputs = markdowns, outputs = [htex_file], tools","be only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir =","dot file into png using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures","file into png using dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures =","[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs =","+= provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to the","(string) The full path to the script to invoke # dir_reference: (string) The","\"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub}",")) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [","] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc","--cd-to-dir-reference \\ gladtex -r 200 -d {outdir} {htex_file} \\ \"\"\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path),","[] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps","for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file =",") def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. #","+= (provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for","mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info)","ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for","target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename","dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, )","inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file","inputs = inputs, tools = [script], outputs = [ebook_epub], command = \"\"\"\\ {script}","for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures","= ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message","= \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path),","directory dir_reference, not the # directory where the build happens! This is needed","for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps +=","# Fixed up paths -- relative to the directory dir_reference, not the #","-o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) ) # run gladtex","{ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles","+= provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths --","= ctx.actions.declare_file(in_file.basename + \".png\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message","for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns),","with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run],","{script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd =","= rule( implementation = _markdown_lib_impl, doc = \"Declares a set of markdown files\",","= \"All the targets you need to make this book work.\", providers =","_strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \"Building","DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl,","attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"], doc = \"The file to","return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx):","inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \"\"\"\\","target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider","= _asymptote_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".asy\"], doc = \"The","{} {}\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell(","(string) The path to a file used for figuring out # the reference","repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path,","[epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name),","{script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps","def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs = {","= rule(implementation = _drawtiming_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".t\"], doc","markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles","default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file(","}, doc = \"Transform an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src):","ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\"{}.pdf\".format(name)) inputs = [epub_metadata, title_yaml] +","reference directory. # # Params: # script_path: (string) The full path to the","= [out_file], tools = [asycc], command = \"\"\"\\ {script} \\ asy -render 5","= inputs, tools = [script], outputs = [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference","file to use for this book\", ), \"metadata_xml\": attr.label( allow_files = True, doc","\\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf])","{ \"srcs\": attr.label_list( allow_files = [\".dot\"], doc = \"The file to compile\", ),","runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs","for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles,","-f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []","for: {}\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command =","= \"Building EPUB for: {}\".format(name), inputs = inputs, tools = [script], outputs =","= \"Building PDF for: {}\".format(name), inputs = inputs, tools = [script], outputs =","= { \"deps\": attr.label_list( doc = \"All the targets you need to make","# This file has been licensed under Apache 2.0 license. Please see the","# untar the equation dir # Maybe this is not needed. tar_command =","directory. # # Params: # script_path: (string) The full path to the script","= [docker_run], command = \"\"\"\\ {script} \\ {cmd} --output \"{out_file}\" \"{in_file}\" \"\"\".format( cmd=cmd,","+ figures ctx.actions.run_shell( progress_message = \"Building PDF for: {}\".format(name), inputs = inputs, tools","EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = {","outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = {","[] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or [])","[epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference","{script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path),","book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx,","= [in_file], outputs = [out_file], tools = [docker_run], command = \"\"\"\\ {script} \\","allow_files = True, doc = \"The title.yaml file to use for this book\",","# directory where the build happens! This is needed because we can not","def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"neato\") neato_png = rule(implementation = _neato_png_impl, attrs = {","\"\"\"\\ {script} \\ --container={container} \\ --dir-reference={dir_reference}\"\"\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd","= [ebook_pdf], command = \"\"\"\\ {script} --cd-to-dir-reference \\ pandoc --epub-metadata={epub_metadata} \\ --mathml -o","html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path),","= _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message =","= [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx,","inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \"Building PDF","equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name)) # untar the","\"The file to compile\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name","file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path,","epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \"{}.untar-out\".format(ctx.label.name))","ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation","= \"Copying {} to {}\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\"cp","= ctx.label.name # steps # run htex on all *md, gives book.htex markdowns","# Copyright (C) 2020 Google Inc. # # This file has been licensed","src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to {}\".format(src.short_path, src_copy.short_path),","mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)","implementation = _ebook_pdf_impl, attrs = { \"deps\": attr.label_list( doc = \"All the targets","ctx.actions.run_shell( progress_message = \"Copying {}\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\"cp {}","script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\" \".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep","\"Transform a graphviz dot file into png using neato\", ) def _dot_png_impl(ctx): return","# Build rules for building ebooks. # This is the container CONTAINER =","files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = {","one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar","def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \"Copying {} to","progress_message = \"graphviz to PNG with {1}: {0}\".format(in_file.short_path, cmd), inputs = [in_file], outputs","dot\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in","dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up","attr.label( default=\"//build:docker_run\", executable=True, cfg=\"host\"), }, doc = \"Transform an asymptote file into png\",","dir_reference.path) # run htexepub to obtain book.epub. # This is gonna be fun!",") def _drawtiming_png_impl(ctx): cmd = \"drawtiming\" docker_run = ctx.executable._script figures = [] for","rules for building ebooks. # This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" #","is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this for quick local runs.","outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ;",") def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs =","outdir = ctx.actions.declare_directory(\"{}.eqn\".format(name)) html_file = ctx.actions.declare_file(\"{}.html\".format(name)) ctx.actions.run_shell( progress_message = \"Extracting equations for: {}\".format(name),",") def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\"{}.mobi\".format(ctx.label.name)) # First provider is EbookInfo, second is","name = ctx.label.name # steps # run htex on all *md, gives book.htex","command based on the # script path and its reference directory. # #","_ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all *md, gives","Maybe this is not needed. tar_command = \"(cd {base} ; tar xvf {archive})","5 -f png -o \"{out_file}\" \"{in_file}\" \"\"\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =","\"Building equation environments for: {}\".format(name), inputs = markdowns, outputs = [htex_file], tools =","based on the # script path and its reference directory. # # Params:","script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep","book.htex markdowns = [] figures = [] for dep in ctx.attr.deps: provider =","root and repo root). def _script_cmd(script_path, dir_reference): return \"\"\"\\ {script} \\ --container={container} \\","rule( implementation = _markdown_lib_impl, doc = \"Declares a set of markdown files\", attrs","= \"Extracting equations for: {}\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools","license. Please see the LICENSE # file at the root of the repository.","outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; tar cf {archive} {dir})\".format( base=outdir_tar.dirname,","providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This","= [] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or","= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub","\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return","markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths","inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message","[mobi_file], command = \"\"\"\\ {script} --cd-to-dir-reference \\ ebook-convert {epub_file} {mobi_file} \\ \"\"\".format( script=script_cmd,","building ebooks. # This is the container CONTAINER = \"filipfilmar/ebook-buildenv:1.1\" # Use this","= ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \"Building","rule(implementation = _neato_png_impl, attrs = { \"srcs\": attr.label_list( allow_files = [\".dot\"], doc =","pandoc -s --gladtex -o {target} {sources} \\ \"\"\".format( script=script_cmd, target=htex_file.path, sources=\" \".join(markdowns_paths)) )","[htex_file], outputs = [outdir, html_file], tools = [script], command = \"\"\"\\ {script} --cd-to-dir-reference","\"Transform an asymptote file into png\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\"{}_{}\".format(ctx.label.name,","markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider =","attr.label_list( allow_files = [\".t\"], doc = \"The file to compile\", ), \"deps\": attr.label_list(","_generalized_graphviz_rule_impl(ctx, \"dot\") dot_png = rule(implementation = _dot_png_impl, attrs = { \"srcs\": attr.label_list( allow_files","progress_message = \"Building EPUB for: {}\".format(name), inputs = inputs, tools = [script], outputs","= _ebook_epub_impl(ctx) # There can be only one such file outputs = default_info.files.to_list()"],"string":"[\n \"pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd,\",\n \"ctx.label.name # steps # run htex on all *md, gives book.htex markdowns =\",\n \") outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf {archive} {dir})\\\".format(\",\n \"outputs = [outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\\",\n \"+ markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs =\",\n \"quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the\",\n \"its reference directory. # # Params: # script_path: (string) The full path to\",\n \"doc = \\\"Generate an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file =\",\n \"captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe this is\",\n \"file into png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png =\",\n \"[outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r\",\n \"= dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file =\",\n \"epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in\",\n \"\\\\ asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd),\",\n \"[out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with\",\n \"file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures\",\n \"ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return\",\n \"markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain\",\n \"\\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the resulting htex\",\n \"-r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), )\",\n \"}, doc = \\\"Generate an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path):\",\n \"using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target\",\n \"equation dir # Maybe this is not needed. tar_command = \\\"(cd {base} ;\",\n \"for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The epub-metadata.xml\",\n \"html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file],\",\n \"markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation =\",\n \"_copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures\",\n \"markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run\",\n \"script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message =\",\n \"needed because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns)\",\n \"script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures\",\n \"directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting\",\n \"targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True,\",\n \"= rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All\",\n \"tar_command = \\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell(\",\n \"_copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs\",\n \"not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [\",\n \"set of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc\",\n \"= [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html\",\n \"target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles =\",\n \"+= [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with\",\n \"\\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd,\",\n \"ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message\",\n \"inputs = inputs, tools = [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script}\",\n \"_ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need\",\n \"_dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file\",\n \"markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\", providers\",\n \"= [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy\",\n \"outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name),\",\n \"# This is duplicated in _ebook_pdf_impl. # steps # run htex on all\",\n \"title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir,\",\n \"[out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\"\",\n \"to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path),\",\n \"[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png =\",\n \"\\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets should be allowed\\\", ), \\\"output\\\":\",\n \"(provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep\",\n \"base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs\",\n \"\\\"The title.yaml file to use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files =\",\n \"markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped =\",\n \"htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf\",\n \"= rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All\",\n \"dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures = []\",\n \"to the directory dir_reference, not the # directory where the build happens! This\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file into\",\n \"script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub.\",\n \"in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo]\",\n \"ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools =\",\n \"deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)\",\n \"and its reference directory. # # Params: # script_path: (string) The full path\",\n \"= figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation =\",\n \"root of the repository. # Build rules for building ebooks. # This is\",\n \"EPUB for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command\",\n \"ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file],\",\n \"dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def\",\n \"script invocation command based on the # script path and its reference directory.\",\n \"--epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference,\",\n \"archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs =\",\n \"[outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub. # This\",\n \"= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation =\",\n \"# Maybe this is not needed. tar_command = \\\"(cd {base} ; tar xvf\",\n \"figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for:\",\n \"doc = \\\"Transform a graphviz dot file into png using dot\\\", ) def\",\n \"attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file to\",\n \"There can be only one such file outputs = default_info.files.to_list() epub_file = outputs[0]\",\n \"dir # Maybe this is not needed. tar_command = \\\"(cd {base} ; tar\",\n \"default_info) = _ebook_epub_impl(ctx) # There can be only one such file outputs =\",\n \"ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ]\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in PDF format\\\" ) def\",\n \"to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools\",\n \"= target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles\",\n \"{htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename))\",\n \"out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider =\",\n \"epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx,\",\n \"= [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o\",\n \"asymptote = rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"],\",\n \"out # the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference):\",\n \"ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles\",\n \"<gh_stars>1-10 # Copyright (C) 2020 Google Inc. # # This file has been\",\n \"htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference,\",\n \"= \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd),\",\n \"= [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def\",\n \"target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep\",\n \"= ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures\",\n \"in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs =\",\n \"[file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd\",\n \"\\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\":\",\n \"src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs =\",\n \"= provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation command based on the\",\n \"return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png\",\n \"\\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\":\",\n \"tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd =\",\n \"generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz\",\n \"= _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name))\",\n \"progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools\",\n \"for building ebooks. # This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use\",\n \"attr.label_list( doc = \\\"The file to compile\\\", providers = [EbookInfo], ), }, )\",\n \"a graphviz dot file into png using dot\\\", ) def _asymptote_impl(ctx): asycc =\",\n \"_ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps #\",\n \"provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] #\",\n \"attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list(\",\n \"200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) )\",\n \"command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path,\",\n \"ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, )\",\n \"docker_run script invocation command based on the # script path and its reference\",\n \"[src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy =\",\n \"= epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell(\",\n \"the equation dir # Maybe this is not needed. tar_command = \\\"(cd {base}\",\n \"[\\\".asy\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The\",\n \"src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures +=\",\n \"target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns +=\",\n \"[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs =\",\n \"at the root of the repository. # Build rules for building ebooks. #\",\n \"title.yaml file to use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True,\",\n \"def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference,\",\n \"equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference\",\n \"output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output],\",\n \"ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs =\",\n \"and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message\",\n \"png using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for\",\n \"for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command\",\n \"\\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path,\",\n \"\\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference,\",\n \"reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script}\",\n \"epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx,\",\n \"= \\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message\",\n \"= \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs =\",\n \"= [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF for:\",\n \"script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the resulting htex to\",\n \"outputs = [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s\",\n \"outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml\",\n \"happens! This is needed because we can not control # figure inclusion. markdowns_paths\",\n \"\\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation command based\",\n \"# This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata)\",\n \"runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo(\",\n \"[captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name =\",\n \"ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs =\",\n \"Google Inc. # # This file has been licensed under Apache 2.0 license.\",\n \"= \\\"Generate an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name))\",\n \"inputs = [in_file], outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script}\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir,\",\n \"[out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\"\",\n \"= ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file,\",\n \"control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd =\",\n \"dot file into png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png\",\n \"command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename)\",\n \"True, doc = \\\"The title.yaml file to use for this book\\\", ), \\\"metadata_xml\\\":\",\n \"attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an\",\n \"files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown\",\n \"command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name\",\n \"[ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf}\",\n \"gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0]\",\n \"PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools =\",\n \"runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle =\",\n \"\\\"The epub-metadata.xml file to use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True,\",\n \"tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png\",\n \"\\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for\",\n \"dependencies, any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label(\",\n \"= ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs\",\n \"= \\\"Transform a graphviz dot file into png using dot\\\", ) def _asymptote_impl(ctx):\",\n \"= [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target} {sources}\",\n \"for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns\",\n \"-- relative to the directory dir_reference, not the # directory where the build\",\n \"-o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for\",\n \"can be only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir\",\n \"name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run\",\n \"-o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), ))\",\n \"# run htex on all *md, gives book.htex markdowns = [] figures =\",\n \"{mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for\",\n \"This is needed because we can not control # figure inclusion. markdowns_paths =\",\n \"title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata,\",\n \"file to use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), },\",\n \"ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote =\",\n \") ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list(\",\n \"_copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path,\",\n \"Fixed up paths -- relative to the directory dir_reference, not the # directory\",\n \"provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path\",\n \"= { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file to compile\\\",\",\n \"{base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message =\",\n \"progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command =\",\n \"= \\\"The file to compile\\\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx):\",\n \"), \\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\", providers = [EbookInfo], ),\",\n \"doc = \\\"The epub-metadata.xml file to use for this book\\\", ), \\\"_script\\\": attr.label(\",\n \"epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles\",\n \"of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc =\",\n \"\\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe this is not needed. tar_command\",\n \"dir_reference = markdowns[0] # Fixed up paths -- relative to the directory dir_reference,\",\n \"runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule(\",\n \"in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ),\",\n \"allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc\",\n \"\\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to\",\n \") ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list(\",\n \"# Params: # script_path: (string) The full path to the script to invoke\",\n \"}, doc = \\\"Generate an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file\",\n \"doc = \\\"The file to compile\\\", providers = [EbookInfo], ), }, ) def\",\n \"ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles =\",\n \"file used for figuring out # the reference directories (build root and repo\",\n \"doc = \\\"Generate an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in the Kindle's MOBI format\\\" )\",\n \"= outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) #\",\n \")) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [\",\n \"\\\"All the targets you need to make this book work.\\\", providers = [EbookInfo],\",\n \"png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = []\",\n \"}, doc = \\\"Transform a timing diagram file into png using drawtiming\\\", )\",\n \"root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER,\",\n \"dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]),\",\n \"= ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in target.files.to_list():\",\n \"= _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you\",\n \"= \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file to\",\n \"epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools\",\n \"_copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] +\",\n \"_markdown_lib_impl, doc = \\\"Declares a set of markdown files\\\", attrs = { \\\"srcs\\\":\",\n \"= ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename,\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram file into png using drawtiming\\\",\",\n \"ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub,\",\n \"in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles,\",\n \"implementation = _markdown_lib_impl, doc = \\\"Declares a set of markdown files\\\", attrs =\",\n \"return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list(\",\n \"{script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd,\",\n \") # run htexepub to obtain book.epub. # This is gonna be fun!\",\n \"title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell(\",\n \"\\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in\",\n \"= ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns)\",\n \"out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell(\",\n \"= \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command,\",\n \"# script path and its reference directory. # # Params: # script_path: (string)\",\n \"\\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in\",\n \"_asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file\",\n \"= [] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns\",\n \"an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path))\",\n \"= [outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub. #\",\n \"equation_outdir], tools = [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\\",\n \"= [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir}\",\n \"markdowns[0] # Fixed up paths -- relative to the directory dir_reference, not the\",\n \") return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying\",\n \"allow_files = [\\\".asy\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc\",\n \"_drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for target in\",\n \"> {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs =\",\n \"_script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs\",\n \"tools = [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc\",\n \"provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file\",\n \"compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets should be allowed\\\",\",\n \"tar_command, ) # run htexepub to obtain book.epub. # This is gonna be\",\n \"continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]),\",\n \"ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This\",\n \"#CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation\",\n \"# Returns the docker_run script invocation command based on the # script path\",\n \"[script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target} {sources} \\\\\",\n \"outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar\",\n \"dir_reference: (string) The path to a file used for figuring out # the\",\n \"figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools =\",\n \"in _ebook_pdf_impl. # steps # run htex on all *md, gives book.htex markdowns\",\n \"{ \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file to compile\\\", ),\",\n \"src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in\",\n \"allow_files = [\\\".t\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc\",\n \"{}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command =\",\n \"\\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub],\",\n \"attr.label( allow_files = True, doc = \\\"The title.yaml file to use for this\",\n \"cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs\",\n \"the build happens! This is needed because we can not control # figure\",\n \"runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib\",\n \"allow_files = [\\\".dot\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc\",\n \"= \\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell(\",\n \"= ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml)\",\n \"\\\"markdowns\\\"]) # Returns the docker_run script invocation command based on the # script\",\n \"figures += provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to\",\n \"src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message =\",\n \"\\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools =\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in EPUB format\\\" )\",\n \"the repository. # Build rules for building ebooks. # This is the container\",\n \"script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in the Kindle's MOBI\",\n \"= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib =\",\n \"src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures +=\",\n \"[] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)]\",\n \"dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns]\",\n \"for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command =\",\n \"file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message\",\n \"# steps # run htex on all *md, gives book.htex markdowns = []\",\n \"\\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path,\",\n \"the root of the repository. # Build rules for building ebooks. # This\",\n \"duplicated in _ebook_pdf_impl. # steps # run htex on all *md, gives book.htex\",\n \"{epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file])\",\n \"--output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for\",\n \"{}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) # run\",\n \"if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in\",\n \"neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx,\",\n \"script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd\",\n \"] asymptote = rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files =\",\n \"-Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []\",\n \"figuring out # the reference directories (build root and repo root). def _script_cmd(script_path,\",\n \"script to invoke # dir_reference: (string) The path to a file used for\",\n \"= ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png =\",\n \"graphviz dot file into png using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script\",\n \"outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs\",\n \"= \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command,\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file into png using neato\\\",\",\n \"ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files\",\n \"tar_command = \\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path)\",\n \"command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference,\",\n \"[_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures\",\n \"[]) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [\",\n \"ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc =\",\n \"base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs\",\n \"}, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl.\",\n \"equation environments for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script],\",\n \"dir_reference, not the # directory where the build happens! This is needed because\",\n \"gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0]\",\n \"= ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name),\",\n \"script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs =\",\n \"# # This file has been licensed under Apache 2.0 license. Please see\",\n \"in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = []\",\n \"path to the script to invoke # dir_reference: (string) The path to a\",\n \"[script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\\",\n \"Build rules for building ebooks. # This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\"\",\n \"ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools = [script],\",\n \"deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps),\",\n \"progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path,\",\n \"ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf\",\n \"= \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any\",\n \"\\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs =\",\n \"\\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in\",\n \"\\\"Generate an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\")\",\n \"= [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue\",\n \"{archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs =\",\n \"), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in\",\n \"markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths\",\n \"epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf =\",\n \"to make this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files =\",\n \"dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ]\",\n \"neato_png = rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"],\",\n \"= \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo\",\n \"\\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run =\",\n \"path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files]\",\n \"outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir\",\n \"= markdowns, outputs = [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\\",\n \"ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf =\",\n \"steps # run htex on all *md, gives book.htex markdowns = [] figures\",\n \"= \\\"Transform a timing diagram file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx,\",\n \"progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command =\",\n \"= ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata)\",\n \"[ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl,\",\n \"container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures =\",\n \"a file used for figuring out # the reference directories (build root and\",\n \"= \\\"Transform an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy =\",\n \"+= ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return\",\n \") ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf {archive}\",\n \"for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns\",\n \"gives book.htex markdowns = [] figures = [] for dep in ctx.attr.deps: provider\",\n \"relative to the directory dir_reference, not the # directory where the build happens!\",\n \"= _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The\",\n \"provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative\",\n \"{}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message\",\n \"EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl,\",\n \"The path to a file used for figuring out # the reference directories\",\n \"--cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), ))\",\n \"ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run htex on\",\n \"_drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file\",\n \"(build root and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container}\",\n \"= { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file to compile\\\",\",\n \"{}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return\",\n \"[outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub to obtain\",\n \"This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml\",\n \"EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation command based on\",\n \"environments for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command\",\n \"if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return\",\n \"[out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}:\",\n \"be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), },\",\n \"provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can\",\n \"_script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd),\",\n \"= ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file into png\\\", )\",\n \"[ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs\",\n \"[equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script =\",\n \"def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for target\",\n \"outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script\",\n \"{ \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown source files\\\", ),\",\n \"= [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png -o\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in the\",\n \"= ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo(\",\n \"\\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The title.yaml file to use for\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in PDF format\\\"\",\n \"[in_file], outputs = [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy\",\n \"epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs =\",\n \"}, doc = \\\"Transform a graphviz dot file into png using neato\\\", )\",\n \"outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output\",\n \"src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path)\",\n \"--dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script\",\n \"src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path)\",\n \"file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file\",\n \"epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps:\",\n \"runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl,\",\n \"dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference\",\n \"= tar_command, ) # run htexepub to obtain book.epub. # This is gonna\",\n \"attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need to\",\n \"to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command\",\n \"allow_files = [\\\".md\\\"], doc = \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc\",\n \"with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file into png\",\n \"outputs = [outdir_tar], command = tar_command, ) # run htexepub to obtain book.epub.\",\n \"= \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools =\",\n \"in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file]\",\n \"2020 Google Inc. # # This file has been licensed under Apache 2.0\",\n \"ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub\",\n \"= \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path,\",\n \"\\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\":\",\n \"drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target\",\n \"command = tar_command, ) # run htexepub to obtain book.epub. # This is\",\n \"Apache 2.0 license. Please see the LICENSE # file at the root of\",\n \"rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the\",\n \"is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml =\",\n \"runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files\",\n \"dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar],\",\n \"the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs. #CONTAINER\",\n \"ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI\",\n \"file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex\",\n \"DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list(\",\n \"= \\\"Transform a graphviz dot file into png using neato\\\", ) def _dot_png_impl(ctx):\",\n \"asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in\",\n \"tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d\",\n \"to invoke # dir_reference: (string) The path to a file used for figuring\",\n \"figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path)\",\n \"docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in\",\n \"= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation\",\n \"[out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs\",\n \") def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in\",\n \"] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files =\",\n \"Copyright (C) 2020 Google Inc. # # This file has been licensed under\",\n \"ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe this is not needed.\",\n \"\\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command =\",\n \"), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \\\"Declares a set\",\n \"generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing\",\n \"rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc =\",\n \"\\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message\",\n \"is not needed. tar_command = \\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format(\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in EPUB\",\n \"ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps)\",\n \"\\\"Declares a set of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files =\",\n \"paths -- relative to the directory dir_reference, not the # directory where the\",\n \"output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message =\",\n \"{script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), )\",\n \"{}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command =\",\n \"in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0])\",\n \"inputs = [htex_file], outputs = [outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\\",\n \"in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles\",\n \"outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram file into png using\",\n \"this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an\",\n \"\\\\ -f html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path),\",\n \"not the # directory where the build happens! This is needed because we\",\n \"file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets should\",\n \"figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG:\",\n \"progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file],\",\n \"ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles =\",\n \"_strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for\",\n \"title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata,\",\n \"= outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation\",\n \"= rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc\",\n \"tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations:\",\n \"outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs =\",\n \"run htexepub to obtain book.epub. # This is gonna be fun! epub_metadata =\",\n \"path and its reference directory. # # Params: # script_path: (string) The full\",\n \"= ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message =\",\n \"{archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs\",\n \"[EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated\",\n \"The full path to the script to invoke # dir_reference: (string) The path\",\n \"This is duplicated in _ebook_pdf_impl. # steps # run htex on all *md,\",\n \"-s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) #\",\n \"target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures\",\n \"a timing diagram file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run\",\n \"{}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), )\",\n \"{1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command\",\n \"= [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format(\",\n \"src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in\",\n \"is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml =\",\n \"= \\\"The epub-metadata.xml file to use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\",\",\n \"ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns +\",\n \"EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return\",\n \"= _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is gonna\",\n \"fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml =\",\n \"return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path),\",\n \"ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo(\",\n \"= \\\"The title.yaml file to use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files\",\n \"to use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc\",\n \"such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar =\",\n \"--cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference,\",\n \"{script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub} {html_file}\",\n \"doc = \\\"The dependencies, any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated\",\n \"= ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps # run htex\",\n \"[in_file], outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd}\",\n \"{ \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file to compile\\\", ),\",\n \"src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return\",\n \"full path to the script to invoke # dir_reference: (string) The path to\",\n \"= { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown source files\\\",\",\n \"files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs =\",\n \"_ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need\",\n \"= [\\\".asy\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc =\",\n \"+= provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths =\",\n \"= _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file,\",\n \"= inputs, tools = [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference\",\n \"+ markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs =\",\n \"(provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)\",\n \"command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3\",\n \"= \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs\",\n \"the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\\",\n \"), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets should be allowed\\\", ),\",\n \"_ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need\",\n \"allow_files = True, doc = \\\"The epub-metadata.xml file to use for this book\\\",\",\n \"attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a\",\n \"not needed. tar_command = \\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname,\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file into png using dot\\\",\",\n \"title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB\",\n \"return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation =\",\n \"_strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name\",\n \"_ebook_pdf_impl. # steps # run htex on all *md, gives book.htex markdowns =\",\n \"this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc\",\n \"[epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building\",\n \"runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation\",\n \"Use this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"])\",\n \"in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ]\",\n \"obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata =\",\n \"deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider:\",\n \"in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs =\",\n \"= [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata}\",\n \"figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl,\",\n \"the LICENSE # file at the root of the repository. # Build rules\",\n \"untar the equation dir # Maybe this is not needed. tar_command = \\\"(cd\",\n \"fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml =\",\n \"\\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src\",\n \"[script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file}\",\n \"html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for:\",\n \"DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list(\",\n \"= ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]),\",\n \"the # script path and its reference directory. # # Params: # script_path:\",\n \"[] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file\",\n \"markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation\",\n \") def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {}\",\n \"epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles =\",\n \"directory where the build happens! This is needed because we can not control\",\n \"{}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\\",\n \"def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list():\",\n \"\\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =\",\n \"because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script\",\n \"\\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message =\",\n \"doc = \\\"The title.yaml file to use for this book\\\", ), \\\"metadata_xml\\\": attr.label(\",\n \"\\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file],\",\n \"provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation command based on the #\",\n \"progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script],\",\n \"DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such file\",\n \"on the resulting htex to obtain html and output directory with figures. outdir\",\n \"= outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe\",\n \"for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, )\",\n \"cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx):\",\n \"inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns\",\n \"\\\"The dependencies, any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\":\",\n \"for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures\",\n \"to the script to invoke # dir_reference: (string) The path to a file\",\n \"ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle\",\n \"needed. tar_command = \\\"(cd {base} ; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename,\",\n \"-o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target\",\n \"ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs\",\n \") def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs:\",\n \"{target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on\",\n \"invocation command based on the # script path and its reference directory. #\",\n \"htex to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file\",\n \"= ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]),\",\n \"= _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The\",\n \"tools = [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert\",\n \"= _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path,\",\n \"_generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for\",\n \"we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script =\",\n \"_dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\":\",\n \"to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file =\",\n \"{}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\\",\n \"{script} \\\\ pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\"\",\n \"epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs =\",\n \"{}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\\",\n \"out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell(\",\n \"# figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path,\",\n \"_script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs = markdowns,\",\n \"EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only\",\n \"target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain\",\n \"markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs = inputs,\",\n \"in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files):\",\n \"into png using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = []\",\n \"script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs =\",\n \"\\\"Transform a timing diagram file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd):\",\n \"= rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All\",\n \"provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to the directory\",\n \"resulting htex to obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name))\",\n \"ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs =\",\n \"Please see the LICENSE # file at the root of the repository. #\",\n \"_script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs\",\n \"ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles\",\n \"be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml\",\n \"is needed because we can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference,\",\n \"DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc =\",\n \"= _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar]\",\n \"runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\":\",\n \"= [captured_output], command = tar_command, ) dir_reference = epub_file script = ctx.executable._script name\",\n \"(C) 2020 Google Inc. # # This file has been licensed under Apache\",\n \"run gladtex on the resulting htex to obtain html and output directory with\",\n \"book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx,\",\n \"\\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\"\",\n \"+= provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file\",\n \"= \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd,\",\n \"book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The epub-metadata.xml file to\",\n \"{cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =\",\n \"dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles,\",\n \"return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list(\",\n \"markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns +=\",\n \"html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200\",\n \"\\\"deps\\\": attr.label_list( doc = \\\"All the targets you need to make this book\",\n \"see the LICENSE # file at the root of the repository. # Build\",\n \"= [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message =\",\n \"= rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc\",\n \"--container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run\",\n \"for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles),\",\n \"\\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {}\",\n \"{0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\\",\n \"htex on all *md, gives book.htex markdowns = [] figures = [] for\",\n \"in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote\",\n \"tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\"\",\n \"and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format(\",\n \"attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list(\",\n \"# the reference directories (build root and repo root). def _script_cmd(script_path, dir_reference): return\",\n \"for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_epub], command =\",\n \"doc = \\\"Transform a graphviz dot file into png using neato\\\", ) def\",\n \"\\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd),\",\n \"script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if\",\n \"to a file used for figuring out # the reference directories (build root\",\n \"in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs\",\n \"figures = [] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures\",\n \"src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs\",\n \"ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script\",\n \"{ \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need to make this\",\n \"command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\"\",\n \"compile\\\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name #\",\n \"return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl,\",\n \"an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def\",\n \"\\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider\",\n \"\\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo =\",\n \"rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the\",\n \"runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation =\",\n \"runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ]\",\n \"attr.label_list( doc = \\\"The dependencies, any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The\",\n \"doc = \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file\",\n \"\\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path),\",\n \"ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message =\",\n \"\\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps:\",\n \"html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)\",\n \"\\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def\",\n \"def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo.\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file into png using\",\n \"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in\",\n \"been licensed under Apache 2.0 license. Please see the LICENSE # file at\",\n \") ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list(\",\n \"= \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format(\",\n \"outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe this\",\n \"diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file],\",\n \"= [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file}\",\n \"-t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference,\",\n \"ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles =\",\n \"asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), )\",\n \"sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain html\",\n \"this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The epub-metadata.xml file\",\n \"\\\".join(markdowns_paths)) ) # run gladtex on the resulting htex to obtain html and\",\n \"inputs, tools = [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\\",\n \"target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures =\",\n \"--mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths),\",\n \"ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ]\",\n \"= dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed\",\n \"\\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for\",\n \"format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second\",\n \"cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs:\",\n \"{script} \\\\ asy -render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path,\",\n \"ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [\",\n \"dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name))\",\n \"pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference,\",\n \"epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub =\",\n \"diagram file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script\",\n \"PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc], command =\",\n \"MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file],\",\n \"{dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir],\",\n \"all *md, gives book.htex markdowns = [] figures = [] for dep in\",\n \"command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format(\",\n \"progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src],\",\n \"= _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The\",\n \"[htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o\",\n \"= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule(\",\n \"+= [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG\",\n \"rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the\",\n \"= True, doc = \\\"The title.yaml file to use for this book\\\", ),\",\n \"+= [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path),\",\n \"= target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for\",\n \"LICENSE # file at the root of the repository. # Build rules for\",\n \"_ebook_epub_impl(ctx) # There can be only one such file outputs = default_info.files.to_list() epub_file\",\n \"# This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx, epub_metadata)\",\n \"html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return\",\n \"files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all\",\n \"), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc =\",\n \"file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps #\",\n \"; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations:\",\n \"\\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep\",\n \"has been licensed under Apache 2.0 license. Please see the LICENSE # file\",\n \"generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote\",\n \"book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook\",\n \"for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target\",\n \"figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns\",\n \"--epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path),\",\n \"a graphviz dot file into png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx,\",\n \"up paths -- relative to the directory dir_reference, not the # directory where\",\n \"in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns\",\n \"\\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The epub-metadata.xml file to use for\",\n \"figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG\",\n \"= [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script\",\n \"into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures =\",\n \"ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\")\",\n \"= _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you\",\n \"{base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving\",\n \"ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs = markdowns, outputs =\",\n \"using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for\",\n \"_neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\":\",\n \"is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be\",\n \"{} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path,\",\n \"{}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference =\",\n \"ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or\",\n \"for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd =\",\n \"= src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path,\",\n \"-f html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference,\",\n \"this is not needed. tar_command = \\\"(cd {base} ; tar xvf {archive}) >\",\n \"return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule(\",\n \"invoke # dir_reference: (string) The path to a file used for figuring out\",\n \"= markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped\",\n \"\\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\",\",\n \"This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml\",\n \"cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src\",\n \"), \\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The title.yaml file to use\",\n \"# file at the root of the repository. # Build rules for building\",\n \"ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir,\",\n \"= \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o\",\n \"= _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns +\",\n \"second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one\",\n \"equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) #\",\n \"# First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) #\",\n \"\\\"Generate an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) #\",\n \"implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets\",\n \"the directory dir_reference, not the # directory where the build happens! This is\",\n \"= [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex\",\n \"markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell(\",\n \"epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path),\",\n \"dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ]\",\n \"= True, doc = \\\"The epub-metadata.xml file to use for this book\\\", ),\",\n \"inputs = [in_file], outputs = [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script}\",\n \"continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles =\",\n \"ebooks. # This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for\",\n \"make this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True,\",\n \"attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file to\",\n \"This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local\",\n \"to compile\\\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name\",\n \") return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for\",\n \"dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation =\",\n \"= [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script}\",\n \"any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\",\",\n \"markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs = markdowns, outputs\",\n \"--gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run\",\n \"this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) #\",\n \"target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures)\",\n \"inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src):\",\n \"need to make this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram file into\",\n \"htexepub to obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0]\",\n \"outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output\",\n \"rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc =\",\n \"[script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\\",\n \"# # Params: # script_path: (string) The full path to the script to\",\n \"markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\":\",\n \"png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying\",\n \"in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps:\",\n \"runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo],\",\n \"ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider\",\n \"directories (build root and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\\",\n \"file has been licensed under Apache 2.0 license. Please see the LICENSE #\",\n \"doc = \\\"Transform an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy\",\n \"\\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps\",\n \"the resulting htex to obtain html and output directory with figures. outdir =\",\n \"DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs =\",\n \"markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The\",\n \"{ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles\",\n \"doc = \\\"All the targets you need to make this book work.\\\", providers\",\n \"captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = {\",\n \"a set of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"],\",\n \"def _generalized_graphviz_rule_impl(ctx, cmd): docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs:\",\n \"ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles =\",\n \"markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message\",\n \"+ figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools\",\n \"runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule(\",\n \"-o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), ))\",\n \"{cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []\",\n \"runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png\",\n \"[out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f\",\n \"runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub =\",\n \"mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return\",\n \"for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures\",\n \"outputs = [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render\",\n \"for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir,\",\n \"into png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation\",\n \"inputs, tools = [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\\",\n \"progress_message = \\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools = [script], outputs\",\n \"_ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info,\",\n \"\\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs =\",\n \"attr.label( allow_files = True, doc = \\\"The epub-metadata.xml file to use for this\",\n \"] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs =\",\n \"build happens! This is needed because we can not control # figure inclusion.\",\n \"First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There\",\n \"only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1]\",\n \"an ebook in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First\",\n \"= [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures\",\n \"epub-metadata.xml file to use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"),\",\n \"\\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps:\",\n \"= \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs\",\n \"doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies,\",\n \"ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc =\",\n \"epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps:\",\n \"[ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name #\",\n \"[src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = []\",\n \"use for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc =\",\n \"gladtex on the resulting htex to obtain html and output directory with figures.\",\n \") deps = [] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not\",\n \"under Apache 2.0 license. Please see the LICENSE # file at the root\",\n \"runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps,\",\n \"implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets\",\n \"title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs\",\n \"= [htex_file], outputs = [outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script}\",\n \"for this book\\\", ), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate\",\n \"CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\"\",\n \"= ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx):\",\n \"= ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. #\",\n \"runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\":\",\n \"doc = \\\"Transform a timing diagram file into png using drawtiming\\\", ) def\",\n \"markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)\",\n \"Inc. # # This file has been licensed under Apache 2.0 license. Please\",\n \"\\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), )\",\n \"EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation\",\n \"[ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation\",\n \"ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message =\",\n \"DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl,\",\n \"targets you need to make this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\":\",\n \"-d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar\",\n \"\\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\"\",\n \"ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy],\",\n \"runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script\",\n \"_generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files\",\n \"command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\\",\n \"runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram file\",\n \"_neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file\",\n \"where the build happens! This is needed because we can not control #\",\n \"markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs = inputs,\",\n \"tools = [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc\",\n \"= _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in PDF format\\\" )\",\n \"ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command\",\n \"markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\":\",\n \"inputs = markdowns, outputs = [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script}\",\n \"= [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format(\",\n \"def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. # steps\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot file\",\n \"{markdowns} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles =\",\n \"command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for\",\n \"figures += provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for\",\n \"= ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src],\",\n \"src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file into png\\\", ) def\",\n \"= [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\"\",\n \"_asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for src\",\n \"container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs. #CONTAINER =\",\n \"PDF for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf], command\",\n \"# script_path: (string) The full path to the script to invoke # dir_reference:\",\n \"ctx.executable._script figures = [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file\",\n \"= { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The file to compile\\\",\",\n \"= figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx,\",\n \"run htex on all *md, gives book.htex markdowns = [] figures = []\",\n \"= [\\\".t\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc =\",\n \"runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures,\",\n \"\\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown source files\\\", ), \\\"deps\\\":\",\n \"runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \\\"Declares a\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in EPUB format\\\"\",\n \"\\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub])\",\n \"Params: # script_path: (string) The full path to the script to invoke #\",\n \"asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell(\",\n \"png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for\",\n \"in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename +\",\n \"ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file,\",\n \"[script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file}\",\n \"runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\":\",\n \"\\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram\",\n \"return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs\",\n \"ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]),\",\n \") def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir,\",\n \"_strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps\",\n \"name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for:\",\n \"\\\\ pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths))\",\n \"xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path),\",\n \"\\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the resulting\",\n \"[\\\".md\\\"], doc = \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The\",\n \"# This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick\",\n \"\\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to\",\n \"runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files\",\n \"file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a graphviz dot\",\n \"\\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles\",\n \"used for figuring out # the reference directories (build root and repo root).\",\n \"EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs = {\",\n \"ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs =\",\n \"file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram\",\n \"This file has been licensed under Apache 2.0 license. Please see the LICENSE\",\n \"_script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building MOBI for: {}\\\".format(name), inputs = [epub_file, equation_outdir],\",\n \") # run gladtex on the resulting htex to obtain html and output\",\n \"of the repository. # Build rules for building ebooks. # This is the\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in PDF\",\n \"True, doc = \\\"The epub-metadata.xml file to use for this book\\\", ), \\\"_script\\\":\",\n \"provider = target[EbookInfo] figures += (provider.figures or []) markdowns += (provider.markdowns or [])\",\n \"+= [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider = target[EbookInfo]\",\n \"\\\"Transform a graphviz dot file into png using dot\\\", ) def _asymptote_impl(ctx): asycc\",\n \"+= (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles =\",\n \"# run gladtex on the resulting htex to obtain html and output directory\",\n \"\\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file], outputs = [out_file], tools = [asycc],\",\n \"[docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path,\",\n \"return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation = _asymptote_impl, attrs\",\n \"\\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools = [script], outputs = [ebook_pdf],\",\n \"outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base}\",\n \"on all *md, gives book.htex markdowns = [] figures = [] for dep\",\n \"should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"),\",\n \"or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return\",\n \"= target[EbookInfo] if not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files =\",\n \"= [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The title.yaml file\",\n \"}, doc = \\\"Transform a graphviz dot file into png using dot\\\", )\",\n \"ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell(\",\n \"drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"],\",\n \"using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl,\",\n \"\\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), )\",\n \"the targets you need to make this book work.\\\", providers = [EbookInfo], ),\",\n \"[ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t\",\n \"files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name =\",\n \"= [\\\".md\\\"], doc = \\\"The markdown source files\\\", ), \\\"deps\\\": attr.label_list( doc =\",\n \"use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The\",\n \"[ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation =\",\n \"= [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This is\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in the Kindle's MOBI format\\\"\",\n \"\\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format(\",\n \"\\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\ \\\"\\\"\\\".format(\",\n \") dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path,\",\n \"Returns the docker_run script invocation command based on the # script path and\",\n \"= \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run script invocation command\",\n \"_strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to\",\n \"*md, gives book.htex markdowns = [] figures = [] for dep in ctx.attr.deps:\",\n \"ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command\",\n \"PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo,\",\n \"[\\\".t\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The\",\n \"src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs\",\n \"def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx):\",\n \"# Use this for quick local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\",\",\n \"htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar\",\n \"to obtain book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata\",\n \"= [outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex\",\n \"tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc -s --gladtex -o {target}\",\n \"\\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files =\",\n \"= \\\"Generate an ebook in EPUB format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\",\",\n \"[]) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps:\",\n \"is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such\",\n \"\\\"output\\\": attr.output(doc=\\\"The generated file\\\"), \\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform\",\n \"in PDF format\\\" ) def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is\",\n \"ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) = _ebook_epub_impl(ctx)\",\n \"src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs\",\n \"# run htexepub to obtain book.epub. # This is gonna be fun! epub_metadata\",\n \"_copy_file_to_workdir(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs\",\n \"= _markdown_lib_impl, doc = \\\"Declares a set of markdown files\\\", attrs = {\",\n \"command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path,\",\n \"book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc =\",\n \"--cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub} {html_file} \\\\\",\n \"markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc\",\n \"# dir_reference: (string) The path to a file used for figuring out #\",\n \"] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc\",\n \"= \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path),\",\n \"script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to PNG with {1}:\",\n \"= [in_file], outputs = [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\\",\n \"target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd\",\n \"\\\"Archiving equations: {}\\\".format(outdir_tar.short_path), inputs = [outdir], outputs = [outdir_tar], command = tar_command, )\",\n \"figures = [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file =\",\n \"graphviz dot file into png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\")\",\n \"files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \\\"Declares\",\n \"file to compile\\\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name =\",\n \"dot_png = rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"],\",\n \"= [] for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src\",\n \"= src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path,\",\n \"outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\\",\n \"= _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub\",\n \"the docker_run script invocation command based on the # script path and its\",\n \"progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file],\",\n \"ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp\",\n \"repository. # Build rules for building ebooks. # This is the container CONTAINER\",\n \"= \\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for target in ctx.attr.srcs: for\",\n \"figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing diagram to\",\n \"return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src\",\n \"outputs = [src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy\",\n \"ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps,\",\n \"figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\")\",\n \"{html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles =\",\n \"[] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns +=\",\n \"= ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for:\",\n \"script_path: (string) The full path to the script to invoke # dir_reference: (string)\",\n \"{outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar =\",\n \"the script to invoke # dir_reference: (string) The path to a file used\",\n \"= ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs =\",\n \"+= ebook_provider.figures runfiles = ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles),\",\n \"provider.figures dir_reference = markdowns[0] htex_file = ctx.actions.declare_file(\\\"{}.htex\\\".format(name)) markdowns_paths = [file.path for file in\",\n \"+ \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"timing\",\n \"tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format(\",\n \"[asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5 -f png -o \\\"{out_file}\\\"\",\n \"= ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns),\",\n \"ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file],\",\n \"= ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml,\",\n \"attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".md\\\"], doc = \\\"The markdown source\",\n \"archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs =\",\n \"= \\\"Declares a set of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list( allow_files\",\n \"html and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell(\",\n \"inputs = [outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub\",\n \"ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir,\",\n \"not ebook_provider: continue deps += ebook_provider.figures runfiles = ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps:\",\n \"] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = { \\\"deps\\\": attr.label_list( doc\",\n \"ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name),\",\n \"= default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output =\",\n \"into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message =\",\n \"= ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info) =\",\n \"epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message\",\n \"[\\\".dot\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The\",\n \"= markdowns[0] # Fixed up paths -- relative to the directory dir_reference, not\",\n \"is duplicated in _ebook_pdf_impl. # steps # run htex on all *md, gives\",\n \"= \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns} \\\\\",\n \"\\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, )\",\n \"= \\\"The dependencies, any targets should be allowed\\\", ), \\\"output\\\": attr.output(doc=\\\"The generated file\\\"),\",\n \"def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all *md,\",\n \"in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on\",\n \"script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs\",\n \"_copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy],\",\n \"{script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference,\",\n \"= rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc\",\n \"2.0 license. Please see the LICENSE # file at the root of the\",\n \"title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns\",\n \"[] for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures +=\",\n \"figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools =\",\n \"(ebook_info, default_info) = _ebook_epub_impl(ctx) # There can be only one such file outputs\",\n \"ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles\",\n \"), \\\"metadata_xml\\\": attr.label( allow_files = True, doc = \\\"The epub-metadata.xml file to use\",\n \"providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The title.yaml\",\n \"ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles,\",\n \"return [ _strip_reference_dir(reference_dir, file.path) for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name\",\n \"ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs = { \\\"deps\\\": attr.label_list( doc =\",\n \"the # directory where the build happens! This is needed because we can\",\n \"work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The\",\n \"files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs\",\n \"for: {}\\\".format(name), inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command\",\n \"= [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata}\",\n \"[src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx):\",\n \"def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs =\",\n \"to use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc =\",\n \"ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools = [script],\",\n \"markdowns, outputs = [htex_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} \\\\ pandoc\",\n \"dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path)\",\n \"executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in EPUB format\\\" ) def\",\n \"outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng\",\n \"= tar_command, ) dir_reference = epub_file script = ctx.executable._script name = ctx.label.name script_cmd\",\n \"[docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} -Tpng -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd,\",\n \"[EbookInfo], ), \\\"title_yaml\\\": attr.label( allow_files = True, doc = \\\"The title.yaml file to\",\n \"default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform a timing diagram file into png\",\n \"= _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY to PNG: {0}\\\".format(in_file.short_path), inputs = [in_file],\",\n \"= [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file script\",\n \"cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target in ctx.attr.deps: ebook_provider\",\n \"in_file = src out_file = ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd =\",\n \"ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building\",\n \"on the # script path and its reference directory. # # Params: #\",\n \"= _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building equation environments for: {}\\\".format(name), inputs =\",\n \"\\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets\",\n \"path to a file used for figuring out # the reference directories (build\",\n \"= [outdir], outputs = [outdir_tar], command = tar_command, ) # run htexepub to\",\n \"+ \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"ASY\",\n \"= \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format(\",\n \"{}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in\",\n \"script path and its reference directory. # # Params: # script_path: (string) The\",\n \"src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs:\",\n \"= ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml]\",\n \"path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file\",\n \"= ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir # Maybe this is not\",\n \"{output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving equations: {}\\\".format(equation_outdir_tar.short_path), inputs = [equation_outdir_tar],\",\n \"markdowns = [] figures = [] for dep in ctx.attr.deps: provider = dep[EbookInfo]\",\n \"tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename) ctx.actions.run_shell( progress_message = \\\"Archiving equations: {}\\\".format(outdir_tar.short_path),\",\n \"+ \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz\",\n \"cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\\",\n \"file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2]\",\n \"source files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\", providers =\",\n \"DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, ) ] ebook_kindle = rule( implementation = _ebook_kindle_impl, attrs\",\n \"to compile\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The dependencies, any targets should be\",\n \"obtain html and output directory with figures. outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name))\",\n \"in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf\",\n \"timing diagram file into png using drawtiming\\\", ) def _generalized_graphviz_rule_impl(ctx, cmd): docker_run =\",\n \"in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference =\",\n \"{} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns = [] for target\",\n \"attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list(\",\n \"command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o {ebook_pdf} {markdowns}\",\n \"inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, dir_reference.path) #\",\n \"doc = \\\"Declares a set of markdown files\\\", attrs = { \\\"srcs\\\": attr.label_list(\",\n \"ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname, archive=outdir_tar.basename, dir=outdir.basename)\",\n \"] markdown_lib = rule( implementation = _markdown_lib_impl, doc = \\\"Declares a set of\",\n \"or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in\",\n \"figures += (provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns)\",\n \"def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in ctx.attr.srcs: for\",\n \"{sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex on the\",\n \"file at the root of the repository. # Build rules for building ebooks.\",\n \"equations for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools = [script],\",\n \"= _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd),\",\n \"format\\\" ) def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [\",\n \"{0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run], command =\",\n \"\\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files =\",\n \"for figuring out # the reference directories (build root and repo root). def\",\n \"rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc =\",\n \"return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns), runfiles=runfiles, ), ] markdown_lib = rule( implementation\",\n \"attr.label_list( doc = \\\"All the targets you need to make this book work.\\\",\",\n \"return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path) for file in\",\n \"# There can be only one such file outputs = default_info.files.to_list() epub_file =\",\n \"runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] asymptote = rule(implementation\",\n \"be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml\",\n \"= [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _markdown_lib_impl(ctx): markdowns =\",\n \"= [\\\".dot\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list( doc =\",\n \"runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo(\",\n \"_script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is gonna be\",\n \"= _copy_file_to_workdir_renamed(ctx, epub_metadata) title_yaml = ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir_renamed(ctx, epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name))\",\n \"_script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, )\",\n \"markdown_lib = rule( implementation = _markdown_lib_impl, doc = \\\"Declares a set of markdown\",\n \"progress_message = \\\"timing diagram to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file],\",\n \"def _strip_reference_dir(reference_dir, path): return path.replace(reference_dir.dirname+\\\"/\\\", \\\"\\\") def _strip_reference_dir_from_files(reference_dir, files): return [ _strip_reference_dir(reference_dir, file.path)\",\n \"script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path,\",\n \"markdowns_paths = [file.path for file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script =\",\n \"epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in\",\n \"png using neato\\\", ) def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation =\",\n \"epub_metadata) ebook_epub = ctx.actions.declare_file(\\\"{}.epub\\\".format(name)) inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns\",\n \"script_cmd = _script_cmd(script.path, dir_reference.path) # run htexepub to obtain book.epub. # This is\",\n \"not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd\",\n \"you need to make this book work.\\\", providers = [EbookInfo], ), \\\"title_yaml\\\": attr.label(\",\n \"local runs. #CONTAINER = \\\"ebook-buildenv:local\\\" EbookInfo = provider(fields=[\\\"figures\\\", \\\"markdowns\\\"]) # Returns the docker_run\",\n \"gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path),\",\n \"for file in files] def _ebook_pdf_impl(ctx): name = ctx.label.name # steps # run\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Generate an ebook in the Kindle's\",\n \"ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0]\",\n \"\\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = [] for target\",\n \"files\\\", ), \\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\", providers = [EbookInfo],\",\n \"-render 5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps\",\n \"_markdown_lib_impl(ctx): markdowns = [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns\",\n \"licensed under Apache 2.0 license. Please see the LICENSE # file at the\",\n \")) runfiles = ctx.runfiles(files=[ebook_epub]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [\",\n \"[src_copy], inputs = [src], command=\\\"cp {} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx,\",\n \"can not control # figure inclusion. markdowns_paths = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script\",\n \"equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the equation dir #\",\n \"\\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference,\",\n \"ctx.runfiles(files=figures+deps) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps),\",\n \"; tar xvf {archive}) > {output}\\\".format( base=equation_outdir_tar.dirname, archive=equation_outdir_tar.basename, output=captured_output.path) ctx.actions.run_shell( progress_message = \\\"Unarchiving\",\n \"\\\"_script\\\": attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file into\",\n \"\\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command\",\n \"\\\"deps\\\": attr.label_list( doc = \\\"The file to compile\\\", providers = [EbookInfo], ), },\",\n \"script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps:\",\n \"= \\\"Building equation environments for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools\",\n \"be only one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir =\",\n \"dot file into png using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures\",\n \"file into png using dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures =\",\n \"[ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs =\",\n \"+= provider.figures dir_reference = markdowns[0] # Fixed up paths -- relative to the\",\n \"(string) The full path to the script to invoke # dir_reference: (string) The\",\n \"\\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ -f html -t epub3 -o {ebook_epub}\",\n \")) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [\",\n \"] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc\",\n \"--cd-to-dir-reference \\\\ gladtex -r 200 -d {outdir} {htex_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, outdir=_strip_reference_dir(dir_reference, outdir.path),\",\n \"[] for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps\",\n \"for target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file =\",\n \") def _ebook_epub_impl(ctx): name = ctx.label.name # This is duplicated in _ebook_pdf_impl. #\",\n \"+= (provider.figures or []) markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for\",\n \"mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is DefaultInfo. (ebook_info, default_info)\",\n \"ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for\",\n \"target in ctx.attr.srcs: for src in target.files.to_list(): in_file = src out_file = ctx.actions.declare_file(in_file.basename\",\n \"dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles, )\",\n \"inputs = [equation_outdir_tar], outputs = [captured_output], command = tar_command, ) dir_reference = epub_file\",\n \"inputs = inputs, tools = [script], outputs = [ebook_epub], command = \\\"\\\"\\\"\\\\ {script}\",\n \"for dep in ctx.attr.deps: provider = dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures\",\n \"= ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(docker_run.path, in_file.path) ctx.actions.run_shell( progress_message\",\n \"= \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path),\",\n \"directory dir_reference, not the # directory where the build happens! This is needed\",\n \"for target in ctx.attr.deps: ebook_provider = target[EbookInfo] if not ebook_provider: continue deps +=\",\n \"# Fixed up paths -- relative to the directory dir_reference, not the #\",\n \"-o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) ) # run gladtex\",\n \"{ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles\",\n \"+= provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths --\",\n \"= ctx.actions.declare_file(in_file.basename + \\\".png\\\") figures += [out_file] script_cmd = _script_cmd(asycc.path, in_file.path) ctx.actions.run_shell( progress_message\",\n \"for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ EbookInfo(figures=figures, markdowns=markdowns), DefaultInfo( files=depset(figures+markdowns),\",\n \"with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs = [out_file], tools = [docker_run],\",\n \"{script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd =\",\n \"= rule( implementation = _markdown_lib_impl, doc = \\\"Declares a set of markdown files\\\",\",\n \"= \\\"All the targets you need to make this book work.\\\", providers =\",\n \"_strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message = \\\"Building\",\n \"DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl,\",\n \"attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file to\",\n \"return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx):\",\n \"inputs = [epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\\",\n \"target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider\",\n \"= _asymptote_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".asy\\\"], doc = \\\"The\",\n \"{} {}\\\".format(src.path, src_copy.path), ) return src_copy def _copy_file_to_workdir(ctx, src): src_copy = ctx.actions.declare_file(src.basename) ctx.actions.run_shell(\",\n \"(string) The path to a file used for figuring out # the reference\",\n \"repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path,\",\n \"[epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name),\",\n \"{script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd, out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps\",\n \"def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs = {\",\n \"= rule(implementation = _drawtiming_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".t\\\"], doc\",\n \"markdowns += (provider.markdowns or []) runfiles = ctx.runfiles(files=figures+markdowns) for dep in ctx.attr.deps: runfiles\",\n \"default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file(\",\n \"}, doc = \\\"Transform an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src):\",\n \"ctx.attr.title_yaml.files.to_list()[0] title_yaml = _copy_file_to_workdir(ctx, title_yaml) ebook_pdf = ctx.actions.declare_file(\\\"{}.pdf\\\".format(name)) inputs = [epub_metadata, title_yaml] +\",\n \"reference directory. # # Params: # script_path: (string) The full path to the\",\n \"= [out_file], tools = [asycc], command = \\\"\\\"\\\"\\\\ {script} \\\\ asy -render 5\",\n \"= inputs, tools = [script], outputs = [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference\",\n \"file to use for this book\\\", ), \\\"metadata_xml\\\": attr.label( allow_files = True, doc\",\n \"\\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf])\",\n \"{ \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc = \\\"The file to compile\\\", ),\",\n \"runfiles=runfiles), ] def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs\",\n \"for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return [ DefaultInfo( files=depset([mobi_file, captured_output]), runfiles=runfiles,\",\n \"-f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps = []\",\n \"for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools = [script], command =\",\n \"= \\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools = [script], outputs =\",\n \"= \\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools = [script], outputs =\",\n \"= { \\\"deps\\\": attr.label_list( doc = \\\"All the targets you need to make\",\n \"# This file has been licensed under Apache 2.0 license. Please see the\",\n \"# untar the equation dir # Maybe this is not needed. tar_command =\",\n \"directory. # # Params: # script_path: (string) The full path to the script\",\n \"= [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\ {cmd} --output \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( cmd=cmd,\",\n \"+ figures ctx.actions.run_shell( progress_message = \\\"Building PDF for: {}\\\".format(name), inputs = inputs, tools\",\n \"EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation = _drawtiming_png_impl, attrs = {\",\n \"outdir_tar]), runfiles=runfiles, ) ] ebook_epub = rule( implementation = _ebook_epub_impl, attrs = {\",\n \"[] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or [])\",\n \"[epub_file, equation_outdir], tools = [script], outputs = [mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference\",\n \"{script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_file=_strip_reference_dir(dir_reference, epub_file.path), mobi_file=_strip_reference_dir(dir_reference, mobi_file.path),\",\n \"book.epub. # This is gonna be fun! epub_metadata = ctx.attr.metadata_xml.files.to_list()[0] epub_metadata = _copy_file_to_workdir(ctx,\",\n \"= [in_file], outputs = [out_file], tools = [docker_run], command = \\\"\\\"\\\"\\\\ {script} \\\\\",\n \"allow_files = True, doc = \\\"The title.yaml file to use for this book\\\",\",\n \"# directory where the build happens! This is needed because we can not\",\n \"def _neato_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"neato\\\") neato_png = rule(implementation = _neato_png_impl, attrs = {\",\n \"\\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\ --dir-reference={dir_reference}\\\"\\\"\\\".format( script=script_path, container=CONTAINER, dir_reference=dir_reference, ) def _drawtiming_png_impl(ctx): cmd\",\n \"= [ebook_pdf], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ pandoc --epub-metadata={epub_metadata} \\\\ --mathml -o\",\n \"html -t epub3 -o {ebook_epub} {html_file} \\\\ \\\"\\\"\\\".format( script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path),\",\n \"= _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path, markdowns_paths[0]) ctx.actions.run_shell( progress_message =\",\n \"= [] for target in ctx.attr.srcs: for src in target.files.to_list(): markdowns += [_copy_file_to_workdir(ctx,\",\n \"inputs = [epub_metadata, title_yaml] + markdowns + figures ctx.actions.run_shell( progress_message = \\\"Building PDF\",\n \"equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name)) # untar the\",\n \"\\\"The file to compile\\\", providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name\",\n \"file in markdowns] markdowns_paths_stripped = _strip_reference_dir_from_files(dir_reference, markdowns) script = ctx.executable._script script_cmd = _script_cmd(script.path,\",\n \"epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar = outputs[2] captured_output = ctx.actions.declare_file( \\\"{}.untar-out\\\".format(ctx.label.name))\",\n \"ctx.runfiles(files = figures) return [ EbookInfo(figures=figures+deps, markdowns=[]), DefaultInfo(files=depset(figures+deps), runfiles=runfiles), ] drawtiming_png = rule(implementation\",\n \"= \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp\",\n \"= ctx.label.name # steps # run htex on all *md, gives book.htex markdowns\",\n \"# Copyright (C) 2020 Google Inc. # # This file has been licensed\",\n \"src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to {}\\\".format(src.short_path, src_copy.short_path),\",\n \"mobi_file=_strip_reference_dir(dir_reference, mobi_file.path), )) runfiles = ctx.runfiles(files=[mobi_file]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles)\",\n \"implementation = _ebook_pdf_impl, attrs = { \\\"deps\\\": attr.label_list( doc = \\\"All the targets\",\n \"ctx.actions.run_shell( progress_message = \\\"Copying {}\\\".format(src.short_path), outputs = [src_copy], inputs = [src], command=\\\"cp {}\",\n \"script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_pdf=_strip_reference_dir(dir_reference, ebook_pdf.path), markdowns=\\\" \\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep\",\n \"\\\"Transform a graphviz dot file into png using neato\\\", ) def _dot_png_impl(ctx): return\",\n \"# Build rules for building ebooks. # This is the container CONTAINER =\",\n \"files=depset([ebook_pdf]), runfiles=runfiles, ) ] ebook_pdf = rule( implementation = _ebook_pdf_impl, attrs = {\",\n \"one such file outputs = default_info.files.to_list() epub_file = outputs[0] equation_outdir = outputs[1] equation_outdir_tar\",\n \"def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name, src.short_path)) ctx.actions.run_shell( progress_message = \\\"Copying {} to\",\n \"progress_message = \\\"graphviz to PNG with {1}: {0}\\\".format(in_file.short_path, cmd), inputs = [in_file], outputs\",\n \"dot\\\", ) def _asymptote_impl(ctx): asycc = ctx.executable._script figures = [] for target in\",\n \"dep[EbookInfo] markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up\",\n \"attr.label( default=\\\"//build:docker_run\\\", executable=True, cfg=\\\"host\\\"), }, doc = \\\"Transform an asymptote file into png\\\",\",\n \"dir_reference.path) # run htexepub to obtain book.epub. # This is gonna be fun!\",\n \") def _drawtiming_png_impl(ctx): cmd = \\\"drawtiming\\\" docker_run = ctx.executable._script figures = [] for\",\n \"rules for building ebooks. # This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" #\",\n \"is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this for quick local runs.\",\n \"outdir.path), htex_file=_strip_reference_dir(dir_reference, htex_file.path), ) ) outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ;\",\n \") def _dot_png_impl(ctx): return _generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs =\",\n \"outdir = ctx.actions.declare_directory(\\\"{}.eqn\\\".format(name)) html_file = ctx.actions.declare_file(\\\"{}.html\\\".format(name)) ctx.actions.run_shell( progress_message = \\\"Extracting equations for: {}\\\".format(name),\",\n \") def _ebook_kindle_impl(ctx): mobi_file = ctx.actions.declare_file(\\\"{}.mobi\\\".format(ctx.label.name)) # First provider is EbookInfo, second is\",\n \"name = ctx.label.name # steps # run htex on all *md, gives book.htex\",\n \"command based on the # script path and its reference directory. # #\",\n \"_ebook_pdf_impl(ctx): name = ctx.label.name # steps # run htex on all *md, gives\",\n \"Maybe this is not needed. tar_command = \\\"(cd {base} ; tar xvf {archive})\",\n \"5 -f png -o \\\"{out_file}\\\" \\\"{in_file}\\\" \\\"\\\"\\\".format( out_file=out_file.path, in_file=in_file.path, script=script_cmd), ) deps =\",\n \"\\\"Building equation environments for: {}\\\".format(name), inputs = markdowns, outputs = [htex_file], tools =\",\n \"based on the # script path and its reference directory. # # Params:\",\n \"script=script_cmd, epub_metadata=_strip_reference_dir(dir_reference, epub_metadata.path), ebook_epub=_strip_reference_dir(dir_reference, ebook_epub.path), html_file=_strip_reference_dir(dir_reference, html_file.path), )) runfiles = ctx.runfiles(files=[ebook_epub]) for dep\",\n \"book.htex markdowns = [] figures = [] for dep in ctx.attr.deps: provider =\",\n \"root and repo root). def _script_cmd(script_path, dir_reference): return \\\"\\\"\\\"\\\\ {script} \\\\ --container={container} \\\\\",\n \"rule( implementation = _markdown_lib_impl, doc = \\\"Declares a set of markdown files\\\", attrs\",\n \"= \\\"Extracting equations for: {}\\\".format(name), inputs = [htex_file], outputs = [outdir, html_file], tools\",\n \"license. Please see the LICENSE # file at the root of the repository.\",\n \"outdir_tar = ctx.actions.declare_file(\\\"{}.tar\\\".format(outdir.basename)) tar_command = \\\"(cd {base} ; tar cf {archive} {dir})\\\".format( base=outdir_tar.dirname,\",\n \"providers = [EbookInfo], ), }, ) def _ebook_epub_impl(ctx): name = ctx.label.name # This\",\n \"= [] for target in ctx.attr.deps: provider = target[EbookInfo] figures += (provider.figures or\",\n \"= runfiles.merge(dep[DefaultInfo].data_runfiles) return [ dep[EbookInfo], DefaultInfo( files=depset([ebook_epub, outdir, outdir_tar]), runfiles=runfiles, ) ] ebook_epub\",\n \"\\\".join(markdowns_paths), )) runfiles = ctx.runfiles(files=[ebook_pdf]) for dep in ctx.attr.deps: runfiles = runfiles.merge(dep[DefaultInfo].data_runfiles) return\",\n \"markdowns += provider.markdowns figures += provider.figures dir_reference = markdowns[0] # Fixed up paths\",\n \"inputs = [epub_metadata, title_yaml, html_file, outdir, outdir_tar] + markdowns + figures ctx.actions.run_shell( progress_message\",\n \"[mobi_file], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference \\\\ ebook-convert {epub_file} {mobi_file} \\\\ \\\"\\\"\\\".format( script=script_cmd,\",\n \"building ebooks. # This is the container CONTAINER = \\\"filipfilmar/ebook-buildenv:1.1\\\" # Use this\",\n \"= ctx.executable._script name = ctx.label.name script_cmd = _script_cmd(script.path, epub_file.path) ctx.actions.run_shell( progress_message = \\\"Building\",\n \"rule(implementation = _neato_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files = [\\\".dot\\\"], doc =\",\n \"pandoc -s --gladtex -o {target} {sources} \\\\ \\\"\\\"\\\".format( script=script_cmd, target=htex_file.path, sources=\\\" \\\".join(markdowns_paths)) )\",\n \"[htex_file], outputs = [outdir, html_file], tools = [script], command = \\\"\\\"\\\"\\\\ {script} --cd-to-dir-reference\",\n \"\\\"Transform an asymptote file into png\\\", ) def _copy_file_to_workdir_renamed(ctx, src): src_copy = ctx.actions.declare_file(\\\"{}_{}\\\".format(ctx.label.name,\",\n \"markdowns += [_copy_file_to_workdir(ctx, src)] figures = [] for target in ctx.attr.deps: provider =\",\n \"attr.label_list( allow_files = [\\\".t\\\"], doc = \\\"The file to compile\\\", ), \\\"deps\\\": attr.label_list(\",\n \"_generalized_graphviz_rule_impl(ctx, \\\"dot\\\") dot_png = rule(implementation = _dot_png_impl, attrs = { \\\"srcs\\\": attr.label_list( allow_files\",\n \"progress_message = \\\"Building EPUB for: {}\\\".format(name), inputs = inputs, tools = [script], outputs\",\n \"= _ebook_epub_impl(ctx) # There can be only one such file outputs = default_info.files.to_list()\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":40,"numItemsPerPage":100,"numTotalItems":12866649,"offset":4000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzU2ODA1NiwianRpIjoiNzRjOTY3NGMtOGQ3OS00NmVmLWI2ZjgtMjYwOWEyZDI0NWRkIiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzU3MTY1NiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.sdi0eU3Uy1Mm4GY4E9swb01nk8amkqHdyC0Xihs_T5JZ4bd8A-BqeS-MZU1RzEGJb8mMYh-DirVHUAc5g3jWDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"sparse/dense matrices for varying matrices along batch, must have the same nonzero locations.",
"default backend can be set globally using `set_global_default_backend()` and locally using `with backend:`.",
"self.memory: int = memory \"\"\" Maximum memory of the device that can be",
"a node in a graph. Use `... |
[
"LazyReCompile COLORS = CURTSIES_COLORS + (\"default\",) CNAMES = dict(zip(\"krgybmcwd\", COLORS)) # hack for",
"= CURTSIES_COLORS + (\"default\",) CNAMES = dict(zip(\"krgybmcwd\", COLORS)) # hack for finding the",
"= \"k\"): \"\"\"Returns FmtStr constructor for a bpython-style color code\"\"\" if letter_color_... |
[
"7, value) self.add_field('ROW_SPACING_UNITS', 's', 1, value) self.add_field('COL_SPACING', 's', 7, value) self.add_field('COL_SPACING_UNITS', 's', 1,",
"__init__(self, value): super(BANDSAType, self).__init__() self.add_field('ROW_SPACING', 's', 7, value) self.add_field('ROW_SPACING_UNITS', 's', 1, value) self.a... |
[
"| pred:%s)\\n\" % (idx, round(loss,2), truth, pred)) #print(obs) return def layer_output(self, layer_id, example_id=0,",
"pred)) #print(obs) return def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False): \"\"\" ``` Prints output",
"help debug models. Uses first example (example_id=0) from tr... |
[
"if VegaZero['transform']['sort']['type'] == 'desc': self.VegaLiteSpec[VegaZero['mark']]['encoding']['x']['sort'] = '-y' else: self.VegaLiteSpec[VegaZero['mark']]['encoding']['x']['sort'] = 'y' return self.VegaLiteSpec[VegaZero['mark']]",
"'.join(filter_part_token).split() filter_part_token = ['&' if x == 'and' e... |
[
"= name self.checker = checker self.block: Optional[ac.Block] = None def update(self, acn: AC)",
"raise JCNotFoundException(f'Jízdní cesta {self.name} neexistuje!') return StepJC.name_to_id[name] def disp_str(self) -> str: return f'Stavění",
"steps self.stepi = 0 def on_start(self) -> None: logging.info('Start'... |
[
"API_PATH class ReportableMixin: \"\"\"Interface for RedditBase classes that can be reported.\"\"\" def report(self,",
"\"\"\"Report this object to the moderators of its subreddit. :param reason: The reason",
"Raises :class:`.APIException` if ``reason`` is longer than 100 characters. Example usage: .. code-bloc... |
[
"def _jpeg_compression(im): assert torch.is_tensor(im) im = ToPILImage()(im) savepath = BytesIO() im.save(savepath, 'JPEG', quality=75)",
"assert torch.is_tensor(im) im = ToPILImage()(im) savepath = BytesIO() im.save(savepath, 'JPEG', quality=75) im =",
"torch.is_tensor(im) im = ToPILImage()(im) savepath = Byte... |
[
"mellonByteFileFromFilePathAndConfigFactory = Factory(MellonByteFileFromFilePathAndConfig) @interface.implementer(mellon.IUnicodeMellonFile) class MellonUnicodeFileFromFilePathAndConfig(object): def __init__(self, file_path, config): self.file_path = file_path",
"import mellon @interface.implementer(mellon.IByteM... |
[
"'MSCeleb1M') Datasource.register_instance('5celeb', __name__ + '.fivecelebface', 'FiveCelebFace') Datasource.register_instance('ffhq', __name__ + '.ffhq', 'FFHQ') Datasource.register_instance('celeba', __name__",
"'.fivecelebface', 'FiveCelebFace') Datasource.register_instance('ffhq', __name__ + '.ffhq', 'FFHQ')... |
[
"[0.87, 0.9, 0.9359999999999999, 0.924, 0.944, 0.944, 0.948, 0.888, 0.868, 0.86, 0.888, 0.9, 0.908,",
"0.827906976744186, 0.8608695652173913, 0.9333333333333333], 'AB010291.1_Bj': [0.95, 0.984, 0.988, 0.984, 0.98, 0.98, 0.98, 0.92, 0.896,",
"0.948, 0.976, 0.976, 0.968, 0.952, 0.896, 0.844, 0.86, 0.908, 0.976, 0... |
[
"self.mean2[0] params[2,1] = 1.0/self.variance2 * self.mean2[1] print params return params def plot_data(self,params=np.array([]),name=\"Naive Bayes\",",
"\"Bayes Optimal\",\"black\") axis.legend() # fig.show() return fig,axis def add_line(self,fig,axis,params,name,colour): x_max = np.max(self.train_X) x_min",
... |
[
"hamming_distance(bs1, bs2) / 2 sizes.update({size: normalized_distance}) return sorted(sizes, key=lambda k: sizes[k])[:n] def hamming_distance(bs1,",
"a \"Crypto 101\" thing. But more people \"know how\" to break it than",
"The distance between: # # this is a test # # and #",
"key. # # This code is going to ... |
[
"def count_parameters(self): return sum(p.numel() for p in self.parameters() if p.requires_grad) def count_encoder_parameters(self): return",
"prediction[b].index_select(0, blank_b)) prediction[b].index_fill_(0, blank_b, 1e-10) else: prediction = self.generator(decoder_outputs.squeeze(1)) prediction = f.softmax(p... |
[
"open('/proc/{}/environ'.format(pid)) as f: for line in f.read().split('\\0'): if not len(line): continue kv =",
"return retcode, ''.join(text), data class DockerDelegate(BaseHandler): def __init__(self): self.compute = DockerCompute() pass def",
"data = ns_exec(inspect['State']['Pid'], event) if exit_code == 0... |
[
"on a timeslice. \\n`timeslice`: a section of trade data with time length equal",
"discrete_prices = {} for timeslice in data: timeslice = [float(i) for i in",
"bought: \"+str(self.exchange.times_bought)) print(\"Times sold: \"+str(self.exchange.times_sold)) print(\"The Market's performance: \"+str(market_perfo... |
[
"return 0 def predict_perceptron_proper(inputs, weights): def step_function(input): return 1 if input > 0",
"0 else 0 def linear_model(inputs, weights): return np.dot(inputs, weights) return step_function(linear_model(inputs, weights)) def",
"return step_function(linear_model(inputs, weights)) def neuron(inputs... |
[
"the z components that we are leaving alone: pad = get_direction_padding_fn(config) direction_size =",
"% w_ix, interp_vis[w_ix], 0, fps=24) for epoch in range(state_dict['epoch'], config['num_epochs']): if config['pbar'] ==",
"module if config['parallel']: G = nn.DataParallel(DataParallelLoss(G)) if config['cr... |
[
"zip(thailand['Year'], thailand['RGDP'], thailand['NGDP'], thailand['GDP_pc'], thailand['Inflation'], thailand['Unemployment_Rate'], thailand['Net_LB'], thailand['Account_Balance']): mycursor.execute(sqlformula13, [a, b, c, d,",
"f, g, h]) sqlformula2 = \"INSERT INTO brunei VALUES(%s, %s, %s, %s, %s,",
"b, c, d... |
[
"not None: subnet_rule = res.group(0) l_subnet_rule = subnet_rule.split() device = l_subnet_rule[2] ip =",
"= '/dev/disk/by-id/*{0}'.format(v_id) device = glob.glob(pattern)[0] gdisk_commands = '\\n'.join([ 'n', '1', '34', '', '',",
"re.match('default .*', line) if res is not None: r_default_route = res.group(0... |
[
"if csv_file == \"Unknown1.csv\": with open(full_path, \"r\") as f: reader = csv.DictReader(f) for",
"open(full_path, \"r\") as f: reader = csv.DictReader(f) for line in reader: cur_record =",
"\"SruDbIdMapTable.csv\": [], \"Network Usage.csv\": [\"TimeStamp\", \"AppId\", \"UserId\", \"InterfaceLuid\", \"L2Prof... |
[
"def test_force_token_to_string(self): request = HttpRequest() test_token = '<KEY>' request.META['CSRF_COOKIE'] = test_token token =",
"from django.http import HttpRequest from django.middleware.csrf import _compare_salted_tokens as equivalent_tokens from django.template.context_processors import",
"import _com... |
[
"index=i+k) for k, x in enumerate(y)] super(Sequence, self).__setslice__(i, j, newvals) try: self._gvalidate() except:",
"hasattr(self, k2): # don't need to create forwarding attribute (set __getattr__) return if",
"% len(args)) for k, v in kwargs.iteritems(): k = self._adapt_key(k) if k in",
"def _get_schema... |
[
"'/' + entity): if os.path.isdir(full_path + '/' + entity): no_of_dirs += 1 new_sub_path",
"+ entity): no_of_dirs += 1 new_sub_path = sub_path + '/' + entity dir_dict",
"full_path + '/' + entity, 'time': get_time(stats)} except FileNotFoundError: errors.append(full_path + '/' +",
"= False if _ignore and to_be... |
[
"write the captured output too with open(name + '.stderr.txt', 'w') as handle: handle.writelines(opts['error_output'])",
"as new output with report_directory(args.output, args.keep_empty, args.output_format) as args.output: # Run the analyzer",
"out language (-x) and architecture (-arch) flags for future proces... |
[
"\"\"\" Facillite l'usage de l'UNICODE \"\"\" def __init__(self, top_left, top_split, top_right, mid_left, mid_split,",
"[TableBorder('+', '+', '+',\\ '+', '+', '+',\\ '+', '+', '+',\\ '-', '|'), TableBorder(u'\\u250c', u'\\u252C',",
"u'\\u251C', u'\\u253C', u'\\u2524',\\ u'\\u2514', u'\\u2534', u'\\u2518',\\ u... |
[
"from django.conf.urls import url from . import views urlpatterns = [ url(r'^register/', views.register),",
"url from . import views urlpatterns = [ url(r'^register/', views.register), url(r'^login/', views.login), url(r'logout/',",
"<gh_stars>0 from django.conf.urls import url from . import views urlpatterns =... |
[
"= config.get(CONF_SI2_KEY) ri4key = config.get(CONF_RI4_KEY) if sitekey and ri4key: sensorname = sensorconf[ATTR_FRIENDLY_NAME] sensors.append(SLDeparturesSensor(",
"to update TL2 for %s...\", self._name) # Object used to create our object.",
"'api_minimization' LIST_SENSOR_TYPES = ['departures', 'status', 'tr... |
[
"for SN coords, redshift, and host galaxy. If redshift is not given for",
"and report its redshift. Returns ( (ra,dec), redshift, host_name, redshift_citation ), with values",
"').split(' ')[0]) citation = resred.group().split(' ')[-1] except AttributeError: redshift = None citation =",
"A quick library to de... |
[
"6.5 \" + \"-85.5 12.5 8.5 \" + \"-83.5 12.5 10.5 \" +",
"if all_cables: for i in range(0, nbCables): childname = 'cableS' + str(i) theta",
"12.5 10.5 \" + \"-77.5 12.5 12.5 \" + \"-62.5 12.5 12.5 \"",
"\")) # Create a CableConstraint object with a name. # the indices are",
"[[-97, 0, 45]], 'withAPullPointL... |
[
"'median' 'score_type': \"coupling\", # #TODO fill in the rest of the options in",
"\"mean\", # 'max', 'median' 'score_type': \"coupling\", # #TODO fill in the rest of",
"'opt_tol': 1e-9, # no limits 'opt_round_g': False, # True 'opt_compute_accuracy': False, # True",
"(1710,1740), (1740,1770), (1770,1800), (... |
[
"data are scaled to have a std of 1 and a mean of",
"zero :param dataset: [xr dataset] input or output data :param std: [xr dataset]",
"properties will not be included as predictors :param exclude_file: [str] path to exclude",
"have multiple discontinuos periods) :param end_dates: [str or list] fmt: \"YYYY-MM... |
[
"integer) copies of a given string. Tools: input function, slicing >>>>>>> f4444ec0d72c645d12694e90df7429456db0611c \"\"\"",
"7. Write a Python program to test whether a number is within 100",
"function : abs() Expected Result : abs(number) -> number Return the absolute value",
"This is a ....... multi-line h... |
[
"class meta(type): @classmethod def __prepare__(metacls, name, bases, **kwds): assert metacls.__name__ == 'meta' assert",
"@classmethod def __prepare__(metacls, name, bases, **kwds): assert metacls.__name__ == 'meta' assert name in",
"attributes, **kwds): assert metacls.__name__ == 'meta' assert name in ['base'... |
[
"for x in range(w): p = img.get(x, y) if dist(p, key) < threshold:",
"r2, g2, b2 = c2 return math.sqrt((r1-r2)**2 + (g1-g2)**2 + (b1-b2)**2) def chroma(img,",
"img.get(x, y) if dist(p, key) < threshold: img.set(x, y, Color.yellow) statue = load_picture(\"photos/statue1.jpg\")",
"range(h): for x in range(w): p... |
[
"* tau # uniform distribution of spot peak times # start well before",
"uniform in sin(latitude) lat = scipy.arcsin(scipy.rand(nspot_tot)) # spot rotation rate optionally depends on",
"up the contributions of individual spots for i in range(nspot_tot): # Spot area",
"time / period0[i] + lon[i] mu = scipy.cos(... |
[
"else is worth learning # FIXME: ass -k-1,-1 for negative field indexing #",
"negative field indexing # FIXME: think into the mess at \"sort\" vs \"LC_ALL=C",
"main(): args = argdoc.parse_args() sys.stderr.write(\"{}\\n\".format(args)) sys.stderr.write(\"{}\\n\".format(argdoc.format_usage().rstrip())) sys.stder... |
[
") def listDevices(): response = doRequest('devices/list', {'supportedMethods': SUPPORTED_METHODS}) logger.debug(\"Number of devices: %i\" %",
"== TELLSTICK_TURNON): state = 'ON' elif (device['state'] == TELLSTICK_TURNOFF): state = 'OFF' elif",
"Dims device. 'device' must be an integer of the device-id\") print... |
[
"api_tag else: suffix = \"observation\" tags = [\"type:%s\" % type_] + api_tag METRICS.incr(\"data.%s.%s\"",
"\"report\" tags = api_tag else: suffix = \"observation\" tags = [\"type:%s\" % type_]",
"target section id # *_map maps fields inside the section from source to",
"or wifis: return report return {} cl... |
[
"embedding_vec_size = 16, combiner = \"sum\", sparse_embedding_name = \"sparse_embedding1\", bottom_name = \"data1\", optimizer",
"dropout_rate=0.5)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct, bottom_names = [\"dropout1\"], top_names = [\"fc2\"], num_output=1024)) model.add(hugectr.De... |
[
"white. \"\"\" LeafSystem.__init__(self) self._point_cloud_ports = {} self._transform_ports = {} self._id_list = id_list self._default_rgb",
"np from pydrake.common.value import AbstractValue from pydrake.math import RigidTransform from pydrake.perception import BaseField,",
"system that takes in N point clouds... |
[
"'female' catanDBObj.person_bio.name_family = family_names[random.randint(0,len(family_names)-1)] catanDBObj.person_bio.age = random.randint(5,90) # message (message, status, location, etc.)",
"centered around Cambridge for i in range(n): # random lat, long lat =",
"random nodes, centered around Cambridge for i... |
[
"00100 # 00011 # Answer: 3 # # Version: 1.0 # 11/13/17 by",
"surrounded by water. # # Example 1: # 11110 # 11010 # 11000",
"< len(grid[0]) and grid[i][j] == \"1\": grid[i][j] = \"0\" map(sink, (i+1, i-1, i,",
"grid[x][y+1] == '1': # grid[x][y+1] = '2' # self.island(x,y+1,grid, m, n) # if",
"n and grid[x][y+... |
[
"fseventsd from tests.formatters import test_lib class FseventsdFormatterTest(test_lib.EventFormatterTestCase): \"\"\"Tests for the fseventsd record event",
"-*- \"\"\"Tests for the fseventsd record event formatter.\"\"\" from __future__ import unicode_literals import",
"record event formatter.\"\"\" from __fut... |
[
"= ModelCheckpoint(conf.logPath+\"/\"+trainString+'/Checkpoint-{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', save_best_only=False, save_weights_only=True) change_lr = LearningRateScheduler(LrPolicy(conf.lr).stepDecay) tbCallBack=TensorBoard(log_dir=conf.logPath+\"/\"+trainString+'/logs', histogram_freq=0, w... |
[
"= functions.flatten(x) self.assertEqual(y.shape, self.g_shape) self.assertEqual(y.dtype, self.dtype) testing.assert_allclose(self.x.flatten(), y.data) def test_forward_cpu(self): self.check_forward(self.x) @attr.gpu def",
"chainer.Variable(x_data) y = functions.flatten(x) self.assertEqual(y.shape, self.g_shape) ... |
[
"('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('updated', models.DateTimeField(auto_now=True)), ('name', models.CharField(max_length=80)), ('description', models.TextField(blank=True)),",
"= [ ('exercises',... |
[
"time_step.step_type == StepType.LAST, **time_step.observation) def reset(self): time_step = self._env.reset() return flatten_observation(time_step.observation)['observations'] def render(self,",
"close(self): if self._viewer: self._viewer.close() self._env.close() self._viewer = None self._env = None def _flat_s... |
[
"return None def materialize(self, context, table_type, table_metadata, value): return None, None def snowflake_table(",
"'required_resource_keys') required_resource_keys.add('snowflake') if callable(name): fn = name return create_lakehouse_table_def( name=fn.__name__, lakehouse_fn=fn, input_tables=[], required_r... |
[
"checks whether the look has any members of: - lambert1 - initialShadingGroup -",
"for any disallowed connections on *all* nodes if any(s in cls.DEFAULT_SHADERS for s",
"['look'] hosts = ['maya'] label = 'Look No Default Shaders' actions = [pype.maya.action.SelectInvalidAction]",
"{\"lambert1\", \"initialShad... |
[
"route @app.route(\"/iris\") def iris(): from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression X,",
"flask import Flask # initialize the app app = Flask(__name__) # execute iris",
"the app app = Flask(__name__) # execute iris function at /iris route @app.route(\"/iris\")",
... |
[
"with rttm files in_emb_dir (str|None): path to directory with i-vectors out_emb_dir (str|None): path",
"open('{}{}'.format(os.path.join(self.in_rttm_dir, file_name), self.rttm_suffix)) as fp: for line in fp: speakers.add(line.split()[7]) logger.info('Loading pickled normalization",
"whole audio features = feat... |
[
"cells') ax.set_xlabel('Time (h)') plt.legend() f = plt.gcf() f.set_size_inches(20.0, 8.0) # alternative: 20.0, 8.0",
"lattice_dict['B'][0] n = int(total_cells**0.5) plt.figure(1) plt.plot(lattice_dict['time'], lattice_dict['E'], label='Empty lattice points') plt.plot(lattice_dict['time'], lattice_dict['D_a'], la... |
[
"of SecurityProviders. :param supported_providers: :type supported_providers: list[~azure.mgmt.network.v2018_10_01.models.VirtualWanSecurityProvider] \"\"\" _attribute_map = { 'supported_providers': {'key':",
"Changes may cause incorrect behavior and will be lost if the code is",
"Code generated by Microsoft (R... |
[
"if regex is not None: date = datetime.date( int(regex.group(\"year\")), int(regex.group(\"month\")), int(regex.group(\"day\"))) else: raise",
"import datetime import re from .exceptions import ObjectIsNotADate def format_date(value, format=\"%d %M %Y\"):",
"%M %Y\"): regex = re.match(r\"(?P<year>\\d{4})-(?P<mo... |
[
"test_path is not None: DataLoader.test = DataLoader.load_image_data_with_label_at_end(os.path.join(DATASET_ROOT_FOLDER, test_path), height=height, length=length) logging.debug('Training data shape:{}'.format(str(DataLoader.train['images'].shape)))",
"numpy as np import os import logging from sklearn.model_select... |
[
"parte mediaidade = ((sum(idadelista))/4) # Adcionei todos os nomes em uma lista nomelista.append(nome)",
"nomelista[indexidade] # ------------------------------------------------------------------- print(f'A media das idades é: {mediaidade}') print(f'A pessoa que tem",
"# Armazenei em maximo o maior valor enco... |
[
"qargs=[0, 1] targ = np.dot(mat, np.kron(np.eye(2), np.kron(mat_b, mat_a))) self.assertEqual(op.dot(op2, qargs=[0, 1]), Operator(targ)) self.assertEqual(op",
"self.UH, np.diag([0, 1])) global_phase_equivalent = matrix_equal( op.data, target, ignore_phase=True) self.assertTrue(global_phase_equivalent) def test_ins... |
[
"import By class feature_modal: title_textbox = (By.ID, \"feature-name\") description_textbox = (By.ID, \"description\") save_button",
"By class feature_modal: title_textbox = (By.ID, \"feature-name\") description_textbox = (By.ID, \"description\") save_button =",
"<gh_stars>0 from selenium.webdriver.common.by ... |
[
"= Request(url2) try: response = urlopen(request3) data3 = response.read() except URLError, e: print",
"key a= station_prop_json[u'properties'][u'timeseries'].keys() i=a[0] url2 =('https://uk-air.defra.gov.uk/sos-ukair/api/v1/timeseries/'+str(i) +'/getData') request3 = Request(url2) try: response =",
"station_p... |
[] |
[
"# Generated by Django 2.0.6 on 2018-07-02 19:13 import core.models from django.db import",
"), migrations.AlterField( model_name='echo', name='audio', field=models.FileField(upload_to=core.models.echo_directory), ), migrations.AlterField( model_name='profile', name='picture', field=models.FileField(blank=True, n... |
[
"test_access_logger_atoms(mock_getpid, mock_datetime): utcnow = datetime.datetime(1843, 1, 1, 0, 0) mock_datetime.datetime.utcnow.return_value = utcnow mock_getpid.return_value",
"'%s {%s} \"%s\" %%X {X} %%%s' assert expected == access_logger._log_format @mock.patch(\"aiohttp.helpers.datetime\") @mock.patch(\"os.... |
[
"\"/lhome/trulsas/openwhisk\" #: Location of output data DATA_DIR = join(FAAS_ROOT, \"..\", \"profiler_results\") SYSTEM_CPU_SET =",
"WORKLOAD_SPECS=join(FAAS_ROOT, \"specs\", \"workloads\") #FAAS_ROOT=\"/home/truls/uni/phd/faas-profiler\" WSK_PATH = \"wsk\" OPENWHISK_PATH = \"/lhome/trulsas/openwhisk\" #: Locati... |
[
"\"Error in sum1 function\" def divide(a,b): try: c = a/b return c except",
"def sum1(a,b): try: c = a+b return c except : print \"Error in",
"except : print \"Error in sum1 function\" def divide(a,b): try: c = a/b",
"function\" def divide(a,b): try: c = a/b return c except : print \"Error",
"divide(a,b): t... |
[
"answer[0].upper() == 'Y': accept_and_add = True if self.mode in (verify_mode.accept_new, verify_mode.overwrite_blindly): accept_and_add =",
"Use known_hosts rewrite instead if using this API warnings.warn(FutureWarning('RadSSH hostkey module is no",
"OFF overwrite_blindly=666 # Concentrated evil ) def printabl... |
[
"] << 8 | \\ key[ block_start + 8 ] k4 = key[",
"( k1 * c2 ) & 0xFFFFFFFF h1 ^= k1 #finalization h1 ^=",
"+ h2 ) & 0xFFFFFFFFFFFFFFFF h2 = ( h2 * 5 + 0x38495ab5",
"h4 ) & 0xFFFFFFFF h1 = fmix( h1 ) h2 = fmix( h2",
"public domain. The authors hereby disclaim copyright to this source code. pure python",
"2 ... |
[
"print('-- create: ExPerson') ExPerson = dlite.classfactory(Person, url=url) print('-- create: person1') person1 = Person('<NAME>',",
"os.path.abspath(os.path.dirname(__file__)) class Person: def __init__(self, name, age, skills): self.name = name self.age =",
"ExPerson = dlite.classfactory(Person, url=url) pri... |
[
"작성하세요. ''' dp = [0] * len(data) dp[0] = data[0] for i in",
"list로 주어질 때, 그 연속 부분 최대합을 반환하는 함수를 작성하세요. ''' dp =",
"3, -2, 9], [9, -10] 등이 있을 수 있다. 이 연속 부분들 중에서",
"최대 100개입니다. ''' import sys def getSubsum(data) : ''' n개의 숫자가 list로 주어질",
"들어, 다음과 같이 8개의 숫자가 있다고 하자. 1 2 -4 5 3 -2",
"할 수는 없다. 따라서 연속 부분 최대합은 5... |
[
"#!/usr/bin/env python3 import unittest from mockdock import dns class DNSTest(unittest.TestCase): def test_build_packet(self): data",
"import dns class DNSTest(unittest.TestCase): def test_build_packet(self): data = b\"^4\\x01\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x06google\\x03com\\x00\\x00\\x01\\x00\\x... |
[
"the fixture functions in this file to make them accessible across multiple test",
"of test session. # use temporary directory for conf home so user conf",
"test_connection delete_index(test_connection) @pytest.fixture(scope=\"function\") def default_config_location(): from escli.conf import __file__ as package... |
[
"{}\".format(local_time['time'], local_time['zone']) server_embed.description = \"Created at {}\".format(time_str) online_members = 0 bot_member = 0",
"online ({:,g}%)\".format( online_members, len(guild.members) - bot_member, round((online_members/(len(guild.members) - bot_member) * 100), 2) )",
"DisplayName.m... |
[
"random_state=seed).reset_index(drop=True) df_ones_training = df_ones.loc[:40000] df_zeros_training = df_zeros.loc[:40000] df_ones_test = df_ones.loc[40000:50000] df_zeros_test = df_zeros.loc[40000:50000]",
"df_ones_training = df_ones.loc[:40000] df_zeros_training = df_zeros.loc[:40000] df_ones_test = df_ones.loc... |
[
"HmassT_INPUTS = _constants.HmassT_INPUTS SmolarT_INPUTS = _constants.SmolarT_INPUTS SmassT_INPUTS = _constants.SmassT_INPUTS TUmolar_INPUTS = _constants.TUmolar_INPUTS TUmass_INPUTS",
"DmassSmass_INPUTS = _constants.DmassSmass_INPUTS DmolarSmolar_INPUTS = _constants.DmolarSmolar_INPUTS DmassUmass_INPUTS = _const... |
[
"from choosen labels for l in labels_choosen: batch_ids += random.sample(self.label_to_entry_ids[l], self.n_samples) if len(batch_ids)",
"generate (if None, then dataset_size / batch_size will be used) n_classes : Number",
"batch_ids += random.sample(self.label_to_entry_ids[l], self.n_samples) if len(batch_ids)... |
[
"See the NOTICE file distributed with this work for additional information regarding copyright",
"the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in",
"language governing permissions and limitations under the License. Ambari Agent \"\"\" import re",
"th... |
[
"range(len(csv)): csv_k[i % k].append(csv[i]) for test in csv_k: train = [] for data",
"to_number(n): return float(n) if re.match(r\"^[0-9\\.]+$\", n) else n def to_columm(line): return list(map(to_number, line.strip().split(\",\")))",
"[] for row in rows: data.append(row[0:4]) labels.append(row[4]) return (dat... |
[
"\"org_specs2_specs2_matcher_2_12\", artifact = \"org.specs2:specs2-matcher_2.12:4.8.3\", artifact_sha256 = \"aadf27b6d015572b2e3842627c09bf0797153dbb329262ea3bcbbce129d51ad8\", srcjar_sha256 = \"01251acc28219aa17aabcb9a26a84e1871aa64980d335cd8f83c2bcea6f4f1be\", deps = [",
"def dependencies(): import_external( n... |
[
"b == 0: print(\"INF\") else: if (d - b * c / a)",
"/ a) == (- b // a): print(- b // a) else: print(\"NO\")",
"if (d - b * c / a) != 0 and (- b",
"and (- b / a) == (- b // a): print(- b //",
"and b == 0: print(\"INF\") else: if (d - b * c /",
"= int(input()) b = int(input()) c = int(input()) d = int(input... |
[
"from collections import Counter import numpy as np self.bins = 3 self.pd =",
"self.bins = 3 self.pd = pd self.LabelEncoder = LabelEncoder self.Counter = Counter self.X",
"for merging for i in range(len(classes_count)): if classes_count[i][1] < min_n_samples: self.Y_classes[self.np.where(self.Y_classes == class... |
[
"def _split_data(tensor, stratify): # 0.7/0.15/0.15 train/val/test split (train_tensor, testval_tensor, train_stratify, testval_stratify) = sklearn.model_selection.train_test_split(tensor,",
"wish.)\") base_loc = base_base_loc + '/SpeechCommands' loc = base_loc + '/speech_commands.tar.gz' if os.path.exists(loc):"... |
[
"datetime from typing import List from flask import Blueprint, jsonify, request, json from",
"'{\"status\": \"error\", \"msg\": \"categories number must be between 1 and 5\"}', 400 categories",
"= Product.query.filter(Product.id == payload['id']) if product: if 'name' in payload.keys(): product.update({'name': ... |
[
"app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r\"bitbucket.org\")",
"BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( \... |
[
"#plt.legend(loc='best') ##plt.savefig('refraction',dpi=200) #plt.show() #x=np.linspace(-5,5,100) # #plt.figure(2) #plt.plot(x,s(x)) #plt.show() # #d=np.linspace(0,5,100) #xa=-d/2 #xb=d/2 #plt.figure(3)",
"matplotlib.pyplot as plt k=1000 n1=2.0 n2=1.0 alpha=np.pi/6.0 beta=np.arcsin(n2/n1*np.sin(alpha)) ya=1.0 xa=... |
[
"import settings from django.core.management.base import BaseCommand from elasticsearch import Elasticsearch class Command(BaseCommand): \"\"\"Clear",
"readthedocs elasticsearch index.\"\"\" from __future__ import absolute_import from django.conf import settings from django.core.management.base",
"elasticsearch... |
[
"= 64 # IMG_SIZE_2 = IMG_SIZE * 2 def run(config): # Update the",
"# Update the config dict as necessary # This is for convenience, to",
"data['img'], data['label'] # Increment the iteration counter state_dict['itr'] += 1 # Make sure",
"nn.DataParallel(G) D = nn.DataParallel(D) # If using EMA, prepare it if c... |
[
"% settings.TMP_DIR) deleteStaleFiles() FileUtil.mkdirP(d) return d def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile",
"def initZipDir(prefix): return makeTempDir(prefix) def finishZipDir(zipDir): zipFile = '%s.zip' % zipDir oldDir =",
"zipFile = '%s.zip' % zipDir oldDir = os... |
[
"we got a well-ordered heap assert_goodheap(tau,len(L)+1) @given(lists(elements=integers())) @settings(max_examples=400,deadline=None) def test_delmin(L): L += [10]",
"k in K:tau.insert(k)#on rajoute les elements de K assert_goodheap(tau,tau.currentSize) x = [] while",
"def __init__(self): #initialise un tas bi... |
[
"Use dict to associate the transformation name with its class. flavor_transformation_dict = {'NoTransformation':",
"the events from given table flux,det,weighted,smeared = params for c in table.columns: if",
"tmax = snmodel.get_time()[-1] if deltat is not None: dt = deltat ntbins =",
"distribution, for each r... |
[
"(1 - dones) * n_step_gamma * next_z distance = target_z - z_dists quantile_huber_loss",
"k * (x.abs() - 0.5 * k)) class CategoricalLoss(Loss): \"\"\"Compute C51 loss\"\"\" def",
"offset: torch.Tensor, ) -> torch.Tensor: b = (target_z - network.v_min) / network.delta_z lb",
"...], data: Tuple[torch.Tensor, ..... |
[
"= self.canvas.create_image(w * 0.5, h * 0.56, image=self.imagelists[self.curr_img]) # images and buttons self.img_w_f",
"font=(\"Courier\", int(h / 30)), text='') self.zoom = self.canvas.create_text(w * 0.12, h * 0.94,",
"[] # unmasked full images self.imgs_pred = [] self.cc = [] for index,",
"- b_logos), in... |
[
"# 'ordertime': '2009-03-04T20:56:08Z', # 'paymentstatus': 'Completed', # 'paymenttype': 'instant', # 'pendingreason': 'None', #",
"# because they're behind paypal's doors. nvp_obj = self.wpp.setExpressCheckout(self.item) self.assertTrue(nvp_obj.ack == \"Success\") ###",
"= True self.item = { 'amt': '9.95', 'in... |
[
"elif(me_i-pe_i>0): print('UP') me_i=me_i-1 else: if(me_j-pe_j>0): print('LEFT') me_j=me_j-1 elif(me_j-pe_j<0): print('RIGHT') me_j=me_j+1 else: break m",
"Dec 7 19:46:40 2020 @author: Intel \"\"\" def displayPathtoPrincess(n,grid): me_i=n//2 me_j=n//2 for i",
"me_j=me_j+1 else: break m = int(input()) grid = []... |
[
"import tabix import math def plotGenes(genes_bed, exons_bed, introns_bed, region_bed, blacklist=None, gene_map=None, plot_gene_ids=True, y_max=None,",
"defaults to None. :type ax: :class:`matplotlib.axes._subplots.AxesSubplot`, optional :return: Nothing to be returned :rtype:",
"ids not to be plotted, defaults... |
[
"\"state set.\"), message_plural=(\"Accept states {} are not members of the fsa's \" \"state",
"current_state = self.start_state for symbol in string: current_state = self.transition_function[(current_state, symbol)] return current_state",
"operator_stack.pop() while len(operator_stack) > 1: binary_operate() re... |
[
"_start_arabic_to_roman(self): self.assertRaises(ValueError, arabic_to_roman, 4000) self.assertEqual(arabic_to_roman(4), \"IV\") self.assertEqual(arabic_to_roman(12), \"XII\") self.assertEqual(arabic_to_roman(20), \"XX\") if __name__ ==",
"#!/usr/bin/env python3 import unittest from roman_number_generator import ... |
[
"import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir def runAfterDownload(self, file_name,",
"infrastructure.satnogClient import SatnogClient import os class ModuleBase: def __init__(self, working_dir): self.working_dir = working_dir",
"os class ModuleBase: def __init__(s... |
[
"back to the # verifier, there is no privacy leak by the ommission.",
"proxy such as nginx for that purpose. \"\"\" import secrets import sys import",
"= [schnorr.BlindSigner() for _co in range(Params.num_components)] lock = threading.Lock() seen_salthashes = set() #",
"even restart / end # without sharing co... |
[
"o no per l'economia\") def filtrar(dataframe, subject1): df = dataframe[dataframe['subject'].str.contains(subject1, case=False)].copy() # Afegim",
"print(\"percentatge not_at_all: {}%\".format(percentnotatall)) grafic4('People_Very', 'People_Not_At_All', percentvery, percentnotatall, \" % Persones\", \"Satisfacc... |
[
"pandoc --epub-metadata={epub_metadata} \\ -f html -t epub3 -o {ebook_epub} {html_file} \\ \"\"\".format( script=script_cmd,",
"ctx.label.name # steps # run htex on all *md, gives book.htex markdowns =",
") outdir_tar = ctx.actions.declare_file(\"{}.tar\".format(outdir.basename)) tar_command = \"(cd {base} ; ta... |