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raamana/pyradigm | pyradigm/pyradigm.py | cli_run | def cli_run():
"""
Command line interface
This interface saves you coding effort to:
- display basic info (classes, sizes etc) about datasets
- display meta data (class membership) for samples
- perform basic arithmetic (add multiple classes or feature sets)
"""
path_list, meta_requested, summary_requested, add_path_list, out_path = parse_args()
# printing info if requested
if path_list:
for ds_path in path_list:
ds = MLDataset(ds_path)
if summary_requested:
print_info(ds, ds_path)
if meta_requested:
print_meta(ds, ds_path)
# combining datasets
if add_path_list:
combine_and_save(add_path_list, out_path)
return | python | def cli_run():
"""
Command line interface
This interface saves you coding effort to:
- display basic info (classes, sizes etc) about datasets
- display meta data (class membership) for samples
- perform basic arithmetic (add multiple classes or feature sets)
"""
path_list, meta_requested, summary_requested, add_path_list, out_path = parse_args()
# printing info if requested
if path_list:
for ds_path in path_list:
ds = MLDataset(ds_path)
if summary_requested:
print_info(ds, ds_path)
if meta_requested:
print_meta(ds, ds_path)
# combining datasets
if add_path_list:
combine_and_save(add_path_list, out_path)
return | [
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raamana/pyradigm | pyradigm/pyradigm.py | print_info | def print_info(ds, ds_path=None):
"Prints basic summary of a given dataset."
if ds_path is None:
bname = ''
else:
bname = basename(ds_path)
dashes = '-' * len(bname)
print('\n{}\n{}\n{:full}'.format(dashes, bname, ds))
return | python | def print_info(ds, ds_path=None):
"Prints basic summary of a given dataset."
if ds_path is None:
bname = ''
else:
bname = basename(ds_path)
dashes = '-' * len(bname)
print('\n{}\n{}\n{:full}'.format(dashes, bname, ds))
return | [
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raamana/pyradigm | pyradigm/pyradigm.py | print_meta | def print_meta(ds, ds_path=None):
"Prints meta data for subjects in given dataset."
print('\n#' + ds_path)
for sub, cls in ds.classes.items():
print('{},{}'.format(sub, cls))
return | python | def print_meta(ds, ds_path=None):
"Prints meta data for subjects in given dataset."
print('\n#' + ds_path)
for sub, cls in ds.classes.items():
print('{},{}'.format(sub, cls))
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raamana/pyradigm | pyradigm/pyradigm.py | combine_and_save | def combine_and_save(add_path_list, out_path):
"""
Combines whatever datasets that can be combined,
and save the bigger dataset to a given location.
"""
add_path_list = list(add_path_list)
# first one!
first_ds_path = add_path_list[0]
print('Starting with {}'.format(first_ds_path))
combined = MLDataset(first_ds_path)
for ds_path in add_path_list[1:]:
try:
combined = combined + MLDataset(ds_path)
except:
print(' Failed to add {}'.format(ds_path))
traceback.print_exc()
else:
print('Successfully added {}'.format(ds_path))
combined.save(out_path)
return | python | def combine_and_save(add_path_list, out_path):
"""
Combines whatever datasets that can be combined,
and save the bigger dataset to a given location.
"""
add_path_list = list(add_path_list)
# first one!
first_ds_path = add_path_list[0]
print('Starting with {}'.format(first_ds_path))
combined = MLDataset(first_ds_path)
for ds_path in add_path_list[1:]:
try:
combined = combined + MLDataset(ds_path)
except:
print(' Failed to add {}'.format(ds_path))
traceback.print_exc()
else:
print('Successfully added {}'.format(ds_path))
combined.save(out_path)
return | [
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raamana/pyradigm | pyradigm/pyradigm.py | get_parser | def get_parser():
"""Argument specifier.
"""
parser = argparse.ArgumentParser(prog='pyradigm')
parser.add_argument('path_list', nargs='*', action='store',
default=None, help='List of paths to display info about.')
parser.add_argument('-m', '--meta', action='store_true', dest='meta_requested',
required=False,
default=False, help='Prints the meta data (subject_id,class).')
parser.add_argument('-i', '--info', action='store_true', dest='summary_requested',
required=False,
default=False,
help='Prints summary info (classes, #samples, #features).')
arithmetic_group = parser.add_argument_group('Options for multiple datasets')
arithmetic_group.add_argument('-a', '--add', nargs='+', action='store',
dest='add_path_list', required=False,
default=None,
help='List of MLDatasets to combine')
arithmetic_group.add_argument('-o', '--out_path', action='store', dest='out_path',
required=False,
default=None,
help='Output path to save the resulting dataset.')
return parser | python | def get_parser():
"""Argument specifier.
"""
parser = argparse.ArgumentParser(prog='pyradigm')
parser.add_argument('path_list', nargs='*', action='store',
default=None, help='List of paths to display info about.')
parser.add_argument('-m', '--meta', action='store_true', dest='meta_requested',
required=False,
default=False, help='Prints the meta data (subject_id,class).')
parser.add_argument('-i', '--info', action='store_true', dest='summary_requested',
required=False,
default=False,
help='Prints summary info (classes, #samples, #features).')
arithmetic_group = parser.add_argument_group('Options for multiple datasets')
arithmetic_group.add_argument('-a', '--add', nargs='+', action='store',
dest='add_path_list', required=False,
default=None,
help='List of MLDatasets to combine')
arithmetic_group.add_argument('-o', '--out_path', action='store', dest='out_path',
required=False,
default=None,
help='Output path to save the resulting dataset.')
return parser | [
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raamana/pyradigm | pyradigm/pyradigm.py | parse_args | def parse_args():
"""Arg parser.
"""
parser = get_parser()
if len(sys.argv) < 2:
parser.print_help()
logging.warning('Too few arguments!')
parser.exit(1)
# parsing
try:
params = parser.parse_args()
except Exception as exc:
print(exc)
raise ValueError('Unable to parse command-line arguments.')
path_list = list()
if params.path_list is not None:
for dpath in params.path_list:
if pexists(dpath):
path_list.append(realpath(dpath))
else:
print('Below dataset does not exist. Ignoring it.\n{}'.format(dpath))
add_path_list = list()
out_path = None
if params.add_path_list is not None:
for dpath in params.add_path_list:
if pexists(dpath):
add_path_list.append(realpath(dpath))
else:
print('Below dataset does not exist. Ignoring it.\n{}'.format(dpath))
if params.out_path is None:
raise ValueError(
'Output path must be specified to save the combined dataset to')
out_path = realpath(params.out_path)
parent_dir = dirname(out_path)
if not pexists(parent_dir):
os.mkdir(parent_dir)
if len(add_path_list) < 2:
raise ValueError('Need a minimum of datasets to combine!!')
# removing duplicates (from regex etc)
path_list = set(path_list)
add_path_list = set(add_path_list)
return path_list, params.meta_requested, params.summary_requested, \
add_path_list, out_path | python | def parse_args():
"""Arg parser.
"""
parser = get_parser()
if len(sys.argv) < 2:
parser.print_help()
logging.warning('Too few arguments!')
parser.exit(1)
# parsing
try:
params = parser.parse_args()
except Exception as exc:
print(exc)
raise ValueError('Unable to parse command-line arguments.')
path_list = list()
if params.path_list is not None:
for dpath in params.path_list:
if pexists(dpath):
path_list.append(realpath(dpath))
else:
print('Below dataset does not exist. Ignoring it.\n{}'.format(dpath))
add_path_list = list()
out_path = None
if params.add_path_list is not None:
for dpath in params.add_path_list:
if pexists(dpath):
add_path_list.append(realpath(dpath))
else:
print('Below dataset does not exist. Ignoring it.\n{}'.format(dpath))
if params.out_path is None:
raise ValueError(
'Output path must be specified to save the combined dataset to')
out_path = realpath(params.out_path)
parent_dir = dirname(out_path)
if not pexists(parent_dir):
os.mkdir(parent_dir)
if len(add_path_list) < 2:
raise ValueError('Need a minimum of datasets to combine!!')
# removing duplicates (from regex etc)
path_list = set(path_list)
add_path_list = set(add_path_list)
return path_list, params.meta_requested, params.summary_requested, \
add_path_list, out_path | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.data_and_labels | def data_and_labels(self):
"""
Dataset features and labels in a matrix form for learning.
Also returns sample_ids in the same order.
Returns
-------
data_matrix : ndarray
2D array of shape [num_samples, num_features]
with features corresponding row-wise to sample_ids
labels : ndarray
Array of numeric labels for each sample corresponding row-wise to sample_ids
sample_ids : list
List of sample ids
"""
sample_ids = np.array(self.keys)
label_dict = self.labels
matrix = np.full([self.num_samples, self.num_features], np.nan)
labels = np.full([self.num_samples, 1], np.nan)
for ix, sample in enumerate(sample_ids):
matrix[ix, :] = self.__data[sample]
labels[ix] = label_dict[sample]
return matrix, np.ravel(labels), sample_ids | python | def data_and_labels(self):
"""
Dataset features and labels in a matrix form for learning.
Also returns sample_ids in the same order.
Returns
-------
data_matrix : ndarray
2D array of shape [num_samples, num_features]
with features corresponding row-wise to sample_ids
labels : ndarray
Array of numeric labels for each sample corresponding row-wise to sample_ids
sample_ids : list
List of sample ids
"""
sample_ids = np.array(self.keys)
label_dict = self.labels
matrix = np.full([self.num_samples, self.num_features], np.nan)
labels = np.full([self.num_samples, 1], np.nan)
for ix, sample in enumerate(sample_ids):
matrix[ix, :] = self.__data[sample]
labels[ix] = label_dict[sample]
return matrix, np.ravel(labels), sample_ids | [
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Array of numeric labels for each sample corresponding row-wise to sample_ids
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.data | def data(self, values, feature_names=None):
"""
Populates this dataset with the provided data.
Usage of this method is discourage (unless you know what you are doing).
Parameters
----------
values : dict
dict of features keyed in by sample ids.
feature_names : list of str
New feature names for the new features, if available.
Raises
------
ValueError
If number of samples does not match the size of existing set, or
If atleast one sample is not provided.
"""
if isinstance(values, dict):
if self.__labels is not None and len(self.__labels) != len(values):
raise ValueError(
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elif len(values) < 1:
raise ValueError('There must be at least 1 sample in the dataset!')
else:
self.__data = values
# update dimensionality
# assuming all keys in dict have same len arrays
self.__num_features = len(values[self.keys[0]])
if feature_names is None:
self.__feature_names = self.__str_names(self.num_features)
else:
self.feature_names = feature_names
else:
raise ValueError('data input must be a dictionary!') | python | def data(self, values, feature_names=None):
"""
Populates this dataset with the provided data.
Usage of this method is discourage (unless you know what you are doing).
Parameters
----------
values : dict
dict of features keyed in by sample ids.
feature_names : list of str
New feature names for the new features, if available.
Raises
------
ValueError
If number of samples does not match the size of existing set, or
If atleast one sample is not provided.
"""
if isinstance(values, dict):
if self.__labels is not None and len(self.__labels) != len(values):
raise ValueError(
'number of samples do not match the previously assigned labels')
elif len(values) < 1:
raise ValueError('There must be at least 1 sample in the dataset!')
else:
self.__data = values
# update dimensionality
# assuming all keys in dict have same len arrays
self.__num_features = len(values[self.keys[0]])
if feature_names is None:
self.__feature_names = self.__str_names(self.num_features)
else:
self.feature_names = feature_names
else:
raise ValueError('data input must be a dictionary!') | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.labels | def labels(self, values):
"""Class labels (such as 1, 2, -1, 'A', 'B' etc.) for each sample in the dataset."""
if isinstance(values, dict):
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elif set(self.keys) != set(list(values)):
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else:
self.__labels = values
else:
raise ValueError('labels input must be a dictionary!') | python | def labels(self, values):
"""Class labels (such as 1, 2, -1, 'A', 'B' etc.) for each sample in the dataset."""
if isinstance(values, dict):
if self.__data is not None and len(self.__data) != len(values):
raise ValueError(
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elif set(self.keys) != set(list(values)):
raise ValueError('sample ids do not match the previously assigned ids.')
else:
self.__labels = values
else:
raise ValueError('labels input must be a dictionary!') | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.classes | def classes(self, values):
"""Classes setter."""
if isinstance(values, dict):
if self.__data is not None and len(self.__data) != len(values):
raise ValueError(
'number of samples do not match the previously assigned data')
elif set(self.keys) != set(list(values)):
raise ValueError('sample ids do not match the previously assigned ids.')
else:
self.__classes = values
else:
raise ValueError('classes input must be a dictionary!') | python | def classes(self, values):
"""Classes setter."""
if isinstance(values, dict):
if self.__data is not None and len(self.__data) != len(values):
raise ValueError(
'number of samples do not match the previously assigned data')
elif set(self.keys) != set(list(values)):
raise ValueError('sample ids do not match the previously assigned ids.')
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self.__classes = values
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.feature_names | def feature_names(self, names):
"Stores the text labels for features"
if len(names) != self.num_features:
raise ValueError("Number of names do not match the number of features!")
if not isinstance(names, (Sequence, np.ndarray, np.generic)):
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self.__feature_names = np.array(names) | python | def feature_names(self, names):
"Stores the text labels for features"
if len(names) != self.num_features:
raise ValueError("Number of names do not match the number of features!")
if not isinstance(names, (Sequence, np.ndarray, np.generic)):
raise ValueError("Input is not a sequence. "
"Ensure names are in the same order "
"and length as features.")
self.__feature_names = np.array(names) | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.glance | def glance(self, nitems=5):
"""Quick and partial glance of the data matrix.
Parameters
----------
nitems : int
Number of items to glance from the dataset.
Default : 5
Returns
-------
dict
"""
nitems = max([1, min([nitems, self.num_samples - 1])])
return self.__take(nitems, iter(self.__data.items())) | python | def glance(self, nitems=5):
"""Quick and partial glance of the data matrix.
Parameters
----------
nitems : int
Number of items to glance from the dataset.
Default : 5
Returns
-------
dict
"""
nitems = max([1, min([nitems, self.num_samples - 1])])
return self.__take(nitems, iter(self.__data.items())) | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.summarize_classes | def summarize_classes(self):
"""
Summary of classes: names, numeric labels and sizes
Returns
-------
tuple : class_set, label_set, class_sizes
class_set : list
List of names of all the classes
label_set : list
Label for each class in class_set
class_sizes : list
Size of each class (number of samples)
"""
class_sizes = np.zeros(len(self.class_set))
for idx, cls in enumerate(self.class_set):
class_sizes[idx] = self.class_sizes[cls]
# TODO consider returning numeric label set e.g. for use in scikit-learn
return self.class_set, self.label_set, class_sizes | python | def summarize_classes(self):
"""
Summary of classes: names, numeric labels and sizes
Returns
-------
tuple : class_set, label_set, class_sizes
class_set : list
List of names of all the classes
label_set : list
Label for each class in class_set
class_sizes : list
Size of each class (number of samples)
"""
class_sizes = np.zeros(len(self.class_set))
for idx, cls in enumerate(self.class_set):
class_sizes[idx] = self.class_sizes[cls]
# TODO consider returning numeric label set e.g. for use in scikit-learn
return self.class_set, self.label_set, class_sizes | [
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label_set : list
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.check_features | def check_features(self, features):
"""
Method to ensure data to be added is not empty and vectorized.
Parameters
----------
features : iterable
Any data that can be converted to a numpy array.
Returns
-------
features : numpy array
Flattened non-empty numpy array.
Raises
------
ValueError
If input data is empty.
"""
if not isinstance(features, np.ndarray):
features = np.asarray(features)
if features.size <= 0:
raise ValueError('provided features are empty.')
if features.ndim > 1:
features = np.ravel(features)
return features | python | def check_features(self, features):
"""
Method to ensure data to be added is not empty and vectorized.
Parameters
----------
features : iterable
Any data that can be converted to a numpy array.
Returns
-------
features : numpy array
Flattened non-empty numpy array.
Raises
------
ValueError
If input data is empty.
"""
if not isinstance(features, np.ndarray):
features = np.asarray(features)
if features.size <= 0:
raise ValueError('provided features are empty.')
if features.ndim > 1:
features = np.ravel(features)
return features | [
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Returns
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features : numpy array
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Raises
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.add_sample | def add_sample(self, sample_id, features, label,
class_id=None,
overwrite=False,
feature_names=None):
"""Adds a new sample to the dataset with its features, label and class ID.
This is the preferred way to construct the dataset.
Parameters
----------
sample_id : str, int
The identifier that uniquely identifies this sample.
features : list, ndarray
The features for this sample
label : int, str
The label for this sample
class_id : int, str
The class for this sample.
If not provided, label converted to a string becomes its ID.
overwrite : bool
If True, allows the overwite of features for an existing subject ID.
Default : False.
feature_names : list
The names for each feature. Assumed to be in the same order as `features`
Raises
------
ValueError
If `sample_id` is already in the MLDataset (and overwrite=False), or
If dimensionality of the current sample does not match the current, or
If `feature_names` do not match existing names
TypeError
If sample to be added is of different data type compared to existing samples.
"""
if sample_id in self.__data and not overwrite:
raise ValueError('{} already exists in this dataset!'.format(sample_id))
# ensuring there is always a class name, even when not provided by the user.
# this is needed, in order for __str__ method to work.
# TODO consider enforcing label to be numeric and class_id to be string
# so portability with other packages is more uniform e.g. for use in scikit-learn
if class_id is None:
class_id = str(label)
features = self.check_features(features)
if self.num_samples <= 0:
self.__data[sample_id] = features
self.__labels[sample_id] = label
self.__classes[sample_id] = class_id
self.__dtype = type(features)
self.__num_features = features.size if isinstance(features,
np.ndarray) else len(
features)
if feature_names is None:
self.__feature_names = self.__str_names(self.num_features)
else:
if self.__num_features != features.size:
raise ValueError('dimensionality of this sample ({}) '
'does not match existing samples ({})'
''.format(features.size, self.__num_features))
if not isinstance(features, self.__dtype):
raise TypeError("Mismatched dtype. Provide {}".format(self.__dtype))
self.__data[sample_id] = features
self.__labels[sample_id] = label
self.__classes[sample_id] = class_id
if feature_names is not None:
# if it was never set, allow it
# class gets here when adding the first sample,
# after dataset was initialized with empty constructor
if self.__feature_names is None:
self.__feature_names = np.array(feature_names)
else: # if set already, ensure a match
if not np.array_equal(self.feature_names, np.array(feature_names)):
raise ValueError(
"supplied feature names do not match the existing names!") | python | def add_sample(self, sample_id, features, label,
class_id=None,
overwrite=False,
feature_names=None):
"""Adds a new sample to the dataset with its features, label and class ID.
This is the preferred way to construct the dataset.
Parameters
----------
sample_id : str, int
The identifier that uniquely identifies this sample.
features : list, ndarray
The features for this sample
label : int, str
The label for this sample
class_id : int, str
The class for this sample.
If not provided, label converted to a string becomes its ID.
overwrite : bool
If True, allows the overwite of features for an existing subject ID.
Default : False.
feature_names : list
The names for each feature. Assumed to be in the same order as `features`
Raises
------
ValueError
If `sample_id` is already in the MLDataset (and overwrite=False), or
If dimensionality of the current sample does not match the current, or
If `feature_names` do not match existing names
TypeError
If sample to be added is of different data type compared to existing samples.
"""
if sample_id in self.__data and not overwrite:
raise ValueError('{} already exists in this dataset!'.format(sample_id))
# ensuring there is always a class name, even when not provided by the user.
# this is needed, in order for __str__ method to work.
# TODO consider enforcing label to be numeric and class_id to be string
# so portability with other packages is more uniform e.g. for use in scikit-learn
if class_id is None:
class_id = str(label)
features = self.check_features(features)
if self.num_samples <= 0:
self.__data[sample_id] = features
self.__labels[sample_id] = label
self.__classes[sample_id] = class_id
self.__dtype = type(features)
self.__num_features = features.size if isinstance(features,
np.ndarray) else len(
features)
if feature_names is None:
self.__feature_names = self.__str_names(self.num_features)
else:
if self.__num_features != features.size:
raise ValueError('dimensionality of this sample ({}) '
'does not match existing samples ({})'
''.format(features.size, self.__num_features))
if not isinstance(features, self.__dtype):
raise TypeError("Mismatched dtype. Provide {}".format(self.__dtype))
self.__data[sample_id] = features
self.__labels[sample_id] = label
self.__classes[sample_id] = class_id
if feature_names is not None:
# if it was never set, allow it
# class gets here when adding the first sample,
# after dataset was initialized with empty constructor
if self.__feature_names is None:
self.__feature_names = np.array(feature_names)
else: # if set already, ensure a match
if not np.array_equal(self.feature_names, np.array(feature_names)):
raise ValueError(
"supplied feature names do not match the existing names!") | [
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The identifier that uniquely identifies this sample.
features : list, ndarray
The features for this sample
label : int, str
The label for this sample
class_id : int, str
The class for this sample.
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If True, allows the overwite of features for an existing subject ID.
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.del_sample | def del_sample(self, sample_id):
"""
Method to remove a sample from the dataset.
Parameters
----------
sample_id : str
sample id to be removed.
Raises
------
UserWarning
If sample id to delete was not found in the dataset.
"""
if sample_id not in self.__data:
warnings.warn('Sample to delete not found in the dataset - nothing to do.')
else:
self.__data.pop(sample_id)
self.__classes.pop(sample_id)
self.__labels.pop(sample_id)
print('{} removed.'.format(sample_id)) | python | def del_sample(self, sample_id):
"""
Method to remove a sample from the dataset.
Parameters
----------
sample_id : str
sample id to be removed.
Raises
------
UserWarning
If sample id to delete was not found in the dataset.
"""
if sample_id not in self.__data:
warnings.warn('Sample to delete not found in the dataset - nothing to do.')
else:
self.__data.pop(sample_id)
self.__classes.pop(sample_id)
self.__labels.pop(sample_id)
print('{} removed.'.format(sample_id)) | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.get_feature_subset | def get_feature_subset(self, subset_idx):
"""
Returns the subset of features indexed numerically.
Parameters
----------
subset_idx : list, ndarray
List of indices to features to be returned
Returns
-------
MLDataset : MLDataset
with subset of features requested.
Raises
------
UnboundLocalError
If input indices are out of bounds for the dataset.
"""
subset_idx = np.asarray(subset_idx)
if not (max(subset_idx) < self.__num_features) and (min(subset_idx) >= 0):
raise UnboundLocalError('indices out of range for the dataset. '
'Max index: {} Min index : 0'.format(
self.__num_features))
sub_data = {sample: features[subset_idx] for sample, features in
self.__data.items()}
new_descr = 'Subset features derived from: \n ' + self.__description
subdataset = MLDataset(data=sub_data,
labels=self.__labels, classes=self.__classes,
description=new_descr,
feature_names=self.__feature_names[subset_idx])
return subdataset | python | def get_feature_subset(self, subset_idx):
"""
Returns the subset of features indexed numerically.
Parameters
----------
subset_idx : list, ndarray
List of indices to features to be returned
Returns
-------
MLDataset : MLDataset
with subset of features requested.
Raises
------
UnboundLocalError
If input indices are out of bounds for the dataset.
"""
subset_idx = np.asarray(subset_idx)
if not (max(subset_idx) < self.__num_features) and (min(subset_idx) >= 0):
raise UnboundLocalError('indices out of range for the dataset. '
'Max index: {} Min index : 0'.format(
self.__num_features))
sub_data = {sample: features[subset_idx] for sample, features in
self.__data.items()}
new_descr = 'Subset features derived from: \n ' + self.__description
subdataset = MLDataset(data=sub_data,
labels=self.__labels, classes=self.__classes,
description=new_descr,
feature_names=self.__feature_names[subset_idx])
return subdataset | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.keys_with_value | def keys_with_value(dictionary, value):
"Returns a subset of keys from the dict with the value supplied."
subset = [key for key in dictionary if dictionary[key] == value]
return subset | python | def keys_with_value(dictionary, value):
"Returns a subset of keys from the dict with the value supplied."
subset = [key for key in dictionary if dictionary[key] == value]
return subset | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.get_class | def get_class(self, class_id):
"""
Returns a smaller dataset belonging to the requested classes.
Parameters
----------
class_id : str or list
identifier(s) of the class(es) to be returned.
Returns
-------
MLDataset
With subset of samples belonging to the given class(es).
Raises
------
ValueError
If one or more of the requested classes do not exist in this dataset.
If the specified id is empty or None
"""
if class_id in [None, '']:
raise ValueError("class id can not be empty or None.")
if isinstance(class_id, str):
class_ids = [class_id, ]
else:
class_ids = class_id
non_existent = set(self.class_set).intersection(set(class_ids))
if len(non_existent) < 1:
raise ValueError(
'These classes {} do not exist in this dataset.'.format(non_existent))
subsets = list()
for class_id in class_ids:
subsets_this_class = self.keys_with_value(self.__classes, class_id)
subsets.extend(subsets_this_class)
return self.get_subset(subsets) | python | def get_class(self, class_id):
"""
Returns a smaller dataset belonging to the requested classes.
Parameters
----------
class_id : str or list
identifier(s) of the class(es) to be returned.
Returns
-------
MLDataset
With subset of samples belonging to the given class(es).
Raises
------
ValueError
If one or more of the requested classes do not exist in this dataset.
If the specified id is empty or None
"""
if class_id in [None, '']:
raise ValueError("class id can not be empty or None.")
if isinstance(class_id, str):
class_ids = [class_id, ]
else:
class_ids = class_id
non_existent = set(self.class_set).intersection(set(class_ids))
if len(non_existent) < 1:
raise ValueError(
'These classes {} do not exist in this dataset.'.format(non_existent))
subsets = list()
for class_id in class_ids:
subsets_this_class = self.keys_with_value(self.__classes, class_id)
subsets.extend(subsets_this_class)
return self.get_subset(subsets) | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.transform | def transform(self, func, func_description=None):
"""
Applies a given a function to the features of each subject
and returns a new dataset with other info unchanged.
Parameters
----------
func : callable
A valid callable that takes in a single ndarray and returns a single ndarray.
Ensure the transformed dimensionality must be the same for all subjects.
If your function requires more than one argument,
use `functools.partial` to freeze all the arguments
except the features for the subject.
func_description : str, optional
Human readable description of the given function.
Returns
-------
xfm_ds : MLDataset
with features obtained from subject-wise transform
Raises
------
TypeError
If given func is not a callable
ValueError
If transformation of any of the subjects features raises an exception.
Examples
--------
Simple:
.. code-block:: python
from pyradigm import MLDataset
thickness = MLDataset(in_path='ADNI_thickness.csv')
pcg_thickness = thickness.apply_xfm(func=get_pcg, description = 'applying ROI mask for PCG')
pcg_median = pcg_thickness.apply_xfm(func=np.median, description='median per subject')
Complex example with function taking more than one argument:
.. code-block:: python
from pyradigm import MLDataset
from functools import partial
import hiwenet
thickness = MLDataset(in_path='ADNI_thickness.csv')
roi_membership = read_roi_membership()
hw = partial(hiwenet, groups = roi_membership)
thickness_hiwenet = thickness.transform(func=hw, description = 'histogram weighted networks')
median_thk_hiwenet = thickness_hiwenet.transform(func=np.median, description='median per subject')
"""
if not callable(func):
raise TypeError('Given function {} is not a callable'.format(func))
xfm_ds = MLDataset()
for sample, data in self.__data.items():
try:
xfm_data = func(data)
except:
print('Unable to transform features for {}. Quitting.'.format(sample))
raise
xfm_ds.add_sample(sample, xfm_data,
label=self.__labels[sample],
class_id=self.__classes[sample])
xfm_ds.description = "{}\n{}".format(func_description, self.__description)
return xfm_ds | python | def transform(self, func, func_description=None):
"""
Applies a given a function to the features of each subject
and returns a new dataset with other info unchanged.
Parameters
----------
func : callable
A valid callable that takes in a single ndarray and returns a single ndarray.
Ensure the transformed dimensionality must be the same for all subjects.
If your function requires more than one argument,
use `functools.partial` to freeze all the arguments
except the features for the subject.
func_description : str, optional
Human readable description of the given function.
Returns
-------
xfm_ds : MLDataset
with features obtained from subject-wise transform
Raises
------
TypeError
If given func is not a callable
ValueError
If transformation of any of the subjects features raises an exception.
Examples
--------
Simple:
.. code-block:: python
from pyradigm import MLDataset
thickness = MLDataset(in_path='ADNI_thickness.csv')
pcg_thickness = thickness.apply_xfm(func=get_pcg, description = 'applying ROI mask for PCG')
pcg_median = pcg_thickness.apply_xfm(func=np.median, description='median per subject')
Complex example with function taking more than one argument:
.. code-block:: python
from pyradigm import MLDataset
from functools import partial
import hiwenet
thickness = MLDataset(in_path='ADNI_thickness.csv')
roi_membership = read_roi_membership()
hw = partial(hiwenet, groups = roi_membership)
thickness_hiwenet = thickness.transform(func=hw, description = 'histogram weighted networks')
median_thk_hiwenet = thickness_hiwenet.transform(func=np.median, description='median per subject')
"""
if not callable(func):
raise TypeError('Given function {} is not a callable'.format(func))
xfm_ds = MLDataset()
for sample, data in self.__data.items():
try:
xfm_data = func(data)
except:
print('Unable to transform features for {}. Quitting.'.format(sample))
raise
xfm_ds.add_sample(sample, xfm_data,
label=self.__labels[sample],
class_id=self.__classes[sample])
xfm_ds.description = "{}\n{}".format(func_description, self.__description)
return xfm_ds | [
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Ensure the transformed dimensionality must be the same for all subjects.
If your function requires more than one argument,
use `functools.partial` to freeze all the arguments
except the features for the subject.
func_description : str, optional
Human readable description of the given function.
Returns
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xfm_ds : MLDataset
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Raises
------
TypeError
If given func is not a callable
ValueError
If transformation of any of the subjects features raises an exception.
Examples
--------
Simple:
.. code-block:: python
from pyradigm import MLDataset
thickness = MLDataset(in_path='ADNI_thickness.csv')
pcg_thickness = thickness.apply_xfm(func=get_pcg, description = 'applying ROI mask for PCG')
pcg_median = pcg_thickness.apply_xfm(func=np.median, description='median per subject')
Complex example with function taking more than one argument:
.. code-block:: python
from pyradigm import MLDataset
from functools import partial
import hiwenet
thickness = MLDataset(in_path='ADNI_thickness.csv')
roi_membership = read_roi_membership()
hw = partial(hiwenet, groups = roi_membership)
thickness_hiwenet = thickness.transform(func=hw, description = 'histogram weighted networks')
median_thk_hiwenet = thickness_hiwenet.transform(func=np.median, description='median per subject') | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.random_subset_ids_by_count | def random_subset_ids_by_count(self, count_per_class=1):
"""
Returns a random subset of sample ids of specified size by count,
within each class.
Parameters
----------
count_per_class : int
Exact number of samples per each class.
Returns
-------
subset : list
Combined list of sample ids from all classes.
"""
class_sizes = self.class_sizes
subsets = list()
if count_per_class < 1:
warnings.warn('Atleast one sample must be selected from each class')
return list()
elif count_per_class >= self.num_samples:
warnings.warn('All samples requested - returning a copy!')
return self.keys
# seeding the random number generator
# random.seed(random_seed)
for class_id, class_size in class_sizes.items():
# samples belonging to the class
this_class = self.keys_with_value(self.classes, class_id)
# shuffling the sample order; shuffling works in-place!
random.shuffle(this_class)
# clipping the range to [0, class_size]
subset_size_this_class = max(0, min(class_size, count_per_class))
if subset_size_this_class < 1 or this_class is None:
# warning if none were selected
warnings.warn('No subjects from class {} were selected.'.format(class_id))
else:
subsets_this_class = this_class[0:count_per_class]
subsets.extend(subsets_this_class)
if len(subsets) > 0:
return subsets
else:
warnings.warn('Zero samples were selected. Returning an empty list!')
return list() | python | def random_subset_ids_by_count(self, count_per_class=1):
"""
Returns a random subset of sample ids of specified size by count,
within each class.
Parameters
----------
count_per_class : int
Exact number of samples per each class.
Returns
-------
subset : list
Combined list of sample ids from all classes.
"""
class_sizes = self.class_sizes
subsets = list()
if count_per_class < 1:
warnings.warn('Atleast one sample must be selected from each class')
return list()
elif count_per_class >= self.num_samples:
warnings.warn('All samples requested - returning a copy!')
return self.keys
# seeding the random number generator
# random.seed(random_seed)
for class_id, class_size in class_sizes.items():
# samples belonging to the class
this_class = self.keys_with_value(self.classes, class_id)
# shuffling the sample order; shuffling works in-place!
random.shuffle(this_class)
# clipping the range to [0, class_size]
subset_size_this_class = max(0, min(class_size, count_per_class))
if subset_size_this_class < 1 or this_class is None:
# warning if none were selected
warnings.warn('No subjects from class {} were selected.'.format(class_id))
else:
subsets_this_class = this_class[0:count_per_class]
subsets.extend(subsets_this_class)
if len(subsets) > 0:
return subsets
else:
warnings.warn('Zero samples were selected. Returning an empty list!')
return list() | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.random_subset_ids | def random_subset_ids(self, perc_per_class=0.5):
"""
Returns a random subset of sample ids (size in percentage) within each class.
Parameters
----------
perc_per_class : float
Fraction of samples per class
Returns
-------
subset : list
Combined list of sample ids from all classes.
Raises
------
ValueError
If no subjects from one or more classes were selected.
UserWarning
If an empty or full dataset is requested.
"""
class_sizes = self.class_sizes
subsets = list()
if perc_per_class <= 0.0:
warnings.warn('Zero percentage requested - returning an empty dataset!')
return list()
elif perc_per_class >= 1.0:
warnings.warn('Full or a larger dataset requested - returning a copy!')
return self.keys
# seeding the random number generator
# random.seed(random_seed)
for class_id, class_size in class_sizes.items():
# samples belonging to the class
this_class = self.keys_with_value(self.classes, class_id)
# shuffling the sample order; shuffling works in-place!
random.shuffle(this_class)
# calculating the requested number of samples
subset_size_this_class = np.int64(np.floor(class_size * perc_per_class))
# clipping the range to [1, n]
subset_size_this_class = max(1, min(class_size, subset_size_this_class))
if subset_size_this_class < 1 or len(this_class) < 1 or this_class is None:
# warning if none were selected
raise ValueError(
'No subjects from class {} were selected.'.format(class_id))
else:
subsets_this_class = this_class[0:subset_size_this_class]
subsets.extend(subsets_this_class)
if len(subsets) > 0:
return subsets
else:
warnings.warn('Zero samples were selected. Returning an empty list!')
return list() | python | def random_subset_ids(self, perc_per_class=0.5):
"""
Returns a random subset of sample ids (size in percentage) within each class.
Parameters
----------
perc_per_class : float
Fraction of samples per class
Returns
-------
subset : list
Combined list of sample ids from all classes.
Raises
------
ValueError
If no subjects from one or more classes were selected.
UserWarning
If an empty or full dataset is requested.
"""
class_sizes = self.class_sizes
subsets = list()
if perc_per_class <= 0.0:
warnings.warn('Zero percentage requested - returning an empty dataset!')
return list()
elif perc_per_class >= 1.0:
warnings.warn('Full or a larger dataset requested - returning a copy!')
return self.keys
# seeding the random number generator
# random.seed(random_seed)
for class_id, class_size in class_sizes.items():
# samples belonging to the class
this_class = self.keys_with_value(self.classes, class_id)
# shuffling the sample order; shuffling works in-place!
random.shuffle(this_class)
# calculating the requested number of samples
subset_size_this_class = np.int64(np.floor(class_size * perc_per_class))
# clipping the range to [1, n]
subset_size_this_class = max(1, min(class_size, subset_size_this_class))
if subset_size_this_class < 1 or len(this_class) < 1 or this_class is None:
# warning if none were selected
raise ValueError(
'No subjects from class {} were selected.'.format(class_id))
else:
subsets_this_class = this_class[0:subset_size_this_class]
subsets.extend(subsets_this_class)
if len(subsets) > 0:
return subsets
else:
warnings.warn('Zero samples were selected. Returning an empty list!')
return list() | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.random_subset | def random_subset(self, perc_in_class=0.5):
"""
Returns a random sub-dataset (of specified size by percentage) within each class.
Parameters
----------
perc_in_class : float
Fraction of samples to be taken from each class.
Returns
-------
subdataset : MLDataset
random sub-dataset of specified size.
"""
subsets = self.random_subset_ids(perc_in_class)
if len(subsets) > 0:
return self.get_subset(subsets)
else:
warnings.warn('Zero samples were selected. Returning an empty dataset!')
return MLDataset() | python | def random_subset(self, perc_in_class=0.5):
"""
Returns a random sub-dataset (of specified size by percentage) within each class.
Parameters
----------
perc_in_class : float
Fraction of samples to be taken from each class.
Returns
-------
subdataset : MLDataset
random sub-dataset of specified size.
"""
subsets = self.random_subset_ids(perc_in_class)
if len(subsets) > 0:
return self.get_subset(subsets)
else:
warnings.warn('Zero samples were selected. Returning an empty dataset!')
return MLDataset() | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.sample_ids_in_class | def sample_ids_in_class(self, class_id):
"""
Returns a list of sample ids belonging to a given class.
Parameters
----------
class_id : str
class id to query.
Returns
-------
subset_ids : list
List of sample ids belonging to a given class.
"""
# subset_ids = [sid for sid in self.keys if self.classes[sid] == class_id]
subset_ids = self.keys_with_value(self.classes, class_id)
return subset_ids | python | def sample_ids_in_class(self, class_id):
"""
Returns a list of sample ids belonging to a given class.
Parameters
----------
class_id : str
class id to query.
Returns
-------
subset_ids : list
List of sample ids belonging to a given class.
"""
# subset_ids = [sid for sid in self.keys if self.classes[sid] == class_id]
subset_ids = self.keys_with_value(self.classes, class_id)
return subset_ids | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.get_subset | def get_subset(self, subset_ids):
"""
Returns a smaller dataset identified by their keys/sample IDs.
Parameters
----------
subset_ids : list
List od sample IDs to extracted from the dataset.
Returns
-------
sub-dataset : MLDataset
sub-dataset containing only requested sample IDs.
"""
num_existing_keys = sum([1 for key in subset_ids if key in self.__data])
if subset_ids is not None and num_existing_keys > 0:
# ensure items are added to data, labels etc in the same order of sample IDs
# TODO come up with a way to do this even when not using OrderedDict()
# putting the access of data, labels and classes in the same loop would
# ensure there is correspondence across the three attributes of the class
data = self.__get_subset_from_dict(self.__data, subset_ids)
labels = self.__get_subset_from_dict(self.__labels, subset_ids)
if self.__classes is not None:
classes = self.__get_subset_from_dict(self.__classes, subset_ids)
else:
classes = None
subdataset = MLDataset(data=data, labels=labels, classes=classes)
# Appending the history
subdataset.description += '\n Subset derived from: ' + self.description
subdataset.feature_names = self.__feature_names
subdataset.__dtype = self.dtype
return subdataset
else:
warnings.warn('subset of IDs requested do not exist in the dataset!')
return MLDataset() | python | def get_subset(self, subset_ids):
"""
Returns a smaller dataset identified by their keys/sample IDs.
Parameters
----------
subset_ids : list
List od sample IDs to extracted from the dataset.
Returns
-------
sub-dataset : MLDataset
sub-dataset containing only requested sample IDs.
"""
num_existing_keys = sum([1 for key in subset_ids if key in self.__data])
if subset_ids is not None and num_existing_keys > 0:
# ensure items are added to data, labels etc in the same order of sample IDs
# TODO come up with a way to do this even when not using OrderedDict()
# putting the access of data, labels and classes in the same loop would
# ensure there is correspondence across the three attributes of the class
data = self.__get_subset_from_dict(self.__data, subset_ids)
labels = self.__get_subset_from_dict(self.__labels, subset_ids)
if self.__classes is not None:
classes = self.__get_subset_from_dict(self.__classes, subset_ids)
else:
classes = None
subdataset = MLDataset(data=data, labels=labels, classes=classes)
# Appending the history
subdataset.description += '\n Subset derived from: ' + self.description
subdataset.feature_names = self.__feature_names
subdataset.__dtype = self.dtype
return subdataset
else:
warnings.warn('subset of IDs requested do not exist in the dataset!')
return MLDataset() | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.get_data_matrix_in_order | def get_data_matrix_in_order(self, subset_ids):
"""
Returns a numpy array of features, rows in the same order as subset_ids
Parameters
----------
subset_ids : list
List od sample IDs to extracted from the dataset.
Returns
-------
matrix : ndarray
Matrix of features, for each id in subset_ids, in order.
"""
if len(subset_ids) < 1:
warnings.warn('subset must have atleast one ID - returning empty matrix!')
return np.empty((0, 0))
if isinstance(subset_ids, set):
raise TypeError('Input set is not ordered, hence can not guarantee order! '
'Must provide a list or tuple.')
if isinstance(subset_ids, str):
subset_ids = [subset_ids, ]
num_existing_keys = sum([1 for key in subset_ids if key in self.__data])
if num_existing_keys < len(subset_ids):
raise ValueError('One or more IDs from subset do not exist in the dataset!')
matrix = np.full((num_existing_keys, self.num_features), np.nan)
for idx, sid in enumerate(subset_ids):
matrix[idx, :] = self.__data[sid]
return matrix | python | def get_data_matrix_in_order(self, subset_ids):
"""
Returns a numpy array of features, rows in the same order as subset_ids
Parameters
----------
subset_ids : list
List od sample IDs to extracted from the dataset.
Returns
-------
matrix : ndarray
Matrix of features, for each id in subset_ids, in order.
"""
if len(subset_ids) < 1:
warnings.warn('subset must have atleast one ID - returning empty matrix!')
return np.empty((0, 0))
if isinstance(subset_ids, set):
raise TypeError('Input set is not ordered, hence can not guarantee order! '
'Must provide a list or tuple.')
if isinstance(subset_ids, str):
subset_ids = [subset_ids, ]
num_existing_keys = sum([1 for key in subset_ids if key in self.__data])
if num_existing_keys < len(subset_ids):
raise ValueError('One or more IDs from subset do not exist in the dataset!')
matrix = np.full((num_existing_keys, self.num_features), np.nan)
for idx, sid in enumerate(subset_ids):
matrix[idx, :] = self.__data[sid]
return matrix | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.get | def get(self, item, not_found_value=None):
"Method like dict.get() which can return specified value if key not found"
if item in self.keys:
return self.__data[item]
else:
return not_found_value | python | def get(self, item, not_found_value=None):
"Method like dict.get() which can return specified value if key not found"
if item in self.keys:
return self.__data[item]
else:
return not_found_value | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.label_set | def label_set(self):
"""Set of labels in the dataset corresponding to class_set."""
label_set = list()
for class_ in self.class_set:
samples_in_class = self.sample_ids_in_class(class_)
label_set.append(self.labels[samples_in_class[0]])
return label_set | python | def label_set(self):
"""Set of labels in the dataset corresponding to class_set."""
label_set = list()
for class_ in self.class_set:
samples_in_class = self.sample_ids_in_class(class_)
label_set.append(self.labels[samples_in_class[0]])
return label_set | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.add_classes | def add_classes(self, classes):
"""
Helper to rename the classes, if provided by a dict keyed in by the orignal keys
Parameters
----------
classes : dict
Dict of class named keyed in by sample IDs.
Raises
------
TypeError
If classes is not a dict.
ValueError
If all samples in dataset are not present in input dict,
or one of they samples in input is not recognized.
"""
if not isinstance(classes, dict):
raise TypeError('Input classes is not a dict!')
if not len(classes) == self.num_samples:
raise ValueError('Too few items - need {} keys'.format(self.num_samples))
if not all([key in self.keys for key in classes]):
raise ValueError('One or more unrecognized keys!')
self.__classes = classes | python | def add_classes(self, classes):
"""
Helper to rename the classes, if provided by a dict keyed in by the orignal keys
Parameters
----------
classes : dict
Dict of class named keyed in by sample IDs.
Raises
------
TypeError
If classes is not a dict.
ValueError
If all samples in dataset are not present in input dict,
or one of they samples in input is not recognized.
"""
if not isinstance(classes, dict):
raise TypeError('Input classes is not a dict!')
if not len(classes) == self.num_samples:
raise ValueError('Too few items - need {} keys'.format(self.num_samples))
if not all([key in self.keys for key in classes]):
raise ValueError('One or more unrecognized keys!')
self.__classes = classes | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.__copy | def __copy(self, other):
"""Copy constructor."""
self.__data = copy.deepcopy(other.data)
self.__classes = copy.deepcopy(other.classes)
self.__labels = copy.deepcopy(other.labels)
self.__dtype = copy.deepcopy(other.dtype)
self.__description = copy.deepcopy(other.description)
self.__feature_names = copy.deepcopy(other.feature_names)
self.__num_features = copy.deepcopy(other.num_features)
return self | python | def __copy(self, other):
"""Copy constructor."""
self.__data = copy.deepcopy(other.data)
self.__classes = copy.deepcopy(other.classes)
self.__labels = copy.deepcopy(other.labels)
self.__dtype = copy.deepcopy(other.dtype)
self.__description = copy.deepcopy(other.description)
self.__feature_names = copy.deepcopy(other.feature_names)
self.__num_features = copy.deepcopy(other.num_features)
return self | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.__load | def __load(self, path):
"""Method to load the serialized dataset from disk."""
try:
path = os.path.abspath(path)
with open(path, 'rb') as df:
# loaded_dataset = pickle.load(df)
self.__data, self.__classes, self.__labels, \
self.__dtype, self.__description, \
self.__num_features, self.__feature_names = pickle.load(df)
# ensure the loaded dataset is valid
self.__validate(self.__data, self.__classes, self.__labels)
except IOError as ioe:
raise IOError('Unable to read the dataset from file: {}', format(ioe))
except:
raise | python | def __load(self, path):
"""Method to load the serialized dataset from disk."""
try:
path = os.path.abspath(path)
with open(path, 'rb') as df:
# loaded_dataset = pickle.load(df)
self.__data, self.__classes, self.__labels, \
self.__dtype, self.__description, \
self.__num_features, self.__feature_names = pickle.load(df)
# ensure the loaded dataset is valid
self.__validate(self.__data, self.__classes, self.__labels)
except IOError as ioe:
raise IOError('Unable to read the dataset from file: {}', format(ioe))
except:
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.__load_arff | def __load_arff(self, arff_path, encode_nonnumeric=False):
"""Loads a given dataset saved in Weka's ARFF format. """
try:
from scipy.io.arff import loadarff
arff_data, arff_meta = loadarff(arff_path)
except:
raise ValueError('Error loading the ARFF dataset!')
attr_names = arff_meta.names()[:-1] # last column is class
attr_types = arff_meta.types()[:-1]
if not encode_nonnumeric:
# ensure all the attributes are numeric
uniq_types = set(attr_types)
if 'numeric' not in uniq_types:
raise ValueError(
'Currently only numeric attributes in ARFF are supported!')
non_numeric = uniq_types.difference({'numeric'})
if len(non_numeric) > 0:
raise ValueError('Non-numeric features provided ({}), '
'without requesting encoding to numeric. '
'Try setting encode_nonnumeric=True '
'or encode features to numeric!'.format(non_numeric))
else:
raise NotImplementedError(
'encoding non-numeric features to numeric is not implemented yet! '
'Encode features beforing to ARFF.')
self.__description = arff_meta.name # to enable it as a label e.g. in neuropredict
# initializing the key containers, before calling self.add_sample
self.__data = OrderedDict()
self.__labels = OrderedDict()
self.__classes = OrderedDict()
num_samples = len(arff_data)
num_digits = len(str(num_samples))
make_id = lambda index: 'row{index:0{nd}d}'.format(index=index, nd=num_digits)
sample_classes = [cls.decode('utf-8') for cls in arff_data['class']]
class_set = set(sample_classes)
label_dict = dict()
# encoding class names to labels 1 to n
for ix, cls in enumerate(class_set):
label_dict[cls] = ix + 1
for index in range(num_samples):
sample = arff_data.take([index])[0].tolist()
sample_attrs = sample[:-1]
sample_class = sample[-1].decode('utf-8')
self.add_sample(sample_id=make_id(index), # ARFF rows do not have an ID
features=sample_attrs,
label=label_dict[sample_class],
class_id=sample_class)
# not necessary to set feature_names=attr_names for each sample,
# as we do it globally after loop
self.__feature_names = attr_names
return | python | def __load_arff(self, arff_path, encode_nonnumeric=False):
"""Loads a given dataset saved in Weka's ARFF format. """
try:
from scipy.io.arff import loadarff
arff_data, arff_meta = loadarff(arff_path)
except:
raise ValueError('Error loading the ARFF dataset!')
attr_names = arff_meta.names()[:-1] # last column is class
attr_types = arff_meta.types()[:-1]
if not encode_nonnumeric:
# ensure all the attributes are numeric
uniq_types = set(attr_types)
if 'numeric' not in uniq_types:
raise ValueError(
'Currently only numeric attributes in ARFF are supported!')
non_numeric = uniq_types.difference({'numeric'})
if len(non_numeric) > 0:
raise ValueError('Non-numeric features provided ({}), '
'without requesting encoding to numeric. '
'Try setting encode_nonnumeric=True '
'or encode features to numeric!'.format(non_numeric))
else:
raise NotImplementedError(
'encoding non-numeric features to numeric is not implemented yet! '
'Encode features beforing to ARFF.')
self.__description = arff_meta.name # to enable it as a label e.g. in neuropredict
# initializing the key containers, before calling self.add_sample
self.__data = OrderedDict()
self.__labels = OrderedDict()
self.__classes = OrderedDict()
num_samples = len(arff_data)
num_digits = len(str(num_samples))
make_id = lambda index: 'row{index:0{nd}d}'.format(index=index, nd=num_digits)
sample_classes = [cls.decode('utf-8') for cls in arff_data['class']]
class_set = set(sample_classes)
label_dict = dict()
# encoding class names to labels 1 to n
for ix, cls in enumerate(class_set):
label_dict[cls] = ix + 1
for index in range(num_samples):
sample = arff_data.take([index])[0].tolist()
sample_attrs = sample[:-1]
sample_class = sample[-1].decode('utf-8')
self.add_sample(sample_id=make_id(index), # ARFF rows do not have an ID
features=sample_attrs,
label=label_dict[sample_class],
class_id=sample_class)
# not necessary to set feature_names=attr_names for each sample,
# as we do it globally after loop
self.__feature_names = attr_names
return | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.save | def save(self, file_path):
"""
Method to save the dataset to disk.
Parameters
----------
file_path : str
File path to save the current dataset to
Raises
------
IOError
If saving to disk is not successful.
"""
# TODO need a file format that is flexible and efficient to allow the following:
# 1) being able to read just meta info without having to load the ENTIRE dataset
# i.e. use case: compatibility check with #subjects, ids and their classes
# 2) random access layout: being able to read features for a single subject!
try:
file_path = os.path.abspath(file_path)
with open(file_path, 'wb') as df:
# pickle.dump(self, df)
pickle.dump((self.__data, self.__classes, self.__labels,
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self.__feature_names),
df)
return
except IOError as ioe:
raise IOError('Unable to save the dataset to file: {}', format(ioe))
except:
raise | python | def save(self, file_path):
"""
Method to save the dataset to disk.
Parameters
----------
file_path : str
File path to save the current dataset to
Raises
------
IOError
If saving to disk is not successful.
"""
# TODO need a file format that is flexible and efficient to allow the following:
# 1) being able to read just meta info without having to load the ENTIRE dataset
# i.e. use case: compatibility check with #subjects, ids and their classes
# 2) random access layout: being able to read features for a single subject!
try:
file_path = os.path.abspath(file_path)
with open(file_path, 'wb') as df:
# pickle.dump(self, df)
pickle.dump((self.__data, self.__classes, self.__labels,
self.__dtype, self.__description, self.__num_features,
self.__feature_names),
df)
return
except IOError as ioe:
raise IOError('Unable to save the dataset to file: {}', format(ioe))
except:
raise | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.__validate | def __validate(data, classes, labels):
"Validator of inputs."
if not isinstance(data, dict):
raise TypeError(
'data must be a dict! keys: sample ID or any unique identifier')
if not isinstance(labels, dict):
raise TypeError(
'labels must be a dict! keys: sample ID or any unique identifier')
if classes is not None:
if not isinstance(classes, dict):
raise TypeError(
'labels must be a dict! keys: sample ID or any unique identifier')
if not len(data) == len(labels) == len(classes):
raise ValueError('Lengths of data, labels and classes do not match!')
if not set(list(data)) == set(list(labels)) == set(list(classes)):
raise ValueError(
'data, classes and labels dictionaries must have the same keys!')
num_features_in_elements = np.unique([sample.size for sample in data.values()])
if len(num_features_in_elements) > 1:
raise ValueError(
'different samples have different number of features - invalid!')
return True | python | def __validate(data, classes, labels):
"Validator of inputs."
if not isinstance(data, dict):
raise TypeError(
'data must be a dict! keys: sample ID or any unique identifier')
if not isinstance(labels, dict):
raise TypeError(
'labels must be a dict! keys: sample ID or any unique identifier')
if classes is not None:
if not isinstance(classes, dict):
raise TypeError(
'labels must be a dict! keys: sample ID or any unique identifier')
if not len(data) == len(labels) == len(classes):
raise ValueError('Lengths of data, labels and classes do not match!')
if not set(list(data)) == set(list(labels)) == set(list(classes)):
raise ValueError(
'data, classes and labels dictionaries must have the same keys!')
num_features_in_elements = np.unique([sample.size for sample in data.values()])
if len(num_features_in_elements) > 1:
raise ValueError(
'different samples have different number of features - invalid!')
return True | [
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raamana/pyradigm | pyradigm/pyradigm.py | MLDataset.extend | def extend(self, other):
"""
Method to extend the dataset vertically (add samples from anotehr dataset).
Parameters
----------
other : MLDataset
second dataset to be combined with the current
(different samples, but same dimensionality)
Raises
------
TypeError
if input is not an MLDataset.
"""
if not isinstance(other, MLDataset):
raise TypeError('Incorrect type of dataset provided!')
# assert self.__dtype==other.dtype, TypeError('Incorrect data type of features!')
for sample in other.keys:
self.add_sample(sample, other.data[sample], other.labels[sample],
other.classes[sample]) | python | def extend(self, other):
"""
Method to extend the dataset vertically (add samples from anotehr dataset).
Parameters
----------
other : MLDataset
second dataset to be combined with the current
(different samples, but same dimensionality)
Raises
------
TypeError
if input is not an MLDataset.
"""
if not isinstance(other, MLDataset):
raise TypeError('Incorrect type of dataset provided!')
# assert self.__dtype==other.dtype, TypeError('Incorrect data type of features!')
for sample in other.keys:
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michal-stuglik/django-blastplus | blastplus/forms.py | validate_word_size | def validate_word_size(word_size, BLAST_SETS):
"""Validate word size in blast/tblastn form. """
blast_min_int_word_size = BLAST_SETS.min_word_size
blast_max_int_word_size = BLAST_SETS.max_word_size
blast_word_size_error = BLAST_SETS.get_word_size_error()
try:
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int_word_size = int(word_size)
if int_word_size < blast_min_int_word_size:
raise forms.ValidationError(blast_word_size_error)
if int_word_size >= blast_max_int_word_size:
raise forms.ValidationError(blast_word_size_error)
except:
raise forms.ValidationError(blast_word_size_error)
return int_word_size | python | def validate_word_size(word_size, BLAST_SETS):
"""Validate word size in blast/tblastn form. """
blast_min_int_word_size = BLAST_SETS.min_word_size
blast_max_int_word_size = BLAST_SETS.max_word_size
blast_word_size_error = BLAST_SETS.get_word_size_error()
try:
if len(word_size) <= 0:
raise forms.ValidationError(blast_word_size_error)
int_word_size = int(word_size)
if int_word_size < blast_min_int_word_size:
raise forms.ValidationError(blast_word_size_error)
if int_word_size >= blast_max_int_word_size:
raise forms.ValidationError(blast_word_size_error)
except:
raise forms.ValidationError(blast_word_size_error)
return int_word_size | [
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michal-stuglik/django-blastplus | blastplus/forms.py | validate_sequence | def validate_sequence(sequence: str, sequence_is_as_nucleotide=True):
"""Validate sequence in blast/tblastn form. """
tmp_seq = tempfile.NamedTemporaryFile(mode="wb+", delete=False)
if len(str(sequence).strip()) == 0:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_ERROR_MSG)
if str(sequence).strip()[0] != ">":
tmp_seq.write(">seq1\n".encode())
tmp_seq.write(sequence.encode())
tmp_seq.close()
records = SeqIO.index(tmp_seq.name, "fasta")
record_count = len(records)
if record_count == 0:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_ERROR_MSG)
if record_count > blast_settings.BLAST_MAX_NUMBER_SEQ_IN_INPUT:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_MAX_SEQ_NUMB_ERROR_MSG)
# read query sequence from temporary file
first_sequence_list_in_file = SeqIO.parse(tmp_seq.name, "fasta")
for sequence in first_sequence_list_in_file:
if len(sequence.seq) <= 10:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_TOO_SHORT_ERROR_MSG)
if sequence_is_as_nucleotide:
check_allowed_letters(str(sequence.seq), ALLOWED_NUCL)
else:
check_allowed_letters(str(sequence.seq), ALLOWED_AMINOACIDS)
return tmp_seq | python | def validate_sequence(sequence: str, sequence_is_as_nucleotide=True):
"""Validate sequence in blast/tblastn form. """
tmp_seq = tempfile.NamedTemporaryFile(mode="wb+", delete=False)
if len(str(sequence).strip()) == 0:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_ERROR_MSG)
if str(sequence).strip()[0] != ">":
tmp_seq.write(">seq1\n".encode())
tmp_seq.write(sequence.encode())
tmp_seq.close()
records = SeqIO.index(tmp_seq.name, "fasta")
record_count = len(records)
if record_count == 0:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_ERROR_MSG)
if record_count > blast_settings.BLAST_MAX_NUMBER_SEQ_IN_INPUT:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_MAX_SEQ_NUMB_ERROR_MSG)
# read query sequence from temporary file
first_sequence_list_in_file = SeqIO.parse(tmp_seq.name, "fasta")
for sequence in first_sequence_list_in_file:
if len(sequence.seq) <= 10:
raise forms.ValidationError(blast_settings.BLAST_CORRECT_SEQ_TOO_SHORT_ERROR_MSG)
if sequence_is_as_nucleotide:
check_allowed_letters(str(sequence.seq), ALLOWED_NUCL)
else:
check_allowed_letters(str(sequence.seq), ALLOWED_AMINOACIDS)
return tmp_seq | [
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michal-stuglik/django-blastplus | blastplus/forms.py | check_allowed_letters | def check_allowed_letters(seq, allowed_letters_as_set):
"""Validate sequence: Rise an error if sequence contains undesirable letters. """
# set of unique letters in sequence
seq_set = set(seq)
not_allowed_letters_in_seq = [x for x in seq_set if str(x).upper() not in allowed_letters_as_set]
if len(not_allowed_letters_in_seq) > 0:
raise forms.ValidationError(
"This sequence type cannot contain letters: " + ", ".join(not_allowed_letters_in_seq)) | python | def check_allowed_letters(seq, allowed_letters_as_set):
"""Validate sequence: Rise an error if sequence contains undesirable letters. """
# set of unique letters in sequence
seq_set = set(seq)
not_allowed_letters_in_seq = [x for x in seq_set if str(x).upper() not in allowed_letters_as_set]
if len(not_allowed_letters_in_seq) > 0:
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tomduck/pandoc-xnos | pandocxnos/core.py | _repeat | def _repeat(func):
"""Repeats func(...) call until something other than None is returned."""
@functools.wraps(func)
def wrapper(*args, **kwargs):
"""Repeats the call until True is returned."""
ret = None
while ret is None:
ret = func(*args, **kwargs)
return ret
return wrapper | python | def _repeat(func):
"""Repeats func(...) call until something other than None is returned."""
@functools.wraps(func)
def wrapper(*args, **kwargs):
"""Repeats the call until True is returned."""
ret = None
while ret is None:
ret = func(*args, **kwargs)
return ret
return wrapper | [
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tomduck/pandoc-xnos | pandocxnos/core.py | init | def init(pandocversion=None, doc=None):
"""Sets or determines the pandoc version. This must be called.
The pandoc version is needed for multi-version support.
See: https://github.com/jgm/pandoc/issues/2640
Returns the pandoc version."""
# This requires some care because we can't be sure that a call to 'pandoc'
# will work. It could be 'pandoc-1.17.0.2' or some other name. Try
# checking the parent process first, and only make a call to 'pandoc' as
# a last resort.
global _PANDOCVERSION # pylint: disable=global-statement
pattern = re.compile(r'^[1-2]\.[0-9]+(?:\.[0-9]+)?(?:\.[0-9]+)?$')
if 'PANDOC_VERSION' in os.environ: # Available for pandoc >= 1.19.1
pandocversion = str(os.environ['PANDOC_VERSION'])
if not pandocversion is None:
# Test the result and if it is OK then store it in _PANDOCVERSION
if pattern.match(pandocversion):
_PANDOCVERSION = pandocversion
return _PANDOCVERSION
else:
msg = 'Cannot understand pandocversion=%s'%pandocversion
raise RuntimeError(msg)
if not doc is None:
if 'pandoc-api-version' in doc:
# This could be either 1.18 or 1.19; there is no way to
# distinguish them (but there isn't a use case in pandoc-fignos
# and friends where it matters)
_PANDOCVERSION = '1.18'
return _PANDOCVERSION
# Get the command
try: # Get the path for the parent process
if os.name == 'nt':
# psutil appears to work differently for windows
command = psutil.Process(os.getpid()).parent().parent().exe()
else:
command = psutil.Process(os.getpid()).parent().exe()
if not os.path.basename(command).startswith('pandoc'):
raise RuntimeError('pandoc not found')
except: # pylint: disable=bare-except
# Call whatever pandoc is available and hope for the best
command = 'pandoc'
# Make the call
try:
# Get the version number and confirm it conforms to expectations
output = subprocess.check_output([command, '-v'])
line = output.decode('utf-8').split('\n')[0]
pandocversion = line.split(' ')[-1].strip()
except: # pylint: disable=bare-except
pandocversion = ''
# Test the result and if it is OK then store it in _PANDOCVERSION
if pattern.match(pandocversion):
_PANDOCVERSION = pandocversion
if _PANDOCVERSION is None:
msg = """Cannot determine pandoc version. Please file an issue at
https://github.com/tomduck/pandocfiltering/issues"""
raise RuntimeError(textwrap.dedent(msg))
return _PANDOCVERSION | python | def init(pandocversion=None, doc=None):
"""Sets or determines the pandoc version. This must be called.
The pandoc version is needed for multi-version support.
See: https://github.com/jgm/pandoc/issues/2640
Returns the pandoc version."""
# This requires some care because we can't be sure that a call to 'pandoc'
# will work. It could be 'pandoc-1.17.0.2' or some other name. Try
# checking the parent process first, and only make a call to 'pandoc' as
# a last resort.
global _PANDOCVERSION # pylint: disable=global-statement
pattern = re.compile(r'^[1-2]\.[0-9]+(?:\.[0-9]+)?(?:\.[0-9]+)?$')
if 'PANDOC_VERSION' in os.environ: # Available for pandoc >= 1.19.1
pandocversion = str(os.environ['PANDOC_VERSION'])
if not pandocversion is None:
# Test the result and if it is OK then store it in _PANDOCVERSION
if pattern.match(pandocversion):
_PANDOCVERSION = pandocversion
return _PANDOCVERSION
else:
msg = 'Cannot understand pandocversion=%s'%pandocversion
raise RuntimeError(msg)
if not doc is None:
if 'pandoc-api-version' in doc:
# This could be either 1.18 or 1.19; there is no way to
# distinguish them (but there isn't a use case in pandoc-fignos
# and friends where it matters)
_PANDOCVERSION = '1.18'
return _PANDOCVERSION
# Get the command
try: # Get the path for the parent process
if os.name == 'nt':
# psutil appears to work differently for windows
command = psutil.Process(os.getpid()).parent().parent().exe()
else:
command = psutil.Process(os.getpid()).parent().exe()
if not os.path.basename(command).startswith('pandoc'):
raise RuntimeError('pandoc not found')
except: # pylint: disable=bare-except
# Call whatever pandoc is available and hope for the best
command = 'pandoc'
# Make the call
try:
# Get the version number and confirm it conforms to expectations
output = subprocess.check_output([command, '-v'])
line = output.decode('utf-8').split('\n')[0]
pandocversion = line.split(' ')[-1].strip()
except: # pylint: disable=bare-except
pandocversion = ''
# Test the result and if it is OK then store it in _PANDOCVERSION
if pattern.match(pandocversion):
_PANDOCVERSION = pandocversion
if _PANDOCVERSION is None:
msg = """Cannot determine pandoc version. Please file an issue at
https://github.com/tomduck/pandocfiltering/issues"""
raise RuntimeError(textwrap.dedent(msg))
return _PANDOCVERSION | [
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tomduck/pandoc-xnos | pandocxnos/core.py | get_meta | def get_meta(meta, name):
"""Retrieves the metadata variable 'name' from the 'meta' dict."""
assert name in meta
data = meta[name]
if data['t'] in ['MetaString', 'MetaBool']:
return data['c']
elif data['t'] == 'MetaInlines':
# Handle bug in pandoc 2.2.3 and 2.2.3.1: Return boolean value rather
# than strings, as appropriate.
if len(data['c']) == 1 and data['c'][0]['t'] == 'Str':
if data['c'][0]['c'] in ['true', 'True', 'TRUE']:
return True
elif data['c'][0]['c'] in ['false', 'False', 'FALSE']:
return False
return stringify(data['c'])
elif data['t'] == 'MetaList':
return [stringify(v['c']) for v in data['c']]
else:
raise RuntimeError("Could not understand metadata variable '%s'." %
name) | python | def get_meta(meta, name):
"""Retrieves the metadata variable 'name' from the 'meta' dict."""
assert name in meta
data = meta[name]
if data['t'] in ['MetaString', 'MetaBool']:
return data['c']
elif data['t'] == 'MetaInlines':
# Handle bug in pandoc 2.2.3 and 2.2.3.1: Return boolean value rather
# than strings, as appropriate.
if len(data['c']) == 1 and data['c'][0]['t'] == 'Str':
if data['c'][0]['c'] in ['true', 'True', 'TRUE']:
return True
elif data['c'][0]['c'] in ['false', 'False', 'FALSE']:
return False
return stringify(data['c'])
elif data['t'] == 'MetaList':
return [stringify(v['c']) for v in data['c']]
else:
raise RuntimeError("Could not understand metadata variable '%s'." %
name) | [
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tomduck/pandoc-xnos | pandocxnos/core.py | elt | def elt(eltType, numargs): # pylint: disable=invalid-name
"""Returns Element(*value) to create pandoc json elements.
This should be used in place of pandocfilters.elt(). This version
ensures that the content is stored in a list, not a tuple.
"""
def Element(*value): # pylint: disable=invalid-name
"""Creates an element."""
el = _elt(eltType, numargs)(*value)
if isinstance(el['c'], tuple):
el['c'] = list(el['c']) # The content should be a list, not tuple
return el
return Element | python | def elt(eltType, numargs): # pylint: disable=invalid-name
"""Returns Element(*value) to create pandoc json elements.
This should be used in place of pandocfilters.elt(). This version
ensures that the content is stored in a list, not a tuple.
"""
def Element(*value): # pylint: disable=invalid-name
"""Creates an element."""
el = _elt(eltType, numargs)(*value)
if isinstance(el['c'], tuple):
el['c'] = list(el['c']) # The content should be a list, not tuple
return el
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tomduck/pandoc-xnos | pandocxnos/core.py | _getel | def _getel(key, value):
"""Returns an element given a key and value."""
if key in ['HorizontalRule', 'Null']:
return elt(key, 0)()
elif key in ['Plain', 'Para', 'BlockQuote', 'BulletList',
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return elt(key, 1)(value)
return elt(key, len(value))(*value) | python | def _getel(key, value):
"""Returns an element given a key and value."""
if key in ['HorizontalRule', 'Null']:
return elt(key, 0)()
elif key in ['Plain', 'Para', 'BlockQuote', 'BulletList',
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return elt(key, 1)(value)
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tomduck/pandoc-xnos | pandocxnos/core.py | quotify | def quotify(x):
"""Replaces Quoted elements in element list 'x' with quoted strings.
Pandoc uses the Quoted element in its json when --smart is enabled.
Output to TeX/pdf automatically triggers --smart.
stringify() ignores Quoted elements. Use quotify() first to replace
Quoted elements in 'x' with quoted strings. 'x' should be a deep copy so
that the underlying document is left untouched.
Returns x."""
def _quotify(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaced Quoted elements with quoted strings."""
if key == 'Quoted':
ret = []
quote = '"' if value[0]['t'] == 'DoubleQuote' else "'"
if value[1][0]['t'] == 'Str':
value[1][0]['c'] = quote + value[1][0]['c']
else:
ret.append(Str(quote))
if value[1][-1]['t'] == 'Str':
value[1][-1]['c'] = value[1][-1]['c'] + quote
ret += value[1]
else:
ret += value[1] + [Str(quote)]
return ret
return None
return walk(walk(x, _quotify, '', {}), join_strings, '', {}) | python | def quotify(x):
"""Replaces Quoted elements in element list 'x' with quoted strings.
Pandoc uses the Quoted element in its json when --smart is enabled.
Output to TeX/pdf automatically triggers --smart.
stringify() ignores Quoted elements. Use quotify() first to replace
Quoted elements in 'x' with quoted strings. 'x' should be a deep copy so
that the underlying document is left untouched.
Returns x."""
def _quotify(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaced Quoted elements with quoted strings."""
if key == 'Quoted':
ret = []
quote = '"' if value[0]['t'] == 'DoubleQuote' else "'"
if value[1][0]['t'] == 'Str':
value[1][0]['c'] = quote + value[1][0]['c']
else:
ret.append(Str(quote))
if value[1][-1]['t'] == 'Str':
value[1][-1]['c'] = value[1][-1]['c'] + quote
ret += value[1]
else:
ret += value[1] + [Str(quote)]
return ret
return None
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tomduck/pandoc-xnos | pandocxnos/core.py | dollarfy | def dollarfy(x):
"""Replaces Math elements in element list 'x' with a $-enclosed string.
stringify() passes through TeX math. Use dollarfy(x) first to replace
Math elements with math strings set in dollars. 'x' should be a deep copy
so that the underlying document is left untouched.
Returns 'x'."""
def _dollarfy(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaces Math elements"""
if key == 'Math':
return Str('$' + value[1] + '$')
return None
return walk(x, _dollarfy, '', {}) | python | def dollarfy(x):
"""Replaces Math elements in element list 'x' with a $-enclosed string.
stringify() passes through TeX math. Use dollarfy(x) first to replace
Math elements with math strings set in dollars. 'x' should be a deep copy
so that the underlying document is left untouched.
Returns 'x'."""
def _dollarfy(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaces Math elements"""
if key == 'Math':
return Str('$' + value[1] + '$')
return None
return walk(x, _dollarfy, '', {}) | [
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tomduck/pandoc-xnos | pandocxnos/core.py | extract_attrs | def extract_attrs(x, n):
"""Extracts attributes from element list 'x' beginning at index 'n'.
The elements encapsulating the attributes (typically a series of Str and
Space elements) are removed from 'x'. Items before index 'n' are left
unchanged.
Returns the attributes in pandoc format. A ValueError is raised if
attributes aren't found. An IndexError is raised if the index 'n' is out
of range."""
# Check for the start of the attributes string
if not (x[n]['t'] == 'Str' and x[n]['c'].startswith('{')):
raise ValueError('Attributes not found.')
# It starts with {, so this *may* be an attributes list. Search for where
# the attributes end. Do not consider } in quoted elements.
seq = [] # A sequence of saved values
quotechar = None # Used to keep track of quotes in strings
flag = False # Flags that an attributes list was found
i = 0 # Initialization
for i, v in enumerate(x[n:]): # Scan through the list
if v and v['t'] == 'Str':
# Scan for } outside of a quote
for j, c in enumerate(v['c']):
if c == quotechar: # This is an end quote
quotechar = None
elif c in ['"', "'"]: # This is an open quote
quotechar = c
elif c == '}' and quotechar is None: # The attributes end here
# Split the string at the } and save the pieces
head, tail = v['c'][:j+1], v['c'][j+1:]
x[n+i] = copy.deepcopy(v)
x[n+i]['c'] = tail
v['c'] = head
flag = True
break
seq.append(v)
if flag:
break
if flag: # Attributes string was found, so process it
# Delete empty and extracted elements
if x[n+i]['t'] == 'Str' and not x[n+i]['c']:
del x[n+i]
del x[n:n+i]
# Process the attrs
attrstr = stringify(dollarfy(quotify(seq))).strip()
attrs = PandocAttributes(attrstr, 'markdown').to_pandoc()
# Remove extranneous quotes from kvs
for i, (k, v) in enumerate(attrs[2]): # pylint: disable=unused-variable
if v[0] == v[-1] == '"' or v[0] == "'" == v[-1] == "'":
attrs[2][i][1] = attrs[2][i][1][1:-1]
# We're done
return attrs
# Attributes not found
raise ValueError('Attributes not found.') | python | def extract_attrs(x, n):
"""Extracts attributes from element list 'x' beginning at index 'n'.
The elements encapsulating the attributes (typically a series of Str and
Space elements) are removed from 'x'. Items before index 'n' are left
unchanged.
Returns the attributes in pandoc format. A ValueError is raised if
attributes aren't found. An IndexError is raised if the index 'n' is out
of range."""
# Check for the start of the attributes string
if not (x[n]['t'] == 'Str' and x[n]['c'].startswith('{')):
raise ValueError('Attributes not found.')
# It starts with {, so this *may* be an attributes list. Search for where
# the attributes end. Do not consider } in quoted elements.
seq = [] # A sequence of saved values
quotechar = None # Used to keep track of quotes in strings
flag = False # Flags that an attributes list was found
i = 0 # Initialization
for i, v in enumerate(x[n:]): # Scan through the list
if v and v['t'] == 'Str':
# Scan for } outside of a quote
for j, c in enumerate(v['c']):
if c == quotechar: # This is an end quote
quotechar = None
elif c in ['"', "'"]: # This is an open quote
quotechar = c
elif c == '}' and quotechar is None: # The attributes end here
# Split the string at the } and save the pieces
head, tail = v['c'][:j+1], v['c'][j+1:]
x[n+i] = copy.deepcopy(v)
x[n+i]['c'] = tail
v['c'] = head
flag = True
break
seq.append(v)
if flag:
break
if flag: # Attributes string was found, so process it
# Delete empty and extracted elements
if x[n+i]['t'] == 'Str' and not x[n+i]['c']:
del x[n+i]
del x[n:n+i]
# Process the attrs
attrstr = stringify(dollarfy(quotify(seq))).strip()
attrs = PandocAttributes(attrstr, 'markdown').to_pandoc()
# Remove extranneous quotes from kvs
for i, (k, v) in enumerate(attrs[2]): # pylint: disable=unused-variable
if v[0] == v[-1] == '"' or v[0] == "'" == v[-1] == "'":
attrs[2][i][1] = attrs[2][i][1][1:-1]
# We're done
return attrs
# Attributes not found
raise ValueError('Attributes not found.') | [
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tomduck/pandoc-xnos | pandocxnos/core.py | _join_strings | def _join_strings(x):
"""Joins adjacent Str elements found in the element list 'x'."""
for i in range(len(x)-1): # Process successive pairs of elements
if x[i]['t'] == 'Str' and x[i+1]['t'] == 'Str':
x[i]['c'] += x[i+1]['c']
del x[i+1] # In-place deletion of element from list
return None # Forces processing to repeat
return True | python | def _join_strings(x):
"""Joins adjacent Str elements found in the element list 'x'."""
for i in range(len(x)-1): # Process successive pairs of elements
if x[i]['t'] == 'Str' and x[i+1]['t'] == 'Str':
x[i]['c'] += x[i+1]['c']
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tomduck/pandoc-xnos | pandocxnos/core.py | join_strings | def join_strings(key, value, fmt, meta): # pylint: disable=unused-argument
"""Joins adjacent Str elements in the 'value' list."""
if key in ['Para', 'Plain']:
_join_strings(value)
elif key == 'Image':
_join_strings(value[-2])
elif key == 'Table':
_join_strings(value[-5]) | python | def join_strings(key, value, fmt, meta): # pylint: disable=unused-argument
"""Joins adjacent Str elements in the 'value' list."""
if key in ['Para', 'Plain']:
_join_strings(value)
elif key == 'Image':
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tomduck/pandoc-xnos | pandocxnos/core.py | _is_broken_ref | def _is_broken_ref(key1, value1, key2, value2):
"""True if this is a broken reference; False otherwise."""
# A link followed by a string may represent a broken reference
if key1 != 'Link' or key2 != 'Str':
return False
# Assemble the parts
n = 0 if _PANDOCVERSION < '1.16' else 1
if isinstance(value1[n][0]['c'], list):
# Occurs when there is quoted text in an actual link. This is not
# a broken link. See Issue #1.
return False
s = value1[n][0]['c'] + value2
# Return True if this matches the reference regex
return True if _REF.match(s) else False | python | def _is_broken_ref(key1, value1, key2, value2):
"""True if this is a broken reference; False otherwise."""
# A link followed by a string may represent a broken reference
if key1 != 'Link' or key2 != 'Str':
return False
# Assemble the parts
n = 0 if _PANDOCVERSION < '1.16' else 1
if isinstance(value1[n][0]['c'], list):
# Occurs when there is quoted text in an actual link. This is not
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return False
s = value1[n][0]['c'] + value2
# Return True if this matches the reference regex
return True if _REF.match(s) else False | [
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tomduck/pandoc-xnos | pandocxnos/core.py | _repair_refs | def _repair_refs(x):
"""Performs the repair on the element list 'x'."""
if _PANDOCVERSION is None:
raise RuntimeError('Module uninitialized. Please call init().')
# Scan the element list x
for i in range(len(x)-1):
# Check for broken references
if _is_broken_ref(x[i]['t'], x[i]['c'] if 'c' in x[i] else [],
x[i+1]['t'], x[i+1]['c'] if 'c' in x[i+1] else []):
# Get the reference string
n = 0 if _PANDOCVERSION < '1.16' else 1
s = x[i]['c'][n][0]['c'] + x[i+1]['c']
# Chop it into pieces. Note that the prefix and suffix may be
# parts of other broken references.
prefix, label, suffix = _REF.match(s).groups()
# Insert the suffix, label and prefix back into x. Do it in this
# order so that the indexing works.
if suffix:
x.insert(i+2, Str(suffix))
x[i+1] = Cite(
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"citationNoteNum":0,
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if prefix:
if i > 0 and x[i-1]['t'] == 'Str':
x[i-1]['c'] = x[i-1]['c'] + prefix
del x[i]
else:
x[i] = Str(prefix)
else:
del x[i]
return None # Forces processing to repeat
return True | python | def _repair_refs(x):
"""Performs the repair on the element list 'x'."""
if _PANDOCVERSION is None:
raise RuntimeError('Module uninitialized. Please call init().')
# Scan the element list x
for i in range(len(x)-1):
# Check for broken references
if _is_broken_ref(x[i]['t'], x[i]['c'] if 'c' in x[i] else [],
x[i+1]['t'], x[i+1]['c'] if 'c' in x[i+1] else []):
# Get the reference string
n = 0 if _PANDOCVERSION < '1.16' else 1
s = x[i]['c'][n][0]['c'] + x[i+1]['c']
# Chop it into pieces. Note that the prefix and suffix may be
# parts of other broken references.
prefix, label, suffix = _REF.match(s).groups()
# Insert the suffix, label and prefix back into x. Do it in this
# order so that the indexing works.
if suffix:
x.insert(i+2, Str(suffix))
x[i+1] = Cite(
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"citationPrefix":[],
"citationSuffix":[],
"citationNoteNum":0,
"citationMode":{"t":"AuthorInText", "c":[]},
"citationHash":0}],
[Str('@' + label)])
if prefix:
if i > 0 and x[i-1]['t'] == 'Str':
x[i-1]['c'] = x[i-1]['c'] + prefix
del x[i]
else:
x[i] = Str(prefix)
else:
del x[i]
return None # Forces processing to repeat
return True | [
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tomduck/pandoc-xnos | pandocxnos/core.py | repair_refs | def repair_refs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Using "-f markdown+autolink_bare_uris" with pandoc < 1.18 splits a
reference like "{@fig:one}" into email Link and Str elements. This
function replaces the mess with the Cite and Str elements we normally
get. Call this before any reference processing."""
if _PANDOCVERSION >= '1.18':
return
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elif key == 'Table':
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tomduck/pandoc-xnos | pandocxnos/core.py | _extract_modifier | def _extract_modifier(x, i, attrs):
"""Extracts the */+/! modifier in front of the Cite at index 'i' of the
element list 'x'. The modifier is stored in 'attrs'. Returns the updated
index 'i'."""
global _cleveref_tex_flag # pylint: disable=global-statement
assert x[i]['t'] == 'Cite'
assert i > 0
# Check the previous element for a modifier in the last character
if x[i-1]['t'] == 'Str':
modifier = x[i-1]['c'][-1]
if not _cleveref_tex_flag and modifier in ['*', '+']:
_cleveref_tex_flag = True
if modifier in ['*', '+', '!']:
attrs[2].append(['modifier', modifier])
if len(x[i-1]['c']) > 1: # Lop the modifier off of the string
x[i-1]['c'] = x[i-1]['c'][:-1]
else: # The element contains only the modifier; delete it
del x[i-1]
i -= 1
return i | python | def _extract_modifier(x, i, attrs):
"""Extracts the */+/! modifier in front of the Cite at index 'i' of the
element list 'x'. The modifier is stored in 'attrs'. Returns the updated
index 'i'."""
global _cleveref_tex_flag # pylint: disable=global-statement
assert x[i]['t'] == 'Cite'
assert i > 0
# Check the previous element for a modifier in the last character
if x[i-1]['t'] == 'Str':
modifier = x[i-1]['c'][-1]
if not _cleveref_tex_flag and modifier in ['*', '+']:
_cleveref_tex_flag = True
if modifier in ['*', '+', '!']:
attrs[2].append(['modifier', modifier])
if len(x[i-1]['c']) > 1: # Lop the modifier off of the string
x[i-1]['c'] = x[i-1]['c'][:-1]
else: # The element contains only the modifier; delete it
del x[i-1]
i -= 1
return i | [
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tomduck/pandoc-xnos | pandocxnos/core.py | _remove_brackets | def _remove_brackets(x, i):
"""Removes curly brackets surrounding the Cite element at index 'i' in
the element list 'x'. It is assumed that the modifier has been
extracted. Empty strings are deleted from 'x'."""
assert x[i]['t'] == 'Cite'
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return
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del x[i+1]
if len(x[i-1]['c']) > 1:
x[i-1]['c'] = x[i-1]['c'][:-1]
else:
del x[i-1] | python | def _remove_brackets(x, i):
"""Removes curly brackets surrounding the Cite element at index 'i' in
the element list 'x'. It is assumed that the modifier has been
extracted. Empty strings are deleted from 'x'."""
assert x[i]['t'] == 'Cite'
assert i > 0 and i < len(x) - 1
# Check if the surrounding elements are strings
if not x[i-1]['t'] == x[i+1]['t'] == 'Str':
return
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tomduck/pandoc-xnos | pandocxnos/core.py | _process_refs | def _process_refs(x, labels):
"""Strips surrounding curly braces and adds modifiers to the
attributes of Cite elements. Only references with labels in the 'labels'
list are processed. Repeats processing (via decorator) until no more
broken references are found."""
# Scan the element list x for Cite elements with known labels
for i, v in enumerate(x):
if v['t'] == 'Cite' and len(v['c']) == 2 and \
_get_label(v['t'], v['c']) in labels:
# A new reference was found; create some empty attributes for it
attrs = ['', [], []]
# Extract the modifiers. 'attrs' is updated in place. Element
# deletion could change the index of the Cite being processed.
if i > 0:
i = _extract_modifier(x, i, attrs)
# Attach the attributes
v['c'].insert(0, attrs)
# Remove surrounding brackets
if i > 0 and i < len(x)-1:
_remove_brackets(x, i)
# The element list may be changed
return None # Forces processing to repeat via _repeat decorator
return True | python | def _process_refs(x, labels):
"""Strips surrounding curly braces and adds modifiers to the
attributes of Cite elements. Only references with labels in the 'labels'
list are processed. Repeats processing (via decorator) until no more
broken references are found."""
# Scan the element list x for Cite elements with known labels
for i, v in enumerate(x):
if v['t'] == 'Cite' and len(v['c']) == 2 and \
_get_label(v['t'], v['c']) in labels:
# A new reference was found; create some empty attributes for it
attrs = ['', [], []]
# Extract the modifiers. 'attrs' is updated in place. Element
# deletion could change the index of the Cite being processed.
if i > 0:
i = _extract_modifier(x, i, attrs)
# Attach the attributes
v['c'].insert(0, attrs)
# Remove surrounding brackets
if i > 0 and i < len(x)-1:
_remove_brackets(x, i)
# The element list may be changed
return None # Forces processing to repeat via _repeat decorator
return True | [
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tomduck/pandoc-xnos | pandocxnos/core.py | process_refs_factory | def process_refs_factory(labels):
"""Returns process_refs(key, value, fmt, meta) action that processes
text around a reference. Only references with labels found in the
'labels' list are processed.
Consider the markdown "{+@fig:1}", which represents a reference to a
figure. "@" denotes a reference, "fig:1" is the reference's label, and
"+" is a modifier. Valid modifiers are '+, '*' and '!'.
This function strips curly braces and adds the modifiers to the attributes
of Cite elements. Cite attributes must be detached before the document is
written to STDOUT because pandoc doesn't recognize them. Alternatively,
use an action from replace_refs_factory() to replace the references
altogether.
"""
# pylint: disable=unused-argument
def process_refs(key, value, fmt, meta):
"""Instates Ref elements."""
# References may occur in a variety of places; we must process them
# all.
if key in ['Para', 'Plain']:
_process_refs(value, labels)
elif key == 'Image':
_process_refs(value[-2], labels)
elif key == 'Table':
_process_refs(value[-5], labels)
elif key == 'Span':
_process_refs(value[-1], labels)
elif key == 'Emph':
_process_refs(value, labels)
elif key == 'Strong':
_process_refs(value, labels)
return process_refs | python | def process_refs_factory(labels):
"""Returns process_refs(key, value, fmt, meta) action that processes
text around a reference. Only references with labels found in the
'labels' list are processed.
Consider the markdown "{+@fig:1}", which represents a reference to a
figure. "@" denotes a reference, "fig:1" is the reference's label, and
"+" is a modifier. Valid modifiers are '+, '*' and '!'.
This function strips curly braces and adds the modifiers to the attributes
of Cite elements. Cite attributes must be detached before the document is
written to STDOUT because pandoc doesn't recognize them. Alternatively,
use an action from replace_refs_factory() to replace the references
altogether.
"""
# pylint: disable=unused-argument
def process_refs(key, value, fmt, meta):
"""Instates Ref elements."""
# References may occur in a variety of places; we must process them
# all.
if key in ['Para', 'Plain']:
_process_refs(value, labels)
elif key == 'Image':
_process_refs(value[-2], labels)
elif key == 'Table':
_process_refs(value[-5], labels)
elif key == 'Span':
_process_refs(value[-1], labels)
elif key == 'Emph':
_process_refs(value, labels)
elif key == 'Strong':
_process_refs(value, labels)
return process_refs | [
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tomduck/pandoc-xnos | pandocxnos/core.py | replace_refs_factory | def replace_refs_factory(references, use_cleveref_default, use_eqref,
plusname, starname, target):
"""Returns replace_refs(key, value, fmt, meta) action that replaces
references with format-specific content. The content is determined using
the 'references' dict, which associates reference labels with numbers or
string tags (e.g., { 'fig:1':1, 'fig:2':2, ...}). If 'use_cleveref_default'
is True, or if "modifier" in the reference's attributes is "+" or "*", then
clever referencing is used; i.e., a name is placed in front of the number
or string tag. The 'plusname' and 'starname' lists give the singular
and plural names for "+" and "*" clever references, respectively. The
'target' is the LaTeX type for clever referencing (e.g., "figure",
"equation", "table", ...)."""
global _cleveref_tex_flag # pylint: disable=global-statement
# Update global if clever referencing is required by default
_cleveref_tex_flag = _cleveref_tex_flag or use_cleveref_default
def _insert_cleveref_fakery(key, value, meta):
r"""Inserts TeX to support clever referencing in LaTeX documents
if the key isn't a RawBlock. If the key is a RawBlock, then check
the value to see if the TeX was already inserted.
The \providecommand macro is used to fake the cleveref package's
behaviour if it is not provided in the template via
\usepackage{cleveref}.
TeX is inserted into the value. Replacement elements are returned.
"""
global _cleveref_tex_flag # pylint: disable=global-statement
comment1 = '% pandoc-xnos: cleveref formatting'
tex1 = [comment1,
r'\crefformat{%s}{%s~#2#1#3}'%(target, plusname[0]),
r'\Crefformat{%s}{%s~#2#1#3}'%(target, starname[0])]
if key == 'RawBlock': # Check for existing cleveref TeX
if value[1].startswith(comment1):
# Append the new portion
value[1] = value[1] + '\n' + '\n'.join(tex1[1:])
_cleveref_tex_flag = False # Cleveref fakery already installed
elif key != 'RawBlock': # Write the cleveref TeX
_cleveref_tex_flag = False # Cancels further attempts
ret = []
# Check first to see if fakery is turned off
if not 'xnos-cleveref-fake' in meta or \
check_bool(get_meta(meta, 'xnos-cleveref-fake')):
# Cleveref fakery
tex2 = [
r'% pandoc-xnos: cleveref fakery',
r'\newcommand{\plusnamesingular}{}',
r'\newcommand{\starnamesingular}{}',
r'\newcommand{\xrefname}[1]{'\
r'\protect\renewcommand{\plusnamesingular}{#1}}',
r'\newcommand{\Xrefname}[1]{'\
r'\protect\renewcommand{\starnamesingular}{#1}}',
r'\providecommand{\cref}{\plusnamesingular~\ref}',
r'\providecommand{\Cref}{\starnamesingular~\ref}',
r'\providecommand{\crefformat}[2]{}',
r'\providecommand{\Crefformat}[2]{}']
ret.append(RawBlock('tex', '\n'.join(tex2)))
ret.append(RawBlock('tex', '\n'.join(tex1)))
return ret
return None
def _cite_replacement(key, value, fmt, meta):
"""Returns context-dependent content to replace a Cite element."""
assert key == 'Cite'
attrs, label = value[0], _get_label(key, value)
attrs = PandocAttributes(attrs, 'pandoc')
assert label in references
# Get the replacement value
text = str(references[label])
# Choose between \Cref, \cref and \ref
use_cleveref = attrs['modifier'] in ['*', '+'] \
if 'modifier' in attrs.kvs else use_cleveref_default
plus = attrs['modifier'] == '+' if 'modifier' in attrs.kvs \
else use_cleveref_default
name = plusname[0] if plus else starname[0] # Name used by cref
# The replacement depends on the output format
if fmt == 'latex':
if use_cleveref:
# Renew commands needed for cleveref fakery
if not 'xnos-cleveref-fake' in meta or \
check_bool(get_meta(meta, 'xnos-cleveref-fake')):
faketex = (r'\xrefname' if plus else r'\Xrefname') + \
'{%s}' % name
else:
faketex = ''
macro = r'\cref' if plus else r'\Cref'
ret = RawInline('tex', r'%s%s{%s}'%(faketex, macro, label))
elif use_eqref:
ret = RawInline('tex', r'\eqref{%s}'%label)
else:
ret = RawInline('tex', r'\ref{%s}'%label)
else:
if use_eqref:
text = '(' + text + ')'
linktext = [Math({"t":"InlineMath", "c":[]}, text[1:-1]) \
if text.startswith('$') and text.endswith('$') \
else Str(text)]
link = elt('Link', 2)(linktext, ['#%s' % label, '']) \
if _PANDOCVERSION < '1.16' else \
Link(['', [], []], linktext, ['#%s' % label, ''])
ret = ([Str(name), Space()] if use_cleveref else []) + [link]
return ret
def replace_refs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaces references with format-specific content."""
if fmt == 'latex' and _cleveref_tex_flag:
# Put the cleveref TeX fakery in front of the first block element
# that isn't a RawBlock.
if not key in ['Plain', 'Para', 'CodeBlock', 'RawBlock',
'BlockQuote', 'OrderedList', 'BulletList',
'DefinitionList', 'Header', 'HorizontalRule',
'Table', 'Div', 'Null']:
return None
# Reconstruct the block element
el = _getel(key, value)
# Insert cleveref TeX in front of the block element
tex = _insert_cleveref_fakery(key, value, meta)
if tex:
return tex + [el]
elif key == 'Cite' and len(value) == 3: # Replace the reference
return _cite_replacement(key, value, fmt, meta)
return None
return replace_refs | python | def replace_refs_factory(references, use_cleveref_default, use_eqref,
plusname, starname, target):
"""Returns replace_refs(key, value, fmt, meta) action that replaces
references with format-specific content. The content is determined using
the 'references' dict, which associates reference labels with numbers or
string tags (e.g., { 'fig:1':1, 'fig:2':2, ...}). If 'use_cleveref_default'
is True, or if "modifier" in the reference's attributes is "+" or "*", then
clever referencing is used; i.e., a name is placed in front of the number
or string tag. The 'plusname' and 'starname' lists give the singular
and plural names for "+" and "*" clever references, respectively. The
'target' is the LaTeX type for clever referencing (e.g., "figure",
"equation", "table", ...)."""
global _cleveref_tex_flag # pylint: disable=global-statement
# Update global if clever referencing is required by default
_cleveref_tex_flag = _cleveref_tex_flag or use_cleveref_default
def _insert_cleveref_fakery(key, value, meta):
r"""Inserts TeX to support clever referencing in LaTeX documents
if the key isn't a RawBlock. If the key is a RawBlock, then check
the value to see if the TeX was already inserted.
The \providecommand macro is used to fake the cleveref package's
behaviour if it is not provided in the template via
\usepackage{cleveref}.
TeX is inserted into the value. Replacement elements are returned.
"""
global _cleveref_tex_flag # pylint: disable=global-statement
comment1 = '% pandoc-xnos: cleveref formatting'
tex1 = [comment1,
r'\crefformat{%s}{%s~#2#1#3}'%(target, plusname[0]),
r'\Crefformat{%s}{%s~#2#1#3}'%(target, starname[0])]
if key == 'RawBlock': # Check for existing cleveref TeX
if value[1].startswith(comment1):
# Append the new portion
value[1] = value[1] + '\n' + '\n'.join(tex1[1:])
_cleveref_tex_flag = False # Cleveref fakery already installed
elif key != 'RawBlock': # Write the cleveref TeX
_cleveref_tex_flag = False # Cancels further attempts
ret = []
# Check first to see if fakery is turned off
if not 'xnos-cleveref-fake' in meta or \
check_bool(get_meta(meta, 'xnos-cleveref-fake')):
# Cleveref fakery
tex2 = [
r'% pandoc-xnos: cleveref fakery',
r'\newcommand{\plusnamesingular}{}',
r'\newcommand{\starnamesingular}{}',
r'\newcommand{\xrefname}[1]{'\
r'\protect\renewcommand{\plusnamesingular}{#1}}',
r'\newcommand{\Xrefname}[1]{'\
r'\protect\renewcommand{\starnamesingular}{#1}}',
r'\providecommand{\cref}{\plusnamesingular~\ref}',
r'\providecommand{\Cref}{\starnamesingular~\ref}',
r'\providecommand{\crefformat}[2]{}',
r'\providecommand{\Crefformat}[2]{}']
ret.append(RawBlock('tex', '\n'.join(tex2)))
ret.append(RawBlock('tex', '\n'.join(tex1)))
return ret
return None
def _cite_replacement(key, value, fmt, meta):
"""Returns context-dependent content to replace a Cite element."""
assert key == 'Cite'
attrs, label = value[0], _get_label(key, value)
attrs = PandocAttributes(attrs, 'pandoc')
assert label in references
# Get the replacement value
text = str(references[label])
# Choose between \Cref, \cref and \ref
use_cleveref = attrs['modifier'] in ['*', '+'] \
if 'modifier' in attrs.kvs else use_cleveref_default
plus = attrs['modifier'] == '+' if 'modifier' in attrs.kvs \
else use_cleveref_default
name = plusname[0] if plus else starname[0] # Name used by cref
# The replacement depends on the output format
if fmt == 'latex':
if use_cleveref:
# Renew commands needed for cleveref fakery
if not 'xnos-cleveref-fake' in meta or \
check_bool(get_meta(meta, 'xnos-cleveref-fake')):
faketex = (r'\xrefname' if plus else r'\Xrefname') + \
'{%s}' % name
else:
faketex = ''
macro = r'\cref' if plus else r'\Cref'
ret = RawInline('tex', r'%s%s{%s}'%(faketex, macro, label))
elif use_eqref:
ret = RawInline('tex', r'\eqref{%s}'%label)
else:
ret = RawInline('tex', r'\ref{%s}'%label)
else:
if use_eqref:
text = '(' + text + ')'
linktext = [Math({"t":"InlineMath", "c":[]}, text[1:-1]) \
if text.startswith('$') and text.endswith('$') \
else Str(text)]
link = elt('Link', 2)(linktext, ['#%s' % label, '']) \
if _PANDOCVERSION < '1.16' else \
Link(['', [], []], linktext, ['#%s' % label, ''])
ret = ([Str(name), Space()] if use_cleveref else []) + [link]
return ret
def replace_refs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Replaces references with format-specific content."""
if fmt == 'latex' and _cleveref_tex_flag:
# Put the cleveref TeX fakery in front of the first block element
# that isn't a RawBlock.
if not key in ['Plain', 'Para', 'CodeBlock', 'RawBlock',
'BlockQuote', 'OrderedList', 'BulletList',
'DefinitionList', 'Header', 'HorizontalRule',
'Table', 'Div', 'Null']:
return None
# Reconstruct the block element
el = _getel(key, value)
# Insert cleveref TeX in front of the block element
tex = _insert_cleveref_fakery(key, value, meta)
if tex:
return tex + [el]
elif key == 'Cite' and len(value) == 3: # Replace the reference
return _cite_replacement(key, value, fmt, meta)
return None
return replace_refs | [
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tomduck/pandoc-xnos | pandocxnos/core.py | attach_attrs_factory | def attach_attrs_factory(f, extract_attrs=extract_attrs, allow_space=False):
"""Returns attach_attrs(key, value, fmt, meta) action that reads and
attaches attributes to unattributed elements generated by the
pandocfilters function f (e.g. pandocfilters.Math, etc).
The extract_attrs() function should read the attributes and raise a
ValueError or IndexError if attributes are not found.
"""
# Get the name
name = f.__closure__[0].cell_contents
def _attach_attrs(x):
"""Extracts and attaches the attributes."""
for i, v in enumerate(x):
if v and v['t'] == name: # Find where the attributes start
n = i+1
if allow_space and n < len(x) and x[n]['t'] == 'Space':
n += 1
try: # Extract the attributes
attrs = extract_attrs(x, n)
x[i]['c'].insert(0, attrs)
except (ValueError, IndexError):
pass
def attach_attrs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Attaches attributes to an element."""
if key in ['Para', 'Plain']:
_attach_attrs(value)
# Image: Add pandoc's figure marker if warranted
if len(value) == 1 and value[0]['t'] == 'Image':
value[0]['c'][-1][1] = 'fig:'
return attach_attrs | python | def attach_attrs_factory(f, extract_attrs=extract_attrs, allow_space=False):
"""Returns attach_attrs(key, value, fmt, meta) action that reads and
attaches attributes to unattributed elements generated by the
pandocfilters function f (e.g. pandocfilters.Math, etc).
The extract_attrs() function should read the attributes and raise a
ValueError or IndexError if attributes are not found.
"""
# Get the name
name = f.__closure__[0].cell_contents
def _attach_attrs(x):
"""Extracts and attaches the attributes."""
for i, v in enumerate(x):
if v and v['t'] == name: # Find where the attributes start
n = i+1
if allow_space and n < len(x) and x[n]['t'] == 'Space':
n += 1
try: # Extract the attributes
attrs = extract_attrs(x, n)
x[i]['c'].insert(0, attrs)
except (ValueError, IndexError):
pass
def attach_attrs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Attaches attributes to an element."""
if key in ['Para', 'Plain']:
_attach_attrs(value)
# Image: Add pandoc's figure marker if warranted
if len(value) == 1 and value[0]['t'] == 'Image':
value[0]['c'][-1][1] = 'fig:'
return attach_attrs | [
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tomduck/pandoc-xnos | pandocxnos/core.py | detach_attrs_factory | def detach_attrs_factory(f):
"""Returns detach_attrs(key, value, fmt, meta) action that detaches
attributes attached to elements of type f (e.g. pandocfilters.Math, etc).
Attributes provided natively by pandoc will be left as is."""
# Get the name and standard length
name = f.__closure__[0].cell_contents
n = f.__closure__[1].cell_contents
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if key == name:
assert len(value) <= n+1
if len(value) == n+1:
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assert len(value[0]) == 3
assert isinstance(value[0][0], STRTYPES)
assert isinstance(value[0][1], list)
assert isinstance(value[0][2], list)
del value[0]
return detach_attrs | python | def detach_attrs_factory(f):
"""Returns detach_attrs(key, value, fmt, meta) action that detaches
attributes attached to elements of type f (e.g. pandocfilters.Math, etc).
Attributes provided natively by pandoc will be left as is."""
# Get the name and standard length
name = f.__closure__[0].cell_contents
n = f.__closure__[1].cell_contents
def detach_attrs(key, value, fmt, meta): # pylint: disable=unused-argument
"""Detaches the attributes."""
if key == name:
assert len(value) <= n+1
if len(value) == n+1:
# Make sure value[0] represents attributes then delete
assert len(value[0]) == 3
assert isinstance(value[0][0], STRTYPES)
assert isinstance(value[0][1], list)
assert isinstance(value[0][2], list)
del value[0]
return detach_attrs | [
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tomduck/pandoc-xnos | pandocxnos/core.py | insert_secnos_factory | def insert_secnos_factory(f):
"""Returns insert_secnos(key, value, fmt, meta) action that inserts
section numbers into the attributes of elements of type f.
"""
# Get the name and standard length
name = f.__closure__[0].cell_contents
n = f.__closure__[1].cell_contents
def insert_secnos(key, value, fmt, meta): # pylint: disable=unused-argument
"""Inserts section numbers into elements attributes."""
global sec # pylint: disable=global-statement
if 'xnos-number-sections' in meta and \
check_bool(get_meta(meta, 'xnos-number-sections')) and \
fmt in ['html', 'html5']:
if key == 'Header':
if 'unnumbered' in value[1][1]:
return
level = value[0]
m = level - len(sec)
if m > 0:
sec.extend([0]*m)
sec[level-1] += 1
sec = sec[:MAXLEVEL]
if key == name:
# Only insert if attributes are attached. Images always have
# attributes.
assert len(value) <= n+1
if name == 'Image' or len(value) == n+1:
# Make sure value[0] represents attributes
assert len(value[0]) == 3
assert isinstance(value[0][0], STRTYPES)
assert isinstance(value[0][1], list)
assert isinstance(value[0][2], list)
# Insert the section number into the attributes
s = '.'.join([str(m) for m in sec])
value[0][2].insert(0, ['secno', s])
return insert_secnos | python | def insert_secnos_factory(f):
"""Returns insert_secnos(key, value, fmt, meta) action that inserts
section numbers into the attributes of elements of type f.
"""
# Get the name and standard length
name = f.__closure__[0].cell_contents
n = f.__closure__[1].cell_contents
def insert_secnos(key, value, fmt, meta): # pylint: disable=unused-argument
"""Inserts section numbers into elements attributes."""
global sec # pylint: disable=global-statement
if 'xnos-number-sections' in meta and \
check_bool(get_meta(meta, 'xnos-number-sections')) and \
fmt in ['html', 'html5']:
if key == 'Header':
if 'unnumbered' in value[1][1]:
return
level = value[0]
m = level - len(sec)
if m > 0:
sec.extend([0]*m)
sec[level-1] += 1
sec = sec[:MAXLEVEL]
if key == name:
# Only insert if attributes are attached. Images always have
# attributes.
assert len(value) <= n+1
if name == 'Image' or len(value) == n+1:
# Make sure value[0] represents attributes
assert len(value[0]) == 3
assert isinstance(value[0][0], STRTYPES)
assert isinstance(value[0][1], list)
assert isinstance(value[0][2], list)
# Insert the section number into the attributes
s = '.'.join([str(m) for m in sec])
value[0][2].insert(0, ['secno', s])
return insert_secnos | [
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tomduck/pandoc-xnos | pandocxnos/core.py | delete_secnos_factory | def delete_secnos_factory(f):
"""Returns delete_secnos(key, value, fmt, meta) action that deletes
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"""
# Get the name and standard length
name = f.__closure__[0].cell_contents
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# Remove the secno attribute
if value[0][2] and value[0][2][0][0] == 'secno':
del value[0][2][0]
return delete_secnos | python | def delete_secnos_factory(f):
"""Returns delete_secnos(key, value, fmt, meta) action that deletes
section numbers from the attributes of elements of type f.
"""
# Get the name and standard length
name = f.__closure__[0].cell_contents
n = f.__closure__[1].cell_contents
def delete_secnos(key, value, fmt, meta): # pylint: disable=unused-argument
"""Deletes section numbers from elements attributes."""
if 'xnos-number-sections' in meta and \
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fmt in ['html', 'html5']:
# Only delete if attributes are attached. Images always have
# attributes.
if key == name:
assert len(value) <= n+1
if name == 'Image' or len(value) == n+1:
# Make sure value[0] represents attributes
assert len(value[0]) == 3
assert isinstance(value[0][0], STRTYPES)
assert isinstance(value[0][1], list)
assert isinstance(value[0][2], list)
# Remove the secno attribute
if value[0][2] and value[0][2][0][0] == 'secno':
del value[0][2][0]
return delete_secnos | [
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tomduck/pandoc-xnos | pandocxnos/core.py | insert_rawblocks_factory | def insert_rawblocks_factory(rawblocks):
r"""Returns insert_rawblocks(key, value, fmt, meta) action that inserts
non-duplicate RawBlock elements.
"""
# pylint: disable=unused-argument
def insert_rawblocks(key, value, fmt, meta):
"""Inserts non-duplicate RawBlock elements."""
if not rawblocks:
return None
# Put the RawBlock elements in front of the first block element that
# isn't also a RawBlock.
if not key in ['Plain', 'Para', 'CodeBlock', 'RawBlock',
'BlockQuote', 'OrderedList', 'BulletList',
'DefinitionList', 'Header', 'HorizontalRule',
'Table', 'Div', 'Null']:
return None
if key == 'RawBlock': # Remove duplicates
rawblock = RawBlock(*value)
if rawblock in rawblocks:
rawblocks.remove(rawblock)
return None
if rawblocks: # Insert blocks
el = _getel(key, value)
return [rawblocks.pop(0) for i in range(len(rawblocks))] + [el]
return None
return insert_rawblocks | python | def insert_rawblocks_factory(rawblocks):
r"""Returns insert_rawblocks(key, value, fmt, meta) action that inserts
non-duplicate RawBlock elements.
"""
# pylint: disable=unused-argument
def insert_rawblocks(key, value, fmt, meta):
"""Inserts non-duplicate RawBlock elements."""
if not rawblocks:
return None
# Put the RawBlock elements in front of the first block element that
# isn't also a RawBlock.
if not key in ['Plain', 'Para', 'CodeBlock', 'RawBlock',
'BlockQuote', 'OrderedList', 'BulletList',
'DefinitionList', 'Header', 'HorizontalRule',
'Table', 'Div', 'Null']:
return None
if key == 'RawBlock': # Remove duplicates
rawblock = RawBlock(*value)
if rawblock in rawblocks:
rawblocks.remove(rawblock)
return None
if rawblocks: # Insert blocks
el = _getel(key, value)
return [rawblocks.pop(0) for i in range(len(rawblocks))] + [el]
return None
return insert_rawblocks | [
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celiao/rtsimple | rtsimple/movies.py | Movies.search | def search(self, **kwargs):
"""Get movies that match the search query string from the API.
Args:
q (optional): plain text search query; remember to URI encode
page_limit (optional): number of search results to show per page,
default=30
page (optional): results page number, default=1
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_path('search')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | python | def search(self, **kwargs):
"""Get movies that match the search query string from the API.
Args:
q (optional): plain text search query; remember to URI encode
page_limit (optional): number of search results to show per page,
default=30
page (optional): results page number, default=1
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_path('search')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | [
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celiao/rtsimple | rtsimple/movies.py | Movies.cast | def cast(self, **kwargs):
"""Get the cast for a movie specified by id from the API.
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('cast')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | python | def cast(self, **kwargs):
"""Get the cast for a movie specified by id from the API.
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('cast')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | [
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celiao/rtsimple | rtsimple/movies.py | Movies.clips | def clips(self, **kwargs):
"""Get related clips and trailers for a movie specified by id
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Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('clips')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | python | def clips(self, **kwargs):
"""Get related clips and trailers for a movie specified by id
from the API.
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('clips')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
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kunitoki/django-custard | custard/templatetags/custard_tags.py | debug | def debug(value):
"""
Simple tag to debug output a variable;
Usage:
{% debug request %}
"""
print("%s %s: " % (type(value), value))
print(dir(value))
print('\n\n')
return '' | python | def debug(value):
"""
Simple tag to debug output a variable;
Usage:
{% debug request %}
"""
print("%s %s: " % (type(value), value))
print(dir(value))
print('\n\n')
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michal-stuglik/django-blastplus | blastplus/utils.py | get_sample_data | def get_sample_data(sample_file):
"""Read and returns sample data to fill form with default sample sequence. """
sequence_sample_in_fasta = None
with open(sample_file) as handle:
sequence_sample_in_fasta = handle.read()
return sequence_sample_in_fasta | python | def get_sample_data(sample_file):
"""Read and returns sample data to fill form with default sample sequence. """
sequence_sample_in_fasta = None
with open(sample_file) as handle:
sequence_sample_in_fasta = handle.read()
return sequence_sample_in_fasta | [
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michal-stuglik/django-blastplus | blastplus/utils.py | blast_records_to_object | def blast_records_to_object(blast_records):
"""Transforms biopython's blast record into blast object defined in django-blastplus app. """
# container for transformed objects
blast_objects_list = []
for blast_record in blast_records:
br = BlastRecord(**{'query': blast_record.query,
'version': blast_record.version,
'expect': blast_record.expect,
'application': blast_record.application,
'reference': blast_record.reference})
for alignment in blast_record.alignments:
al = Alignment(**{
'hit_def': alignment.hit_def,
'title': alignment.title,
'length': alignment.length,
})
for hsp in alignment.hsps:
h = Hsp(**{
'align_length': hsp.align_length,
'bits': hsp.bits,
'expect': hsp.expect,
'frame': hsp.frame,
'gaps': hsp.gaps,
'identities': hsp.identities,
'match': hsp.match,
'num_alignments': hsp.num_alignments,
'positives': hsp.positives,
'query': hsp.query,
'query_end': hsp.query_end,
'query_start': hsp.query_start,
'sbjct': hsp.sbjct,
'sbjct_end': hsp.sbjct_end,
'sbjct_start': hsp.sbjct_start,
'score': hsp.score,
'strand': hsp.strand,
'str': str(hsp),
})
al.hsp_list.append(h)
br.alignments.append(al)
blast_objects_list.append(br)
return blast_objects_list | python | def blast_records_to_object(blast_records):
"""Transforms biopython's blast record into blast object defined in django-blastplus app. """
# container for transformed objects
blast_objects_list = []
for blast_record in blast_records:
br = BlastRecord(**{'query': blast_record.query,
'version': blast_record.version,
'expect': blast_record.expect,
'application': blast_record.application,
'reference': blast_record.reference})
for alignment in blast_record.alignments:
al = Alignment(**{
'hit_def': alignment.hit_def,
'title': alignment.title,
'length': alignment.length,
})
for hsp in alignment.hsps:
h = Hsp(**{
'align_length': hsp.align_length,
'bits': hsp.bits,
'expect': hsp.expect,
'frame': hsp.frame,
'gaps': hsp.gaps,
'identities': hsp.identities,
'match': hsp.match,
'num_alignments': hsp.num_alignments,
'positives': hsp.positives,
'query': hsp.query,
'query_end': hsp.query_end,
'query_start': hsp.query_start,
'sbjct': hsp.sbjct,
'sbjct_end': hsp.sbjct_end,
'sbjct_start': hsp.sbjct_start,
'score': hsp.score,
'strand': hsp.strand,
'str': str(hsp),
})
al.hsp_list.append(h)
br.alignments.append(al)
blast_objects_list.append(br)
return blast_objects_list | [
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michal-stuglik/django-blastplus | blastplus/utils.py | run_blast_commands | def run_blast_commands(ncbicommandline_method, **keywords):
"""Runs blastplus/tblastn search, collects result and pass as a xml temporary file. """
# temporary files for output
blast_out_tmp = tempfile.NamedTemporaryFile(mode="w+",delete=False)
keywords['out'] = blast_out_tmp.name
# unpack query temp file object
query_file_object_tmp = keywords['query']
keywords['query'] = query_file_object_tmp.name
stderr = ''
error_string = ''
try:
# formating blastplus command
blastplusx_cline = ncbicommandline_method(**keywords)
stdout, stderr = blastplusx_cline()
except ApplicationError as e:
error_string = "Runtime error: " + stderr + "\n" + e.cmd
# remove query temp file
os.unlink(query_file_object_tmp.name)
# os.remove(query_file_object_tmp.name)
return blast_out_tmp, error_string | python | def run_blast_commands(ncbicommandline_method, **keywords):
"""Runs blastplus/tblastn search, collects result and pass as a xml temporary file. """
# temporary files for output
blast_out_tmp = tempfile.NamedTemporaryFile(mode="w+",delete=False)
keywords['out'] = blast_out_tmp.name
# unpack query temp file object
query_file_object_tmp = keywords['query']
keywords['query'] = query_file_object_tmp.name
stderr = ''
error_string = ''
try:
# formating blastplus command
blastplusx_cline = ncbicommandline_method(**keywords)
stdout, stderr = blastplusx_cline()
except ApplicationError as e:
error_string = "Runtime error: " + stderr + "\n" + e.cmd
# remove query temp file
os.unlink(query_file_object_tmp.name)
# os.remove(query_file_object_tmp.name)
return blast_out_tmp, error_string | [
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michal-stuglik/django-blastplus | blastplus/utils.py | get_annotation | def get_annotation(db_path, db_list):
""" Checks if database is set as annotated. """
annotated = False
for db in db_list:
if db["path"] == db_path:
annotated = db["annotated"]
break
return annotated | python | def get_annotation(db_path, db_list):
""" Checks if database is set as annotated. """
annotated = False
for db in db_list:
if db["path"] == db_path:
annotated = db["annotated"]
break
return annotated | [
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compbrain/dexcom_reader | dexcom_reader/util.py | find_usbserial | def find_usbserial(vendor, product):
"""Find the tty device for a given usbserial devices identifiers.
Args:
vendor: (int) something like 0x0000
product: (int) something like 0x0000
Returns:
String, like /dev/ttyACM0 or /dev/tty.usb...
"""
if platform.system() == 'Linux':
vendor, product = [('%04x' % (x)).strip() for x in (vendor, product)]
return linux_find_usbserial(vendor, product)
elif platform.system() == 'Darwin':
return osx_find_usbserial(vendor, product)
else:
raise NotImplementedError('Cannot find serial ports on %s'
% platform.system()) | python | def find_usbserial(vendor, product):
"""Find the tty device for a given usbserial devices identifiers.
Args:
vendor: (int) something like 0x0000
product: (int) something like 0x0000
Returns:
String, like /dev/ttyACM0 or /dev/tty.usb...
"""
if platform.system() == 'Linux':
vendor, product = [('%04x' % (x)).strip() for x in (vendor, product)]
return linux_find_usbserial(vendor, product)
elif platform.system() == 'Darwin':
return osx_find_usbserial(vendor, product)
else:
raise NotImplementedError('Cannot find serial ports on %s'
% platform.system()) | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_fields | def create_fields(self, base_model=models.Model, base_manager=models.Manager):
"""
This method will create a model which will hold field types defined
at runtime for each ContentType.
:param base_model: base model class to inherit from
:return:
"""
CONTENT_TYPES = self.content_types_query
class CustomContentTypeFieldManager(base_manager):
pass
@python_2_unicode_compatible
class CustomContentTypeField(base_model):
DATATYPE_CHOICES = (
(CUSTOM_TYPE_TEXT, _('text')),
(CUSTOM_TYPE_INTEGER, _('integer')),
(CUSTOM_TYPE_FLOAT, _('float')),
(CUSTOM_TYPE_TIME, _('time')),
(CUSTOM_TYPE_DATE, _('date')),
(CUSTOM_TYPE_DATETIME, _('datetime')),
(CUSTOM_TYPE_BOOLEAN, _('boolean')),
)
content_type = models.ForeignKey(ContentType,
verbose_name=_('content type'),
related_name='+',
limit_choices_to=CONTENT_TYPES)
name = models.CharField(_('name'), max_length=100, db_index=True)
label = models.CharField(_('label'), max_length=100)
data_type = models.CharField(_('data type'), max_length=8, choices=DATATYPE_CHOICES, db_index=True)
help_text = models.CharField(_('help text'), max_length=200, blank=True, null=True)
required = models.BooleanField(_('required'), default=False)
searchable = models.BooleanField(_('searchable'), default=True)
initial = models.CharField(_('initial'), max_length=200, blank=True, null=True)
min_length = models.PositiveIntegerField(_('min length'), blank=True, null=True)
max_length = models.PositiveIntegerField(_('max length'), blank=True, null=True)
min_value = models.FloatField(_('min value'), blank=True, null=True)
max_value = models.FloatField(_('max value'), blank=True, null=True)
objects = CustomContentTypeFieldManager()
class Meta:
verbose_name = _('custom field')
verbose_name_plural = _('custom fields')
abstract = True
def save(self, *args, **kwargs):
super(CustomContentTypeField, self).save(*args, **kwargs)
def clean(self):
# if field is required must issue a initial value
if self.required:
# TODO - must create values for all instances that have not
#print model.objects.values_list('pk', flat=True)
#print self.field.filter(content_type=self.content_type)
#objs = self.field.filter(content_type=self.content_type) \
# .exclude(object_id__in=model.objects.values_list('pk', flat=True))
#for obj in objs:
# print obj
pass
def _check_validate_already_defined_in_model(self):
model = self.content_type.model_class()
if self.name in [f.name for f in model._meta.fields]:
raise ValidationError({ 'name': (_('Custom field already defined as model field for content type %(model_name)s') % {'model_name': model.__name__},) })
def _check_validate_already_defined_in_custom_fields(self):
model = self.content_type.model_class()
qs = self.__class__._default_manager.filter(
content_type=self.content_type,
name=self.name,
)
if not self._state.adding and self.pk is not None:
qs = qs.exclude(pk=self.pk)
if qs.exists():
raise ValidationError({ 'name': (_('Custom field already defined for content type %(model_name)s') % {'model_name': model.__name__},) })
def __str__(self):
return "%s" % self.name
return CustomContentTypeField | python | def create_fields(self, base_model=models.Model, base_manager=models.Manager):
"""
This method will create a model which will hold field types defined
at runtime for each ContentType.
:param base_model: base model class to inherit from
:return:
"""
CONTENT_TYPES = self.content_types_query
class CustomContentTypeFieldManager(base_manager):
pass
@python_2_unicode_compatible
class CustomContentTypeField(base_model):
DATATYPE_CHOICES = (
(CUSTOM_TYPE_TEXT, _('text')),
(CUSTOM_TYPE_INTEGER, _('integer')),
(CUSTOM_TYPE_FLOAT, _('float')),
(CUSTOM_TYPE_TIME, _('time')),
(CUSTOM_TYPE_DATE, _('date')),
(CUSTOM_TYPE_DATETIME, _('datetime')),
(CUSTOM_TYPE_BOOLEAN, _('boolean')),
)
content_type = models.ForeignKey(ContentType,
verbose_name=_('content type'),
related_name='+',
limit_choices_to=CONTENT_TYPES)
name = models.CharField(_('name'), max_length=100, db_index=True)
label = models.CharField(_('label'), max_length=100)
data_type = models.CharField(_('data type'), max_length=8, choices=DATATYPE_CHOICES, db_index=True)
help_text = models.CharField(_('help text'), max_length=200, blank=True, null=True)
required = models.BooleanField(_('required'), default=False)
searchable = models.BooleanField(_('searchable'), default=True)
initial = models.CharField(_('initial'), max_length=200, blank=True, null=True)
min_length = models.PositiveIntegerField(_('min length'), blank=True, null=True)
max_length = models.PositiveIntegerField(_('max length'), blank=True, null=True)
min_value = models.FloatField(_('min value'), blank=True, null=True)
max_value = models.FloatField(_('max value'), blank=True, null=True)
objects = CustomContentTypeFieldManager()
class Meta:
verbose_name = _('custom field')
verbose_name_plural = _('custom fields')
abstract = True
def save(self, *args, **kwargs):
super(CustomContentTypeField, self).save(*args, **kwargs)
def clean(self):
# if field is required must issue a initial value
if self.required:
# TODO - must create values for all instances that have not
#print model.objects.values_list('pk', flat=True)
#print self.field.filter(content_type=self.content_type)
#objs = self.field.filter(content_type=self.content_type) \
# .exclude(object_id__in=model.objects.values_list('pk', flat=True))
#for obj in objs:
# print obj
pass
def _check_validate_already_defined_in_model(self):
model = self.content_type.model_class()
if self.name in [f.name for f in model._meta.fields]:
raise ValidationError({ 'name': (_('Custom field already defined as model field for content type %(model_name)s') % {'model_name': model.__name__},) })
def _check_validate_already_defined_in_custom_fields(self):
model = self.content_type.model_class()
qs = self.__class__._default_manager.filter(
content_type=self.content_type,
name=self.name,
)
if not self._state.adding and self.pk is not None:
qs = qs.exclude(pk=self.pk)
if qs.exists():
raise ValidationError({ 'name': (_('Custom field already defined for content type %(model_name)s') % {'model_name': model.__name__},) })
def __str__(self):
return "%s" % self.name
return CustomContentTypeField | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_values | def create_values(self, base_model=models.Model, base_manager=models.Manager):
"""
This method will create a model which will hold field values for
field types of custom_field_model.
:param base_model:
:param base_manager:
:return:
"""
_builder = self
class CustomContentTypeFieldValueManager(base_manager):
def create(self, **kwargs):
"""
Subclass create in order to be able to use "value" in kwargs
instead of using "value_%s" passing also type directly
"""
if 'value' in kwargs:
value = kwargs.pop('value')
created_object = super(CustomContentTypeFieldValueManager, self).create(**kwargs)
created_object.value = value
return created_object
else:
return super(CustomContentTypeFieldValueManager, self).create(**kwargs)
@python_2_unicode_compatible
class CustomContentTypeFieldValue(base_model):
custom_field = models.ForeignKey('.'.join(_builder.fields_model),
verbose_name=_('custom field'),
related_name='+')
content_type = models.ForeignKey(ContentType, editable=False,
verbose_name=_('content type'),
limit_choices_to=_builder.content_types_query)
object_id = models.PositiveIntegerField(_('object id'), db_index=True)
content_object = GenericForeignKey('content_type', 'object_id')
value_text = models.TextField(blank=True, null=True)
value_integer = models.IntegerField(blank=True, null=True)
value_float = models.FloatField(blank=True, null=True)
value_time = models.TimeField(blank=True, null=True)
value_date = models.DateField(blank=True, null=True)
value_datetime = models.DateTimeField(blank=True, null=True)
value_boolean = models.NullBooleanField(blank=True)
objects = CustomContentTypeFieldValueManager()
def _get_value(self):
return getattr(self, 'value_%s' % self.custom_field.data_type)
def _set_value(self, new_value):
setattr(self, 'value_%s' % self.custom_field.data_type, new_value)
value = property(_get_value, _set_value)
class Meta:
unique_together = ('custom_field', 'content_type', 'object_id')
verbose_name = _('custom field value')
verbose_name_plural = _('custom field values')
abstract = True
def save(self, *args, **kwargs):
# save content type as user shouldn't be able to change it
self.content_type = self.custom_field.content_type
super(CustomContentTypeFieldValue, self).save(*args, **kwargs)
def validate_unique(self, exclude=None):
qs = self.__class__._default_manager.filter(
custom_field=self.custom_field,
content_type=self.custom_field.content_type,
object_id=self.object_id,
)
if not self._state.adding and self.pk is not None:
qs = qs.exclude(pk=self.pk)
if qs.exists():
raise ValidationError({ NON_FIELD_ERRORS: (_('A value for this custom field already exists'),) })
def __str__(self):
return "%s: %s" % (self.custom_field.name, self.value)
return CustomContentTypeFieldValue | python | def create_values(self, base_model=models.Model, base_manager=models.Manager):
"""
This method will create a model which will hold field values for
field types of custom_field_model.
:param base_model:
:param base_manager:
:return:
"""
_builder = self
class CustomContentTypeFieldValueManager(base_manager):
def create(self, **kwargs):
"""
Subclass create in order to be able to use "value" in kwargs
instead of using "value_%s" passing also type directly
"""
if 'value' in kwargs:
value = kwargs.pop('value')
created_object = super(CustomContentTypeFieldValueManager, self).create(**kwargs)
created_object.value = value
return created_object
else:
return super(CustomContentTypeFieldValueManager, self).create(**kwargs)
@python_2_unicode_compatible
class CustomContentTypeFieldValue(base_model):
custom_field = models.ForeignKey('.'.join(_builder.fields_model),
verbose_name=_('custom field'),
related_name='+')
content_type = models.ForeignKey(ContentType, editable=False,
verbose_name=_('content type'),
limit_choices_to=_builder.content_types_query)
object_id = models.PositiveIntegerField(_('object id'), db_index=True)
content_object = GenericForeignKey('content_type', 'object_id')
value_text = models.TextField(blank=True, null=True)
value_integer = models.IntegerField(blank=True, null=True)
value_float = models.FloatField(blank=True, null=True)
value_time = models.TimeField(blank=True, null=True)
value_date = models.DateField(blank=True, null=True)
value_datetime = models.DateTimeField(blank=True, null=True)
value_boolean = models.NullBooleanField(blank=True)
objects = CustomContentTypeFieldValueManager()
def _get_value(self):
return getattr(self, 'value_%s' % self.custom_field.data_type)
def _set_value(self, new_value):
setattr(self, 'value_%s' % self.custom_field.data_type, new_value)
value = property(_get_value, _set_value)
class Meta:
unique_together = ('custom_field', 'content_type', 'object_id')
verbose_name = _('custom field value')
verbose_name_plural = _('custom field values')
abstract = True
def save(self, *args, **kwargs):
# save content type as user shouldn't be able to change it
self.content_type = self.custom_field.content_type
super(CustomContentTypeFieldValue, self).save(*args, **kwargs)
def validate_unique(self, exclude=None):
qs = self.__class__._default_manager.filter(
custom_field=self.custom_field,
content_type=self.custom_field.content_type,
object_id=self.object_id,
)
if not self._state.adding and self.pk is not None:
qs = qs.exclude(pk=self.pk)
if qs.exists():
raise ValidationError({ NON_FIELD_ERRORS: (_('A value for this custom field already exists'),) })
def __str__(self):
return "%s: %s" % (self.custom_field.name, self.value)
return CustomContentTypeFieldValue | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_manager | def create_manager(self, base_manager=models.Manager):
"""
This will create the custom Manager that will use the fields_model and values_model
respectively.
:param base_manager: the base manager class to inherit from
:return:
"""
_builder = self
class CustomManager(base_manager):
def search(self, search_data, custom_args={}):
"""
Search inside the custom fields for this model for any match
of search_data and returns existing model instances
:param search_data:
:param custom_args:
:return:
"""
query = None
lookups = (
'%s__%s' % ('value_text', 'icontains'),
)
content_type = ContentType.objects.get_for_model(self.model)
custom_args = dict({ 'content_type': content_type, 'searchable': True }, **custom_args)
custom_fields = dict((f.name, f) for f in _builder.fields_model_class.objects.filter(**custom_args))
for value_lookup in lookups:
for key, f in custom_fields.items():
found = _builder.values_model_class.objects.filter(**{ 'custom_field': f,
'content_type': content_type,
value_lookup: search_data })
if found.count() > 0:
if query is None:
query = Q()
query = query & Q(**{ str('%s__in' % self.model._meta.pk.name):
[obj.object_id for obj in found] })
if query is None:
return self.get_queryset().none()
return self.get_queryset().filter(query)
return CustomManager | python | def create_manager(self, base_manager=models.Manager):
"""
This will create the custom Manager that will use the fields_model and values_model
respectively.
:param base_manager: the base manager class to inherit from
:return:
"""
_builder = self
class CustomManager(base_manager):
def search(self, search_data, custom_args={}):
"""
Search inside the custom fields for this model for any match
of search_data and returns existing model instances
:param search_data:
:param custom_args:
:return:
"""
query = None
lookups = (
'%s__%s' % ('value_text', 'icontains'),
)
content_type = ContentType.objects.get_for_model(self.model)
custom_args = dict({ 'content_type': content_type, 'searchable': True }, **custom_args)
custom_fields = dict((f.name, f) for f in _builder.fields_model_class.objects.filter(**custom_args))
for value_lookup in lookups:
for key, f in custom_fields.items():
found = _builder.values_model_class.objects.filter(**{ 'custom_field': f,
'content_type': content_type,
value_lookup: search_data })
if found.count() > 0:
if query is None:
query = Q()
query = query & Q(**{ str('%s__in' % self.model._meta.pk.name):
[obj.object_id for obj in found] })
if query is None:
return self.get_queryset().none()
return self.get_queryset().filter(query)
return CustomManager | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_mixin | def create_mixin(self):
"""
This will create the custom Model Mixin to attach to your custom field
enabled model.
:return:
"""
_builder = self
class CustomModelMixin(object):
@cached_property
def _content_type(self):
return ContentType.objects.get_for_model(self)
@classmethod
def get_model_custom_fields(cls):
""" Return a list of custom fields for this model, callable at model level """
return _builder.fields_model_class.objects.filter(content_type=ContentType.objects.get_for_model(cls))
def get_custom_fields(self):
""" Return a list of custom fields for this model """
return _builder.fields_model_class.objects.filter(content_type=self._content_type)
def get_custom_value(self, field):
""" Get a value for a specified custom field """
return _builder.values_model_class.objects.get(custom_field=field,
content_type=self._content_type,
object_id=self.pk)
def set_custom_value(self, field, value):
""" Set a value for a specified custom field """
custom_value, created = \
_builder.values_model_class.objects.get_or_create(custom_field=field,
content_type=self._content_type,
object_id=self.pk)
custom_value.value = value
custom_value.full_clean()
custom_value.save()
return custom_value
#def __getattr__(self, name):
# """ Get a value for a specified custom field """
# try:
# obj = _builder.values_model_class.objects.get(custom_field__name=name,
# content_type=self._content_type,
# object_id=self.pk)
# return obj.value
# except ObjectDoesNotExist:
# pass
# return super(CustomModelMixin, self).__getattr__(name)
return CustomModelMixin | python | def create_mixin(self):
"""
This will create the custom Model Mixin to attach to your custom field
enabled model.
:return:
"""
_builder = self
class CustomModelMixin(object):
@cached_property
def _content_type(self):
return ContentType.objects.get_for_model(self)
@classmethod
def get_model_custom_fields(cls):
""" Return a list of custom fields for this model, callable at model level """
return _builder.fields_model_class.objects.filter(content_type=ContentType.objects.get_for_model(cls))
def get_custom_fields(self):
""" Return a list of custom fields for this model """
return _builder.fields_model_class.objects.filter(content_type=self._content_type)
def get_custom_value(self, field):
""" Get a value for a specified custom field """
return _builder.values_model_class.objects.get(custom_field=field,
content_type=self._content_type,
object_id=self.pk)
def set_custom_value(self, field, value):
""" Set a value for a specified custom field """
custom_value, created = \
_builder.values_model_class.objects.get_or_create(custom_field=field,
content_type=self._content_type,
object_id=self.pk)
custom_value.value = value
custom_value.full_clean()
custom_value.save()
return custom_value
#def __getattr__(self, name):
# """ Get a value for a specified custom field """
# try:
# obj = _builder.values_model_class.objects.get(custom_field__name=name,
# content_type=self._content_type,
# object_id=self.pk)
# return obj.value
# except ObjectDoesNotExist:
# pass
# return super(CustomModelMixin, self).__getattr__(name)
return CustomModelMixin | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_modelform | def create_modelform(self, base_form=forms.ModelForm,
field_types=settings.CUSTOM_FIELD_TYPES,
widget_types=settings.CUSTOM_WIDGET_TYPES):
"""
This creates the class that implements a ModelForm that knows about
the custom fields
:param base_form:
:param field_types:
:param widget_types:
:return:
"""
_builder = self
class CustomFieldModelBaseForm(base_form):
def __init__(self, *args, **kwargs):
"""
Constructor
"""
# additional form variables
self.custom_classes = None
self.is_custom_form = True
self.instance = None
# construct the form
super(CustomFieldModelBaseForm, self).__init__(*args, **kwargs)
# init custom fields from model in the form
self.init_custom_fields()
def clean(self):
"""
Clean the form
"""
cleaned_data = super(CustomFieldModelBaseForm, self).clean()
return cleaned_data
def save(self, commit=True):
"""
Save the form
"""
self.instance = super(CustomFieldModelBaseForm, self).save(commit=commit)
if self.instance and commit:
self.instance.save()
self.save_custom_fields()
return self.instance
def init_custom_fields(self):
"""
Populate the ``form.fields[]`` with the additional fields coming from
the custom fields models.
"""
content_type = self.get_content_type()
fields = self.get_fields_for_content_type(content_type)
for f in fields:
name = str(f.name)
initial = f.initial
self.fields[name] = self.get_formfield_for_field(f)
self.fields[name].is_custom = True
self.fields[name].label = f.label
self.fields[name].required = f.required
self.fields[name].widget = self.get_widget_for_field(f)
if self.instance and self.instance.pk:
value = self.search_value_for_field(f,
content_type,
self.instance.pk)
if len(value) > 0:
initial = value[0].value
self.fields[name].initial = self.initial[name] = initial
def save_custom_fields(self):
""" Perform save and validation over the custom fields """
if not self.instance.pk:
raise Exception("The model instance has not been saved. Have you called instance.save() ?")
content_type = self.get_content_type()
fields = self.get_fields_for_content_type(content_type)
for f in fields:
name = str(f.name)
fv = self.search_value_for_field(f,
content_type,
self.instance.pk)
if len(fv) > 0:
value = fv[0]
value.value = self.cleaned_data[name]
else:
value = self.create_value_for_field(f,
self.instance.pk,
self.cleaned_data[name])
value.save()
def get_model(self):
"""
Returns the actual model this ``ModelForm`` is referring to
"""
return self._meta.model
def get_content_type(self):
"""
Returns the content type instance of the model this ``ModelForm`` is
referring to
"""
return ContentType.objects.get_for_model(self.get_model())
def get_formfield_for_field(self, field):
"""
Returns the defined formfield instance built from the type of the field
:param field: custom field instance
:return: the formfield instance
"""
field_attrs = {
'label': field.label,
'help_text': field.help_text,
'required': field.required,
}
if field.data_type == CUSTOM_TYPE_TEXT:
#widget_attrs = {}
if field.min_length:
field_attrs['min_length'] = field.min_length
if field.max_length:
field_attrs['max_length'] = field.max_length
# widget_attrs['maxlength'] = field.max_length
#field_attrs['widget'] = widgets.AdminTextInputWidget(attrs=widget_attrs)
elif field.data_type == CUSTOM_TYPE_INTEGER:
if field.min_value: field_attrs['min_value'] = int(float(field.min_value))
if field.max_value: field_attrs['max_value'] = int(float(field.max_value))
#field_attrs['widget'] = spinner.IntegerSpinnerWidget(attrs=field_attrs)
elif field.data_type == CUSTOM_TYPE_FLOAT:
if field.min_value: field_attrs['min_value'] = float(field.min_value)
if field.max_value: field_attrs['max_value'] = float(field.max_value)
#field_attrs['widget'] = spinner.SpinnerWidget(attrs=field_attrs)
elif field.data_type == CUSTOM_TYPE_TIME:
#field_attrs['widget'] = date.TimePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_DATE:
#field_attrs['widget'] = date.DatePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_DATETIME:
#field_attrs['widget'] = date.DateTimePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_BOOLEAN:
pass
field_type = import_class(field_types[field.data_type])
return field_type(**field_attrs)
def get_widget_for_field(self, field, attrs={}):
"""
Returns the defined widget type instance built from the type of the field
:param field: custom field instance
:param attrs: attributes of widgets
:return: the widget instance
"""
return import_class(widget_types[field.data_type])(**attrs)
def get_fields_for_content_type(self, content_type):
"""
Returns all fields for a given content type
Example implementation:
return MyCustomField.objects.filter(content_type=content_type)
:param content_type: content type to search
:return: the custom field instances
"""
return _builder.fields_model_class.objects.filter(content_type=content_type)
def search_value_for_field(self, field, content_type, object_id):
"""
This function will return the CustomFieldValue instance for a given
field of an object that has the given content_type
Example implementation:
return MyCustomFieldValue.objects.filter(custom_field=field,
content_type=content_type,
object_id=object_id)
:param field: the custom field instance
:param content_type: the content type instance
:param object_id: the object id this value is referring to
:return: CustomFieldValue queryset
"""
return _builder.values_model_class.objects.filter(custom_field=field,
content_type=content_type,
object_id=object_id)
def create_value_for_field(self, field, object_id, value):
"""
Create a value for a given field of an object
Example implementation:
return MyCustomFieldValue(custom_field=field,
object_id=object_id,
value=value)
:param field: the custom field instance
:param object_id: the object id this value is referring to
:param value: the value to set
:return: the value instance (not saved!)
"""
return _builder.values_model_class(custom_field=field,
object_id=object_id,
value=value)
return CustomFieldModelBaseForm | python | def create_modelform(self, base_form=forms.ModelForm,
field_types=settings.CUSTOM_FIELD_TYPES,
widget_types=settings.CUSTOM_WIDGET_TYPES):
"""
This creates the class that implements a ModelForm that knows about
the custom fields
:param base_form:
:param field_types:
:param widget_types:
:return:
"""
_builder = self
class CustomFieldModelBaseForm(base_form):
def __init__(self, *args, **kwargs):
"""
Constructor
"""
# additional form variables
self.custom_classes = None
self.is_custom_form = True
self.instance = None
# construct the form
super(CustomFieldModelBaseForm, self).__init__(*args, **kwargs)
# init custom fields from model in the form
self.init_custom_fields()
def clean(self):
"""
Clean the form
"""
cleaned_data = super(CustomFieldModelBaseForm, self).clean()
return cleaned_data
def save(self, commit=True):
"""
Save the form
"""
self.instance = super(CustomFieldModelBaseForm, self).save(commit=commit)
if self.instance and commit:
self.instance.save()
self.save_custom_fields()
return self.instance
def init_custom_fields(self):
"""
Populate the ``form.fields[]`` with the additional fields coming from
the custom fields models.
"""
content_type = self.get_content_type()
fields = self.get_fields_for_content_type(content_type)
for f in fields:
name = str(f.name)
initial = f.initial
self.fields[name] = self.get_formfield_for_field(f)
self.fields[name].is_custom = True
self.fields[name].label = f.label
self.fields[name].required = f.required
self.fields[name].widget = self.get_widget_for_field(f)
if self.instance and self.instance.pk:
value = self.search_value_for_field(f,
content_type,
self.instance.pk)
if len(value) > 0:
initial = value[0].value
self.fields[name].initial = self.initial[name] = initial
def save_custom_fields(self):
""" Perform save and validation over the custom fields """
if not self.instance.pk:
raise Exception("The model instance has not been saved. Have you called instance.save() ?")
content_type = self.get_content_type()
fields = self.get_fields_for_content_type(content_type)
for f in fields:
name = str(f.name)
fv = self.search_value_for_field(f,
content_type,
self.instance.pk)
if len(fv) > 0:
value = fv[0]
value.value = self.cleaned_data[name]
else:
value = self.create_value_for_field(f,
self.instance.pk,
self.cleaned_data[name])
value.save()
def get_model(self):
"""
Returns the actual model this ``ModelForm`` is referring to
"""
return self._meta.model
def get_content_type(self):
"""
Returns the content type instance of the model this ``ModelForm`` is
referring to
"""
return ContentType.objects.get_for_model(self.get_model())
def get_formfield_for_field(self, field):
"""
Returns the defined formfield instance built from the type of the field
:param field: custom field instance
:return: the formfield instance
"""
field_attrs = {
'label': field.label,
'help_text': field.help_text,
'required': field.required,
}
if field.data_type == CUSTOM_TYPE_TEXT:
#widget_attrs = {}
if field.min_length:
field_attrs['min_length'] = field.min_length
if field.max_length:
field_attrs['max_length'] = field.max_length
# widget_attrs['maxlength'] = field.max_length
#field_attrs['widget'] = widgets.AdminTextInputWidget(attrs=widget_attrs)
elif field.data_type == CUSTOM_TYPE_INTEGER:
if field.min_value: field_attrs['min_value'] = int(float(field.min_value))
if field.max_value: field_attrs['max_value'] = int(float(field.max_value))
#field_attrs['widget'] = spinner.IntegerSpinnerWidget(attrs=field_attrs)
elif field.data_type == CUSTOM_TYPE_FLOAT:
if field.min_value: field_attrs['min_value'] = float(field.min_value)
if field.max_value: field_attrs['max_value'] = float(field.max_value)
#field_attrs['widget'] = spinner.SpinnerWidget(attrs=field_attrs)
elif field.data_type == CUSTOM_TYPE_TIME:
#field_attrs['widget'] = date.TimePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_DATE:
#field_attrs['widget'] = date.DatePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_DATETIME:
#field_attrs['widget'] = date.DateTimePickerWidget()
pass
elif field.data_type == CUSTOM_TYPE_BOOLEAN:
pass
field_type = import_class(field_types[field.data_type])
return field_type(**field_attrs)
def get_widget_for_field(self, field, attrs={}):
"""
Returns the defined widget type instance built from the type of the field
:param field: custom field instance
:param attrs: attributes of widgets
:return: the widget instance
"""
return import_class(widget_types[field.data_type])(**attrs)
def get_fields_for_content_type(self, content_type):
"""
Returns all fields for a given content type
Example implementation:
return MyCustomField.objects.filter(content_type=content_type)
:param content_type: content type to search
:return: the custom field instances
"""
return _builder.fields_model_class.objects.filter(content_type=content_type)
def search_value_for_field(self, field, content_type, object_id):
"""
This function will return the CustomFieldValue instance for a given
field of an object that has the given content_type
Example implementation:
return MyCustomFieldValue.objects.filter(custom_field=field,
content_type=content_type,
object_id=object_id)
:param field: the custom field instance
:param content_type: the content type instance
:param object_id: the object id this value is referring to
:return: CustomFieldValue queryset
"""
return _builder.values_model_class.objects.filter(custom_field=field,
content_type=content_type,
object_id=object_id)
def create_value_for_field(self, field, object_id, value):
"""
Create a value for a given field of an object
Example implementation:
return MyCustomFieldValue(custom_field=field,
object_id=object_id,
value=value)
:param field: the custom field instance
:param object_id: the object id this value is referring to
:param value: the value to set
:return: the value instance (not saved!)
"""
return _builder.values_model_class(custom_field=field,
object_id=object_id,
value=value)
return CustomFieldModelBaseForm | [
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kunitoki/django-custard | custard/builder.py | CustomFieldsBuilder.create_modeladmin | def create_modeladmin(self, base_admin=admin.ModelAdmin):
"""
This creates the class that implements a ModelForm that knows about
the custom fields
:param base_admin:
:return:
"""
_builder = self
class CustomFieldModelBaseAdmin(base_admin):
def __init__(self, *args, **kwargs):
super(CustomFieldModelBaseAdmin, self).__init__(*args, **kwargs)
def save_model(self, request, obj, form, change):
obj.save()
if hasattr(form, 'save_custom_fields'):
form.save_custom_fields()
return CustomFieldModelBaseAdmin | python | def create_modeladmin(self, base_admin=admin.ModelAdmin):
"""
This creates the class that implements a ModelForm that knows about
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:param base_admin:
:return:
"""
_builder = self
class CustomFieldModelBaseAdmin(base_admin):
def __init__(self, *args, **kwargs):
super(CustomFieldModelBaseAdmin, self).__init__(*args, **kwargs)
def save_model(self, request, obj, form, change):
obj.save()
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form.save_custom_fields()
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pudo/banal | banal/cache.py | bytes_iter | def bytes_iter(obj):
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elif isinstance(obj, six.string_types):
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yield out
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try:
obj = sorted(obj)
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for item in obj:
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pudo/banal | banal/cache.py | hash_data | def hash_data(obj):
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collect = sha1()
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text = text.encode('utf-8')
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"""Generate a SHA1 from a complex object."""
collect = sha1()
for text in bytes_iter(obj):
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text = text.encode('utf-8')
collect.update(text)
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ProgVal/markovgen | markovgen/markovgen.py | Markov.triples | def triples(self, words):
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"""
if len(words) < 3:
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"""
if len(words) < 3:
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schettino72/import-deps | import_deps/__init__.py | ast_imports | def ast_imports(file_path):
"""get list of import from python module
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"""
with open(file_path, 'r') as fp:
text = fp.read()
mod_ast = ast.parse(text, file_path)
finder = _ImportsFinder()
finder.visit(mod_ast)
return finder.imports | python | def ast_imports(file_path):
"""get list of import from python module
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"""
with open(file_path, 'r') as fp:
text = fp.read()
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finder.visit(mod_ast)
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schettino72/import-deps | import_deps/__init__.py | _ImportsFinder.visit_Import | def visit_Import(self, node):
"""callback for 'import' statement"""
self.imports.extend((None, n.name, n.asname, None)
for n in node.names)
ast.NodeVisitor.generic_visit(self, node) | python | def visit_Import(self, node):
"""callback for 'import' statement"""
self.imports.extend((None, n.name, n.asname, None)
for n in node.names)
ast.NodeVisitor.generic_visit(self, node) | [
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schettino72/import-deps | import_deps/__init__.py | _ImportsFinder.visit_ImportFrom | def visit_ImportFrom(self, node):
"""callback for 'import from' statement"""
self.imports.extend((node.module, n.name, n.asname, node.level)
for n in node.names)
ast.NodeVisitor.generic_visit(self, node) | python | def visit_ImportFrom(self, node):
"""callback for 'import from' statement"""
self.imports.extend((node.module, n.name, n.asname, node.level)
for n in node.names)
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schettino72/import-deps | import_deps/__init__.py | PyModule._get_fqn | def _get_fqn(cls, path):
"""get full qualified name as list of strings
:return: (list - str) of path segments from top package to given path
"""
name_list = [path.stem]
current_path = path
# move to parent path until parent path is a python package
while cls.is_pkg(current_path.parent):
name_list.append(current_path.parent.name)
current_path = current_path.parent
return list(reversed(name_list)) | python | def _get_fqn(cls, path):
"""get full qualified name as list of strings
:return: (list - str) of path segments from top package to given path
"""
name_list = [path.stem]
current_path = path
# move to parent path until parent path is a python package
while cls.is_pkg(current_path.parent):
name_list.append(current_path.parent.name)
current_path = current_path.parent
return list(reversed(name_list)) | [
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schettino72/import-deps | import_deps/__init__.py | ModuleSet._get_imported_module | def _get_imported_module(self, module_name):
"""try to get imported module reference by its name"""
# if imported module on module_set add to list
imp_mod = self.by_name.get(module_name)
if imp_mod:
return imp_mod
# last part of import section might not be a module
# remove last section
no_obj = module_name.rsplit('.', 1)[0]
imp_mod2 = self.by_name.get(no_obj)
if imp_mod2:
return imp_mod2
# special case for __init__
if module_name in self.pkgs:
pkg_name = module_name + ".__init__"
return self.by_name[pkg_name]
if no_obj in self.pkgs:
pkg_name = no_obj + ".__init__"
return self.by_name[pkg_name] | python | def _get_imported_module(self, module_name):
"""try to get imported module reference by its name"""
# if imported module on module_set add to list
imp_mod = self.by_name.get(module_name)
if imp_mod:
return imp_mod
# last part of import section might not be a module
# remove last section
no_obj = module_name.rsplit('.', 1)[0]
imp_mod2 = self.by_name.get(no_obj)
if imp_mod2:
return imp_mod2
# special case for __init__
if module_name in self.pkgs:
pkg_name = module_name + ".__init__"
return self.by_name[pkg_name]
if no_obj in self.pkgs:
pkg_name = no_obj + ".__init__"
return self.by_name[pkg_name] | [
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schettino72/import-deps | import_deps/__init__.py | ModuleSet.get_imports | def get_imports(self, module, return_fqn=False):
"""return set of imported modules that are in self
:param module: PyModule
:return: (set - str) of path names
"""
# print('####', module.fqn)
# print(self.by_name.keys(), '\n\n')
imports = set()
raw_imports = ast_imports(module.path)
for import_entry in raw_imports:
# join 'from' and 'import' part of import statement
full = ".".join(s for s in import_entry[:2] if s)
import_level = import_entry[3]
if import_level:
# intra package imports
intra = '.'.join(module.fqn[:-import_level] + [full])
imported = self._get_imported_module(intra)
else:
imported = self._get_imported_module(full)
if imported:
if return_fqn:
imports.add('.'.join(imported.fqn))
else:
imports.add(imported.path)
return imports | python | def get_imports(self, module, return_fqn=False):
"""return set of imported modules that are in self
:param module: PyModule
:return: (set - str) of path names
"""
# print('####', module.fqn)
# print(self.by_name.keys(), '\n\n')
imports = set()
raw_imports = ast_imports(module.path)
for import_entry in raw_imports:
# join 'from' and 'import' part of import statement
full = ".".join(s for s in import_entry[:2] if s)
import_level = import_entry[3]
if import_level:
# intra package imports
intra = '.'.join(module.fqn[:-import_level] + [full])
imported = self._get_imported_module(intra)
else:
imported = self._get_imported_module(full)
if imported:
if return_fqn:
imports.add('.'.join(imported.fqn))
else:
imports.add(imported.path)
return imports | [
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rapidpro/dash | dash/orgs/models.py | Invitation.generate_random_string | def generate_random_string(cls, length):
"""
Generatesa a [length] characters alpha numeric secret
"""
# avoid things that could be mistaken ex: 'I' and '1'
letters = "23456789ABCDEFGHJKLMNPQRSTUVWXYZ"
return "".join([random.choice(letters) for _ in range(length)]) | python | def generate_random_string(cls, length):
"""
Generatesa a [length] characters alpha numeric secret
"""
# avoid things that could be mistaken ex: 'I' and '1'
letters = "23456789ABCDEFGHJKLMNPQRSTUVWXYZ"
return "".join([random.choice(letters) for _ in range(length)]) | [
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HDI-Project/RDT | examples/airbnb.py | run_airbnb_demo | def run_airbnb_demo(data_dir):
"""HyperTransfomer will transform back and forth data airbnb data."""
# Setup
meta_file = os.path.join(data_dir, 'Airbnb_demo_meta.json')
transformer_list = ['NumberTransformer', 'DTTransformer', 'CatTransformer']
ht = HyperTransformer(meta_file)
# Run
transformed = ht.fit_transform(transformer_list=transformer_list)
result = ht.reverse_transform(tables=transformed)
# Check
assert result.keys() == ht.table_dict.keys()
for name, table in result.items():
assert not result[name].isnull().all().all() | python | def run_airbnb_demo(data_dir):
"""HyperTransfomer will transform back and forth data airbnb data."""
# Setup
meta_file = os.path.join(data_dir, 'Airbnb_demo_meta.json')
transformer_list = ['NumberTransformer', 'DTTransformer', 'CatTransformer']
ht = HyperTransformer(meta_file)
# Run
transformed = ht.fit_transform(transformer_list=transformer_list)
result = ht.reverse_transform(tables=transformed)
# Check
assert result.keys() == ht.table_dict.keys()
for name, table in result.items():
assert not result[name].isnull().all().all() | [
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noxdafox/vminspect | vminspect/winevtx.py | WinEventLog.eventlog | def eventlog(self, path):
"""Iterates over the Events contained within the log at the given path.
For each Event, yields a XML string.
"""
self.logger.debug("Parsing Event log file %s.", path)
with NamedTemporaryFile(buffering=0) as tempfile:
self._filesystem.download(path, tempfile.name)
file_header = FileHeader(tempfile.read(), 0)
for xml_string, _ in evtx_file_xml_view(file_header):
yield xml_string | python | def eventlog(self, path):
"""Iterates over the Events contained within the log at the given path.
For each Event, yields a XML string.
"""
self.logger.debug("Parsing Event log file %s.", path)
with NamedTemporaryFile(buffering=0) as tempfile:
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file_header = FileHeader(tempfile.read(), 0)
for xml_string, _ in evtx_file_xml_view(file_header):
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raphaelgyory/django-rest-messaging-centrifugo | rest_messaging_centrifugo/signals.py | publish_message_to_centrifugo | def publish_message_to_centrifugo(sender, instance, created, **kwargs):
""" Publishes each saved message to Centrifugo. """
if created is True:
client = Client("{0}api/".format(getattr(settings, "CENTRIFUGE_ADDRESS")), getattr(settings, "CENTRIFUGE_SECRET"))
# we ensure the client is still in the thread (he may have left or have been removed)
active_participants = [participation.participant.id for participation in Participation.objects.filter(thread=instance.thread, date_left__isnull=True).select_related('participant')]
client.publish(
build_channel(settings.CENTRIFUGO_MESSAGE_NAMESPACE, instance.thread.id, active_participants),
{
"id": instance.id,
"body": instance.body,
"sender": instance.sender.id,
"thread": instance.thread.id,
"sent_at": str(instance.sent_at),
"is_notification": True, # ATTENTION: check against sender too to be sure to not notify him his message
}
) | python | def publish_message_to_centrifugo(sender, instance, created, **kwargs):
""" Publishes each saved message to Centrifugo. """
if created is True:
client = Client("{0}api/".format(getattr(settings, "CENTRIFUGE_ADDRESS")), getattr(settings, "CENTRIFUGE_SECRET"))
# we ensure the client is still in the thread (he may have left or have been removed)
active_participants = [participation.participant.id for participation in Participation.objects.filter(thread=instance.thread, date_left__isnull=True).select_related('participant')]
client.publish(
build_channel(settings.CENTRIFUGO_MESSAGE_NAMESPACE, instance.thread.id, active_participants),
{
"id": instance.id,
"body": instance.body,
"sender": instance.sender.id,
"thread": instance.thread.id,
"sent_at": str(instance.sent_at),
"is_notification": True, # ATTENTION: check against sender too to be sure to not notify him his message
}
) | [
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raphaelgyory/django-rest-messaging-centrifugo | rest_messaging_centrifugo/signals.py | publish_participation_to_thread | def publish_participation_to_thread(sender, instance, created, **kwargs):
""" Warns users everytime a thread including them is published. This is done via channel subscription. """
if kwargs.get('created_and_add_participants') is True:
request_participant_id = kwargs.get('request_participant_id')
if request_participant_id is not None:
client = Client("{0}api/".format(getattr(settings, "CENTRIFUGE_ADDRESS")), getattr(settings, "CENTRIFUGE_SECRET"))
active_participants = [participation.participant for participation in Participation.objects.filter(thread=instance, date_left__isnull=True).select_related('participant')]
for participant in active_participants:
client.publish(
build_channel(settings.CENTRIFUGO_THREAD_NAMESPACE, participant.id, [participant.id]),
{
"message_channel_to_connect_to": build_channel(settings.CENTRIFUGO_MESSAGE_NAMESPACE, instance.id, [p.id for p in active_participants])
}
) | python | def publish_participation_to_thread(sender, instance, created, **kwargs):
""" Warns users everytime a thread including them is published. This is done via channel subscription. """
if kwargs.get('created_and_add_participants') is True:
request_participant_id = kwargs.get('request_participant_id')
if request_participant_id is not None:
client = Client("{0}api/".format(getattr(settings, "CENTRIFUGE_ADDRESS")), getattr(settings, "CENTRIFUGE_SECRET"))
active_participants = [participation.participant for participation in Participation.objects.filter(thread=instance, date_left__isnull=True).select_related('participant')]
for participant in active_participants:
client.publish(
build_channel(settings.CENTRIFUGO_THREAD_NAMESPACE, participant.id, [participant.id]),
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"message_channel_to_connect_to": build_channel(settings.CENTRIFUGO_MESSAGE_NAMESPACE, instance.id, [p.id for p in active_participants])
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.getFileAndName | def getFileAndName(self, *args, **kwargs):
'''
Give a requested page (note: the arguments for this call are forwarded to getpage()),
return the content at the target URL and the filename for the target content as a
2-tuple (pgctnt, hName) for the content at the target URL.
The filename specified in the content-disposition header is used, if present. Otherwise,
the last section of the url path segment is treated as the filename.
'''
pgctnt, hName, mime = self.getFileNameMime(*args, **kwargs)
return pgctnt, hName | python | def getFileAndName(self, *args, **kwargs):
'''
Give a requested page (note: the arguments for this call are forwarded to getpage()),
return the content at the target URL and the filename for the target content as a
2-tuple (pgctnt, hName) for the content at the target URL.
The filename specified in the content-disposition header is used, if present. Otherwise,
the last section of the url path segment is treated as the filename.
'''
pgctnt, hName, mime = self.getFileNameMime(*args, **kwargs)
return pgctnt, hName | [
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.getFileNameMime | def getFileNameMime(self, requestedUrl, *args, **kwargs):
'''
Give a requested page (note: the arguments for this call are forwarded to getpage()),
return the content at the target URL, the filename for the target content, and
the mimetype for the content at the target URL, as a 3-tuple (pgctnt, hName, mime).
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'''
if 'returnMultiple' in kwargs:
raise Exceptions.ArgumentError("getFileAndName cannot be called with 'returnMultiple'", requestedUrl)
if 'soup' in kwargs and kwargs['soup']:
raise Exceptions.ArgumentError("getFileAndName contradicts the 'soup' directive!", requestedUrl)
kwargs["returnMultiple"] = True
pgctnt, pghandle = self.getpage(requestedUrl, *args, **kwargs)
info = pghandle.info()
if not 'Content-Disposition' in info:
hName = ''
elif not 'filename=' in info['Content-Disposition']:
hName = ''
else:
hName = info['Content-Disposition'].split('filename=')[1]
# Unquote filename if it's quoted.
if ((hName.startswith("'") and hName.endswith("'")) or hName.startswith('"') and hName.endswith('"')) and len(hName) >= 2:
hName = hName[1:-1]
mime = info.get_content_type()
if not hName.strip():
requestedUrl = pghandle.geturl()
hName = urllib.parse.urlsplit(requestedUrl).path.split("/")[-1].strip()
if "/" in hName:
hName = hName.split("/")[-1]
return pgctnt, hName, mime | python | def getFileNameMime(self, requestedUrl, *args, **kwargs):
'''
Give a requested page (note: the arguments for this call are forwarded to getpage()),
return the content at the target URL, the filename for the target content, and
the mimetype for the content at the target URL, as a 3-tuple (pgctnt, hName, mime).
The filename specified in the content-disposition header is used, if present. Otherwise,
the last section of the url path segment is treated as the filename.
'''
if 'returnMultiple' in kwargs:
raise Exceptions.ArgumentError("getFileAndName cannot be called with 'returnMultiple'", requestedUrl)
if 'soup' in kwargs and kwargs['soup']:
raise Exceptions.ArgumentError("getFileAndName contradicts the 'soup' directive!", requestedUrl)
kwargs["returnMultiple"] = True
pgctnt, pghandle = self.getpage(requestedUrl, *args, **kwargs)
info = pghandle.info()
if not 'Content-Disposition' in info:
hName = ''
elif not 'filename=' in info['Content-Disposition']:
hName = ''
else:
hName = info['Content-Disposition'].split('filename=')[1]
# Unquote filename if it's quoted.
if ((hName.startswith("'") and hName.endswith("'")) or hName.startswith('"') and hName.endswith('"')) and len(hName) >= 2:
hName = hName[1:-1]
mime = info.get_content_type()
if not hName.strip():
requestedUrl = pghandle.geturl()
hName = urllib.parse.urlsplit(requestedUrl).path.split("/")[-1].strip()
if "/" in hName:
hName = hName.split("/")[-1]
return pgctnt, hName, mime | [
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.__check_suc_cookie | def __check_suc_cookie(self, components):
'''
This is only called if we're on a known sucuri-"protected" site.
As such, if we do *not* have a sucuri cloudproxy cookie, we can assume we need to
do the normal WAF step-through.
'''
netloc = components.netloc.lower()
for cookie in self.cj:
if cookie.domain_specified and (cookie.domain.lower().endswith(netloc)
or (cookie.domain.lower().endswith("127.0.0.1") and (
components.path == "/sucuri_shit_3" or components.path == "/sucuri_shit_2" ))): # Allow testing
if "sucuri_cloudproxy_uuid_" in cookie.name:
return
self.log.info("Missing cloudproxy cookie for known sucuri wrapped site. Doing a pre-emptive chromium fetch.")
raise Exceptions.SucuriWrapper("WAF Shit", str(components)) | python | def __check_suc_cookie(self, components):
'''
This is only called if we're on a known sucuri-"protected" site.
As such, if we do *not* have a sucuri cloudproxy cookie, we can assume we need to
do the normal WAF step-through.
'''
netloc = components.netloc.lower()
for cookie in self.cj:
if cookie.domain_specified and (cookie.domain.lower().endswith(netloc)
or (cookie.domain.lower().endswith("127.0.0.1") and (
components.path == "/sucuri_shit_3" or components.path == "/sucuri_shit_2" ))): # Allow testing
if "sucuri_cloudproxy_uuid_" in cookie.name:
return
self.log.info("Missing cloudproxy cookie for known sucuri wrapped site. Doing a pre-emptive chromium fetch.")
raise Exceptions.SucuriWrapper("WAF Shit", str(components)) | [
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.__pre_check | def __pre_check(self, requestedUrl):
'''
Allow the pre-emptive fetching of sites with a full browser if they're known
to be dick hosters.
'''
components = urllib.parse.urlsplit(requestedUrl)
netloc_l = components.netloc.lower()
if netloc_l in Domain_Constants.SUCURI_GARBAGE_SITE_NETLOCS:
self.__check_suc_cookie(components)
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self.__check_cf_cookie(components)
elif components.path == '/sucuri_shit_2':
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elif components.path == '/sucuri_shit_3':
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elif components.path == '/cloudflare_under_attack_shit_2':
self.__check_cf_cookie(components)
elif components.path == '/cloudflare_under_attack_shit_3':
self.__check_cf_cookie(components) | python | def __pre_check(self, requestedUrl):
'''
Allow the pre-emptive fetching of sites with a full browser if they're known
to be dick hosters.
'''
components = urllib.parse.urlsplit(requestedUrl)
netloc_l = components.netloc.lower()
if netloc_l in Domain_Constants.SUCURI_GARBAGE_SITE_NETLOCS:
self.__check_suc_cookie(components)
elif netloc_l in Domain_Constants.CF_GARBAGE_SITE_NETLOCS:
self.__check_cf_cookie(components)
elif components.path == '/sucuri_shit_2':
self.__check_suc_cookie(components)
elif components.path == '/sucuri_shit_3':
self.__check_suc_cookie(components)
elif components.path == '/cloudflare_under_attack_shit_2':
self.__check_cf_cookie(components)
elif components.path == '/cloudflare_under_attack_shit_3':
self.__check_cf_cookie(components) | [
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.__decompressContent | def __decompressContent(self, coding, pgctnt):
"""
This is really obnoxious
"""
#preLen = len(pgctnt)
if coding == 'deflate':
compType = "deflate"
bits_opts = [
-zlib.MAX_WBITS, # deflate
zlib.MAX_WBITS, # zlib
zlib.MAX_WBITS | 16, # gzip
zlib.MAX_WBITS | 32, # "automatic header detection"
0, # Try to guess from header
# Try all the raw window options.
-8, -9, -10, -11, -12, -13, -14, -15,
# Stream with zlib headers
8, 9, 10, 11, 12, 13, 14, 15,
# With gzip header+trailer
8+16, 9+16, 10+16, 11+16, 12+16, 13+16, 14+16, 15+16,
# Automatic detection
8+32, 9+32, 10+32, 11+32, 12+32, 13+32, 14+32, 15+32,
]
err = None
for wbits_val in bits_opts:
try:
pgctnt = zlib.decompress(pgctnt, wbits_val)
return compType, pgctnt
except zlib.error as e:
err = e
# We can't get here without err having thrown.
raise err
elif coding == 'gzip':
compType = "gzip"
buf = io.BytesIO(pgctnt)
f = gzip.GzipFile(fileobj=buf)
pgctnt = f.read()
elif coding == "sdch":
raise Exceptions.ContentTypeError("Wait, someone other then google actually supports SDCH compression (%s)?" % pgreq)
else:
compType = "none"
return compType, pgctnt | python | def __decompressContent(self, coding, pgctnt):
"""
This is really obnoxious
"""
#preLen = len(pgctnt)
if coding == 'deflate':
compType = "deflate"
bits_opts = [
-zlib.MAX_WBITS, # deflate
zlib.MAX_WBITS, # zlib
zlib.MAX_WBITS | 16, # gzip
zlib.MAX_WBITS | 32, # "automatic header detection"
0, # Try to guess from header
# Try all the raw window options.
-8, -9, -10, -11, -12, -13, -14, -15,
# Stream with zlib headers
8, 9, 10, 11, 12, 13, 14, 15,
# With gzip header+trailer
8+16, 9+16, 10+16, 11+16, 12+16, 13+16, 14+16, 15+16,
# Automatic detection
8+32, 9+32, 10+32, 11+32, 12+32, 13+32, 14+32, 15+32,
]
err = None
for wbits_val in bits_opts:
try:
pgctnt = zlib.decompress(pgctnt, wbits_val)
return compType, pgctnt
except zlib.error as e:
err = e
# We can't get here without err having thrown.
raise err
elif coding == 'gzip':
compType = "gzip"
buf = io.BytesIO(pgctnt)
f = gzip.GzipFile(fileobj=buf)
pgctnt = f.read()
elif coding == "sdch":
raise Exceptions.ContentTypeError("Wait, someone other then google actually supports SDCH compression (%s)?" % pgreq)
else:
compType = "none"
return compType, pgctnt | [
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fake-name/WebRequest | WebRequest/WebRequestClass.py | WebGetRobust.addSeleniumCookie | def addSeleniumCookie(self, cookieDict):
'''
Install a cookie exported from a selenium webdriver into
the active opener
'''
# print cookieDict
cookie = http.cookiejar.Cookie(
version = 0,
name = cookieDict['name'],
value = cookieDict['value'],
port = None,
port_specified = False,
domain = cookieDict['domain'],
domain_specified = True,
domain_initial_dot = False,
path = cookieDict['path'],
path_specified = False,
secure = cookieDict['secure'],
expires = cookieDict['expiry'] if 'expiry' in cookieDict else None,
discard = False,
comment = None,
comment_url = None,
rest = {"httponly":"%s" % cookieDict['httponly'] if 'httponly' in cookieDict else False},
rfc2109 = False
)
self.addCookie(cookie) | python | def addSeleniumCookie(self, cookieDict):
'''
Install a cookie exported from a selenium webdriver into
the active opener
'''
# print cookieDict
cookie = http.cookiejar.Cookie(
version = 0,
name = cookieDict['name'],
value = cookieDict['value'],
port = None,
port_specified = False,
domain = cookieDict['domain'],
domain_specified = True,
domain_initial_dot = False,
path = cookieDict['path'],
path_specified = False,
secure = cookieDict['secure'],
expires = cookieDict['expiry'] if 'expiry' in cookieDict else None,
discard = False,
comment = None,
comment_url = None,
rest = {"httponly":"%s" % cookieDict['httponly'] if 'httponly' in cookieDict else False},
rfc2109 = False
)
self.addCookie(cookie) | [
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jasonwyatt/Flask-ErrorMail | flask_errormail/__init__.py | mail_on_500 | def mail_on_500(app, recipients, sender='noreply@localhost'):
'''Main function for setting up Flask-ErrorMail to send e-mails when 500
errors occur.
:param app: Flask Application Object
:type app: flask.Flask
:param recipients: List of recipient email addresses.
:type recipients: list or tuple
:param sender: Email address that should be listed as the sender. Defaults
to 'noreply@localhost'
:type sender: string
'''
#importing locally, so that the dependencies are only required if
# mail_on_500 is used.
from flask import request as __request
from flask_mail import Mail as __Mail
from flask_mail import Message as __Message
mail = __Mail(app)
# create a closure to track the sender and recipients
def email_exception(exception):
'''Handles the exception message from Flask by sending an email to the
recipients defined in the call to mail_on_500.
'''
msg = __Message("[Flask|ErrorMail] Exception Detected",
sender=sender,
recipients=recipients)
msg_contents = [
'Traceback:',
'='*80,
traceback.format_exc(),
]
msg_contents.append('\n')
msg_contents.append('Request Information:')
msg_contents.append('='*80)
environ = __request.environ
environkeys = sorted(environ.keys())
for key in environkeys:
msg_contents.append('%s: %s' % (key, environ.get(key)))
msg.body = '\n'.join(msg_contents) + '\n'
mail.send(msg)
app.register_error_handler(500, email_exception) | python | def mail_on_500(app, recipients, sender='noreply@localhost'):
'''Main function for setting up Flask-ErrorMail to send e-mails when 500
errors occur.
:param app: Flask Application Object
:type app: flask.Flask
:param recipients: List of recipient email addresses.
:type recipients: list or tuple
:param sender: Email address that should be listed as the sender. Defaults
to 'noreply@localhost'
:type sender: string
'''
#importing locally, so that the dependencies are only required if
# mail_on_500 is used.
from flask import request as __request
from flask_mail import Mail as __Mail
from flask_mail import Message as __Message
mail = __Mail(app)
# create a closure to track the sender and recipients
def email_exception(exception):
'''Handles the exception message from Flask by sending an email to the
recipients defined in the call to mail_on_500.
'''
msg = __Message("[Flask|ErrorMail] Exception Detected",
sender=sender,
recipients=recipients)
msg_contents = [
'Traceback:',
'='*80,
traceback.format_exc(),
]
msg_contents.append('\n')
msg_contents.append('Request Information:')
msg_contents.append('='*80)
environ = __request.environ
environkeys = sorted(environ.keys())
for key in environkeys:
msg_contents.append('%s: %s' % (key, environ.get(key)))
msg.body = '\n'.join(msg_contents) + '\n'
mail.send(msg)
app.register_error_handler(500, email_exception) | [
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brbsix/pip-utils | pip_utils/dependents.py | command_dependents | def command_dependents(options):
"""Command launched by CLI."""
dependents = dependencies(options.package, options.recursive, options.info)
if dependents:
print(*dependents, sep='\n') | python | def command_dependents(options):
"""Command launched by CLI."""
dependents = dependencies(options.package, options.recursive, options.info)
if dependents:
print(*dependents, sep='\n') | [
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brbsix/pip-utils | pip_utils/dependents.py | dependencies | def dependencies(dist, recursive=False, info=False):
"""Yield distribution's dependencies."""
def case_sorted(items):
"""Return unique list sorted in case-insensitive order."""
return sorted(set(items), key=lambda i: i.lower())
def requires(distribution):
"""Return the requirements for a distribution."""
if recursive:
req = set(pkg_resources.require(distribution.project_name))
req.remove(distribution)
return {r.as_requirement() for r in req}
return distribution.requires()
def modifier(distribution):
"""Return project's name or full requirement string."""
return str(distribution) if info else distribution.project_name
return case_sorted(modifier(r) for r in requires(dist)) | python | def dependencies(dist, recursive=False, info=False):
"""Yield distribution's dependencies."""
def case_sorted(items):
"""Return unique list sorted in case-insensitive order."""
return sorted(set(items), key=lambda i: i.lower())
def requires(distribution):
"""Return the requirements for a distribution."""
if recursive:
req = set(pkg_resources.require(distribution.project_name))
req.remove(distribution)
return {r.as_requirement() for r in req}
return distribution.requires()
def modifier(distribution):
"""Return project's name or full requirement string."""
return str(distribution) if info else distribution.project_name
return case_sorted(modifier(r) for r in requires(dist)) | [
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rapidpro/dash | dash/orgs/context_processors.py | user_group_perms_processor | def user_group_perms_processor(request):
"""
return context variables with org permissions to the user.
"""
org = None
group = None
if hasattr(request, "user"):
if request.user.is_anonymous:
group = None
else:
group = request.user.get_org_group()
org = request.user.get_org()
if group:
context = dict(org_perms=GroupPermWrapper(group))
else:
context = dict()
# make sure user_org is set on our request based on their session
context["user_org"] = org
return context | python | def user_group_perms_processor(request):
"""
return context variables with org permissions to the user.
"""
org = None
group = None
if hasattr(request, "user"):
if request.user.is_anonymous:
group = None
else:
group = request.user.get_org_group()
org = request.user.get_org()
if group:
context = dict(org_perms=GroupPermWrapper(group))
else:
context = dict()
# make sure user_org is set on our request based on their session
context["user_org"] = org
return context | [
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"context",
"variables",
"with",
"org",
"permissions",
"to",
"the",
"user",
"."
] | train | https://github.com/rapidpro/dash/blob/e9dc05b31b86fe3fe72e956975d1ee0a275ac016/dash/orgs/context_processors.py#L43-L65 |
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