max_stars_repo_path stringlengths 4 286 | max_stars_repo_name stringlengths 5 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.03M | content_cleaned stringlengths 6 1.03M | language stringclasses 111 values | language_score float64 0.03 1 | comments stringlengths 0 556k | edu_score float64 0.32 5.03 | edu_int_score int64 0 5 |
|---|---|---|---|---|---|---|---|---|---|---|
getdata.py | teejaytanmay/image_object_localization_flipkart | 0 | 6614251 | # coding: utf-8
from PIL import Image
import numpy as np
import pickle
import pandas as pd
def Normalize(image,mean,std):
for channel in range(3):
image[:,:,channel]=(image[:,:,channel]-mean[channel])/std[channel]
return image
id_to_data={}
id_to_size={}
imgs = pd.read_csv("/media/teejay/TJ HDD2/data/training.csv")
images = imgs.image_name
for i in range(64):
path=images[i]
image=Image.open("/media/teejay/TJ HDD2/data/images/"+path).convert('RGB')
id_to_size[i]=np.array(image,dtype=np.float32).shape[0:2]
# image=image.resize((224,224))
image=np.array(image,dtype=np.float32)
# image=image/255
# image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225])
id_to_data[i]=image
l=list(id_to_data.values())
m=list(id_to_size.values())
id_to_data=np.array(l)
id_to_size=np.array(m)
f=open("/media/teejay/TJ HDD2/data/id_to_data","wb+")
pickle.dump(id_to_data,f)
f=open("/media/teejay/TJ HDD2/data/id_to_size","wb+")
pickle.dump(id_to_size,f)
# id_to_box={}
# with open("./data/images.txt") as f:
# lines=f.read().splitlines()
# for line in lines:
# id,path=line.split(" ",1)
# image=Image.open("./data/images/"+path).convert('RGB')
# id_to_size[int(id)]=np.array(image,dtype=np.float32).shape[0:2]
# image=image.resize((224,224))
# image=np.array(image,dtype=np.float32)
# image=image/255
# image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225])
# id_to_data[int(id)]=image
# id_to_data=np.array(list(id_to_data.values()))
# id_to_size=np.array(list(id_to_size.values()))
# f=open("./id_to_data","wb+")
# pickle.dump(id_to_data,f,protocol=4)
# f=open("./id_to_size","wb+")
# pickle.dump(id_to_size,f,protocol=4)
id_to_box={}
# print (id_to_size.shape[0])
# for i in range(id_to_size.shape[0]):
imgs.x1 = imgs.x1/id_to_size[1][1]
imgs.x2 = imgs.x2/id_to_size[0][0]
imgs.y1 = imgs.y1/id_to_size[1][1]
imgs.y2 = imgs.y2/id_to_size[0][0]
for i in range(id_to_size.shape[0]):
id_to_box[i] = np.array([imgs.x1[i],imgs.x2[i],imgs.y1[i],imgs.y2[i]])
# imgs.head(5)
# with open("./data/bounding_boxes.txt") as f:
# lines=f.read().splitlines()
# for line in lines:
# id,box=line.split(" ",1)
# box=np.array([float(i) for i in box.split(" ")],dtype=np.float32)
# box[0]=box[0]/id_to_size[int(id)-1][1]*224
# box[1]=box[1]/id_to_size[int(id)-1][0]*224
# box[2]=box[2]/id_to_size[int(id)-1][1]*224
# box[3]=box[3]/id_to_size[int(id)-1][0]*224
# id_to_box[int(id)]=box
n=list(id_to_box.values())
id_to_box=np.array(n)
f=open("/media/teejay/TJ HDD2/data/id_to_box","wb+")
pickle.dump(id_to_box,f)
# id_to_box=np.array(list(id_to_box.values()))
# f=open("./id_to_box","wb+")
# pickle.dump(id_to_box,f,protocol=4)
| # coding: utf-8
from PIL import Image
import numpy as np
import pickle
import pandas as pd
def Normalize(image,mean,std):
for channel in range(3):
image[:,:,channel]=(image[:,:,channel]-mean[channel])/std[channel]
return image
id_to_data={}
id_to_size={}
imgs = pd.read_csv("/media/teejay/TJ HDD2/data/training.csv")
images = imgs.image_name
for i in range(64):
path=images[i]
image=Image.open("/media/teejay/TJ HDD2/data/images/"+path).convert('RGB')
id_to_size[i]=np.array(image,dtype=np.float32).shape[0:2]
# image=image.resize((224,224))
image=np.array(image,dtype=np.float32)
# image=image/255
# image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225])
id_to_data[i]=image
l=list(id_to_data.values())
m=list(id_to_size.values())
id_to_data=np.array(l)
id_to_size=np.array(m)
f=open("/media/teejay/TJ HDD2/data/id_to_data","wb+")
pickle.dump(id_to_data,f)
f=open("/media/teejay/TJ HDD2/data/id_to_size","wb+")
pickle.dump(id_to_size,f)
# id_to_box={}
# with open("./data/images.txt") as f:
# lines=f.read().splitlines()
# for line in lines:
# id,path=line.split(" ",1)
# image=Image.open("./data/images/"+path).convert('RGB')
# id_to_size[int(id)]=np.array(image,dtype=np.float32).shape[0:2]
# image=image.resize((224,224))
# image=np.array(image,dtype=np.float32)
# image=image/255
# image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225])
# id_to_data[int(id)]=image
# id_to_data=np.array(list(id_to_data.values()))
# id_to_size=np.array(list(id_to_size.values()))
# f=open("./id_to_data","wb+")
# pickle.dump(id_to_data,f,protocol=4)
# f=open("./id_to_size","wb+")
# pickle.dump(id_to_size,f,protocol=4)
id_to_box={}
# print (id_to_size.shape[0])
# for i in range(id_to_size.shape[0]):
imgs.x1 = imgs.x1/id_to_size[1][1]
imgs.x2 = imgs.x2/id_to_size[0][0]
imgs.y1 = imgs.y1/id_to_size[1][1]
imgs.y2 = imgs.y2/id_to_size[0][0]
for i in range(id_to_size.shape[0]):
id_to_box[i] = np.array([imgs.x1[i],imgs.x2[i],imgs.y1[i],imgs.y2[i]])
# imgs.head(5)
# with open("./data/bounding_boxes.txt") as f:
# lines=f.read().splitlines()
# for line in lines:
# id,box=line.split(" ",1)
# box=np.array([float(i) for i in box.split(" ")],dtype=np.float32)
# box[0]=box[0]/id_to_size[int(id)-1][1]*224
# box[1]=box[1]/id_to_size[int(id)-1][0]*224
# box[2]=box[2]/id_to_size[int(id)-1][1]*224
# box[3]=box[3]/id_to_size[int(id)-1][0]*224
# id_to_box[int(id)]=box
n=list(id_to_box.values())
id_to_box=np.array(n)
f=open("/media/teejay/TJ HDD2/data/id_to_box","wb+")
pickle.dump(id_to_box,f)
# id_to_box=np.array(list(id_to_box.values()))
# f=open("./id_to_box","wb+")
# pickle.dump(id_to_box,f,protocol=4)
| en | 0.313772 | # coding: utf-8 # image=image.resize((224,224)) # image=image/255 # image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225]) # id_to_box={} # with open("./data/images.txt") as f: # lines=f.read().splitlines() # for line in lines: # id,path=line.split(" ",1) # image=Image.open("./data/images/"+path).convert('RGB') # id_to_size[int(id)]=np.array(image,dtype=np.float32).shape[0:2] # image=image.resize((224,224)) # image=np.array(image,dtype=np.float32) # image=image/255 # image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225]) # id_to_data[int(id)]=image # id_to_data=np.array(list(id_to_data.values())) # id_to_size=np.array(list(id_to_size.values())) # f=open("./id_to_data","wb+") # pickle.dump(id_to_data,f,protocol=4) # f=open("./id_to_size","wb+") # pickle.dump(id_to_size,f,protocol=4) # print (id_to_size.shape[0]) # for i in range(id_to_size.shape[0]): # imgs.head(5) # with open("./data/bounding_boxes.txt") as f: # lines=f.read().splitlines() # for line in lines: # id,box=line.split(" ",1) # box=np.array([float(i) for i in box.split(" ")],dtype=np.float32) # box[0]=box[0]/id_to_size[int(id)-1][1]*224 # box[1]=box[1]/id_to_size[int(id)-1][0]*224 # box[2]=box[2]/id_to_size[int(id)-1][1]*224 # box[3]=box[3]/id_to_size[int(id)-1][0]*224 # id_to_box[int(id)]=box # id_to_box=np.array(list(id_to_box.values())) # f=open("./id_to_box","wb+") # pickle.dump(id_to_box,f,protocol=4) | 2.738078 | 3 |
make_word_list.py | schufo/lyrics-aligner | 2 | 6614252 | <filename>make_word_list.py
"""
Generates a .txt-file with all unique words in a dataset. This .txt/file
can be used to translate words into phoneme sequences with the
CMU pronunciation dictionary (http://www.speech.cs.cmu.edu/tools/lextool.html)
"""
import argparse
import os
import glob
parser = argparse.ArgumentParser(description='Word list generation')
parser.add_argument('lyrics', type=str, help='path to a directory with lyrics stored in .txt-files')
parser.add_argument('--dataset-name', type=str, default='dataset1')
args = parser.parse_args()
unique_words = set()
lyrics_files = glob.glob(os.path.join(args.lyrics, '*.txt'))
assert len(lyrics_files) > 0, 'No .txt-files found in {}'.format(args.lyrics)
# go through .txt-files and save unique words in the unique_words set
for file in lyrics_files:
with open(file) as word_file:
lines = word_file.readlines()
for line in lines:
line = line.lower().replace('\n', '').replace('’', "'")
clean_line = ''.join(c for c in line if c.isalnum() or c in ["'", ' '])
if clean_line == ' ' or clean_line == '': continue
words = clean_line.split(' ')
for word in words:
unique_words.add(word)
unique_words.remove('')
# create .txt-file
word_file_path = 'files/{}_word_list.txt'.format(args.dataset_name)
assert not os.path.isfile(word_file_path), 'file {} exists already. Delete or choose different' \
' file to avoid appending to existing file'.format(word_file_path)
# write words in .txt-file
words_file = open(word_file_path, 'a')
for word in sorted(unique_words):
words_file.write(word + '\n')
words_file.close()
# create empty .txt-file which will contain the output of the CMU pronuciation dictionary.
empty_file_path = 'files/{}_word2phonemes.txt'.format(args.dataset_name)
empty_file = open(empty_file_path, 'a')
empty_file.write('')
empty_file.close()
| <filename>make_word_list.py
"""
Generates a .txt-file with all unique words in a dataset. This .txt/file
can be used to translate words into phoneme sequences with the
CMU pronunciation dictionary (http://www.speech.cs.cmu.edu/tools/lextool.html)
"""
import argparse
import os
import glob
parser = argparse.ArgumentParser(description='Word list generation')
parser.add_argument('lyrics', type=str, help='path to a directory with lyrics stored in .txt-files')
parser.add_argument('--dataset-name', type=str, default='dataset1')
args = parser.parse_args()
unique_words = set()
lyrics_files = glob.glob(os.path.join(args.lyrics, '*.txt'))
assert len(lyrics_files) > 0, 'No .txt-files found in {}'.format(args.lyrics)
# go through .txt-files and save unique words in the unique_words set
for file in lyrics_files:
with open(file) as word_file:
lines = word_file.readlines()
for line in lines:
line = line.lower().replace('\n', '').replace('’', "'")
clean_line = ''.join(c for c in line if c.isalnum() or c in ["'", ' '])
if clean_line == ' ' or clean_line == '': continue
words = clean_line.split(' ')
for word in words:
unique_words.add(word)
unique_words.remove('')
# create .txt-file
word_file_path = 'files/{}_word_list.txt'.format(args.dataset_name)
assert not os.path.isfile(word_file_path), 'file {} exists already. Delete or choose different' \
' file to avoid appending to existing file'.format(word_file_path)
# write words in .txt-file
words_file = open(word_file_path, 'a')
for word in sorted(unique_words):
words_file.write(word + '\n')
words_file.close()
# create empty .txt-file which will contain the output of the CMU pronuciation dictionary.
empty_file_path = 'files/{}_word2phonemes.txt'.format(args.dataset_name)
empty_file = open(empty_file_path, 'a')
empty_file.write('')
empty_file.close()
| en | 0.752932 | Generates a .txt-file with all unique words in a dataset. This .txt/file can be used to translate words into phoneme sequences with the CMU pronunciation dictionary (http://www.speech.cs.cmu.edu/tools/lextool.html) # go through .txt-files and save unique words in the unique_words set # create .txt-file # write words in .txt-file # create empty .txt-file which will contain the output of the CMU pronuciation dictionary. | 3.710906 | 4 |
Courses/Udacity/CS101/Lesson_2.5_How_To_Solve_Problems/21-Define_nextDay/supplied/studentMain.py | leparrav/Playground | 1 | 6614253 | # By Websten from forums
#
# Given your birthday and the current date, calculate your age in days.
# Compensate for leap days.
# Assume that the birthday and current date are correct dates (and no time travel).
# Simply put, if you were born 1 Jan 2012 and todays date is 2 Jan 2012
# you are 1 day old.
#
# Hint
# A whole year is 365 days, 366 if a leap year.
def nextDay(year, month, day):
"""
Returns the year, month, day of the next day.
Simple version: assume every month has 30 days.
"""
# YOUR CODE HERE
return | # By Websten from forums
#
# Given your birthday and the current date, calculate your age in days.
# Compensate for leap days.
# Assume that the birthday and current date are correct dates (and no time travel).
# Simply put, if you were born 1 Jan 2012 and todays date is 2 Jan 2012
# you are 1 day old.
#
# Hint
# A whole year is 365 days, 366 if a leap year.
def nextDay(year, month, day):
"""
Returns the year, month, day of the next day.
Simple version: assume every month has 30 days.
"""
# YOUR CODE HERE
return | en | 0.938688 | # By Websten from forums # # Given your birthday and the current date, calculate your age in days. # Compensate for leap days. # Assume that the birthday and current date are correct dates (and no time travel). # Simply put, if you were born 1 Jan 2012 and todays date is 2 Jan 2012 # you are 1 day old. # # Hint # A whole year is 365 days, 366 if a leap year. Returns the year, month, day of the next day. Simple version: assume every month has 30 days. # YOUR CODE HERE | 4.069884 | 4 |
pytorch_privacy/analysis/online_accountant.py | MJHutchinson/PytorchPrivacy | 2 | 6614254 | class OnlineAccountant(object):
""" A class to perform accounting in an
online manner to speed up experiments. requires
an accountancy method to have an online method. """
def __init__(self,
accountancy_update_method,
ledger=None,
accountancy_parameters=None):
"""
:param accountancy_update_method: A method to compute the desired accountancy in and
online fashion. Should take as parameters some list of new privacy queries to update the
privacy for, and some tracking variable specific to the method (E.g. log moments for the
moment accountant). This method should fuction if the tracking variable are None.
:param ledger: Some initial ledger. May be None
:param accountancy_parameters: Some parameters to pass to the accountancy update method.
E.g. the target epsilon or delta, maximum log moment...
"""
self._accountancy_update_method = accountancy_update_method
self._accountancy_parameters = accountancy_parameters
self._ledger = []
self._tracking_parameters = None
self._position = 0
if ledger is None:
ledger = []
self._privacy_bound = self.update_privacy(ledger)
def update_privacy(self, incremented_ledger):
""" Update the current privacy bound using new additions to the ledger.
:param incremented_ledger: The new ledger. Assumes that the only differences
from previously seen ledger is the new entries. This should be of the formatted
ledger type.
:return: The new privacy bound.
"""
self._ledger = incremented_ledger
new_entries = self._ledger[self._position:]
self._privacy_bound, self._tracking_parameters = self._accountancy_update_method(
new_entries,
self._tracking_parameters,
**self._accountancy_parameters
)
self._position = len(self._ledger)
return self._privacy_bound
@property
def privacy_bound(self):
return self._privacy_bound
| class OnlineAccountant(object):
""" A class to perform accounting in an
online manner to speed up experiments. requires
an accountancy method to have an online method. """
def __init__(self,
accountancy_update_method,
ledger=None,
accountancy_parameters=None):
"""
:param accountancy_update_method: A method to compute the desired accountancy in and
online fashion. Should take as parameters some list of new privacy queries to update the
privacy for, and some tracking variable specific to the method (E.g. log moments for the
moment accountant). This method should fuction if the tracking variable are None.
:param ledger: Some initial ledger. May be None
:param accountancy_parameters: Some parameters to pass to the accountancy update method.
E.g. the target epsilon or delta, maximum log moment...
"""
self._accountancy_update_method = accountancy_update_method
self._accountancy_parameters = accountancy_parameters
self._ledger = []
self._tracking_parameters = None
self._position = 0
if ledger is None:
ledger = []
self._privacy_bound = self.update_privacy(ledger)
def update_privacy(self, incremented_ledger):
""" Update the current privacy bound using new additions to the ledger.
:param incremented_ledger: The new ledger. Assumes that the only differences
from previously seen ledger is the new entries. This should be of the formatted
ledger type.
:return: The new privacy bound.
"""
self._ledger = incremented_ledger
new_entries = self._ledger[self._position:]
self._privacy_bound, self._tracking_parameters = self._accountancy_update_method(
new_entries,
self._tracking_parameters,
**self._accountancy_parameters
)
self._position = len(self._ledger)
return self._privacy_bound
@property
def privacy_bound(self):
return self._privacy_bound
| en | 0.782924 | A class to perform accounting in an online manner to speed up experiments. requires an accountancy method to have an online method. :param accountancy_update_method: A method to compute the desired accountancy in and online fashion. Should take as parameters some list of new privacy queries to update the privacy for, and some tracking variable specific to the method (E.g. log moments for the moment accountant). This method should fuction if the tracking variable are None. :param ledger: Some initial ledger. May be None :param accountancy_parameters: Some parameters to pass to the accountancy update method. E.g. the target epsilon or delta, maximum log moment... Update the current privacy bound using new additions to the ledger. :param incremented_ledger: The new ledger. Assumes that the only differences from previously seen ledger is the new entries. This should be of the formatted ledger type. :return: The new privacy bound. | 3.669649 | 4 |
Motospeed-ck62/f5.py | godrix/motospeed-ck62-fix-key | 0 | 6614255 | keyboard.send_key("<f5>") | keyboard.send_key("<f5>") | none | 1 | 1.167948 | 1 | |
tests/test_alldistinct.py | Abhisheknishant/iteration_utilities | 0 | 6614256 | <gh_stars>0
# Licensed under Apache License Version 2.0 - see LICENSE
import pytest
from iteration_utilities import all_distinct
import helper_funcs as _hf
from helper_cls import T
def test_alldistinct_empty1():
assert all_distinct([])
def test_alldistinct_normal1():
assert all_distinct([T(1), T(2), T(3)])
def test_alldistinct_normal2():
assert not all_distinct([T(1), T(1), T(1)])
def test_alldistinct_normal3():
# generator
assert all_distinct((i for i in [T(1), T(2), T(3)]))
def test_alldistinct_unhashable1():
assert all_distinct([{T('a'): T(1)}, {T('a'): T(2)}])
def test_alldistinct_unhashable2():
assert not all_distinct([{T('a'): T(1)}, {T('a'): T(1)}])
def test_alldistinct_failure1():
with pytest.raises(_hf.FailIter.EXC_TYP, match= _hf.FailIter.EXC_MSG):
all_distinct(_hf.FailIter())
def test_alldistinct_failure2():
# Test that a failing iterator doesn't raise a SystemError
with pytest.raises(_hf.FailNext.EXC_TYP, match=_hf.FailNext.EXC_MSG):
all_distinct(_hf.FailNext())
def test_alldistinct_failure3():
# Failure when comparing the object to the objects in the list
with pytest.raises(_hf.FailEqNoHash.EXC_TYP, match=_hf.FailEqNoHash.EXC_MSG):
all_distinct([[T(1)], _hf.FailEqNoHash()])
def test_alldistinct_failure4():
# Failure (no TypeError) when trying to hash the value
with pytest.raises(_hf.FailHash.EXC_TYP, match=_hf.FailHash.EXC_MSG):
all_distinct([T(1), _hf.FailHash()])
@_hf.skip_on_pypy_because_cache_next_works_differently
def test_alldistinct_failure5():
# Changing next method
with pytest.raises(_hf.CacheNext.EXC_TYP, match=_hf.CacheNext.EXC_MSG):
all_distinct(_hf.CacheNext(1))
| # Licensed under Apache License Version 2.0 - see LICENSE
import pytest
from iteration_utilities import all_distinct
import helper_funcs as _hf
from helper_cls import T
def test_alldistinct_empty1():
assert all_distinct([])
def test_alldistinct_normal1():
assert all_distinct([T(1), T(2), T(3)])
def test_alldistinct_normal2():
assert not all_distinct([T(1), T(1), T(1)])
def test_alldistinct_normal3():
# generator
assert all_distinct((i for i in [T(1), T(2), T(3)]))
def test_alldistinct_unhashable1():
assert all_distinct([{T('a'): T(1)}, {T('a'): T(2)}])
def test_alldistinct_unhashable2():
assert not all_distinct([{T('a'): T(1)}, {T('a'): T(1)}])
def test_alldistinct_failure1():
with pytest.raises(_hf.FailIter.EXC_TYP, match= _hf.FailIter.EXC_MSG):
all_distinct(_hf.FailIter())
def test_alldistinct_failure2():
# Test that a failing iterator doesn't raise a SystemError
with pytest.raises(_hf.FailNext.EXC_TYP, match=_hf.FailNext.EXC_MSG):
all_distinct(_hf.FailNext())
def test_alldistinct_failure3():
# Failure when comparing the object to the objects in the list
with pytest.raises(_hf.FailEqNoHash.EXC_TYP, match=_hf.FailEqNoHash.EXC_MSG):
all_distinct([[T(1)], _hf.FailEqNoHash()])
def test_alldistinct_failure4():
# Failure (no TypeError) when trying to hash the value
with pytest.raises(_hf.FailHash.EXC_TYP, match=_hf.FailHash.EXC_MSG):
all_distinct([T(1), _hf.FailHash()])
@_hf.skip_on_pypy_because_cache_next_works_differently
def test_alldistinct_failure5():
# Changing next method
with pytest.raises(_hf.CacheNext.EXC_TYP, match=_hf.CacheNext.EXC_MSG):
all_distinct(_hf.CacheNext(1)) | en | 0.799902 | # Licensed under Apache License Version 2.0 - see LICENSE # generator # Test that a failing iterator doesn't raise a SystemError # Failure when comparing the object to the objects in the list # Failure (no TypeError) when trying to hash the value # Changing next method | 2.304506 | 2 |
djskeletor/dashboard/urls.py | carthagecollege/django-djskeletor | 0 | 6614257 | # -*- coding: utf-8 -*-
"""URLs for all views."""
from django.urls import path
from djskeletor.dashboard import views
urlpatterns = [
path(
'search/',
views.search, name='dashboard_search'
),
path('', views.home, name='home'),
]
| # -*- coding: utf-8 -*-
"""URLs for all views."""
from django.urls import path
from djskeletor.dashboard import views
urlpatterns = [
path(
'search/',
views.search, name='dashboard_search'
),
path('', views.home, name='home'),
]
| en | 0.836984 | # -*- coding: utf-8 -*- URLs for all views. | 1.639342 | 2 |
chapter-05/Exercise_5_6.py | yuetsin/CS-902 | 1 | 6614258 | # Exercise 5.6
from Tkinter import *
root = Tk()
c = Canvas(root, width=550, height=400, bg='gray')
c.pack()
centralPoint = (300, 175)
colors = ['yellow', 'red', 'blue', 'black', 'white']
i = 0
while i < 5:
c.create_oval(275 - i * 20, 275 - i * 20, 200 +
i * 20, 200 + i * 20, fill='', outline=colors[i], width=8)
i += 1
root.mainloop()
| # Exercise 5.6
from Tkinter import *
root = Tk()
c = Canvas(root, width=550, height=400, bg='gray')
c.pack()
centralPoint = (300, 175)
colors = ['yellow', 'red', 'blue', 'black', 'white']
i = 0
while i < 5:
c.create_oval(275 - i * 20, 275 - i * 20, 200 +
i * 20, 200 + i * 20, fill='', outline=colors[i], width=8)
i += 1
root.mainloop()
| en | 0.767826 | # Exercise 5.6 | 3.505714 | 4 |
dobby/examples/passwordinput.py | gabrielcsapo/dobby | 0 | 6614259 | import dobby
class Graze(dobby.App):
def startup(self):
main_container = dobby.Container()
main_password = dobby.PasswordInput(placeholder="Password")
main_container.add(main_password)
main_container.constrain(main_password.TOP == main_container.TOP + 5)
main_container.constrain(main_password.RIGHT == main_container.RIGHT - 5)
main_container.constrain(main_password.LEFT == main_container.LEFT + 5)
app.main_window.content = main_container
if __name__ == '__main__':
app = Graze('Graze', 'org.pybee.graze')
app.main_loop() | import dobby
class Graze(dobby.App):
def startup(self):
main_container = dobby.Container()
main_password = dobby.PasswordInput(placeholder="Password")
main_container.add(main_password)
main_container.constrain(main_password.TOP == main_container.TOP + 5)
main_container.constrain(main_password.RIGHT == main_container.RIGHT - 5)
main_container.constrain(main_password.LEFT == main_container.LEFT + 5)
app.main_window.content = main_container
if __name__ == '__main__':
app = Graze('Graze', 'org.pybee.graze')
app.main_loop() | none | 1 | 2.583961 | 3 | |
gpugwas/viz.py | VibhuJawa/GPU-GWAS | 4 | 6614260 | #!/opt/conda/envs/rapids/bin/python
"""
Pre-reqs:
---------
/opt/conda/envs/rapids/bin/pip install \
dash \
jupyter-dash \
dash_bootstrap_components \
dash_core_components \
dash_html_components
"""
import os
import cudf
import plotly.graph_objects as go
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
# from dash.dependencies import Input, Output, State, ALL
# from plotly.offline import init_notebook_mode
# init_notebook_mode(connected = True)
EXT_STYLES = ['https://codepen.io/chriddyp/pen/bWLwgP.css', dbc.themes.BOOTSTRAP]
class ManhattanPlot:
def __init__(self, qq_spec, manhattan_spec, fig_path=None):
self.app = dash.Dash( __name__, external_stylesheets=EXT_STYLES)
self.qq_spec = qq_spec
self.manhattan_spec = manhattan_spec
self.fig_path = fig_path
self.app.layout, self.manhattan_figure = self._construct()
def start(self, host=None, port=5000):
return self.app.run_server(
debug=False, use_reloader=False, host=host, port=port)
def _construct_qq(self):
x_values = self.qq_spec['df'][self.qq_spec['x_axis']]
y_values = self.qq_spec['df'][self.qq_spec['y_axis']]
x_max = float(x_values.max())
y_max = float(y_values.max())
scatter_marker = go.Scattergl({
'x': self.qq_spec['df'][self.qq_spec['x_axis']].to_array(),
'y': y_values.to_array(),
'mode': 'markers',
'marker': {
'size': 2,
'color': '#406278',
},
})
scatter_line = go.Scattergl({
'x': [0, x_max],
'y': [0, y_max],
'mode': 'lines',
'line': {
'width': 2,
'color': 'orange',
},
})
scatter_fig = go.Figure(
data = [scatter_marker, scatter_line],
layout = {
'title': 'Q-Q Plot',
'showlegend': False,
'grid': {
'columns' : 1,
},
})
return scatter_fig
def _construct_manhatten(self):
chroms = self.manhattan_spec['df'][self.manhattan_spec['group_by']].unique().to_array()
start_position = -0.5
scatter_traces = []
for chrom in chroms:
query = '%s == %s' % (self.manhattan_spec['group_by'], chrom)
cdf = self.manhattan_spec['df'].query(query)
x_array = cdf[self.manhattan_spec['x_axis']] + start_position
scatter_trace = go.Scattergl({
'x': x_array.to_array(),
'y': cdf[self.manhattan_spec['y_axis']].to_array(),
'name': 'Chromosome ' + str(chrom),
'mode': 'markers',
'marker': {
'size': 2,
'color': '#406278' if (start_position - 0.5) % 2 == 0 else '#e32636',
},
})
scatter_traces.append(scatter_trace)
start_position += 1
manhattan_fig = go.Figure(
data = scatter_traces,
layout = {
'title': 'GWAS Manhattan Plot',
'showlegend': False,
'grid': {
'columns' : 1,
},
'xaxis': {
'showgrid': False,
'gridwidth': 1,
'ticks': 'outside',
'zeroline': False,
'tickvals': [t for t in range(int(start_position + 0.5))],
'ticktext': [str(t) for t in chroms],
}})
# plotly.offline.iplot({ "data": manhattan_fig, "layout": go.Layout(title="Sine wave")})
return manhattan_fig
def _construct(self):
manhattan_fig = self._construct_manhatten()
# qq_plot_fig = self._construct_qq()
if self.fig_path:
manhattan_fig.write_html(
os.path.join(self.fig_path, "manhattan.html"))
#qq_plot_fig.write_html(
# os.path.join(self.fig_path, "qq_plot.html"))
layout = html.Div([
html.Div(
children=[
dcc.Markdown(
"""
**GWAS**
"""),
# html.Div([dcc.Graph(id='qq_plot_fig', figure=qq_plot_fig),]),
html.Div([dcc.Graph(id='manhattan-figure', figure=manhattan_fig),]),
]),
])
return layout, manhattan_fig#, qq_plot_fig
def main():
df = cudf.read_csv('./data/data.csv')
qq_spec = {}
qq_spec['df'] = df
qq_spec['x_axis'] = 'P'
qq_spec['y_axis'] = 'ZSCORE'
manhattan_spec = {}
manhattan_spec['df'] = df
manhattan_spec['group_by'] = 'CHR'
manhattan_spec['x_axis'] = 'P'
manhattan_spec['y_axis'] = 'ZSCORE'
fig_path = None
plot = ManhattanPlot(qq_spec, manhattan_spec, fig_path)
plot.start()
if __name__=='__main__':
main()
| #!/opt/conda/envs/rapids/bin/python
"""
Pre-reqs:
---------
/opt/conda/envs/rapids/bin/pip install \
dash \
jupyter-dash \
dash_bootstrap_components \
dash_core_components \
dash_html_components
"""
import os
import cudf
import plotly.graph_objects as go
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
# from dash.dependencies import Input, Output, State, ALL
# from plotly.offline import init_notebook_mode
# init_notebook_mode(connected = True)
EXT_STYLES = ['https://codepen.io/chriddyp/pen/bWLwgP.css', dbc.themes.BOOTSTRAP]
class ManhattanPlot:
def __init__(self, qq_spec, manhattan_spec, fig_path=None):
self.app = dash.Dash( __name__, external_stylesheets=EXT_STYLES)
self.qq_spec = qq_spec
self.manhattan_spec = manhattan_spec
self.fig_path = fig_path
self.app.layout, self.manhattan_figure = self._construct()
def start(self, host=None, port=5000):
return self.app.run_server(
debug=False, use_reloader=False, host=host, port=port)
def _construct_qq(self):
x_values = self.qq_spec['df'][self.qq_spec['x_axis']]
y_values = self.qq_spec['df'][self.qq_spec['y_axis']]
x_max = float(x_values.max())
y_max = float(y_values.max())
scatter_marker = go.Scattergl({
'x': self.qq_spec['df'][self.qq_spec['x_axis']].to_array(),
'y': y_values.to_array(),
'mode': 'markers',
'marker': {
'size': 2,
'color': '#406278',
},
})
scatter_line = go.Scattergl({
'x': [0, x_max],
'y': [0, y_max],
'mode': 'lines',
'line': {
'width': 2,
'color': 'orange',
},
})
scatter_fig = go.Figure(
data = [scatter_marker, scatter_line],
layout = {
'title': 'Q-Q Plot',
'showlegend': False,
'grid': {
'columns' : 1,
},
})
return scatter_fig
def _construct_manhatten(self):
chroms = self.manhattan_spec['df'][self.manhattan_spec['group_by']].unique().to_array()
start_position = -0.5
scatter_traces = []
for chrom in chroms:
query = '%s == %s' % (self.manhattan_spec['group_by'], chrom)
cdf = self.manhattan_spec['df'].query(query)
x_array = cdf[self.manhattan_spec['x_axis']] + start_position
scatter_trace = go.Scattergl({
'x': x_array.to_array(),
'y': cdf[self.manhattan_spec['y_axis']].to_array(),
'name': 'Chromosome ' + str(chrom),
'mode': 'markers',
'marker': {
'size': 2,
'color': '#406278' if (start_position - 0.5) % 2 == 0 else '#e32636',
},
})
scatter_traces.append(scatter_trace)
start_position += 1
manhattan_fig = go.Figure(
data = scatter_traces,
layout = {
'title': 'GWAS Manhattan Plot',
'showlegend': False,
'grid': {
'columns' : 1,
},
'xaxis': {
'showgrid': False,
'gridwidth': 1,
'ticks': 'outside',
'zeroline': False,
'tickvals': [t for t in range(int(start_position + 0.5))],
'ticktext': [str(t) for t in chroms],
}})
# plotly.offline.iplot({ "data": manhattan_fig, "layout": go.Layout(title="Sine wave")})
return manhattan_fig
def _construct(self):
manhattan_fig = self._construct_manhatten()
# qq_plot_fig = self._construct_qq()
if self.fig_path:
manhattan_fig.write_html(
os.path.join(self.fig_path, "manhattan.html"))
#qq_plot_fig.write_html(
# os.path.join(self.fig_path, "qq_plot.html"))
layout = html.Div([
html.Div(
children=[
dcc.Markdown(
"""
**GWAS**
"""),
# html.Div([dcc.Graph(id='qq_plot_fig', figure=qq_plot_fig),]),
html.Div([dcc.Graph(id='manhattan-figure', figure=manhattan_fig),]),
]),
])
return layout, manhattan_fig#, qq_plot_fig
def main():
df = cudf.read_csv('./data/data.csv')
qq_spec = {}
qq_spec['df'] = df
qq_spec['x_axis'] = 'P'
qq_spec['y_axis'] = 'ZSCORE'
manhattan_spec = {}
manhattan_spec['df'] = df
manhattan_spec['group_by'] = 'CHR'
manhattan_spec['x_axis'] = 'P'
manhattan_spec['y_axis'] = 'ZSCORE'
fig_path = None
plot = ManhattanPlot(qq_spec, manhattan_spec, fig_path)
plot.start()
if __name__=='__main__':
main()
| en | 0.462285 | #!/opt/conda/envs/rapids/bin/python Pre-reqs: --------- /opt/conda/envs/rapids/bin/pip install \ dash \ jupyter-dash \ dash_bootstrap_components \ dash_core_components \ dash_html_components # from dash.dependencies import Input, Output, State, ALL # from plotly.offline import init_notebook_mode # init_notebook_mode(connected = True) # plotly.offline.iplot({ "data": manhattan_fig, "layout": go.Layout(title="Sine wave")}) # qq_plot_fig = self._construct_qq() #qq_plot_fig.write_html( # os.path.join(self.fig_path, "qq_plot.html")) **GWAS** # html.Div([dcc.Graph(id='qq_plot_fig', figure=qq_plot_fig),]), #, qq_plot_fig | 2.313073 | 2 |
pyzmq/perf/perf.py | Surfndez/source-publish | 0 | 6614261 | #!/usr/bin/env python
# coding: utf-8
# Copyright (C) PyZMQ Developers
# Distributed under the terms of the Modified BSD License.
#
# Some original test code Copyright (c) 2007-2010 iMatix Corporation,
# Used under LGPLv3
import argparse
from multiprocessing import Process
import time
try:
now = time.monotonic
except AttributeError:
now = time.time
import zmq
def parse_args(argv=None):
parser = argparse.ArgumentParser(description='Run a zmq performance test')
parser.add_argument('-p', '--poll', action='store_true',
help='use a zmq Poller instead of raw send/recv')
parser.add_argument('-c', '--copy', action='store_true',
help='copy messages instead of using zero-copy')
parser.add_argument('-s', '--size', type=int, default=10240,
help='size (in bytes) of the test message')
parser.add_argument('-n', '--count', type=int, default=10240,
help='number of test messages to send')
parser.add_argument('--url', dest='url', type=str, default='tcp://127.0.0.1:5555',
help='the zmq URL on which to run the test')
parser.add_argument(dest='test', type=str, default='lat', choices=['lat', 'thr'],
help='which test to run')
return parser.parse_args(argv)
def latency_echo(url, count, poll, copy):
"""echo messages on a REP socket
Should be started before `latency`
"""
ctx = zmq.Context()
s = ctx.socket(zmq.REP)
if poll:
p = zmq.Poller()
p.register(s)
s.bind(url)
block = zmq.NOBLOCK if poll else 0
for i in range(count):
if poll:
res = p.poll()
msg = s.recv(block, copy=copy)
if poll:
res = p.poll()
s.send(msg, block, copy=copy)
msg = s.recv()
assert msg == b'done'
s.close()
ctx.term()
def latency(url, count, size, poll, copy):
"""Perform a latency test"""
ctx = zmq.Context()
s = ctx.socket(zmq.REQ)
s.setsockopt(zmq.LINGER, -1)
s.connect(url)
if poll:
p = zmq.Poller()
p.register(s)
msg = b' ' * size
block = zmq.NOBLOCK if poll else 0
time.sleep(1)
start = now()
for i in range (0, count):
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLOUT)
s.send(msg, block, copy=copy)
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLIN)
msg = s.recv(block, copy=copy)
assert len(msg) == size
elapsed = now() - start
s.send(b'done')
latency = 1e6 * elapsed / (count * 2.)
print ("message size : %8i [B]" % (size, ))
print ("roundtrip count: %8i [msgs]" % (count, ))
print ("mean latency : %12.3f [µs]" % (latency, ))
print ("test time : %12.3f [s]" % (elapsed, ))
def pusher(url, count, size, poll, copy):
"""send a bunch of messages on a PUSH socket"""
ctx = zmq.Context()
s = ctx.socket(zmq.PUSH)
# Add your socket options here.
# For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM.
if poll:
p = zmq.Poller()
p.register(s)
s.connect(url)
msg = zmq.Message(b' ' * size)
block = zmq.NOBLOCK if poll else 0
for i in range(count):
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLOUT)
s.send(msg, block, copy=copy)
s.close()
ctx.term()
def throughput(url, count, size, poll, copy):
"""recv a bunch of messages on a PULL socket
Should be started before `pusher`
"""
ctx = zmq.Context()
s = ctx.socket(zmq.PULL)
# Add your socket options here.
# For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM.
if poll:
p = zmq.Poller()
p.register(s)
s.bind(url)
block = zmq.NOBLOCK if poll else 0
# Wait for the other side to connect.
msg = s.recv()
assert len (msg) == size
start = now()
for i in range (count-1):
if poll:
res = p.poll()
msg = s.recv(block, copy=copy)
elapsed = now() - start
throughput = (float(count)) / float(elapsed)
megabits = float(throughput * size * 8) / 1e6
print ("message size : %8i [B]" % (size, ))
print ("message count : %8i [msgs]" % (count, ))
print ("mean throughput: %8.0f [msg/s]" % (throughput, ))
print ("mean throughput: %12.3f [Mb/s]" % (megabits, ))
print ("test time : %12.3f [s]" % (elapsed, ))
def main():
args = parse_args()
tic = time.time()
if args.test == 'lat':
bg = Process(target=latency_echo, args=(args.url, args.count, args.poll, args.copy))
bg.start()
latency(args.url, args.count, args.size, args.poll, args.copy)
elif args.test == 'thr':
bg = Process(target=throughput, args=(args.url, args.count, args.size, args.poll, args.copy))
bg.start()
pusher(args.url, args.count, args.size, args.poll, args.copy)
bg.join()
toc = time.time()
if (toc - tic) < 3:
print ("For best results, tests should take at least a few seconds.")
if __name__ == '__main__':
main()
| #!/usr/bin/env python
# coding: utf-8
# Copyright (C) PyZMQ Developers
# Distributed under the terms of the Modified BSD License.
#
# Some original test code Copyright (c) 2007-2010 iMatix Corporation,
# Used under LGPLv3
import argparse
from multiprocessing import Process
import time
try:
now = time.monotonic
except AttributeError:
now = time.time
import zmq
def parse_args(argv=None):
parser = argparse.ArgumentParser(description='Run a zmq performance test')
parser.add_argument('-p', '--poll', action='store_true',
help='use a zmq Poller instead of raw send/recv')
parser.add_argument('-c', '--copy', action='store_true',
help='copy messages instead of using zero-copy')
parser.add_argument('-s', '--size', type=int, default=10240,
help='size (in bytes) of the test message')
parser.add_argument('-n', '--count', type=int, default=10240,
help='number of test messages to send')
parser.add_argument('--url', dest='url', type=str, default='tcp://127.0.0.1:5555',
help='the zmq URL on which to run the test')
parser.add_argument(dest='test', type=str, default='lat', choices=['lat', 'thr'],
help='which test to run')
return parser.parse_args(argv)
def latency_echo(url, count, poll, copy):
"""echo messages on a REP socket
Should be started before `latency`
"""
ctx = zmq.Context()
s = ctx.socket(zmq.REP)
if poll:
p = zmq.Poller()
p.register(s)
s.bind(url)
block = zmq.NOBLOCK if poll else 0
for i in range(count):
if poll:
res = p.poll()
msg = s.recv(block, copy=copy)
if poll:
res = p.poll()
s.send(msg, block, copy=copy)
msg = s.recv()
assert msg == b'done'
s.close()
ctx.term()
def latency(url, count, size, poll, copy):
"""Perform a latency test"""
ctx = zmq.Context()
s = ctx.socket(zmq.REQ)
s.setsockopt(zmq.LINGER, -1)
s.connect(url)
if poll:
p = zmq.Poller()
p.register(s)
msg = b' ' * size
block = zmq.NOBLOCK if poll else 0
time.sleep(1)
start = now()
for i in range (0, count):
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLOUT)
s.send(msg, block, copy=copy)
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLIN)
msg = s.recv(block, copy=copy)
assert len(msg) == size
elapsed = now() - start
s.send(b'done')
latency = 1e6 * elapsed / (count * 2.)
print ("message size : %8i [B]" % (size, ))
print ("roundtrip count: %8i [msgs]" % (count, ))
print ("mean latency : %12.3f [µs]" % (latency, ))
print ("test time : %12.3f [s]" % (elapsed, ))
def pusher(url, count, size, poll, copy):
"""send a bunch of messages on a PUSH socket"""
ctx = zmq.Context()
s = ctx.socket(zmq.PUSH)
# Add your socket options here.
# For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM.
if poll:
p = zmq.Poller()
p.register(s)
s.connect(url)
msg = zmq.Message(b' ' * size)
block = zmq.NOBLOCK if poll else 0
for i in range(count):
if poll:
res = p.poll()
assert(res[0][1] & zmq.POLLOUT)
s.send(msg, block, copy=copy)
s.close()
ctx.term()
def throughput(url, count, size, poll, copy):
"""recv a bunch of messages on a PULL socket
Should be started before `pusher`
"""
ctx = zmq.Context()
s = ctx.socket(zmq.PULL)
# Add your socket options here.
# For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM.
if poll:
p = zmq.Poller()
p.register(s)
s.bind(url)
block = zmq.NOBLOCK if poll else 0
# Wait for the other side to connect.
msg = s.recv()
assert len (msg) == size
start = now()
for i in range (count-1):
if poll:
res = p.poll()
msg = s.recv(block, copy=copy)
elapsed = now() - start
throughput = (float(count)) / float(elapsed)
megabits = float(throughput * size * 8) / 1e6
print ("message size : %8i [B]" % (size, ))
print ("message count : %8i [msgs]" % (count, ))
print ("mean throughput: %8.0f [msg/s]" % (throughput, ))
print ("mean throughput: %12.3f [Mb/s]" % (megabits, ))
print ("test time : %12.3f [s]" % (elapsed, ))
def main():
args = parse_args()
tic = time.time()
if args.test == 'lat':
bg = Process(target=latency_echo, args=(args.url, args.count, args.poll, args.copy))
bg.start()
latency(args.url, args.count, args.size, args.poll, args.copy)
elif args.test == 'thr':
bg = Process(target=throughput, args=(args.url, args.count, args.size, args.poll, args.copy))
bg.start()
pusher(args.url, args.count, args.size, args.poll, args.copy)
bg.join()
toc = time.time()
if (toc - tic) < 3:
print ("For best results, tests should take at least a few seconds.")
if __name__ == '__main__':
main()
| en | 0.690346 | #!/usr/bin/env python # coding: utf-8 # Copyright (C) PyZMQ Developers # Distributed under the terms of the Modified BSD License. # # Some original test code Copyright (c) 2007-2010 iMatix Corporation, # Used under LGPLv3 echo messages on a REP socket Should be started before `latency` Perform a latency test send a bunch of messages on a PUSH socket # Add your socket options here. # For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM. recv a bunch of messages on a PULL socket Should be started before `pusher` # Add your socket options here. # For example ZMQ_RATE, ZMQ_RECOVERY_IVL and ZMQ_MCAST_LOOP for PGM. # Wait for the other side to connect. | 2.556052 | 3 |
cms/tests/plugins.py | s-a-s-forks/django-cms | 1 | 6614262 | <reponame>s-a-s-forks/django-cms
# -*- coding: utf-8 -*-
from cms.exceptions import PluginAlreadyRegistered, PluginNotRegistered
from cms.models import Page, Placeholder
from cms.models.pluginmodel import CMSPlugin
from cms.plugin_base import CMSPluginBase
from cms.plugin_pool import plugin_pool
from cms.plugins.file.models import File
from cms.plugins.googlemap.models import GoogleMap
from cms.plugins.inherit.models import InheritPagePlaceholder
from cms.plugins.text.models import Text
from cms.plugins.text.utils import (plugin_tags_to_id_list,
plugin_tags_to_admin_html)
from cms.test.testcases import (CMSTestCase, URL_CMS_PAGE, URL_CMS_PAGE_ADD,
URL_CMS_PLUGIN_ADD, URL_CMS_PLUGIN_EDIT, URL_CMS_PAGE_CHANGE,
URL_CMS_PLUGIN_REMOVE)
from cms.test.util.context_managers import SettingsOverride
from django.conf import settings
from django.contrib.auth.models import User
from django.core.files.uploadedfile import SimpleUploadedFile
from django.core.urlresolvers import reverse
from django.forms.widgets import Media
from django.template import RequestContext
from testapp.pluginapp.models import Article, Section
from testapp.pluginapp.plugins.manytomany_rel.models import ArticlePluginModel
import os
class DumbFixturePlugin(CMSPluginBase):
model = CMSPlugin
name = "Dumb Test Plugin. It does nothing."
render_template = ""
admin_preview = False
def render(self, context, instance, placeholder):
return context
class PluginsTestBaseCase(CMSTestCase):
def setUp(self):
self.super_user = User(username="test", is_staff = True, is_active = True, is_superuser = True)
self.super_user.set_password("<PASSWORD>")
self.super_user.save()
self.slave = User(username="slave", is_staff=True, is_active=True, is_superuser=False)
self.slave.set_password("<PASSWORD>")
self.slave.save()
self.login_user(self.super_user)
self.FIRST_LANG = settings.LANGUAGES[0][0]
self.SECOND_LANG = settings.LANGUAGES[1][0]
# REFACTOR - the publish and appove methods exist in this file and in permmod.py - should they be in base?
def publish_page(self, page, approve=False, user=None, published_check=True):
if user:
self.login_user(user)
# publish / approve page by master
response = self.client.post(URL_CMS_PAGE + "%d/change-status/" % page.pk, {1 :1})
self.assertEqual(response.status_code, 200)
if not approve:
return self.reload_page(page)
# approve
page = self.approve_page(page)
if published_check:
# must have public object now
assert(page.publisher_public)
# and public object must be published
assert(page.publisher_public.published)
return page
def approve_page(self, page):
response = self.client.get(URL_CMS_PAGE + "%d/approve/" % page.pk)
self.assertRedirects(response, URL_CMS_PAGE)
# reload page
return self.reload_page(page)
def get_request(self, *args, **kwargs):
request = super(PluginsTestBaseCase, self).get_request(*args, **kwargs)
request.placeholder_media = Media()
return request
class PluginsTestCase(PluginsTestBaseCase):
def test_01_add_edit_plugin(self):
"""
Test that you can add a text plugin
"""
# add a new text plugin
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
response = self.client.get(edit_url)
self.assertEquals(response.status_code, 200)
data = {
"body":"Hello World"
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
txt = Text.objects.all()[0]
self.assertEquals("Hello World", txt.body)
def test_02_copy_plugins(self):
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(len(settings.LANGUAGES) > 1, True)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
text_plugin_pk = int(response.content)
self.assertEquals(text_plugin_pk, CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
data = {
"body":"Hello World"
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
txt = Text.objects.all()[0]
self.assertEquals("Hello World", txt.body)
# add an inline link
#/admin/cms/page/2799/edit-plugin/17570/add-plugin/
#http://127.0.0.1/admin/cms/page/2799/edit-plugin/17570/edit-plugin/17574/?_popup=1
add_url = '%s%s/add-plugin/' % (URL_CMS_PLUGIN_EDIT, text_plugin_pk)
data = {
'plugin_type': "LinkPlugin",
"parent_id": txt.pk,
"language": settings.LANGUAGES[0][0],
}
response = self.client.post(add_url, data)
link_pk = response.content
self.assertEqual(response.status_code, 200)
# edit the inline link plugin
edit_url = '%s%s/edit-plugin/%s/' % (URL_CMS_PLUGIN_EDIT, text_plugin_pk, link_pk)
data = {
'name': "A Link",
'url': "http://www.divio.ch",
}
response = self.client.post(edit_url, data)
self.assertEqual(response.status_code, 200)
self.assertEqual(CMSPlugin.objects.get(pk=link_pk).parent.pk, txt.pk)
#create 2nd language page
page_data['language'] = settings.LANGUAGES[1][0]
page_data['title'] += " %s" % settings.LANGUAGES[1][0]
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % settings.LANGUAGES[1][0], page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.all().count(), 2)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder':page.placeholders.get(slot="body").pk,
'language':settings.LANGUAGES[1][0],
'copy_from':settings.LANGUAGES[0][0],
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 1)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=settings.LANGUAGES[0][0]).count(), 2)
self.assertEquals(CMSPlugin.objects.filter(language=settings.LANGUAGES[1][0]).count(), 2)
self.assertEquals(CMSPlugin.objects.all().count(), 4)
# assert plugin tree
for link in CMSPlugin.objects.filter(plugin_type="LinkPlugin"):
self.assertNotEqual(link.parent, None)
for text in Text.objects.all():
self.assertEquals(text.body, "Hello World")
def test_03_remove_plugin_before_published(self):
"""
When removing a draft plugin we would expect the public copy of the plugin to also be removed
"""
# add a page
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
# delete the plugin
plugin_data = {
'plugin_id': int(response.content)
}
remove_url = URL_CMS_PLUGIN_REMOVE
response = self.client.post(remove_url, plugin_data)
self.assertEquals(response.status_code, 200)
# there should be no plugins
self.assertEquals(0, CMSPlugin.objects.all().count())
def test_04_remove_plugin_after_published(self):
# add a page
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
plugin_id = int(response.content)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
# publish page
response = self.client.post(URL_CMS_PAGE + "%d/change-status/" % page.pk, {1 :1})
self.assertEqual(response.status_code, 200)
# there should now be two plugins - 1 draft, 1 public
self.assertEquals(CMSPlugin.objects.all().count(), 2)
# delete the plugin
plugin_data = {
'plugin_id': plugin_id
}
remove_url = URL_CMS_PLUGIN_REMOVE
response = self.client.post(remove_url, plugin_data)
self.assertEquals(response.status_code, 200)
# there should be no plugins
self.assertEquals(CMSPlugin.objects.all().count(), 0)
def test_05_remove_plugin_not_associated_to_page(self):
"""
Test case for PlaceholderField
"""
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
ph = Placeholder(slot="subplugin")
ph.save()
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder': ph.pk,
'parent': int(response.content)
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
# no longer allowed for security reasons
self.assertEqual(response.status_code, 404)
def test_07_register_plugin_twice_should_raise(self):
number_of_plugins_before = len(plugin_pool.get_all_plugins())
# The first time we register the plugin is should work
plugin_pool.register_plugin(DumbFixturePlugin)
# Let's add it a second time. We should catch and exception
raised = False
try:
plugin_pool.register_plugin(DumbFixturePlugin)
except PluginAlreadyRegistered:
raised = True
self.assertTrue(raised)
# Let's also unregister the plugin now, and assert it's not in the
# pool anymore
plugin_pool.unregister_plugin(DumbFixturePlugin)
# Let's make sure we have the same number of plugins as before:
number_of_plugins_after = len(plugin_pool.get_all_plugins())
self.assertEqual(number_of_plugins_before, number_of_plugins_after)
def test_08_unregister_non_existing_plugin_should_raise(self):
number_of_plugins_before = len(plugin_pool.get_all_plugins())
raised = False
try:
# There should not be such a plugin registered if the others tests
# don't leak plugins
plugin_pool.unregister_plugin(DumbFixturePlugin)
except PluginNotRegistered:
raised = True
self.assertTrue(raised)
# Let's count, to make sure we didn't remove a plugin accidentally.
number_of_plugins_after = len(plugin_pool.get_all_plugins())
self.assertEqual(number_of_plugins_before, number_of_plugins_after)
def test_09_iheritplugin_media(self):
"""
Test case for InheritPagePlaceholder
"""
inheritfrompage = self.create_page(title='page to inherit from')
body = inheritfrompage.placeholders.get(slot="body")
plugin = GoogleMap(
plugin_type='GoogleMapPlugin',
placeholder=body,
position=1,
language=settings.LANGUAGE_CODE, lat=1, lng=1)
plugin.insert_at(None, position='last-child', commit=True)
page = self.create_page(title='inherit from page')
inherited_body = page.placeholders.get(slot="body")
inherit_plugin = InheritPagePlaceholder(
plugin_type='InheritPagePlaceholderPlugin',
placeholder=inherited_body,
position=1,
language=settings.LANGUAGE_CODE,
from_page=inheritfrompage,
from_language=settings.LANGUAGE_CODE)
inherit_plugin.insert_at(None, position='last-child', commit=True)
request = self.get_request()
context = RequestContext(request, {})
inherit_plugin.render_plugin(context, inherited_body)
self.assertEquals(unicode(request.placeholder_media).find('maps.google.com') != -1, True)
def test_10_fileplugin_icon_uppercase(self):
page = self.create_page(title='testpage')
body = page.placeholders.get(slot="body")
plugin = File(
plugin_type='FilePlugin',
placeholder=body,
position=1,
language=settings.LANGUAGE_CODE,
)
plugin.file.save("UPPERCASE.JPG", SimpleUploadedFile("UPPERCASE.jpg", "content"), False)
plugin.insert_at(None, position='last-child', commit=True)
self.assertNotEquals(plugin.get_icon_url().find('jpg'), -1)
response = self.client.get(plugin.get_icon_url(), follow=True)
self.assertEqual(response.status_code, 200)
# Nuke everything in the storage location directory (since removing just
# our file would still leave a useless directory structure)
#
# By the way, plugin.file.storage.delete(plugin.file.name) does not work
# since the delete method is a pass... See reversion.storage.delete()
storage_location = plugin.file.storage.location # This is ".../media/"
for root, dirs, files in os.walk(storage_location, topdown=False):
# We need to walk() the directory tree since rmdir() does not allow
# to remove non-empty directories...
for name in files:
# Start by killing all files we walked
os.remove(os.path.join(root, name))
for name in dirs:
# Now all directories we walked...
os.rmdir(os.path.join(root, name))
def test_11_copy_textplugin(self):
"""
Test that copying of textplugins replaces references to copied plugins
"""
page = self.create_page()
placeholder = page.placeholders.get(slot='body')
plugin_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin_base.insert_at(None, position='last-child', commit=False)
plugin = Text(body='')
plugin_base.set_base_attr(plugin)
plugin.save()
plugin_ref_1_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin_ref_1_base.insert_at(plugin_base, position='last-child', commit=False)
plugin_ref_1 = Text(body='')
plugin_ref_1_base.set_base_attr(plugin_ref_1)
plugin_ref_1.save()
plugin_ref_2_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=2,
language=self.FIRST_LANG)
plugin_ref_2_base.insert_at(plugin_base, position='last-child', commit=False)
plugin_ref_2 = Text(body='')
plugin_ref_2_base.set_base_attr(plugin_ref_2)
plugin_ref_2.save()
plugin.body = plugin_tags_to_admin_html(' {{ plugin_object %s }} {{ plugin_object %s }} ' % (str(plugin_ref_1.pk), str(plugin_ref_2.pk)))
plugin.save()
self.assertEquals(plugin.pk, 1)
page_data = self.get_new_page_data()
#create 2nd language page
page_data.update({
'language': self.SECOND_LANG,
'title': "%s %s" % (page.get_title(), self.SECOND_LANG),
})
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % self.SECOND_LANG, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 0)
self.assertEquals(CMSPlugin.objects.count(), 3)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder': placeholder.pk,
'language': self.SECOND_LANG,
'copy_from': self.FIRST_LANG,
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 3)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.count(), 6)
new_plugin = Text.objects.get(pk=6)
self.assertEquals(plugin_tags_to_id_list(new_plugin.body), [u'4', u'5'])
class PluginManyToManyTestCase(PluginsTestBaseCase):
def setUp(self):
self.super_user = User(username="test", is_staff = True, is_active = True, is_superuser = True)
self.super_user.set_password("<PASSWORD>")
self.super_user.save()
self.slave = User(username="slave", is_staff=True, is_active=True, is_superuser=False)
self.slave.set_password("<PASSWORD>")
self.slave.save()
self.login_user(self.super_user)
# create 3 sections
self.sections = []
self.section_pks = []
for i in range(3):
section = Section.objects.create(name="section %s" %i)
self.sections.append(section)
self.section_pks.append(section.pk)
self.section_count = len(self.sections)
# create 10 articles by section
for section in self.sections:
for j in range(10):
Article.objects.create(
title="article %s" % j,
section=section
)
self.FIRST_LANG = settings.LANGUAGES[0][0]
self.SECOND_LANG = settings.LANGUAGES[1][0]
def test_01_add_plugin_with_m2m(self):
# add a new text plugin
page_data = self.get_new_page_data()
self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
plugin_data = {
'plugin_type': "ArticlePlugin",
'language': self.FIRST_LANG,
'placeholder': placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
response = self.client.get(edit_url)
self.assertEquals(response.status_code, 200)
data = {
'title': "Articles Plugin 1",
"sections": self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEqual(response.status_code, 200)
self.assertEqual(ArticlePluginModel.objects.count(), 1)
plugin = ArticlePluginModel.objects.all()[0]
self.assertEquals(self.section_count, plugin.sections.count())
def test_01_add_plugin_with_m2m_and_publisher(self):
page_data = self.get_new_page_data()
self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
# add a plugin
plugin_data = {
'plugin_type': "ArticlePlugin",
'language': self.FIRST_LANG,
'placeholder': placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(1, CMSPlugin.objects.all().count())
articles_plugin_pk = int(response.content)
self.assertEquals(articles_plugin_pk, CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
data = {
'title': "Articles Plugin 1",
'sections': self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
self.assertEquals(1, ArticlePluginModel.objects.count())
articles_plugin = ArticlePluginModel.objects.all()[0]
self.assertEquals(u'Articles Plugin 1', articles_plugin.title)
self.assertEquals(self.section_count, articles_plugin.sections.count())
# check publish box
page = self.publish_page(page)
# there should now be two plugins - 1 draft, 1 public
self.assertEquals(2, ArticlePluginModel.objects.all().count())
db_counts = [plugin.sections.count() for plugin in ArticlePluginModel.objects.all()]
expected = [self.section_count for i in range(len(db_counts))]
self.assertEqual(expected, db_counts)
def test_03_copy_plugin_with_m2m(self):
page = self.create_page()
placeholder = page.placeholders.get(slot='body')
plugin = ArticlePluginModel(
plugin_type='ArticlePlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin.insert_at(None, position='last-child', commit=True)
edit_url = URL_CMS_PLUGIN_EDIT + str(plugin.pk) + "/"
data = {
'title': "Articles Plugin 1",
"sections": self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
self.assertEqual(ArticlePluginModel.objects.count(), 1)
self.assertEqual(ArticlePluginModel.objects.all()[0].sections.count(), self.section_count)
page_data = self.get_new_page_data()
#create 2nd language page
page_data.update({
'language': self.SECOND_LANG,
'title': "%s %s" % (page.get_title(), self.SECOND_LANG),
})
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % self.SECOND_LANG, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 0)
self.assertEquals(CMSPlugin.objects.count(), 1)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder': placeholder.pk,
'language': self.SECOND_LANG,
'copy_from': self.FIRST_LANG,
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 1)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.count(), 2)
db_counts = [plugin.sections.count() for plugin in ArticlePluginModel.objects.all()]
expected = [self.section_count for i in range(len(db_counts))]
self.assertEqual(expected, db_counts)
| # -*- coding: utf-8 -*-
from cms.exceptions import PluginAlreadyRegistered, PluginNotRegistered
from cms.models import Page, Placeholder
from cms.models.pluginmodel import CMSPlugin
from cms.plugin_base import CMSPluginBase
from cms.plugin_pool import plugin_pool
from cms.plugins.file.models import File
from cms.plugins.googlemap.models import GoogleMap
from cms.plugins.inherit.models import InheritPagePlaceholder
from cms.plugins.text.models import Text
from cms.plugins.text.utils import (plugin_tags_to_id_list,
plugin_tags_to_admin_html)
from cms.test.testcases import (CMSTestCase, URL_CMS_PAGE, URL_CMS_PAGE_ADD,
URL_CMS_PLUGIN_ADD, URL_CMS_PLUGIN_EDIT, URL_CMS_PAGE_CHANGE,
URL_CMS_PLUGIN_REMOVE)
from cms.test.util.context_managers import SettingsOverride
from django.conf import settings
from django.contrib.auth.models import User
from django.core.files.uploadedfile import SimpleUploadedFile
from django.core.urlresolvers import reverse
from django.forms.widgets import Media
from django.template import RequestContext
from testapp.pluginapp.models import Article, Section
from testapp.pluginapp.plugins.manytomany_rel.models import ArticlePluginModel
import os
class DumbFixturePlugin(CMSPluginBase):
model = CMSPlugin
name = "Dumb Test Plugin. It does nothing."
render_template = ""
admin_preview = False
def render(self, context, instance, placeholder):
return context
class PluginsTestBaseCase(CMSTestCase):
def setUp(self):
self.super_user = User(username="test", is_staff = True, is_active = True, is_superuser = True)
self.super_user.set_password("<PASSWORD>")
self.super_user.save()
self.slave = User(username="slave", is_staff=True, is_active=True, is_superuser=False)
self.slave.set_password("<PASSWORD>")
self.slave.save()
self.login_user(self.super_user)
self.FIRST_LANG = settings.LANGUAGES[0][0]
self.SECOND_LANG = settings.LANGUAGES[1][0]
# REFACTOR - the publish and appove methods exist in this file and in permmod.py - should they be in base?
def publish_page(self, page, approve=False, user=None, published_check=True):
if user:
self.login_user(user)
# publish / approve page by master
response = self.client.post(URL_CMS_PAGE + "%d/change-status/" % page.pk, {1 :1})
self.assertEqual(response.status_code, 200)
if not approve:
return self.reload_page(page)
# approve
page = self.approve_page(page)
if published_check:
# must have public object now
assert(page.publisher_public)
# and public object must be published
assert(page.publisher_public.published)
return page
def approve_page(self, page):
response = self.client.get(URL_CMS_PAGE + "%d/approve/" % page.pk)
self.assertRedirects(response, URL_CMS_PAGE)
# reload page
return self.reload_page(page)
def get_request(self, *args, **kwargs):
request = super(PluginsTestBaseCase, self).get_request(*args, **kwargs)
request.placeholder_media = Media()
return request
class PluginsTestCase(PluginsTestBaseCase):
def test_01_add_edit_plugin(self):
"""
Test that you can add a text plugin
"""
# add a new text plugin
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
response = self.client.get(edit_url)
self.assertEquals(response.status_code, 200)
data = {
"body":"Hello World"
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
txt = Text.objects.all()[0]
self.assertEquals("Hello World", txt.body)
def test_02_copy_plugins(self):
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(len(settings.LANGUAGES) > 1, True)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
text_plugin_pk = int(response.content)
self.assertEquals(text_plugin_pk, CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
data = {
"body":"Hello World"
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
txt = Text.objects.all()[0]
self.assertEquals("Hello World", txt.body)
# add an inline link
#/admin/cms/page/2799/edit-plugin/17570/add-plugin/
#http://127.0.0.1/admin/cms/page/2799/edit-plugin/17570/edit-plugin/17574/?_popup=1
add_url = '%s%s/add-plugin/' % (URL_CMS_PLUGIN_EDIT, text_plugin_pk)
data = {
'plugin_type': "LinkPlugin",
"parent_id": txt.pk,
"language": settings.LANGUAGES[0][0],
}
response = self.client.post(add_url, data)
link_pk = response.content
self.assertEqual(response.status_code, 200)
# edit the inline link plugin
edit_url = '%s%s/edit-plugin/%s/' % (URL_CMS_PLUGIN_EDIT, text_plugin_pk, link_pk)
data = {
'name': "A Link",
'url': "http://www.divio.ch",
}
response = self.client.post(edit_url, data)
self.assertEqual(response.status_code, 200)
self.assertEqual(CMSPlugin.objects.get(pk=link_pk).parent.pk, txt.pk)
#create 2nd language page
page_data['language'] = settings.LANGUAGES[1][0]
page_data['title'] += " %s" % settings.LANGUAGES[1][0]
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % settings.LANGUAGES[1][0], page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.all().count(), 2)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder':page.placeholders.get(slot="body").pk,
'language':settings.LANGUAGES[1][0],
'copy_from':settings.LANGUAGES[0][0],
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 1)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=settings.LANGUAGES[0][0]).count(), 2)
self.assertEquals(CMSPlugin.objects.filter(language=settings.LANGUAGES[1][0]).count(), 2)
self.assertEquals(CMSPlugin.objects.all().count(), 4)
# assert plugin tree
for link in CMSPlugin.objects.filter(plugin_type="LinkPlugin"):
self.assertNotEqual(link.parent, None)
for text in Text.objects.all():
self.assertEquals(text.body, "Hello World")
def test_03_remove_plugin_before_published(self):
"""
When removing a draft plugin we would expect the public copy of the plugin to also be removed
"""
# add a page
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
# delete the plugin
plugin_data = {
'plugin_id': int(response.content)
}
remove_url = URL_CMS_PLUGIN_REMOVE
response = self.client.post(remove_url, plugin_data)
self.assertEquals(response.status_code, 200)
# there should be no plugins
self.assertEquals(0, CMSPlugin.objects.all().count())
def test_04_remove_plugin_after_published(self):
# add a page
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
plugin_id = int(response.content)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
# publish page
response = self.client.post(URL_CMS_PAGE + "%d/change-status/" % page.pk, {1 :1})
self.assertEqual(response.status_code, 200)
# there should now be two plugins - 1 draft, 1 public
self.assertEquals(CMSPlugin.objects.all().count(), 2)
# delete the plugin
plugin_data = {
'plugin_id': plugin_id
}
remove_url = URL_CMS_PLUGIN_REMOVE
response = self.client.post(remove_url, plugin_data)
self.assertEquals(response.status_code, 200)
# there should be no plugins
self.assertEquals(CMSPlugin.objects.all().count(), 0)
def test_05_remove_plugin_not_associated_to_page(self):
"""
Test case for PlaceholderField
"""
page_data = self.get_new_page_data()
response = self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
# add a plugin
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder':page.placeholders.get(slot="body").pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(CMSPlugin.objects.all().count(), 1)
ph = Placeholder(slot="subplugin")
ph.save()
plugin_data = {
'plugin_type':"TextPlugin",
'language':settings.LANGUAGES[0][0],
'placeholder': ph.pk,
'parent': int(response.content)
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
# no longer allowed for security reasons
self.assertEqual(response.status_code, 404)
def test_07_register_plugin_twice_should_raise(self):
number_of_plugins_before = len(plugin_pool.get_all_plugins())
# The first time we register the plugin is should work
plugin_pool.register_plugin(DumbFixturePlugin)
# Let's add it a second time. We should catch and exception
raised = False
try:
plugin_pool.register_plugin(DumbFixturePlugin)
except PluginAlreadyRegistered:
raised = True
self.assertTrue(raised)
# Let's also unregister the plugin now, and assert it's not in the
# pool anymore
plugin_pool.unregister_plugin(DumbFixturePlugin)
# Let's make sure we have the same number of plugins as before:
number_of_plugins_after = len(plugin_pool.get_all_plugins())
self.assertEqual(number_of_plugins_before, number_of_plugins_after)
def test_08_unregister_non_existing_plugin_should_raise(self):
number_of_plugins_before = len(plugin_pool.get_all_plugins())
raised = False
try:
# There should not be such a plugin registered if the others tests
# don't leak plugins
plugin_pool.unregister_plugin(DumbFixturePlugin)
except PluginNotRegistered:
raised = True
self.assertTrue(raised)
# Let's count, to make sure we didn't remove a plugin accidentally.
number_of_plugins_after = len(plugin_pool.get_all_plugins())
self.assertEqual(number_of_plugins_before, number_of_plugins_after)
def test_09_iheritplugin_media(self):
"""
Test case for InheritPagePlaceholder
"""
inheritfrompage = self.create_page(title='page to inherit from')
body = inheritfrompage.placeholders.get(slot="body")
plugin = GoogleMap(
plugin_type='GoogleMapPlugin',
placeholder=body,
position=1,
language=settings.LANGUAGE_CODE, lat=1, lng=1)
plugin.insert_at(None, position='last-child', commit=True)
page = self.create_page(title='inherit from page')
inherited_body = page.placeholders.get(slot="body")
inherit_plugin = InheritPagePlaceholder(
plugin_type='InheritPagePlaceholderPlugin',
placeholder=inherited_body,
position=1,
language=settings.LANGUAGE_CODE,
from_page=inheritfrompage,
from_language=settings.LANGUAGE_CODE)
inherit_plugin.insert_at(None, position='last-child', commit=True)
request = self.get_request()
context = RequestContext(request, {})
inherit_plugin.render_plugin(context, inherited_body)
self.assertEquals(unicode(request.placeholder_media).find('maps.google.com') != -1, True)
def test_10_fileplugin_icon_uppercase(self):
page = self.create_page(title='testpage')
body = page.placeholders.get(slot="body")
plugin = File(
plugin_type='FilePlugin',
placeholder=body,
position=1,
language=settings.LANGUAGE_CODE,
)
plugin.file.save("UPPERCASE.JPG", SimpleUploadedFile("UPPERCASE.jpg", "content"), False)
plugin.insert_at(None, position='last-child', commit=True)
self.assertNotEquals(plugin.get_icon_url().find('jpg'), -1)
response = self.client.get(plugin.get_icon_url(), follow=True)
self.assertEqual(response.status_code, 200)
# Nuke everything in the storage location directory (since removing just
# our file would still leave a useless directory structure)
#
# By the way, plugin.file.storage.delete(plugin.file.name) does not work
# since the delete method is a pass... See reversion.storage.delete()
storage_location = plugin.file.storage.location # This is ".../media/"
for root, dirs, files in os.walk(storage_location, topdown=False):
# We need to walk() the directory tree since rmdir() does not allow
# to remove non-empty directories...
for name in files:
# Start by killing all files we walked
os.remove(os.path.join(root, name))
for name in dirs:
# Now all directories we walked...
os.rmdir(os.path.join(root, name))
def test_11_copy_textplugin(self):
"""
Test that copying of textplugins replaces references to copied plugins
"""
page = self.create_page()
placeholder = page.placeholders.get(slot='body')
plugin_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin_base.insert_at(None, position='last-child', commit=False)
plugin = Text(body='')
plugin_base.set_base_attr(plugin)
plugin.save()
plugin_ref_1_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin_ref_1_base.insert_at(plugin_base, position='last-child', commit=False)
plugin_ref_1 = Text(body='')
plugin_ref_1_base.set_base_attr(plugin_ref_1)
plugin_ref_1.save()
plugin_ref_2_base = CMSPlugin(
plugin_type='TextPlugin',
placeholder=placeholder,
position=2,
language=self.FIRST_LANG)
plugin_ref_2_base.insert_at(plugin_base, position='last-child', commit=False)
plugin_ref_2 = Text(body='')
plugin_ref_2_base.set_base_attr(plugin_ref_2)
plugin_ref_2.save()
plugin.body = plugin_tags_to_admin_html(' {{ plugin_object %s }} {{ plugin_object %s }} ' % (str(plugin_ref_1.pk), str(plugin_ref_2.pk)))
plugin.save()
self.assertEquals(plugin.pk, 1)
page_data = self.get_new_page_data()
#create 2nd language page
page_data.update({
'language': self.SECOND_LANG,
'title': "%s %s" % (page.get_title(), self.SECOND_LANG),
})
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % self.SECOND_LANG, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 0)
self.assertEquals(CMSPlugin.objects.count(), 3)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder': placeholder.pk,
'language': self.SECOND_LANG,
'copy_from': self.FIRST_LANG,
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 3)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 3)
self.assertEquals(CMSPlugin.objects.count(), 6)
new_plugin = Text.objects.get(pk=6)
self.assertEquals(plugin_tags_to_id_list(new_plugin.body), [u'4', u'5'])
class PluginManyToManyTestCase(PluginsTestBaseCase):
def setUp(self):
self.super_user = User(username="test", is_staff = True, is_active = True, is_superuser = True)
self.super_user.set_password("<PASSWORD>")
self.super_user.save()
self.slave = User(username="slave", is_staff=True, is_active=True, is_superuser=False)
self.slave.set_password("<PASSWORD>")
self.slave.save()
self.login_user(self.super_user)
# create 3 sections
self.sections = []
self.section_pks = []
for i in range(3):
section = Section.objects.create(name="section %s" %i)
self.sections.append(section)
self.section_pks.append(section.pk)
self.section_count = len(self.sections)
# create 10 articles by section
for section in self.sections:
for j in range(10):
Article.objects.create(
title="article %s" % j,
section=section
)
self.FIRST_LANG = settings.LANGUAGES[0][0]
self.SECOND_LANG = settings.LANGUAGES[1][0]
def test_01_add_plugin_with_m2m(self):
# add a new text plugin
page_data = self.get_new_page_data()
self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
plugin_data = {
'plugin_type': "ArticlePlugin",
'language': self.FIRST_LANG,
'placeholder': placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
response = self.client.get(edit_url)
self.assertEquals(response.status_code, 200)
data = {
'title': "Articles Plugin 1",
"sections": self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEqual(response.status_code, 200)
self.assertEqual(ArticlePluginModel.objects.count(), 1)
plugin = ArticlePluginModel.objects.all()[0]
self.assertEquals(self.section_count, plugin.sections.count())
def test_01_add_plugin_with_m2m_and_publisher(self):
page_data = self.get_new_page_data()
self.client.post(URL_CMS_PAGE_ADD, page_data)
page = Page.objects.all()[0]
placeholder = page.placeholders.get(slot="body")
# add a plugin
plugin_data = {
'plugin_type': "ArticlePlugin",
'language': self.FIRST_LANG,
'placeholder': placeholder.pk,
}
response = self.client.post(URL_CMS_PLUGIN_ADD, plugin_data)
self.assertEquals(response.status_code, 200)
self.assertEquals(int(response.content), CMSPlugin.objects.all()[0].pk)
# there should be only 1 plugin
self.assertEquals(1, CMSPlugin.objects.all().count())
articles_plugin_pk = int(response.content)
self.assertEquals(articles_plugin_pk, CMSPlugin.objects.all()[0].pk)
# now edit the plugin
edit_url = URL_CMS_PLUGIN_EDIT + response.content + "/"
data = {
'title': "Articles Plugin 1",
'sections': self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
self.assertEquals(1, ArticlePluginModel.objects.count())
articles_plugin = ArticlePluginModel.objects.all()[0]
self.assertEquals(u'Articles Plugin 1', articles_plugin.title)
self.assertEquals(self.section_count, articles_plugin.sections.count())
# check publish box
page = self.publish_page(page)
# there should now be two plugins - 1 draft, 1 public
self.assertEquals(2, ArticlePluginModel.objects.all().count())
db_counts = [plugin.sections.count() for plugin in ArticlePluginModel.objects.all()]
expected = [self.section_count for i in range(len(db_counts))]
self.assertEqual(expected, db_counts)
def test_03_copy_plugin_with_m2m(self):
page = self.create_page()
placeholder = page.placeholders.get(slot='body')
plugin = ArticlePluginModel(
plugin_type='ArticlePlugin',
placeholder=placeholder,
position=1,
language=self.FIRST_LANG)
plugin.insert_at(None, position='last-child', commit=True)
edit_url = URL_CMS_PLUGIN_EDIT + str(plugin.pk) + "/"
data = {
'title': "Articles Plugin 1",
"sections": self.section_pks
}
response = self.client.post(edit_url, data)
self.assertEquals(response.status_code, 200)
self.assertEqual(ArticlePluginModel.objects.count(), 1)
self.assertEqual(ArticlePluginModel.objects.all()[0].sections.count(), self.section_count)
page_data = self.get_new_page_data()
#create 2nd language page
page_data.update({
'language': self.SECOND_LANG,
'title': "%s %s" % (page.get_title(), self.SECOND_LANG),
})
response = self.client.post(URL_CMS_PAGE_CHANGE % page.pk + "?language=%s" % self.SECOND_LANG, page_data)
self.assertRedirects(response, URL_CMS_PAGE)
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 0)
self.assertEquals(CMSPlugin.objects.count(), 1)
self.assertEquals(Page.objects.all().count(), 1)
copy_data = {
'placeholder': placeholder.pk,
'language': self.SECOND_LANG,
'copy_from': self.FIRST_LANG,
}
response = self.client.post(URL_CMS_PAGE + "copy-plugins/", copy_data)
self.assertEquals(response.status_code, 200)
self.assertEqual(response.content.count('<li '), 1)
# assert copy success
self.assertEquals(CMSPlugin.objects.filter(language=self.FIRST_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.filter(language=self.SECOND_LANG).count(), 1)
self.assertEquals(CMSPlugin.objects.count(), 2)
db_counts = [plugin.sections.count() for plugin in ArticlePluginModel.objects.all()]
expected = [self.section_count for i in range(len(db_counts))]
self.assertEqual(expected, db_counts) | en | 0.848298 | # -*- coding: utf-8 -*- # REFACTOR - the publish and appove methods exist in this file and in permmod.py - should they be in base? # publish / approve page by master # approve # must have public object now # and public object must be published # reload page Test that you can add a text plugin # add a new text plugin # now edit the plugin # now edit the plugin # add an inline link #/admin/cms/page/2799/edit-plugin/17570/add-plugin/ #http://127.0.0.1/admin/cms/page/2799/edit-plugin/17570/edit-plugin/17574/?_popup=1 # edit the inline link plugin #create 2nd language page # assert copy success # assert plugin tree When removing a draft plugin we would expect the public copy of the plugin to also be removed # add a page # add a plugin # there should be only 1 plugin # delete the plugin # there should be no plugins # add a page # add a plugin # there should be only 1 plugin # publish page # there should now be two plugins - 1 draft, 1 public # delete the plugin # there should be no plugins Test case for PlaceholderField # add a plugin # there should be only 1 plugin # no longer allowed for security reasons # The first time we register the plugin is should work # Let's add it a second time. We should catch and exception # Let's also unregister the plugin now, and assert it's not in the # pool anymore # Let's make sure we have the same number of plugins as before: # There should not be such a plugin registered if the others tests # don't leak plugins # Let's count, to make sure we didn't remove a plugin accidentally. Test case for InheritPagePlaceholder # Nuke everything in the storage location directory (since removing just # our file would still leave a useless directory structure) # # By the way, plugin.file.storage.delete(plugin.file.name) does not work # since the delete method is a pass... See reversion.storage.delete() # This is ".../media/" # We need to walk() the directory tree since rmdir() does not allow # to remove non-empty directories... # Start by killing all files we walked # Now all directories we walked... Test that copying of textplugins replaces references to copied plugins #create 2nd language page # assert copy success # create 3 sections # create 10 articles by section # add a new text plugin # now edit the plugin # add a plugin # there should be only 1 plugin # now edit the plugin # check publish box # there should now be two plugins - 1 draft, 1 public #create 2nd language page # assert copy success | 1.801171 | 2 |
test_project/test_project/ext_db_sqlite3_settings.py | xiva-wgt/django-fias | 108 | 6614263 | from .settings import *
DATABASES['fias'] = {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(PROJECT_PATH, 'fias.sqlite'),
}
| from .settings import *
DATABASES['fias'] = {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(PROJECT_PATH, 'fias.sqlite'),
}
| none | 1 | 1.222459 | 1 | |
src/chains/migrations/0006_i18n.py | tough-dev-school/education-backend | 62 | 6614264 | <filename>src/chains/migrations/0006_i18n.py
# Generated by Django 3.2.12 on 2022-03-19 11:48
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('products', '0019_CourseDisplayInLMS'),
('chains', '0005_MessageAdminSpeedup'),
]
operations = [
migrations.AlterField(
model_name='chain',
name='course',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='products.course', verbose_name='Course'),
),
migrations.AlterField(
model_name='chain',
name='name',
field=models.CharField(max_length=256, verbose_name='Name'),
),
migrations.AlterField(
model_name='chain',
name='sending_is_active',
field=models.BooleanField(default=False, verbose_name='Sending is active'),
),
migrations.AlterField(
model_name='message',
name='chain',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chains.chain', verbose_name='Chain'),
),
migrations.AlterField(
model_name='message',
name='delay',
field=models.BigIntegerField(default=0, help_text='86400 for day, 604800 for week', verbose_name='Delay (minutes)'),
),
migrations.AlterField(
model_name='message',
name='name',
field=models.CharField(max_length=256, verbose_name='Name'),
),
migrations.AlterField(
model_name='message',
name='parent',
field=models.ForeignKey(blank=True, help_text='Messages without parent will be sent upon start', null=True, on_delete=django.db.models.deletion.PROTECT, related_name='children', to='chains.message', verbose_name='Parent'),
),
migrations.AlterField(
model_name='message',
name='template_id',
field=models.CharField(max_length=256, verbose_name='Template id'),
),
]
| <filename>src/chains/migrations/0006_i18n.py
# Generated by Django 3.2.12 on 2022-03-19 11:48
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('products', '0019_CourseDisplayInLMS'),
('chains', '0005_MessageAdminSpeedup'),
]
operations = [
migrations.AlterField(
model_name='chain',
name='course',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='products.course', verbose_name='Course'),
),
migrations.AlterField(
model_name='chain',
name='name',
field=models.CharField(max_length=256, verbose_name='Name'),
),
migrations.AlterField(
model_name='chain',
name='sending_is_active',
field=models.BooleanField(default=False, verbose_name='Sending is active'),
),
migrations.AlterField(
model_name='message',
name='chain',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chains.chain', verbose_name='Chain'),
),
migrations.AlterField(
model_name='message',
name='delay',
field=models.BigIntegerField(default=0, help_text='86400 for day, 604800 for week', verbose_name='Delay (minutes)'),
),
migrations.AlterField(
model_name='message',
name='name',
field=models.CharField(max_length=256, verbose_name='Name'),
),
migrations.AlterField(
model_name='message',
name='parent',
field=models.ForeignKey(blank=True, help_text='Messages without parent will be sent upon start', null=True, on_delete=django.db.models.deletion.PROTECT, related_name='children', to='chains.message', verbose_name='Parent'),
),
migrations.AlterField(
model_name='message',
name='template_id',
field=models.CharField(max_length=256, verbose_name='Template id'),
),
]
| en | 0.808352 | # Generated by Django 3.2.12 on 2022-03-19 11:48 | 1.431325 | 1 |
GetStockData/getstockdata.py | scotthuang1989/FundAnalysis | 0 | 6614265 | from urllib.request import urlopen
def GetStockData(stock_code,start,end,filename):
#stock_code, end with .ss (shanghai) or .sz (shenzhen)
url_template = "http://ichart.yahoo.com/table.csv?s={0}&a={1}&b={2}&c={3}&d={4}&e={5}&f={6}&g=d"
url_data = url_template.format(stock_code,start.month-1,start.day,start.year,end.month-1,end.day,end.year)
print(url_data)
url_response = urlopen(url_data)
file_data = open(filename,'w')
file_data.write(url_response.read().decode("utf-8"))
file_data.close()
return True;
# except:
| from urllib.request import urlopen
def GetStockData(stock_code,start,end,filename):
#stock_code, end with .ss (shanghai) or .sz (shenzhen)
url_template = "http://ichart.yahoo.com/table.csv?s={0}&a={1}&b={2}&c={3}&d={4}&e={5}&f={6}&g=d"
url_data = url_template.format(stock_code,start.month-1,start.day,start.year,end.month-1,end.day,end.year)
print(url_data)
url_response = urlopen(url_data)
file_data = open(filename,'w')
file_data.write(url_response.read().decode("utf-8"))
file_data.close()
return True;
# except:
| en | 0.759552 | #stock_code, end with .ss (shanghai) or .sz (shenzhen) # except: | 3.196837 | 3 |
apps/drug_target_interaction/graph_dta/src/model.py | agave233/PaddleHelix | 454 | 6614266 | <reponame>agave233/PaddleHelix
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
DTA model
"""
import paddle
import paddle.nn as nn
import pgl
from pahelix.networks.compound_encoder import AtomEmbedding
from pahelix.utils.protein_tools import ProteinTokenizer
from pahelix.networks.gnn_block import MeanPool
class CompoundGNNModel(nn.Layer):
"""
| CompoundGNNModel, implementation of the variant GNN models in paper
``GraphDTA: Predicting drug-target binding affinity with graph neural networks``.
Public Functions:
- ``forward``: forward to create the compound representation.
"""
def __init__(self, config):
super(CompoundGNNModel, self).__init__()
self.hidden_size = config['hidden_size']
self.embed_dim = config['embed_dim']
self.output_dim = config['output_dim']
self.dropout_rate = config['dropout_rate']
self.layer_num = config['layer_num']
self.gnn_type = config['gnn_type']
self.gat_nheads = config.get('gat_nheads', 10)
self.activation = config.get('activation', 'relu')
self.atomic_numeric_feat_dim = config.get(
'atomic_numeric_feat_dim', 28)
self.atom_names = config['atom_names']
self.bond_names = config['bond_names']
self.atom_embedding = AtomEmbedding(self.atom_names, self.embed_dim)
self.gnn_layers = nn.LayerList()
if self.gnn_type == 'gcn':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GCNConv(
self._get_in_size(layer_id),
self.hidden_size,
activation=self.activation))
self.graph_pool = pgl.nn.GraphPool(pool_type='max')
self.fc = nn.Linear(self.hidden_size, self.output_dim)
elif self.gnn_type == 'gat':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GATConv(
self._get_in_size(layer_id, self.gat_nheads),
self.hidden_size,
activation=self.activation,
num_heads=self.gat_nheads,
feat_drop=self.dropout_rate,
attn_drop=self.dropout_rate))
self.graph_pool = pgl.nn.GraphPool(pool_type='max')
in_size = self.hidden_size * self.gat_nheads
self.fc = nn.Linear(in_size, self.output_dim)
elif self.gnn_type == 'gin':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GINConv(
self._get_in_size(layer_id),
self.hidden_size,
activation=self.activation))
self.gnn_layers.append(
nn.BatchNorm1D(self.hidden_size))
self.graph_pool = pgl.nn.GraphPool(pool_type='sum')
self.fc = nn.Linear(self.hidden_size, self.output_dim)
elif self.gnn_type == "gat_gcn":
self.gnn_layers.append(pgl.nn.GATConv(
self._get_in_size(0),
self.hidden_size,
activation=self.activation,
num_heads=self.gat_nheads,
feat_drop=0.0,
attn_drop=0.0))
self.gnn_layers.append(pgl.nn.GCNConv(
self.hidden_size * self.gat_nheads,
self.hidden_size * self.gat_nheads,
activation=self.activation))
self.graph_max_pool = pgl.nn.GraphPool(pool_type='max')
self.graph_avg_pool = MeanPool()
dim = self.hidden_size * self.gat_nheads * 2
self.fc1 = nn.Linear(dim, 1500)
self.act1 = nn.ReLU()
self.fc2 = nn.Linear(1500, self.output_dim)
self.dropout = nn.Dropout(p=self.dropout_rate)
def _get_in_size(self, layer_id, gat_heads=None):
in_size = self.embed_dim + self.atomic_numeric_feat_dim
gat_heads = 1 if gat_heads is None else gat_heads
if layer_id > 0:
in_size = self.hidden_size * gat_heads
return in_size
def _mol_encoder(self, graph):
x = self.atom_embedding(graph.node_feat)
x = paddle.squeeze(x, axis=1)
x = paddle.concat([x, graph.node_feat['atom_numeric_feat']], axis=1)
return x
def forward(self, graph):
"""Forward function.
Args:
graph (pgl.Graph): a PGL Graph instance.
"""
feat = self._mol_encoder(graph)
for i in range(len(self.gnn_layers)):
if isinstance(self.gnn_layers[i], nn.BatchNorm1D):
feat = self.gnn_layers[i](feat)
else:
feat = self.gnn_layers[i](graph, feat)
if self.gnn_type == 'gat_gcn':
x1 = self.graph_max_pool(graph, feat)
x2 = self.graph_avg_pool(graph, feat)
feat = paddle.concat([x1, x2], axis=1)
feat = self.dropout(self.act1(self.fc1(feat)))
feat = self.fc2(feat)
else:
feat = self.graph_pool(graph, feat)
feat = self.dropout(self.fc(feat))
return feat
class ProteinSequenceModel(nn.Layer):
"""
| ProteinSequenceModel, implementation of Conv1D model for protein representation.
Public Functions:
- ``forward``: forward to create protein sequence representation.
"""
def __init__(self, config):
super(ProteinSequenceModel, self).__init__()
self.config = config
self.output_dim = config['output_dim']
self.embed_dim = config['embed_dim']
self.max_protein_len = config['max_protein_len']
self.vocab_size = len(ProteinTokenizer.vocab)
self.num_filters = config.get('num_filters', 32)
self.pool_type = config.get('pool_type', 'mean')
self.initializer_range = config.get('initializer_range', 0.02)
self.protein_embeddings =nn.Embedding(
self.vocab_size, self.embed_dim,
weight_attr=nn.initializer.TruncatedNormal(
std=self.initializer_range))
self.conv1d = nn.Conv1D(
self.embed_dim, self.num_filters,
kernel_size=8, padding='SAME', data_format='NLC')
if self.max_protein_len < 0:
self.fc = nn.Linear(self.num_filters, self.output_dim)
else:
self.fc = nn.Linear(self.num_filters * self.max_protein_len, self.output_dim)
def forward(self, token, mask):
"""Forward.
Args:
token (Tensor): a tensor that represents the amino acid sequence as IDs.
mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding.
"""
token_emb = self.protein_embeddings(token)
feat = self.conv1d(token_emb)
if self.max_protein_len < 0:
# average pooling
feat = feat * paddle.unsqueeze(mask, 2)
feat = paddle.sum(feat, axis=1) / paddle.sum(mask, 1, keepdim=True)
else:
feat = paddle.reshape(feat, [-1, self.max_protein_len * self.num_filters])
feat = self.fc(feat)
return feat
class DTAModel(nn.Layer):
"""
| DTAModel, implementation of the network architecture in GraphDTA.
Public Functions:
- ``forward``: forward.
"""
def __init__(self, config):
super(DTAModel, self).__init__()
self.dropout_rate = config['dropout_rate']
self.compound_model = CompoundGNNModel(config['compound'])
self.protein_model = ProteinSequenceModel(config['protein'])
self.fc1 = nn.Linear(self.compound_model.output_dim + self.protein_model.output_dim, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 1)
self.act = nn.ReLU()
self.dropout = nn.Dropout(p=self.dropout_rate)
def forward(self, graph, protein_token, protein_mask):
"""Forward function.
Args:
graph (pgl.Graph): a PGL Graph instance.
protein_token (Tensor): a tensor that represents the amino acid sequence as IDs.
protein_mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding.
"""
compound_repr = self.compound_model(graph)
protein_repr = self.protein_model(protein_token, protein_mask)
compound_protein = paddle.concat(
[compound_repr, protein_repr], axis=1)
h = self.dropout(self.act(self.fc1(compound_protein)))
h = self.dropout(self.act(self.fc2(h)))
pred = self.fc3(h)
return pred
class DTAModelCriterion(nn.Layer):
"""
| DTAModelCriterion, implementation of MSE loss for DTA model.
Public Functions:
- ``forward``: forward function.
"""
def __init__(self):
super(DTAModelCriterion, self).__init__()
def forward(self, pred, label):
"""Forward function.
Args:
pred (Tensor): affinity predictions, i.e. output from DTAModel.
label (Tensor): affinity label.
"""
loss = nn.functional.square_error_cost(pred, label)
loss = paddle.mean(loss)
return loss
| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
DTA model
"""
import paddle
import paddle.nn as nn
import pgl
from pahelix.networks.compound_encoder import AtomEmbedding
from pahelix.utils.protein_tools import ProteinTokenizer
from pahelix.networks.gnn_block import MeanPool
class CompoundGNNModel(nn.Layer):
"""
| CompoundGNNModel, implementation of the variant GNN models in paper
``GraphDTA: Predicting drug-target binding affinity with graph neural networks``.
Public Functions:
- ``forward``: forward to create the compound representation.
"""
def __init__(self, config):
super(CompoundGNNModel, self).__init__()
self.hidden_size = config['hidden_size']
self.embed_dim = config['embed_dim']
self.output_dim = config['output_dim']
self.dropout_rate = config['dropout_rate']
self.layer_num = config['layer_num']
self.gnn_type = config['gnn_type']
self.gat_nheads = config.get('gat_nheads', 10)
self.activation = config.get('activation', 'relu')
self.atomic_numeric_feat_dim = config.get(
'atomic_numeric_feat_dim', 28)
self.atom_names = config['atom_names']
self.bond_names = config['bond_names']
self.atom_embedding = AtomEmbedding(self.atom_names, self.embed_dim)
self.gnn_layers = nn.LayerList()
if self.gnn_type == 'gcn':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GCNConv(
self._get_in_size(layer_id),
self.hidden_size,
activation=self.activation))
self.graph_pool = pgl.nn.GraphPool(pool_type='max')
self.fc = nn.Linear(self.hidden_size, self.output_dim)
elif self.gnn_type == 'gat':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GATConv(
self._get_in_size(layer_id, self.gat_nheads),
self.hidden_size,
activation=self.activation,
num_heads=self.gat_nheads,
feat_drop=self.dropout_rate,
attn_drop=self.dropout_rate))
self.graph_pool = pgl.nn.GraphPool(pool_type='max')
in_size = self.hidden_size * self.gat_nheads
self.fc = nn.Linear(in_size, self.output_dim)
elif self.gnn_type == 'gin':
for layer_id in range(self.layer_num):
self.gnn_layers.append(pgl.nn.GINConv(
self._get_in_size(layer_id),
self.hidden_size,
activation=self.activation))
self.gnn_layers.append(
nn.BatchNorm1D(self.hidden_size))
self.graph_pool = pgl.nn.GraphPool(pool_type='sum')
self.fc = nn.Linear(self.hidden_size, self.output_dim)
elif self.gnn_type == "gat_gcn":
self.gnn_layers.append(pgl.nn.GATConv(
self._get_in_size(0),
self.hidden_size,
activation=self.activation,
num_heads=self.gat_nheads,
feat_drop=0.0,
attn_drop=0.0))
self.gnn_layers.append(pgl.nn.GCNConv(
self.hidden_size * self.gat_nheads,
self.hidden_size * self.gat_nheads,
activation=self.activation))
self.graph_max_pool = pgl.nn.GraphPool(pool_type='max')
self.graph_avg_pool = MeanPool()
dim = self.hidden_size * self.gat_nheads * 2
self.fc1 = nn.Linear(dim, 1500)
self.act1 = nn.ReLU()
self.fc2 = nn.Linear(1500, self.output_dim)
self.dropout = nn.Dropout(p=self.dropout_rate)
def _get_in_size(self, layer_id, gat_heads=None):
in_size = self.embed_dim + self.atomic_numeric_feat_dim
gat_heads = 1 if gat_heads is None else gat_heads
if layer_id > 0:
in_size = self.hidden_size * gat_heads
return in_size
def _mol_encoder(self, graph):
x = self.atom_embedding(graph.node_feat)
x = paddle.squeeze(x, axis=1)
x = paddle.concat([x, graph.node_feat['atom_numeric_feat']], axis=1)
return x
def forward(self, graph):
"""Forward function.
Args:
graph (pgl.Graph): a PGL Graph instance.
"""
feat = self._mol_encoder(graph)
for i in range(len(self.gnn_layers)):
if isinstance(self.gnn_layers[i], nn.BatchNorm1D):
feat = self.gnn_layers[i](feat)
else:
feat = self.gnn_layers[i](graph, feat)
if self.gnn_type == 'gat_gcn':
x1 = self.graph_max_pool(graph, feat)
x2 = self.graph_avg_pool(graph, feat)
feat = paddle.concat([x1, x2], axis=1)
feat = self.dropout(self.act1(self.fc1(feat)))
feat = self.fc2(feat)
else:
feat = self.graph_pool(graph, feat)
feat = self.dropout(self.fc(feat))
return feat
class ProteinSequenceModel(nn.Layer):
"""
| ProteinSequenceModel, implementation of Conv1D model for protein representation.
Public Functions:
- ``forward``: forward to create protein sequence representation.
"""
def __init__(self, config):
super(ProteinSequenceModel, self).__init__()
self.config = config
self.output_dim = config['output_dim']
self.embed_dim = config['embed_dim']
self.max_protein_len = config['max_protein_len']
self.vocab_size = len(ProteinTokenizer.vocab)
self.num_filters = config.get('num_filters', 32)
self.pool_type = config.get('pool_type', 'mean')
self.initializer_range = config.get('initializer_range', 0.02)
self.protein_embeddings =nn.Embedding(
self.vocab_size, self.embed_dim,
weight_attr=nn.initializer.TruncatedNormal(
std=self.initializer_range))
self.conv1d = nn.Conv1D(
self.embed_dim, self.num_filters,
kernel_size=8, padding='SAME', data_format='NLC')
if self.max_protein_len < 0:
self.fc = nn.Linear(self.num_filters, self.output_dim)
else:
self.fc = nn.Linear(self.num_filters * self.max_protein_len, self.output_dim)
def forward(self, token, mask):
"""Forward.
Args:
token (Tensor): a tensor that represents the amino acid sequence as IDs.
mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding.
"""
token_emb = self.protein_embeddings(token)
feat = self.conv1d(token_emb)
if self.max_protein_len < 0:
# average pooling
feat = feat * paddle.unsqueeze(mask, 2)
feat = paddle.sum(feat, axis=1) / paddle.sum(mask, 1, keepdim=True)
else:
feat = paddle.reshape(feat, [-1, self.max_protein_len * self.num_filters])
feat = self.fc(feat)
return feat
class DTAModel(nn.Layer):
"""
| DTAModel, implementation of the network architecture in GraphDTA.
Public Functions:
- ``forward``: forward.
"""
def __init__(self, config):
super(DTAModel, self).__init__()
self.dropout_rate = config['dropout_rate']
self.compound_model = CompoundGNNModel(config['compound'])
self.protein_model = ProteinSequenceModel(config['protein'])
self.fc1 = nn.Linear(self.compound_model.output_dim + self.protein_model.output_dim, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 1)
self.act = nn.ReLU()
self.dropout = nn.Dropout(p=self.dropout_rate)
def forward(self, graph, protein_token, protein_mask):
"""Forward function.
Args:
graph (pgl.Graph): a PGL Graph instance.
protein_token (Tensor): a tensor that represents the amino acid sequence as IDs.
protein_mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding.
"""
compound_repr = self.compound_model(graph)
protein_repr = self.protein_model(protein_token, protein_mask)
compound_protein = paddle.concat(
[compound_repr, protein_repr], axis=1)
h = self.dropout(self.act(self.fc1(compound_protein)))
h = self.dropout(self.act(self.fc2(h)))
pred = self.fc3(h)
return pred
class DTAModelCriterion(nn.Layer):
"""
| DTAModelCriterion, implementation of MSE loss for DTA model.
Public Functions:
- ``forward``: forward function.
"""
def __init__(self):
super(DTAModelCriterion, self).__init__()
def forward(self, pred, label):
"""Forward function.
Args:
pred (Tensor): affinity predictions, i.e. output from DTAModel.
label (Tensor): affinity label.
"""
loss = nn.functional.square_error_cost(pred, label)
loss = paddle.mean(loss)
return loss | en | 0.803854 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. DTA model | CompoundGNNModel, implementation of the variant GNN models in paper ``GraphDTA: Predicting drug-target binding affinity with graph neural networks``. Public Functions: - ``forward``: forward to create the compound representation. Forward function. Args: graph (pgl.Graph): a PGL Graph instance. | ProteinSequenceModel, implementation of Conv1D model for protein representation. Public Functions: - ``forward``: forward to create protein sequence representation. Forward. Args: token (Tensor): a tensor that represents the amino acid sequence as IDs. mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding. # average pooling | DTAModel, implementation of the network architecture in GraphDTA. Public Functions: - ``forward``: forward. Forward function. Args: graph (pgl.Graph): a PGL Graph instance. protein_token (Tensor): a tensor that represents the amino acid sequence as IDs. protein_mask (Tensor): a tensor that marks whether the position is a valid amino acid or a padding. | DTAModelCriterion, implementation of MSE loss for DTA model. Public Functions: - ``forward``: forward function. Forward function. Args: pred (Tensor): affinity predictions, i.e. output from DTAModel. label (Tensor): affinity label. | 2.12956 | 2 |
App/softwares_env/softwares/maya_wizard/auto_hair.py | Wizard-collab/wizard | 0 | 6614267 | import sys
if sys.platform == "win32":
import ctypes
ctypes.windll.kernel32.SetDllDirectoryA(None)
import maya.standalone
maya.standalone.initialize()
from wizard.tools import log
from wizard.asset import main as asset_core
from wizard.vars import defaults
from wizard.prefs.main import prefs
from wizard.tools import utility as utils
from wizard.project import wall
import traceback
import logging
import copy
import os
import sys
from softwares.maya_wizard.export_anim import export_anim
from softwares.maya_wizard.export_fur import export_fur
from wizard.asset.reference import references
path_to_append = os.path.abspath('softwares/')
sys.path.append(path_to_append)
from softwares.maya_wizard import reference_asset
from wizard.asset import checker
import maya.cmds as cmds
#cmds.loadPlugin( allPlugins=True )
logger = log.pipe_log(__name__)
logger.info(path_to_append)
class auto_hair():
def __init__(self, string_asset, file, nspace_list, frange, comment = None, set_done = 1, refresh_assets = 0):
self.asset = asset_core.string_to_asset(string_asset)
self.string_asset = string_asset
self.file = file
self.nspace_list = nspace_list
self.frange = frange
self.references_dic = prefs().asset(self.asset).software.references
self.comment = comment
self.set_done = set_done
self.refresh_assets = refresh_assets
def auto_hair(self):
for nspace in self.nspace_list:
self.export_anim(nspace)
self.rig_asset = asset_core.string_to_asset(self.references_dic[nspace][defaults._asset_key_])
if self.get_grooming_asset():
if self.match_geos():
if self.create_new_scene():
if self.get_exported_asset():
self.add_anim_reference()
self.add_grooming_reference()
self.build_scene()
self.blendshape_shapes()
self.export_hair()
if self.set_done:
print('status:Done !')
else:
logger.warning("No {} publish found for this asset : {}-{}".format(defaults._hair_, self.rig_asset.category, self.rig_asset.name))
def build_scene(self):
os.environ[defaults._asset_var_] = utils.asset_to_string(self.cfx_asset)
cmds.file( f=True, new=True )
reference_asset.import_anim()
reference_asset.import_hair()
cmds.file( rename=self.cfx_scene )
cmds.file( save=True, type='mayaAscii', f=True )
def blendshape_shapes(self):
cmds.namespace( set=self.animation_namespace )
obj_list = cmds.namespaceInfo( listNamespace=True )
logger.info(obj_list)
anim_obj_list = []
for obj in obj_list:
logger.info(obj)
relatives_list = cmds.listRelatives(obj, shapes = 1)
if relatives_list and relatives_list != [] and len(relatives_list) == 1:
if cmds.objectType(relatives_list[0]) == 'mesh':
anim_obj_list.append(obj)
logger.info(anim_obj_list)
for obj in anim_obj_list:
groom_obj = obj.replace(self.animation_namespace,
'{}:{}'.format(self.hair_nspace, self.groom_geo_namespace))
logger.info(obj)
logger.info(groom_obj)
try:
self.blend(obj, groom_obj)
except:
logger.info("Can't blendshape {} and {}".format(obj, groom_obj))
cmds.file( save=True, type='mayaAscii', f=True )
def blend(self, base, target):
cmds.namespace( set=':' )
cmds.select(cl=1)
blendShapeName = '{}_blendShape'.format(base.split(':')[-1])
cmds.blendShape(base, target, origin='world', name=blendShapeName, tc=1 )
cmds.setAttr(blendShapeName+'.'+(base.split(':')[-1]), 1)
def add_anim_reference(self):
references(self.cfx_asset).remove_all_references()
count = references(self.cfx_asset).add_reference(self.export_asset, 0,1)
self.animation_namespace = references(self.cfx_asset).get_name_space(self.export_asset, count)
def match_geos(self):
match = None
rig_references = prefs().asset(self.rig_asset).software.references
rig_geo_asset = None
for reference in rig_references.keys():
asset = asset_core.string_to_asset(rig_references[reference][defaults._asset_key_])
logger.info(asset.variant)
logger.info(self.rig_asset.variant)
if asset.stage == defaults._geo_ and asset.name == self.rig_asset.name:
rig_geo_asset = asset
break
groom_references = prefs().asset(self.grooming_asset).software.references
grooming_geo_asset = None
for reference in groom_references.keys():
asset = asset_core.string_to_asset(groom_references[reference][defaults._asset_key_])
if asset.stage == defaults._geo_ and asset.name == self.grooming_asset.name:
grooming_geo_asset = asset
self.groom_geo_namespace = reference
break
if rig_geo_asset:
if grooming_geo_asset:
if rig_geo_asset.export_version == grooming_geo_asset.export_version:
match = 1
else:
logger.warning("The geo imported in rig and the geo imported in grooming doesn't matchs")
else:
logger.warning("No geo imported in the grooming scene")
else:
logger.warning("No geo imported in the rig scene")
return match
def get_grooming_asset(self):
self.grooming_asset = copy.deepcopy(self.rig_asset)
self.grooming_asset.stage = defaults._hair_
presence = None
if checker.check_stage_existence(self.grooming_asset):
self.grooming_asset.variant = self.rig_asset.variant
if not checker.check_variant_existence(self.grooming_asset):
self.grooming_asset.variant = prefs().asset(self.grooming_asset).stage.default_variant
if checker.check_variant_existence(self.grooming_asset):
self.grooming_asset.export_asset = prefs().asset(self.grooming_asset).export_root.default_export_asset
if self.grooming_asset.export_asset:
self.grooming_asset.export_version = prefs().asset(self.grooming_asset).export.last_version
presence = 1
return presence
def add_grooming_reference(self):
count = references(self.cfx_asset).add_reference(self.grooming_asset, 0,1)
self.hair_nspace = references(self.cfx_asset).get_name_space(self.grooming_asset, count)
def export_anim(self, nspace):
export_anim(self.string_asset, self.file, [nspace], self.frange, set_done = 0, refresh_assets = self.refresh_assets).export_anim()
def export_hair(self):
string_asset = utils.asset_to_string(self.cfx_asset)
export_fur(string_asset, self.cfx_scene, [self.hair_nspace], self.frange, set_done = 0).export_fur()
def create_new_scene(self):
stage_exists = 0
variant_exists = 0
self.cfx_asset = copy.deepcopy(self.asset)
self.cfx_asset.stage = defaults._cfx_
self.cfx_asset.variant = 'auto_hair'
if not checker.check_stage_existence(self.cfx_asset):
self.cfx_asset.variant = None
self.cfx_asset.software = None
self.cfx_asset.version = None
self.cfx_asset.export_asset = None
self.cfx_asset.export_version = None
if self.cfx_asset.create():
stage_exists = 1
else:
stage_exists = 1
self.cfx_asset.variant = 'auto_hair'
if not checker.check_variant_existence(self.cfx_asset):
logger.info('LOL')
self.cfx_asset.software = None
self.cfx_asset.version = None
self.cfx_asset.export_asset = None
self.cfx_asset.export_version = None
if self.cfx_asset.create():
variant_exists = 1
else:
variant_exists = 1
if variant_exists and stage_exists:
prefs().asset(self.cfx_asset).stage.set_default_variant('auto_hair')
self.cfx_asset.software = prefs().asset(self.cfx_asset).variant.default_software
self.cfx_asset.version = prefs().asset(self.cfx_asset).software.get_new_version()
prefs().asset(self.cfx_asset).software.new_version(self.cfx_asset.version)
self.cfx_scene = self.cfx_asset.file
return (variant_exists * stage_exists)
def get_exported_asset(self):
self.export_asset = copy.deepcopy(self.asset)
self.export_asset.export_asset = prefs().asset(self.export_asset).export_root.default_export_asset
self.export_asset.export_version = prefs().asset(self.export_asset).export.last_version
file = prefs().asset(self.export_asset).export.full_file
if os.path.isfile(file):
return 1
else:
return 0
| import sys
if sys.platform == "win32":
import ctypes
ctypes.windll.kernel32.SetDllDirectoryA(None)
import maya.standalone
maya.standalone.initialize()
from wizard.tools import log
from wizard.asset import main as asset_core
from wizard.vars import defaults
from wizard.prefs.main import prefs
from wizard.tools import utility as utils
from wizard.project import wall
import traceback
import logging
import copy
import os
import sys
from softwares.maya_wizard.export_anim import export_anim
from softwares.maya_wizard.export_fur import export_fur
from wizard.asset.reference import references
path_to_append = os.path.abspath('softwares/')
sys.path.append(path_to_append)
from softwares.maya_wizard import reference_asset
from wizard.asset import checker
import maya.cmds as cmds
#cmds.loadPlugin( allPlugins=True )
logger = log.pipe_log(__name__)
logger.info(path_to_append)
class auto_hair():
def __init__(self, string_asset, file, nspace_list, frange, comment = None, set_done = 1, refresh_assets = 0):
self.asset = asset_core.string_to_asset(string_asset)
self.string_asset = string_asset
self.file = file
self.nspace_list = nspace_list
self.frange = frange
self.references_dic = prefs().asset(self.asset).software.references
self.comment = comment
self.set_done = set_done
self.refresh_assets = refresh_assets
def auto_hair(self):
for nspace in self.nspace_list:
self.export_anim(nspace)
self.rig_asset = asset_core.string_to_asset(self.references_dic[nspace][defaults._asset_key_])
if self.get_grooming_asset():
if self.match_geos():
if self.create_new_scene():
if self.get_exported_asset():
self.add_anim_reference()
self.add_grooming_reference()
self.build_scene()
self.blendshape_shapes()
self.export_hair()
if self.set_done:
print('status:Done !')
else:
logger.warning("No {} publish found for this asset : {}-{}".format(defaults._hair_, self.rig_asset.category, self.rig_asset.name))
def build_scene(self):
os.environ[defaults._asset_var_] = utils.asset_to_string(self.cfx_asset)
cmds.file( f=True, new=True )
reference_asset.import_anim()
reference_asset.import_hair()
cmds.file( rename=self.cfx_scene )
cmds.file( save=True, type='mayaAscii', f=True )
def blendshape_shapes(self):
cmds.namespace( set=self.animation_namespace )
obj_list = cmds.namespaceInfo( listNamespace=True )
logger.info(obj_list)
anim_obj_list = []
for obj in obj_list:
logger.info(obj)
relatives_list = cmds.listRelatives(obj, shapes = 1)
if relatives_list and relatives_list != [] and len(relatives_list) == 1:
if cmds.objectType(relatives_list[0]) == 'mesh':
anim_obj_list.append(obj)
logger.info(anim_obj_list)
for obj in anim_obj_list:
groom_obj = obj.replace(self.animation_namespace,
'{}:{}'.format(self.hair_nspace, self.groom_geo_namespace))
logger.info(obj)
logger.info(groom_obj)
try:
self.blend(obj, groom_obj)
except:
logger.info("Can't blendshape {} and {}".format(obj, groom_obj))
cmds.file( save=True, type='mayaAscii', f=True )
def blend(self, base, target):
cmds.namespace( set=':' )
cmds.select(cl=1)
blendShapeName = '{}_blendShape'.format(base.split(':')[-1])
cmds.blendShape(base, target, origin='world', name=blendShapeName, tc=1 )
cmds.setAttr(blendShapeName+'.'+(base.split(':')[-1]), 1)
def add_anim_reference(self):
references(self.cfx_asset).remove_all_references()
count = references(self.cfx_asset).add_reference(self.export_asset, 0,1)
self.animation_namespace = references(self.cfx_asset).get_name_space(self.export_asset, count)
def match_geos(self):
match = None
rig_references = prefs().asset(self.rig_asset).software.references
rig_geo_asset = None
for reference in rig_references.keys():
asset = asset_core.string_to_asset(rig_references[reference][defaults._asset_key_])
logger.info(asset.variant)
logger.info(self.rig_asset.variant)
if asset.stage == defaults._geo_ and asset.name == self.rig_asset.name:
rig_geo_asset = asset
break
groom_references = prefs().asset(self.grooming_asset).software.references
grooming_geo_asset = None
for reference in groom_references.keys():
asset = asset_core.string_to_asset(groom_references[reference][defaults._asset_key_])
if asset.stage == defaults._geo_ and asset.name == self.grooming_asset.name:
grooming_geo_asset = asset
self.groom_geo_namespace = reference
break
if rig_geo_asset:
if grooming_geo_asset:
if rig_geo_asset.export_version == grooming_geo_asset.export_version:
match = 1
else:
logger.warning("The geo imported in rig and the geo imported in grooming doesn't matchs")
else:
logger.warning("No geo imported in the grooming scene")
else:
logger.warning("No geo imported in the rig scene")
return match
def get_grooming_asset(self):
self.grooming_asset = copy.deepcopy(self.rig_asset)
self.grooming_asset.stage = defaults._hair_
presence = None
if checker.check_stage_existence(self.grooming_asset):
self.grooming_asset.variant = self.rig_asset.variant
if not checker.check_variant_existence(self.grooming_asset):
self.grooming_asset.variant = prefs().asset(self.grooming_asset).stage.default_variant
if checker.check_variant_existence(self.grooming_asset):
self.grooming_asset.export_asset = prefs().asset(self.grooming_asset).export_root.default_export_asset
if self.grooming_asset.export_asset:
self.grooming_asset.export_version = prefs().asset(self.grooming_asset).export.last_version
presence = 1
return presence
def add_grooming_reference(self):
count = references(self.cfx_asset).add_reference(self.grooming_asset, 0,1)
self.hair_nspace = references(self.cfx_asset).get_name_space(self.grooming_asset, count)
def export_anim(self, nspace):
export_anim(self.string_asset, self.file, [nspace], self.frange, set_done = 0, refresh_assets = self.refresh_assets).export_anim()
def export_hair(self):
string_asset = utils.asset_to_string(self.cfx_asset)
export_fur(string_asset, self.cfx_scene, [self.hair_nspace], self.frange, set_done = 0).export_fur()
def create_new_scene(self):
stage_exists = 0
variant_exists = 0
self.cfx_asset = copy.deepcopy(self.asset)
self.cfx_asset.stage = defaults._cfx_
self.cfx_asset.variant = 'auto_hair'
if not checker.check_stage_existence(self.cfx_asset):
self.cfx_asset.variant = None
self.cfx_asset.software = None
self.cfx_asset.version = None
self.cfx_asset.export_asset = None
self.cfx_asset.export_version = None
if self.cfx_asset.create():
stage_exists = 1
else:
stage_exists = 1
self.cfx_asset.variant = 'auto_hair'
if not checker.check_variant_existence(self.cfx_asset):
logger.info('LOL')
self.cfx_asset.software = None
self.cfx_asset.version = None
self.cfx_asset.export_asset = None
self.cfx_asset.export_version = None
if self.cfx_asset.create():
variant_exists = 1
else:
variant_exists = 1
if variant_exists and stage_exists:
prefs().asset(self.cfx_asset).stage.set_default_variant('auto_hair')
self.cfx_asset.software = prefs().asset(self.cfx_asset).variant.default_software
self.cfx_asset.version = prefs().asset(self.cfx_asset).software.get_new_version()
prefs().asset(self.cfx_asset).software.new_version(self.cfx_asset.version)
self.cfx_scene = self.cfx_asset.file
return (variant_exists * stage_exists)
def get_exported_asset(self):
self.export_asset = copy.deepcopy(self.asset)
self.export_asset.export_asset = prefs().asset(self.export_asset).export_root.default_export_asset
self.export_asset.export_version = prefs().asset(self.export_asset).export.last_version
file = prefs().asset(self.export_asset).export.full_file
if os.path.isfile(file):
return 1
else:
return 0
| ru | 0.135395 | #cmds.loadPlugin( allPlugins=True ) | 1.870782 | 2 |
polls/application/dictionary.py | jphacks/B_2015 | 0 | 6614268 | import requests
from bs4 import BeautifulSoup
def make_synonym_dict(word):
#word = input()
synonym_dict={word:[]}
url = "https://thesaurus.weblio.jp/content/" + word
#headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.80 Safari/537.36'}
r = requests.get(url)
html = r.text
bs = BeautifulSoup(html, 'html.parser')
try:
synonyms_table = bs.find_all("td" ,class_="nwntsR")
#synonyms_table = bs.find_all("div" ,class_="Nwnts")
for synonyms in synonyms_table:
synonyms = synonyms.find_all("li")#class_='crosslink')
#meanings = bs.select_one("#main > div:nth-of-type(13) > div > div.Nwnts > table > tbody > tr:nth-of-type(2) > td.nwntsR > ul > li:nth-of-type(1) > a").text
for synonym in synonyms:
if synonym.find(class_='crosslink')!=None:
synonym = synonym.find(class_='crosslink')
synonym_dict[word] += synonym.contents
#print(synonym_dict)
return synonym_dict
except AttributeError:
meanings = "そのような言葉は見つからなかったよ...。ごめんね。"
print(meanings)
return {}
synonym_dict={}
synonym_dict = make_synonym_dict("ぬこ")
synonym_dict
| import requests
from bs4 import BeautifulSoup
def make_synonym_dict(word):
#word = input()
synonym_dict={word:[]}
url = "https://thesaurus.weblio.jp/content/" + word
#headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.80 Safari/537.36'}
r = requests.get(url)
html = r.text
bs = BeautifulSoup(html, 'html.parser')
try:
synonyms_table = bs.find_all("td" ,class_="nwntsR")
#synonyms_table = bs.find_all("div" ,class_="Nwnts")
for synonyms in synonyms_table:
synonyms = synonyms.find_all("li")#class_='crosslink')
#meanings = bs.select_one("#main > div:nth-of-type(13) > div > div.Nwnts > table > tbody > tr:nth-of-type(2) > td.nwntsR > ul > li:nth-of-type(1) > a").text
for synonym in synonyms:
if synonym.find(class_='crosslink')!=None:
synonym = synonym.find(class_='crosslink')
synonym_dict[word] += synonym.contents
#print(synonym_dict)
return synonym_dict
except AttributeError:
meanings = "そのような言葉は見つからなかったよ...。ごめんね。"
print(meanings)
return {}
synonym_dict={}
synonym_dict = make_synonym_dict("ぬこ")
synonym_dict
| en | 0.289113 | #word = input() #headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.80 Safari/537.36'} #synonyms_table = bs.find_all("div" ,class_="Nwnts") #class_='crosslink') #meanings = bs.select_one("#main > div:nth-of-type(13) > div > div.Nwnts > table > tbody > tr:nth-of-type(2) > td.nwntsR > ul > li:nth-of-type(1) > a").text #print(synonym_dict) | 3.10405 | 3 |
utils.py | davtoh/product-web-page | 1 | 6614269 | import re
def get_referer_view(request, default=None):
'''
Return the referer view of the current request
Example:
def some_view(request):
...
referer_view = get_referer_view(request)
return HttpResponseRedirect(referer_view, '/accounts/login/')
'''
# https://djangosnippets.org/snippets/1474/
# if the user typed the url directly in the browser's address bar
referer = request.META.get('HTTP_REFERER')
if not referer:
return default
# remove the protocol and split the url at the slashes
referer = re.sub('^https?:\/\/', '', referer).split('/')
if referer[0] != request.META.get('SERVER_NAME'):
return default
# add the slash at the relative path's view and finished
referer = u'/' + u'/'.join(referer[1:])
return referer | import re
def get_referer_view(request, default=None):
'''
Return the referer view of the current request
Example:
def some_view(request):
...
referer_view = get_referer_view(request)
return HttpResponseRedirect(referer_view, '/accounts/login/')
'''
# https://djangosnippets.org/snippets/1474/
# if the user typed the url directly in the browser's address bar
referer = request.META.get('HTTP_REFERER')
if not referer:
return default
# remove the protocol and split the url at the slashes
referer = re.sub('^https?:\/\/', '', referer).split('/')
if referer[0] != request.META.get('SERVER_NAME'):
return default
# add the slash at the relative path's view and finished
referer = u'/' + u'/'.join(referer[1:])
return referer | en | 0.631761 | Return the referer view of the current request Example: def some_view(request): ... referer_view = get_referer_view(request) return HttpResponseRedirect(referer_view, '/accounts/login/') # https://djangosnippets.org/snippets/1474/ # if the user typed the url directly in the browser's address bar # remove the protocol and split the url at the slashes # add the slash at the relative path's view and finished | 2.79878 | 3 |
server/app/services/publish/views/__init__.py | goodfree/ActorCloud | 173 | 6614270 | <reponame>goodfree/ActorCloud<gh_stars>100-1000
from flask import Blueprint
bp = Blueprint('publish', __name__)
from . import devices # noqa: E402
from . import timers # noqa: E402
__all__ = [
'bp', 'devices', 'timers'
]
| from flask import Blueprint
bp = Blueprint('publish', __name__)
from . import devices # noqa: E402
from . import timers # noqa: E402
__all__ = [
'bp', 'devices', 'timers'
] | uz | 0.245811 | # noqa: E402 # noqa: E402 | 1.336698 | 1 |
test/simplerw.py | rhjdvsgsgks/RPi.GPIO-Odroid | 9 | 6614271 | <reponame>rhjdvsgsgks/RPi.GPIO-Odroid<gh_stars>1-10
#Read state of GPIO output on GPIO input
#Use jumper wire from pin 13 to pin 31
#XU4 without shifter-shield pin 13 to pin 19
import RPi.GPIO as GPIO
import time
LedPinW = 27 # pin13, bcm27
LedPinR = 6 # pin31, bcm6
def setup():
GPIO.setmode(GPIO.BCM) # Number GPIOs by BCM chip numbering scheme
GPIO.setup(LedPinR, GPIO.IN, pull_up_down=GPIO.PUD_UP) # Set LedPinR mode input
GPIO.setup(LedPinW, GPIO.OUT) # Set LedPinW mode to output
GPIO.output(LedPinW, GPIO.HIGH) # Set LedPinW pin high
def blink():
while True:
GPIO.output(LedPinW, GPIO.HIGH) # LedPinW high
time.sleep(2)
pstate=GPIO.input(LedPinR) # Read LedPinR
print("*****Pin state (LedPinW HIGH) ", pstate, "*****\n")
time.sleep(2)
GPIO.output(LedPinW, GPIO.LOW) # LedPinW low
time.sleep(2)
pstate=GPIO.input(LedPinR) # Read LedPinR
print("*****Pin state (LedPinW LOW) ", pstate, "*****\n")
time.sleep(2)
def shutdown():
GPIO.output(LedPinW, GPIO.LOW) # LedPinW low
GPIO.setup(LedPinW, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # LedPinW input
GPIO.cleanup()
if __name__ == '__main__': # Program start
print('To read output correctly, jumper pin 13 (bcm27) to pin 31 (bcm6)')
print('Press Ctrl-C to exit')
setup()
print("Hardware information: ", GPIO.RPI_INFO)
try:
blink()
except KeyboardInterrupt: # When 'Ctrl+C' is pressed, shut down cleanly
shutdown()
| #Read state of GPIO output on GPIO input
#Use jumper wire from pin 13 to pin 31
#XU4 without shifter-shield pin 13 to pin 19
import RPi.GPIO as GPIO
import time
LedPinW = 27 # pin13, bcm27
LedPinR = 6 # pin31, bcm6
def setup():
GPIO.setmode(GPIO.BCM) # Number GPIOs by BCM chip numbering scheme
GPIO.setup(LedPinR, GPIO.IN, pull_up_down=GPIO.PUD_UP) # Set LedPinR mode input
GPIO.setup(LedPinW, GPIO.OUT) # Set LedPinW mode to output
GPIO.output(LedPinW, GPIO.HIGH) # Set LedPinW pin high
def blink():
while True:
GPIO.output(LedPinW, GPIO.HIGH) # LedPinW high
time.sleep(2)
pstate=GPIO.input(LedPinR) # Read LedPinR
print("*****Pin state (LedPinW HIGH) ", pstate, "*****\n")
time.sleep(2)
GPIO.output(LedPinW, GPIO.LOW) # LedPinW low
time.sleep(2)
pstate=GPIO.input(LedPinR) # Read LedPinR
print("*****Pin state (LedPinW LOW) ", pstate, "*****\n")
time.sleep(2)
def shutdown():
GPIO.output(LedPinW, GPIO.LOW) # LedPinW low
GPIO.setup(LedPinW, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # LedPinW input
GPIO.cleanup()
if __name__ == '__main__': # Program start
print('To read output correctly, jumper pin 13 (bcm27) to pin 31 (bcm6)')
print('Press Ctrl-C to exit')
setup()
print("Hardware information: ", GPIO.RPI_INFO)
try:
blink()
except KeyboardInterrupt: # When 'Ctrl+C' is pressed, shut down cleanly
shutdown() | en | 0.751535 | #Read state of GPIO output on GPIO input #Use jumper wire from pin 13 to pin 31 #XU4 without shifter-shield pin 13 to pin 19 # pin13, bcm27 # pin31, bcm6 # Number GPIOs by BCM chip numbering scheme # Set LedPinR mode input # Set LedPinW mode to output # Set LedPinW pin high # LedPinW high # Read LedPinR # LedPinW low # Read LedPinR # LedPinW low # LedPinW input # Program start # When 'Ctrl+C' is pressed, shut down cleanly | 3.350093 | 3 |
project/project_code/azure/TestCases/test_azuresqldb.py | cybertraining-dsc/fa19-516-147 | 0 | 6614272 | <gh_stars>0
import requests
import logging
import json
import jsonpath
def test_get_database():
# url = 'http://0.0.0.0:8080/cloudmesh/v3/ui/#/Database%20Registry/cloudmesh.database.get'
url = 'http://0.0.0.0:8080/cloudmesh/v3/database'
response = requests.get(url)
assert response.status_code == 200
print(response.content)
'''
def test_create_new_database():
file = open('create_database.json','r')
json_input =file.read()
request_json = json.loads(json_input)
# put request with JSON input dfata
response = requests.put(url, request_json)
#validate response code
assert response.status_code ==200
print(response.header.get('Content-Length'))
#parse response to Json format
response_json = json.loads(response.text)
#pick id uisng Json Path
id = jsonpath.jsonpath(respone_json,'id')
print(id[0])
'''
def test_get_schema():
url = 'http://0.0.0.0:8080/cloudmesh/v3/database/absdb/schema/all'
response = requests.get(url)
assert response.status_code == 200
print(response.content)
def test_put_schema():
url = 'http://0.0.0.0:8080/cloudmesh/v3/database/testdb/schema/pycheck_sch'
# file = open('create_database.json','r')
# json_input =file.read()
# request_json = json.loads(json_input)
# put request with JSON input dfata
response = requests.put(url)
#validate response code
assert response.status_code == 500
#print(response.header.get('Content-Length'))
#parse response to Json format
#response_json = json.loads(response.text)
#pick id uisng Json Path
#id = jsonpath.jsonpath(respone_json,'id')
#print(id[0]) | import requests
import logging
import json
import jsonpath
def test_get_database():
# url = 'http://0.0.0.0:8080/cloudmesh/v3/ui/#/Database%20Registry/cloudmesh.database.get'
url = 'http://0.0.0.0:8080/cloudmesh/v3/database'
response = requests.get(url)
assert response.status_code == 200
print(response.content)
'''
def test_create_new_database():
file = open('create_database.json','r')
json_input =file.read()
request_json = json.loads(json_input)
# put request with JSON input dfata
response = requests.put(url, request_json)
#validate response code
assert response.status_code ==200
print(response.header.get('Content-Length'))
#parse response to Json format
response_json = json.loads(response.text)
#pick id uisng Json Path
id = jsonpath.jsonpath(respone_json,'id')
print(id[0])
'''
def test_get_schema():
url = 'http://0.0.0.0:8080/cloudmesh/v3/database/absdb/schema/all'
response = requests.get(url)
assert response.status_code == 200
print(response.content)
def test_put_schema():
url = 'http://0.0.0.0:8080/cloudmesh/v3/database/testdb/schema/pycheck_sch'
# file = open('create_database.json','r')
# json_input =file.read()
# request_json = json.loads(json_input)
# put request with JSON input dfata
response = requests.put(url)
#validate response code
assert response.status_code == 500
#print(response.header.get('Content-Length'))
#parse response to Json format
#response_json = json.loads(response.text)
#pick id uisng Json Path
#id = jsonpath.jsonpath(respone_json,'id')
#print(id[0]) | en | 0.32606 | # url = 'http://0.0.0.0:8080/cloudmesh/v3/ui/#/Database%20Registry/cloudmesh.database.get' def test_create_new_database(): file = open('create_database.json','r') json_input =file.read() request_json = json.loads(json_input) # put request with JSON input dfata response = requests.put(url, request_json) #validate response code assert response.status_code ==200 print(response.header.get('Content-Length')) #parse response to Json format response_json = json.loads(response.text) #pick id uisng Json Path id = jsonpath.jsonpath(respone_json,'id') print(id[0]) # file = open('create_database.json','r') # json_input =file.read() # request_json = json.loads(json_input) # put request with JSON input dfata #validate response code #print(response.header.get('Content-Length')) #parse response to Json format #response_json = json.loads(response.text) #pick id uisng Json Path #id = jsonpath.jsonpath(respone_json,'id') #print(id[0]) | 2.563223 | 3 |
packages/m5flowui/v1.4.0/generic/frozen/flowlib/units/_makey.py | TheVinhLuong102/micropy-stubs | 18 | 6614273 | <reponame>TheVinhLuong102/micropy-stubs
import unit, i2c_bus
MAKEY_I2C_ADDR = const(0x51)
class Makey:
def __init__(self, port):
self.i2c = i2c_bus.get(port)
self._available()
self.sing_map = [261, 293, 329, 349, 392, 440, 494, 294]
def _available(self):
if self.i2c.is_ready(MAKEY_I2C_ADDR) or self.i2c.is_ready(MAKEY_I2C_ADDR):
pass
else:
raise unit.Unit("Makey unit maybe not connect")
def _updateValue(self):
value = 0
data = self.i2c.readfrom(MAKEY_I2C_ADDR, 2)
value = data[0]|(data[1] << 8)
return value
@property
def valueAll(self):
return self._updateValue()
@property
def value(self):
value = self._updateValue()
for i in range(16):
if (value >> i) & 0x01:
return i
return -1
# def playPiano(self, beat):
# key_value = self.get_value()
# time.sleep_ms(1)
# for i in range(8):
# if (key_value >> i) & 0x01:
# speaker.sing(self.sing_map[i], beat)
# break
def deinit(self):
pass | import unit, i2c_bus
MAKEY_I2C_ADDR = const(0x51)
class Makey:
def __init__(self, port):
self.i2c = i2c_bus.get(port)
self._available()
self.sing_map = [261, 293, 329, 349, 392, 440, 494, 294]
def _available(self):
if self.i2c.is_ready(MAKEY_I2C_ADDR) or self.i2c.is_ready(MAKEY_I2C_ADDR):
pass
else:
raise unit.Unit("Makey unit maybe not connect")
def _updateValue(self):
value = 0
data = self.i2c.readfrom(MAKEY_I2C_ADDR, 2)
value = data[0]|(data[1] << 8)
return value
@property
def valueAll(self):
return self._updateValue()
@property
def value(self):
value = self._updateValue()
for i in range(16):
if (value >> i) & 0x01:
return i
return -1
# def playPiano(self, beat):
# key_value = self.get_value()
# time.sleep_ms(1)
# for i in range(8):
# if (key_value >> i) & 0x01:
# speaker.sing(self.sing_map[i], beat)
# break
def deinit(self):
pass | en | 0.185827 | # def playPiano(self, beat): # key_value = self.get_value() # time.sleep_ms(1) # for i in range(8): # if (key_value >> i) & 0x01: # speaker.sing(self.sing_map[i], beat) # break | 2.68408 | 3 |
modules/effects/__init__.py | bira37/puzzle-effect-filter | 1 | 6614274 | # __init__.py
from .effects_handler import apply_v1
from .effects_handler import add_background
from .effects_handler import apply_relief_and_shadow | # __init__.py
from .effects_handler import apply_v1
from .effects_handler import add_background
from .effects_handler import apply_relief_and_shadow | ar | 0.447093 | # __init__.py | 1.023618 | 1 |
homeschool/reports/views.py | brandonmcclure/homeschool | 0 | 6614275 | <gh_stars>0
import datetime
import io
import zipfile
from decimal import ROUND_HALF_UP, Decimal
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.db.models import Q
from django.http import HttpResponse
from django.shortcuts import get_object_or_404
from django.views.generic import TemplateView
from homeschool.courses.models import CourseResource
from homeschool.schools.models import SchoolYear
from homeschool.students.models import Coursework, Enrollment, Grade
class ReportsIndexView(LoginRequiredMixin, TemplateView):
template_name = "reports/index.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context["nav_link"] = "reports"
user = self.request.user
context["enrollments"] = (
Enrollment.objects.filter(grade_level__school_year__school__admin=user)
.select_related("student", "grade_level", "grade_level__school_year")
.order_by("-grade_level__school_year__start_date", "student")
)
context["school_years"] = SchoolYear.objects.filter(
school__admin=user
).order_by("-start_date")
return context
class BundleView(LoginRequiredMixin, TemplateView):
template_name = "reports/bundle.html"
def get_context_data(self, **kwargs):
user = self.request.user
context = super().get_context_data(**kwargs)
context["school_year"] = get_object_or_404(
SchoolYear, pk=self.kwargs["pk"], school__admin=user
)
return context
@login_required
def create_bundle(request, pk):
user = request.user
get_object_or_404(SchoolYear, pk=pk, school__admin=user)
zip_file_data = io.BytesIO()
with zipfile.ZipFile(zip_file_data, "w") as zip_file:
zip_file.writestr("file1.txt", b"hello world")
zip_file.writestr("file2.txt", b"hello world")
filename = "bundle.zip"
return HttpResponse(
zip_file_data.getbuffer(),
headers={
"Content-Type": "application/zip",
"Content-Disposition": f'attachment; filename="{filename}"',
},
)
class AttendanceReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/attendance_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
context["school_dates"] = self._build_school_dates(enrollment)
context["total_days_attended"] = sum(
1 for school_date in context["school_dates"] if school_date["attended"]
)
return context
def _build_school_dates(self, enrollment):
"""Collect all the school dates in the year to the end or today."""
dates_with_work = set(
Coursework.objects.filter(
student=enrollment.student,
course_task__course__grade_levels__in=[enrollment.grade_level],
).values_list("completed_date", flat=True)
)
school_dates = []
school_year = enrollment.grade_level.school_year
school_date = school_year.start_date
end_date = min(school_year.end_date, self.request.user.get_local_today())
while school_date <= end_date:
school_dates.append(
{
"date": school_date,
"is_school_day": school_year.runs_on(school_date),
"is_break": school_year.is_break(
school_date, student=enrollment.student
),
"attended": school_date in dates_with_work,
}
)
school_date += datetime.timedelta(days=1)
return school_dates
class ProgressReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/progress_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
course_id = self.request.GET.get("course")
if course_id:
qs_filter = Q(graded_work__course_task__course__id=course_id)
else:
qs_filter = Q(
graded_work__course_task__course__grade_levels__in=[
enrollment.grade_level
]
)
grades = (
Grade.objects.filter(qs_filter, student=enrollment.student)
# Include secondary ordering so tasks are ordered in the course.
.order_by(
"graded_work__course_task__course", "graded_work__course_task"
).select_related(
"graded_work__course_task", "graded_work__course_task__course"
)
)
self._mixin_coursework(grades, enrollment.student)
context["courses"] = self._build_courses_info(grades)
return context
def _mixin_coursework(self, grades, student):
"""Mix in the coursework for the grades.
Coursework is added to the grades to display the completed dates.
It is possible for a user to add a grade without the student finishing the task
so the coursework can be None.
"""
tasks = [grade.graded_work.course_task for grade in grades]
coursework_by_task_id = {
coursework.course_task_id: coursework
for coursework in Coursework.objects.filter(
student=student, course_task__in=tasks
)
}
for grade in grades:
grade.coursework = coursework_by_task_id.get(
grade.graded_work.course_task_id
)
def _build_courses_info(self, grades):
"""Regroup the grades into an appropriate display structure for the template.
Grades must be sorted by course.
"""
if not grades:
return []
courses = []
course = None
course_info = {}
for grade in grades:
next_course = grade.graded_work.course_task.course
if course != next_course:
# Don't compute average until a course is collected.
# On the first iteration when course is None, nothing is collected yet.
if course is not None:
self._compute_course_average(course_info)
course = next_course
course_info = {"course": course, "grades": [grade]}
courses.append(course_info)
else:
course_info["grades"].append(grade)
# Compute average of last course to catch the edge case.
self._compute_course_average(course_info)
return courses
def _compute_course_average(self, course_info):
"""Compute the average for the course based on collected grades."""
grades = course_info["grades"]
average = sum(grade.score for grade in grades) / len(grades)
# Sane rounding.
course_info["course_average"] = int(Decimal(average).quantize(0, ROUND_HALF_UP))
class ResourceReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/resource_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
context["resources"] = (
CourseResource.objects.filter(
course__grade_levels__in=[enrollment.grade_level]
)
.select_related("course")
.order_by("course")
)
return context
| import datetime
import io
import zipfile
from decimal import ROUND_HALF_UP, Decimal
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.db.models import Q
from django.http import HttpResponse
from django.shortcuts import get_object_or_404
from django.views.generic import TemplateView
from homeschool.courses.models import CourseResource
from homeschool.schools.models import SchoolYear
from homeschool.students.models import Coursework, Enrollment, Grade
class ReportsIndexView(LoginRequiredMixin, TemplateView):
template_name = "reports/index.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context["nav_link"] = "reports"
user = self.request.user
context["enrollments"] = (
Enrollment.objects.filter(grade_level__school_year__school__admin=user)
.select_related("student", "grade_level", "grade_level__school_year")
.order_by("-grade_level__school_year__start_date", "student")
)
context["school_years"] = SchoolYear.objects.filter(
school__admin=user
).order_by("-start_date")
return context
class BundleView(LoginRequiredMixin, TemplateView):
template_name = "reports/bundle.html"
def get_context_data(self, **kwargs):
user = self.request.user
context = super().get_context_data(**kwargs)
context["school_year"] = get_object_or_404(
SchoolYear, pk=self.kwargs["pk"], school__admin=user
)
return context
@login_required
def create_bundle(request, pk):
user = request.user
get_object_or_404(SchoolYear, pk=pk, school__admin=user)
zip_file_data = io.BytesIO()
with zipfile.ZipFile(zip_file_data, "w") as zip_file:
zip_file.writestr("file1.txt", b"hello world")
zip_file.writestr("file2.txt", b"hello world")
filename = "bundle.zip"
return HttpResponse(
zip_file_data.getbuffer(),
headers={
"Content-Type": "application/zip",
"Content-Disposition": f'attachment; filename="{filename}"',
},
)
class AttendanceReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/attendance_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
context["school_dates"] = self._build_school_dates(enrollment)
context["total_days_attended"] = sum(
1 for school_date in context["school_dates"] if school_date["attended"]
)
return context
def _build_school_dates(self, enrollment):
"""Collect all the school dates in the year to the end or today."""
dates_with_work = set(
Coursework.objects.filter(
student=enrollment.student,
course_task__course__grade_levels__in=[enrollment.grade_level],
).values_list("completed_date", flat=True)
)
school_dates = []
school_year = enrollment.grade_level.school_year
school_date = school_year.start_date
end_date = min(school_year.end_date, self.request.user.get_local_today())
while school_date <= end_date:
school_dates.append(
{
"date": school_date,
"is_school_day": school_year.runs_on(school_date),
"is_break": school_year.is_break(
school_date, student=enrollment.student
),
"attended": school_date in dates_with_work,
}
)
school_date += datetime.timedelta(days=1)
return school_dates
class ProgressReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/progress_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
course_id = self.request.GET.get("course")
if course_id:
qs_filter = Q(graded_work__course_task__course__id=course_id)
else:
qs_filter = Q(
graded_work__course_task__course__grade_levels__in=[
enrollment.grade_level
]
)
grades = (
Grade.objects.filter(qs_filter, student=enrollment.student)
# Include secondary ordering so tasks are ordered in the course.
.order_by(
"graded_work__course_task__course", "graded_work__course_task"
).select_related(
"graded_work__course_task", "graded_work__course_task__course"
)
)
self._mixin_coursework(grades, enrollment.student)
context["courses"] = self._build_courses_info(grades)
return context
def _mixin_coursework(self, grades, student):
"""Mix in the coursework for the grades.
Coursework is added to the grades to display the completed dates.
It is possible for a user to add a grade without the student finishing the task
so the coursework can be None.
"""
tasks = [grade.graded_work.course_task for grade in grades]
coursework_by_task_id = {
coursework.course_task_id: coursework
for coursework in Coursework.objects.filter(
student=student, course_task__in=tasks
)
}
for grade in grades:
grade.coursework = coursework_by_task_id.get(
grade.graded_work.course_task_id
)
def _build_courses_info(self, grades):
"""Regroup the grades into an appropriate display structure for the template.
Grades must be sorted by course.
"""
if not grades:
return []
courses = []
course = None
course_info = {}
for grade in grades:
next_course = grade.graded_work.course_task.course
if course != next_course:
# Don't compute average until a course is collected.
# On the first iteration when course is None, nothing is collected yet.
if course is not None:
self._compute_course_average(course_info)
course = next_course
course_info = {"course": course, "grades": [grade]}
courses.append(course_info)
else:
course_info["grades"].append(grade)
# Compute average of last course to catch the edge case.
self._compute_course_average(course_info)
return courses
def _compute_course_average(self, course_info):
"""Compute the average for the course based on collected grades."""
grades = course_info["grades"]
average = sum(grade.score for grade in grades) / len(grades)
# Sane rounding.
course_info["course_average"] = int(Decimal(average).quantize(0, ROUND_HALF_UP))
class ResourceReportView(LoginRequiredMixin, TemplateView):
template_name = "reports/resource_report.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
user = self.request.user
enrollment = get_object_or_404(
Enrollment.objects.select_related(
"student", "grade_level", "grade_level__school_year"
),
pk=self.kwargs["pk"],
grade_level__school_year__school=user.school,
)
context["grade_level"] = enrollment.grade_level
context["school_year"] = enrollment.grade_level.school_year
context["student"] = enrollment.student
context["resources"] = (
CourseResource.objects.filter(
course__grade_levels__in=[enrollment.grade_level]
)
.select_related("course")
.order_by("course")
)
return context | en | 0.921535 | Collect all the school dates in the year to the end or today. # Include secondary ordering so tasks are ordered in the course. Mix in the coursework for the grades. Coursework is added to the grades to display the completed dates. It is possible for a user to add a grade without the student finishing the task so the coursework can be None. Regroup the grades into an appropriate display structure for the template. Grades must be sorted by course. # Don't compute average until a course is collected. # On the first iteration when course is None, nothing is collected yet. # Compute average of last course to catch the edge case. Compute the average for the course based on collected grades. # Sane rounding. | 1.949279 | 2 |
JYTools/MyRequests.py | meisanggou/Tools | 0 | 6614276 | <reponame>meisanggou/Tools
#! /usr/bin/env python
# coding: utf-8
import thread
import requests
from json import dumps as json_dumps
__author__ = 'ZhouHeng'
class RequestsManager(object):
def __init__(self, conn_error_code=-1, http_error_code=1):
self._req = requests.session()
self.conn_error_code = conn_error_code
self.http_error_code = http_error_code
self.verify_http_code = True
@property
def auth(self):
return self._req.auth
@auth.setter
def auth(self, v):
self._req.auth = v
@property
def headers(self):
return self._req.headers
@headers.setter
def headers(self, v):
self._req.headers = v
def options(self, url, **kwargs):
return self._req.options(url, **kwargs)
def head(self, url, **kwargs):
return self._req.head(url, **kwargs)
def get(self, url, **kwargs):
return self.request("get", url, **kwargs)
def post(self, url, data=None, json=None, **kwargs):
return self.request("post", url, data=data, json=json, **kwargs)
def put(self, url, data=None, **kwargs):
return self.request("put", url, data=data, **kwargs)
def delete(self, url, **kwargs):
return self.request("delete", url, **kwargs)
def close(self):
self._req.close()
def request(self, method, url, **kwargs):
as_thread = kwargs.pop("as_thread", False)
if as_thread is True:
return thread.start_new_thread(self.request, (method, url), kwargs)
if "allow_redirects" not in kwargs:
kwargs["allow_redirects"] = True
body = kwargs.pop("body", None)
if body is not None:
if method == "GET":
kwargs["params"] = body
elif method == "POST" or method == "GET" or method == "DELETE":
kwargs["json"] = body
try:
resp = self._req.request(method, url, **kwargs)
except requests.ConnectionError as ce:
if hasattr(ce.message, "reason") is True:
msg = ce.message.reason
else:
msg = ce.message
raise JYRequestsException(self.conn_error_code, url, message=msg, **kwargs)
if self.verify_http_code is True and resp.status_code != 200:
raise JYRequestsException(self.http_error_code, url, http_code=resp.status_code, **kwargs)
return resp
class JYRequestsException(Exception):
def __init__(self, error_type, url, **kwargs):
self.error_type = error_type
self.url = url
if "http_code" in kwargs:
self.http_code = kwargs["http_code"]
else:
self.http_code = 0
if "message" in kwargs:
self.message = str(kwargs["message"])
else:
self.message = ""
self.json = None
self.data = None
if "json" in kwargs:
self.json = kwargs["json"]
if "data" in kwargs:
self.data = kwargs["data"]
def __str__(self):
exp_msg = {"url": self.url, "error": self.message, "http_code": self.http_code}
if self.data is not None:
exp_msg["data"] = self.data
if self.json is not None:
exp_msg["json"] = self.json
return json_dumps(exp_msg)
| #! /usr/bin/env python
# coding: utf-8
import thread
import requests
from json import dumps as json_dumps
__author__ = 'ZhouHeng'
class RequestsManager(object):
def __init__(self, conn_error_code=-1, http_error_code=1):
self._req = requests.session()
self.conn_error_code = conn_error_code
self.http_error_code = http_error_code
self.verify_http_code = True
@property
def auth(self):
return self._req.auth
@auth.setter
def auth(self, v):
self._req.auth = v
@property
def headers(self):
return self._req.headers
@headers.setter
def headers(self, v):
self._req.headers = v
def options(self, url, **kwargs):
return self._req.options(url, **kwargs)
def head(self, url, **kwargs):
return self._req.head(url, **kwargs)
def get(self, url, **kwargs):
return self.request("get", url, **kwargs)
def post(self, url, data=None, json=None, **kwargs):
return self.request("post", url, data=data, json=json, **kwargs)
def put(self, url, data=None, **kwargs):
return self.request("put", url, data=data, **kwargs)
def delete(self, url, **kwargs):
return self.request("delete", url, **kwargs)
def close(self):
self._req.close()
def request(self, method, url, **kwargs):
as_thread = kwargs.pop("as_thread", False)
if as_thread is True:
return thread.start_new_thread(self.request, (method, url), kwargs)
if "allow_redirects" not in kwargs:
kwargs["allow_redirects"] = True
body = kwargs.pop("body", None)
if body is not None:
if method == "GET":
kwargs["params"] = body
elif method == "POST" or method == "GET" or method == "DELETE":
kwargs["json"] = body
try:
resp = self._req.request(method, url, **kwargs)
except requests.ConnectionError as ce:
if hasattr(ce.message, "reason") is True:
msg = ce.message.reason
else:
msg = ce.message
raise JYRequestsException(self.conn_error_code, url, message=msg, **kwargs)
if self.verify_http_code is True and resp.status_code != 200:
raise JYRequestsException(self.http_error_code, url, http_code=resp.status_code, **kwargs)
return resp
class JYRequestsException(Exception):
def __init__(self, error_type, url, **kwargs):
self.error_type = error_type
self.url = url
if "http_code" in kwargs:
self.http_code = kwargs["http_code"]
else:
self.http_code = 0
if "message" in kwargs:
self.message = str(kwargs["message"])
else:
self.message = ""
self.json = None
self.data = None
if "json" in kwargs:
self.json = kwargs["json"]
if "data" in kwargs:
self.data = kwargs["data"]
def __str__(self):
exp_msg = {"url": self.url, "error": self.message, "http_code": self.http_code}
if self.data is not None:
exp_msg["data"] = self.data
if self.json is not None:
exp_msg["json"] = self.json
return json_dumps(exp_msg) | en | 0.321189 | #! /usr/bin/env python # coding: utf-8 | 2.716016 | 3 |
setup.py | scalabli/neuraldig | 0 | 6614277 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from setuptools import setup
setup(
name="neuraldig",
install_requires=[
"numpy==1.21.0",
"scipy==1.5",
"pandas==1.0",
"scikit-learn==0.22",
"joblib==0.15",
"nibabel==3.0.0",
"lxml",
"quo",
],
)
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from setuptools import setup
setup(
name="neuraldig",
install_requires=[
"numpy==1.21.0",
"scipy==1.5",
"pandas==1.0",
"scikit-learn==0.22",
"joblib==0.15",
"nibabel==3.0.0",
"lxml",
"quo",
],
)
| en | 0.308914 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- | 1.081448 | 1 |
projects/07/Parser.py | anArkitect/Nand2Tetris | 0 | 6614278 | <filename>projects/07/Parser.py
import re
import sys
class Parser(object):
C_ARITHMETIC = 0
C_PUSH = 1
C_POP = 2
C_LABAL = 3
C_GOTO = 4
C_IF = 5
C_FUNCTION = 6
C_RETURN = 7
C_CALL = 8
arithmetics = ['add', 'sub', 'neg', 'eq', 'gt', 'lt', 'and', 'or', 'not']
_cmd_type = -1
# store the cotent of .vm file
_lines = []
def __init__(self, input_file):
file_name = input_file
if not file_name.endswith('.vm'):
print('Only .vm files are supported')
exit(1)
with open(file_name, 'r') as new_file:
self._lines = new_file.read().split('\n')
#print(self._lines)
def has_more_commands(self):
return self._lines != []
def advance(self):
if self.has_more_commands():
new_command = self._remove_comment(self._lines.pop(0))
if new_command == '':
return ''
else:
tokens = new_command.split()
if tokens[0] in self.arithmetics:
self._cmd_type = self.C_ARITHMETIC
return tokens
elif tokens[0] == 'push':
self._cmd_type = self.C_PUSH
return tokens
elif tokens[0] == 'pop':
self._cmd_type = self.C_POP
return tokens
else:
print("There is no more commands in .vm file, ERROR #1")
exit(1)
def get_command_type(self):
return self._cmd_type
def _remove_comment(self, line):
_comment_pattern_1 = re.compile(r'/\*.*?\*/')
_comment_pattern_2 = re.compile(r'//.*')
_new_line = _comment_pattern_2.sub('', _comment_pattern_1.sub('', line))
return _new_line
def output(self):
while(self.has_more_commands):
val = self.advance()
if val != '':
#print("command type: " + str(self.get_command_type()))
print(val) | <filename>projects/07/Parser.py
import re
import sys
class Parser(object):
C_ARITHMETIC = 0
C_PUSH = 1
C_POP = 2
C_LABAL = 3
C_GOTO = 4
C_IF = 5
C_FUNCTION = 6
C_RETURN = 7
C_CALL = 8
arithmetics = ['add', 'sub', 'neg', 'eq', 'gt', 'lt', 'and', 'or', 'not']
_cmd_type = -1
# store the cotent of .vm file
_lines = []
def __init__(self, input_file):
file_name = input_file
if not file_name.endswith('.vm'):
print('Only .vm files are supported')
exit(1)
with open(file_name, 'r') as new_file:
self._lines = new_file.read().split('\n')
#print(self._lines)
def has_more_commands(self):
return self._lines != []
def advance(self):
if self.has_more_commands():
new_command = self._remove_comment(self._lines.pop(0))
if new_command == '':
return ''
else:
tokens = new_command.split()
if tokens[0] in self.arithmetics:
self._cmd_type = self.C_ARITHMETIC
return tokens
elif tokens[0] == 'push':
self._cmd_type = self.C_PUSH
return tokens
elif tokens[0] == 'pop':
self._cmd_type = self.C_POP
return tokens
else:
print("There is no more commands in .vm file, ERROR #1")
exit(1)
def get_command_type(self):
return self._cmd_type
def _remove_comment(self, line):
_comment_pattern_1 = re.compile(r'/\*.*?\*/')
_comment_pattern_2 = re.compile(r'//.*')
_new_line = _comment_pattern_2.sub('', _comment_pattern_1.sub('', line))
return _new_line
def output(self):
while(self.has_more_commands):
val = self.advance()
if val != '':
#print("command type: " + str(self.get_command_type()))
print(val) | en | 0.266871 | # store the cotent of .vm file #print(self._lines) #1") #print("command type: " + str(self.get_command_type())) | 3.127014 | 3 |
lektor_markdown_mactutor.py | davidferguson/lektor-markdown-mactutor | 2 | 6614279 | <reponame>davidferguson/lektor-markdown-mactutor
# -*- coding: utf-8 -*-
from lektor.pluginsystem import Plugin
import mistune
import re
import commands.m_link
import commands.gl_link
import commands.ac_link
import commands.e_link
import commands.translation
import commands.reference
import commands.ovl_text
import commands.center_text
import commands.sub_text
import commands.sup_text
import commands.color_text
import commands.bgcolor_text
import commands.math_inline
import commands.text_inline
# list of plugins here. to add new ones, import them and add them to this list
plugins = [
commands.m_link,
commands.gl_link,
commands.ac_link,
commands.e_link,
commands.translation,
commands.reference,
commands.ovl_text,
commands.center_text,
commands.sub_text,
commands.sup_text,
commands.color_text,
commands.bgcolor_text,
commands.math_inline,
commands.text_inline
]
class MarkdownMactutorPlugin(Plugin):
name = 'Markdown MacTutor'
description = u'Lektor plugin that adds custom markdown syntax used for MacTutor.'
def on_markdown_config(self, config, **extra):
# create inline and block lexers
inline_lexer = mistune.InlineLexer(mistune.Renderer())
block_lexer = mistune.BlockLexer()
# does an lexer already exist? if so, use that
if 'inline' in config.options:
inline_lexer = config.options['inline']
if 'block' in config.options:
block_lexer = config.options['inline']
# loop through all enabled plugins
for plugin in plugins:
# select the correct lexer
lexer = inline_lexer
if plugin.type == 'block':
lexer = block_lexer
# add the plugin in
setattr(lexer.rules, plugin.name, plugin.regex)
# if there is a position and renderer, add that in too
if hasattr(plugin, 'position') and hasattr(plugin, 'render'):
lexer.default_rules.insert(plugin.position, plugin.name)
setattr(lexer, 'output_%s' % plugin.name, plugin.render)
# set the config to use these custom lexers
config.options['inline'] = inline_lexer
config.options['block'] = block_lexer
| # -*- coding: utf-8 -*-
from lektor.pluginsystem import Plugin
import mistune
import re
import commands.m_link
import commands.gl_link
import commands.ac_link
import commands.e_link
import commands.translation
import commands.reference
import commands.ovl_text
import commands.center_text
import commands.sub_text
import commands.sup_text
import commands.color_text
import commands.bgcolor_text
import commands.math_inline
import commands.text_inline
# list of plugins here. to add new ones, import them and add them to this list
plugins = [
commands.m_link,
commands.gl_link,
commands.ac_link,
commands.e_link,
commands.translation,
commands.reference,
commands.ovl_text,
commands.center_text,
commands.sub_text,
commands.sup_text,
commands.color_text,
commands.bgcolor_text,
commands.math_inline,
commands.text_inline
]
class MarkdownMactutorPlugin(Plugin):
name = 'Markdown MacTutor'
description = u'Lektor plugin that adds custom markdown syntax used for MacTutor.'
def on_markdown_config(self, config, **extra):
# create inline and block lexers
inline_lexer = mistune.InlineLexer(mistune.Renderer())
block_lexer = mistune.BlockLexer()
# does an lexer already exist? if so, use that
if 'inline' in config.options:
inline_lexer = config.options['inline']
if 'block' in config.options:
block_lexer = config.options['inline']
# loop through all enabled plugins
for plugin in plugins:
# select the correct lexer
lexer = inline_lexer
if plugin.type == 'block':
lexer = block_lexer
# add the plugin in
setattr(lexer.rules, plugin.name, plugin.regex)
# if there is a position and renderer, add that in too
if hasattr(plugin, 'position') and hasattr(plugin, 'render'):
lexer.default_rules.insert(plugin.position, plugin.name)
setattr(lexer, 'output_%s' % plugin.name, plugin.render)
# set the config to use these custom lexers
config.options['inline'] = inline_lexer
config.options['block'] = block_lexer | en | 0.826251 | # -*- coding: utf-8 -*- # list of plugins here. to add new ones, import them and add them to this list # create inline and block lexers # does an lexer already exist? if so, use that # loop through all enabled plugins # select the correct lexer # add the plugin in # if there is a position and renderer, add that in too # set the config to use these custom lexers | 2.574155 | 3 |
Labeling/labeled-3.py | ChiNasa511/FGCB-REU | 0 | 6614280 | f = mh.gaussian_filter(f, 4)
f = (f> f.mean())
imshow(f)
show()
| f = mh.gaussian_filter(f, 4)
f = (f> f.mean())
imshow(f)
show()
| none | 1 | 1.913413 | 2 | |
cnn/parameters_init.py | EmanueleLM/CNN | 1 | 6614281 | # -*- coding: utf-8 -*-
"""
Created on Tue Nov 28 19:40:10 2018
@author: Emanuele
Parameters' initializaton functions: provides both the functions and the dictionary
to initialize the weights of a given layer.
"""
import numpy as np
def uniform(weights, bias=None, args=None):
if args is None:
if bias is None:
weights = np.random.uniform(.0, 1., size=weights.shape)
return weights
else:
weights = np.random.uniform(.0, 1., size=weights.shape)
bias = np.random.uniform(.0, 1., size=bias.shape)
return weights, bias
else:
if bias is None:
weights = np.random.uniform(args[0], args[1], size=weights.shape)
return weights
else:
weights = np.random.uniform(args[0], args[1], size=weights.shape)
bias = np.random.uniform(args[0], args[1], size=bias.shape)
return weights, bias
def random(weights, bias=None, args=None):
if args is None:
if bias is None:
weights = np.random.rand(weights.shape[0], weights.shape[1])
return weights
else:
weights = np.random.rand(weights.shape[0], weights.shape[1])
bias = np.random.rand(bias.shape[0], bias.shape[1])
return weights, bias
else:
if bias is None:
weights = np.random.rand(weights.shape[0], weights.shape[1])
return weights
else:
weights = np.random.rand(weights.shape[0], weights.shape[1])
bias = np.random.rand(bias.shape[0], bias.shape[1])
return weights, bias
dict_parameters_init = { 'uniform': uniform,
'random': random
}
| # -*- coding: utf-8 -*-
"""
Created on Tue Nov 28 19:40:10 2018
@author: Emanuele
Parameters' initializaton functions: provides both the functions and the dictionary
to initialize the weights of a given layer.
"""
import numpy as np
def uniform(weights, bias=None, args=None):
if args is None:
if bias is None:
weights = np.random.uniform(.0, 1., size=weights.shape)
return weights
else:
weights = np.random.uniform(.0, 1., size=weights.shape)
bias = np.random.uniform(.0, 1., size=bias.shape)
return weights, bias
else:
if bias is None:
weights = np.random.uniform(args[0], args[1], size=weights.shape)
return weights
else:
weights = np.random.uniform(args[0], args[1], size=weights.shape)
bias = np.random.uniform(args[0], args[1], size=bias.shape)
return weights, bias
def random(weights, bias=None, args=None):
if args is None:
if bias is None:
weights = np.random.rand(weights.shape[0], weights.shape[1])
return weights
else:
weights = np.random.rand(weights.shape[0], weights.shape[1])
bias = np.random.rand(bias.shape[0], bias.shape[1])
return weights, bias
else:
if bias is None:
weights = np.random.rand(weights.shape[0], weights.shape[1])
return weights
else:
weights = np.random.rand(weights.shape[0], weights.shape[1])
bias = np.random.rand(bias.shape[0], bias.shape[1])
return weights, bias
dict_parameters_init = { 'uniform': uniform,
'random': random
}
| en | 0.768341 | # -*- coding: utf-8 -*- Created on Tue Nov 28 19:40:10 2018 @author: Emanuele Parameters' initializaton functions: provides both the functions and the dictionary to initialize the weights of a given layer. | 3.700682 | 4 |
build/lib/divis/pipelines.py | niu-lab/DIVIS | 3 | 6614282 | <filename>build/lib/divis/pipelines.py<gh_stars>1-10
import sys
import os
from divis.utils import dir_create
from divis.flows import FLOWS_DICT
from divis.macros import DEFAULT_MACROS_DICT
from divis.macros import read_macros, write_macros
from divis.steps import flow_run
from multiprocessing import Process
def somatic_pipeline(preview, input_macros_file, out_dir):
# create out directory
out_dir = os.path.abspath(out_dir)
dir_create(out_dir)
total_macros = read_macros(input_macros_file)
# align
normal_r1 = total_macros.get("NORMAL_R1")
if not normal_r1:
sys.stderr.write("can't find NORMAL_R1" + os.linesep)
exit(1)
normal_r2 = total_macros.get("NORMAL_R2")
if not normal_r2:
sys.stderr.write("can't find NORMAL_R2" + os.linesep)
exit(1)
tumor_r1 = total_macros.get("TUMOR_R1")
if not tumor_r1:
sys.stderr.write("can't find TUMOR_R1" + os.linesep)
exit(1)
tumor_r2 = total_macros.get("TUMOR_R2")
if not tumor_r2:
sys.stderr.write("can't find TUMOR_R2" + os.linesep)
exit(1)
sample_name = total_macros.get("SAMPLE_NAME")
if not sample_name:
sys.stderr.write("can't find SAMPLE_NAME" + os.linesep)
exit(1)
platform = total_macros.get("PLATFORM")
if not platform:
sys.stderr.write("can't find PLATFORM" + os.linesep)
exit(1)
normal_rg = '\'@RG\\tID:{sample_name}_N\\tSM:{sample_name}_N\\tLB:{sample_name}_N\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
tumor_rg = '\'@RG\\tID:{sample_name}_T\\tSM:{sample_name}_T\\tLB:{sample_name}_T\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
align_macros = read_macros(DEFAULT_MACROS_DICT.get("align"))
for align_macro in align_macros:
if align_macro in total_macros:
align_macros[align_macro] = total_macros[align_macro]
# normal align macro
align_macros["R1"] = normal_r1
align_macros["R2"] = normal_r2
align_macros["RG"] = normal_rg
align_macros["SAMPLE_NAME"] = "{}_normal".format(sample_name)
normal_macros_file = os.path.join(out_dir, "normal.align.macros")
write_macros(align_macros, normal_macros_file)
# tumor align macros
align_macros["R1"] = tumor_r1
align_macros["R2"] = tumor_r2
align_macros["RG"] = tumor_rg
align_macros["SAMPLE_NAME"] = "{}_tumor".format(sample_name)
tumor_macros_file = os.path.join(out_dir, "tumor.align.macros")
write_macros(align_macros, tumor_macros_file)
# normal align
normal_align_dir = os.path.join(out_dir, "normal_align")
print("------ normal align ------")
normal_align_thread = Process(target=flow_run,
args=(preview,
FLOWS_DICT.get("align"),
normal_macros_file,
normal_align_dir))
normal_align_thread.start()
# tumor align
tumor_align_dir = os.path.join(out_dir, "tumor_align")
print("------ tumor align ------")
tumor_align_thread = Process(target=flow_run,
args=(preview,
FLOWS_DICT.get("align"),
tumor_macros_file,
tumor_align_dir))
tumor_align_thread.start()
normal_align_thread.join()
tumor_align_thread.join()
# VSP variants calling
# varscan somatic
# print("------ varscan somatic ------")
# varscan_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("varscan_somatic"))
# for macro in varscan_somatic_macros:
# if macro in total_macros:
# varscan_somatic_macros[macro] = total_macros[macro]
#
# varscan_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# varscan_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# varscan_somatic_macros_file = os.path.join(out_dir, "varscan_somatic.macros")
# write_macros(varscan_somatic_macros, varscan_somatic_macros_file)
# varscan_somatic_dir = os.path.join(out_dir, "varscan_somatic")
# varscan_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("varscan_somatic"),
# varscan_somatic_macros_file,
# varscan_somatic_dir))
# varscan_thread.start()
#
# # strelka somatic
# print("------ strelka somatic ------")
# strelka_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("strelka_somatic"))
# for macro in strelka_somatic_macros:
# if macro in total_macros:
# strelka_somatic_macros[macro] = total_macros[macro]
#
# strelka_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# strelka_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# strelka_somatic_macros_file = os.path.join(out_dir, "strelka_somatic.macros")
# write_macros(strelka_somatic_macros, strelka_somatic_macros_file)
# strelka_somatic_dir = os.path.join(out_dir, "strelka_somatic")
# strelka_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("strelka_somatic"),
# strelka_somatic_macros_file,
# strelka_somatic_dir))
# strelka_thread.start()
#
# # pindel somatic
# print("------ pindel somatic ------")
# pindel_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("pindel_somatic"))
# for macro in pindel_somatic_macros:
# if macro in total_macros:
# pindel_somatic_macros[macro] = total_macros[macro]
#
# pindel_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# pindel_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# pindel_somatic_macros_file = os.path.join(out_dir, "pindel_somatic.macros")
# write_macros(pindel_somatic_macros, pindel_somatic_macros_file)
# pindel_somatic_dir = os.path.join(out_dir, "pindel_somatic")
# pindel_thread = Process(target=flow_run, args=(preview,
# FLOWS_DICT.get("pindel_somatic"),
# pindel_somatic_macros_file,
# pindel_somatic_dir))
# pindel_thread.start()
#
# varscan_thread.join()
# strelka_thread.join()
# pindel_thread.join()
#
# # annotation
# print("------ oncotator annotation ------")
# oncotator_macros = read_macros(DEFAULT_MACROS_DICT.get("oncotator"))
# for macro in oncotator_macros:
# if macro in total_macros:
# oncotator_macros[macro] = total_macros[macro]
#
# vcfs = "{varscan_dir}/{sample_name}.indel.vcf " \
# "{varscan_dir}/{sample_name}.snp.vcf " \
# "{strelka_dir}/strelk_out/results/passed.somatic.indels.vcf " \
# "{strelka_dir}/strelk_out/results/passed.somatic.snvs.vcf " \
# "{pindel_dir}/{sample_name}.somatic.delete.vcf " \
# "{pindel_dir}/{sample_name}.somatic.insert.vcf ".format(varscan_dir=varscan_somatic_dir,
# strelka_dir=strelka_somatic_dir,
# pindel_dir=pindel_somatic_dir,
# sample_name=sample_name)
# oncotator_macros["VCFS"] = vcfs
# oncotator_macros["NORMAL_BARCODE"] = "{}_normal".format(sample_name)
# oncotator_macros["TUMOR_BARCODE"] = "{}_tumor".format(sample_name)
# oncotator_macros_file = os.path.join(out_dir, "oncotator.macros")
# write_macros(oncotator_macros, oncotator_macros_file)
# oncotator_dir = os.path.join(out_dir, "oncotator")
# oncotator_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("oncotator"),
# oncotator_macros_file,
# oncotator_dir))
# oncotator_thread.start()
# oncotator_thread.join()
pass
def gatk4_haplotypecaller_germline_pipeline(preview, in_macro_file, out_dir):
# create out directory
out_dir = os.path.abspath(out_dir)
dir_create(out_dir)
total_macros = read_macros(in_macro_file)
# quality control
qc_macros = read_macros(DEFAULT_MACROS_DICT.get("qc"))
for qc_macro in qc_macros:
if qc_macro in total_macros:
qc_macros[qc_macro] = total_macros[qc_macro]
qc_macros_file = os.path.join(out_dir, "qc.macros")
write_macros(qc_macros, qc_macros_file)
qc_dir = os.path.join(out_dir, "qc")
print("------ quality control ------")
flow_run(preview, FLOWS_DICT.get("qc"), qc_macros_file, qc_dir)
# alignment
align_macros = read_macros(DEFAULT_MACROS_DICT.get("align"))
for align_macro in align_macros:
if align_macro in total_macros:
align_macros[align_macro] = total_macros[align_macro]
sample_name = total_macros.get("SAMPLE_NAME")
platform = total_macros.get("PLATFORM")
rg = '\'@RG\\tID:{sample_name}\\tSM:{sample_name}\\tLB:{sample_name}\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
align_macros["RG"] = rg
align_macros_file = os.path.join(out_dir, "align.macros")
write_macros(align_macros, align_macros_file)
align_dir = os.path.join(out_dir, "align")
print("------ align ------")
flow_run(preview, FLOWS_DICT.get("align"), align_macros_file, align_dir)
# variants calling
bam_file = os.path.join(align_dir, "{}.bam".format(sample_name))
gatk4_haplotypecaller_macros = read_macros(DEFAULT_MACROS_DICT.get("gatk4_haplotypecaller_germline"))
for gatk4_haplotypecaller_macro in gatk4_haplotypecaller_macros:
if gatk4_haplotypecaller_macro in total_macros:
gatk4_haplotypecaller_macros[gatk4_haplotypecaller_macro] = total_macros[gatk4_haplotypecaller_macro]
gatk4_haplotypecaller_macros["BAM_FILE"] = bam_file
gatk4_haplotypecaller_macros_file = os.path.join(out_dir, "gatk4_haplotypecaller_germline.macros")
write_macros(gatk4_haplotypecaller_macros, gatk4_haplotypecaller_macros_file)
gatk4_haplotypecaller_dir = os.path.join(out_dir, "gatk4_haplotypecaller")
print("------ variants calling ------")
flow_run(preview, FLOWS_DICT.get("gatk4_haplotypecaller_germline"), gatk4_haplotypecaller_macros_file,
gatk4_haplotypecaller_dir)
pass
PIPELINE_FUNCS = {
"wes_somatic": somatic_pipeline,
"wgs_somatic": somatic_pipeline,
"panel_germline": gatk4_haplotypecaller_germline_pipeline,
}
def pipeline_run(pipeline_name, preview, input_macros_file, out_dir):
func = PIPELINE_FUNCS.get(pipeline_name)
if not func:
sys.stderr.write("can't find {} pipeline".format(pipeline_name) + os.linesep)
func(preview, input_macros_file, out_dir)
| <filename>build/lib/divis/pipelines.py<gh_stars>1-10
import sys
import os
from divis.utils import dir_create
from divis.flows import FLOWS_DICT
from divis.macros import DEFAULT_MACROS_DICT
from divis.macros import read_macros, write_macros
from divis.steps import flow_run
from multiprocessing import Process
def somatic_pipeline(preview, input_macros_file, out_dir):
# create out directory
out_dir = os.path.abspath(out_dir)
dir_create(out_dir)
total_macros = read_macros(input_macros_file)
# align
normal_r1 = total_macros.get("NORMAL_R1")
if not normal_r1:
sys.stderr.write("can't find NORMAL_R1" + os.linesep)
exit(1)
normal_r2 = total_macros.get("NORMAL_R2")
if not normal_r2:
sys.stderr.write("can't find NORMAL_R2" + os.linesep)
exit(1)
tumor_r1 = total_macros.get("TUMOR_R1")
if not tumor_r1:
sys.stderr.write("can't find TUMOR_R1" + os.linesep)
exit(1)
tumor_r2 = total_macros.get("TUMOR_R2")
if not tumor_r2:
sys.stderr.write("can't find TUMOR_R2" + os.linesep)
exit(1)
sample_name = total_macros.get("SAMPLE_NAME")
if not sample_name:
sys.stderr.write("can't find SAMPLE_NAME" + os.linesep)
exit(1)
platform = total_macros.get("PLATFORM")
if not platform:
sys.stderr.write("can't find PLATFORM" + os.linesep)
exit(1)
normal_rg = '\'@RG\\tID:{sample_name}_N\\tSM:{sample_name}_N\\tLB:{sample_name}_N\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
tumor_rg = '\'@RG\\tID:{sample_name}_T\\tSM:{sample_name}_T\\tLB:{sample_name}_T\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
align_macros = read_macros(DEFAULT_MACROS_DICT.get("align"))
for align_macro in align_macros:
if align_macro in total_macros:
align_macros[align_macro] = total_macros[align_macro]
# normal align macro
align_macros["R1"] = normal_r1
align_macros["R2"] = normal_r2
align_macros["RG"] = normal_rg
align_macros["SAMPLE_NAME"] = "{}_normal".format(sample_name)
normal_macros_file = os.path.join(out_dir, "normal.align.macros")
write_macros(align_macros, normal_macros_file)
# tumor align macros
align_macros["R1"] = tumor_r1
align_macros["R2"] = tumor_r2
align_macros["RG"] = tumor_rg
align_macros["SAMPLE_NAME"] = "{}_tumor".format(sample_name)
tumor_macros_file = os.path.join(out_dir, "tumor.align.macros")
write_macros(align_macros, tumor_macros_file)
# normal align
normal_align_dir = os.path.join(out_dir, "normal_align")
print("------ normal align ------")
normal_align_thread = Process(target=flow_run,
args=(preview,
FLOWS_DICT.get("align"),
normal_macros_file,
normal_align_dir))
normal_align_thread.start()
# tumor align
tumor_align_dir = os.path.join(out_dir, "tumor_align")
print("------ tumor align ------")
tumor_align_thread = Process(target=flow_run,
args=(preview,
FLOWS_DICT.get("align"),
tumor_macros_file,
tumor_align_dir))
tumor_align_thread.start()
normal_align_thread.join()
tumor_align_thread.join()
# VSP variants calling
# varscan somatic
# print("------ varscan somatic ------")
# varscan_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("varscan_somatic"))
# for macro in varscan_somatic_macros:
# if macro in total_macros:
# varscan_somatic_macros[macro] = total_macros[macro]
#
# varscan_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# varscan_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# varscan_somatic_macros_file = os.path.join(out_dir, "varscan_somatic.macros")
# write_macros(varscan_somatic_macros, varscan_somatic_macros_file)
# varscan_somatic_dir = os.path.join(out_dir, "varscan_somatic")
# varscan_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("varscan_somatic"),
# varscan_somatic_macros_file,
# varscan_somatic_dir))
# varscan_thread.start()
#
# # strelka somatic
# print("------ strelka somatic ------")
# strelka_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("strelka_somatic"))
# for macro in strelka_somatic_macros:
# if macro in total_macros:
# strelka_somatic_macros[macro] = total_macros[macro]
#
# strelka_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# strelka_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# strelka_somatic_macros_file = os.path.join(out_dir, "strelka_somatic.macros")
# write_macros(strelka_somatic_macros, strelka_somatic_macros_file)
# strelka_somatic_dir = os.path.join(out_dir, "strelka_somatic")
# strelka_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("strelka_somatic"),
# strelka_somatic_macros_file,
# strelka_somatic_dir))
# strelka_thread.start()
#
# # pindel somatic
# print("------ pindel somatic ------")
# pindel_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("pindel_somatic"))
# for macro in pindel_somatic_macros:
# if macro in total_macros:
# pindel_somatic_macros[macro] = total_macros[macro]
#
# pindel_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name))
# pindel_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name))
# pindel_somatic_macros_file = os.path.join(out_dir, "pindel_somatic.macros")
# write_macros(pindel_somatic_macros, pindel_somatic_macros_file)
# pindel_somatic_dir = os.path.join(out_dir, "pindel_somatic")
# pindel_thread = Process(target=flow_run, args=(preview,
# FLOWS_DICT.get("pindel_somatic"),
# pindel_somatic_macros_file,
# pindel_somatic_dir))
# pindel_thread.start()
#
# varscan_thread.join()
# strelka_thread.join()
# pindel_thread.join()
#
# # annotation
# print("------ oncotator annotation ------")
# oncotator_macros = read_macros(DEFAULT_MACROS_DICT.get("oncotator"))
# for macro in oncotator_macros:
# if macro in total_macros:
# oncotator_macros[macro] = total_macros[macro]
#
# vcfs = "{varscan_dir}/{sample_name}.indel.vcf " \
# "{varscan_dir}/{sample_name}.snp.vcf " \
# "{strelka_dir}/strelk_out/results/passed.somatic.indels.vcf " \
# "{strelka_dir}/strelk_out/results/passed.somatic.snvs.vcf " \
# "{pindel_dir}/{sample_name}.somatic.delete.vcf " \
# "{pindel_dir}/{sample_name}.somatic.insert.vcf ".format(varscan_dir=varscan_somatic_dir,
# strelka_dir=strelka_somatic_dir,
# pindel_dir=pindel_somatic_dir,
# sample_name=sample_name)
# oncotator_macros["VCFS"] = vcfs
# oncotator_macros["NORMAL_BARCODE"] = "{}_normal".format(sample_name)
# oncotator_macros["TUMOR_BARCODE"] = "{}_tumor".format(sample_name)
# oncotator_macros_file = os.path.join(out_dir, "oncotator.macros")
# write_macros(oncotator_macros, oncotator_macros_file)
# oncotator_dir = os.path.join(out_dir, "oncotator")
# oncotator_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("oncotator"),
# oncotator_macros_file,
# oncotator_dir))
# oncotator_thread.start()
# oncotator_thread.join()
pass
def gatk4_haplotypecaller_germline_pipeline(preview, in_macro_file, out_dir):
# create out directory
out_dir = os.path.abspath(out_dir)
dir_create(out_dir)
total_macros = read_macros(in_macro_file)
# quality control
qc_macros = read_macros(DEFAULT_MACROS_DICT.get("qc"))
for qc_macro in qc_macros:
if qc_macro in total_macros:
qc_macros[qc_macro] = total_macros[qc_macro]
qc_macros_file = os.path.join(out_dir, "qc.macros")
write_macros(qc_macros, qc_macros_file)
qc_dir = os.path.join(out_dir, "qc")
print("------ quality control ------")
flow_run(preview, FLOWS_DICT.get("qc"), qc_macros_file, qc_dir)
# alignment
align_macros = read_macros(DEFAULT_MACROS_DICT.get("align"))
for align_macro in align_macros:
if align_macro in total_macros:
align_macros[align_macro] = total_macros[align_macro]
sample_name = total_macros.get("SAMPLE_NAME")
platform = total_macros.get("PLATFORM")
rg = '\'@RG\\tID:{sample_name}\\tSM:{sample_name}\\tLB:{sample_name}\\tPL:{platform}\''.format(
sample_name=sample_name,
platform=platform)
align_macros["RG"] = rg
align_macros_file = os.path.join(out_dir, "align.macros")
write_macros(align_macros, align_macros_file)
align_dir = os.path.join(out_dir, "align")
print("------ align ------")
flow_run(preview, FLOWS_DICT.get("align"), align_macros_file, align_dir)
# variants calling
bam_file = os.path.join(align_dir, "{}.bam".format(sample_name))
gatk4_haplotypecaller_macros = read_macros(DEFAULT_MACROS_DICT.get("gatk4_haplotypecaller_germline"))
for gatk4_haplotypecaller_macro in gatk4_haplotypecaller_macros:
if gatk4_haplotypecaller_macro in total_macros:
gatk4_haplotypecaller_macros[gatk4_haplotypecaller_macro] = total_macros[gatk4_haplotypecaller_macro]
gatk4_haplotypecaller_macros["BAM_FILE"] = bam_file
gatk4_haplotypecaller_macros_file = os.path.join(out_dir, "gatk4_haplotypecaller_germline.macros")
write_macros(gatk4_haplotypecaller_macros, gatk4_haplotypecaller_macros_file)
gatk4_haplotypecaller_dir = os.path.join(out_dir, "gatk4_haplotypecaller")
print("------ variants calling ------")
flow_run(preview, FLOWS_DICT.get("gatk4_haplotypecaller_germline"), gatk4_haplotypecaller_macros_file,
gatk4_haplotypecaller_dir)
pass
PIPELINE_FUNCS = {
"wes_somatic": somatic_pipeline,
"wgs_somatic": somatic_pipeline,
"panel_germline": gatk4_haplotypecaller_germline_pipeline,
}
def pipeline_run(pipeline_name, preview, input_macros_file, out_dir):
func = PIPELINE_FUNCS.get(pipeline_name)
if not func:
sys.stderr.write("can't find {} pipeline".format(pipeline_name) + os.linesep)
func(preview, input_macros_file, out_dir)
| en | 0.335403 | # create out directory # align # normal align macro # tumor align macros # normal align # tumor align # VSP variants calling # varscan somatic # print("------ varscan somatic ------") # varscan_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("varscan_somatic")) # for macro in varscan_somatic_macros: # if macro in total_macros: # varscan_somatic_macros[macro] = total_macros[macro] # # varscan_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name)) # varscan_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name)) # varscan_somatic_macros_file = os.path.join(out_dir, "varscan_somatic.macros") # write_macros(varscan_somatic_macros, varscan_somatic_macros_file) # varscan_somatic_dir = os.path.join(out_dir, "varscan_somatic") # varscan_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("varscan_somatic"), # varscan_somatic_macros_file, # varscan_somatic_dir)) # varscan_thread.start() # # # strelka somatic # print("------ strelka somatic ------") # strelka_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("strelka_somatic")) # for macro in strelka_somatic_macros: # if macro in total_macros: # strelka_somatic_macros[macro] = total_macros[macro] # # strelka_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name)) # strelka_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name)) # strelka_somatic_macros_file = os.path.join(out_dir, "strelka_somatic.macros") # write_macros(strelka_somatic_macros, strelka_somatic_macros_file) # strelka_somatic_dir = os.path.join(out_dir, "strelka_somatic") # strelka_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("strelka_somatic"), # strelka_somatic_macros_file, # strelka_somatic_dir)) # strelka_thread.start() # # # pindel somatic # print("------ pindel somatic ------") # pindel_somatic_macros = read_macros(DEFAULT_MACROS_DICT.get("pindel_somatic")) # for macro in pindel_somatic_macros: # if macro in total_macros: # pindel_somatic_macros[macro] = total_macros[macro] # # pindel_somatic_macros["NORMAL_BAM"] = os.path.join(normal_align_dir, "{}_normal.bam".format(sample_name)) # pindel_somatic_macros["TUMOR_BAM"] = os.path.join(tumor_align_dir, "{}_tumor.bam".format(sample_name)) # pindel_somatic_macros_file = os.path.join(out_dir, "pindel_somatic.macros") # write_macros(pindel_somatic_macros, pindel_somatic_macros_file) # pindel_somatic_dir = os.path.join(out_dir, "pindel_somatic") # pindel_thread = Process(target=flow_run, args=(preview, # FLOWS_DICT.get("pindel_somatic"), # pindel_somatic_macros_file, # pindel_somatic_dir)) # pindel_thread.start() # # varscan_thread.join() # strelka_thread.join() # pindel_thread.join() # # # annotation # print("------ oncotator annotation ------") # oncotator_macros = read_macros(DEFAULT_MACROS_DICT.get("oncotator")) # for macro in oncotator_macros: # if macro in total_macros: # oncotator_macros[macro] = total_macros[macro] # # vcfs = "{varscan_dir}/{sample_name}.indel.vcf " \ # "{varscan_dir}/{sample_name}.snp.vcf " \ # "{strelka_dir}/strelk_out/results/passed.somatic.indels.vcf " \ # "{strelka_dir}/strelk_out/results/passed.somatic.snvs.vcf " \ # "{pindel_dir}/{sample_name}.somatic.delete.vcf " \ # "{pindel_dir}/{sample_name}.somatic.insert.vcf ".format(varscan_dir=varscan_somatic_dir, # strelka_dir=strelka_somatic_dir, # pindel_dir=pindel_somatic_dir, # sample_name=sample_name) # oncotator_macros["VCFS"] = vcfs # oncotator_macros["NORMAL_BARCODE"] = "{}_normal".format(sample_name) # oncotator_macros["TUMOR_BARCODE"] = "{}_tumor".format(sample_name) # oncotator_macros_file = os.path.join(out_dir, "oncotator.macros") # write_macros(oncotator_macros, oncotator_macros_file) # oncotator_dir = os.path.join(out_dir, "oncotator") # oncotator_thread = Process(target=flow_run, args=(preview, FLOWS_DICT.get("oncotator"), # oncotator_macros_file, # oncotator_dir)) # oncotator_thread.start() # oncotator_thread.join() # create out directory # quality control # alignment # variants calling | 2.116731 | 2 |
ic_marathon_app/admin.py | sarifern/ciscorunning | 0 | 6614283 | from django.contrib import admin
from .models import Workout, Profile
from django.utils.html import format_html
# Register your models here.
class WorkoutAdmin(admin.ModelAdmin):
list_display = ('uuid', 'belongs_to', 'distance', 'image_tag')
list_filter = ['belongs_to']
def image_tag(self, obj):
return format_html(
'<img src="{}" width="600px" height="600px"/>'.format(
obj.photo_evidence.url))
admin.site.register(Workout, WorkoutAdmin)
class ProfileAdmin(admin.ModelAdmin):
list_display = ('user', 'cec', 'user_goal', 'distance')
list_filter = ['user', 'cec']
admin.site.register(Profile, ProfileAdmin)
| from django.contrib import admin
from .models import Workout, Profile
from django.utils.html import format_html
# Register your models here.
class WorkoutAdmin(admin.ModelAdmin):
list_display = ('uuid', 'belongs_to', 'distance', 'image_tag')
list_filter = ['belongs_to']
def image_tag(self, obj):
return format_html(
'<img src="{}" width="600px" height="600px"/>'.format(
obj.photo_evidence.url))
admin.site.register(Workout, WorkoutAdmin)
class ProfileAdmin(admin.ModelAdmin):
list_display = ('user', 'cec', 'user_goal', 'distance')
list_filter = ['user', 'cec']
admin.site.register(Profile, ProfileAdmin)
| en | 0.968259 | # Register your models here. | 1.896459 | 2 |
isar/events/rulesservice.py | zardosht/isar | 0 | 6614284 | <reponame>zardosht/isar<gh_stars>0
import logging
from threading import Thread
from isar.events import events, eventmanager
from isar.services.service import Service
logger = logging.getLogger("isar.scene.rulesservice")
class RulesService(Service):
def __init__(self, service_name):
super().__init__(service_name)
self.actions_service = None
self.__scenes_model = None
self.current_scene = None
self.register_for_all_events()
def register_for_all_events(self):
for event_class_name in events.event_types:
eventmanager.register_listener(event_class_name, self)
def set_scenes_model(self, scenes_model):
self.__scenes_model = scenes_model
def set_current_scene(self, current_scene):
self.current_scene = current_scene
def on_event(self, event):
if self.current_scene is None:
logger.error("self.scene is None. Return.")
return
for rule in self.current_scene.get_rules():
if rule.event == event:
t = Thread(name="RuleServiceFireRuleThread", target=rule.fire)
t.start()
| import logging
from threading import Thread
from isar.events import events, eventmanager
from isar.services.service import Service
logger = logging.getLogger("isar.scene.rulesservice")
class RulesService(Service):
def __init__(self, service_name):
super().__init__(service_name)
self.actions_service = None
self.__scenes_model = None
self.current_scene = None
self.register_for_all_events()
def register_for_all_events(self):
for event_class_name in events.event_types:
eventmanager.register_listener(event_class_name, self)
def set_scenes_model(self, scenes_model):
self.__scenes_model = scenes_model
def set_current_scene(self, current_scene):
self.current_scene = current_scene
def on_event(self, event):
if self.current_scene is None:
logger.error("self.scene is None. Return.")
return
for rule in self.current_scene.get_rules():
if rule.event == event:
t = Thread(name="RuleServiceFireRuleThread", target=rule.fire)
t.start() | none | 1 | 2.457962 | 2 | |
vgazer/install/custom_installer/sdl2_gpu.py | edomin/vgazer | 2 | 6614285 | import os
from vgazer.command import RunCommand
from vgazer.config.cmake import ConfigCmake
from vgazer.exceptions import CommandError
from vgazer.exceptions import InstallError
from vgazer.platform import GetArFullPath
from vgazer.platform import GetCc
from vgazer.platform import GetInstallPrefix
from vgazer.platform import GetSoPrefix
from vgazer.platform import GetSoFilename
from vgazer.store.temp import StoreTemp
from vgazer.working_dir import WorkingDir
def GetVersionFromSource(filename):
with open(filename) as f:
data = f.read()
lines = data.splitlines()
for line in lines:
if "#define SDL_GPU_VERSION_MAJOR" in line:
versionMajor = line.split(" ")[2]
if "#define SDL_GPU_VERSION_MINOR" in line:
versionMinor = line.split(" ")[2]
if "#define SDL_GPU_VERSION_PATCH" in line:
versionPatch = line.split(" ")[2]
return "{major}.{minor}.{patch}".format(major=versionMajor,
minor=versionMinor, patch=versionPatch)
def Install(auth, software, platform, platformData, mirrors, verbose):
configCmake = ConfigCmake(platformData)
configCmake.GenerateCrossFile()
installPrefix = GetInstallPrefix(platformData)
ar = GetArFullPath(platformData["target"])
cc = GetCc(platformData["target"])
os = platformData["target"].GetOs()
soPrefix = GetSoPrefix(platformData)
soFilename = GetSoFilename(platformData["target"], "SDL2_gpu")
storeTemp = StoreTemp()
storeTemp.ResolveEmptySubdirectory(software)
tempPath = storeTemp.GetSubdirectoryPath(software)
try:
with WorkingDir(tempPath):
RunCommand(
["git", "clone", "https://github.com/grimfang4/sdl-gpu.git",
"sdl2-gpu"],
verbose)
clonedDir = os.path.join(tempPath, "sdl2-gpu")
with WorkingDir(clonedDir):
RunCommand(
["sed", "-i",
"-e", '/\t\t\tlink_libraries (${GLEW_LIBRARIES})/i \t\t\tadd_definitions("-DGLEW_STATIC")',
"./CMakeLists.txt"],
verbose)
RunCommand(["mkdir", "build"], verbose)
sdlGpuHeader = os.path.join(clonedDir, "include/SDL_gpu.h")
version = GetVersionFromSource(sdlGpuHeader)
if os == "linux":
soLibname = "libSDL2_gpu.so"
installedLibPrefix = installPrefix + "/SDL_gpu-" + version + "/lib"
elif os == "windows":
soLibname = "libSDL2_gpu.dll"
installedLibPrefix = installPrefix + "/SDL_gpu-MINGW-" + version + "/lib"
buildDir = os.path.join(clonedDir, "build")
with WorkingDir(buildDir):
RunCommand(
[cc, "-c", "../src/externals/stb_image/stb_image.c",
"-o", "../src/externals/stb_image/stb_image.o", "-O2", "-Wall",
"-mmmx", "-msse", "-msse2", "-mfpmath=sse", "-fPIC",
"-I" + installPrefix + "/include"],
verbose)
RunCommand(
[ar, "rcs", "../src/externals/stb_image/libstbi.a",
"../src/externals/stb_image/stb_image.o"],
verbose)
RunCommand(
[cc, "-c", "../src/externals/stb_image_write/stb_image_write.c",
"-o", "../src/externals/stb_image_write/stb_image_write.o",
"-O2", "-Wall", "-mmmx", "-msse", "-msse2", "-mfpmath=sse",
"-fPIC", "-I" + installPrefix + "/include"],
verbose)
RunCommand(
[ar, "rcs", "../src/externals/stb_image_write/libstbi_write.a",
"../src/externals/stb_image_write/stb_image_write.o"],
verbose)
RunCommand(
[
"cmake", "..", "-G", "Unix Makefiles",
"-DCMAKE_TOOLCHAIN_FILE=" + configCmake.GetCrossFileName(),
"-DCMAKE_INSTALL_PREFIX=" + installPrefix,
"-DSDL_gpu_INSTALL=ON", "-DSDL_gpu_BUILD_DEMOS=OFF",
"-DSDL_gpu_USE_SYSTEM_GLEW=ON",
"-DSTBI_INCLUDE_DIR=" + installPrefix + "/include",
"-DSTBI_LIBRARY=" + buildDir
+ "/../src/externals/stb_image/libstbi.a",
"-DSTBI_FOUND=TRUE",
"-DSTBI_WRITE_INCLUDE_DIR=" + installPrefix + "/include",
"-DSTBI_WRITE_LIBRARY=" + buildDir
+ "/../src/externals/stb_image_write/libstbi_write.a",
"-DSTBI_WRITE_FOUND=TRUE",
"-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON", "-DCMAKE_AR=" + ar
],
verbose)
RunCommand(["make"], verbose)
RunCommand(["make", "install"], verbose)
RunCommand(
["mv", installedLibPrefix + "/" + soLibname,
soPrefix + "/" + soFilename],
verbose)
RunCommand(
["mv", installedLibPrefix + "/libSDL2_gpu.a",
installPrefix + "/lib/libSDL2_gpu.a"],
verbose)
RunCommand(
["rm", "-rf", installPrefix + "/SDL_gpu-" + version],
verbose)
except CommandError:
print("VGAZER: Unable to install", software)
raise InstallError(software + " not installed")
print("VGAZER:", software, "installed")
| import os
from vgazer.command import RunCommand
from vgazer.config.cmake import ConfigCmake
from vgazer.exceptions import CommandError
from vgazer.exceptions import InstallError
from vgazer.platform import GetArFullPath
from vgazer.platform import GetCc
from vgazer.platform import GetInstallPrefix
from vgazer.platform import GetSoPrefix
from vgazer.platform import GetSoFilename
from vgazer.store.temp import StoreTemp
from vgazer.working_dir import WorkingDir
def GetVersionFromSource(filename):
with open(filename) as f:
data = f.read()
lines = data.splitlines()
for line in lines:
if "#define SDL_GPU_VERSION_MAJOR" in line:
versionMajor = line.split(" ")[2]
if "#define SDL_GPU_VERSION_MINOR" in line:
versionMinor = line.split(" ")[2]
if "#define SDL_GPU_VERSION_PATCH" in line:
versionPatch = line.split(" ")[2]
return "{major}.{minor}.{patch}".format(major=versionMajor,
minor=versionMinor, patch=versionPatch)
def Install(auth, software, platform, platformData, mirrors, verbose):
configCmake = ConfigCmake(platformData)
configCmake.GenerateCrossFile()
installPrefix = GetInstallPrefix(platformData)
ar = GetArFullPath(platformData["target"])
cc = GetCc(platformData["target"])
os = platformData["target"].GetOs()
soPrefix = GetSoPrefix(platformData)
soFilename = GetSoFilename(platformData["target"], "SDL2_gpu")
storeTemp = StoreTemp()
storeTemp.ResolveEmptySubdirectory(software)
tempPath = storeTemp.GetSubdirectoryPath(software)
try:
with WorkingDir(tempPath):
RunCommand(
["git", "clone", "https://github.com/grimfang4/sdl-gpu.git",
"sdl2-gpu"],
verbose)
clonedDir = os.path.join(tempPath, "sdl2-gpu")
with WorkingDir(clonedDir):
RunCommand(
["sed", "-i",
"-e", '/\t\t\tlink_libraries (${GLEW_LIBRARIES})/i \t\t\tadd_definitions("-DGLEW_STATIC")',
"./CMakeLists.txt"],
verbose)
RunCommand(["mkdir", "build"], verbose)
sdlGpuHeader = os.path.join(clonedDir, "include/SDL_gpu.h")
version = GetVersionFromSource(sdlGpuHeader)
if os == "linux":
soLibname = "libSDL2_gpu.so"
installedLibPrefix = installPrefix + "/SDL_gpu-" + version + "/lib"
elif os == "windows":
soLibname = "libSDL2_gpu.dll"
installedLibPrefix = installPrefix + "/SDL_gpu-MINGW-" + version + "/lib"
buildDir = os.path.join(clonedDir, "build")
with WorkingDir(buildDir):
RunCommand(
[cc, "-c", "../src/externals/stb_image/stb_image.c",
"-o", "../src/externals/stb_image/stb_image.o", "-O2", "-Wall",
"-mmmx", "-msse", "-msse2", "-mfpmath=sse", "-fPIC",
"-I" + installPrefix + "/include"],
verbose)
RunCommand(
[ar, "rcs", "../src/externals/stb_image/libstbi.a",
"../src/externals/stb_image/stb_image.o"],
verbose)
RunCommand(
[cc, "-c", "../src/externals/stb_image_write/stb_image_write.c",
"-o", "../src/externals/stb_image_write/stb_image_write.o",
"-O2", "-Wall", "-mmmx", "-msse", "-msse2", "-mfpmath=sse",
"-fPIC", "-I" + installPrefix + "/include"],
verbose)
RunCommand(
[ar, "rcs", "../src/externals/stb_image_write/libstbi_write.a",
"../src/externals/stb_image_write/stb_image_write.o"],
verbose)
RunCommand(
[
"cmake", "..", "-G", "Unix Makefiles",
"-DCMAKE_TOOLCHAIN_FILE=" + configCmake.GetCrossFileName(),
"-DCMAKE_INSTALL_PREFIX=" + installPrefix,
"-DSDL_gpu_INSTALL=ON", "-DSDL_gpu_BUILD_DEMOS=OFF",
"-DSDL_gpu_USE_SYSTEM_GLEW=ON",
"-DSTBI_INCLUDE_DIR=" + installPrefix + "/include",
"-DSTBI_LIBRARY=" + buildDir
+ "/../src/externals/stb_image/libstbi.a",
"-DSTBI_FOUND=TRUE",
"-DSTBI_WRITE_INCLUDE_DIR=" + installPrefix + "/include",
"-DSTBI_WRITE_LIBRARY=" + buildDir
+ "/../src/externals/stb_image_write/libstbi_write.a",
"-DSTBI_WRITE_FOUND=TRUE",
"-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON", "-DCMAKE_AR=" + ar
],
verbose)
RunCommand(["make"], verbose)
RunCommand(["make", "install"], verbose)
RunCommand(
["mv", installedLibPrefix + "/" + soLibname,
soPrefix + "/" + soFilename],
verbose)
RunCommand(
["mv", installedLibPrefix + "/libSDL2_gpu.a",
installPrefix + "/lib/libSDL2_gpu.a"],
verbose)
RunCommand(
["rm", "-rf", installPrefix + "/SDL_gpu-" + version],
verbose)
except CommandError:
print("VGAZER: Unable to install", software)
raise InstallError(software + " not installed")
print("VGAZER:", software, "installed")
| none | 1 | 2.067563 | 2 | |
Course/syntax/example_4.py | zevgenia/Python_shultais | 0 | 6614286 | <gh_stars>0
d = 10
l = ("A", "B", "C")
sl = {"en": "one", "ru": "один"}
a = b = c = 10
a *= 5
print(a, b, c)
# Каноническая форма
x = "строка"
# Приваивание кортежей
a, b = 'a', 'b'
print('a:', a)
print('b:', b)
#A, B = ('A', 'B')
#print('A:', A)
#print('B:', B)
# Приваивание списков
d, e = ["d", "e"]
print('d:', d)
print('e:', e)
#d, e, f = ["d", "e"]
#d, e, f = ["d", "e", "f", "g"]
# f = ["a", "b", "c"]
# Приваивание последовательностей
s, t, r, o, k, a = "строка"
a, b, c = ["строка", 45, {1: "one", 2: "two"}]
print('a:', a)
print('b:', b)
print('c:', c)
# Расширенное распаковывание последовательностей
a, *b, c = "A", "B1", "B2", "B3", "C"
print('a:', a)
print('b:', b)
print('c:', c)
# Групповое присваивание одного значения
a = b = c = 0
#c = 0
#b = c
#a = b
# Комбинированное присваивание
a = 10
a += 20
#a = a + 20
print(a) | d = 10
l = ("A", "B", "C")
sl = {"en": "one", "ru": "один"}
a = b = c = 10
a *= 5
print(a, b, c)
# Каноническая форма
x = "строка"
# Приваивание кортежей
a, b = 'a', 'b'
print('a:', a)
print('b:', b)
#A, B = ('A', 'B')
#print('A:', A)
#print('B:', B)
# Приваивание списков
d, e = ["d", "e"]
print('d:', d)
print('e:', e)
#d, e, f = ["d", "e"]
#d, e, f = ["d", "e", "f", "g"]
# f = ["a", "b", "c"]
# Приваивание последовательностей
s, t, r, o, k, a = "строка"
a, b, c = ["строка", 45, {1: "one", 2: "two"}]
print('a:', a)
print('b:', b)
print('c:', c)
# Расширенное распаковывание последовательностей
a, *b, c = "A", "B1", "B2", "B3", "C"
print('a:', a)
print('b:', b)
print('c:', c)
# Групповое присваивание одного значения
a = b = c = 0
#c = 0
#b = c
#a = b
# Комбинированное присваивание
a = 10
a += 20
#a = a + 20
print(a) | ru | 0.819429 | # Каноническая форма # Приваивание кортежей #A, B = ('A', 'B') #print('A:', A) #print('B:', B) # Приваивание списков #d, e, f = ["d", "e"] #d, e, f = ["d", "e", "f", "g"] # f = ["a", "b", "c"] # Приваивание последовательностей # Расширенное распаковывание последовательностей # Групповое присваивание одного значения #c = 0 #b = c #a = b # Комбинированное присваивание #a = a + 20 | 3.880517 | 4 |
example/hello/__init__.py | mamaz/tdd-example | 2 | 6614287 |
def test_hello_function_should_return_hello():
pass |
def test_hello_function_should_return_hello():
pass | none | 1 | 0.95068 | 1 | |
pyronear/utils/__init__.py | JoaoFdC/PyroNear | 0 | 6614288 | <filename>pyronear/utils/__init__.py<gh_stars>0
from .collect_env import get_pretty_env_info
del collect_env
| <filename>pyronear/utils/__init__.py<gh_stars>0
from .collect_env import get_pretty_env_info
del collect_env
| none | 1 | 1.043531 | 1 | |
pycdt/corrections/kumagai_correction.py | hitarth64/pycdt | 0 | 6614289 | """
This module computes finite size supercell charge corrections for
defects in anistropic systems using extended Freysoldt (or Kumagai) method
developed by Kumagai and Oba.
Kumagai method includes
a) anisotropic PC energy
b) potential alignment by atomic site averaging at Wigner Seitz cell
edge
If you use the corrections implemented in this module, cite
a) Kumagai and Oba, Phys. Rev. B. 89, 195205 (2014) and
b) Freysoldt, Neugebauer, and Van <NAME>,
Phys. Status Solidi B. 248, 1067-1076 (2011) and
in addition to the pycdt paper
"""
__author__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>, <EMAIL>'
import math
import logging
import numpy as np
from pymatgen.io.vasp.outputs import Locpot, Outcar
from pymatgen.core.lattice import Lattice
from pycdt.corrections.utils import *
from pycdt.utils.units import hart_to_ev
import warnings
norm = np.linalg.norm
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def kumagai_init(structure, dieltens):
angset = structure.lattice.get_cartesian_coords(1)
dieltens = np.array(dieltens)
if not len(dieltens.shape):
dieltens = dieltens*np.identity(3)
elif len(dieltens.shape) == 1:
dieltens = np.diagflat(dieltens)
logging.getLogger(__name__).debug('Lattice constants (in Angs): '
+ str(cleanlat(angset)))
[a1, a2, a3] = ang_to_bohr * angset # convert to bohr
bohrset = [a1, a2, a3]
vol = np.dot(a1, np.cross(a2, a3))
logging.getLogger(__name__).debug('Lattice constants (in Bohr): '
+ str(cleanlat([a1, a2, a3])))
determ = np.linalg.det(dieltens)
invdiel = np.linalg.inv(dieltens)
logging.getLogger(__name__).debug('inv dielectric tensor: ' + str(invdiel))
return angset, bohrset, vol, determ, invdiel
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def real_sum(a1, a2, a3, r, q, dieltens, gamma, tolerance):
invdiel = np.linalg.inv(dieltens)
determ = np.linalg.det(dieltens)
realpre = q / np.sqrt(determ)
tolerance /= hart_to_ev
#Real space sum by converging with respect to real space vectors
#create list of real space vectors that satisfy |i*a1+j*a2+k*a3|<=N
Nmaxlength = 40 #tolerance for stopping real space sum convergence
N = 2
r_sums = []
while N < Nmaxlength:
r_sum = 0.0
if norm(r):
for i in range(-N, N+1):
for j in range(-N, N+1):
for k in range(-N, N+1):
r_vec = i*a1 + j*a2 + k*a3 - r
loc_res = np.dot(r_vec, np.dot(invdiel, r_vec))
nmr = math.erfc(gamma * np.sqrt(loc_res))
dmr = np.sqrt(determ * loc_res)
r_sum += nmr / dmr
else:
for i in range(-N, N+1):
for j in range(-N, N+1):
for k in range(-N, N+1):
if i == j == k == 0:
continue
else:
r_vec = i*a1 + j*a2 + k*a3
loc_res = np.dot(r_vec, np.dot(invdiel, r_vec))
nmr = math.erfc(gamma * np.sqrt(loc_res))
dmr = np.sqrt(determ * loc_res)
r_sum += nmr / dmr
r_sums.append([N, realpre * r_sum])
if N == Nmaxlength-1:
logging.getLogger(__name__).warning(
'Direct part could not converge with real space translation '
'tolerance of {} for gamma {}'.format(Nmaxlength-1, gamma))
return
elif len(r_sums) > 3:
if abs(abs(r_sums[-1][1]) - abs(r_sums[-2][1])) < tolerance:
r_sum = r_sums[-1][1]
logging.debug("gamma is {}".format(gamma))
logging.getLogger(__name__).debug(
"convergence for real summatin term occurs at step {} "
"where real sum is {}".format(N, r_sum * hart_to_ev))
break
N += 1
return r_sum
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def get_g_sum_at_r(g_sum, structure, dim, r):
"""
Args:
g_sum: Reciprocal summation calculated from reciprocal_sum method
structure: Bulk structure pymatgen object
dim : ngxf dimension
r: Position relative to defect (in cartesian coords)
Returns:
reciprocal summ value at g_sum[i_rx,j_ry,k_rz]
"""
fraccoord = structure.lattice.get_fractional_coords(r)
i, j, k = getgridind(structure, dim, fraccoord)
return g_sum[i, j, k]
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def anisotropic_madelung_potential(structure, dim, g_sum, r, dieltens, q,
gamma, tolerance):
"""
Compute the anisotropic Madelung potential at r not equal to 0.
For r=(0,0,0) use anisotropic_pc_energy function
Args:
structure: Bulk pymatgen structure type
dim : ngxf dimension
g_sum: Precomputed reciprocal sum for all r_vectors
r: r vector (in cartesian coordinates) relative to defect position.
Non zero r is expected
dieltens: dielectric tensor
q: Point charge (in units of e+)
tolerance: Tolerance parameter for numerical convergence
gamma (float): Convergence parameter
silence (bool): Verbosity flag. If False, messages are printed.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
structure, dieltens)
recippartreal = q * get_g_sum_at_r(g_sum, structure, dim, r)
directpart = real_sum(a1, a2, a3, r, q, dieltens, gamma, tolerance)
#now add up total madelung potential part with two extra parts:
#self interaction term
selfint = q * np.pi / (vol * (gamma ** 2))
logging.getLogger(__name__).debug('self interaction piece is {}'.format(
selfint * hart_to_ev))
pot = hart_to_ev * (directpart + recippartreal - selfint)
return pot
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def anisotropic_pc_energy(structure, g_sum, dieltens, q, gamma, tolerance):
"""
Compute the anistropic periodic point charge interaction energy.
Args:
structure: Bulk pymatgen structure type
g_sum : comes from KumagaiBulkInit class
dieltens: dielectric tensor
q: Point charge (in units of e+)
gamma : convergence parameter optimized in KumagaiBulkInit class
silence (bool): Verbosity flag. If False, messages are printed.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
structure, dieltens)
g_part = q*g_sum[0,0,0]
r_part = real_sum(a1, a2, a3, [0,0,0], q, dieltens, gamma, tolerance)
selfint = q*np.pi / (vol * (gamma**2)) #self interaction term
#surface term (only for r not at origin)
surfterm = 2*gamma*q / np.sqrt(np.pi*determ)
logger = logging.getLogger(__name__)
logger.debug('reciprocal part: {}'.format(g_part * hart_to_ev))
logger.debug('real part: {}'.format(r_part * hart_to_ev))
logger.debug('self interaction part: {}'.format(selfint * hart_to_ev))
logger.debug('surface term: {}'.format(surfterm * hart_to_ev))
pc_energy = -(q*0.5*hart_to_ev) * (r_part + g_part - selfint - surfterm)
logging.debug('Final PC Energy term: {} eV'.format(pc_energy))
return pc_energy
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def getgridind(structure, dim, r, gridavg=0.0):
"""
Computes the index of a point, r, in the locpot grid
Args:
structure:
Pymatgen structure object
dim:
dimension of FFT grid (NGXF dimension list in VASP)
r:
Relative co-ordinates with respect to abc lattice vectors
gridavg:
If you want to do atomic site averaging, set gridavg to
the radius of the atom at r
Returns:
[i,j,k]: Indices as list
TODO: Once final, remove the getgridind inside disttrans function
"""
abc = structure.lattice.abc
grdind = []
if gridavg:
radvals = [] #radius in terms of indices
dxvals = []
for i in range(3):
if r[i] < 0:
while r[i] < 0:
r[i] += 1
elif r[i] >= 1:
while r[i] >= 1:
r[i] -= 1
r[i] *= abc[i]
num_pts = dim[i]
x = [now_num / float(num_pts) * abc[i] for now_num in range(num_pts)]
dx = x[1] - x[0]
x_rprojection_delta_abs = np.absolute(x - r[i])
ind = np.argmin(x_rprojection_delta_abs)
if x_rprojection_delta_abs[ind] > dx*1.1: #to avoid numerical errors
logger = logging.getLogger(__name__)
logger.error("Input position not within the locpot grid")
logger.error("%d, %d, %f", i, ind, r)
logger.error("%f", x_rprojection_delta_abs)
raise ValueError("Input position is not within the locpot grid")
grdind.append(ind)
if gridavg:
radvals.append(int(np.ceil(gridavg/dx)))
dxvals.append(dx)
if gridavg:
grdindfull = []
for i in range(-radvals[0], radvals[0]+1):
for j in range(-radvals[1], radvals[1]+1):
for k in range(-radvals[2], radvals[2]+1):
dtoc = [i*dxvals[0], j*dxvals[1], k*dxvals[2]]
if norm(dtoc) < gridavg:
ival = (i+grdind[0]) % dim[0]
jval = (j+grdind[1]) % dim[1]
kval = (k+grdind[2]) % dim[2]
grdindfull.append((ival, jval, kval))
grdind = grdindfull
return grdind
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def disttrans(struct, defstruct, defpos=None):
"""
To calculate distance from defect to each atom and finding NGX grid
pts at each atom.
Args:
struct: Bulk structure object
defstruct: Defect structure object
defpos: (if known) defect position as a pymatgen Site object within bulk supercell
"""
#Find defect location in bulk and defect cells
blksite, defsite = find_defect_pos(struct, defstruct, defpos=defpos)
logger = logging.getLogger(__name__)
if blksite is None and defsite is None:
logger.error('Not able to determine defect site')
return
if blksite is None:
logger.debug('Found defect to be Interstitial type at %s',
repr(defsite))
elif defsite is None:
logger.debug('Found defect to be Vacancy type at %s', repr(blksite))
else:
logger.debug('Found defect to be antisite/subsitution type at %s ' \
' in bulk, and %s in defect cell',
repr(blksite), repr(defsite))
if blksite is None:
blksite = defsite
elif defsite is None:
defsite = blksite
def_ccoord = blksite[:]
defcell_def_ccoord = defsite[:]
if len(struct.sites) >= len(defstruct.sites):
sitelist = struct.sites[:]
else: #for interstitial list
sitelist = defstruct.sites[:]
#better image getter since pymatgen wasnt working well for this
def returnclosestr(vec):
from operator import itemgetter
listvals = []
abclats = defstruct.lattice.matrix
trylist = [-1, 0, 1]
for i in trylist:
for j in trylist:
for k in trylist:
transvec = i*abclats[0] + j*abclats[1] + k*abclats[2]
rnew = vec - (defcell_def_ccoord + transvec)
listvals.append([norm(rnew), rnew, transvec])
listvals.sort(key=itemgetter(0))
return listvals[0] #will return [dist,r to defect, and transvec for defect]
grid_sites = {} # dictionary with indices keys in order of structure list
for i in sitelist:
if np.array_equal(i.coords, def_ccoord):
logging.debug('Site {} is defect! Skipping '.format(i))
continue
blksite, defsite = closestsites(struct, defstruct, i.coords)
blkindex = blksite[-1]
defindex = defsite[-1]
dcart_coord = defsite[0].coords
closeimage = returnclosestr(dcart_coord)
cart_reldef = closeimage[1]
defdist = closeimage[0]
if abs(norm(cart_reldef) - defdist) > 0.1:
logger.warning('Image locater issue encountered for site = %d',
blkindex)
logger.warning('In defect supercell')
logger.warning('Distance should be %f', defdist)
logger.warning('But, calculated distance is %f', norm(cart_reldef))
if blkindex in grid_sites:
logger.warning('Index %d already exists in potinddict!', blkindex)
logger.warning('Overwriting information.')
grid_sites[blkindex] = {
'dist': defdist,
'cart': dcart_coord,
'cart_reldef': cart_reldef,
'siteobj': [i.coords, i.frac_coords, i.species_string],
'bulk_site_index': blkindex,
'def_site_index': defindex}
return grid_sites
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def wigner_seitz_radius(structure):
"""
Calculate the Wigner Seitz radius for the given structure.
Args:
structure: pymatgen Structure object
"""
wz = structure.lattice.get_wigner_seitz_cell()
dist = []
for facet in wz:
midpt = np.mean(np.array(facet), axis=0)
dist.append(norm(midpt))
wsrad = min(dist)
return wsrad
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def read_ES_avg_fromlocpot(locpot):
"""
Reads Electrostatic potential at each atomic
site from Locpot Pymatgen object
"""
structure = locpot.structure
radii = {specie: 1.0 for specie in set(structure.species)}
# TODO: The above radii could be smarter (related to ENAUG?)
# but turns out you get a similar result to Outcar differences
# when taking locpot avgd differences
ES_data = {'sampling_radii': radii, 'ngxf_dims': locpot.dim}
pot = []
for site in structure.sites:
indexlist = getgridind(structure, locpot.dim, site.frac_coords,
gridavg=radii[site.specie])
samplevals = []
for u,v,w in indexlist:
samplevals.append(locpot.data["total"][u][v][w])
pot.append(np.mean(samplevals))
ES_data.update({'potential': pot})
return ES_data
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
class KumagaiBulkInit(object):
"""
Compute the anisotropic madelung potential array from the bulk
locpot. This helps in evaluating the bulk supercell related part
once to speed up the calculations.
"""
def __init__(self, structure, dim, epsilon, encut=520, tolerance=0.0001,
optgamma=False):
"""
Args
structure:
Pymatgen structure object of bulk cell
dim:
Fine FFT grid dimensions as a list
For vasp this is NGXF grid dimensions
epsilon:
Dielectric tensor
encut (float):
Energy cutoff for optimal gamma
tolerance (float):
Accuracy parameter
optgamma:
if you know optimized gamma, give its value.
Otherwise it will be computed.
"""
self.structure = structure
self.dim = dim
self.epsilon = epsilon
self.encut = encut
self.tolerance = tolerance
#self.silence = silence
if not optgamma:
self.gamma = self.find_optimal_gamma()
else:
self.gamma = optgamma
self.g_sum = self.reciprocal_sum()
logging.getLogger(__name__).info('optimized gamma: %f', self.gamma)
def find_optimal_gamma(self):
"""
Find optimal gamma by evaluating the brute force reciprocal
summation and seeing when the values are on the order of 1,
This calculation is the anisotropic Madelung potential at r = (0,0,0).
Note this only requires the STRUCTURE not the LOCPOT object.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
self.structure, self.epsilon)
optgam = None
#do brute force recip summation
def get_recippart(encut, gamma):
recippart = 0.0
for rec in genrecip(a1, a2, a3, encut):
Gdotdiel = np.dot(rec, np.dot(self.epsilon, rec))
summand = math.exp(-Gdotdiel / (4 * (gamma ** 2))) / Gdotdiel
recippart += summand
recippart *= 4*np.pi/vol
return recippart, 0.0
def do_summation(gamma):
# Do recip sum until it is bigger than 1eV
# First do Recip space sum convergence with respect to encut for
# this gamma
encut = 20 #start with small encut for expediency
recippartreal1, recippartimag1 = get_recippart(encut, gamma)
encut += 10
recippartreal, recippartimag = get_recippart(encut, gamma)
converge = [recippartreal1, recippartreal]
logger = logging.getLogger(__name__)
while abs(abs(converge[0]) - abs(converge[1])) * hart_to_ev > \
self.tolerance:
encut += 10
recippartreal, recippartimag = get_recippart(encut, gamma)
converge.reverse()
converge[1] = recippartreal
if encut > self.encut:
msg = 'Optimal gamma not found at {} eV cutoff'.format(
self.encut)
logger.error(msg)
raise ValueError(msg)
if abs(recippartimag) * hart_to_ev > self.tolerance:
logger.error("Imaginary part of reciprocal sum not converged.")
logger.error("Imaginary sum value is {} (eV)".format(
recippartimag * hart_to_ev))
return None, None
logger.debug('Reciprocal sum converged to %f eV',
recippartreal * hart_to_ev)
logger.debug('Convergin encut = %d eV', encut)
if (abs(converge[1]) * hart_to_ev < 1 and not optgam):
logger.warning('Reciprocal summation value is less than 1 eV.')
logger.warning('Might lead to errors')
logger.warning('Change gamma.')
return None, 'Try Again'
return recippartreal, gamma
logger = logging.getLogger(__name__)
#start with gamma s.t. gamma*L=5 (this is optimal)
#optimizing gamma for the reciprocal sum to improve convergence
gamma = 5.0/(vol ** (1/3.0))
optimal_gamma_found = False
while not optimal_gamma_found:
recippartreal, optgamma = do_summation(gamma)
if optgamma == gamma:
logger.debug('optimized gamma found to be %f', optgamma)
optimal_gamma_found = True
elif 'Try Again' in optgamma:
gamma *= 1.5
else:
logger.error('Had problem in gamma optimization process.')
return None
if gamma > 50:
logger.error('Could not optimize gamma before gamma = %d', 50)
return None
return optgamma
def reciprocal_sum(self):
"""
Compute the reciprocal summation in the anisotropic Madelung
potential.
TODO: Get the input to fft cut by half by using rfft instead of fft
"""
logger = logging.getLogger(__name__)
logger.debug('Reciprocal summation in Madeling potential')
over_atob = 1.0 / ang_to_bohr
atob3 = ang_to_bohr ** 3
latt = self.structure.lattice
vol = latt.volume * atob3 # in Bohr^3
reci_latt = latt.reciprocal_lattice
[b1, b2, b3] = reci_latt.get_cartesian_coords(1)
b1 = np.array(b1) * over_atob # In 1/Bohr
b2 = np.array(b2) * over_atob
b3 = np.array(b3) * over_atob
nx, ny, nz = self.dim
logging.debug('nx: %d, ny: %d, nz: %d', nx, ny, nz)
ind1 = np.arange(nx)
for i in range(int(nx/2), nx):
ind1[i] = i - nx
ind2 = np.arange(ny)
for i in range(int(ny/2), ny):
ind2[i] = i - ny
ind3 = np.arange(nz)
for i in range(int(nz/2), nz):
ind3[i] = i - nz
g_array = np.zeros(self.dim, np.dtype('c16'))
gamm2 = 4*(self.gamma**2)
for i in ind1:
for j in ind2:
for k in ind3:
g = i*b1 + j*b2 + k*b3
g_eps_g = np.dot(g, np.dot(self.epsilon, g))
if i == j == k == 0:
continue
else:
g_array[i,j,k] = math.exp(-g_eps_g/gamm2) / g_eps_g
r_array = np.fft.fftn(g_array)
over_vol = 4*np.pi/vol # Multiply with q later
r_array *= over_vol
r_arr_real = np.real(r_array)
r_arr_imag = np.imag(r_array)
max_imag = r_arr_imag.max()
logger.debug('Max imaginary part found to be %f', max_imag)
return r_arr_real
warnings.warn("Replacing PyCDT usage of Kumagai base classes and plotting with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
class KumagaiCorrection(object):
"""
Extended freysoldt correction developed by Kumagai and Oba.
"""
def __init__(self, dielectric_tensor, q, gamma, g_sum, bulk_structure,
defect_structure, energy_cutoff=520, madetol=0.0001,
lengths=None, **kw):
"""
Args:
dielectric_tensor:
Macroscopic dielectric tensor
Include ionic also if defect is relaxed, othewise ion clamped.
Can be a matrix array or scalar.
q:
Charge associated with the defect. Typically integer
gamma:
Convergence parameter. Obtained from KumagaiBulkPart
g_sum:
value that is dependent on the Bulk only.
Obtained from KumagaiBulkPart
bulk_structure:
bulk Pymatgen structure object. Need to specify this if
using Outcar method for atomic site avg.
(If you specify outcar files for bulk_file_path but dont
specify structure then code will break)
(TO DO: resolve this dumb dependency by being smarter
about where structure comes from?)
defect_structure:
defect structure. Needed if using Outcar method
energy_cutoff:
Energy for plane wave cutoff (in eV).
If not given, Materials Project default 520 eV is used.
madetol:
Tolerance for convergence of energy terms in eV
lengths:
Lengths of axes, for speeding up plotting slightly
keywords:
1) bulk_locpot: Bulk Locpot file path OR Bulk Locpot
defect_locpot: Defect Locpot file path or defect Locpot
2) (Or) bulk_outcar: Bulk Outcar file path
defect_outcar: Defect outcar file path
3) defect_position: Defect position as a pymatgen Site object in the bulk supercell structure
NOTE: this is optional but recommended, if not provided then analysis is done to find
the defect position; this analysis has been rigorously tested, but has broken in an example with
severe long range relaxation
(at which point you probably should not be including the defect in your analysis...)
"""
if isinstance(dielectric_tensor, int) or \
isinstance(dielectric_tensor, float):
self.dieltens = np.identity(3) * dielectric_tensor
else:
self.dieltens = np.array(dielectric_tensor)
if 'bulk_locpot' in kw:
if isinstance(kw['bulk_locpot'], Locpot):
self.locpot_blk = kw['bulk_locpot']
else:
self.locpot_blk = Locpot.from_file(kw['bulk_locpot'])
if isinstance(kw['defect_locpot'], Locpot):
self.locpot_def = kw['defect_locpot']
else:
self.locpot_def = Locpot.from_file(kw['defect_locpot'])
self.dim = self.locpot_blk.dim
self.outcar_blk = None
self.outcar_def = None
self.do_outcar_method = False
if 'bulk_outcar' in kw:
self.outcar_blk = Outcar(str(kw['bulk_outcar']))
self.outcar_def = Outcar(str(kw['defect_outcar']))
self.do_outcar_method = True
self.locpot_blk = None
self.locpot_def = None
self.dim = self.outcar_blk.ngf
if 'defect_position' in kw:
self._defpos = kw['defect_position']
else:
self._defpos = None
self.madetol = madetol
self.q = q
self.encut = energy_cutoff
self.structure = bulk_structure
self.defstructure = defect_structure
self.gamma = gamma
self.g_sum = g_sum
self.lengths=lengths
def correction(self, title=None, partflag='All'):
"""
Computes the extended Freysoldt correction for anistropic systems
developed by <NAME> and <NAME> (Ref: PRB 89, 195205 (2014)
Args:
title:
If plot of potential averaging process is wanted set title
partflag:
Specifies the part of correction computed
'pc': periodic interaction of defect charges (point charge) only
'potalign': potential alignmnet correction only,
'All' (default): pc and potalign combined into one value,
'AllSplit' for correction in form [PC, potterm, full]
"""
logger = logging.getLogger(__name__)
logger.info('This is Kumagai Correction.')
if not self.q:
if partflag == 'AllSplit':
return [0., 0., 0.]
else:
return 0.0
if partflag != 'potalign':
energy_pc = self.pc()
if partflag != 'pc':
potalign = self.potalign(title=title)
#logger.info('Kumagai Correction details:')
#if partflag != 'potalign':
# logger.info('PCenergy (E_lat) = %f', round(energy_pc, 5))
#if partflag != 'pc':
# logger.info('potential alignment (-q*delta V) = %f',
# round(potalign, 5))
if partflag in ['All','AllSplit']:
logger.info('Total Kumagai correction = %f',
round(energy_pc+potalign, 5))
if partflag == 'pc':
return round(energy_pc, 5)
elif partflag == 'potalign':
return round(potalign, 5)
elif partflag == 'All':
return round(energy_pc+potalign, 5)
else:
return map(lambda x: round(x, 5),
[energy_pc, potalign, energy_pc+potalign])
def pc(self):
energy_pc = anisotropic_pc_energy(
self.structure, self.g_sum, self.dieltens, self.q,
self.gamma, self.madetol)
logger = logging.getLogger(__name__)
logger.info('PC energy determined to be %f eV (%f Hartree)',
energy_pc, energy_pc/hart_to_ev)
return energy_pc
def potalign(self, title=None, output_sr=False):
"""
Potential alignment for Kumagai method
Args:
title: Title for the plot. None will not generate the plot
output_sr allows for output of the short range potential
(Good for delocalization analysis)
"""
logger = logging.getLogger(__name__)
logger.info('\nRunning potential alignment (atomic site averaging)')
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
self.structure, self.dieltens)
potinddict = disttrans(self.structure, self.defstructure, defpos=self._defpos)
minlat = min(norm(a1), norm(a2), norm(a3))
lat_perc_diffs = [100 * abs(norm(a1) - norm(lat)) / minlat for lat \
in [a2, a3]]
lat_perc_diffs.append(100 * abs(norm(a2) - norm(a3)) / minlat)
if not all(i < 45 for i in lat_perc_diffs):
logger.warning('Detected that cell was not very cubic.')
logger.warning('Sampling atoms outside wigner-seitz cell may '\
'not be optimal')
wsrad = wigner_seitz_radius(self.structure)
logger.debug('wsrad %f', wsrad)
for i in potinddict.keys():
logger.debug("Atom %d, distance: %f", i, potinddict[i]['dist'])
if potinddict[i]['dist'] > wsrad:
potinddict[i]['OutsideWS'] = True
else:
potinddict[i]['OutsideWS'] = False
if not self.do_outcar_method:
puredat = read_ES_avg_fromlocpot(self.locpot_blk)
defdat = read_ES_avg_fromlocpot(self.locpot_def)
else:
puredat = {'potential': self.outcar_blk.electrostatic_potential}
defdat = {'potential': self.outcar_def.electrostatic_potential}
jup = 0
for i in potinddict.keys():
jup += 1
if (not title and not potinddict[i]['OutsideWS']):
#dont need to calculate inside WS if not printing plot
continue
j = potinddict[i]['def_site_index'] #assuming zero defined
k = potinddict[i]['bulk_site_index']
v_qb = defdat['potential'][j] - puredat['potential'][k]
cart_reldef = potinddict[i]['cart_reldef']
v_pc = anisotropic_madelung_potential(
self.structure, self.dim, self.g_sum, cart_reldef,
self.dieltens, self.q, self.gamma, self.madetol)
v_qb *= -1 #change charge sign convention
potinddict[i]['Vpc'] = v_pc
potinddict[i]['Vqb'] = v_qb
logger.debug('Atom: %d, anisotropic madelung potential: %f',
i, v_pc)
logger.debug('Atom: %d, bulk/defect difference = %f', i, v_qb)
if title:
fullspecset = self.structure.species
specset = list(set(fullspecset))
shade, forplot = {}, {}
for i in specset:
shade[i.symbol] = {'r': [], 'Vpc': [], 'Vqb': []}
forplot[i.symbol] = {'r': [], 'Vpc': [], 'Vqb': [],'sites':[]}
forcorrection = []
for i in potinddict.keys():
if (not title and not potinddict[i]['OutsideWS']):
continue
if potinddict[i]['OutsideWS']:
forcorrection.append(potinddict[i]['Vqb']-potinddict[i]['Vpc'])
if title:
elt = fullspecset[i].symbol
shade[elt]['r'].append(potinddict[i]['dist'])
shade[elt]['Vpc'].append(potinddict[i]['Vpc'])
shade[elt]['Vqb'].append(potinddict[i]['Vqb'])
if title:
elt = fullspecset[i].symbol
forplot[elt]['r'].append(potinddict[i]['dist'])
forplot[elt]['Vpc'].append(potinddict[i]['Vpc'])
forplot[elt]['Vqb'].append(potinddict[i]['Vqb'])
forplot[elt]['sites'].append(potinddict[i]['siteobj'])
potalign = np.mean(forcorrection)
if title:
forplot['EXTRA'] = {'wsrad': wsrad, 'potalign': potalign}
try:
forplot['EXTRA']['lengths']=self.structure.lattice.abc
except:
forplot['EXTRA']['lengths']=self.lengths
if title != 'written':
KumagaiCorrection.plot(forplot, title=title)
else:
#TODO: use a more descriptive fname that describes the defect
from monty.serialization import dumpfn
from monty.json import MontyEncoder
fname = 'KumagaiData.json'
dumpfn(forplot, fname, cls=MontyEncoder)
logger.info('potential alignment (site averaging): %f',
np.mean(forcorrection))
logger.info('Potential correction energy: %f eV',
-self.q * np.mean(forcorrection))
if output_sr:
outpot = {'sampled': forcorrection, 'alldata':potinddict}
return ((-self.q * np.mean(forcorrection)), outpot) #pot align energy correction (eV)
else:
return (-self.q * np.mean(forcorrection)) #pot align energy correction (eV)
@classmethod
def plot(cls, forplot, title):
"""
Plotting of locpot data
TODO: Rename forplot to a more descriptive name
"""
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
plt.figure()
plt.clf()
collis = ['b', 'g', 'c', 'm', 'y', 'w', 'k']
ylis = []
rlis = []
for i in range(len(forplot.keys())):
inkey = list(forplot.keys())[i]
if inkey == 'EXTRA':
continue
for k in forplot[inkey]['r']:
rlis.append(k)
for k in ['Vqb', 'Vpc']:
for u in forplot[inkey][k]:
ylis.append(u)
plt.plot(forplot[inkey]['r'], forplot[inkey]['Vqb'],
color=collis[i], marker='^', linestyle='None',
label=str(inkey) + ': $V_{q/b}$')
plt.plot(forplot[inkey]['r'], forplot[inkey]['Vpc'],
color=collis[i], marker='o', linestyle='None',
label=str(inkey) + ': $V_{pc}$')
full = []
for i in forplot.keys():
if i == 'EXTRA':
continue
for k in range(len(forplot[i]['Vpc'])):
full.append([
forplot[i]['r'][k],
forplot[i]['Vqb'][k] - forplot[i]['Vpc'][k]
])
realfull = sorted(full, key=lambda x: x[0])
r, y = [], []
for i in realfull:
r.append(i[0])
y.append(i[1])
wsrad = forplot['EXTRA']['wsrad']
potalign = forplot['EXTRA']['potalign']
plt.plot(r, y, color=collis[-1], marker='x', linestyle='None',
label='$V_{q/b}$ - $V_{pc}$')
plt.xlabel('Distance from defect ($\AA$)',fontsize=20)
plt.ylabel('Potential (V)',fontsize=20)
x = np.arange(wsrad, max(forplot['EXTRA']['lengths']), 0.01)
plt.fill_between(x, min(ylis) - 1, max(ylis) + 1, facecolor='red',
alpha=0.15, label='sampling region')
plt.axhline(y=potalign, linewidth=0.5, color='red',
label='pot. align. / q')
fontP = FontProperties()
fontP.set_size('small')
plt.legend(bbox_to_anchor=(1.05, 0.5), prop=fontP)
plt.axhline(y=0, linewidth=0.2, color='black')
plt.ylim([min(ylis) - 0.5, max(ylis) + 0.5])
plt.xlim([0, max(rlis) + 3])
plt.title('%s atomic site potential plot' % title)
plt.savefig('%s_kumagaisiteavgPlot.pdf' % title)
@classmethod
def plot_from_datfile(cls, name='KumagaiData.json', title='default'):
"""
Takes data file called 'name' and does plotting.
Good for later plotting of locpot data after running run_correction()
"""
from monty.serialization import loadfn
from monty.json import MontyDecoder
forplot = loadfn(name, cls=MontyDecoder)
cls.plot(forplot, title=title)
| """
This module computes finite size supercell charge corrections for
defects in anistropic systems using extended Freysoldt (or Kumagai) method
developed by Kumagai and Oba.
Kumagai method includes
a) anisotropic PC energy
b) potential alignment by atomic site averaging at Wigner Seitz cell
edge
If you use the corrections implemented in this module, cite
a) Kumagai and Oba, Phys. Rev. B. 89, 195205 (2014) and
b) Freysoldt, Neugebauer, and Van <NAME>,
Phys. Status Solidi B. 248, 1067-1076 (2011) and
in addition to the pycdt paper
"""
__author__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>, <EMAIL>'
import math
import logging
import numpy as np
from pymatgen.io.vasp.outputs import Locpot, Outcar
from pymatgen.core.lattice import Lattice
from pycdt.corrections.utils import *
from pycdt.utils.units import hart_to_ev
import warnings
norm = np.linalg.norm
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def kumagai_init(structure, dieltens):
angset = structure.lattice.get_cartesian_coords(1)
dieltens = np.array(dieltens)
if not len(dieltens.shape):
dieltens = dieltens*np.identity(3)
elif len(dieltens.shape) == 1:
dieltens = np.diagflat(dieltens)
logging.getLogger(__name__).debug('Lattice constants (in Angs): '
+ str(cleanlat(angset)))
[a1, a2, a3] = ang_to_bohr * angset # convert to bohr
bohrset = [a1, a2, a3]
vol = np.dot(a1, np.cross(a2, a3))
logging.getLogger(__name__).debug('Lattice constants (in Bohr): '
+ str(cleanlat([a1, a2, a3])))
determ = np.linalg.det(dieltens)
invdiel = np.linalg.inv(dieltens)
logging.getLogger(__name__).debug('inv dielectric tensor: ' + str(invdiel))
return angset, bohrset, vol, determ, invdiel
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def real_sum(a1, a2, a3, r, q, dieltens, gamma, tolerance):
invdiel = np.linalg.inv(dieltens)
determ = np.linalg.det(dieltens)
realpre = q / np.sqrt(determ)
tolerance /= hart_to_ev
#Real space sum by converging with respect to real space vectors
#create list of real space vectors that satisfy |i*a1+j*a2+k*a3|<=N
Nmaxlength = 40 #tolerance for stopping real space sum convergence
N = 2
r_sums = []
while N < Nmaxlength:
r_sum = 0.0
if norm(r):
for i in range(-N, N+1):
for j in range(-N, N+1):
for k in range(-N, N+1):
r_vec = i*a1 + j*a2 + k*a3 - r
loc_res = np.dot(r_vec, np.dot(invdiel, r_vec))
nmr = math.erfc(gamma * np.sqrt(loc_res))
dmr = np.sqrt(determ * loc_res)
r_sum += nmr / dmr
else:
for i in range(-N, N+1):
for j in range(-N, N+1):
for k in range(-N, N+1):
if i == j == k == 0:
continue
else:
r_vec = i*a1 + j*a2 + k*a3
loc_res = np.dot(r_vec, np.dot(invdiel, r_vec))
nmr = math.erfc(gamma * np.sqrt(loc_res))
dmr = np.sqrt(determ * loc_res)
r_sum += nmr / dmr
r_sums.append([N, realpre * r_sum])
if N == Nmaxlength-1:
logging.getLogger(__name__).warning(
'Direct part could not converge with real space translation '
'tolerance of {} for gamma {}'.format(Nmaxlength-1, gamma))
return
elif len(r_sums) > 3:
if abs(abs(r_sums[-1][1]) - abs(r_sums[-2][1])) < tolerance:
r_sum = r_sums[-1][1]
logging.debug("gamma is {}".format(gamma))
logging.getLogger(__name__).debug(
"convergence for real summatin term occurs at step {} "
"where real sum is {}".format(N, r_sum * hart_to_ev))
break
N += 1
return r_sum
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def get_g_sum_at_r(g_sum, structure, dim, r):
"""
Args:
g_sum: Reciprocal summation calculated from reciprocal_sum method
structure: Bulk structure pymatgen object
dim : ngxf dimension
r: Position relative to defect (in cartesian coords)
Returns:
reciprocal summ value at g_sum[i_rx,j_ry,k_rz]
"""
fraccoord = structure.lattice.get_fractional_coords(r)
i, j, k = getgridind(structure, dim, fraccoord)
return g_sum[i, j, k]
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def anisotropic_madelung_potential(structure, dim, g_sum, r, dieltens, q,
gamma, tolerance):
"""
Compute the anisotropic Madelung potential at r not equal to 0.
For r=(0,0,0) use anisotropic_pc_energy function
Args:
structure: Bulk pymatgen structure type
dim : ngxf dimension
g_sum: Precomputed reciprocal sum for all r_vectors
r: r vector (in cartesian coordinates) relative to defect position.
Non zero r is expected
dieltens: dielectric tensor
q: Point charge (in units of e+)
tolerance: Tolerance parameter for numerical convergence
gamma (float): Convergence parameter
silence (bool): Verbosity flag. If False, messages are printed.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
structure, dieltens)
recippartreal = q * get_g_sum_at_r(g_sum, structure, dim, r)
directpart = real_sum(a1, a2, a3, r, q, dieltens, gamma, tolerance)
#now add up total madelung potential part with two extra parts:
#self interaction term
selfint = q * np.pi / (vol * (gamma ** 2))
logging.getLogger(__name__).debug('self interaction piece is {}'.format(
selfint * hart_to_ev))
pot = hart_to_ev * (directpart + recippartreal - selfint)
return pot
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def anisotropic_pc_energy(structure, g_sum, dieltens, q, gamma, tolerance):
"""
Compute the anistropic periodic point charge interaction energy.
Args:
structure: Bulk pymatgen structure type
g_sum : comes from KumagaiBulkInit class
dieltens: dielectric tensor
q: Point charge (in units of e+)
gamma : convergence parameter optimized in KumagaiBulkInit class
silence (bool): Verbosity flag. If False, messages are printed.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
structure, dieltens)
g_part = q*g_sum[0,0,0]
r_part = real_sum(a1, a2, a3, [0,0,0], q, dieltens, gamma, tolerance)
selfint = q*np.pi / (vol * (gamma**2)) #self interaction term
#surface term (only for r not at origin)
surfterm = 2*gamma*q / np.sqrt(np.pi*determ)
logger = logging.getLogger(__name__)
logger.debug('reciprocal part: {}'.format(g_part * hart_to_ev))
logger.debug('real part: {}'.format(r_part * hart_to_ev))
logger.debug('self interaction part: {}'.format(selfint * hart_to_ev))
logger.debug('surface term: {}'.format(surfterm * hart_to_ev))
pc_energy = -(q*0.5*hart_to_ev) * (r_part + g_part - selfint - surfterm)
logging.debug('Final PC Energy term: {} eV'.format(pc_energy))
return pc_energy
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def getgridind(structure, dim, r, gridavg=0.0):
"""
Computes the index of a point, r, in the locpot grid
Args:
structure:
Pymatgen structure object
dim:
dimension of FFT grid (NGXF dimension list in VASP)
r:
Relative co-ordinates with respect to abc lattice vectors
gridavg:
If you want to do atomic site averaging, set gridavg to
the radius of the atom at r
Returns:
[i,j,k]: Indices as list
TODO: Once final, remove the getgridind inside disttrans function
"""
abc = structure.lattice.abc
grdind = []
if gridavg:
radvals = [] #radius in terms of indices
dxvals = []
for i in range(3):
if r[i] < 0:
while r[i] < 0:
r[i] += 1
elif r[i] >= 1:
while r[i] >= 1:
r[i] -= 1
r[i] *= abc[i]
num_pts = dim[i]
x = [now_num / float(num_pts) * abc[i] for now_num in range(num_pts)]
dx = x[1] - x[0]
x_rprojection_delta_abs = np.absolute(x - r[i])
ind = np.argmin(x_rprojection_delta_abs)
if x_rprojection_delta_abs[ind] > dx*1.1: #to avoid numerical errors
logger = logging.getLogger(__name__)
logger.error("Input position not within the locpot grid")
logger.error("%d, %d, %f", i, ind, r)
logger.error("%f", x_rprojection_delta_abs)
raise ValueError("Input position is not within the locpot grid")
grdind.append(ind)
if gridavg:
radvals.append(int(np.ceil(gridavg/dx)))
dxvals.append(dx)
if gridavg:
grdindfull = []
for i in range(-radvals[0], radvals[0]+1):
for j in range(-radvals[1], radvals[1]+1):
for k in range(-radvals[2], radvals[2]+1):
dtoc = [i*dxvals[0], j*dxvals[1], k*dxvals[2]]
if norm(dtoc) < gridavg:
ival = (i+grdind[0]) % dim[0]
jval = (j+grdind[1]) % dim[1]
kval = (k+grdind[2]) % dim[2]
grdindfull.append((ival, jval, kval))
grdind = grdindfull
return grdind
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def disttrans(struct, defstruct, defpos=None):
"""
To calculate distance from defect to each atom and finding NGX grid
pts at each atom.
Args:
struct: Bulk structure object
defstruct: Defect structure object
defpos: (if known) defect position as a pymatgen Site object within bulk supercell
"""
#Find defect location in bulk and defect cells
blksite, defsite = find_defect_pos(struct, defstruct, defpos=defpos)
logger = logging.getLogger(__name__)
if blksite is None and defsite is None:
logger.error('Not able to determine defect site')
return
if blksite is None:
logger.debug('Found defect to be Interstitial type at %s',
repr(defsite))
elif defsite is None:
logger.debug('Found defect to be Vacancy type at %s', repr(blksite))
else:
logger.debug('Found defect to be antisite/subsitution type at %s ' \
' in bulk, and %s in defect cell',
repr(blksite), repr(defsite))
if blksite is None:
blksite = defsite
elif defsite is None:
defsite = blksite
def_ccoord = blksite[:]
defcell_def_ccoord = defsite[:]
if len(struct.sites) >= len(defstruct.sites):
sitelist = struct.sites[:]
else: #for interstitial list
sitelist = defstruct.sites[:]
#better image getter since pymatgen wasnt working well for this
def returnclosestr(vec):
from operator import itemgetter
listvals = []
abclats = defstruct.lattice.matrix
trylist = [-1, 0, 1]
for i in trylist:
for j in trylist:
for k in trylist:
transvec = i*abclats[0] + j*abclats[1] + k*abclats[2]
rnew = vec - (defcell_def_ccoord + transvec)
listvals.append([norm(rnew), rnew, transvec])
listvals.sort(key=itemgetter(0))
return listvals[0] #will return [dist,r to defect, and transvec for defect]
grid_sites = {} # dictionary with indices keys in order of structure list
for i in sitelist:
if np.array_equal(i.coords, def_ccoord):
logging.debug('Site {} is defect! Skipping '.format(i))
continue
blksite, defsite = closestsites(struct, defstruct, i.coords)
blkindex = blksite[-1]
defindex = defsite[-1]
dcart_coord = defsite[0].coords
closeimage = returnclosestr(dcart_coord)
cart_reldef = closeimage[1]
defdist = closeimage[0]
if abs(norm(cart_reldef) - defdist) > 0.1:
logger.warning('Image locater issue encountered for site = %d',
blkindex)
logger.warning('In defect supercell')
logger.warning('Distance should be %f', defdist)
logger.warning('But, calculated distance is %f', norm(cart_reldef))
if blkindex in grid_sites:
logger.warning('Index %d already exists in potinddict!', blkindex)
logger.warning('Overwriting information.')
grid_sites[blkindex] = {
'dist': defdist,
'cart': dcart_coord,
'cart_reldef': cart_reldef,
'siteobj': [i.coords, i.frac_coords, i.species_string],
'bulk_site_index': blkindex,
'def_site_index': defindex}
return grid_sites
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def wigner_seitz_radius(structure):
"""
Calculate the Wigner Seitz radius for the given structure.
Args:
structure: pymatgen Structure object
"""
wz = structure.lattice.get_wigner_seitz_cell()
dist = []
for facet in wz:
midpt = np.mean(np.array(facet), axis=0)
dist.append(norm(midpt))
wsrad = min(dist)
return wsrad
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
def read_ES_avg_fromlocpot(locpot):
"""
Reads Electrostatic potential at each atomic
site from Locpot Pymatgen object
"""
structure = locpot.structure
radii = {specie: 1.0 for specie in set(structure.species)}
# TODO: The above radii could be smarter (related to ENAUG?)
# but turns out you get a similar result to Outcar differences
# when taking locpot avgd differences
ES_data = {'sampling_radii': radii, 'ngxf_dims': locpot.dim}
pot = []
for site in structure.sites:
indexlist = getgridind(structure, locpot.dim, site.frac_coords,
gridavg=radii[site.specie])
samplevals = []
for u,v,w in indexlist:
samplevals.append(locpot.data["total"][u][v][w])
pot.append(np.mean(samplevals))
ES_data.update({'potential': pot})
return ES_data
warnings.warn("Replacing PyCDT usage of Kumagai base classes with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
class KumagaiBulkInit(object):
"""
Compute the anisotropic madelung potential array from the bulk
locpot. This helps in evaluating the bulk supercell related part
once to speed up the calculations.
"""
def __init__(self, structure, dim, epsilon, encut=520, tolerance=0.0001,
optgamma=False):
"""
Args
structure:
Pymatgen structure object of bulk cell
dim:
Fine FFT grid dimensions as a list
For vasp this is NGXF grid dimensions
epsilon:
Dielectric tensor
encut (float):
Energy cutoff for optimal gamma
tolerance (float):
Accuracy parameter
optgamma:
if you know optimized gamma, give its value.
Otherwise it will be computed.
"""
self.structure = structure
self.dim = dim
self.epsilon = epsilon
self.encut = encut
self.tolerance = tolerance
#self.silence = silence
if not optgamma:
self.gamma = self.find_optimal_gamma()
else:
self.gamma = optgamma
self.g_sum = self.reciprocal_sum()
logging.getLogger(__name__).info('optimized gamma: %f', self.gamma)
def find_optimal_gamma(self):
"""
Find optimal gamma by evaluating the brute force reciprocal
summation and seeing when the values are on the order of 1,
This calculation is the anisotropic Madelung potential at r = (0,0,0).
Note this only requires the STRUCTURE not the LOCPOT object.
"""
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
self.structure, self.epsilon)
optgam = None
#do brute force recip summation
def get_recippart(encut, gamma):
recippart = 0.0
for rec in genrecip(a1, a2, a3, encut):
Gdotdiel = np.dot(rec, np.dot(self.epsilon, rec))
summand = math.exp(-Gdotdiel / (4 * (gamma ** 2))) / Gdotdiel
recippart += summand
recippart *= 4*np.pi/vol
return recippart, 0.0
def do_summation(gamma):
# Do recip sum until it is bigger than 1eV
# First do Recip space sum convergence with respect to encut for
# this gamma
encut = 20 #start with small encut for expediency
recippartreal1, recippartimag1 = get_recippart(encut, gamma)
encut += 10
recippartreal, recippartimag = get_recippart(encut, gamma)
converge = [recippartreal1, recippartreal]
logger = logging.getLogger(__name__)
while abs(abs(converge[0]) - abs(converge[1])) * hart_to_ev > \
self.tolerance:
encut += 10
recippartreal, recippartimag = get_recippart(encut, gamma)
converge.reverse()
converge[1] = recippartreal
if encut > self.encut:
msg = 'Optimal gamma not found at {} eV cutoff'.format(
self.encut)
logger.error(msg)
raise ValueError(msg)
if abs(recippartimag) * hart_to_ev > self.tolerance:
logger.error("Imaginary part of reciprocal sum not converged.")
logger.error("Imaginary sum value is {} (eV)".format(
recippartimag * hart_to_ev))
return None, None
logger.debug('Reciprocal sum converged to %f eV',
recippartreal * hart_to_ev)
logger.debug('Convergin encut = %d eV', encut)
if (abs(converge[1]) * hart_to_ev < 1 and not optgam):
logger.warning('Reciprocal summation value is less than 1 eV.')
logger.warning('Might lead to errors')
logger.warning('Change gamma.')
return None, 'Try Again'
return recippartreal, gamma
logger = logging.getLogger(__name__)
#start with gamma s.t. gamma*L=5 (this is optimal)
#optimizing gamma for the reciprocal sum to improve convergence
gamma = 5.0/(vol ** (1/3.0))
optimal_gamma_found = False
while not optimal_gamma_found:
recippartreal, optgamma = do_summation(gamma)
if optgamma == gamma:
logger.debug('optimized gamma found to be %f', optgamma)
optimal_gamma_found = True
elif 'Try Again' in optgamma:
gamma *= 1.5
else:
logger.error('Had problem in gamma optimization process.')
return None
if gamma > 50:
logger.error('Could not optimize gamma before gamma = %d', 50)
return None
return optgamma
def reciprocal_sum(self):
"""
Compute the reciprocal summation in the anisotropic Madelung
potential.
TODO: Get the input to fft cut by half by using rfft instead of fft
"""
logger = logging.getLogger(__name__)
logger.debug('Reciprocal summation in Madeling potential')
over_atob = 1.0 / ang_to_bohr
atob3 = ang_to_bohr ** 3
latt = self.structure.lattice
vol = latt.volume * atob3 # in Bohr^3
reci_latt = latt.reciprocal_lattice
[b1, b2, b3] = reci_latt.get_cartesian_coords(1)
b1 = np.array(b1) * over_atob # In 1/Bohr
b2 = np.array(b2) * over_atob
b3 = np.array(b3) * over_atob
nx, ny, nz = self.dim
logging.debug('nx: %d, ny: %d, nz: %d', nx, ny, nz)
ind1 = np.arange(nx)
for i in range(int(nx/2), nx):
ind1[i] = i - nx
ind2 = np.arange(ny)
for i in range(int(ny/2), ny):
ind2[i] = i - ny
ind3 = np.arange(nz)
for i in range(int(nz/2), nz):
ind3[i] = i - nz
g_array = np.zeros(self.dim, np.dtype('c16'))
gamm2 = 4*(self.gamma**2)
for i in ind1:
for j in ind2:
for k in ind3:
g = i*b1 + j*b2 + k*b3
g_eps_g = np.dot(g, np.dot(self.epsilon, g))
if i == j == k == 0:
continue
else:
g_array[i,j,k] = math.exp(-g_eps_g/gamm2) / g_eps_g
r_array = np.fft.fftn(g_array)
over_vol = 4*np.pi/vol # Multiply with q later
r_array *= over_vol
r_arr_real = np.real(r_array)
r_arr_imag = np.imag(r_array)
max_imag = r_arr_imag.max()
logger.debug('Max imaginary part found to be %f', max_imag)
return r_arr_real
warnings.warn("Replacing PyCDT usage of Kumagai base classes and plotting with calls to "
"corresponding objects in pymatgen.analysis.defects.corrections\n"
"All core Kumagai code will be removed with Version 2.5 of PyCDT."
" (note these functions all exist in pymatgen)",
DeprecationWarning)
class KumagaiCorrection(object):
"""
Extended freysoldt correction developed by Kumagai and Oba.
"""
def __init__(self, dielectric_tensor, q, gamma, g_sum, bulk_structure,
defect_structure, energy_cutoff=520, madetol=0.0001,
lengths=None, **kw):
"""
Args:
dielectric_tensor:
Macroscopic dielectric tensor
Include ionic also if defect is relaxed, othewise ion clamped.
Can be a matrix array or scalar.
q:
Charge associated with the defect. Typically integer
gamma:
Convergence parameter. Obtained from KumagaiBulkPart
g_sum:
value that is dependent on the Bulk only.
Obtained from KumagaiBulkPart
bulk_structure:
bulk Pymatgen structure object. Need to specify this if
using Outcar method for atomic site avg.
(If you specify outcar files for bulk_file_path but dont
specify structure then code will break)
(TO DO: resolve this dumb dependency by being smarter
about where structure comes from?)
defect_structure:
defect structure. Needed if using Outcar method
energy_cutoff:
Energy for plane wave cutoff (in eV).
If not given, Materials Project default 520 eV is used.
madetol:
Tolerance for convergence of energy terms in eV
lengths:
Lengths of axes, for speeding up plotting slightly
keywords:
1) bulk_locpot: Bulk Locpot file path OR Bulk Locpot
defect_locpot: Defect Locpot file path or defect Locpot
2) (Or) bulk_outcar: Bulk Outcar file path
defect_outcar: Defect outcar file path
3) defect_position: Defect position as a pymatgen Site object in the bulk supercell structure
NOTE: this is optional but recommended, if not provided then analysis is done to find
the defect position; this analysis has been rigorously tested, but has broken in an example with
severe long range relaxation
(at which point you probably should not be including the defect in your analysis...)
"""
if isinstance(dielectric_tensor, int) or \
isinstance(dielectric_tensor, float):
self.dieltens = np.identity(3) * dielectric_tensor
else:
self.dieltens = np.array(dielectric_tensor)
if 'bulk_locpot' in kw:
if isinstance(kw['bulk_locpot'], Locpot):
self.locpot_blk = kw['bulk_locpot']
else:
self.locpot_blk = Locpot.from_file(kw['bulk_locpot'])
if isinstance(kw['defect_locpot'], Locpot):
self.locpot_def = kw['defect_locpot']
else:
self.locpot_def = Locpot.from_file(kw['defect_locpot'])
self.dim = self.locpot_blk.dim
self.outcar_blk = None
self.outcar_def = None
self.do_outcar_method = False
if 'bulk_outcar' in kw:
self.outcar_blk = Outcar(str(kw['bulk_outcar']))
self.outcar_def = Outcar(str(kw['defect_outcar']))
self.do_outcar_method = True
self.locpot_blk = None
self.locpot_def = None
self.dim = self.outcar_blk.ngf
if 'defect_position' in kw:
self._defpos = kw['defect_position']
else:
self._defpos = None
self.madetol = madetol
self.q = q
self.encut = energy_cutoff
self.structure = bulk_structure
self.defstructure = defect_structure
self.gamma = gamma
self.g_sum = g_sum
self.lengths=lengths
def correction(self, title=None, partflag='All'):
"""
Computes the extended Freysoldt correction for anistropic systems
developed by <NAME> and <NAME> (Ref: PRB 89, 195205 (2014)
Args:
title:
If plot of potential averaging process is wanted set title
partflag:
Specifies the part of correction computed
'pc': periodic interaction of defect charges (point charge) only
'potalign': potential alignmnet correction only,
'All' (default): pc and potalign combined into one value,
'AllSplit' for correction in form [PC, potterm, full]
"""
logger = logging.getLogger(__name__)
logger.info('This is Kumagai Correction.')
if not self.q:
if partflag == 'AllSplit':
return [0., 0., 0.]
else:
return 0.0
if partflag != 'potalign':
energy_pc = self.pc()
if partflag != 'pc':
potalign = self.potalign(title=title)
#logger.info('Kumagai Correction details:')
#if partflag != 'potalign':
# logger.info('PCenergy (E_lat) = %f', round(energy_pc, 5))
#if partflag != 'pc':
# logger.info('potential alignment (-q*delta V) = %f',
# round(potalign, 5))
if partflag in ['All','AllSplit']:
logger.info('Total Kumagai correction = %f',
round(energy_pc+potalign, 5))
if partflag == 'pc':
return round(energy_pc, 5)
elif partflag == 'potalign':
return round(potalign, 5)
elif partflag == 'All':
return round(energy_pc+potalign, 5)
else:
return map(lambda x: round(x, 5),
[energy_pc, potalign, energy_pc+potalign])
def pc(self):
energy_pc = anisotropic_pc_energy(
self.structure, self.g_sum, self.dieltens, self.q,
self.gamma, self.madetol)
logger = logging.getLogger(__name__)
logger.info('PC energy determined to be %f eV (%f Hartree)',
energy_pc, energy_pc/hart_to_ev)
return energy_pc
def potalign(self, title=None, output_sr=False):
"""
Potential alignment for Kumagai method
Args:
title: Title for the plot. None will not generate the plot
output_sr allows for output of the short range potential
(Good for delocalization analysis)
"""
logger = logging.getLogger(__name__)
logger.info('\nRunning potential alignment (atomic site averaging)')
angset, [a1, a2, a3], vol, determ, invdiel = kumagai_init(
self.structure, self.dieltens)
potinddict = disttrans(self.structure, self.defstructure, defpos=self._defpos)
minlat = min(norm(a1), norm(a2), norm(a3))
lat_perc_diffs = [100 * abs(norm(a1) - norm(lat)) / minlat for lat \
in [a2, a3]]
lat_perc_diffs.append(100 * abs(norm(a2) - norm(a3)) / minlat)
if not all(i < 45 for i in lat_perc_diffs):
logger.warning('Detected that cell was not very cubic.')
logger.warning('Sampling atoms outside wigner-seitz cell may '\
'not be optimal')
wsrad = wigner_seitz_radius(self.structure)
logger.debug('wsrad %f', wsrad)
for i in potinddict.keys():
logger.debug("Atom %d, distance: %f", i, potinddict[i]['dist'])
if potinddict[i]['dist'] > wsrad:
potinddict[i]['OutsideWS'] = True
else:
potinddict[i]['OutsideWS'] = False
if not self.do_outcar_method:
puredat = read_ES_avg_fromlocpot(self.locpot_blk)
defdat = read_ES_avg_fromlocpot(self.locpot_def)
else:
puredat = {'potential': self.outcar_blk.electrostatic_potential}
defdat = {'potential': self.outcar_def.electrostatic_potential}
jup = 0
for i in potinddict.keys():
jup += 1
if (not title and not potinddict[i]['OutsideWS']):
#dont need to calculate inside WS if not printing plot
continue
j = potinddict[i]['def_site_index'] #assuming zero defined
k = potinddict[i]['bulk_site_index']
v_qb = defdat['potential'][j] - puredat['potential'][k]
cart_reldef = potinddict[i]['cart_reldef']
v_pc = anisotropic_madelung_potential(
self.structure, self.dim, self.g_sum, cart_reldef,
self.dieltens, self.q, self.gamma, self.madetol)
v_qb *= -1 #change charge sign convention
potinddict[i]['Vpc'] = v_pc
potinddict[i]['Vqb'] = v_qb
logger.debug('Atom: %d, anisotropic madelung potential: %f',
i, v_pc)
logger.debug('Atom: %d, bulk/defect difference = %f', i, v_qb)
if title:
fullspecset = self.structure.species
specset = list(set(fullspecset))
shade, forplot = {}, {}
for i in specset:
shade[i.symbol] = {'r': [], 'Vpc': [], 'Vqb': []}
forplot[i.symbol] = {'r': [], 'Vpc': [], 'Vqb': [],'sites':[]}
forcorrection = []
for i in potinddict.keys():
if (not title and not potinddict[i]['OutsideWS']):
continue
if potinddict[i]['OutsideWS']:
forcorrection.append(potinddict[i]['Vqb']-potinddict[i]['Vpc'])
if title:
elt = fullspecset[i].symbol
shade[elt]['r'].append(potinddict[i]['dist'])
shade[elt]['Vpc'].append(potinddict[i]['Vpc'])
shade[elt]['Vqb'].append(potinddict[i]['Vqb'])
if title:
elt = fullspecset[i].symbol
forplot[elt]['r'].append(potinddict[i]['dist'])
forplot[elt]['Vpc'].append(potinddict[i]['Vpc'])
forplot[elt]['Vqb'].append(potinddict[i]['Vqb'])
forplot[elt]['sites'].append(potinddict[i]['siteobj'])
potalign = np.mean(forcorrection)
if title:
forplot['EXTRA'] = {'wsrad': wsrad, 'potalign': potalign}
try:
forplot['EXTRA']['lengths']=self.structure.lattice.abc
except:
forplot['EXTRA']['lengths']=self.lengths
if title != 'written':
KumagaiCorrection.plot(forplot, title=title)
else:
#TODO: use a more descriptive fname that describes the defect
from monty.serialization import dumpfn
from monty.json import MontyEncoder
fname = 'KumagaiData.json'
dumpfn(forplot, fname, cls=MontyEncoder)
logger.info('potential alignment (site averaging): %f',
np.mean(forcorrection))
logger.info('Potential correction energy: %f eV',
-self.q * np.mean(forcorrection))
if output_sr:
outpot = {'sampled': forcorrection, 'alldata':potinddict}
return ((-self.q * np.mean(forcorrection)), outpot) #pot align energy correction (eV)
else:
return (-self.q * np.mean(forcorrection)) #pot align energy correction (eV)
@classmethod
def plot(cls, forplot, title):
"""
Plotting of locpot data
TODO: Rename forplot to a more descriptive name
"""
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
plt.figure()
plt.clf()
collis = ['b', 'g', 'c', 'm', 'y', 'w', 'k']
ylis = []
rlis = []
for i in range(len(forplot.keys())):
inkey = list(forplot.keys())[i]
if inkey == 'EXTRA':
continue
for k in forplot[inkey]['r']:
rlis.append(k)
for k in ['Vqb', 'Vpc']:
for u in forplot[inkey][k]:
ylis.append(u)
plt.plot(forplot[inkey]['r'], forplot[inkey]['Vqb'],
color=collis[i], marker='^', linestyle='None',
label=str(inkey) + ': $V_{q/b}$')
plt.plot(forplot[inkey]['r'], forplot[inkey]['Vpc'],
color=collis[i], marker='o', linestyle='None',
label=str(inkey) + ': $V_{pc}$')
full = []
for i in forplot.keys():
if i == 'EXTRA':
continue
for k in range(len(forplot[i]['Vpc'])):
full.append([
forplot[i]['r'][k],
forplot[i]['Vqb'][k] - forplot[i]['Vpc'][k]
])
realfull = sorted(full, key=lambda x: x[0])
r, y = [], []
for i in realfull:
r.append(i[0])
y.append(i[1])
wsrad = forplot['EXTRA']['wsrad']
potalign = forplot['EXTRA']['potalign']
plt.plot(r, y, color=collis[-1], marker='x', linestyle='None',
label='$V_{q/b}$ - $V_{pc}$')
plt.xlabel('Distance from defect ($\AA$)',fontsize=20)
plt.ylabel('Potential (V)',fontsize=20)
x = np.arange(wsrad, max(forplot['EXTRA']['lengths']), 0.01)
plt.fill_between(x, min(ylis) - 1, max(ylis) + 1, facecolor='red',
alpha=0.15, label='sampling region')
plt.axhline(y=potalign, linewidth=0.5, color='red',
label='pot. align. / q')
fontP = FontProperties()
fontP.set_size('small')
plt.legend(bbox_to_anchor=(1.05, 0.5), prop=fontP)
plt.axhline(y=0, linewidth=0.2, color='black')
plt.ylim([min(ylis) - 0.5, max(ylis) + 0.5])
plt.xlim([0, max(rlis) + 3])
plt.title('%s atomic site potential plot' % title)
plt.savefig('%s_kumagaisiteavgPlot.pdf' % title)
@classmethod
def plot_from_datfile(cls, name='KumagaiData.json', title='default'):
"""
Takes data file called 'name' and does plotting.
Good for later plotting of locpot data after running run_correction()
"""
from monty.serialization import loadfn
from monty.json import MontyDecoder
forplot = loadfn(name, cls=MontyDecoder)
cls.plot(forplot, title=title)
| en | 0.739713 | This module computes finite size supercell charge corrections for defects in anistropic systems using extended Freysoldt (or Kumagai) method developed by Kumagai and Oba. Kumagai method includes a) anisotropic PC energy b) potential alignment by atomic site averaging at Wigner Seitz cell edge If you use the corrections implemented in this module, cite a) Kumagai and Oba, Phys. Rev. B. 89, 195205 (2014) and b) Freysoldt, Neugebauer, and Van <NAME>, Phys. Status Solidi B. 248, 1067-1076 (2011) and in addition to the pycdt paper # convert to bohr #Real space sum by converging with respect to real space vectors #create list of real space vectors that satisfy |i*a1+j*a2+k*a3|<=N #tolerance for stopping real space sum convergence Args: g_sum: Reciprocal summation calculated from reciprocal_sum method structure: Bulk structure pymatgen object dim : ngxf dimension r: Position relative to defect (in cartesian coords) Returns: reciprocal summ value at g_sum[i_rx,j_ry,k_rz] Compute the anisotropic Madelung potential at r not equal to 0. For r=(0,0,0) use anisotropic_pc_energy function Args: structure: Bulk pymatgen structure type dim : ngxf dimension g_sum: Precomputed reciprocal sum for all r_vectors r: r vector (in cartesian coordinates) relative to defect position. Non zero r is expected dieltens: dielectric tensor q: Point charge (in units of e+) tolerance: Tolerance parameter for numerical convergence gamma (float): Convergence parameter silence (bool): Verbosity flag. If False, messages are printed. #now add up total madelung potential part with two extra parts: #self interaction term Compute the anistropic periodic point charge interaction energy. Args: structure: Bulk pymatgen structure type g_sum : comes from KumagaiBulkInit class dieltens: dielectric tensor q: Point charge (in units of e+) gamma : convergence parameter optimized in KumagaiBulkInit class silence (bool): Verbosity flag. If False, messages are printed. #self interaction term #surface term (only for r not at origin) Computes the index of a point, r, in the locpot grid Args: structure: Pymatgen structure object dim: dimension of FFT grid (NGXF dimension list in VASP) r: Relative co-ordinates with respect to abc lattice vectors gridavg: If you want to do atomic site averaging, set gridavg to the radius of the atom at r Returns: [i,j,k]: Indices as list TODO: Once final, remove the getgridind inside disttrans function #radius in terms of indices #to avoid numerical errors To calculate distance from defect to each atom and finding NGX grid pts at each atom. Args: struct: Bulk structure object defstruct: Defect structure object defpos: (if known) defect position as a pymatgen Site object within bulk supercell #Find defect location in bulk and defect cells #for interstitial list #better image getter since pymatgen wasnt working well for this #will return [dist,r to defect, and transvec for defect] # dictionary with indices keys in order of structure list Calculate the Wigner Seitz radius for the given structure. Args: structure: pymatgen Structure object Reads Electrostatic potential at each atomic site from Locpot Pymatgen object # TODO: The above radii could be smarter (related to ENAUG?) # but turns out you get a similar result to Outcar differences # when taking locpot avgd differences Compute the anisotropic madelung potential array from the bulk locpot. This helps in evaluating the bulk supercell related part once to speed up the calculations. Args structure: Pymatgen structure object of bulk cell dim: Fine FFT grid dimensions as a list For vasp this is NGXF grid dimensions epsilon: Dielectric tensor encut (float): Energy cutoff for optimal gamma tolerance (float): Accuracy parameter optgamma: if you know optimized gamma, give its value. Otherwise it will be computed. #self.silence = silence Find optimal gamma by evaluating the brute force reciprocal summation and seeing when the values are on the order of 1, This calculation is the anisotropic Madelung potential at r = (0,0,0). Note this only requires the STRUCTURE not the LOCPOT object. #do brute force recip summation # Do recip sum until it is bigger than 1eV # First do Recip space sum convergence with respect to encut for # this gamma #start with small encut for expediency #start with gamma s.t. gamma*L=5 (this is optimal) #optimizing gamma for the reciprocal sum to improve convergence Compute the reciprocal summation in the anisotropic Madelung potential. TODO: Get the input to fft cut by half by using rfft instead of fft # in Bohr^3 # In 1/Bohr # Multiply with q later Extended freysoldt correction developed by Kumagai and Oba. Args: dielectric_tensor: Macroscopic dielectric tensor Include ionic also if defect is relaxed, othewise ion clamped. Can be a matrix array or scalar. q: Charge associated with the defect. Typically integer gamma: Convergence parameter. Obtained from KumagaiBulkPart g_sum: value that is dependent on the Bulk only. Obtained from KumagaiBulkPart bulk_structure: bulk Pymatgen structure object. Need to specify this if using Outcar method for atomic site avg. (If you specify outcar files for bulk_file_path but dont specify structure then code will break) (TO DO: resolve this dumb dependency by being smarter about where structure comes from?) defect_structure: defect structure. Needed if using Outcar method energy_cutoff: Energy for plane wave cutoff (in eV). If not given, Materials Project default 520 eV is used. madetol: Tolerance for convergence of energy terms in eV lengths: Lengths of axes, for speeding up plotting slightly keywords: 1) bulk_locpot: Bulk Locpot file path OR Bulk Locpot defect_locpot: Defect Locpot file path or defect Locpot 2) (Or) bulk_outcar: Bulk Outcar file path defect_outcar: Defect outcar file path 3) defect_position: Defect position as a pymatgen Site object in the bulk supercell structure NOTE: this is optional but recommended, if not provided then analysis is done to find the defect position; this analysis has been rigorously tested, but has broken in an example with severe long range relaxation (at which point you probably should not be including the defect in your analysis...) Computes the extended Freysoldt correction for anistropic systems developed by <NAME> and <NAME> (Ref: PRB 89, 195205 (2014) Args: title: If plot of potential averaging process is wanted set title partflag: Specifies the part of correction computed 'pc': periodic interaction of defect charges (point charge) only 'potalign': potential alignmnet correction only, 'All' (default): pc and potalign combined into one value, 'AllSplit' for correction in form [PC, potterm, full] #logger.info('Kumagai Correction details:') #if partflag != 'potalign': # logger.info('PCenergy (E_lat) = %f', round(energy_pc, 5)) #if partflag != 'pc': # logger.info('potential alignment (-q*delta V) = %f', # round(potalign, 5)) Potential alignment for Kumagai method Args: title: Title for the plot. None will not generate the plot output_sr allows for output of the short range potential (Good for delocalization analysis) #dont need to calculate inside WS if not printing plot #assuming zero defined #change charge sign convention #TODO: use a more descriptive fname that describes the defect #pot align energy correction (eV) #pot align energy correction (eV) Plotting of locpot data TODO: Rename forplot to a more descriptive name Takes data file called 'name' and does plotting. Good for later plotting of locpot data after running run_correction() | 2.33127 | 2 |
src/python/T0/StorageManager/StorageManagerAPI.py | silviodonato/T0 | 6 | 6614290 | """
_StorageManagerAPI_
Contains all the code for interfacing with the StorageManager
"""
import logging
import threading
import time
from WMCore.DAOFactory import DAOFactory
knownStreamers = set()
def injectNewData(dbInterfaceStorageManager,
dbInterfaceHltConf,
dbInterfaceSMNotify,
streamerPNN,
minRun = None,
maxRun = None,
injectRun = None):
"""
_injectNewData_
Replaces the old-style file notification injecton into the Tier0.
Queries the StorageManager database for new data and injects it into the Tier0.
These queries will find duplicates, ie. data that was already found and
processed in a previous polling cycle. Code has to be robust against that.
Needs to be passed the PNN on which streamer files are located
"""
logging.debug("injectNewData()")
myThread = threading.currentThread()
daoFactory = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = myThread.dbi)
daoFactoryStorageManager = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceStorageManager)
daoFactoryHltConf = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceHltConf)
if dbInterfaceSMNotify:
daoFactorySMNotify = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceSMNotify)
insertFileStatusDAO = daoFactorySMNotify(classname = "SMNotification.InsertOfflineFileStatus")
getNewDataDAO = daoFactoryStorageManager(classname = "StorageManager.GetNewData")
getRunInfoDAO = daoFactoryHltConf(classname = "StorageManager.GetRunInfo")
insertRunDAO = daoFactory(classname = "RunConfig.InsertRun")
insertStreamDAO = daoFactory(classname = "RunConfig.InsertStream")
insertCMSSWVersionDAO = daoFactory(classname = "RunConfig.InsertCMSSWVersion")
insertStreamCMSSWVersionDAO = daoFactory(classname = "RunConfig.InsertStreamCMSSWVersion")
insertLumiDAO = daoFactory(classname = "RunConfig.InsertLumiSection")
insertStreamerDAO = daoFactory(classname = "RunConfig.InsertStreamer")
newData = getNewDataDAO.execute(minRun = minRun,
maxRun = maxRun,
injectRun = injectRun,
transaction = False)
# remove already processed files
newData[:] = [newFile for newFile in newData if newFile['p5_id'] not in knownStreamers]
logging.debug("StoragemanagerAPI: found %d new files", len(newData))
newRuns = set()
newRunStreams = {}
for newFile in newData:
run = newFile['run']
stream = newFile['stream']
newRuns.add(newFile['run'])
if run not in newRunStreams:
newRunStreams[run] = set()
if stream not in newRunStreams[run]:
newRunStreams[run].add(stream)
logging.debug("StoragemanagerAPI: found %d new runs", len(newRuns))
cmsswVersions = set()
streams = set()
bindRunHltKey = []
bindRunStreamCMSSW = []
for run in sorted(list(newRuns)):
(hltkey, cmssw) = getRunInfoDAO.execute(run = run, transaction = False)
logging.debug("StorageManagerAPI: run = %d, hltkey = %s, cmssw = %s", run, hltkey, cmssw)
if hltkey and cmssw:
cmssw = '_'.join(cmssw.split('_')[0:4]) # only consider base release
cmsswVersions.add(cmssw)
bindRunHltKey.append( { 'RUN': run,
'HLTKEY': hltkey } )
for stream in newRunStreams[run]:
streams.add(stream)
bindRunStreamCMSSW.append( { 'RUN': run,
'STREAM': stream,
'VERSION': cmssw } )
else:
# can't retrieve hltkey and cmssw for run, ignore any data for it
newRuns.remove(run)
if len(bindRunHltKey) > 0:
insertRunDAO.execute(binds = bindRunHltKey, transaction = False)
bindStream = []
for stream in streams:
bindStream.append( { 'STREAM': stream } )
if len(bindStream) > 0:
insertStreamDAO.execute(binds = bindStream, transaction = False)
bindCMSSW = []
for cmssw in cmsswVersions:
bindCMSSW.append( { 'VERSION': cmssw } )
if len(bindCMSSW) > 0:
insertCMSSWVersionDAO.execute(binds = bindCMSSW, transaction = False)
if len(bindRunStreamCMSSW) > 0:
insertStreamCMSSWVersionDAO.execute(binds = bindRunStreamCMSSW, transaction = False)
lumis = set()
bindStreamer = []
bindInsertFileStatus = []
for newFile in newData:
run = newFile['run']
if run not in newRuns:
continue
lumi = newFile['lumi']
lumis.add((run,lumi))
if newFile['filename'] == 'run289461_ls0020_streamExpressCosmics_StorageManager.dat':
newFile['path'] = '/store/t0streamer/Data/ExpressCosmics/000/289/461'
bindStreamer.append( { 'LFN': newFile['path'] + '/' + newFile['filename'],
'P5_ID': newFile['p5_id'],
'RUN': run,
'LUMI': lumi,
'STREAM': newFile['stream'],
'FILESIZE': newFile['filesize'],
'EVENTS': newFile['events'],
'TIME': int(time.time()) } )
if dbInterfaceSMNotify:
bindInsertFileStatus.append( { 'P5_ID': newFile['p5_id'],
'FILENAME': newFile['filename'] } )
bindLumi = []
for lumi in lumis:
bindLumi.append( { 'RUN': lumi[0],
'LUMI': lumi[1] } )
if len(bindLumi) > 0:
insertLumiDAO.execute(binds = bindLumi, transaction = False)
if len(bindStreamer) > 0:
insertStreamerDAO.execute(streamerPNN, binds = bindStreamer, transaction = False)
if len(bindInsertFileStatus) > 0:
insertFileStatusDAO.execute(bindInsertFileStatus, transaction = False)
for x in bindStreamer:
knownStreamers.add(x['P5_ID'])
return
def markRepacked(dbInterfaceSMNotify):
"""
_markRepacked_
Find all finished streamers for closed all run/stream
Update the StorageManager notification table
Update the streamer status to finished (deleted = 1)
"""
if not dbInterfaceSMNotify:
return
logging.debug("updateFileStatus()")
myThread = threading.currentThread()
daoFactory = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = myThread.dbi)
daoFactorySMNotify = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceSMNotify)
getFinishedStreamersDAO = daoFactory(classname = "SMNotification.GetFinishedStreamers")
updateFileStatusDAO = daoFactorySMNotify(classname = "SMNotification.UpdateOfflineFileStatus")
markStreamersFinishedDAO = daoFactory(classname = "SMNotification.MarkStreamersFinished")
finishedStreamers = getFinishedStreamersDAO.execute(transaction = False)
streamers = []
bindUpdateFileStatus = []
for (streamer_id, p5_id) in finishedStreamers:
streamers.append(streamer_id)
bindUpdateFileStatus.append( { 'P5_ID': p5_id } )
if len(bindUpdateFileStatus) > 0:
updateFileStatusDAO.execute(bindUpdateFileStatus, transaction = False)
if len(streamers) > 0:
markStreamersFinishedDAO.execute(streamers, transaction = False)
return
| """
_StorageManagerAPI_
Contains all the code for interfacing with the StorageManager
"""
import logging
import threading
import time
from WMCore.DAOFactory import DAOFactory
knownStreamers = set()
def injectNewData(dbInterfaceStorageManager,
dbInterfaceHltConf,
dbInterfaceSMNotify,
streamerPNN,
minRun = None,
maxRun = None,
injectRun = None):
"""
_injectNewData_
Replaces the old-style file notification injecton into the Tier0.
Queries the StorageManager database for new data and injects it into the Tier0.
These queries will find duplicates, ie. data that was already found and
processed in a previous polling cycle. Code has to be robust against that.
Needs to be passed the PNN on which streamer files are located
"""
logging.debug("injectNewData()")
myThread = threading.currentThread()
daoFactory = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = myThread.dbi)
daoFactoryStorageManager = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceStorageManager)
daoFactoryHltConf = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceHltConf)
if dbInterfaceSMNotify:
daoFactorySMNotify = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceSMNotify)
insertFileStatusDAO = daoFactorySMNotify(classname = "SMNotification.InsertOfflineFileStatus")
getNewDataDAO = daoFactoryStorageManager(classname = "StorageManager.GetNewData")
getRunInfoDAO = daoFactoryHltConf(classname = "StorageManager.GetRunInfo")
insertRunDAO = daoFactory(classname = "RunConfig.InsertRun")
insertStreamDAO = daoFactory(classname = "RunConfig.InsertStream")
insertCMSSWVersionDAO = daoFactory(classname = "RunConfig.InsertCMSSWVersion")
insertStreamCMSSWVersionDAO = daoFactory(classname = "RunConfig.InsertStreamCMSSWVersion")
insertLumiDAO = daoFactory(classname = "RunConfig.InsertLumiSection")
insertStreamerDAO = daoFactory(classname = "RunConfig.InsertStreamer")
newData = getNewDataDAO.execute(minRun = minRun,
maxRun = maxRun,
injectRun = injectRun,
transaction = False)
# remove already processed files
newData[:] = [newFile for newFile in newData if newFile['p5_id'] not in knownStreamers]
logging.debug("StoragemanagerAPI: found %d new files", len(newData))
newRuns = set()
newRunStreams = {}
for newFile in newData:
run = newFile['run']
stream = newFile['stream']
newRuns.add(newFile['run'])
if run not in newRunStreams:
newRunStreams[run] = set()
if stream not in newRunStreams[run]:
newRunStreams[run].add(stream)
logging.debug("StoragemanagerAPI: found %d new runs", len(newRuns))
cmsswVersions = set()
streams = set()
bindRunHltKey = []
bindRunStreamCMSSW = []
for run in sorted(list(newRuns)):
(hltkey, cmssw) = getRunInfoDAO.execute(run = run, transaction = False)
logging.debug("StorageManagerAPI: run = %d, hltkey = %s, cmssw = %s", run, hltkey, cmssw)
if hltkey and cmssw:
cmssw = '_'.join(cmssw.split('_')[0:4]) # only consider base release
cmsswVersions.add(cmssw)
bindRunHltKey.append( { 'RUN': run,
'HLTKEY': hltkey } )
for stream in newRunStreams[run]:
streams.add(stream)
bindRunStreamCMSSW.append( { 'RUN': run,
'STREAM': stream,
'VERSION': cmssw } )
else:
# can't retrieve hltkey and cmssw for run, ignore any data for it
newRuns.remove(run)
if len(bindRunHltKey) > 0:
insertRunDAO.execute(binds = bindRunHltKey, transaction = False)
bindStream = []
for stream in streams:
bindStream.append( { 'STREAM': stream } )
if len(bindStream) > 0:
insertStreamDAO.execute(binds = bindStream, transaction = False)
bindCMSSW = []
for cmssw in cmsswVersions:
bindCMSSW.append( { 'VERSION': cmssw } )
if len(bindCMSSW) > 0:
insertCMSSWVersionDAO.execute(binds = bindCMSSW, transaction = False)
if len(bindRunStreamCMSSW) > 0:
insertStreamCMSSWVersionDAO.execute(binds = bindRunStreamCMSSW, transaction = False)
lumis = set()
bindStreamer = []
bindInsertFileStatus = []
for newFile in newData:
run = newFile['run']
if run not in newRuns:
continue
lumi = newFile['lumi']
lumis.add((run,lumi))
if newFile['filename'] == 'run289461_ls0020_streamExpressCosmics_StorageManager.dat':
newFile['path'] = '/store/t0streamer/Data/ExpressCosmics/000/289/461'
bindStreamer.append( { 'LFN': newFile['path'] + '/' + newFile['filename'],
'P5_ID': newFile['p5_id'],
'RUN': run,
'LUMI': lumi,
'STREAM': newFile['stream'],
'FILESIZE': newFile['filesize'],
'EVENTS': newFile['events'],
'TIME': int(time.time()) } )
if dbInterfaceSMNotify:
bindInsertFileStatus.append( { 'P5_ID': newFile['p5_id'],
'FILENAME': newFile['filename'] } )
bindLumi = []
for lumi in lumis:
bindLumi.append( { 'RUN': lumi[0],
'LUMI': lumi[1] } )
if len(bindLumi) > 0:
insertLumiDAO.execute(binds = bindLumi, transaction = False)
if len(bindStreamer) > 0:
insertStreamerDAO.execute(streamerPNN, binds = bindStreamer, transaction = False)
if len(bindInsertFileStatus) > 0:
insertFileStatusDAO.execute(bindInsertFileStatus, transaction = False)
for x in bindStreamer:
knownStreamers.add(x['P5_ID'])
return
def markRepacked(dbInterfaceSMNotify):
"""
_markRepacked_
Find all finished streamers for closed all run/stream
Update the StorageManager notification table
Update the streamer status to finished (deleted = 1)
"""
if not dbInterfaceSMNotify:
return
logging.debug("updateFileStatus()")
myThread = threading.currentThread()
daoFactory = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = myThread.dbi)
daoFactorySMNotify = DAOFactory(package = "T0.WMBS",
logger = logging,
dbinterface = dbInterfaceSMNotify)
getFinishedStreamersDAO = daoFactory(classname = "SMNotification.GetFinishedStreamers")
updateFileStatusDAO = daoFactorySMNotify(classname = "SMNotification.UpdateOfflineFileStatus")
markStreamersFinishedDAO = daoFactory(classname = "SMNotification.MarkStreamersFinished")
finishedStreamers = getFinishedStreamersDAO.execute(transaction = False)
streamers = []
bindUpdateFileStatus = []
for (streamer_id, p5_id) in finishedStreamers:
streamers.append(streamer_id)
bindUpdateFileStatus.append( { 'P5_ID': p5_id } )
if len(bindUpdateFileStatus) > 0:
updateFileStatusDAO.execute(bindUpdateFileStatus, transaction = False)
if len(streamers) > 0:
markStreamersFinishedDAO.execute(streamers, transaction = False)
return
| en | 0.919641 | _StorageManagerAPI_ Contains all the code for interfacing with the StorageManager _injectNewData_ Replaces the old-style file notification injecton into the Tier0. Queries the StorageManager database for new data and injects it into the Tier0. These queries will find duplicates, ie. data that was already found and processed in a previous polling cycle. Code has to be robust against that. Needs to be passed the PNN on which streamer files are located # remove already processed files # only consider base release # can't retrieve hltkey and cmssw for run, ignore any data for it _markRepacked_ Find all finished streamers for closed all run/stream Update the StorageManager notification table Update the streamer status to finished (deleted = 1) | 2.268322 | 2 |
machine_learning/over_sampling_by_replication.py | xwkuang5/code-fragments | 0 | 6614291 | """
This script tests the intuition behind the Synthetic Minority Over-sampling
Technique (SMOTE). Bascially, the intution behind the technique is that
simply over-sample the minority class by replicating will lead to overfitting.
The authors argue that replicating minority samples will cause the decision
boundary to become overly specific. Intuitively, if we do not have prior
knowledge about the distribution of the input data, we should not mislead
the learning algorithm to think that way, which arguably is what replication
is doing. Therefore, the SMOTE algorithm can be thought of as a more
conservative way of doing data replication: we do not have 100% confidence
that the data will appear exactly the same as the sampled data. However, we
believe that unseen data should be close to the sampled data and its neighbors.
It is hard to replicate the results exactly without going into more details about
creating an artificial datasets. However, the figure shown by this script should
provide some evidence that the intution may be true to some extent. The second
plot in the figure shows that the algorithm learns more decision boundaries for the
second class due to over-sampling, which may (or may not) be a result of overfitting.
Installed packages:
Package Version
---------------- -------
cycler 0.10.0
imbalanced-learn 0.3.3
imblearn 0.0
kiwisolver 1.0.1
matplotlib 2.2.2
numpy 1.14.3
pip 10.0.1
pyparsing 2.2.0
python-dateutil 2.7.2
pytz 2018.4
scikit-learn 0.19.1
scipy 1.1.0
setuptools 39.1.0
six 1.11.0
wheel 0.31.0
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_classification
from imblearn.over_sampling import RandomOverSampler
X, y = make_classification(
n_samples=5000,
n_features=2,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2,
weights=[0.95, 0.05],
flip_y=0,
class_sep=0.1,
random_state=0)
# over-sample the minority class
ros = RandomOverSampler({1: 3000}, random_state=0)
X_resampled, y_resampled = ros.fit_sample(X, y)
# create mesh grid
x_min = min(min(X[:, 0]), min(X_resampled[:, 0]))
x_max = max(max(X[:, 0]), max(X_resampled[:, 0]))
y_min = min(min(X[:, 1]), min(X_resampled[:, 1]))
y_max = max(max(X[:, 1]), max(X_resampled[:, 1]))
xx, yy = np.meshgrid(
np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(2, 1, sharex='col', figsize=(10, 8))
"""
Raw data
"""
clf = DecisionTreeClassifier(max_depth=4)
clf.fit(X[:, :2], y)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[0].contourf(xx, yy, Z, alpha=0.4)
axarr[0].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k')
axarr[0].set_title("Original data")
"""
Minority-class-over-sampled data
"""
clf_resampled = DecisionTreeClassifier(max_depth=4)
clf_resampled.fit(X_resampled[:, :2], y_resampled)
Z = clf_resampled.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[1].contourf(xx, yy, Z, alpha=0.4)
axarr[1].scatter(
X_resampled[:, 0], X_resampled[:, 1], c=y_resampled, s=20, edgecolor='k')
axarr[1].set_title("Over-sampled data")
plt.show()
| """
This script tests the intuition behind the Synthetic Minority Over-sampling
Technique (SMOTE). Bascially, the intution behind the technique is that
simply over-sample the minority class by replicating will lead to overfitting.
The authors argue that replicating minority samples will cause the decision
boundary to become overly specific. Intuitively, if we do not have prior
knowledge about the distribution of the input data, we should not mislead
the learning algorithm to think that way, which arguably is what replication
is doing. Therefore, the SMOTE algorithm can be thought of as a more
conservative way of doing data replication: we do not have 100% confidence
that the data will appear exactly the same as the sampled data. However, we
believe that unseen data should be close to the sampled data and its neighbors.
It is hard to replicate the results exactly without going into more details about
creating an artificial datasets. However, the figure shown by this script should
provide some evidence that the intution may be true to some extent. The second
plot in the figure shows that the algorithm learns more decision boundaries for the
second class due to over-sampling, which may (or may not) be a result of overfitting.
Installed packages:
Package Version
---------------- -------
cycler 0.10.0
imbalanced-learn 0.3.3
imblearn 0.0
kiwisolver 1.0.1
matplotlib 2.2.2
numpy 1.14.3
pip 10.0.1
pyparsing 2.2.0
python-dateutil 2.7.2
pytz 2018.4
scikit-learn 0.19.1
scipy 1.1.0
setuptools 39.1.0
six 1.11.0
wheel 0.31.0
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_classification
from imblearn.over_sampling import RandomOverSampler
X, y = make_classification(
n_samples=5000,
n_features=2,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2,
weights=[0.95, 0.05],
flip_y=0,
class_sep=0.1,
random_state=0)
# over-sample the minority class
ros = RandomOverSampler({1: 3000}, random_state=0)
X_resampled, y_resampled = ros.fit_sample(X, y)
# create mesh grid
x_min = min(min(X[:, 0]), min(X_resampled[:, 0]))
x_max = max(max(X[:, 0]), max(X_resampled[:, 0]))
y_min = min(min(X[:, 1]), min(X_resampled[:, 1]))
y_max = max(max(X[:, 1]), max(X_resampled[:, 1]))
xx, yy = np.meshgrid(
np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(2, 1, sharex='col', figsize=(10, 8))
"""
Raw data
"""
clf = DecisionTreeClassifier(max_depth=4)
clf.fit(X[:, :2], y)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[0].contourf(xx, yy, Z, alpha=0.4)
axarr[0].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k')
axarr[0].set_title("Original data")
"""
Minority-class-over-sampled data
"""
clf_resampled = DecisionTreeClassifier(max_depth=4)
clf_resampled.fit(X_resampled[:, :2], y_resampled)
Z = clf_resampled.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[1].contourf(xx, yy, Z, alpha=0.4)
axarr[1].scatter(
X_resampled[:, 0], X_resampled[:, 1], c=y_resampled, s=20, edgecolor='k')
axarr[1].set_title("Over-sampled data")
plt.show()
| en | 0.904805 | This script tests the intuition behind the Synthetic Minority Over-sampling Technique (SMOTE). Bascially, the intution behind the technique is that simply over-sample the minority class by replicating will lead to overfitting. The authors argue that replicating minority samples will cause the decision boundary to become overly specific. Intuitively, if we do not have prior knowledge about the distribution of the input data, we should not mislead the learning algorithm to think that way, which arguably is what replication is doing. Therefore, the SMOTE algorithm can be thought of as a more conservative way of doing data replication: we do not have 100% confidence that the data will appear exactly the same as the sampled data. However, we believe that unseen data should be close to the sampled data and its neighbors. It is hard to replicate the results exactly without going into more details about creating an artificial datasets. However, the figure shown by this script should provide some evidence that the intution may be true to some extent. The second plot in the figure shows that the algorithm learns more decision boundaries for the second class due to over-sampling, which may (or may not) be a result of overfitting. Installed packages: Package Version ---------------- ------- cycler 0.10.0 imbalanced-learn 0.3.3 imblearn 0.0 kiwisolver 1.0.1 matplotlib 2.2.2 numpy 1.14.3 pip 10.0.1 pyparsing 2.2.0 python-dateutil 2.7.2 pytz 2018.4 scikit-learn 0.19.1 scipy 1.1.0 setuptools 39.1.0 six 1.11.0 wheel 0.31.0 # over-sample the minority class # create mesh grid Raw data Minority-class-over-sampled data | 3.228227 | 3 |
ACME/geometry/metric/quadrilateral/quadrilateral_shape_metric.py | mauriziokovacic/ACME | 3 | 6614292 | from .quadrilateral_metric import *
class QuadrilateralShapeMetric(QuadrilateralMetric):
def __init__(self):
super(QuadrilateralShapeMetric, self).__init__(
name='Quadrilateral Shape',
dimension='1',
acceptable_range=Range(min=0.3, max=1),
normal_range=Range(min=0, max=1),
full_range=Range(min=0, max=1),
q_for_unit=1,
)
def eval(self, P, T):
L = torch.pow(torch.cat(self.edge_lengths(P, T), dim=1), 2)
a = torch.cat(self.areas(P, T), dim=1)
return 2*torch.min(torch.cat((a[:, 0] / (L[:, 0] + L[:, 3]),
a[:, 1] / (L[:, 1] + L[:, 0]),
a[:, 2] / (L[:, 2] + L[:, 1]),
a[:, 3] / (L[:, 3] + L[:, 2]),
), dim=1),
dim=1, keepdim=True)[0]
| from .quadrilateral_metric import *
class QuadrilateralShapeMetric(QuadrilateralMetric):
def __init__(self):
super(QuadrilateralShapeMetric, self).__init__(
name='Quadrilateral Shape',
dimension='1',
acceptable_range=Range(min=0.3, max=1),
normal_range=Range(min=0, max=1),
full_range=Range(min=0, max=1),
q_for_unit=1,
)
def eval(self, P, T):
L = torch.pow(torch.cat(self.edge_lengths(P, T), dim=1), 2)
a = torch.cat(self.areas(P, T), dim=1)
return 2*torch.min(torch.cat((a[:, 0] / (L[:, 0] + L[:, 3]),
a[:, 1] / (L[:, 1] + L[:, 0]),
a[:, 2] / (L[:, 2] + L[:, 1]),
a[:, 3] / (L[:, 3] + L[:, 2]),
), dim=1),
dim=1, keepdim=True)[0]
| none | 1 | 2.477068 | 2 | |
vanderwijk.iivvoo.kar/default.py | iivvoo/kodiekar | 1 | 6614293 | import time
import json
import sys
import xbmc
import xbmcgui
import xbmcaddon
import xbmcplugin
import xbmcvfs
__addon_name__ = 'vanderwijk.iivvoo.kar'
class Log(object):
DEBUG = 0
INFO = 1
NOTICE = 2
WARNING = 3
ERROR = 4
SEVERE = 5
FATAL = 6
NONE = 7
def log(self, msg, level=NOTICE):
xbmc.log(msg=msg, level=level)
def debug(self, msg):
self.log(msg, self.DEBUG)
def info(self, msg):
self.log(msg, self.INFO)
def error(self, msg):
self.log(msg, self.ERROR)
DBURL = "http://pi.m3r.nl/db.sqlite"
"""
Download the database, store it locally, open it as file,
use it to provide additional navigation and searching
"""
log = Log()
# args contains the plugin id and an optional path / args. Urlparse it.
log.info("KAR startup, args: {0}".format(" ".join(sys.argv)))
import os, sys
LIB_DIR = xbmc.translatePath( os.path.join( xbmcaddon.Addon(id=__addon_name__).getAddonInfo('path'), 'resources', 'lib' ) )
sys.path.append (LIB_DIR)
DEBUG = xbmcaddon.Addon(id=__addon_name__).getSetting('debug')
import easywebdav
import requests
import urlparse, urllib
import sqlite3
class DB(object):
def __init__(self, filename):
log.info("Opening database {0}".format(filename))
self.filename = filename
self._db = sqlite3.connect(self.filename)
self._cursor = self._db.cursor()
def execute(self, statement, *values):
log.info("EXEC {0} {1}".format(statement, ",".join(values)))
res = self._cursor.execute(statement, values)
self._db.commit()
return res
class KVStore(DB):
def __init__(self, filename):
super(KVStore, self).__init__(filename)
self.execute("""CREATE TABLE IF NOT EXISTS kvstore
(key TEXT PRIMARY KEY, value BLOB)""")
def put(self, key, value):
self.execute("""INSERT OR REPLACE INTO kvstore (key, value) VALUES (?, ?)""", key, value)
def get(self, key):
res = self.execute("""SELECT key, value FROM kvstore WHERE key = ?""", key)
if res is None:
return None
item = res.fetchone()
if item is None:
return None
return item[1]
class MediaDB(DB):
def genres(self):
""" fetch genres from the database """
def search(self, type, query="", genre=None, year=None):
""" query media databases based on certain clauses """
if type == "movie":
table = "movieinfo"
else:
table = "tvshowinfo"
res = self.execute("""SELECT path, title
FROM {0}
WHERE lower(title) like ?""".format(table),
'%{0}%'.format(query.strip().lower())
)
return res.fetchall()
class RecentlyPlayed(object):
LIMIT = 20
def __init__(self, kvstore):
self.kvstore = kvstore
def get(self):
stored_raw = self.kvstore.get('recent')
if stored_raw is None:
return []
log.info("Stored recent found: {0}".format(stored_raw))
stored = json.loads(stored_raw)
return stored
def add(self, file):
""" get folder, make it nice readable,
add it to store """
current = self.get() or []
folder, file = file.rsplit('/', 1)
newcurrent = [(folder, file)]
for fol, fil in current[:self.LIMIT-1]:
if fol != folder:
newcurrent.append((fol, fil))
self.kvstore.put('recent', json.dumps(newcurrent))
class MediaFile(object):
"""
handle urls, translate it into components such as
- filename
- extension
- parent folder
- parent-parent folder
.. etc
Possible also provide de DAV interfacing, wrapping directories/
files directly in MediaFile (..Folder) object?
"""
class KarException(Exception):
pass
class SearchDialog(xbmcgui.WindowXMLDialog):
"""
Not used for now. Building dialogs for Kodi is a complicated,
buggy and cumbersome task:
- everything has to be specified: sizes, positions of all controls
- background for the dialog
- handling of events is primitive
- .. and sometimes it simply wont work. You'll find that a certain
setup cannot be made to work.
as an alternative, a primitive folder-based navigation with Dialog.input
is used in stead
"""
# http://kodi.wiki/view/WindowXML
# http://kodi.wiki/view/HOW-TO:Add_a_new_window_or_dialog_via_skinning
CONTROL_SEARCH_VIDEO = 26
CONTROL_SEARCH_SHOWS = 27
CONTROL_CANCEL = 28
CONTROL_GENRELIST = 12001
def onInit(self):
self.s = xbmcgui.ControlList(0, 240, 1120, 160)
self.s.setItemHeight(40)
self.addControl(self.s)
self.s.addItem("Hello World")
self.s.addItem("Bye World")
self.s.addItem("Kodi == crap")
def onClick(self, control):
log.info("onClick {0}".format(str(control)))
if control == self.CONTROL_CANCEL:
self.close()
if control == self.CONTROL_SEARCH_VIDEO:
self.close()
if control == self.CONTROL_SEARCH_SHOWS:
self.close()
if control == self.CONTROL_GENRELIST:
log.info("Genre selected {0}".format(str(self.s.getSelectedItem().getLabel())))
def xonAction(self, action):
log.info("onAction {0} {1} {2}".format(action.getId(), action.getButtonCode(), action))
log.info(str(self.s.getSelectedItem().getLabel()))
# ACTION_MOUSE_LEFT_CLICK
if action.getId() == xbmcgui.ACTION_PREVIOUS_MENU:
self.close()
if action.getId() == xbmcgui.ACTION_PARENT_DIR:
self.close()
def onControl(self, control):
log.info("onControl {0}".format(str(control)))
class Kar(object):
METADB_EXPIRE = 3600
def __init__(self, argv):
try:
self.args = dict(urlparse.parse_qsl(sys.argv[2].lstrip('?')))
except IndexError:
self.args = {}
self.plugin_url = argv[0]
self.addon_handle = int(sys.argv[1])
self.addon = xbmcaddon.Addon()
self.davhost = self.addon.getSetting('davhost')
self.davport = int(self.addon.getSetting('davport'))
log.info("Configured DAV URL " + self.davhost)
log.info("Configured DAV port {0}".format(self.davport))
self.pluginid = self.addon.getAddonInfo('id')
self.addonname = self.addon.getAddonInfo('name')
self.dav = easywebdav.connect(self.davhost, port=self.davport)
self.data_path = os.path.join(xbmc.translatePath("special://profile/addon_data/{0}".format(self.pluginid)))
if not xbmcvfs.exists(self.data_path):
xbmcvfs.mkdirs(self.data_path)
self.store = KVStore(os.path.join(self.data_path, "kar_kvstore.sqlite"))
self.recent = RecentlyPlayed(self.store)
mediadb_path = self.clone_db()
self.mediadb = MediaDB(mediadb_path)
def clone_db(self):
dbpath = os.path.join(self.data_path, "meta.db")
st = xbmcvfs.Stat(dbpath)
modified = st.st_mtime()
log.info("AGE: {0}".format(modified - time.time()))
if modified < time.time() - self.METADB_EXPIRE or st.st_size() < 1024 * 1024:
r = requests.get(DBURL, stream=True)
with open(dbpath, "wb") as metadb:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive chunks
metadb.write(chunk)
metadb.flush()
log.info("Meta DB copied")
return dbpath
def debug():
""" invoke remote debugger """
import rpdb2
rpdb2.start_embedded_debugger('pw')
def url(self, **kwargs):
return self.plugin_url + "?" + urllib.urlencode(kwargs)
# handle commands
def run(self):
if self.davhost == "example.org":
dialog = xbmcgui.Dialog()
dialog.ok("Please configure first",
"Please configure the add-on first!",
"You can do this through the context menu")
return
# need this?
xbmcplugin.setContent(self.addon_handle, 'movies')
command = self.args.get('command', 'main')
log.info("COMMAND " + command + " - " + repr(self.args))
try:
if hasattr(self, 'cmd_' + command):
getattr(self, 'cmd_' + command)(self.args)
else:
self.cmd_main(self.args)
except KarException as e:
dialog = xbmcgui.Dialog()
dialog.ok("Error occurred",
str(e))
return
def cmd_main(self, args):
li = xbmcgui.ListItem('Browse Kar', iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Search Kar', iconImage='icon_search.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="search"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Watchlist', iconImage='DefaultMusicPlaylists.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="watchlist"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Favorites', iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="favorites"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Recently Watched', iconImage='DefaultInProgressShows.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="recent"), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
def find_video_art(self, files, name):
""" given a video 'foo.xxx', find video art 'foo.tbn' in files and
return its url, or return default art """
artname = name.rsplit('.', 1)[0] + '.tbn'
for f in files:
filename = urlparse.urlparse(f.name.rsplit('/')[-1]).path
if filename == artname:
return f.name
return 'DefaultVideo.png'
def cmd_watchlist(self, args):
pass
def cmd_favorites(self, args):
pass
def cmd_search(self, args):
# sd = SearchDialog("search-dialog.xml", self.addon.getAddonInfo('path'), 'default', '0')
# sd.doModal()
options = (dict(title="Shows by String", type="show", clause="str"),
dict(title="Shows by Genre", type="show", clause="genre"),
dict(title="Shows by year", type="show", clause="year"),
dict(title="Movies by String", type="movie", clause="str"),
dict(title="Movies by Genre", type="movie", clause="genre"),
dict(title="Movies by year", type="movie", clause="year"))
clause = args.get('clause')
type = args.get('type')
if type and clause:
d = xbmcgui.Dialog()
res = d.input("Enter search")
log.info("You searched {0}".format(str(res)))
matches = self.mediadb.search(type, res)
# log.info("MATCH {0}".format(str(matches)))
for match in matches:
path = match[0]
if path.startswith("/data/"):
path = path[5:]
## XXX Reuse the browse art magic here
li = xbmcgui.ListItem(match[1], iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse", path=path), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
else:
for option in options:
li = xbmcgui.ListItem(option['title'], iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="search", type=option['type'], clause=option['clause']), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
def cmd_browse(self, args):
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_TITLE_IGNORE_THE)
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_FILE)
path = args.get('path', '')
log.info("Kar path " + path);
try:
files = self.dav.ls(path)
except requests.ConnectionError as e:
raise KarException(str(e))
# werkt niet
# win = xbmcgui.Window(xbmcgui.getCurrentWindowId())
# win.setProperty('title', 'Hello World')
## if there aren't too many seasons (S01, s01, season01, scan for a
## seasonXX.tbn file and use it as folder art
for f in files: # [:5]: # XXX restrict, for now
url = f.name
# only use the last path part, no slashes
name = urlparse.urlparse(url).path.rsplit('/')[-1]
ext = name.rsplit('.')[-1]
readable_name = urllib.unquote(name)
if name.startswith("."):
continue
if not name:
continue # skip /
isfolder = False
if f.contenttype == "httpd/unix-directory":
isfolder = True
command = "browse"
fanart_path = path + '/' + name + '/' + 'fanart.jpg'
folderart_path = path + '/' + name + '/' + 'folder.jpg'
fanart_url = "http://{0}:{1}{2}".format(self.davhost, self.davport, fanart_path)
folderart_url = "http://{0}:{1}{2}".format(self.davhost, self.davport, folderart_path)
li = xbmcgui.ListItem(readable_name,
iconImage='DefaultFolder.png')
li.setInfo("video", {"title": readable_name})
## This overrides the iconImage in ListItem. If it's not present,
## it means no iamge at all
li.setArt({'thumb': folderart_url,
'fanart': fanart_url})
elif ext not in ('mp4', 'avi', 'mkv'):
continue
else:
command = "play"
li = xbmcgui.ListItem(readable_name, iconImage='DefaultVideo.png')
li.setInfo("video", { "title": readable_name, "size": f.size})
# scan in the furrent 'files' for a .tbn equiv. If it's
# there, use it as art
art = self.find_video_art(files, name)
# fanart could be series fanart in parent folder
li.setArt({'thumb': art,
'fanart': art})
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command=command, path=path + '/' + name), listitem=li, isFolder=isfolder)
xbmcplugin.endOfDirectory(self.addon_handle)
def cmd_play(self, args):
# xbmcgui.Dialog().ok(self.addonname, "PLAY", args.get('path', '?'), "?")
player = xbmc.Player()
path = args.get('path', '')
url = 'http://{0}:{1}{2}'.format(self.davhost, self.davport, urllib.quote(path))
log.info("PLAY " + url)
name = urlparse.urlparse(url).path.rsplit('/')[-1]
readable_name = urllib.unquote(name)
li = xbmcgui.ListItem(readable_name, iconImage='DefaultVideo.png')
li.setInfo("video", { "Title": readable_name })
self.recent.add(path)
player.play(url, li)
def cmd_recent(self, args):
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)
recent = self.recent.get()
for folder, file in recent:
## reuse stuff in browse!
log.info("RECENT {0}".format(folder))
readable_folder = urllib.unquote(folder)
readable_file = urllib.unquote(file)
readable_folder = " - ".join(readable_folder.lstrip('/').split("/"))
entry = "{0} -> {1}".format(readable_folder, readable_file)
li = xbmcgui.ListItem(entry,
iconImage='DefaultFolder.png')
li.setInfo("video", {"title": entry})
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse", path=folder), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
k = Kar(sys.argv)
k.run()
| import time
import json
import sys
import xbmc
import xbmcgui
import xbmcaddon
import xbmcplugin
import xbmcvfs
__addon_name__ = 'vanderwijk.iivvoo.kar'
class Log(object):
DEBUG = 0
INFO = 1
NOTICE = 2
WARNING = 3
ERROR = 4
SEVERE = 5
FATAL = 6
NONE = 7
def log(self, msg, level=NOTICE):
xbmc.log(msg=msg, level=level)
def debug(self, msg):
self.log(msg, self.DEBUG)
def info(self, msg):
self.log(msg, self.INFO)
def error(self, msg):
self.log(msg, self.ERROR)
DBURL = "http://pi.m3r.nl/db.sqlite"
"""
Download the database, store it locally, open it as file,
use it to provide additional navigation and searching
"""
log = Log()
# args contains the plugin id and an optional path / args. Urlparse it.
log.info("KAR startup, args: {0}".format(" ".join(sys.argv)))
import os, sys
LIB_DIR = xbmc.translatePath( os.path.join( xbmcaddon.Addon(id=__addon_name__).getAddonInfo('path'), 'resources', 'lib' ) )
sys.path.append (LIB_DIR)
DEBUG = xbmcaddon.Addon(id=__addon_name__).getSetting('debug')
import easywebdav
import requests
import urlparse, urllib
import sqlite3
class DB(object):
def __init__(self, filename):
log.info("Opening database {0}".format(filename))
self.filename = filename
self._db = sqlite3.connect(self.filename)
self._cursor = self._db.cursor()
def execute(self, statement, *values):
log.info("EXEC {0} {1}".format(statement, ",".join(values)))
res = self._cursor.execute(statement, values)
self._db.commit()
return res
class KVStore(DB):
def __init__(self, filename):
super(KVStore, self).__init__(filename)
self.execute("""CREATE TABLE IF NOT EXISTS kvstore
(key TEXT PRIMARY KEY, value BLOB)""")
def put(self, key, value):
self.execute("""INSERT OR REPLACE INTO kvstore (key, value) VALUES (?, ?)""", key, value)
def get(self, key):
res = self.execute("""SELECT key, value FROM kvstore WHERE key = ?""", key)
if res is None:
return None
item = res.fetchone()
if item is None:
return None
return item[1]
class MediaDB(DB):
def genres(self):
""" fetch genres from the database """
def search(self, type, query="", genre=None, year=None):
""" query media databases based on certain clauses """
if type == "movie":
table = "movieinfo"
else:
table = "tvshowinfo"
res = self.execute("""SELECT path, title
FROM {0}
WHERE lower(title) like ?""".format(table),
'%{0}%'.format(query.strip().lower())
)
return res.fetchall()
class RecentlyPlayed(object):
LIMIT = 20
def __init__(self, kvstore):
self.kvstore = kvstore
def get(self):
stored_raw = self.kvstore.get('recent')
if stored_raw is None:
return []
log.info("Stored recent found: {0}".format(stored_raw))
stored = json.loads(stored_raw)
return stored
def add(self, file):
""" get folder, make it nice readable,
add it to store """
current = self.get() or []
folder, file = file.rsplit('/', 1)
newcurrent = [(folder, file)]
for fol, fil in current[:self.LIMIT-1]:
if fol != folder:
newcurrent.append((fol, fil))
self.kvstore.put('recent', json.dumps(newcurrent))
class MediaFile(object):
"""
handle urls, translate it into components such as
- filename
- extension
- parent folder
- parent-parent folder
.. etc
Possible also provide de DAV interfacing, wrapping directories/
files directly in MediaFile (..Folder) object?
"""
class KarException(Exception):
pass
class SearchDialog(xbmcgui.WindowXMLDialog):
"""
Not used for now. Building dialogs for Kodi is a complicated,
buggy and cumbersome task:
- everything has to be specified: sizes, positions of all controls
- background for the dialog
- handling of events is primitive
- .. and sometimes it simply wont work. You'll find that a certain
setup cannot be made to work.
as an alternative, a primitive folder-based navigation with Dialog.input
is used in stead
"""
# http://kodi.wiki/view/WindowXML
# http://kodi.wiki/view/HOW-TO:Add_a_new_window_or_dialog_via_skinning
CONTROL_SEARCH_VIDEO = 26
CONTROL_SEARCH_SHOWS = 27
CONTROL_CANCEL = 28
CONTROL_GENRELIST = 12001
def onInit(self):
self.s = xbmcgui.ControlList(0, 240, 1120, 160)
self.s.setItemHeight(40)
self.addControl(self.s)
self.s.addItem("Hello World")
self.s.addItem("Bye World")
self.s.addItem("Kodi == crap")
def onClick(self, control):
log.info("onClick {0}".format(str(control)))
if control == self.CONTROL_CANCEL:
self.close()
if control == self.CONTROL_SEARCH_VIDEO:
self.close()
if control == self.CONTROL_SEARCH_SHOWS:
self.close()
if control == self.CONTROL_GENRELIST:
log.info("Genre selected {0}".format(str(self.s.getSelectedItem().getLabel())))
def xonAction(self, action):
log.info("onAction {0} {1} {2}".format(action.getId(), action.getButtonCode(), action))
log.info(str(self.s.getSelectedItem().getLabel()))
# ACTION_MOUSE_LEFT_CLICK
if action.getId() == xbmcgui.ACTION_PREVIOUS_MENU:
self.close()
if action.getId() == xbmcgui.ACTION_PARENT_DIR:
self.close()
def onControl(self, control):
log.info("onControl {0}".format(str(control)))
class Kar(object):
METADB_EXPIRE = 3600
def __init__(self, argv):
try:
self.args = dict(urlparse.parse_qsl(sys.argv[2].lstrip('?')))
except IndexError:
self.args = {}
self.plugin_url = argv[0]
self.addon_handle = int(sys.argv[1])
self.addon = xbmcaddon.Addon()
self.davhost = self.addon.getSetting('davhost')
self.davport = int(self.addon.getSetting('davport'))
log.info("Configured DAV URL " + self.davhost)
log.info("Configured DAV port {0}".format(self.davport))
self.pluginid = self.addon.getAddonInfo('id')
self.addonname = self.addon.getAddonInfo('name')
self.dav = easywebdav.connect(self.davhost, port=self.davport)
self.data_path = os.path.join(xbmc.translatePath("special://profile/addon_data/{0}".format(self.pluginid)))
if not xbmcvfs.exists(self.data_path):
xbmcvfs.mkdirs(self.data_path)
self.store = KVStore(os.path.join(self.data_path, "kar_kvstore.sqlite"))
self.recent = RecentlyPlayed(self.store)
mediadb_path = self.clone_db()
self.mediadb = MediaDB(mediadb_path)
def clone_db(self):
dbpath = os.path.join(self.data_path, "meta.db")
st = xbmcvfs.Stat(dbpath)
modified = st.st_mtime()
log.info("AGE: {0}".format(modified - time.time()))
if modified < time.time() - self.METADB_EXPIRE or st.st_size() < 1024 * 1024:
r = requests.get(DBURL, stream=True)
with open(dbpath, "wb") as metadb:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive chunks
metadb.write(chunk)
metadb.flush()
log.info("Meta DB copied")
return dbpath
def debug():
""" invoke remote debugger """
import rpdb2
rpdb2.start_embedded_debugger('pw')
def url(self, **kwargs):
return self.plugin_url + "?" + urllib.urlencode(kwargs)
# handle commands
def run(self):
if self.davhost == "example.org":
dialog = xbmcgui.Dialog()
dialog.ok("Please configure first",
"Please configure the add-on first!",
"You can do this through the context menu")
return
# need this?
xbmcplugin.setContent(self.addon_handle, 'movies')
command = self.args.get('command', 'main')
log.info("COMMAND " + command + " - " + repr(self.args))
try:
if hasattr(self, 'cmd_' + command):
getattr(self, 'cmd_' + command)(self.args)
else:
self.cmd_main(self.args)
except KarException as e:
dialog = xbmcgui.Dialog()
dialog.ok("Error occurred",
str(e))
return
def cmd_main(self, args):
li = xbmcgui.ListItem('Browse Kar', iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Search Kar', iconImage='icon_search.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="search"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Watchlist', iconImage='DefaultMusicPlaylists.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="watchlist"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Favorites', iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="favorites"), listitem=li, isFolder=True)
li = xbmcgui.ListItem('Recently Watched', iconImage='DefaultInProgressShows.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="recent"), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
def find_video_art(self, files, name):
""" given a video 'foo.xxx', find video art 'foo.tbn' in files and
return its url, or return default art """
artname = name.rsplit('.', 1)[0] + '.tbn'
for f in files:
filename = urlparse.urlparse(f.name.rsplit('/')[-1]).path
if filename == artname:
return f.name
return 'DefaultVideo.png'
def cmd_watchlist(self, args):
pass
def cmd_favorites(self, args):
pass
def cmd_search(self, args):
# sd = SearchDialog("search-dialog.xml", self.addon.getAddonInfo('path'), 'default', '0')
# sd.doModal()
options = (dict(title="Shows by String", type="show", clause="str"),
dict(title="Shows by Genre", type="show", clause="genre"),
dict(title="Shows by year", type="show", clause="year"),
dict(title="Movies by String", type="movie", clause="str"),
dict(title="Movies by Genre", type="movie", clause="genre"),
dict(title="Movies by year", type="movie", clause="year"))
clause = args.get('clause')
type = args.get('type')
if type and clause:
d = xbmcgui.Dialog()
res = d.input("Enter search")
log.info("You searched {0}".format(str(res)))
matches = self.mediadb.search(type, res)
# log.info("MATCH {0}".format(str(matches)))
for match in matches:
path = match[0]
if path.startswith("/data/"):
path = path[5:]
## XXX Reuse the browse art magic here
li = xbmcgui.ListItem(match[1], iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse", path=path), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
else:
for option in options:
li = xbmcgui.ListItem(option['title'], iconImage='DefaultVideo.png')
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="search", type=option['type'], clause=option['clause']), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
def cmd_browse(self, args):
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_TITLE_IGNORE_THE)
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_FILE)
path = args.get('path', '')
log.info("Kar path " + path);
try:
files = self.dav.ls(path)
except requests.ConnectionError as e:
raise KarException(str(e))
# werkt niet
# win = xbmcgui.Window(xbmcgui.getCurrentWindowId())
# win.setProperty('title', 'Hello World')
## if there aren't too many seasons (S01, s01, season01, scan for a
## seasonXX.tbn file and use it as folder art
for f in files: # [:5]: # XXX restrict, for now
url = f.name
# only use the last path part, no slashes
name = urlparse.urlparse(url).path.rsplit('/')[-1]
ext = name.rsplit('.')[-1]
readable_name = urllib.unquote(name)
if name.startswith("."):
continue
if not name:
continue # skip /
isfolder = False
if f.contenttype == "httpd/unix-directory":
isfolder = True
command = "browse"
fanart_path = path + '/' + name + '/' + 'fanart.jpg'
folderart_path = path + '/' + name + '/' + 'folder.jpg'
fanart_url = "http://{0}:{1}{2}".format(self.davhost, self.davport, fanart_path)
folderart_url = "http://{0}:{1}{2}".format(self.davhost, self.davport, folderart_path)
li = xbmcgui.ListItem(readable_name,
iconImage='DefaultFolder.png')
li.setInfo("video", {"title": readable_name})
## This overrides the iconImage in ListItem. If it's not present,
## it means no iamge at all
li.setArt({'thumb': folderart_url,
'fanart': fanart_url})
elif ext not in ('mp4', 'avi', 'mkv'):
continue
else:
command = "play"
li = xbmcgui.ListItem(readable_name, iconImage='DefaultVideo.png')
li.setInfo("video", { "title": readable_name, "size": f.size})
# scan in the furrent 'files' for a .tbn equiv. If it's
# there, use it as art
art = self.find_video_art(files, name)
# fanart could be series fanart in parent folder
li.setArt({'thumb': art,
'fanart': art})
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command=command, path=path + '/' + name), listitem=li, isFolder=isfolder)
xbmcplugin.endOfDirectory(self.addon_handle)
def cmd_play(self, args):
# xbmcgui.Dialog().ok(self.addonname, "PLAY", args.get('path', '?'), "?")
player = xbmc.Player()
path = args.get('path', '')
url = 'http://{0}:{1}{2}'.format(self.davhost, self.davport, urllib.quote(path))
log.info("PLAY " + url)
name = urlparse.urlparse(url).path.rsplit('/')[-1]
readable_name = urllib.unquote(name)
li = xbmcgui.ListItem(readable_name, iconImage='DefaultVideo.png')
li.setInfo("video", { "Title": readable_name })
self.recent.add(path)
player.play(url, li)
def cmd_recent(self, args):
xbmcplugin.addSortMethod(self.addon_handle, xbmcplugin.SORT_METHOD_UNSORTED)
recent = self.recent.get()
for folder, file in recent:
## reuse stuff in browse!
log.info("RECENT {0}".format(folder))
readable_folder = urllib.unquote(folder)
readable_file = urllib.unquote(file)
readable_folder = " - ".join(readable_folder.lstrip('/').split("/"))
entry = "{0} -> {1}".format(readable_folder, readable_file)
li = xbmcgui.ListItem(entry,
iconImage='DefaultFolder.png')
li.setInfo("video", {"title": entry})
xbmcplugin.addDirectoryItem(handle=self.addon_handle, url=self.url(command="browse", path=folder), listitem=li, isFolder=True)
xbmcplugin.endOfDirectory(self.addon_handle)
k = Kar(sys.argv)
k.run()
| en | 0.729725 | Download the database, store it locally, open it as file, use it to provide additional navigation and searching # args contains the plugin id and an optional path / args. Urlparse it. CREATE TABLE IF NOT EXISTS kvstore (key TEXT PRIMARY KEY, value BLOB) INSERT OR REPLACE INTO kvstore (key, value) VALUES (?, ?) SELECT key, value FROM kvstore WHERE key = ? fetch genres from the database query media databases based on certain clauses SELECT path, title FROM {0} WHERE lower(title) like ? get folder, make it nice readable, add it to store handle urls, translate it into components such as - filename - extension - parent folder - parent-parent folder .. etc Possible also provide de DAV interfacing, wrapping directories/ files directly in MediaFile (..Folder) object? Not used for now. Building dialogs for Kodi is a complicated, buggy and cumbersome task: - everything has to be specified: sizes, positions of all controls - background for the dialog - handling of events is primitive - .. and sometimes it simply wont work. You'll find that a certain setup cannot be made to work. as an alternative, a primitive folder-based navigation with Dialog.input is used in stead # http://kodi.wiki/view/WindowXML # http://kodi.wiki/view/HOW-TO:Add_a_new_window_or_dialog_via_skinning # ACTION_MOUSE_LEFT_CLICK # filter out keep-alive chunks invoke remote debugger # handle commands # need this? given a video 'foo.xxx', find video art 'foo.tbn' in files and return its url, or return default art # sd = SearchDialog("search-dialog.xml", self.addon.getAddonInfo('path'), 'default', '0') # sd.doModal() # log.info("MATCH {0}".format(str(matches))) ## XXX Reuse the browse art magic here # werkt niet # win = xbmcgui.Window(xbmcgui.getCurrentWindowId()) # win.setProperty('title', 'Hello World') ## if there aren't too many seasons (S01, s01, season01, scan for a ## seasonXX.tbn file and use it as folder art # [:5]: # XXX restrict, for now # only use the last path part, no slashes # skip / ## This overrides the iconImage in ListItem. If it's not present, ## it means no iamge at all # scan in the furrent 'files' for a .tbn equiv. If it's # there, use it as art # fanart could be series fanart in parent folder # xbmcgui.Dialog().ok(self.addonname, "PLAY", args.get('path', '?'), "?") ## reuse stuff in browse! | 2.323498 | 2 |
libtrack/elfmod/vstruct/defs/dns.py | columbia/libtrack | 40 | 6614294 | <gh_stars>10-100
import vstruct
from vstruct.primitives import *
DNS_FLAG_RESPONSE = 0x8000
DNS_FLAG_AUTHORITATIVE = 0x0400
DNS_TYPE_A = 1
DNS_TYPE_CNAME = 5
DNS_CLASS_IN = 1
class DnsNamePart(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.length = v_uint8()
self.namepart = v_str()
def pcb_length(self):
size = self.length
if size == 0xc0: size = 1 # FIXME offsets for name...
self.vsGetField('namepart').vsSetLength(size)
def isNameTerm(self):
if self.length == 0:
return True
if self.length == 0xc0:
return True
return False
class DnsName(vstruct.VArray):
def __init__(self):
vstruct.VStruct.__init__(self)
def getFullName(self, dnspkt):
r = []
for fname,fobj in self.vsGetFields():
if fobj.length == 0xc0:
newn = DnsName()
# FIXME redundant parsing...
newn.vsParse(dnspkt, ord(fobj.namepart))
r.append( newn.getFullName(dnspkt) )
else:
r.append(fobj.namepart)
return '.'.join(r)
def vsParse(self, bytes, offset=0):
self.vsClearFields()
while offset < len(bytes):
np = DnsNamePart()
offset = np.vsParse(bytes, offset=offset)
self.vsAddElement(np)
if np.isNameTerm():
break
return offset
class DnsQuery(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.qname = DnsName()
self.qtype = v_uint16(bigend=True)
self.qclass = v_uint16(bigend=True)
class DnsQueryArray(vstruct.VArray):
def __init__(self, reccnt):
vstruct.VArray.__init__(self)
for i in xrange(reccnt):
self.vsAddElement( DnsQuery() )
class DnsAnswer(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.qname = DnsName()
self.qtype = v_uint16(bigend=True)
self.qclass = v_uint16(bigend=True)
self.qttl = v_uint32(bigend=True)
self.dlength = v_uint16(bigend=True)
self.qdata = v_bytes()
def pcb_dlength(self):
size = self.dlength
self.vsGetField('qdata').vsSetLength(size)
class DnsAnswerArray(vstruct.VArray):
def __init__(self, reccnt):
vstruct.VArray.__init__(self)
for i in xrange(reccnt):
self.vsAddElement( DnsAnswer() )
class DnsPacket(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
#self.length = v_uint16(bigend=True)
self.transid = v_uint16(bigend=True)
self.flags = v_uint16(bigend=True)
self.ques_cnt = v_uint16(bigend=True)
self.answ_cnt = v_uint16(bigend=True)
self.auth_cnt = v_uint16(bigend=True)
self.addt_cnt = v_uint16(bigend=True)
self.records = vstruct.VStruct()
self.records.queries = DnsQueryArray(0)
self.records.answers = DnsAnswerArray(0)
self.records.authns = DnsAnswerArray(0)
self.records.addtl = DnsAnswerArray(0)
def pcb_ques_cnt(self):
self.records.queries = DnsQueryArray( self.ques_cnt )
def pcb_answ_cnt(self):
self.records.answers = DnsAnswerArray( self.answ_cnt )
def pcb_auth_cnt(self):
self.records.authns = DnsAnswerArray( self.auth_cnt )
def pcb_addt_cnt(self):
self.records.addtl = DnsAnswerArray( self.addt_cnt )
| import vstruct
from vstruct.primitives import *
DNS_FLAG_RESPONSE = 0x8000
DNS_FLAG_AUTHORITATIVE = 0x0400
DNS_TYPE_A = 1
DNS_TYPE_CNAME = 5
DNS_CLASS_IN = 1
class DnsNamePart(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.length = v_uint8()
self.namepart = v_str()
def pcb_length(self):
size = self.length
if size == 0xc0: size = 1 # FIXME offsets for name...
self.vsGetField('namepart').vsSetLength(size)
def isNameTerm(self):
if self.length == 0:
return True
if self.length == 0xc0:
return True
return False
class DnsName(vstruct.VArray):
def __init__(self):
vstruct.VStruct.__init__(self)
def getFullName(self, dnspkt):
r = []
for fname,fobj in self.vsGetFields():
if fobj.length == 0xc0:
newn = DnsName()
# FIXME redundant parsing...
newn.vsParse(dnspkt, ord(fobj.namepart))
r.append( newn.getFullName(dnspkt) )
else:
r.append(fobj.namepart)
return '.'.join(r)
def vsParse(self, bytes, offset=0):
self.vsClearFields()
while offset < len(bytes):
np = DnsNamePart()
offset = np.vsParse(bytes, offset=offset)
self.vsAddElement(np)
if np.isNameTerm():
break
return offset
class DnsQuery(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.qname = DnsName()
self.qtype = v_uint16(bigend=True)
self.qclass = v_uint16(bigend=True)
class DnsQueryArray(vstruct.VArray):
def __init__(self, reccnt):
vstruct.VArray.__init__(self)
for i in xrange(reccnt):
self.vsAddElement( DnsQuery() )
class DnsAnswer(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
self.qname = DnsName()
self.qtype = v_uint16(bigend=True)
self.qclass = v_uint16(bigend=True)
self.qttl = v_uint32(bigend=True)
self.dlength = v_uint16(bigend=True)
self.qdata = v_bytes()
def pcb_dlength(self):
size = self.dlength
self.vsGetField('qdata').vsSetLength(size)
class DnsAnswerArray(vstruct.VArray):
def __init__(self, reccnt):
vstruct.VArray.__init__(self)
for i in xrange(reccnt):
self.vsAddElement( DnsAnswer() )
class DnsPacket(vstruct.VStruct):
def __init__(self):
vstruct.VStruct.__init__(self)
#self.length = v_uint16(bigend=True)
self.transid = v_uint16(bigend=True)
self.flags = v_uint16(bigend=True)
self.ques_cnt = v_uint16(bigend=True)
self.answ_cnt = v_uint16(bigend=True)
self.auth_cnt = v_uint16(bigend=True)
self.addt_cnt = v_uint16(bigend=True)
self.records = vstruct.VStruct()
self.records.queries = DnsQueryArray(0)
self.records.answers = DnsAnswerArray(0)
self.records.authns = DnsAnswerArray(0)
self.records.addtl = DnsAnswerArray(0)
def pcb_ques_cnt(self):
self.records.queries = DnsQueryArray( self.ques_cnt )
def pcb_answ_cnt(self):
self.records.answers = DnsAnswerArray( self.answ_cnt )
def pcb_auth_cnt(self):
self.records.authns = DnsAnswerArray( self.auth_cnt )
def pcb_addt_cnt(self):
self.records.addtl = DnsAnswerArray( self.addt_cnt ) | en | 0.496131 | # FIXME offsets for name... # FIXME redundant parsing... #self.length = v_uint16(bigend=True) | 2.232058 | 2 |
venv/Lib/site-packages/nipype/interfaces/semtools/brains/__init__.py | richung99/digitizePlots | 585 | 6614295 | <gh_stars>100-1000
# -*- coding: utf-8 -*-
from .segmentation import SimilarityIndex, BRAINSTalairach, BRAINSTalairachMask
from .utilities import (
HistogramMatchingFilter,
GenerateEdgeMapImage,
GeneratePurePlugMask,
)
from .classify import BRAINSPosteriorToContinuousClass
| # -*- coding: utf-8 -*-
from .segmentation import SimilarityIndex, BRAINSTalairach, BRAINSTalairachMask
from .utilities import (
HistogramMatchingFilter,
GenerateEdgeMapImage,
GeneratePurePlugMask,
)
from .classify import BRAINSPosteriorToContinuousClass | en | 0.769321 | # -*- coding: utf-8 -*- | 1.214938 | 1 |
Python3/216.combination-sum-iii.py | 610yilingliu/leetcode | 0 | 6614296 | <gh_stars>0
#
# @lc app=leetcode id=216 lang=python3
#
# [216] Combination Sum III
#
# @lc code=start
class Solution:
def combinationSum3(self, k, n):
if k == 0 or n == 0:
return []
self.ans = []
self.k = k
self.back(n, 0, [])
return self.ans
def back(self, rest, count, path):
if rest == 0 and count == self.k:
self.ans.append(path)
if rest > (self.k - count) * 9:
return
if rest < self.k - count:
return
if rest < 0:
return
for i in range(1, 10):
if not path or (path and i > path[-1]):
self.back(rest - i, count + 1, path + [i])
if __name__ == '__main__':
a = Solution()
b = a.combinationSum3(3, 7)
print(b)
# @lc code=end
| #
# @lc app=leetcode id=216 lang=python3
#
# [216] Combination Sum III
#
# @lc code=start
class Solution:
def combinationSum3(self, k, n):
if k == 0 or n == 0:
return []
self.ans = []
self.k = k
self.back(n, 0, [])
return self.ans
def back(self, rest, count, path):
if rest == 0 and count == self.k:
self.ans.append(path)
if rest > (self.k - count) * 9:
return
if rest < self.k - count:
return
if rest < 0:
return
for i in range(1, 10):
if not path or (path and i > path[-1]):
self.back(rest - i, count + 1, path + [i])
if __name__ == '__main__':
a = Solution()
b = a.combinationSum3(3, 7)
print(b)
# @lc code=end | en | 0.361279 | # # @lc app=leetcode id=216 lang=python3 # # [216] Combination Sum III # # @lc code=start # @lc code=end | 3.201464 | 3 |
cudos-explorer-sync-to-node/checks.py | CudoVentures/cudos-infrastructure-monitoring | 0 | 6614297 | import datetime
import settings
import err
import query
import re
node_stats = []
recorded_errors = {}
def healthy(node_height: int) -> bool:
global node_stats
node_stats.append(node_height)
if len(node_stats) == settings.SELF_CHECK_INTERVAL:
try:
average = abs((sum(node_stats) / len(node_stats)) - node_height)
if average <= settings.MIN_AVERAGE:
return False
finally:
node_stats = []
return True
def check_sync() -> list:
errors = []
global recorded_errors
# NODE
address = settings.NODE_API + settings.END_POINT_FOR_LAST_BLOCK
node_msg = ""
try:
node_ip = re.search(r"\b([\d]{1,3}\.){3}[\d]{1,3}\b", address).group()
instance = f'<{settings.GCLOUD_SEARCH + node_ip}|GCLOUD>'
except AttributeError:
instance = settings.NODE_API
node_height, error = query.height(address)
if not node_height:
node_msg = f"{err.getting_height} node deployed @ {instance} @ {datetime.datetime.now()} {error}"
if not healthy(node_height):
node_msg = f"Node deployed @ {instance} might be stuck on block {node_height} @ {datetime.datetime.now()}"
if not node_msg:
# Checking height of the two explorers only if node is OK
for i in range(1, 3):
explorer_name = f"V{i} Explorer"
explorer_msg = ""
address = settings.EXPLORER_V1_HOST if i == 1 \
else settings.EXPLORER_V2_HOST
explorer_height, error = query.height(address + settings.HEALTHCHECK_ENDPOINT)
if not explorer_height:
explorer_msg = f"{err.getting_height} {explorer_name} @ {datetime.datetime.now()} with error {error}"
elif abs(explorer_height - node_height) >= settings.MAX_SYNC_TOLERANCE:
explorer_msg = f"{explorer_name} {err.stuck_behind} {abs(explorer_height - node_height)} " \
f"blocks @ {datetime.datetime.now()}"
if explorer_msg:
errors.append(explorer_msg)
recorded_errors[explorer_name] = explorer_msg
else:
errors.append(node_msg)
recorded_errors[address] = node_msg
return errors
def msg_type(msg: str) -> dict:
status_starting_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": f"Monitoring started!\n"
f" - Alive Status & Reminders:\n"
f" every {settings.REMINDER} hours\n"
f" - Allowed Block Delay:\n"
f" {settings.MAX_SYNC_TOLERANCE} blocks per {settings.SCHEDULE_TIME} minutes\n"
f" - Node Health Check:\n"
f" minimum"
f" {int(settings.MIN_AVERAGE) * int(settings.SELF_CHECK_INTERVAL)} blocks per"
f" {int(settings.SELF_CHECK_INTERVAL) * int(settings.SCHEDULE_TIME)} minutes",
"short": "false",
}
]
}
]
}
status_ok_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": "All synced",
"short": "false",
}
]
}
]
}
status_resume_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": "Back ONLINE",
"short": "false",
}
]
}
]
}
status_silent_message = {
"username": "Sync info",
"icon_emoji": ":large_orange_circle:",
"attachments": [
{
"color": "#D1C432",
"fields": [
{
"value": "Entering silent mode",
"short": "false",
}
]
}
]
}
status_remind_message = {
"username": "Sync reminder",
"icon_emoji": ":exclamation:",
"attachments": [
{
"fields": [
{
"value": "Unresolved error",
"short": "true",
}
]
}
]
}
if msg == "Status - OK":
return status_ok_message
elif msg == "Status - RESUME":
return status_resume_message
elif msg == "Status - SILENT":
return status_silent_message
elif msg == "Status - REMIND":
return status_remind_message
elif msg == "Start monitoring":
return status_starting_message
return {
"username": "Sync alert",
"icon_emoji": ":red_circle:",
"attachments": [
{
"color": "#FF0000",
"fields": [
{
"value": msg,
"short": "false",
}
]
}
]
}
| import datetime
import settings
import err
import query
import re
node_stats = []
recorded_errors = {}
def healthy(node_height: int) -> bool:
global node_stats
node_stats.append(node_height)
if len(node_stats) == settings.SELF_CHECK_INTERVAL:
try:
average = abs((sum(node_stats) / len(node_stats)) - node_height)
if average <= settings.MIN_AVERAGE:
return False
finally:
node_stats = []
return True
def check_sync() -> list:
errors = []
global recorded_errors
# NODE
address = settings.NODE_API + settings.END_POINT_FOR_LAST_BLOCK
node_msg = ""
try:
node_ip = re.search(r"\b([\d]{1,3}\.){3}[\d]{1,3}\b", address).group()
instance = f'<{settings.GCLOUD_SEARCH + node_ip}|GCLOUD>'
except AttributeError:
instance = settings.NODE_API
node_height, error = query.height(address)
if not node_height:
node_msg = f"{err.getting_height} node deployed @ {instance} @ {datetime.datetime.now()} {error}"
if not healthy(node_height):
node_msg = f"Node deployed @ {instance} might be stuck on block {node_height} @ {datetime.datetime.now()}"
if not node_msg:
# Checking height of the two explorers only if node is OK
for i in range(1, 3):
explorer_name = f"V{i} Explorer"
explorer_msg = ""
address = settings.EXPLORER_V1_HOST if i == 1 \
else settings.EXPLORER_V2_HOST
explorer_height, error = query.height(address + settings.HEALTHCHECK_ENDPOINT)
if not explorer_height:
explorer_msg = f"{err.getting_height} {explorer_name} @ {datetime.datetime.now()} with error {error}"
elif abs(explorer_height - node_height) >= settings.MAX_SYNC_TOLERANCE:
explorer_msg = f"{explorer_name} {err.stuck_behind} {abs(explorer_height - node_height)} " \
f"blocks @ {datetime.datetime.now()}"
if explorer_msg:
errors.append(explorer_msg)
recorded_errors[explorer_name] = explorer_msg
else:
errors.append(node_msg)
recorded_errors[address] = node_msg
return errors
def msg_type(msg: str) -> dict:
status_starting_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": f"Monitoring started!\n"
f" - Alive Status & Reminders:\n"
f" every {settings.REMINDER} hours\n"
f" - Allowed Block Delay:\n"
f" {settings.MAX_SYNC_TOLERANCE} blocks per {settings.SCHEDULE_TIME} minutes\n"
f" - Node Health Check:\n"
f" minimum"
f" {int(settings.MIN_AVERAGE) * int(settings.SELF_CHECK_INTERVAL)} blocks per"
f" {int(settings.SELF_CHECK_INTERVAL) * int(settings.SCHEDULE_TIME)} minutes",
"short": "false",
}
]
}
]
}
status_ok_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": "All synced",
"short": "false",
}
]
}
]
}
status_resume_message = {
"username": "Sync info",
"icon_emoji": ":large_green_circle:",
"attachments": [
{
"color": "#32D132",
"fields": [
{
"value": "Back ONLINE",
"short": "false",
}
]
}
]
}
status_silent_message = {
"username": "Sync info",
"icon_emoji": ":large_orange_circle:",
"attachments": [
{
"color": "#D1C432",
"fields": [
{
"value": "Entering silent mode",
"short": "false",
}
]
}
]
}
status_remind_message = {
"username": "Sync reminder",
"icon_emoji": ":exclamation:",
"attachments": [
{
"fields": [
{
"value": "Unresolved error",
"short": "true",
}
]
}
]
}
if msg == "Status - OK":
return status_ok_message
elif msg == "Status - RESUME":
return status_resume_message
elif msg == "Status - SILENT":
return status_silent_message
elif msg == "Status - REMIND":
return status_remind_message
elif msg == "Start monitoring":
return status_starting_message
return {
"username": "Sync alert",
"icon_emoji": ":red_circle:",
"attachments": [
{
"color": "#FF0000",
"fields": [
{
"value": msg,
"short": "false",
}
]
}
]
}
| en | 0.809825 | # NODE # Checking height of the two explorers only if node is OK | 2.528068 | 3 |
tests/databases/ensembl/utils.py | RNAcentral/rnacentral-import-pipeline | 1 | 6614298 | <filename>tests/databases/ensembl/utils.py
"""
Copyright [2009-2017] EMBL-European Bioinformatics Institute
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest as ut
from Bio import SeqIO
from databases.ensembl import data
from databases.ensembl.helpers import bio as helpers
class Base(ut.TestCase): # pylint: disable=R0904
filename = None
importer_class = None
@classmethod
def setUpClass(cls):
cls.features = {}
if not cls.filename:
return
for feature in cls.record.features:
key = None
if helpers.is_gene(feature):
key = helpers.gene(feature)
elif helpers.is_ncrna(feature):
key = helpers.transcript(feature) or helpers.standard_name(feature)
if not key:
continue
cls.features[key] = feature
def setUp(self):
self.importer = None
if self.importer_class and self.filename:
self.importer = self.importer_class("data/rfam/families.tsv")
def data(self):
with open(self.filename, "rb") as raw:
for entry in self.importer.data(raw):
yield entry
def entries_for(self, feature_key):
feature = self.features[feature_key]
summary = self.summary_of(helpers.gene(feature))
entries = self.importer.rnacentral_entries(self.record, summary, feature)
return list(entries)
def entry_for(self, feature_key):
entries = self.entries_for(feature_key)
assert len(entries) == 1
return entries[0]
| <filename>tests/databases/ensembl/utils.py
"""
Copyright [2009-2017] EMBL-European Bioinformatics Institute
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest as ut
from Bio import SeqIO
from databases.ensembl import data
from databases.ensembl.helpers import bio as helpers
class Base(ut.TestCase): # pylint: disable=R0904
filename = None
importer_class = None
@classmethod
def setUpClass(cls):
cls.features = {}
if not cls.filename:
return
for feature in cls.record.features:
key = None
if helpers.is_gene(feature):
key = helpers.gene(feature)
elif helpers.is_ncrna(feature):
key = helpers.transcript(feature) or helpers.standard_name(feature)
if not key:
continue
cls.features[key] = feature
def setUp(self):
self.importer = None
if self.importer_class and self.filename:
self.importer = self.importer_class("data/rfam/families.tsv")
def data(self):
with open(self.filename, "rb") as raw:
for entry in self.importer.data(raw):
yield entry
def entries_for(self, feature_key):
feature = self.features[feature_key]
summary = self.summary_of(helpers.gene(feature))
entries = self.importer.rnacentral_entries(self.record, summary, feature)
return list(entries)
def entry_for(self, feature_key):
entries = self.entries_for(feature_key)
assert len(entries) == 1
return entries[0]
| en | 0.827854 | Copyright [2009-2017] EMBL-European Bioinformatics Institute Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. # pylint: disable=R0904 | 2.041537 | 2 |
research/recommend/Fat-DeepFFM/eval.py | leelige/mindspore | 77 | 6614299 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===========================================================================
""" eval model"""
import argparse
import os
from src.config import ModelConfig
from src.dataset import get_mindrecord_dataset
from src.fat_deepffm import ModelBuilder
from src.metrics import AUCMetric
from mindspore import context, Model
from mindspore.common import set_seed
from mindspore.train.serialization import load_checkpoint, load_param_into_net
parser = argparse.ArgumentParser(description='CTR Prediction')
parser.add_argument('--dataset_path', type=str, default="/data/FM/mindrecord", help='Dataset path')
parser.add_argument('--ckpt_path', type=str, default="/checkpoint/Fat-DeepFFM-24_5166.ckpt", help='Checkpoint path')
parser.add_argument('--eval_file_name', type=str, default="./auc.log",
help='Auc log file path. Default: "./auc.log"')
parser.add_argument('--loss_file_name', type=str, default="./loss.log",
help='Loss log file path. Default: "./loss.log"')
parser.add_argument('--device_target', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
help="device target, support Ascend, GPU and CPU.")
parser.add_argument('--device_id', type=int, default=0, choices=(0, 1, 2, 3, 4, 5, 6, 7),
help="device target, support Ascend, GPU and CPU.")
args = parser.parse_args()
rank_size = int(os.environ.get("RANK_SIZE", 1))
print("rank_size", rank_size)
set_seed(1)
if __name__ == '__main__':
model_config = ModelConfig()
device_id = int(os.getenv('DEVICE_ID', default=args.device_id))
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
device_id=device_id)
print("Load dataset...")
train_net, test_net = ModelBuilder(model_config).get_train_eval_net()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=test_net, metrics={"AUC": auc_metric})
ds_test = get_mindrecord_dataset(args.dataset_path, train_mode=False)
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(train_net, param_dict)
print("Training started...")
res = model.eval(ds_test, dataset_sink_mode=False)
out_str = f'AUC: {list(res.values())[0]}'
print(res)
print(out_str)
| # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===========================================================================
""" eval model"""
import argparse
import os
from src.config import ModelConfig
from src.dataset import get_mindrecord_dataset
from src.fat_deepffm import ModelBuilder
from src.metrics import AUCMetric
from mindspore import context, Model
from mindspore.common import set_seed
from mindspore.train.serialization import load_checkpoint, load_param_into_net
parser = argparse.ArgumentParser(description='CTR Prediction')
parser.add_argument('--dataset_path', type=str, default="/data/FM/mindrecord", help='Dataset path')
parser.add_argument('--ckpt_path', type=str, default="/checkpoint/Fat-DeepFFM-24_5166.ckpt", help='Checkpoint path')
parser.add_argument('--eval_file_name', type=str, default="./auc.log",
help='Auc log file path. Default: "./auc.log"')
parser.add_argument('--loss_file_name', type=str, default="./loss.log",
help='Loss log file path. Default: "./loss.log"')
parser.add_argument('--device_target', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
help="device target, support Ascend, GPU and CPU.")
parser.add_argument('--device_id', type=int, default=0, choices=(0, 1, 2, 3, 4, 5, 6, 7),
help="device target, support Ascend, GPU and CPU.")
args = parser.parse_args()
rank_size = int(os.environ.get("RANK_SIZE", 1))
print("rank_size", rank_size)
set_seed(1)
if __name__ == '__main__':
model_config = ModelConfig()
device_id = int(os.getenv('DEVICE_ID', default=args.device_id))
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
device_id=device_id)
print("Load dataset...")
train_net, test_net = ModelBuilder(model_config).get_train_eval_net()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=test_net, metrics={"AUC": auc_metric})
ds_test = get_mindrecord_dataset(args.dataset_path, train_mode=False)
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(train_net, param_dict)
print("Training started...")
res = model.eval(ds_test, dataset_sink_mode=False)
out_str = f'AUC: {list(res.values())[0]}'
print(res)
print(out_str)
| en | 0.807926 | # Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =========================================================================== eval model | 1.884698 | 2 |
src/anaplan_api/ResourceParserFile.py | pieter-pot/anaplan-api | 0 | 6614300 | <gh_stars>0
from .ResourceParserFactory import ResourceParserFactory
from .AnaplanResourceFile import AnaplanResourceFile
class ResourceParserFile(ResourceParserFactory):
def get_parser(self, response: dict) -> AnaplanResourceFile:
"""Get a parser object for list of Anaplan files
:param response: JSON list of files in an Anaplan model
:type response: dict
:return: Initialized object containing parsed list of files.
:rtype: AnaplanResourceFile
"""
return AnaplanResourceFile(response)
| from .ResourceParserFactory import ResourceParserFactory
from .AnaplanResourceFile import AnaplanResourceFile
class ResourceParserFile(ResourceParserFactory):
def get_parser(self, response: dict) -> AnaplanResourceFile:
"""Get a parser object for list of Anaplan files
:param response: JSON list of files in an Anaplan model
:type response: dict
:return: Initialized object containing parsed list of files.
:rtype: AnaplanResourceFile
"""
return AnaplanResourceFile(response) | en | 0.660024 | Get a parser object for list of Anaplan files :param response: JSON list of files in an Anaplan model :type response: dict :return: Initialized object containing parsed list of files. :rtype: AnaplanResourceFile | 2.668106 | 3 |
rotkehlchen/tests/test_no_missing_init.py | coblee/rotki | 137 | 6614301 | import os
from typing import Set
def find_directories_with_missing_init(path: str) -> Set[str]:
package_dirs: Set[str] = set()
py_directories: Set[str] = set()
for root, dirs, files in os.walk(path):
try:
dirs.remove("__pycache__")
except ValueError:
pass
for name in files:
if name == "__init__.py":
package_dirs.add(root)
if name.endswith(".py"):
py_directories.add(root)
return py_directories - package_dirs
def test_no_missing_init():
"""Test that there is no directories missing an __init__.py file
The reason for this is some linting tools like mypy and pylint don't check the
directories that are missing the files.
"""
rotki_path = os.path.abspath(os.path.join(os.path.abspath(__file__), "..", ".."))
print(f"\nScanning {rotki_path}")
directories_with_missing_init = find_directories_with_missing_init(rotki_path)
if directories_with_missing_init:
print("The following directories are missing '__init__.py' files:")
for directory in directories_with_missing_init:
print(directory)
assert not directories_with_missing_init, "some directories are missing __init__.py files"
| import os
from typing import Set
def find_directories_with_missing_init(path: str) -> Set[str]:
package_dirs: Set[str] = set()
py_directories: Set[str] = set()
for root, dirs, files in os.walk(path):
try:
dirs.remove("__pycache__")
except ValueError:
pass
for name in files:
if name == "__init__.py":
package_dirs.add(root)
if name.endswith(".py"):
py_directories.add(root)
return py_directories - package_dirs
def test_no_missing_init():
"""Test that there is no directories missing an __init__.py file
The reason for this is some linting tools like mypy and pylint don't check the
directories that are missing the files.
"""
rotki_path = os.path.abspath(os.path.join(os.path.abspath(__file__), "..", ".."))
print(f"\nScanning {rotki_path}")
directories_with_missing_init = find_directories_with_missing_init(rotki_path)
if directories_with_missing_init:
print("The following directories are missing '__init__.py' files:")
for directory in directories_with_missing_init:
print(directory)
assert not directories_with_missing_init, "some directories are missing __init__.py files"
| en | 0.930677 | Test that there is no directories missing an __init__.py file The reason for this is some linting tools like mypy and pylint don't check the directories that are missing the files. | 3.21206 | 3 |
tensorflow_lattice/python/premade.py | synergy-robotics-a-b/lattice | 0 | 6614302 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF Lattice premade models implement typical monotonic model architectures.
You can use TFL premade models to easily construct commonly used monotonic model
architectures. To construct a TFL premade model, construct a model configuration
from `tfl.configs` and pass it to the premade model constructor. Note that the
inputs to the model should match the order in which they are defined in the
feature configs.
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_lattice_model = tfl.premade.CalibratedLattice(
model_config=model_config)
calibrated_lattice_model.compile(...)
calibrated_lattice_model.fit(...)
```
Supported models are defined in `tfl.configs`. Each model architecture can be
used the same as any other `tf.keras.Model`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from . import categorical_calibration_layer
from . import configs
from . import lattice_layer
from . import linear_layer
from . import pwl_calibration_layer
from . import pwl_calibration_lib
from absl import logging
import enum
import numpy as np
import six
import tensorflow as tf
# Layer names used for layers in the premade models.
INPUT_LAYER_NAME = 'tfl_input'
CALIB_LAYER_NAME = 'tfl_calib'
LATTICE_LAYER_NAME = 'tfl_lattice'
LINEAR_LAYER_NAME = 'tfl_linear'
OUTPUT_CALIB_LAYER_NAME = 'tfl_output_calib'
# Prefix for passthrough (identity) nodes for shared calibration.
# These nodes pass shared calibrated values to submodels in an ensemble.
CALIB_PASSTHROUGH_NAME = 'tfl_calib_passthrough'
# Prefix for defining feature calibrator regularizers.
_INPUT_CALIB_REGULARIZER_PREFIX = 'calib_'
# Prefix for defining output calibrator regularizers.
_OUTPUT_CALIB_REGULARIZER_PREFIX = 'output_calib_'
def _input_calibration_regularizers(model_config, feature_config):
"""Returns pwl layer regularizers defined in the model and feature configs."""
regularizer_configs = []
regularizer_configs.extend(feature_config.regularizer_configs or [])
regularizer_configs.extend(model_config.regularizer_configs or [])
return [(r.name.replace(_INPUT_CALIB_REGULARIZER_PREFIX, ''), r.l1, r.l2)
for r in regularizer_configs
if r.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX)]
def _output_calibration_regularizers(model_config):
"""Returns output calibration regularizers defined in the model config."""
return [(r.name.replace(_OUTPUT_CALIB_REGULARIZER_PREFIX, ''), r.l1, r.l2)
for r in model_config.regularizer_configs or []
if r.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)]
def _lattice_regularizers(model_config, feature_configs):
"""Returns lattice regularizers defined in the model and feature configs."""
# dict from regularizer name to pair of per feature l1 and l2 amounts.
regularizers_dict = {}
n_dims = len(feature_configs)
for index, feature_config in enumerate(feature_configs):
for regularizer_config in feature_config.regularizer_configs or []:
if not (
regularizer_config.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX) or
regularizer_config.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)):
if regularizer_config.name not in regularizers_dict:
regularizers_dict[regularizer_config.name] = ([0.0] * n_dims,
[0.0] * n_dims)
regularizers_dict[
regularizer_config.name][0][index] += regularizer_config.l1
regularizers_dict[
regularizer_config.name][1][index] += regularizer_config.l2
regularizers = [(k,) + v for k, v in regularizers_dict.items()]
for regularizer_config in model_config.regularizer_configs or []:
if not (
regularizer_config.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX) or
regularizer_config.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)):
regularizers.append((regularizer_config.name, regularizer_config.l1,
regularizer_config.l2))
return regularizers
class _LayerOutputRange(enum.Enum):
"""Enum to indicate the output range based on the input of the next layers."""
MODEL_OUTPUT = 1
INPUT_TO_LATTICE = 2
INPUT_TO_FINAL_CALIBRATION = 3
def _output_range(layer_output_range, model_config, feature_config=None):
"""Returns min/max/init_min/init_max for a given output range."""
if layer_output_range == _LayerOutputRange.INPUT_TO_LATTICE:
if feature_config is None:
raise ValueError('Expecting feature config for lattice inputs.')
output_init_min = output_min = 0.0
output_init_max = output_max = feature_config.lattice_size - 1.0
elif layer_output_range == _LayerOutputRange.MODEL_OUTPUT:
output_min = model_config.output_min
output_max = model_config.output_max
output_init_min = np.min(model_config.output_initialization)
output_init_max = np.max(model_config.output_initialization)
elif layer_output_range == _LayerOutputRange.INPUT_TO_FINAL_CALIBRATION:
output_init_min = output_min = 0.0
output_init_max = output_max = 1.0
else:
raise ValueError('Unsupported layer output range.')
return output_min, output_max, output_init_min, output_init_max
def _input_layer(feature_configs, dtype):
"""Creates a calibration layer."""
input_layer = {}
for feature_config in feature_configs:
layer_name = '{}_{}'.format(INPUT_LAYER_NAME, feature_config.name)
if feature_config.num_buckets:
input_layer[feature_config.name] = tf.keras.Input(
shape=(1,), dtype=tf.int32, name=layer_name)
else:
input_layer[feature_config.name] = tf.keras.Input(
shape=(1,), dtype=dtype, name=layer_name)
return input_layer
def _calibration_layers(calibration_input_layer, feature_configs, model_config,
layer_output_range, submodels, separate_calibrators,
dtype):
"""Creates a calibration layer for `submodels` as list of list of features."""
# Create a list of (feature_name, calibration_output_idx) pairs for each
# submodel. When using shared calibration, all submodels will have
# calibration_output_idx = 0.
submodels_input_features = []
calibration_last_index = collections.defaultdict(int)
for submodel in submodels:
submodel_input_features = []
submodels_input_features.append(submodel_input_features)
for feature_name in submodel:
submodel_input_features.append(
(feature_name, calibration_last_index[feature_name]))
if separate_calibrators:
calibration_last_index[feature_name] += 1
calibration_output = {}
for feature_config in feature_configs:
feature_name = feature_config.name
units = max(calibration_last_index[feature_name], 1)
calibration_input = calibration_input_layer[feature_name]
layer_name = '{}_{}'.format(CALIB_LAYER_NAME, feature_name)
(output_min, output_max, output_init_min,
output_init_max) = _output_range(layer_output_range, model_config,
feature_config)
if feature_config.num_buckets:
kernel_initializer = tf.compat.v1.random_uniform_initializer(
output_init_min, output_init_max)
calibrated = (
categorical_calibration_layer.CategoricalCalibration(
num_buckets=feature_config.num_buckets,
units=units,
output_min=output_min,
output_max=output_max,
kernel_initializer=kernel_initializer,
monotonicities=feature_config.monotonicity if isinstance(
feature_config.monotonicity, list) else None,
default_input_value=feature_config.default_value,
dtype=dtype,
name=layer_name)(calibration_input))
else:
kernel_regularizer = _input_calibration_regularizers(
model_config, feature_config)
monotonicity = feature_config.monotonicity
if (pwl_calibration_lib.canonicalize_monotonicity(monotonicity) == 0 and
feature_config.pwl_calibration_always_monotonic):
monotonicity = 1
kernel_initializer = pwl_calibration_layer.UniformOutputInitializer(
output_min=output_init_min,
output_max=output_init_max,
monotonicity=monotonicity)
calibrated = (
pwl_calibration_layer.PWLCalibration(
units=units,
input_keypoints=feature_config.pwl_calibration_input_keypoints,
output_min=output_min,
output_max=output_max,
clamp_min=feature_config.pwl_calibration_clamp_min,
clamp_max=feature_config.pwl_calibration_clamp_max,
missing_input_value=feature_config.default_value,
impute_missing=(feature_config.default_value is not None),
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
monotonicity=monotonicity,
convexity=feature_config.pwl_calibration_convexity,
dtype=dtype,
name=layer_name)(calibration_input))
if units == 1:
calibration_output[feature_name] = [calibrated]
else:
calibration_output[feature_name] = tf.split(calibrated, units, axis=1)
# Create passthrough nodes for each submodel input so that we can recover
# the model structure for plotting and analysis.
# {CALIB_PASSTHROUGH_NAME}_{feature_name}_
# {calibration_output_idx}_{submodel_idx}_{submodel_input_idx}
submodels_inputs = []
for submodel_idx, submodel_input_features in enumerate(
submodels_input_features):
submodel_inputs = []
submodels_inputs.append(submodel_inputs)
for (submodel_input_idx,
(feature_name,
calibration_output_idx)) in enumerate(submodel_input_features):
passthrough_name = '{}_{}_{}_{}_{}'.format(CALIB_PASSTHROUGH_NAME,
feature_name,
calibration_output_idx,
submodel_idx,
submodel_input_idx)
submodel_inputs.append(
tf.identity(
calibration_output[feature_name][calibration_output_idx],
name=passthrough_name))
return submodels_inputs
def _monotonicities_from_feature_configs(feature_configs):
"""Returns list of monotonicities defined in the given feature_configs."""
monotonicities = []
for feature_config in feature_configs:
if not feature_config.monotonicity:
monotonicities.append(0)
elif (isinstance(feature_config.monotonicity, six.string_types) and
feature_config.monotonicity.lower() == 'none'):
monotonicities.append(0)
else:
monotonicities.append(1)
return monotonicities
def _dominance_constraints_from_feature_configs(feature_configs):
"""Returns list of dominance constraints in the given feature_configs."""
feature_names = [feature_config.name for feature_config in feature_configs]
monotonic_dominances = []
for dominant_idx, dominant_feature_config in enumerate(feature_configs):
for dominance_config in dominant_feature_config.dominates or []:
if dominance_config.feature_name in feature_names:
weak_idx = feature_names.index(dominance_config.feature_name)
if dominance_config.dominance_type == 'monotonic':
monotonic_dominances.append((dominant_idx, weak_idx))
else:
raise ValueError('Unrecognized dominance type: {}'.format(
dominance_config.dominance_type))
return monotonic_dominances
def _linear_layer(linear_input, feature_configs, model_config, weighted_average,
submodel_index, dtype):
"""Creates a linear layer initialized to be an average."""
layer_name = '{}_{}'.format(LINEAR_LAYER_NAME, submodel_index)
linear_input = tf.keras.layers.Concatenate(axis=1)(linear_input)
num_input_dims = len(feature_configs)
kernel_initializer = tf.compat.v1.constant_initializer(
[1.0 / num_input_dims] * num_input_dims)
bias_initializer = tf.compat.v1.constant_initializer(0)
if weighted_average:
# Linear coefficients should be possitive and sum up to one.
linear_monotonicities = [1] * num_input_dims
normalization_order = 1
use_bias = False
else:
linear_monotonicities = _monotonicities_from_feature_configs(
feature_configs)
normalization_order = None
use_bias = model_config.use_bias
monotonic_dominances = _dominance_constraints_from_feature_configs(
feature_configs)
return linear_layer.Linear(
num_input_dims=num_input_dims,
monotonicities=linear_monotonicities,
monotonic_dominances=monotonic_dominances,
use_bias=use_bias,
normalization_order=normalization_order,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
dtype=dtype,
name=layer_name)(
linear_input)
def _lattice_layer(lattice_input, feature_configs, model_config,
layer_output_range, submodel_index, is_inside_ensemble,
dtype):
"""Creates a lattice layer."""
layer_name = '{}_{}'.format(LATTICE_LAYER_NAME, submodel_index)
(output_min, output_max, output_init_min,
output_init_max) = _output_range(layer_output_range, model_config)
feature_names = [feature_config.name for feature_config in feature_configs]
lattice_sizes = [
feature_config.lattice_size for feature_config in feature_configs
]
lattice_monotonicities = _monotonicities_from_feature_configs(feature_configs)
lattice_unimodalities = [
feature_config.unimodality for feature_config in feature_configs
]
lattice_regularizers = _lattice_regularizers(model_config, feature_configs)
# Construct trust constraints within this lattice.
edgeworth_trusts = []
trapezoid_trusts = []
for conditional_idx, conditional_feature_config in enumerate(feature_configs):
for trust_config in conditional_feature_config.reflects_trust_in or []:
if trust_config.feature_name in feature_names:
main_idx = feature_names.index(trust_config.feature_name)
if trust_config.trust_type == 'edgeworth':
edgeworth_trusts.append(
(main_idx, conditional_idx, trust_config.direction))
elif trust_config.trust_type == 'trapezoid':
trapezoid_trusts.append(
(main_idx, conditional_idx, trust_config.direction))
else:
raise ValueError('Unrecognized trust type: {}'.format(
trust_config.trust_type))
elif is_inside_ensemble and trust_config.trust_type == 'trapezoid':
logging.warning(
'A "main" feature (%s) for a trapezoid trust constraint is not '
'present in a lattice that includes the "conditional" feature '
'(%s). In an ensemble model, this can result in constraint '
'violations. Consider manually setting the ensemble structure if '
'this constraint needs to be satisfied.', trust_config.feature_name,
conditional_feature_config.name)
monotonic_dominances = _dominance_constraints_from_feature_configs(
feature_configs)
kernel_initializer = lattice_layer.LinearInitializer(
lattice_sizes=lattice_sizes,
monotonicities=lattice_monotonicities,
unimodalities=lattice_unimodalities,
output_min=output_init_min,
output_max=output_init_max)
return lattice_layer.Lattice(
lattice_sizes=lattice_sizes,
monotonicities=lattice_monotonicities,
unimodalities=lattice_unimodalities,
edgeworth_trusts=edgeworth_trusts,
trapezoid_trusts=trapezoid_trusts,
monotonic_dominances=monotonic_dominances,
output_min=output_min,
output_max=output_max,
clip_inputs=False,
kernel_regularizer=lattice_regularizers,
kernel_initializer=kernel_initializer,
dtype=dtype,
name=layer_name)(
lattice_input)
def _output_calibration_layer(output_calibration_input, model_config, dtype):
"""Creates a monotonic output calibration layer with inputs range [0, 1]."""
# kernel format: bias followed by diffs between consecutive keypoint outputs.
kernel_init_values = np.ediff1d(
model_config.output_initialization,
to_begin=model_config.output_initialization[0])
input_keypoints = np.linspace(0.0, 1.0, num=len(kernel_init_values))
kernel_initializer = tf.compat.v1.constant_initializer(kernel_init_values)
kernel_regularizer = _output_calibration_regularizers(model_config)
return pwl_calibration_layer.PWLCalibration(
input_keypoints=input_keypoints,
output_min=model_config.output_min,
output_max=model_config.output_max,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
monotonicity=1,
dtype=dtype,
name=OUTPUT_CALIB_LAYER_NAME)(
output_calibration_input)
# TODO: add support for serialization and object scoping or annoations.
class CalibratedLatticeEnsemble(tf.keras.Model):
"""Premade model for Tensorflow calibrated lattice ensemble models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLatticeEnsembleConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeEnsembleConfig(...)
calibrated_lattice_ensemble_model = tfl.premade.CalibratedLatticeEnsemble(
model_config=model_config)
calibrated_lattice_ensemble_model.compile(...)
calibrated_lattice_ensemble_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLatticeEnsemble` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLatticeEnsembleConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=_LayerOutputRange.INPUT_TO_LATTICE,
submodels=model_config.lattices,
separate_calibrators=model_config.separate_calibrators,
dtype=dtype)
lattice_outputs = []
for submodel_index, (lattice_feature_names, lattice_input) in enumerate(
zip(model_config.lattices, submodels_inputs)):
lattice_feature_configs = [
model_config.feature_config_by_name(feature_name)
for feature_name in lattice_feature_names
]
lattice_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION if
model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
lattice_outputs.append(
_lattice_layer(
lattice_input=lattice_input,
feature_configs=lattice_feature_configs,
model_config=model_config,
layer_output_range=lattice_layer_output_range,
submodel_index=submodel_index,
is_inside_ensemble=True,
dtype=dtype))
if len(lattice_outputs) > 1:
averaged_lattice_output = tf.keras.layers.Average()(lattice_outputs)
else:
averaged_lattice_output = lattice_outputs[0]
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=averaged_lattice_output,
model_config=model_config,
dtype=dtype)
else:
model_output = averaged_lattice_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLatticeEnsemble, self).__init__(
inputs=inputs, outputs=model_output)
class CalibratedLattice(tf.keras.Model):
"""Premade model for Tensorflow calibrated lattice models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLatticeConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_lattice_model = tfl.premade.CalibratedLattice(
model_config=model_config)
calibrated_lattice_model.compile(...)
calibrated_lattice_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLattice` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLatticeConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=_LayerOutputRange.INPUT_TO_LATTICE,
submodels=[[
feature_config.name
for feature_config in model_config.feature_configs
]],
separate_calibrators=False,
dtype=dtype)
lattice_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION
if model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
lattice_output = _lattice_layer(
lattice_input=submodels_inputs[0],
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=lattice_layer_output_range,
submodel_index=0,
is_inside_ensemble=False,
dtype=dtype)
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=lattice_output,
model_config=model_config,
dtype=dtype)
else:
model_output = lattice_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLattice, self).__init__(inputs=inputs, outputs=model_output)
class CalibratedLinear(tf.keras.Model):
"""Premade model for Tensorflow calibrated linear models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLinearConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_linear_model = tfl.premade.CalibratedLinear(
model_config=model_config)
calibrated_linear_model.compile(...)
calibrated_linear_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLinear` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLinearConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
calibration_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION
if model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=calibration_layer_output_range,
submodels=[[
feature_config.name
for feature_config in model_config.feature_configs
]],
separate_calibrators=False,
dtype=dtype)
weighted_average = (
model_config.output_min is not None or
model_config.output_max is not None or model_config.output_calibration)
linear_output = _linear_layer(
linear_input=submodels_inputs[0],
feature_configs=model_config.feature_configs,
model_config=model_config,
weighted_average=weighted_average,
submodel_index=0,
dtype=dtype)
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=linear_output,
model_config=model_config,
dtype=dtype)
else:
model_output = linear_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLinear, self).__init__(inputs=inputs, outputs=model_output)
| # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF Lattice premade models implement typical monotonic model architectures.
You can use TFL premade models to easily construct commonly used monotonic model
architectures. To construct a TFL premade model, construct a model configuration
from `tfl.configs` and pass it to the premade model constructor. Note that the
inputs to the model should match the order in which they are defined in the
feature configs.
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_lattice_model = tfl.premade.CalibratedLattice(
model_config=model_config)
calibrated_lattice_model.compile(...)
calibrated_lattice_model.fit(...)
```
Supported models are defined in `tfl.configs`. Each model architecture can be
used the same as any other `tf.keras.Model`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from . import categorical_calibration_layer
from . import configs
from . import lattice_layer
from . import linear_layer
from . import pwl_calibration_layer
from . import pwl_calibration_lib
from absl import logging
import enum
import numpy as np
import six
import tensorflow as tf
# Layer names used for layers in the premade models.
INPUT_LAYER_NAME = 'tfl_input'
CALIB_LAYER_NAME = 'tfl_calib'
LATTICE_LAYER_NAME = 'tfl_lattice'
LINEAR_LAYER_NAME = 'tfl_linear'
OUTPUT_CALIB_LAYER_NAME = 'tfl_output_calib'
# Prefix for passthrough (identity) nodes for shared calibration.
# These nodes pass shared calibrated values to submodels in an ensemble.
CALIB_PASSTHROUGH_NAME = 'tfl_calib_passthrough'
# Prefix for defining feature calibrator regularizers.
_INPUT_CALIB_REGULARIZER_PREFIX = 'calib_'
# Prefix for defining output calibrator regularizers.
_OUTPUT_CALIB_REGULARIZER_PREFIX = 'output_calib_'
def _input_calibration_regularizers(model_config, feature_config):
"""Returns pwl layer regularizers defined in the model and feature configs."""
regularizer_configs = []
regularizer_configs.extend(feature_config.regularizer_configs or [])
regularizer_configs.extend(model_config.regularizer_configs or [])
return [(r.name.replace(_INPUT_CALIB_REGULARIZER_PREFIX, ''), r.l1, r.l2)
for r in regularizer_configs
if r.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX)]
def _output_calibration_regularizers(model_config):
"""Returns output calibration regularizers defined in the model config."""
return [(r.name.replace(_OUTPUT_CALIB_REGULARIZER_PREFIX, ''), r.l1, r.l2)
for r in model_config.regularizer_configs or []
if r.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)]
def _lattice_regularizers(model_config, feature_configs):
"""Returns lattice regularizers defined in the model and feature configs."""
# dict from regularizer name to pair of per feature l1 and l2 amounts.
regularizers_dict = {}
n_dims = len(feature_configs)
for index, feature_config in enumerate(feature_configs):
for regularizer_config in feature_config.regularizer_configs or []:
if not (
regularizer_config.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX) or
regularizer_config.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)):
if regularizer_config.name not in regularizers_dict:
regularizers_dict[regularizer_config.name] = ([0.0] * n_dims,
[0.0] * n_dims)
regularizers_dict[
regularizer_config.name][0][index] += regularizer_config.l1
regularizers_dict[
regularizer_config.name][1][index] += regularizer_config.l2
regularizers = [(k,) + v for k, v in regularizers_dict.items()]
for regularizer_config in model_config.regularizer_configs or []:
if not (
regularizer_config.name.startswith(_INPUT_CALIB_REGULARIZER_PREFIX) or
regularizer_config.name.startswith(_OUTPUT_CALIB_REGULARIZER_PREFIX)):
regularizers.append((regularizer_config.name, regularizer_config.l1,
regularizer_config.l2))
return regularizers
class _LayerOutputRange(enum.Enum):
"""Enum to indicate the output range based on the input of the next layers."""
MODEL_OUTPUT = 1
INPUT_TO_LATTICE = 2
INPUT_TO_FINAL_CALIBRATION = 3
def _output_range(layer_output_range, model_config, feature_config=None):
"""Returns min/max/init_min/init_max for a given output range."""
if layer_output_range == _LayerOutputRange.INPUT_TO_LATTICE:
if feature_config is None:
raise ValueError('Expecting feature config for lattice inputs.')
output_init_min = output_min = 0.0
output_init_max = output_max = feature_config.lattice_size - 1.0
elif layer_output_range == _LayerOutputRange.MODEL_OUTPUT:
output_min = model_config.output_min
output_max = model_config.output_max
output_init_min = np.min(model_config.output_initialization)
output_init_max = np.max(model_config.output_initialization)
elif layer_output_range == _LayerOutputRange.INPUT_TO_FINAL_CALIBRATION:
output_init_min = output_min = 0.0
output_init_max = output_max = 1.0
else:
raise ValueError('Unsupported layer output range.')
return output_min, output_max, output_init_min, output_init_max
def _input_layer(feature_configs, dtype):
"""Creates a calibration layer."""
input_layer = {}
for feature_config in feature_configs:
layer_name = '{}_{}'.format(INPUT_LAYER_NAME, feature_config.name)
if feature_config.num_buckets:
input_layer[feature_config.name] = tf.keras.Input(
shape=(1,), dtype=tf.int32, name=layer_name)
else:
input_layer[feature_config.name] = tf.keras.Input(
shape=(1,), dtype=dtype, name=layer_name)
return input_layer
def _calibration_layers(calibration_input_layer, feature_configs, model_config,
layer_output_range, submodels, separate_calibrators,
dtype):
"""Creates a calibration layer for `submodels` as list of list of features."""
# Create a list of (feature_name, calibration_output_idx) pairs for each
# submodel. When using shared calibration, all submodels will have
# calibration_output_idx = 0.
submodels_input_features = []
calibration_last_index = collections.defaultdict(int)
for submodel in submodels:
submodel_input_features = []
submodels_input_features.append(submodel_input_features)
for feature_name in submodel:
submodel_input_features.append(
(feature_name, calibration_last_index[feature_name]))
if separate_calibrators:
calibration_last_index[feature_name] += 1
calibration_output = {}
for feature_config in feature_configs:
feature_name = feature_config.name
units = max(calibration_last_index[feature_name], 1)
calibration_input = calibration_input_layer[feature_name]
layer_name = '{}_{}'.format(CALIB_LAYER_NAME, feature_name)
(output_min, output_max, output_init_min,
output_init_max) = _output_range(layer_output_range, model_config,
feature_config)
if feature_config.num_buckets:
kernel_initializer = tf.compat.v1.random_uniform_initializer(
output_init_min, output_init_max)
calibrated = (
categorical_calibration_layer.CategoricalCalibration(
num_buckets=feature_config.num_buckets,
units=units,
output_min=output_min,
output_max=output_max,
kernel_initializer=kernel_initializer,
monotonicities=feature_config.monotonicity if isinstance(
feature_config.monotonicity, list) else None,
default_input_value=feature_config.default_value,
dtype=dtype,
name=layer_name)(calibration_input))
else:
kernel_regularizer = _input_calibration_regularizers(
model_config, feature_config)
monotonicity = feature_config.monotonicity
if (pwl_calibration_lib.canonicalize_monotonicity(monotonicity) == 0 and
feature_config.pwl_calibration_always_monotonic):
monotonicity = 1
kernel_initializer = pwl_calibration_layer.UniformOutputInitializer(
output_min=output_init_min,
output_max=output_init_max,
monotonicity=monotonicity)
calibrated = (
pwl_calibration_layer.PWLCalibration(
units=units,
input_keypoints=feature_config.pwl_calibration_input_keypoints,
output_min=output_min,
output_max=output_max,
clamp_min=feature_config.pwl_calibration_clamp_min,
clamp_max=feature_config.pwl_calibration_clamp_max,
missing_input_value=feature_config.default_value,
impute_missing=(feature_config.default_value is not None),
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
monotonicity=monotonicity,
convexity=feature_config.pwl_calibration_convexity,
dtype=dtype,
name=layer_name)(calibration_input))
if units == 1:
calibration_output[feature_name] = [calibrated]
else:
calibration_output[feature_name] = tf.split(calibrated, units, axis=1)
# Create passthrough nodes for each submodel input so that we can recover
# the model structure for plotting and analysis.
# {CALIB_PASSTHROUGH_NAME}_{feature_name}_
# {calibration_output_idx}_{submodel_idx}_{submodel_input_idx}
submodels_inputs = []
for submodel_idx, submodel_input_features in enumerate(
submodels_input_features):
submodel_inputs = []
submodels_inputs.append(submodel_inputs)
for (submodel_input_idx,
(feature_name,
calibration_output_idx)) in enumerate(submodel_input_features):
passthrough_name = '{}_{}_{}_{}_{}'.format(CALIB_PASSTHROUGH_NAME,
feature_name,
calibration_output_idx,
submodel_idx,
submodel_input_idx)
submodel_inputs.append(
tf.identity(
calibration_output[feature_name][calibration_output_idx],
name=passthrough_name))
return submodels_inputs
def _monotonicities_from_feature_configs(feature_configs):
"""Returns list of monotonicities defined in the given feature_configs."""
monotonicities = []
for feature_config in feature_configs:
if not feature_config.monotonicity:
monotonicities.append(0)
elif (isinstance(feature_config.monotonicity, six.string_types) and
feature_config.monotonicity.lower() == 'none'):
monotonicities.append(0)
else:
monotonicities.append(1)
return monotonicities
def _dominance_constraints_from_feature_configs(feature_configs):
"""Returns list of dominance constraints in the given feature_configs."""
feature_names = [feature_config.name for feature_config in feature_configs]
monotonic_dominances = []
for dominant_idx, dominant_feature_config in enumerate(feature_configs):
for dominance_config in dominant_feature_config.dominates or []:
if dominance_config.feature_name in feature_names:
weak_idx = feature_names.index(dominance_config.feature_name)
if dominance_config.dominance_type == 'monotonic':
monotonic_dominances.append((dominant_idx, weak_idx))
else:
raise ValueError('Unrecognized dominance type: {}'.format(
dominance_config.dominance_type))
return monotonic_dominances
def _linear_layer(linear_input, feature_configs, model_config, weighted_average,
submodel_index, dtype):
"""Creates a linear layer initialized to be an average."""
layer_name = '{}_{}'.format(LINEAR_LAYER_NAME, submodel_index)
linear_input = tf.keras.layers.Concatenate(axis=1)(linear_input)
num_input_dims = len(feature_configs)
kernel_initializer = tf.compat.v1.constant_initializer(
[1.0 / num_input_dims] * num_input_dims)
bias_initializer = tf.compat.v1.constant_initializer(0)
if weighted_average:
# Linear coefficients should be possitive and sum up to one.
linear_monotonicities = [1] * num_input_dims
normalization_order = 1
use_bias = False
else:
linear_monotonicities = _monotonicities_from_feature_configs(
feature_configs)
normalization_order = None
use_bias = model_config.use_bias
monotonic_dominances = _dominance_constraints_from_feature_configs(
feature_configs)
return linear_layer.Linear(
num_input_dims=num_input_dims,
monotonicities=linear_monotonicities,
monotonic_dominances=monotonic_dominances,
use_bias=use_bias,
normalization_order=normalization_order,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
dtype=dtype,
name=layer_name)(
linear_input)
def _lattice_layer(lattice_input, feature_configs, model_config,
layer_output_range, submodel_index, is_inside_ensemble,
dtype):
"""Creates a lattice layer."""
layer_name = '{}_{}'.format(LATTICE_LAYER_NAME, submodel_index)
(output_min, output_max, output_init_min,
output_init_max) = _output_range(layer_output_range, model_config)
feature_names = [feature_config.name for feature_config in feature_configs]
lattice_sizes = [
feature_config.lattice_size for feature_config in feature_configs
]
lattice_monotonicities = _monotonicities_from_feature_configs(feature_configs)
lattice_unimodalities = [
feature_config.unimodality for feature_config in feature_configs
]
lattice_regularizers = _lattice_regularizers(model_config, feature_configs)
# Construct trust constraints within this lattice.
edgeworth_trusts = []
trapezoid_trusts = []
for conditional_idx, conditional_feature_config in enumerate(feature_configs):
for trust_config in conditional_feature_config.reflects_trust_in or []:
if trust_config.feature_name in feature_names:
main_idx = feature_names.index(trust_config.feature_name)
if trust_config.trust_type == 'edgeworth':
edgeworth_trusts.append(
(main_idx, conditional_idx, trust_config.direction))
elif trust_config.trust_type == 'trapezoid':
trapezoid_trusts.append(
(main_idx, conditional_idx, trust_config.direction))
else:
raise ValueError('Unrecognized trust type: {}'.format(
trust_config.trust_type))
elif is_inside_ensemble and trust_config.trust_type == 'trapezoid':
logging.warning(
'A "main" feature (%s) for a trapezoid trust constraint is not '
'present in a lattice that includes the "conditional" feature '
'(%s). In an ensemble model, this can result in constraint '
'violations. Consider manually setting the ensemble structure if '
'this constraint needs to be satisfied.', trust_config.feature_name,
conditional_feature_config.name)
monotonic_dominances = _dominance_constraints_from_feature_configs(
feature_configs)
kernel_initializer = lattice_layer.LinearInitializer(
lattice_sizes=lattice_sizes,
monotonicities=lattice_monotonicities,
unimodalities=lattice_unimodalities,
output_min=output_init_min,
output_max=output_init_max)
return lattice_layer.Lattice(
lattice_sizes=lattice_sizes,
monotonicities=lattice_monotonicities,
unimodalities=lattice_unimodalities,
edgeworth_trusts=edgeworth_trusts,
trapezoid_trusts=trapezoid_trusts,
monotonic_dominances=monotonic_dominances,
output_min=output_min,
output_max=output_max,
clip_inputs=False,
kernel_regularizer=lattice_regularizers,
kernel_initializer=kernel_initializer,
dtype=dtype,
name=layer_name)(
lattice_input)
def _output_calibration_layer(output_calibration_input, model_config, dtype):
"""Creates a monotonic output calibration layer with inputs range [0, 1]."""
# kernel format: bias followed by diffs between consecutive keypoint outputs.
kernel_init_values = np.ediff1d(
model_config.output_initialization,
to_begin=model_config.output_initialization[0])
input_keypoints = np.linspace(0.0, 1.0, num=len(kernel_init_values))
kernel_initializer = tf.compat.v1.constant_initializer(kernel_init_values)
kernel_regularizer = _output_calibration_regularizers(model_config)
return pwl_calibration_layer.PWLCalibration(
input_keypoints=input_keypoints,
output_min=model_config.output_min,
output_max=model_config.output_max,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
monotonicity=1,
dtype=dtype,
name=OUTPUT_CALIB_LAYER_NAME)(
output_calibration_input)
# TODO: add support for serialization and object scoping or annoations.
class CalibratedLatticeEnsemble(tf.keras.Model):
"""Premade model for Tensorflow calibrated lattice ensemble models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLatticeEnsembleConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeEnsembleConfig(...)
calibrated_lattice_ensemble_model = tfl.premade.CalibratedLatticeEnsemble(
model_config=model_config)
calibrated_lattice_ensemble_model.compile(...)
calibrated_lattice_ensemble_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLatticeEnsemble` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLatticeEnsembleConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=_LayerOutputRange.INPUT_TO_LATTICE,
submodels=model_config.lattices,
separate_calibrators=model_config.separate_calibrators,
dtype=dtype)
lattice_outputs = []
for submodel_index, (lattice_feature_names, lattice_input) in enumerate(
zip(model_config.lattices, submodels_inputs)):
lattice_feature_configs = [
model_config.feature_config_by_name(feature_name)
for feature_name in lattice_feature_names
]
lattice_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION if
model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
lattice_outputs.append(
_lattice_layer(
lattice_input=lattice_input,
feature_configs=lattice_feature_configs,
model_config=model_config,
layer_output_range=lattice_layer_output_range,
submodel_index=submodel_index,
is_inside_ensemble=True,
dtype=dtype))
if len(lattice_outputs) > 1:
averaged_lattice_output = tf.keras.layers.Average()(lattice_outputs)
else:
averaged_lattice_output = lattice_outputs[0]
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=averaged_lattice_output,
model_config=model_config,
dtype=dtype)
else:
model_output = averaged_lattice_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLatticeEnsemble, self).__init__(
inputs=inputs, outputs=model_output)
class CalibratedLattice(tf.keras.Model):
"""Premade model for Tensorflow calibrated lattice models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLatticeConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_lattice_model = tfl.premade.CalibratedLattice(
model_config=model_config)
calibrated_lattice_model.compile(...)
calibrated_lattice_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLattice` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLatticeConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=_LayerOutputRange.INPUT_TO_LATTICE,
submodels=[[
feature_config.name
for feature_config in model_config.feature_configs
]],
separate_calibrators=False,
dtype=dtype)
lattice_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION
if model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
lattice_output = _lattice_layer(
lattice_input=submodels_inputs[0],
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=lattice_layer_output_range,
submodel_index=0,
is_inside_ensemble=False,
dtype=dtype)
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=lattice_output,
model_config=model_config,
dtype=dtype)
else:
model_output = lattice_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLattice, self).__init__(inputs=inputs, outputs=model_output)
class CalibratedLinear(tf.keras.Model):
"""Premade model for Tensorflow calibrated linear models.
Creates a `tf.keras.Model` for the model architecture specified by the
`model_config`, which should a `tfl.configs.CalibratedLinearConfig`
Note that the inputs to the model should match the
order in which they are defined in the feature configs.
Example:
```python
model_config = tfl.configs.CalibratedLatticeConfig(...)
calibrated_linear_model = tfl.premade.CalibratedLinear(
model_config=model_config)
calibrated_linear_model.compile(...)
calibrated_linear_model.fit(...)
```
"""
def __init__(self, model_config, dtype=tf.float32):
"""Initializes a `CalibratedLinear` instance.
Args:
model_config: Model configuration object describing model architecutre.
Should be one of the model configs in `tfl.configs`.
dtype: dtype of layers used in the model.
"""
# Check that proper config has been given.
if not isinstance(model_config, configs.CalibratedLinearConfig):
raise ValueError('Invalid config type: {}'.format(type(model_config)))
# Get feature configs and construct model.
input_layer = _input_layer(
feature_configs=model_config.feature_configs, dtype=dtype)
calibration_layer_output_range = (
_LayerOutputRange.INPUT_TO_FINAL_CALIBRATION
if model_config.output_calibration else _LayerOutputRange.MODEL_OUTPUT)
submodels_inputs = _calibration_layers(
calibration_input_layer=input_layer,
feature_configs=model_config.feature_configs,
model_config=model_config,
layer_output_range=calibration_layer_output_range,
submodels=[[
feature_config.name
for feature_config in model_config.feature_configs
]],
separate_calibrators=False,
dtype=dtype)
weighted_average = (
model_config.output_min is not None or
model_config.output_max is not None or model_config.output_calibration)
linear_output = _linear_layer(
linear_input=submodels_inputs[0],
feature_configs=model_config.feature_configs,
model_config=model_config,
weighted_average=weighted_average,
submodel_index=0,
dtype=dtype)
if model_config.output_calibration:
model_output = _output_calibration_layer(
output_calibration_input=linear_output,
model_config=model_config,
dtype=dtype)
else:
model_output = linear_output
# Define inputs and initialize model.
inputs = [
input_layer[feature_config.name]
for feature_config in model_config.feature_configs
]
super(CalibratedLinear, self).__init__(inputs=inputs, outputs=model_output)
| en | 0.699835 | # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. TF Lattice premade models implement typical monotonic model architectures. You can use TFL premade models to easily construct commonly used monotonic model architectures. To construct a TFL premade model, construct a model configuration from `tfl.configs` and pass it to the premade model constructor. Note that the inputs to the model should match the order in which they are defined in the feature configs. ```python model_config = tfl.configs.CalibratedLatticeConfig(...) calibrated_lattice_model = tfl.premade.CalibratedLattice( model_config=model_config) calibrated_lattice_model.compile(...) calibrated_lattice_model.fit(...) ``` Supported models are defined in `tfl.configs`. Each model architecture can be used the same as any other `tf.keras.Model`. # Layer names used for layers in the premade models. # Prefix for passthrough (identity) nodes for shared calibration. # These nodes pass shared calibrated values to submodels in an ensemble. # Prefix for defining feature calibrator regularizers. # Prefix for defining output calibrator regularizers. Returns pwl layer regularizers defined in the model and feature configs. Returns output calibration regularizers defined in the model config. Returns lattice regularizers defined in the model and feature configs. # dict from regularizer name to pair of per feature l1 and l2 amounts. Enum to indicate the output range based on the input of the next layers. Returns min/max/init_min/init_max for a given output range. Creates a calibration layer. Creates a calibration layer for `submodels` as list of list of features. # Create a list of (feature_name, calibration_output_idx) pairs for each # submodel. When using shared calibration, all submodels will have # calibration_output_idx = 0. # Create passthrough nodes for each submodel input so that we can recover # the model structure for plotting and analysis. # {CALIB_PASSTHROUGH_NAME}_{feature_name}_ # {calibration_output_idx}_{submodel_idx}_{submodel_input_idx} Returns list of monotonicities defined in the given feature_configs. Returns list of dominance constraints in the given feature_configs. Creates a linear layer initialized to be an average. # Linear coefficients should be possitive and sum up to one. Creates a lattice layer. # Construct trust constraints within this lattice. Creates a monotonic output calibration layer with inputs range [0, 1]. # kernel format: bias followed by diffs between consecutive keypoint outputs. # TODO: add support for serialization and object scoping or annoations. Premade model for Tensorflow calibrated lattice ensemble models. Creates a `tf.keras.Model` for the model architecture specified by the `model_config`, which should a `tfl.configs.CalibratedLatticeEnsembleConfig` Note that the inputs to the model should match the order in which they are defined in the feature configs. Example: ```python model_config = tfl.configs.CalibratedLatticeEnsembleConfig(...) calibrated_lattice_ensemble_model = tfl.premade.CalibratedLatticeEnsemble( model_config=model_config) calibrated_lattice_ensemble_model.compile(...) calibrated_lattice_ensemble_model.fit(...) ``` Initializes a `CalibratedLatticeEnsemble` instance. Args: model_config: Model configuration object describing model architecutre. Should be one of the model configs in `tfl.configs`. dtype: dtype of layers used in the model. # Check that proper config has been given. # Get feature configs and construct model. # Define inputs and initialize model. Premade model for Tensorflow calibrated lattice models. Creates a `tf.keras.Model` for the model architecture specified by the `model_config`, which should a `tfl.configs.CalibratedLatticeConfig` Note that the inputs to the model should match the order in which they are defined in the feature configs. Example: ```python model_config = tfl.configs.CalibratedLatticeConfig(...) calibrated_lattice_model = tfl.premade.CalibratedLattice( model_config=model_config) calibrated_lattice_model.compile(...) calibrated_lattice_model.fit(...) ``` Initializes a `CalibratedLattice` instance. Args: model_config: Model configuration object describing model architecutre. Should be one of the model configs in `tfl.configs`. dtype: dtype of layers used in the model. # Check that proper config has been given. # Get feature configs and construct model. # Define inputs and initialize model. Premade model for Tensorflow calibrated linear models. Creates a `tf.keras.Model` for the model architecture specified by the `model_config`, which should a `tfl.configs.CalibratedLinearConfig` Note that the inputs to the model should match the order in which they are defined in the feature configs. Example: ```python model_config = tfl.configs.CalibratedLatticeConfig(...) calibrated_linear_model = tfl.premade.CalibratedLinear( model_config=model_config) calibrated_linear_model.compile(...) calibrated_linear_model.fit(...) ``` Initializes a `CalibratedLinear` instance. Args: model_config: Model configuration object describing model architecutre. Should be one of the model configs in `tfl.configs`. dtype: dtype of layers used in the model. # Check that proper config has been given. # Get feature configs and construct model. # Define inputs and initialize model. | 2.587067 | 3 |
images/serializers.py | mistakes-consortium/igng | 1 | 6614303 | <reponame>mistakes-consortium/igng
import datetime
from rest_framework.reverse import reverse
from taggit_serializer.serializers import TagListSerializerField, TaggitSerializer
from images.fields import Base64ImageField
from images.models import Gallery, Image, EXIFEntry
from rest_framework import serializers
import pytz
class GallerySerializer(serializers.ModelSerializer):
uuid = serializers.CharField(read_only=True)
class Meta:
model = Gallery
fields = ('uuid', 'user', 'updated', 'updated_u', 'rel_start', 'rel_end', 'title', 'private')
updated_u = serializers.SerializerMethodField()
def get_updated_u(self, obj):
return (obj.updated - datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)).total_seconds()
class EXIFSerializer(serializers.ModelSerializer):
key = serializers.CharField(source="key.key")
value = serializers.CharField(source="value.value")
class Meta:
model = EXIFEntry
fields = ('key', 'value')
class ImageSerializer(TaggitSerializer, serializers.ModelSerializer):
full_url = serializers.SerializerMethodField()
thumb_url = serializers.SerializerMethodField()
tiny_thumb_url = serializers.SerializerMethodField()
gallery = serializers.CharField(source="gallery.uuid")
uploaded_u = serializers.SerializerMethodField()
exif_data = EXIFSerializer(read_only=True, many=True)
tags = TagListSerializerField(required=False)
uuid = serializers.CharField(read_only=True)
class Meta:
model = Image
fields = ('uuid', 'user', 'gallery', 'uploaded', 'uploaded_u', 'title', 'uuid',
'full_url', 'thumb_url', 'tiny_thumb_url', 'exif_data', 'tags')
def get_full_url(self, obj):
return obj.full_fixed.url
def get_thumb_url(self, obj):
return obj.thumb.url
def get_tiny_thumb_url(self, obj):
return obj.tiny_thumb.url
def get_uploaded_u(self, obj):
return (obj.uploaded - datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)).total_seconds()
class ImageUploadSerializer(TaggitSerializer,serializers.ModelSerializer):
uuid = serializers.CharField(read_only=True)
original = serializers.FileField()
tags = TagListSerializerField(required=False)
class Meta:
model = Image
fields = ('user', 'gallery', 'title', 'original', 'tags', 'uuid')
class PasteImageUploadSerializer(serializers.ModelSerializer):
original = Base64ImageField(max_length=None, use_url=True,)
gallery = serializers.SlugRelatedField(required=False, queryset=Gallery.objects.all(),slug_field="uuid")
def get_fields(self, *args, **kwargs):
fields = super(PasteImageUploadSerializer, self).get_fields(*args, **kwargs)
if not 'request' in self.context or self.context['request'] == None: # Documentation Needs it
fields['gallery'].queryset = Gallery.objects.none()
else:
fields['gallery'].queryset = Gallery.objects.filter(user=(self.context['request'].user))
return fields
class Meta:
model = Image
fields = ('original', 'gallery')
def validate_gallery(self, value):
u = self.context['request'].user
if u.galleries.filter(uuid=value.uuid).exists():
# print "YAY"
return value
elif value == None:
# print "NULL"
return value
# print "BOO "
raise serializers.ValidationError("Non-existent Gallery")
class PasteReturnSerializer(serializers.ModelSerializer):
tiny_thumb_url = serializers.SerializerMethodField()
image_page = serializers.SerializerMethodField()
class Meta:
model = Image
fields = ('tiny_thumb_url', 'image_page')
# fields = ("original", )
def get_tiny_thumb_url(self, obj):
# print dir(obj)
return obj.tiny_thumb.url
def get_image_page(self, obj):
return reverse("upload_success", args=[obj.uuid]) | import datetime
from rest_framework.reverse import reverse
from taggit_serializer.serializers import TagListSerializerField, TaggitSerializer
from images.fields import Base64ImageField
from images.models import Gallery, Image, EXIFEntry
from rest_framework import serializers
import pytz
class GallerySerializer(serializers.ModelSerializer):
uuid = serializers.CharField(read_only=True)
class Meta:
model = Gallery
fields = ('uuid', 'user', 'updated', 'updated_u', 'rel_start', 'rel_end', 'title', 'private')
updated_u = serializers.SerializerMethodField()
def get_updated_u(self, obj):
return (obj.updated - datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)).total_seconds()
class EXIFSerializer(serializers.ModelSerializer):
key = serializers.CharField(source="key.key")
value = serializers.CharField(source="value.value")
class Meta:
model = EXIFEntry
fields = ('key', 'value')
class ImageSerializer(TaggitSerializer, serializers.ModelSerializer):
full_url = serializers.SerializerMethodField()
thumb_url = serializers.SerializerMethodField()
tiny_thumb_url = serializers.SerializerMethodField()
gallery = serializers.CharField(source="gallery.uuid")
uploaded_u = serializers.SerializerMethodField()
exif_data = EXIFSerializer(read_only=True, many=True)
tags = TagListSerializerField(required=False)
uuid = serializers.CharField(read_only=True)
class Meta:
model = Image
fields = ('uuid', 'user', 'gallery', 'uploaded', 'uploaded_u', 'title', 'uuid',
'full_url', 'thumb_url', 'tiny_thumb_url', 'exif_data', 'tags')
def get_full_url(self, obj):
return obj.full_fixed.url
def get_thumb_url(self, obj):
return obj.thumb.url
def get_tiny_thumb_url(self, obj):
return obj.tiny_thumb.url
def get_uploaded_u(self, obj):
return (obj.uploaded - datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)).total_seconds()
class ImageUploadSerializer(TaggitSerializer,serializers.ModelSerializer):
uuid = serializers.CharField(read_only=True)
original = serializers.FileField()
tags = TagListSerializerField(required=False)
class Meta:
model = Image
fields = ('user', 'gallery', 'title', 'original', 'tags', 'uuid')
class PasteImageUploadSerializer(serializers.ModelSerializer):
original = Base64ImageField(max_length=None, use_url=True,)
gallery = serializers.SlugRelatedField(required=False, queryset=Gallery.objects.all(),slug_field="uuid")
def get_fields(self, *args, **kwargs):
fields = super(PasteImageUploadSerializer, self).get_fields(*args, **kwargs)
if not 'request' in self.context or self.context['request'] == None: # Documentation Needs it
fields['gallery'].queryset = Gallery.objects.none()
else:
fields['gallery'].queryset = Gallery.objects.filter(user=(self.context['request'].user))
return fields
class Meta:
model = Image
fields = ('original', 'gallery')
def validate_gallery(self, value):
u = self.context['request'].user
if u.galleries.filter(uuid=value.uuid).exists():
# print "YAY"
return value
elif value == None:
# print "NULL"
return value
# print "BOO "
raise serializers.ValidationError("Non-existent Gallery")
class PasteReturnSerializer(serializers.ModelSerializer):
tiny_thumb_url = serializers.SerializerMethodField()
image_page = serializers.SerializerMethodField()
class Meta:
model = Image
fields = ('tiny_thumb_url', 'image_page')
# fields = ("original", )
def get_tiny_thumb_url(self, obj):
# print dir(obj)
return obj.tiny_thumb.url
def get_image_page(self, obj):
return reverse("upload_success", args=[obj.uuid]) | en | 0.615393 | # Documentation Needs it # print "YAY" # print "NULL" # print "BOO " # fields = ("original", ) # print dir(obj) | 2.23523 | 2 |
catalog/urls.py | choia/django-library-tutorial | 0 | 6614304 | <reponame>choia/django-library-tutorial
from django.conf.urls import include, url
from . import views
# Application URL Mapper
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'^authors/$', views.AuthorListView.as_view(), name='authors'),
url(r'^authors/(?P<pk>\d+)$', views.AuthorDetailView.as_view(), name='author-detail'),
url(r'^books/$', views.BookListView.as_view(), name='books'),
url(r'^book/(?P<pk>\d+)$', views.BookDetailView.as_view(), name='book-detail'),
]
# User Loaned Book URL Conf
urlpatterns += [
url(r'^mybooks/$', views.LoanedBookUserListView.as_view(), name='borrowed-book'),
url(r'^borrowed/$', views.AllBorrowedBookListView.as_view(), name='all-borrowed-book'),
]
# Renew Book URL Conf
urlpatterns += [
url(r'^book/(?P<pk>[-\w]+)/renew/$', views.renew_book, name='renew-book'),
]
# Author Model URL Conf
urlpatterns += [
url(r'^author/create/$', views.AuthorCreate.as_view(), name='author-create'),
url(r'^author/(?P<pk>\d+)/update/$', views.AuthorUpdate.as_view(), name='author-update'),
url(r'^author/(?P<pk>\d+)/delete/$', views.AuthorDelete.as_view(), name='author-delete'),
] | from django.conf.urls import include, url
from . import views
# Application URL Mapper
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'^authors/$', views.AuthorListView.as_view(), name='authors'),
url(r'^authors/(?P<pk>\d+)$', views.AuthorDetailView.as_view(), name='author-detail'),
url(r'^books/$', views.BookListView.as_view(), name='books'),
url(r'^book/(?P<pk>\d+)$', views.BookDetailView.as_view(), name='book-detail'),
]
# User Loaned Book URL Conf
urlpatterns += [
url(r'^mybooks/$', views.LoanedBookUserListView.as_view(), name='borrowed-book'),
url(r'^borrowed/$', views.AllBorrowedBookListView.as_view(), name='all-borrowed-book'),
]
# Renew Book URL Conf
urlpatterns += [
url(r'^book/(?P<pk>[-\w]+)/renew/$', views.renew_book, name='renew-book'),
]
# Author Model URL Conf
urlpatterns += [
url(r'^author/create/$', views.AuthorCreate.as_view(), name='author-create'),
url(r'^author/(?P<pk>\d+)/update/$', views.AuthorUpdate.as_view(), name='author-update'),
url(r'^author/(?P<pk>\d+)/delete/$', views.AuthorDelete.as_view(), name='author-delete'),
] | en | 0.410163 | # Application URL Mapper # User Loaned Book URL Conf # Renew Book URL Conf # Author Model URL Conf | 2.080863 | 2 |
improver_tests/calibration/rainforests_calibration/test_ApplyRainForestsCalibration.py | mspelman07/improver | 0 | 6614305 | <filename>improver_tests/calibration/rainforests_calibration/test_ApplyRainForestsCalibration.py
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown copyright. The Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Unit tests for the ApplyRainForestsCalibration class."""
import sys
import numpy as np
import pytest
try:
import treelite_runtime
except ModuleNotFoundError:
TREELITE_ENABLED = False
else:
TREELITE_ENABLED = True
from improver.calibration.rainforest_calibration import ApplyRainForestsCalibration
lightgbm = pytest.importorskip("lightgbm")
class MockBooster:
def __init__(self, model_file, **kwargs):
self.model_class = "lightgbm-Booster"
self.model_file = model_file
def reset_parameter(self, params):
self.threads = params.get("num_threads")
return self
class MockPredictor:
def __init__(self, libpath, nthread, **kwargs):
self.model_class = "treelite-Predictor"
self.threads = nthread
self.model_file = libpath
@pytest.mark.parametrize("lightgbm_keys", (True, False))
@pytest.mark.parametrize("ordered_inputs", (True, False))
@pytest.mark.parametrize("treelite_model", (TREELITE_ENABLED, False))
@pytest.mark.parametrize("treelite_file", (True, False))
def test__init__(
lightgbm_keys,
ordered_inputs,
treelite_model,
treelite_file,
monkeypatch,
model_config,
error_thresholds,
):
"""Test treelite models are loaded if model_config correctly defines them. If all thresholds
contain treelite model AND the treelite module is available, treelite Predictor is returned,
otherwise return lightgbm Boosters. Checks outputs are ordered when inputs can be unordered.
If neither treelite nor lightgbm configs are complete, a ValueError is expected."""
if treelite_model:
monkeypatch.setattr(treelite_runtime, "Predictor", MockPredictor)
else:
monkeypatch.setitem(sys.modules, "treelite_runtime", None)
monkeypatch.setattr(lightgbm, "Booster", MockBooster)
if not treelite_file:
# Model type should default to lightgbm if there are any treelite models
# missing across any thresholds
model_config["0.0000"].pop("treelite_model", None)
if not ordered_inputs:
tmp_value = model_config.pop("0.0000", None)
model_config["0.0000"] = tmp_value
if not lightgbm_keys:
for t, d in model_config.items():
d.pop("lightgbm_model")
if treelite_model and treelite_file:
expected_class = "treelite-Predictor"
elif lightgbm_keys:
expected_class = "lightgbm-Booster"
else:
with pytest.raises(ValueError, match="Path to lightgbm model missing"):
ApplyRainForestsCalibration(model_config, threads=8)
return
result = ApplyRainForestsCalibration(model_config, threads=8)
for model in result.tree_models:
assert model.model_class == expected_class
assert model.threads == 8
assert result.treelite_enabled is treelite_model
assert np.all(result.error_thresholds == error_thresholds)
for threshold, model in zip(result.error_thresholds, result.tree_models):
assert f"{threshold:06.4f}" in model.model_file
| <filename>improver_tests/calibration/rainforests_calibration/test_ApplyRainForestsCalibration.py
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown copyright. The Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Unit tests for the ApplyRainForestsCalibration class."""
import sys
import numpy as np
import pytest
try:
import treelite_runtime
except ModuleNotFoundError:
TREELITE_ENABLED = False
else:
TREELITE_ENABLED = True
from improver.calibration.rainforest_calibration import ApplyRainForestsCalibration
lightgbm = pytest.importorskip("lightgbm")
class MockBooster:
def __init__(self, model_file, **kwargs):
self.model_class = "lightgbm-Booster"
self.model_file = model_file
def reset_parameter(self, params):
self.threads = params.get("num_threads")
return self
class MockPredictor:
def __init__(self, libpath, nthread, **kwargs):
self.model_class = "treelite-Predictor"
self.threads = nthread
self.model_file = libpath
@pytest.mark.parametrize("lightgbm_keys", (True, False))
@pytest.mark.parametrize("ordered_inputs", (True, False))
@pytest.mark.parametrize("treelite_model", (TREELITE_ENABLED, False))
@pytest.mark.parametrize("treelite_file", (True, False))
def test__init__(
lightgbm_keys,
ordered_inputs,
treelite_model,
treelite_file,
monkeypatch,
model_config,
error_thresholds,
):
"""Test treelite models are loaded if model_config correctly defines them. If all thresholds
contain treelite model AND the treelite module is available, treelite Predictor is returned,
otherwise return lightgbm Boosters. Checks outputs are ordered when inputs can be unordered.
If neither treelite nor lightgbm configs are complete, a ValueError is expected."""
if treelite_model:
monkeypatch.setattr(treelite_runtime, "Predictor", MockPredictor)
else:
monkeypatch.setitem(sys.modules, "treelite_runtime", None)
monkeypatch.setattr(lightgbm, "Booster", MockBooster)
if not treelite_file:
# Model type should default to lightgbm if there are any treelite models
# missing across any thresholds
model_config["0.0000"].pop("treelite_model", None)
if not ordered_inputs:
tmp_value = model_config.pop("0.0000", None)
model_config["0.0000"] = tmp_value
if not lightgbm_keys:
for t, d in model_config.items():
d.pop("lightgbm_model")
if treelite_model and treelite_file:
expected_class = "treelite-Predictor"
elif lightgbm_keys:
expected_class = "lightgbm-Booster"
else:
with pytest.raises(ValueError, match="Path to lightgbm model missing"):
ApplyRainForestsCalibration(model_config, threads=8)
return
result = ApplyRainForestsCalibration(model_config, threads=8)
for model in result.tree_models:
assert model.model_class == expected_class
assert model.threads == 8
assert result.treelite_enabled is treelite_model
assert np.all(result.error_thresholds == error_thresholds)
for threshold, model in zip(result.error_thresholds, result.tree_models):
assert f"{threshold:06.4f}" in model.model_file
| en | 0.709322 | # -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown copyright. The Met Office. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. Unit tests for the ApplyRainForestsCalibration class. Test treelite models are loaded if model_config correctly defines them. If all thresholds contain treelite model AND the treelite module is available, treelite Predictor is returned, otherwise return lightgbm Boosters. Checks outputs are ordered when inputs can be unordered. If neither treelite nor lightgbm configs are complete, a ValueError is expected. # Model type should default to lightgbm if there are any treelite models # missing across any thresholds | 1.408288 | 1 |
botx/clients/types/message_payload.py | ExpressApp/pybotx | 13 | 6614306 | <reponame>ExpressApp/pybotx<gh_stars>10-100
"""Shape that is used for messages from bot."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from botx.models.constants import MAXIMUM_TEXT_LENGTH
from botx.models.entities import Mention
from botx.models.enums import Statuses
from botx.models.messages.sending.options import ResultPayloadOptions
from botx.models.typing import BubbleMarkup, KeyboardMarkup
try:
from typing import Literal # noqa: WPS433
except ImportError:
from typing_extensions import Literal # type: ignore # noqa: WPS433, WPS440, F401
class ResultPayload(BaseModel):
"""Data that is sent when bot answers on command or send notification."""
#: status of operation.
status: Literal[Statuses.ok] = Statuses.ok
#: body for new message from bot.
body: str = Field("", max_length=MAXIMUM_TEXT_LENGTH)
#: message metadata.
metadata: Dict[str, Any] = {}
#: options for `notification` and `command_result` API entities.
opts: ResultPayloadOptions = ResultPayloadOptions()
#: keyboard that will be used for new message.
keyboard: KeyboardMarkup = []
#: bubble elements that will be showed under new message.
bubble: BubbleMarkup = []
#: mentions that BotX API will append before new message text.
mentions: List[Mention] = []
class UpdatePayload(BaseModel):
"""Data that is sent when bot updates message."""
#: status of operation.
status: Literal[Statuses.ok] = Statuses.ok
#: new body in message.
body: Optional[str] = Field(None, max_length=MAXIMUM_TEXT_LENGTH)
#: message metadata.
metadata: Optional[Dict[str, Any]] = None
#: new keyboard that will be used for new message.
keyboard: Optional[KeyboardMarkup] = None
#: new bubble elements that will be showed under new message.
bubble: Optional[BubbleMarkup] = None
#: new mentions that BotX API will append before new message text.
mentions: Optional[List[Mention]] = None
class InternalBotNotificationPayload(BaseModel):
"""Data that is sent in internal bot notification."""
#: message data
message: str
#: extra information about notification sender
sender: Optional[str]
| """Shape that is used for messages from bot."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from botx.models.constants import MAXIMUM_TEXT_LENGTH
from botx.models.entities import Mention
from botx.models.enums import Statuses
from botx.models.messages.sending.options import ResultPayloadOptions
from botx.models.typing import BubbleMarkup, KeyboardMarkup
try:
from typing import Literal # noqa: WPS433
except ImportError:
from typing_extensions import Literal # type: ignore # noqa: WPS433, WPS440, F401
class ResultPayload(BaseModel):
"""Data that is sent when bot answers on command or send notification."""
#: status of operation.
status: Literal[Statuses.ok] = Statuses.ok
#: body for new message from bot.
body: str = Field("", max_length=MAXIMUM_TEXT_LENGTH)
#: message metadata.
metadata: Dict[str, Any] = {}
#: options for `notification` and `command_result` API entities.
opts: ResultPayloadOptions = ResultPayloadOptions()
#: keyboard that will be used for new message.
keyboard: KeyboardMarkup = []
#: bubble elements that will be showed under new message.
bubble: BubbleMarkup = []
#: mentions that BotX API will append before new message text.
mentions: List[Mention] = []
class UpdatePayload(BaseModel):
"""Data that is sent when bot updates message."""
#: status of operation.
status: Literal[Statuses.ok] = Statuses.ok
#: new body in message.
body: Optional[str] = Field(None, max_length=MAXIMUM_TEXT_LENGTH)
#: message metadata.
metadata: Optional[Dict[str, Any]] = None
#: new keyboard that will be used for new message.
keyboard: Optional[KeyboardMarkup] = None
#: new bubble elements that will be showed under new message.
bubble: Optional[BubbleMarkup] = None
#: new mentions that BotX API will append before new message text.
mentions: Optional[List[Mention]] = None
class InternalBotNotificationPayload(BaseModel):
"""Data that is sent in internal bot notification."""
#: message data
message: str
#: extra information about notification sender
sender: Optional[str] | en | 0.732331 | Shape that is used for messages from bot. # noqa: WPS433 # type: ignore # noqa: WPS433, WPS440, F401 Data that is sent when bot answers on command or send notification. #: status of operation. #: body for new message from bot. #: message metadata. #: options for `notification` and `command_result` API entities. #: keyboard that will be used for new message. #: bubble elements that will be showed under new message. #: mentions that BotX API will append before new message text. Data that is sent when bot updates message. #: status of operation. #: new body in message. #: message metadata. #: new keyboard that will be used for new message. #: new bubble elements that will be showed under new message. #: new mentions that BotX API will append before new message text. Data that is sent in internal bot notification. #: message data #: extra information about notification sender | 2.777603 | 3 |
array.py | EdgarMoncloa/Algorithm_Genetic_Gecademica | 0 | 6614307 | <reponame>EdgarMoncloa/Algorithm_Genetic_Gecademica
import random
import time
num_parameters = 4
num_populations = 5
num_classes = 100
num_subject_matters = 80
num_classrooms = 28
num_horaries = 5
num_teachers = 60
min_value = 1
pressure = 3 # individuos que se seleccionan para reporduccion
mutation_probability = .50
def create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries):
asigned_class = []
asigned_class.append(random.randint(min_value, num_subject_matters))
asigned_class.append(random.randint(min_value, num_classrooms))
asigned_class.append(random.randint(min_value, num_horaries))
asigned_class.append(random.randint(min_value, num_teachers))
return asigned_class
def create_individual(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries):
individual = []
for idx in range(0, num_classes):
individual.append(create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
))
return individual
def create_population():
return [create_individual(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
) for i in range(0, num_populations)]
def fitness_calculate(individual):
fitness = 0
for idx_element, element in enumerate(individual):
individual_compare = individual.copy()
individual_compare.pop(idx_element)
for compare_element in individual_compare:
for idx in range(0, num_parameters):
if element[idx] == compare_element[idx]:
fitness -= 1
if fitness == 0 :
print("-----------------------------------------------------FITNES 0")
print(individual)
quit()
return fitness
def selection_and_reproduction(population):
population_punctuated = [
(fitness_calculate(element), element)for element in population]
population_punctuated = [
element[1] for element in sorted(population_punctuated)]
population = population_punctuated.copy()
population_punctuated = population_punctuated[(len(population)-pressure):]
# Mix
for idx in range(len(population)-pressure):
# Select one point to cut the individual
cut_point = random.randint(1, num_classes)
# Select two parents
parents = random.sample(population_punctuated, 2)
# Mix genetic material
population[idx][:cut_point] = parents[0][:cut_point]
population[idx][cut_point:] = parents[1][cut_point:]
return population
def mutation(population):
for idx in range(len(population)-pressure):
if random.random() <= mutation_probability:
cut_point = random.randint(0, num_classes-1)
new_class = create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
)
while(new_class == population[idx][cut_point]):
new_class = create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
)
population[idx][cut_point]=new_class
return population
def print_data(population):
print("VALORES")
for element in population:
print(element)
print("FITNESS")
for idx,element in enumerate(population):
print("{} : {}".format(idx,fitness_calculate(element)))
return
start_time = time.time()
# Create First population
population = create_population()
# Print
# print('-----> PRIMERA POBLACION <-----')
# print_data(population)
# Algorithm
for i in range(100000):
# Selection and rerpoduction
if i % 1000 == 0:
print('----------{}-----------------'.format(i))
population = selection_and_reproduction(population)
population = mutation(population)
# Last population
print('-----> ULTIMA POBLACION <-----')
# print_data(population)
# print('(---------{}--------------)'.format(time.time-start_time)) | import random
import time
num_parameters = 4
num_populations = 5
num_classes = 100
num_subject_matters = 80
num_classrooms = 28
num_horaries = 5
num_teachers = 60
min_value = 1
pressure = 3 # individuos que se seleccionan para reporduccion
mutation_probability = .50
def create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries):
asigned_class = []
asigned_class.append(random.randint(min_value, num_subject_matters))
asigned_class.append(random.randint(min_value, num_classrooms))
asigned_class.append(random.randint(min_value, num_horaries))
asigned_class.append(random.randint(min_value, num_teachers))
return asigned_class
def create_individual(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries):
individual = []
for idx in range(0, num_classes):
individual.append(create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
))
return individual
def create_population():
return [create_individual(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
) for i in range(0, num_populations)]
def fitness_calculate(individual):
fitness = 0
for idx_element, element in enumerate(individual):
individual_compare = individual.copy()
individual_compare.pop(idx_element)
for compare_element in individual_compare:
for idx in range(0, num_parameters):
if element[idx] == compare_element[idx]:
fitness -= 1
if fitness == 0 :
print("-----------------------------------------------------FITNES 0")
print(individual)
quit()
return fitness
def selection_and_reproduction(population):
population_punctuated = [
(fitness_calculate(element), element)for element in population]
population_punctuated = [
element[1] for element in sorted(population_punctuated)]
population = population_punctuated.copy()
population_punctuated = population_punctuated[(len(population)-pressure):]
# Mix
for idx in range(len(population)-pressure):
# Select one point to cut the individual
cut_point = random.randint(1, num_classes)
# Select two parents
parents = random.sample(population_punctuated, 2)
# Mix genetic material
population[idx][:cut_point] = parents[0][:cut_point]
population[idx][cut_point:] = parents[1][cut_point:]
return population
def mutation(population):
for idx in range(len(population)-pressure):
if random.random() <= mutation_probability:
cut_point = random.randint(0, num_classes-1)
new_class = create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
)
while(new_class == population[idx][cut_point]):
new_class = create_class(
num_teachers,
num_classrooms,
num_subject_matters,
num_horaries
)
population[idx][cut_point]=new_class
return population
def print_data(population):
print("VALORES")
for element in population:
print(element)
print("FITNESS")
for idx,element in enumerate(population):
print("{} : {}".format(idx,fitness_calculate(element)))
return
start_time = time.time()
# Create First population
population = create_population()
# Print
# print('-----> PRIMERA POBLACION <-----')
# print_data(population)
# Algorithm
for i in range(100000):
# Selection and rerpoduction
if i % 1000 == 0:
print('----------{}-----------------'.format(i))
population = selection_and_reproduction(population)
population = mutation(population)
# Last population
print('-----> ULTIMA POBLACION <-----')
# print_data(population)
# print('(---------{}--------------)'.format(time.time-start_time)) | en | 0.382077 | # individuos que se seleccionan para reporduccion # Mix # Select one point to cut the individual # Select two parents # Mix genetic material # Create First population # Print # print('-----> PRIMERA POBLACION <-----') # print_data(population) # Algorithm # Selection and rerpoduction # Last population # print_data(population) # print('(---------{}--------------)'.format(time.time-start_time)) | 3.20634 | 3 |
models/generator.py | martinoywa/creative-gan | 2 | 6614308 | <filename>models/generator.py
import torch
import torch.nn as nn
from pathlib import Path
# number of color channels in the input images. For color images this is 3
nc = 3
# length of latent vector z
nz = 100
# Size of feature maps in generator
ngf = 64
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
# weight initialization
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# generator mode
G = Generator()
# apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
G.apply(weights_init)
# loading the trained model
checkpoint = Path('models/checkpoints/cars-0.0.6-Monday.pt')
G.load_state_dict(torch.load(checkpoint, map_location='cpu'))
def generate(latent_vector):
# fake image
with torch.no_grad():
fake = G(latent_vector)#.detach().cpu()
return fake
| <filename>models/generator.py
import torch
import torch.nn as nn
from pathlib import Path
# number of color channels in the input images. For color images this is 3
nc = 3
# length of latent vector z
nz = 100
# Size of feature maps in generator
ngf = 64
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
# weight initialization
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# generator mode
G = Generator()
# apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
G.apply(weights_init)
# loading the trained model
checkpoint = Path('models/checkpoints/cars-0.0.6-Monday.pt')
G.load_state_dict(torch.load(checkpoint, map_location='cpu'))
def generate(latent_vector):
# fake image
with torch.no_grad():
fake = G(latent_vector)#.detach().cpu()
return fake
| en | 0.674383 | # number of color channels in the input images. For color images this is 3 # length of latent vector z # Size of feature maps in generator # input is Z, going into a convolution # state size. (ngf*8) x 4 x 4 # state size. (ngf*4) x 8 x 8 # state size. (ngf*2) x 16 x 16 # state size. (ngf) x 32 x 32 # state size. (nc) x 64 x 64 # weight initialization # generator mode # apply the weights_init function to randomly initialize all weights # to mean=0, stdev=0.2. # loading the trained model # fake image #.detach().cpu() | 2.56986 | 3 |
cloudops_setup.py | wfclark/emrg | 0 | 6614309 | import urllib2
import datetime
import time
import psycopg2
from subprocess import call, Popen
import os
os.system('sudo apt-get update')
os.system('sudo apt-get install apache2')
os.system('sudo apt-get build-dep build-essential')
os.system('sudo apt-get install lamp-server')
os.system('sudo wget -qO- https://apt.boundlessgeo.com/gpg.key | apt-key add -')
os.system('sudo echo "deb https://apt.boundlessgeo.com/suite/latest/ubuntu/ trusty main" > /etc/apt/sources.list.d/opengeo.list')
os.system('sudo apt-get update')
os.system('apt-cache search opengeo')
os.system('sudo apt-get install opengeo')
os.system("sudo sh -c "echo 'ProxyPassReverse /geoexplorer http://localhost:8080/geoexplorer' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /geoeditor http://localhost:8080/geoeditor' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /geoeditor http://localhost:8080/geoeditor' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /geowebcache http://localhost:8080/geowebcache' >> /etc/apache2/sites-available/000-default.conf"")
os.sytem("sudo sh -c "echo 'ProxyPassReverse /geowebcache http://localhost:8080/geowebcache' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /dashboard http://localhost:8080/dashboard' >> /etc/apache2/sites-available/000-default.conf""
os.system("sudo sh -c "echo 'ProxyPassReverse /dashboard http://localhost:8080/dashboard' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /recipes http://localhost:8080/recipes' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /recipes http://localhost:8080/recipes' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /opengeo-docs http://localhost:8080/opengeo-docs' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /opengeo-docs http://localhost:8080/opengeo-docs' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo ' ' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo '</VirtualHost>' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo chmod -R 755 /var/www")
os.system("sudo service apache2 restart")
| import urllib2
import datetime
import time
import psycopg2
from subprocess import call, Popen
import os
os.system('sudo apt-get update')
os.system('sudo apt-get install apache2')
os.system('sudo apt-get build-dep build-essential')
os.system('sudo apt-get install lamp-server')
os.system('sudo wget -qO- https://apt.boundlessgeo.com/gpg.key | apt-key add -')
os.system('sudo echo "deb https://apt.boundlessgeo.com/suite/latest/ubuntu/ trusty main" > /etc/apt/sources.list.d/opengeo.list')
os.system('sudo apt-get update')
os.system('apt-cache search opengeo')
os.system('sudo apt-get install opengeo')
os.system("sudo sh -c "echo 'ProxyPassReverse /geoexplorer http://localhost:8080/geoexplorer' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /geoeditor http://localhost:8080/geoeditor' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /geoeditor http://localhost:8080/geoeditor' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /geowebcache http://localhost:8080/geowebcache' >> /etc/apache2/sites-available/000-default.conf"")
os.sytem("sudo sh -c "echo 'ProxyPassReverse /geowebcache http://localhost:8080/geowebcache' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /dashboard http://localhost:8080/dashboard' >> /etc/apache2/sites-available/000-default.conf""
os.system("sudo sh -c "echo 'ProxyPassReverse /dashboard http://localhost:8080/dashboard' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /recipes http://localhost:8080/recipes' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /recipes http://localhost:8080/recipes' >>/etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPass /opengeo-docs http://localhost:8080/opengeo-docs' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo 'ProxyPassReverse /opengeo-docs http://localhost:8080/opengeo-docs' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo ' ' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo sh -c "echo '</VirtualHost>' >> /etc/apache2/sites-available/000-default.conf"")
os.system("sudo chmod -R 755 /var/www")
os.system("sudo service apache2 restart")
| none | 1 | 2.557209 | 3 | |
mmflow/datasets/samplers/distributed_sampler.py | open-mmlab/mmflow | 481 | 6614310 | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Iterator, Optional, Sequence
import torch
import torch.distributed as dist
from torch.utils.data import Dataset
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
from mmflow.core.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):
"""DistributedSampler inheriting from
`torch.utils.data.DistributedSampler`.
This distributed sampler is compatible Pytorch==1.5, as there is no
`seed` argument in Pytorch==1.5.
Args:
datasets (Dataset): the dataset will be loaded.
num_replicas (int, optional): Number of processes participating in
distributed training. By default, world_size is retrieved from the
current distributed group.
rank (int, optional): Rank of the current process within num_replicas.
By default, rank is retrieved from the current distributed group.
shuffle (bool): If True (default), sampler will shuffle the indices.
seed (int): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
"""
def __init__(self,
dataset: Dataset,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
seed=0) -> None:
super().__init__(
dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
# In distributed sampling, different ranks should sample
# non-overlapped data in the dataset. Therefore, this function
# is used to make sure that each rank shuffles the data indices
# in the same order based on the same seed. Then different
# ranks could use different indices to select non-overlapped
# data from the same data list.
self.seed = sync_random_seed(seed)
def __iter__(self) -> Iterator:
"""
Yields:
Iterator: iterator of indices for rank.
"""
# deterministically shuffle based on epoch
if self.shuffle:
g = torch.Generator()
# When :attr:`shuffle=True`, this ensures all replicas
# use a different random ordering for each epoch.
# Otherwise, the next iteration of this sampler will
# yield the same ordering.
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
class MixedBatchDistributedSampler(Sampler):
"""Distributed Sampler for mixed data batch.
Args:
datasets (list): List of datasets will be loaded.
sample_ratio (list): List of the ratio of each dataset in a batch, e.g.
datasets=[DatasetA, DatasetB], sample_ratio=[0.25, 0.75],
sample_per_gpu=1, gpus=8, it means 2 gpus load DatasetA, and 6 gpus
load DatasetB. The length of datasets must be equal to length of
sample_ratio.
num_replicas (int, optional): Number of processes participating in
distributed training. By default, world_size is retrieved from the
current distributed group.
rank (int, optional): Rank of the current process within num_replicas.
By default, rank is retrieved from the current distributed group.
shuffle (bool): If True (default), sampler will shuffle the indices.
seed (int): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
"""
def __init__(self,
datasets: Sequence[Dataset],
sample_ratio: Sequence[float],
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
seed: int = 0) -> None:
# base class `Sampler` do nothing in `__init__` function
# super().__init__()
assert len(datasets) == len(sample_ratio)
assert sum(sample_ratio) == 1.
if num_replicas is None:
if not dist.is_available():
raise RuntimeError(
'Requires distributed package to be available')
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError(
'Requires distributed package to be available')
rank = dist.get_rank()
if rank >= num_replicas or rank < 0:
raise ValueError(
f'Invalid rank {rank}, rank should be in the interval'
f' [0, {num_replicas - 1}]')
self.datasets = datasets
self.num_replicas = num_replicas
self.datasets_num_replicas = [
math.ceil(num_replicas * r) for r in sample_ratio
]
self.cumulative_replicas = []
t = 0
for nr in self.datasets_num_replicas:
t += nr
self.cumulative_replicas.append(t)
self.datasets_length = [len(d) for d in datasets]
self.cumulative_datasets_length = []
t = 0
for dl in self.datasets_length:
t += dl
self.cumulative_datasets_length.append(t)
# the smallest num_sample
self.datasets_num_samples = [
math.ceil(length / ratio) for length, ratio in zip(
self.datasets_length, self.datasets_num_replicas)
]
self.num_samples = min(self.datasets_num_samples)
# the dataset that decides the num_samples and total_size
self.key_dataset = self.datasets_num_samples.index(self.num_samples)
self.key_dataset_length = self.datasets_length[self.key_dataset]
self.total_size = [
self.num_samples * nr for nr in self.datasets_num_replicas
]
self.rank = rank
self.epoch = 0
self.shuffle = shuffle
# In distributed sampling, different ranks should sample
# non-overlapped data in the dataset. Therefore, this function
# is used to make sure that each rank shuffles the data indices
# in the same order based on the same seed. Then different
# ranks could use different indices to select non-overlapped
# data from the same data list.
self.seed = sync_random_seed(seed)
def __iter__(self) -> Iterator:
"""
Yields:
Iterator: iterator of indices for current rank.
"""
# datasets map different rank
for dataset_idx, cumulative_replicas_ in enumerate(
self.cumulative_replicas):
if self.rank < cumulative_replicas_:
break
# deterministically shuffle each datasets based on epoch
if self.shuffle:
g = torch.Generator()
# When :attr:`shuffle=True`, this ensures all replicas
# use a different random ordering for each epoch.
# Otherwise, the next iteration of this sampler will
# yield the same ordering.
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(
self.datasets_length[dataset_idx], generator=g).tolist()
else:
indices = torch.arange(self.datasets_length[dataset_idx]).tolist()
if self.total_size[dataset_idx] > len(indices):
# add extra samples for key_dataset to make it evenly divisible
indices += indices[:(self.total_size[dataset_idx] - len(indices))]
else:
indices = indices[:self.total_size[dataset_idx]]
assert len(indices) == self.total_size[dataset_idx]
# subsample
last_cumulative_replicas = 0 \
if dataset_idx == 0 else self.cumulative_replicas[dataset_idx - 1]
indices = indices[(
self.rank - last_cumulative_replicas
):self.total_size[dataset_idx]:self.datasets_num_replicas[dataset_idx]]
assert len(indices) == self.num_samples
# find the dataset for this rank
last_cumulative_length = 0 \
if dataset_idx == 0 else \
self.cumulative_datasets_length[dataset_idx-1]
indices = [idx + last_cumulative_length for idx in indices]
return iter(indices)
def __len__(self) -> int:
"""Get the combined dataset length."""
return self.num_samples
def set_epoch(self, epoch: int) -> None:
r"""
Sets the epoch for this sampler. When :attr:`shuffle=True`, this
ensures all replicas use a different random ordering for each epoch.
Otherwise, the next iteration of this sampler will yield the same
ordering.
Arguments:
epoch (int): Epoch number.
"""
self.epoch = epoch
| # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Iterator, Optional, Sequence
import torch
import torch.distributed as dist
from torch.utils.data import Dataset
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
from mmflow.core.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):
"""DistributedSampler inheriting from
`torch.utils.data.DistributedSampler`.
This distributed sampler is compatible Pytorch==1.5, as there is no
`seed` argument in Pytorch==1.5.
Args:
datasets (Dataset): the dataset will be loaded.
num_replicas (int, optional): Number of processes participating in
distributed training. By default, world_size is retrieved from the
current distributed group.
rank (int, optional): Rank of the current process within num_replicas.
By default, rank is retrieved from the current distributed group.
shuffle (bool): If True (default), sampler will shuffle the indices.
seed (int): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
"""
def __init__(self,
dataset: Dataset,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
seed=0) -> None:
super().__init__(
dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
# In distributed sampling, different ranks should sample
# non-overlapped data in the dataset. Therefore, this function
# is used to make sure that each rank shuffles the data indices
# in the same order based on the same seed. Then different
# ranks could use different indices to select non-overlapped
# data from the same data list.
self.seed = sync_random_seed(seed)
def __iter__(self) -> Iterator:
"""
Yields:
Iterator: iterator of indices for rank.
"""
# deterministically shuffle based on epoch
if self.shuffle:
g = torch.Generator()
# When :attr:`shuffle=True`, this ensures all replicas
# use a different random ordering for each epoch.
# Otherwise, the next iteration of this sampler will
# yield the same ordering.
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
class MixedBatchDistributedSampler(Sampler):
"""Distributed Sampler for mixed data batch.
Args:
datasets (list): List of datasets will be loaded.
sample_ratio (list): List of the ratio of each dataset in a batch, e.g.
datasets=[DatasetA, DatasetB], sample_ratio=[0.25, 0.75],
sample_per_gpu=1, gpus=8, it means 2 gpus load DatasetA, and 6 gpus
load DatasetB. The length of datasets must be equal to length of
sample_ratio.
num_replicas (int, optional): Number of processes participating in
distributed training. By default, world_size is retrieved from the
current distributed group.
rank (int, optional): Rank of the current process within num_replicas.
By default, rank is retrieved from the current distributed group.
shuffle (bool): If True (default), sampler will shuffle the indices.
seed (int): random seed used to shuffle the sampler if
:attr:`shuffle=True`. This number should be identical across all
processes in the distributed group. Default: ``0``.
"""
def __init__(self,
datasets: Sequence[Dataset],
sample_ratio: Sequence[float],
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
seed: int = 0) -> None:
# base class `Sampler` do nothing in `__init__` function
# super().__init__()
assert len(datasets) == len(sample_ratio)
assert sum(sample_ratio) == 1.
if num_replicas is None:
if not dist.is_available():
raise RuntimeError(
'Requires distributed package to be available')
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError(
'Requires distributed package to be available')
rank = dist.get_rank()
if rank >= num_replicas or rank < 0:
raise ValueError(
f'Invalid rank {rank}, rank should be in the interval'
f' [0, {num_replicas - 1}]')
self.datasets = datasets
self.num_replicas = num_replicas
self.datasets_num_replicas = [
math.ceil(num_replicas * r) for r in sample_ratio
]
self.cumulative_replicas = []
t = 0
for nr in self.datasets_num_replicas:
t += nr
self.cumulative_replicas.append(t)
self.datasets_length = [len(d) for d in datasets]
self.cumulative_datasets_length = []
t = 0
for dl in self.datasets_length:
t += dl
self.cumulative_datasets_length.append(t)
# the smallest num_sample
self.datasets_num_samples = [
math.ceil(length / ratio) for length, ratio in zip(
self.datasets_length, self.datasets_num_replicas)
]
self.num_samples = min(self.datasets_num_samples)
# the dataset that decides the num_samples and total_size
self.key_dataset = self.datasets_num_samples.index(self.num_samples)
self.key_dataset_length = self.datasets_length[self.key_dataset]
self.total_size = [
self.num_samples * nr for nr in self.datasets_num_replicas
]
self.rank = rank
self.epoch = 0
self.shuffle = shuffle
# In distributed sampling, different ranks should sample
# non-overlapped data in the dataset. Therefore, this function
# is used to make sure that each rank shuffles the data indices
# in the same order based on the same seed. Then different
# ranks could use different indices to select non-overlapped
# data from the same data list.
self.seed = sync_random_seed(seed)
def __iter__(self) -> Iterator:
"""
Yields:
Iterator: iterator of indices for current rank.
"""
# datasets map different rank
for dataset_idx, cumulative_replicas_ in enumerate(
self.cumulative_replicas):
if self.rank < cumulative_replicas_:
break
# deterministically shuffle each datasets based on epoch
if self.shuffle:
g = torch.Generator()
# When :attr:`shuffle=True`, this ensures all replicas
# use a different random ordering for each epoch.
# Otherwise, the next iteration of this sampler will
# yield the same ordering.
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(
self.datasets_length[dataset_idx], generator=g).tolist()
else:
indices = torch.arange(self.datasets_length[dataset_idx]).tolist()
if self.total_size[dataset_idx] > len(indices):
# add extra samples for key_dataset to make it evenly divisible
indices += indices[:(self.total_size[dataset_idx] - len(indices))]
else:
indices = indices[:self.total_size[dataset_idx]]
assert len(indices) == self.total_size[dataset_idx]
# subsample
last_cumulative_replicas = 0 \
if dataset_idx == 0 else self.cumulative_replicas[dataset_idx - 1]
indices = indices[(
self.rank - last_cumulative_replicas
):self.total_size[dataset_idx]:self.datasets_num_replicas[dataset_idx]]
assert len(indices) == self.num_samples
# find the dataset for this rank
last_cumulative_length = 0 \
if dataset_idx == 0 else \
self.cumulative_datasets_length[dataset_idx-1]
indices = [idx + last_cumulative_length for idx in indices]
return iter(indices)
def __len__(self) -> int:
"""Get the combined dataset length."""
return self.num_samples
def set_epoch(self, epoch: int) -> None:
r"""
Sets the epoch for this sampler. When :attr:`shuffle=True`, this
ensures all replicas use a different random ordering for each epoch.
Otherwise, the next iteration of this sampler will yield the same
ordering.
Arguments:
epoch (int): Epoch number.
"""
self.epoch = epoch
| en | 0.772394 | # Copyright (c) OpenMMLab. All rights reserved. DistributedSampler inheriting from `torch.utils.data.DistributedSampler`. This distributed sampler is compatible Pytorch==1.5, as there is no `seed` argument in Pytorch==1.5. Args: datasets (Dataset): the dataset will be loaded. num_replicas (int, optional): Number of processes participating in distributed training. By default, world_size is retrieved from the current distributed group. rank (int, optional): Rank of the current process within num_replicas. By default, rank is retrieved from the current distributed group. shuffle (bool): If True (default), sampler will shuffle the indices. seed (int): random seed used to shuffle the sampler if :attr:`shuffle=True`. This number should be identical across all processes in the distributed group. Default: ``0``. # In distributed sampling, different ranks should sample # non-overlapped data in the dataset. Therefore, this function # is used to make sure that each rank shuffles the data indices # in the same order based on the same seed. Then different # ranks could use different indices to select non-overlapped # data from the same data list. Yields: Iterator: iterator of indices for rank. # deterministically shuffle based on epoch # When :attr:`shuffle=True`, this ensures all replicas # use a different random ordering for each epoch. # Otherwise, the next iteration of this sampler will # yield the same ordering. # add extra samples to make it evenly divisible # subsample Distributed Sampler for mixed data batch. Args: datasets (list): List of datasets will be loaded. sample_ratio (list): List of the ratio of each dataset in a batch, e.g. datasets=[DatasetA, DatasetB], sample_ratio=[0.25, 0.75], sample_per_gpu=1, gpus=8, it means 2 gpus load DatasetA, and 6 gpus load DatasetB. The length of datasets must be equal to length of sample_ratio. num_replicas (int, optional): Number of processes participating in distributed training. By default, world_size is retrieved from the current distributed group. rank (int, optional): Rank of the current process within num_replicas. By default, rank is retrieved from the current distributed group. shuffle (bool): If True (default), sampler will shuffle the indices. seed (int): random seed used to shuffle the sampler if :attr:`shuffle=True`. This number should be identical across all processes in the distributed group. Default: ``0``. # base class `Sampler` do nothing in `__init__` function # super().__init__() # the smallest num_sample # the dataset that decides the num_samples and total_size # In distributed sampling, different ranks should sample # non-overlapped data in the dataset. Therefore, this function # is used to make sure that each rank shuffles the data indices # in the same order based on the same seed. Then different # ranks could use different indices to select non-overlapped # data from the same data list. Yields: Iterator: iterator of indices for current rank. # datasets map different rank # deterministically shuffle each datasets based on epoch # When :attr:`shuffle=True`, this ensures all replicas # use a different random ordering for each epoch. # Otherwise, the next iteration of this sampler will # yield the same ordering. # add extra samples for key_dataset to make it evenly divisible # subsample # find the dataset for this rank Get the combined dataset length. Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas use a different random ordering for each epoch. Otherwise, the next iteration of this sampler will yield the same ordering. Arguments: epoch (int): Epoch number. | 2.727302 | 3 |
frozen_lake/q_learning.py | elton-choi/rl-tutorial | 0 | 6614311 | <filename>frozen_lake/q_learning.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import gym
from time import sleep
import math
class QLearning:
def __init__(self, env, alpha=0.1, gamma=0.8, epsilon=0.1):
self.env = env
self.alpha = alpha
self.gamma = gamma
self.epsilon = epsilon
self.n_state = env.observation_space.n
self.n_action = env.action_space.n
self.q_table = np.zeros((self.n_state, self.n_action))
self.S = np.arange(self.n_state)
self.A = np.arange(self.n_action)
self.str_a = ['LEFT', 'DOWN', 'RIGHT', 'UP']
def explore(self, s):
# epsilon-greedy
if np.random.rand() < self.epsilon:
a = np.random.choice(self.A)
else: # greedy: 1-e
a = np.argmax(self.q_table[s])
# print('s=%d, a=%s\n' % (s, self.str_a[a]))
return a
def learn(self, s, a, r, s_):
# print('\nbefore update')
# self.print_q_table()
# print('\ns=%d, a=%d, r=%.2f, s_=%d'%(s, a, r, s_))
self.q_table[s][a] = (1-self.alpha)*self.q_table[s][a] + self.alpha * ( r + self.gamma * np.max(self.q_table[s_]) - self.q_table[s][a] )
# print('\nafter q table')
# self.print_q_table()
# temp = input('Go next?')
# print('----\n')
def print_q_table(self):
print('q_table')
print('action = LEFT, DOWN, RIGHT, UP')
for state in range(self.n_state):
for action in range(self.n_action):
if action == self.n_action - 1:
print('%.1f ' % self.q_table[state][action], end='')
else:
print('%.1f, ' % self.q_table[state][action], end='')
print('| ', end='')
n_square = np.sqrt(self.n_state)
if state % n_square == n_square-1:
print('')
def run_episode(self, render = False, key = False, wait = 0.0):
obs = self.env.reset()
total_reward = 0
step_idx = 0
while True:
if render:
self.env.render()
if key:
temp_key = input('')
elif wait > 0.0:
sleep(wait)
a = self.explore(obs)
next_obs, reward, done , _ = self.env.step(a)
# print('obs = %d, a = %d, next_obs = %d\n'%(obs, a, next_obs))
# temp_key = input('')
obs = next_obs
# total_reward += (self.gamma ** step_idx * reward)
total_reward += (reward)
step_idx += 1
if done:
break
return (step_idx, reward, total_reward)
if __name__ == '__main__':
# env = gym.make("FrozenLake-v0")
env = gym.make("FrozenLake8x8-v0")
agent = QLearning(env, alpha=0.8, gamma=0.9, epsilon=0.2)
# before learning
step_idx, reward, total_reward = agent.run_episode(render=False)
print('Run episode before learning')
print('Total reward is %.3f\n\n' % total_reward)
# q learning
for i in range(10000):
obs = env.reset()
t=1
t_list = []
action_list = []
reward_list = []
obs_list = []
while True:
# env.render()
action = agent.explore(obs) # epsilon greedy
next_obs, reward, done, info = env.step(action)
obs_list.append(obs)
action_list.append(action)
if done:
if reward == 0:
reward = -1
reward_list.append(reward)
agent.learn(obs, action, reward, next_obs) # q-learning
t_list.append(t)
if reward == 1:
print("%dth Episode finished after %d timesteps with average reward %.3f" % (i+1, t, sum(reward_list)/t))
print("Observation list = {}".format(obs_list))
print("Action list = {}".format(action_list))
break
else:
if reward == 0:
reward = -0.1
reward_list.append(reward)
agent.learn(obs, action, reward, next_obs) # q-learning
obs = next_obs
t=t+1
agent.print_q_table()
print("Shortest time step to reach a goal is %d"%min(t_list))
input('Next?')
reward_list = []
step_list = []
n_episode = 1000
agent.epsilon = 0.0
for i in range(n_episode):
(step_idx, reward, total_reward) = agent.run_episode(render=True)
reward_list.append(total_reward)
if reward == 1:
step_list.append(step_idx)
print("%dth episode finished at %d step with total reward %.3f" % (i, step_idx, total_reward))
input('Next?')
print('\nRun episodes(%d) after learning' % n_episode)
print('Total reward average is %.3f' % (sum(reward_list)/n_episode))
print('Shortest time step is %d' % min(step_list)) | <filename>frozen_lake/q_learning.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import gym
from time import sleep
import math
class QLearning:
def __init__(self, env, alpha=0.1, gamma=0.8, epsilon=0.1):
self.env = env
self.alpha = alpha
self.gamma = gamma
self.epsilon = epsilon
self.n_state = env.observation_space.n
self.n_action = env.action_space.n
self.q_table = np.zeros((self.n_state, self.n_action))
self.S = np.arange(self.n_state)
self.A = np.arange(self.n_action)
self.str_a = ['LEFT', 'DOWN', 'RIGHT', 'UP']
def explore(self, s):
# epsilon-greedy
if np.random.rand() < self.epsilon:
a = np.random.choice(self.A)
else: # greedy: 1-e
a = np.argmax(self.q_table[s])
# print('s=%d, a=%s\n' % (s, self.str_a[a]))
return a
def learn(self, s, a, r, s_):
# print('\nbefore update')
# self.print_q_table()
# print('\ns=%d, a=%d, r=%.2f, s_=%d'%(s, a, r, s_))
self.q_table[s][a] = (1-self.alpha)*self.q_table[s][a] + self.alpha * ( r + self.gamma * np.max(self.q_table[s_]) - self.q_table[s][a] )
# print('\nafter q table')
# self.print_q_table()
# temp = input('Go next?')
# print('----\n')
def print_q_table(self):
print('q_table')
print('action = LEFT, DOWN, RIGHT, UP')
for state in range(self.n_state):
for action in range(self.n_action):
if action == self.n_action - 1:
print('%.1f ' % self.q_table[state][action], end='')
else:
print('%.1f, ' % self.q_table[state][action], end='')
print('| ', end='')
n_square = np.sqrt(self.n_state)
if state % n_square == n_square-1:
print('')
def run_episode(self, render = False, key = False, wait = 0.0):
obs = self.env.reset()
total_reward = 0
step_idx = 0
while True:
if render:
self.env.render()
if key:
temp_key = input('')
elif wait > 0.0:
sleep(wait)
a = self.explore(obs)
next_obs, reward, done , _ = self.env.step(a)
# print('obs = %d, a = %d, next_obs = %d\n'%(obs, a, next_obs))
# temp_key = input('')
obs = next_obs
# total_reward += (self.gamma ** step_idx * reward)
total_reward += (reward)
step_idx += 1
if done:
break
return (step_idx, reward, total_reward)
if __name__ == '__main__':
# env = gym.make("FrozenLake-v0")
env = gym.make("FrozenLake8x8-v0")
agent = QLearning(env, alpha=0.8, gamma=0.9, epsilon=0.2)
# before learning
step_idx, reward, total_reward = agent.run_episode(render=False)
print('Run episode before learning')
print('Total reward is %.3f\n\n' % total_reward)
# q learning
for i in range(10000):
obs = env.reset()
t=1
t_list = []
action_list = []
reward_list = []
obs_list = []
while True:
# env.render()
action = agent.explore(obs) # epsilon greedy
next_obs, reward, done, info = env.step(action)
obs_list.append(obs)
action_list.append(action)
if done:
if reward == 0:
reward = -1
reward_list.append(reward)
agent.learn(obs, action, reward, next_obs) # q-learning
t_list.append(t)
if reward == 1:
print("%dth Episode finished after %d timesteps with average reward %.3f" % (i+1, t, sum(reward_list)/t))
print("Observation list = {}".format(obs_list))
print("Action list = {}".format(action_list))
break
else:
if reward == 0:
reward = -0.1
reward_list.append(reward)
agent.learn(obs, action, reward, next_obs) # q-learning
obs = next_obs
t=t+1
agent.print_q_table()
print("Shortest time step to reach a goal is %d"%min(t_list))
input('Next?')
reward_list = []
step_list = []
n_episode = 1000
agent.epsilon = 0.0
for i in range(n_episode):
(step_idx, reward, total_reward) = agent.run_episode(render=True)
reward_list.append(total_reward)
if reward == 1:
step_list.append(step_idx)
print("%dth episode finished at %d step with total reward %.3f" % (i, step_idx, total_reward))
input('Next?')
print('\nRun episodes(%d) after learning' % n_episode)
print('Total reward average is %.3f' % (sum(reward_list)/n_episode))
print('Shortest time step is %d' % min(step_list)) | en | 0.329031 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- # epsilon-greedy # greedy: 1-e # print('s=%d, a=%s\n' % (s, self.str_a[a])) # print('\nbefore update') # self.print_q_table() # print('\ns=%d, a=%d, r=%.2f, s_=%d'%(s, a, r, s_)) # print('\nafter q table') # self.print_q_table() # temp = input('Go next?') # print('----\n') # print('obs = %d, a = %d, next_obs = %d\n'%(obs, a, next_obs)) # temp_key = input('') # total_reward += (self.gamma ** step_idx * reward) # env = gym.make("FrozenLake-v0") # before learning # q learning # env.render() # epsilon greedy # q-learning # q-learning | 3.180002 | 3 |
examples/plot_ITER_plasmas.py | fusion-energy/plasmaboundaries | 4 | 6614312 | <reponame>fusion-energy/plasmaboundaries<gh_stars>1-10
# plasma-boundaries
# This script is an implementation of the method described in
# “One size fits all” analytic solutions to the Grad–Shafranov equation
# <NAME> and <NAME>, Physics of Plamas 17 032502 (2010)
# https://doi.org/10.1063/1.3328818
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
import plasmaboundaries
# plasma parameters
params = plasmaboundaries.ITER
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 4.8))
for ax, config in zip(
[ax1, ax2, ax3],
["non-null", "single-null", "double-null"]):
# compute psi
psi = plasmaboundaries.compute_psi(params, config=config)
# plot the results
xmin, xmax = 0.6, 1.35
ymin, ymax = -0.8, 0.7
x = np.arange(xmin, xmax, step=0.01)
y = np.arange(ymin, ymax, step=0.01)
X, Y = np.meshgrid(x, y)
Z = psi(X, Y) # compute magnetic flux
# add filled contours
levels2 = np.unique(np.linspace(Z.min(), -Z.min(), num=100))
norm = mcolors.TwoSlopeNorm(vmin=Z.min(), vcenter=0., vmax=-Z.min())
CSF = ax.contourf(
X, Y, Z, levels=levels2, cmap="coolwarm", norm=norm,
vmax=-Z.min(), extend="max")
# add contours
levels = np.unique(
np.append(
np.linspace(Z.min(), 0, num=10), np.linspace(0, Z.max(), num=20)))
CS = ax.contour(
X, Y, Z, levels=levels[levels != 0], colors="black",
linestyles="solid")
separatrix = ax.contour(
X, Y, Z, levels=[0], colors="black", linestyles="dashed")
ax.clabel(separatrix, inline=True, fmt=r"$\Psi = $%.0f")
ax.set_title('ITER ' + config)
ax.set_xlabel('Radius $R/R_0$')
ax.set_aspect("equal")
ax1.set_ylabel('Height $Z/R_0$')
plt.colorbar(CSF, label="Magnetic flux $\Psi$", format="%.3f")
plt.show()
| # plasma-boundaries
# This script is an implementation of the method described in
# “One size fits all” analytic solutions to the Grad–Shafranov equation
# <NAME> and <NAME>, Physics of Plamas 17 032502 (2010)
# https://doi.org/10.1063/1.3328818
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
import plasmaboundaries
# plasma parameters
params = plasmaboundaries.ITER
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 4.8))
for ax, config in zip(
[ax1, ax2, ax3],
["non-null", "single-null", "double-null"]):
# compute psi
psi = plasmaboundaries.compute_psi(params, config=config)
# plot the results
xmin, xmax = 0.6, 1.35
ymin, ymax = -0.8, 0.7
x = np.arange(xmin, xmax, step=0.01)
y = np.arange(ymin, ymax, step=0.01)
X, Y = np.meshgrid(x, y)
Z = psi(X, Y) # compute magnetic flux
# add filled contours
levels2 = np.unique(np.linspace(Z.min(), -Z.min(), num=100))
norm = mcolors.TwoSlopeNorm(vmin=Z.min(), vcenter=0., vmax=-Z.min())
CSF = ax.contourf(
X, Y, Z, levels=levels2, cmap="coolwarm", norm=norm,
vmax=-Z.min(), extend="max")
# add contours
levels = np.unique(
np.append(
np.linspace(Z.min(), 0, num=10), np.linspace(0, Z.max(), num=20)))
CS = ax.contour(
X, Y, Z, levels=levels[levels != 0], colors="black",
linestyles="solid")
separatrix = ax.contour(
X, Y, Z, levels=[0], colors="black", linestyles="dashed")
ax.clabel(separatrix, inline=True, fmt=r"$\Psi = $%.0f")
ax.set_title('ITER ' + config)
ax.set_xlabel('Radius $R/R_0$')
ax.set_aspect("equal")
ax1.set_ylabel('Height $Z/R_0$')
plt.colorbar(CSF, label="Magnetic flux $\Psi$", format="%.3f")
plt.show() | en | 0.714658 | # plasma-boundaries # This script is an implementation of the method described in # “One size fits all” analytic solutions to the Grad–Shafranov equation # <NAME> and <NAME>, Physics of Plamas 17 032502 (2010) # https://doi.org/10.1063/1.3328818 # plasma parameters # compute psi # plot the results # compute magnetic flux # add filled contours # add contours | 2.298936 | 2 |
search_service/search/serializer_utils.py | digirati-co-uk/madoc-search-service | 0 | 6614313 | from django.core.validators import URLValidator
from django.core.exceptions import ValidationError
from django.utils.text import slugify
import json
from bs4 import BeautifulSoup
from collections import defaultdict
from ordered_set import OrderedSet
from dateutil import parser
import bleach
import logging
import itertools
logger = logging.getLogger(__name__)
pg_languages = [
"danish",
"dutch",
"english",
"finnish",
"french",
"german",
"hungarian",
"italian",
"norwegian",
"portuguese",
"romanian",
"russian",
"spanish",
"swedish",
"turkish",
]
def resources_by_type(iiif, iiif_type=("Canvas",), master_resources=None):
"""
Iterate a Presentation API 3 manifest and produce a list of resources by type, e.g. Canvases
or Annotations.
"""
if not master_resources:
working_resources = []
else:
working_resources = master_resources
if (items := iiif.get("items", None)) is not None:
if any([isinstance(item, list) for item in items]):
resources = [c for c in itertools.chain.from_iterable(items) if c.get("type") is not None]
else:
resources = [c for c in items if c.get("type") is not None]
filtered_resources = [r for r in resources if r.get("type") in iiif_type]
if filtered_resources:
working_resources += filtered_resources
else:
for f in resources:
working_resources += resources_by_type(
iiif=f, iiif_type=iiif_type, master_resources=filtered_resources
)
return working_resources
def iiif_to_presentationapiresourcemodel(data_dict):
"""
Somewhat hacky transformation of an incoming data object for the serializer
into the correct format for the model
"""
lookup_dict = {
"@id": {"model_key": "identifier", "default": None, "choices": None},
"identifier": {"model_key": "identifier", "default": None, "choices": None},
"@type": {
"model_key": "type",
"default": "Man",
"choices": (
("Col", "Collection"),
("Col", "sc:Collection"),
("Man", "Manifest"),
("Man", "sc:Manifest"),
("Seq", "Sequence"),
("Seq", "sc:Sequence"),
("Rng", "Range"),
("Rng", "sc:Range"),
("Cvs", "Canvas"),
("Cvs", "sc:Canvas"),
),
},
"type": {
"model_key": "type",
"default": "Man",
"choices": (
("Col", "Collection"),
("Man", "Manifest"),
("Seq", "Sequence"),
("Rng", "Range"),
("Cvs", "Canvas"),
),
},
"label": {"model_key": "label", "default": None, "choices": None},
"viewingDirection": {
"model_key": "viewing_direction",
"default": "l2",
"choices": (
("l2r", "left-to-right"),
("r2l", "right-to-left"),
("t2b", "top-to-bottom"),
("b2t", "bottom-to-top"),
),
},
"viewingHint": {
"model_key": "viewing_hint",
"default": "paged",
"choices": (
("ind", "individuals"),
("pgd", "paged"),
("cnt", "continuous"),
("mpt", "multi-part"),
("npg", "non-paged"),
("top", "top"),
("fac", "facing-pages"),
),
},
"description": {"model_key": "description", "default": None, "choices": None},
"attribution": {"model_key": "attribution", "default": None, "choices": None},
"license": {"model_key": "license", "default": None, "choices": None},
"metadata": {"model_key": "metadata", "default": None, "choices": None},
}
return_dict = {}
if data_dict.get("metadata"):
if isinstance((data_dict["metadata"]), str):
data_dict["metadata"] = json.load(data_dict["metadata"])
for k, v in data_dict.items():
lookup_result = lookup_dict.get(k)
if lookup_result:
if not lookup_result.get("choices"):
return_dict[lookup_result["model_key"]] = v
else:
if v in [c[0] for c in lookup_result["choices"]]:
return_dict[lookup_result["model_key"]] = v
elif v in [c[1] for c in lookup_result["choices"]]:
return_dict[lookup_result["model_key"]] = [
c[0] for c in lookup_result["choices"] if c[1] == v
][0]
else:
return_dict[lookup_result["model_key"]] = lookup_result.get("default")
if return_dict.get("license"):
val = URLValidator()
try:
val(return_dict["license"])
except ValidationError:
del return_dict["license"]
return return_dict
def get_language_data(lang_code=None, langbase=None):
if lang_code:
if "-" in lang_code:
lang_code = lang_code.split("-")[0]
if len(lang_code) == 2:
language_data = [x for x in langbase if x[1] == lang_code]
if language_data:
if language_data[0][-1].lower() in pg_languages:
pg_lang = language_data[0][-1].lower()
else:
pg_lang = None
return {
"language_iso639_2": language_data[0][0],
"language_iso639_1": language_data[0][1],
"language_display": language_data[0][-1].lower(),
"language_pg": pg_lang,
}
elif len(lang_code) == 3:
language_data = [x for x in langbase if x[0] == lang_code]
if language_data:
if language_data[0][-1].lower() in pg_languages:
pg_lang = language_data[0][-1].lower()
else:
pg_lang = None
return {
"language_iso639_2": language_data[0][0],
"language_iso639_1": language_data[0][1],
"language_display": language_data[0][-1].lower(),
"language_pg": pg_lang,
}
return {
"language_iso639_2": None,
"language_iso639_1": None,
"language_display": None,
"language_pg": None,
}
def process_field(
field_instance,
key,
default_language,
lang_base,
field_type="descriptive",
field_indexable_type="text",
):
val = None
lang = default_language
subtype = key
field_data = []
if field_instance:
if not field_instance.get("label"):
# Problem here with multilanguage label field
for val_lang, val in field_instance.items():
if val_lang in ["@none", "none"]:
lang = default_language
else:
lang = val_lang
if val:
for v in val:
v = str(v)
if field_indexable_type == "text":
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable": BeautifulSoup(v, "html.parser").text,
"original_content": {subtype: bleach.clean(v)},
**get_language_data(lang_code=lang, langbase=lang_base),
}
)
elif field_indexable_type == "date":
"""
This assumes a single navDate, but we translate this into a datetime
range via adding two indexables.
"""
try:
parsed_date = parser.parse(v)
except ValueError:
parsed_date = None
if parsed_date:
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_start": parsed_date,
"indexable_date_range_end": parsed_date,
"original_content": {subtype: bleach.clean(v)},
}
)
else:
indexable_values = []
label_values = field_instance.get("label", {})
if field_values:=field_instance.get("value"):
for lang, vals in field_values.items():
if labels:= label_values.get(lang):
subtype = labels[0]
if lang in ["@none", "none"]:
lang = default_language
language_data = get_language_data(lang_code=lang, langbase=lang_base)
for v in vals:
if field_indexable_type == "text":
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable": BeautifulSoup(v, "html.parser").text,
"original_content": {subtype: v},
**language_data,
}
)
elif field_indexable_type == "date":
"""
This assumes a single navDate, but we translate this into a datetime
range via adding two indexables.
"""
try:
parsed_date = parser.parse(v)
except ValueError:
parsed_date = None
if parsed_date:
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_start": parsed_date,
"original_content": {subtype: v},
}
)
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_end": parsed_date,
"original_content": {subtype: v},
}
)
return field_data
return
def flatten_iiif_descriptive(iiif, default_language=None, lang_base=None):
"""
Flatten the descriptive fields in a Presentation API into a list of dicts
that can be passed to the Indexables model and serializers
"""
field_data = []
dict_fields = [
("label", "descriptive", "text"),
("requiredStatement", "descriptive", "text"),
("summary", "descriptive", "text"),
("metadata", "metadata", "text"),
("navDate", "descriptive", "date"),
]
for d in dict_fields:
if iiif.get(d[0]):
if isinstance(iiif[d[0]], dict):
field_instances = [iiif[d[0]]]
elif isinstance(iiif[d[0]], list):
field_instances = iiif[d[0]]
else:
# This might be just a string, e.g. navDate
# There is no language or label, so we just pass it through with the language set to None
field_instances = [{"none": [iiif[d[0]]]}]
if field_instances:
for field_instance in field_instances:
returned_data = process_field(
field_instance=field_instance,
lang_base=lang_base,
default_language=default_language,
key=d[0],
field_type=d[1],
field_indexable_type=d[2],
)
if returned_data:
field_data += returned_data
if field_data:
return field_data
else:
return
def simplify_selector(selector):
"""
Simplify a selector from the OCR intermediate format or capture model format
into a compact representation
"selector": {
"id": "0db4fdc1-73dd-4555-95da-7cbc746c980c",
"state": {
"height": "60",
"width": "20",
"x": "821",
"y": "644"
},
"type": "box-selector"
},
Becomes (XYWH):
832,644,20,60
"""
if selector:
if selector.get("state"):
if (selector_type := selector.get("type")) is not None:
if selector_type == "box-selector":
selector_list = [
selector["state"].get("x"),
selector["state"].get("y"),
selector["state"].get("width"),
selector["state"].get("height"),
]
if all([x is not None for x in selector_list]):
try:
return {selector_type: [int(x) for x in selector_list]}
except ValueError:
return
return
def simplify_ocr(ocr):
"""
Simplify ocr to just a single continuous page of text, with selectors.
"""
simplified = dict(text=[], selector=defaultdict(list))
if ocr.get("paragraph"):
for paragraph in ocr["paragraph"]:
if paragraph.get("properties"):
if paragraph["properties"].get("lines"):
for line in paragraph["properties"]["lines"]:
if line.get("properties"):
if line["properties"].get("text"):
for text in line["properties"]["text"]:
simplified["text"].append(text.get("value"))
selector_obj = simplify_selector(text["selector"])
if selector_obj:
for k, v in selector_obj.items():
simplified["selector"][k].append(v)
simplified["indexable"] = " ".join([t for t in simplified["text"] if t])
simplified["original_content"] = simplified["indexable"]
simplified["subtype"] = "intermediate"
return [simplified]
def simplify_label(s):
return ".".join(OrderedSet(s.split(".")))
def recurse_properties(properties, indexables=None, doc_subtype=None, target=None):
if not indexables:
indexables = []
if properties:
if properties.get("properties"): # This is a nested model so recurse into that
indexables += recurse_properties(
properties=properties.get("properties"),
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(properties.get("type", ""))])
),
)
if properties.get("value"): # This is just the content of a list of values so index them
d = {
"subtype": simplify_label(doc_subtype),
"indexable": properties.get("value"),
"original_content": properties.get("value"),
"content_id": properties["id"],
"resource_id": target,
}
# Check for selector
if properties.get("selector"):
d["selector"] = {
k: [v]
for k, v in simplify_selector(properties.get("selector")).items()
if simplify_selector(properties.get("selector")) is not None
}
indexables.append(d)
else: # Iterate through the keys in the dictionary
for property_key, property_value in properties.items():
# It's a list, so we should extract the indexables from each one
if isinstance(property_value, list):
for x in property_value:
indexables += recurse_properties(
properties=x,
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(property_key)])
),
)
# It's a dictionary
if isinstance(property_value, dict):
# To Do: Work out why this isn't working (some sort of simple nesting issue)
# indexables += recurse_properties(
# properties=property_value,
# doc_subtype=simplify_label(".".join([doc_subtype, slugify(property_key)])),
# )
if property_value.get("value"):
d = {
"subtype": simplify_label(
".".join([doc_subtype, slugify(property_value.get("label", ""))])
),
"indexable": property_value.get("value"),
"original_content": property_value.get("value"),
"content_id": property_value["id"],
"resource_id": target,
}
if property_value.get("selector"):
d["selector"] = {
k: [v]
for k, v in simplify_selector(
property_value.get("selector")
).items()
if simplify_selector(property_value.get("selector")) is not None
}
indexables.append(d)
if property_value.get("properties"):
indexables += recurse_properties(
properties=property_value.get("properties"),
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(property_value.get("type", ""))])
),
)
return indexables
def simplify_capturemodel(capturemodel):
"""
Function for parsing a capture model into indexables
"""
if (document := capturemodel.get("document")) is not None:
indexables = []
doc_subtype = document.get("type")
if (targets := capturemodel.get("target")) is not None:
target = targets[-1].get("id")
else:
target = None
if document.get("properties"):
# This has regions of interest
if (regions := document["properties"].get("region")) is not None:
for region in regions:
if region.get("value"):
indexables.append(
{
"subtype": ".".join(
[doc_subtype, slugify(region.get("label", ""))]
),
"indexable": region.get("value"),
"original_content": region.get("value"),
"selector": {
k: [v]
for k, v in simplify_selector(region.get("selector")).items()
},
"content_id": region["id"],
"resource_id": target,
}
)
else:
# This is some sort of entity type tagging task, or other non region of interest
# so we are going to recurse into the nesting
indexables += recurse_properties(
properties=document.get("properties"), doc_subtype=doc_subtype, target=target
)
return indexables
return
def calc_offsets(obj):
"""
The search "hit" should have a 'fullsnip' annotation which is a the entire
text of the indexable resource, with <start_sel> and <end_sel> wrapping each
highlighted word.
Check if there's a selector on the indexable, and then if there's a box-selector
use this to generate a list of xywh coordinates by retrieving the selector by
its index from a list of lists
"""
if hasattr(obj, "fullsnip"):
words = obj.fullsnip.split(" ")
offsets = []
if words:
for i, word in enumerate(words):
if "<start_sel>" in word and "<end_sel>" in word:
offsets.append(i)
if offsets:
if obj.selector:
if (boxes := obj.selector.get("box-selector")) is not None:
box_list = []
for x in offsets:
try:
box_list.append(boxes[x])
except (IndexError, ValueError):
pass
if box_list:
return box_list # [boxes[x] for x in offsets if boxes[x]]
else:
return
return
class ActionBasedSerializerMixin(object):
serializer_mapping = {
"default": None,
}
def get_serializer_class(self):
logger.info(self.action)
if serializer_class := self.serializer_mapping.get(self.action):
return serializer_class
elif serializer_class := self.serializer_mapping.get("default"):
return serializer_class
else:
return self.serializer_class
class MethodBasedSerializerMixin(object):
serializer_mapping = {
"default": None,
}
def get_serializer_class(self):
logger.info(self.request.method)
if serializer_class := self.serializer_mapping.get(self.request.method.lower()):
return serializer_class
elif serializer_class := self.serializer_mapping.get("default"):
return serializer_class
else:
return self.serializer_class | from django.core.validators import URLValidator
from django.core.exceptions import ValidationError
from django.utils.text import slugify
import json
from bs4 import BeautifulSoup
from collections import defaultdict
from ordered_set import OrderedSet
from dateutil import parser
import bleach
import logging
import itertools
logger = logging.getLogger(__name__)
pg_languages = [
"danish",
"dutch",
"english",
"finnish",
"french",
"german",
"hungarian",
"italian",
"norwegian",
"portuguese",
"romanian",
"russian",
"spanish",
"swedish",
"turkish",
]
def resources_by_type(iiif, iiif_type=("Canvas",), master_resources=None):
"""
Iterate a Presentation API 3 manifest and produce a list of resources by type, e.g. Canvases
or Annotations.
"""
if not master_resources:
working_resources = []
else:
working_resources = master_resources
if (items := iiif.get("items", None)) is not None:
if any([isinstance(item, list) for item in items]):
resources = [c for c in itertools.chain.from_iterable(items) if c.get("type") is not None]
else:
resources = [c for c in items if c.get("type") is not None]
filtered_resources = [r for r in resources if r.get("type") in iiif_type]
if filtered_resources:
working_resources += filtered_resources
else:
for f in resources:
working_resources += resources_by_type(
iiif=f, iiif_type=iiif_type, master_resources=filtered_resources
)
return working_resources
def iiif_to_presentationapiresourcemodel(data_dict):
"""
Somewhat hacky transformation of an incoming data object for the serializer
into the correct format for the model
"""
lookup_dict = {
"@id": {"model_key": "identifier", "default": None, "choices": None},
"identifier": {"model_key": "identifier", "default": None, "choices": None},
"@type": {
"model_key": "type",
"default": "Man",
"choices": (
("Col", "Collection"),
("Col", "sc:Collection"),
("Man", "Manifest"),
("Man", "sc:Manifest"),
("Seq", "Sequence"),
("Seq", "sc:Sequence"),
("Rng", "Range"),
("Rng", "sc:Range"),
("Cvs", "Canvas"),
("Cvs", "sc:Canvas"),
),
},
"type": {
"model_key": "type",
"default": "Man",
"choices": (
("Col", "Collection"),
("Man", "Manifest"),
("Seq", "Sequence"),
("Rng", "Range"),
("Cvs", "Canvas"),
),
},
"label": {"model_key": "label", "default": None, "choices": None},
"viewingDirection": {
"model_key": "viewing_direction",
"default": "l2",
"choices": (
("l2r", "left-to-right"),
("r2l", "right-to-left"),
("t2b", "top-to-bottom"),
("b2t", "bottom-to-top"),
),
},
"viewingHint": {
"model_key": "viewing_hint",
"default": "paged",
"choices": (
("ind", "individuals"),
("pgd", "paged"),
("cnt", "continuous"),
("mpt", "multi-part"),
("npg", "non-paged"),
("top", "top"),
("fac", "facing-pages"),
),
},
"description": {"model_key": "description", "default": None, "choices": None},
"attribution": {"model_key": "attribution", "default": None, "choices": None},
"license": {"model_key": "license", "default": None, "choices": None},
"metadata": {"model_key": "metadata", "default": None, "choices": None},
}
return_dict = {}
if data_dict.get("metadata"):
if isinstance((data_dict["metadata"]), str):
data_dict["metadata"] = json.load(data_dict["metadata"])
for k, v in data_dict.items():
lookup_result = lookup_dict.get(k)
if lookup_result:
if not lookup_result.get("choices"):
return_dict[lookup_result["model_key"]] = v
else:
if v in [c[0] for c in lookup_result["choices"]]:
return_dict[lookup_result["model_key"]] = v
elif v in [c[1] for c in lookup_result["choices"]]:
return_dict[lookup_result["model_key"]] = [
c[0] for c in lookup_result["choices"] if c[1] == v
][0]
else:
return_dict[lookup_result["model_key"]] = lookup_result.get("default")
if return_dict.get("license"):
val = URLValidator()
try:
val(return_dict["license"])
except ValidationError:
del return_dict["license"]
return return_dict
def get_language_data(lang_code=None, langbase=None):
if lang_code:
if "-" in lang_code:
lang_code = lang_code.split("-")[0]
if len(lang_code) == 2:
language_data = [x for x in langbase if x[1] == lang_code]
if language_data:
if language_data[0][-1].lower() in pg_languages:
pg_lang = language_data[0][-1].lower()
else:
pg_lang = None
return {
"language_iso639_2": language_data[0][0],
"language_iso639_1": language_data[0][1],
"language_display": language_data[0][-1].lower(),
"language_pg": pg_lang,
}
elif len(lang_code) == 3:
language_data = [x for x in langbase if x[0] == lang_code]
if language_data:
if language_data[0][-1].lower() in pg_languages:
pg_lang = language_data[0][-1].lower()
else:
pg_lang = None
return {
"language_iso639_2": language_data[0][0],
"language_iso639_1": language_data[0][1],
"language_display": language_data[0][-1].lower(),
"language_pg": pg_lang,
}
return {
"language_iso639_2": None,
"language_iso639_1": None,
"language_display": None,
"language_pg": None,
}
def process_field(
field_instance,
key,
default_language,
lang_base,
field_type="descriptive",
field_indexable_type="text",
):
val = None
lang = default_language
subtype = key
field_data = []
if field_instance:
if not field_instance.get("label"):
# Problem here with multilanguage label field
for val_lang, val in field_instance.items():
if val_lang in ["@none", "none"]:
lang = default_language
else:
lang = val_lang
if val:
for v in val:
v = str(v)
if field_indexable_type == "text":
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable": BeautifulSoup(v, "html.parser").text,
"original_content": {subtype: bleach.clean(v)},
**get_language_data(lang_code=lang, langbase=lang_base),
}
)
elif field_indexable_type == "date":
"""
This assumes a single navDate, but we translate this into a datetime
range via adding two indexables.
"""
try:
parsed_date = parser.parse(v)
except ValueError:
parsed_date = None
if parsed_date:
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_start": parsed_date,
"indexable_date_range_end": parsed_date,
"original_content": {subtype: bleach.clean(v)},
}
)
else:
indexable_values = []
label_values = field_instance.get("label", {})
if field_values:=field_instance.get("value"):
for lang, vals in field_values.items():
if labels:= label_values.get(lang):
subtype = labels[0]
if lang in ["@none", "none"]:
lang = default_language
language_data = get_language_data(lang_code=lang, langbase=lang_base)
for v in vals:
if field_indexable_type == "text":
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable": BeautifulSoup(v, "html.parser").text,
"original_content": {subtype: v},
**language_data,
}
)
elif field_indexable_type == "date":
"""
This assumes a single navDate, but we translate this into a datetime
range via adding two indexables.
"""
try:
parsed_date = parser.parse(v)
except ValueError:
parsed_date = None
if parsed_date:
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_start": parsed_date,
"original_content": {subtype: v},
}
)
field_data.append(
{
"type": field_type,
"subtype": subtype.lower(),
"indexable_date_range_end": parsed_date,
"original_content": {subtype: v},
}
)
return field_data
return
def flatten_iiif_descriptive(iiif, default_language=None, lang_base=None):
"""
Flatten the descriptive fields in a Presentation API into a list of dicts
that can be passed to the Indexables model and serializers
"""
field_data = []
dict_fields = [
("label", "descriptive", "text"),
("requiredStatement", "descriptive", "text"),
("summary", "descriptive", "text"),
("metadata", "metadata", "text"),
("navDate", "descriptive", "date"),
]
for d in dict_fields:
if iiif.get(d[0]):
if isinstance(iiif[d[0]], dict):
field_instances = [iiif[d[0]]]
elif isinstance(iiif[d[0]], list):
field_instances = iiif[d[0]]
else:
# This might be just a string, e.g. navDate
# There is no language or label, so we just pass it through with the language set to None
field_instances = [{"none": [iiif[d[0]]]}]
if field_instances:
for field_instance in field_instances:
returned_data = process_field(
field_instance=field_instance,
lang_base=lang_base,
default_language=default_language,
key=d[0],
field_type=d[1],
field_indexable_type=d[2],
)
if returned_data:
field_data += returned_data
if field_data:
return field_data
else:
return
def simplify_selector(selector):
"""
Simplify a selector from the OCR intermediate format or capture model format
into a compact representation
"selector": {
"id": "0db4fdc1-73dd-4555-95da-7cbc746c980c",
"state": {
"height": "60",
"width": "20",
"x": "821",
"y": "644"
},
"type": "box-selector"
},
Becomes (XYWH):
832,644,20,60
"""
if selector:
if selector.get("state"):
if (selector_type := selector.get("type")) is not None:
if selector_type == "box-selector":
selector_list = [
selector["state"].get("x"),
selector["state"].get("y"),
selector["state"].get("width"),
selector["state"].get("height"),
]
if all([x is not None for x in selector_list]):
try:
return {selector_type: [int(x) for x in selector_list]}
except ValueError:
return
return
def simplify_ocr(ocr):
"""
Simplify ocr to just a single continuous page of text, with selectors.
"""
simplified = dict(text=[], selector=defaultdict(list))
if ocr.get("paragraph"):
for paragraph in ocr["paragraph"]:
if paragraph.get("properties"):
if paragraph["properties"].get("lines"):
for line in paragraph["properties"]["lines"]:
if line.get("properties"):
if line["properties"].get("text"):
for text in line["properties"]["text"]:
simplified["text"].append(text.get("value"))
selector_obj = simplify_selector(text["selector"])
if selector_obj:
for k, v in selector_obj.items():
simplified["selector"][k].append(v)
simplified["indexable"] = " ".join([t for t in simplified["text"] if t])
simplified["original_content"] = simplified["indexable"]
simplified["subtype"] = "intermediate"
return [simplified]
def simplify_label(s):
return ".".join(OrderedSet(s.split(".")))
def recurse_properties(properties, indexables=None, doc_subtype=None, target=None):
if not indexables:
indexables = []
if properties:
if properties.get("properties"): # This is a nested model so recurse into that
indexables += recurse_properties(
properties=properties.get("properties"),
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(properties.get("type", ""))])
),
)
if properties.get("value"): # This is just the content of a list of values so index them
d = {
"subtype": simplify_label(doc_subtype),
"indexable": properties.get("value"),
"original_content": properties.get("value"),
"content_id": properties["id"],
"resource_id": target,
}
# Check for selector
if properties.get("selector"):
d["selector"] = {
k: [v]
for k, v in simplify_selector(properties.get("selector")).items()
if simplify_selector(properties.get("selector")) is not None
}
indexables.append(d)
else: # Iterate through the keys in the dictionary
for property_key, property_value in properties.items():
# It's a list, so we should extract the indexables from each one
if isinstance(property_value, list):
for x in property_value:
indexables += recurse_properties(
properties=x,
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(property_key)])
),
)
# It's a dictionary
if isinstance(property_value, dict):
# To Do: Work out why this isn't working (some sort of simple nesting issue)
# indexables += recurse_properties(
# properties=property_value,
# doc_subtype=simplify_label(".".join([doc_subtype, slugify(property_key)])),
# )
if property_value.get("value"):
d = {
"subtype": simplify_label(
".".join([doc_subtype, slugify(property_value.get("label", ""))])
),
"indexable": property_value.get("value"),
"original_content": property_value.get("value"),
"content_id": property_value["id"],
"resource_id": target,
}
if property_value.get("selector"):
d["selector"] = {
k: [v]
for k, v in simplify_selector(
property_value.get("selector")
).items()
if simplify_selector(property_value.get("selector")) is not None
}
indexables.append(d)
if property_value.get("properties"):
indexables += recurse_properties(
properties=property_value.get("properties"),
doc_subtype=simplify_label(
".".join([doc_subtype, slugify(property_value.get("type", ""))])
),
)
return indexables
def simplify_capturemodel(capturemodel):
"""
Function for parsing a capture model into indexables
"""
if (document := capturemodel.get("document")) is not None:
indexables = []
doc_subtype = document.get("type")
if (targets := capturemodel.get("target")) is not None:
target = targets[-1].get("id")
else:
target = None
if document.get("properties"):
# This has regions of interest
if (regions := document["properties"].get("region")) is not None:
for region in regions:
if region.get("value"):
indexables.append(
{
"subtype": ".".join(
[doc_subtype, slugify(region.get("label", ""))]
),
"indexable": region.get("value"),
"original_content": region.get("value"),
"selector": {
k: [v]
for k, v in simplify_selector(region.get("selector")).items()
},
"content_id": region["id"],
"resource_id": target,
}
)
else:
# This is some sort of entity type tagging task, or other non region of interest
# so we are going to recurse into the nesting
indexables += recurse_properties(
properties=document.get("properties"), doc_subtype=doc_subtype, target=target
)
return indexables
return
def calc_offsets(obj):
"""
The search "hit" should have a 'fullsnip' annotation which is a the entire
text of the indexable resource, with <start_sel> and <end_sel> wrapping each
highlighted word.
Check if there's a selector on the indexable, and then if there's a box-selector
use this to generate a list of xywh coordinates by retrieving the selector by
its index from a list of lists
"""
if hasattr(obj, "fullsnip"):
words = obj.fullsnip.split(" ")
offsets = []
if words:
for i, word in enumerate(words):
if "<start_sel>" in word and "<end_sel>" in word:
offsets.append(i)
if offsets:
if obj.selector:
if (boxes := obj.selector.get("box-selector")) is not None:
box_list = []
for x in offsets:
try:
box_list.append(boxes[x])
except (IndexError, ValueError):
pass
if box_list:
return box_list # [boxes[x] for x in offsets if boxes[x]]
else:
return
return
class ActionBasedSerializerMixin(object):
serializer_mapping = {
"default": None,
}
def get_serializer_class(self):
logger.info(self.action)
if serializer_class := self.serializer_mapping.get(self.action):
return serializer_class
elif serializer_class := self.serializer_mapping.get("default"):
return serializer_class
else:
return self.serializer_class
class MethodBasedSerializerMixin(object):
serializer_mapping = {
"default": None,
}
def get_serializer_class(self):
logger.info(self.request.method)
if serializer_class := self.serializer_mapping.get(self.request.method.lower()):
return serializer_class
elif serializer_class := self.serializer_mapping.get("default"):
return serializer_class
else:
return self.serializer_class | en | 0.807046 | Iterate a Presentation API 3 manifest and produce a list of resources by type, e.g. Canvases or Annotations. Somewhat hacky transformation of an incoming data object for the serializer into the correct format for the model # Problem here with multilanguage label field This assumes a single navDate, but we translate this into a datetime range via adding two indexables. This assumes a single navDate, but we translate this into a datetime range via adding two indexables. Flatten the descriptive fields in a Presentation API into a list of dicts that can be passed to the Indexables model and serializers # This might be just a string, e.g. navDate # There is no language or label, so we just pass it through with the language set to None Simplify a selector from the OCR intermediate format or capture model format into a compact representation "selector": { "id": "0db4fdc1-73dd-4555-95da-7cbc746c980c", "state": { "height": "60", "width": "20", "x": "821", "y": "644" }, "type": "box-selector" }, Becomes (XYWH): 832,644,20,60 Simplify ocr to just a single continuous page of text, with selectors. # This is a nested model so recurse into that # This is just the content of a list of values so index them # Check for selector # Iterate through the keys in the dictionary # It's a list, so we should extract the indexables from each one # It's a dictionary # To Do: Work out why this isn't working (some sort of simple nesting issue) # indexables += recurse_properties( # properties=property_value, # doc_subtype=simplify_label(".".join([doc_subtype, slugify(property_key)])), # ) Function for parsing a capture model into indexables # This has regions of interest # This is some sort of entity type tagging task, or other non region of interest # so we are going to recurse into the nesting The search "hit" should have a 'fullsnip' annotation which is a the entire text of the indexable resource, with <start_sel> and <end_sel> wrapping each highlighted word. Check if there's a selector on the indexable, and then if there's a box-selector use this to generate a list of xywh coordinates by retrieving the selector by its index from a list of lists # [boxes[x] for x in offsets if boxes[x]] | 2.160444 | 2 |
groups/migrations/0004_auto_20200510_0509.py | 3crabs/class-book | 1 | 6614314 | # Generated by Django 3.0.6 on 2020-05-09 22:09
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('groups', '0003_student'),
]
operations = [
migrations.RenameField(
model_name='group',
old_name='subject',
new_name='subjects',
),
]
| # Generated by Django 3.0.6 on 2020-05-09 22:09
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('groups', '0003_student'),
]
operations = [
migrations.RenameField(
model_name='group',
old_name='subject',
new_name='subjects',
),
]
| en | 0.802708 | # Generated by Django 3.0.6 on 2020-05-09 22:09 | 1.735571 | 2 |
gen_valset.py | likesum/deepFnF | 8 | 6614315 | #!/usr/bin/env python3
import os
import numpy as np
import tensorflow as tf
from utils.dataset import Dataset
outpath = 'data/valset'
if not os.path.exists(outpath):
os.makedirs(outpath)
TLIST = 'data/train.txt'
VLIST = 'data/val.txt'
def gamma(img):
return img**(1/2.2)
BSZ = 1
IMSZ = 448
dataset = Dataset(TLIST, VLIST, bsz=BSZ, psz=IMSZ, onfly_val=True)
example = dataset.batches[0]
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
dataset.init_handles(sess)
dataset.swap_val(sess)
c = 0
while True:
try:
data = sess.run(example)
np.savez('%s/%d.npz' % (outpath, c), **data)
c += 1
except tf.errors.OutOfRangeError:
break
| #!/usr/bin/env python3
import os
import numpy as np
import tensorflow as tf
from utils.dataset import Dataset
outpath = 'data/valset'
if not os.path.exists(outpath):
os.makedirs(outpath)
TLIST = 'data/train.txt'
VLIST = 'data/val.txt'
def gamma(img):
return img**(1/2.2)
BSZ = 1
IMSZ = 448
dataset = Dataset(TLIST, VLIST, bsz=BSZ, psz=IMSZ, onfly_val=True)
example = dataset.batches[0]
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
dataset.init_handles(sess)
dataset.swap_val(sess)
c = 0
while True:
try:
data = sess.run(example)
np.savez('%s/%d.npz' % (outpath, c), **data)
c += 1
except tf.errors.OutOfRangeError:
break
| fr | 0.221828 | #!/usr/bin/env python3 | 2.342383 | 2 |
tests/ci/commit_status_helper.py | zhongyuankai/ClickHouse | 1 | 6614316 | #!/usr/bin/env python3
import time
from env_helper import GITHUB_REPOSITORY
from ci_config import CI_CONFIG
RETRY = 5
def override_status(status, check_name):
if CI_CONFIG["tests_config"][check_name].get("force_tests", False):
return "success"
return status
def get_commit(gh, commit_sha, retry_count=RETRY):
for i in range(retry_count):
try:
repo = gh.get_repo(GITHUB_REPOSITORY)
commit = repo.get_commit(commit_sha)
return commit
except Exception as ex:
if i == retry_count - 1:
raise ex
time.sleep(i)
# just suppress warning
return None
def post_commit_status(gh, sha, check_name, description, state, report_url):
for i in range(RETRY):
try:
commit = get_commit(gh, sha, 1)
commit.create_status(
context=check_name,
description=description,
state=state,
target_url=report_url,
)
break
except Exception as ex:
if i == RETRY - 1:
raise ex
time.sleep(i)
| #!/usr/bin/env python3
import time
from env_helper import GITHUB_REPOSITORY
from ci_config import CI_CONFIG
RETRY = 5
def override_status(status, check_name):
if CI_CONFIG["tests_config"][check_name].get("force_tests", False):
return "success"
return status
def get_commit(gh, commit_sha, retry_count=RETRY):
for i in range(retry_count):
try:
repo = gh.get_repo(GITHUB_REPOSITORY)
commit = repo.get_commit(commit_sha)
return commit
except Exception as ex:
if i == retry_count - 1:
raise ex
time.sleep(i)
# just suppress warning
return None
def post_commit_status(gh, sha, check_name, description, state, report_url):
for i in range(RETRY):
try:
commit = get_commit(gh, sha, 1)
commit.create_status(
context=check_name,
description=description,
state=state,
target_url=report_url,
)
break
except Exception as ex:
if i == RETRY - 1:
raise ex
time.sleep(i)
| en | 0.21501 | #!/usr/bin/env python3 # just suppress warning | 2.419401 | 2 |
packageopt/services/agents/implementations/daily_traded_volume_money_agent.py | nspostnov/for-article-optimal-position-liquidation | 0 | 6614317 | from ..abstract_base_classes.agent import Agent
__all__ = ['DailyTradedVolumeMoneyAgent']
class DailyTradedVolumeMoneyAgent(Agent):
def __init__(self, dailytradedvolumemoneyrepo, dailytradedvolumemoneysolver):
self._dailytradedvolumemoneyrepo = dailytradedvolumemoneyrepo
self._dailytradedvolumemoneysolver = dailytradedvolumemoneysolver
def get(self, key):
dailytradedvolumemoney = self._dailytradedvolumemoneyrepo.get(key)
if dailytradedvolumemoney is None:
dailytradedvolumemoney = self._dailytradedvolumemoneysolver.calculate(key)
self._dailytradedvolumemoneyrepo.set(dailytradedvolumemoney)
dailytradedvolumemoney = self._dailytradedvolumemoneyrepo.get(key)
return dailytradedvolumemoney
| from ..abstract_base_classes.agent import Agent
__all__ = ['DailyTradedVolumeMoneyAgent']
class DailyTradedVolumeMoneyAgent(Agent):
def __init__(self, dailytradedvolumemoneyrepo, dailytradedvolumemoneysolver):
self._dailytradedvolumemoneyrepo = dailytradedvolumemoneyrepo
self._dailytradedvolumemoneysolver = dailytradedvolumemoneysolver
def get(self, key):
dailytradedvolumemoney = self._dailytradedvolumemoneyrepo.get(key)
if dailytradedvolumemoney is None:
dailytradedvolumemoney = self._dailytradedvolumemoneysolver.calculate(key)
self._dailytradedvolumemoneyrepo.set(dailytradedvolumemoney)
dailytradedvolumemoney = self._dailytradedvolumemoneyrepo.get(key)
return dailytradedvolumemoney
| none | 1 | 3.109981 | 3 | |
Fig4_1Dtrack/unfamiliar_RNN1.25x/src_1comp_etalow/shared_setting.py | TatsuyaHaga/preplaymodel_codes | 1 | 6614318 |
time_pitch=1.0
sim_len_sec=50.0
NE=300
Nsominh=100
Ndndinh=100
Ninput=500
Ndistractor=200
|
time_pitch=1.0
sim_len_sec=50.0
NE=300
Nsominh=100
Ndndinh=100
Ninput=500
Ndistractor=200
| none | 1 | 0.888564 | 1 | |
dyndns_update.py | cptaffe/dyndns_update | 0 | 6614319 | #!/usr/bin/env python3
import requests, datetime
from time import sleep
urls = ['http://cpt.hopper.pw:LBbRhmu3gV@ipv4.www.hopper.pw/nic/update']
# basic auth to hopper.pw updates
while True:
for url in urls:
try:
r = requests.get(url, auth=('cpt.hopper.pw', 'LBbRhmu3gV'))
print("response @", datetime.datetime.now(), ":", r.text)
except requests.exceptions.RequestException as e:
print(e, file=sys.stderr) # print to stderr
sleep(300) # sleep for five minutes
| #!/usr/bin/env python3
import requests, datetime
from time import sleep
urls = ['http://cpt.hopper.pw:LBbRhmu3gV@ipv4.www.hopper.pw/nic/update']
# basic auth to hopper.pw updates
while True:
for url in urls:
try:
r = requests.get(url, auth=('cpt.hopper.pw', 'LBbRhmu3gV'))
print("response @", datetime.datetime.now(), ":", r.text)
except requests.exceptions.RequestException as e:
print(e, file=sys.stderr) # print to stderr
sleep(300) # sleep for five minutes
| en | 0.489195 | #!/usr/bin/env python3 # basic auth to hopper.pw updates # print to stderr # sleep for five minutes | 2.574272 | 3 |
interview/leet/537_Complex_Number_Multiplication.py | eroicaleo/LearningPython | 1 | 6614320 | #!/usr/bin/env python
# Example 1:
# Input: "1+1i", "1+1i"
# Output: "0+2i"
# Explanation: (1 + i) * (1 + i) = 1 + i2 + 2 * i = 2i, and you need convert it to the form of 0+2i.
# Example 2:
# Input: "1+-1i", "1+-1i"
# Output: "0+-2i"
# Explanation: (1 - i) * (1 - i) = 1 + i2 - 2 * i = -2i, and you need convert it to the form of 0+-2i.
class Solution:
def complexNumberMultiply(self, a: str, b: str) -> str:
a_l, b_l = a.split('+'), b.split('+')
ar, ai = int(a_l[0]), int(a_l[1][:-1])
br, bi = int(b_l[0]), int(b_l[1][:-1])
print(ar, ai, br, bi)
return f'{ar*br-ai*bi}+{ar*bi+ai*br}i'
a, b = "1+-1i", "1+-1i"
a, b = "1+1i", "1+1i"
sol = Solution()
print(sol.complexNumberMultiply(a, b))
| #!/usr/bin/env python
# Example 1:
# Input: "1+1i", "1+1i"
# Output: "0+2i"
# Explanation: (1 + i) * (1 + i) = 1 + i2 + 2 * i = 2i, and you need convert it to the form of 0+2i.
# Example 2:
# Input: "1+-1i", "1+-1i"
# Output: "0+-2i"
# Explanation: (1 - i) * (1 - i) = 1 + i2 - 2 * i = -2i, and you need convert it to the form of 0+-2i.
class Solution:
def complexNumberMultiply(self, a: str, b: str) -> str:
a_l, b_l = a.split('+'), b.split('+')
ar, ai = int(a_l[0]), int(a_l[1][:-1])
br, bi = int(b_l[0]), int(b_l[1][:-1])
print(ar, ai, br, bi)
return f'{ar*br-ai*bi}+{ar*bi+ai*br}i'
a, b = "1+-1i", "1+-1i"
a, b = "1+1i", "1+1i"
sol = Solution()
print(sol.complexNumberMultiply(a, b))
| en | 0.54551 | #!/usr/bin/env python # Example 1: # Input: "1+1i", "1+1i" # Output: "0+2i" # Explanation: (1 + i) * (1 + i) = 1 + i2 + 2 * i = 2i, and you need convert it to the form of 0+2i. # Example 2: # Input: "1+-1i", "1+-1i" # Output: "0+-2i" # Explanation: (1 - i) * (1 - i) = 1 + i2 - 2 * i = -2i, and you need convert it to the form of 0+-2i. | 4.040774 | 4 |
crumbs/information.py | alunduil/crumbs | 10 | 6614321 | # Copyright (C) 2015 by <NAME> <<EMAIL>>
#
# crumbs is freely distributable under the terms of an MIT-style license.
# See COPYING or http://www.opensource.org/licenses/mit-license.php.
NAME = 'crumbs'
VERSION = '2.1.1'
DESCRIPTION = 'Generalized all-in-one parameters module.'
AUTHOR = '<NAME>'
AUTHOR_EMAIL = '<EMAIL>'
URL = 'https://github.com/alunduil/crumbs'
LICENSE = 'MIT'
COPYRIGHT = '2015'
| # Copyright (C) 2015 by <NAME> <<EMAIL>>
#
# crumbs is freely distributable under the terms of an MIT-style license.
# See COPYING or http://www.opensource.org/licenses/mit-license.php.
NAME = 'crumbs'
VERSION = '2.1.1'
DESCRIPTION = 'Generalized all-in-one parameters module.'
AUTHOR = '<NAME>'
AUTHOR_EMAIL = '<EMAIL>'
URL = 'https://github.com/alunduil/crumbs'
LICENSE = 'MIT'
COPYRIGHT = '2015'
| en | 0.682219 | # Copyright (C) 2015 by <NAME> <<EMAIL>> # # crumbs is freely distributable under the terms of an MIT-style license. # See COPYING or http://www.opensource.org/licenses/mit-license.php. | 0.837089 | 1 |
hw/DataStructure2019-PJ2/random-gen.py | Riteme/test | 3 | 6614322 | <reponame>Riteme/test
#!/usr/bin/pypy
from sys import *
from random import *
n, m, q, K, C = map(int, argv[1:])
print n, m
# for v in xrange(2, n + 1):
# u = randint(1, v - 1)
# w = randint(1, C)
# print u, v, w
# m -= n - 1
for i in xrange(m):
u = randint(1, n)
v = randint(1, n)
w = randint(1, C)
print u, v, w
print q
for i in xrange(q):
s = randint(1, n)
t = randint(1, n)
k = randint(0, K)
idx = [randint(1, m) for j in xrange(k)]
print s, t, k, ' '.join(map(str, idx)) | #!/usr/bin/pypy
from sys import *
from random import *
n, m, q, K, C = map(int, argv[1:])
print n, m
# for v in xrange(2, n + 1):
# u = randint(1, v - 1)
# w = randint(1, C)
# print u, v, w
# m -= n - 1
for i in xrange(m):
u = randint(1, n)
v = randint(1, n)
w = randint(1, C)
print u, v, w
print q
for i in xrange(q):
s = randint(1, n)
t = randint(1, n)
k = randint(0, K)
idx = [randint(1, m) for j in xrange(k)]
print s, t, k, ' '.join(map(str, idx)) | en | 0.385732 | #!/usr/bin/pypy # for v in xrange(2, n + 1): # u = randint(1, v - 1) # w = randint(1, C) # print u, v, w # m -= n - 1 | 2.6617 | 3 |
bare_python/s19_07_import_mod_func_only.py | AndreiHondrari/python_exploration | 3 | 6614323 | #!python3
from ut import p
p("from mod1 import something")
_temp = __import__("mod1")
something = _temp.something
something()
| #!python3
from ut import p
p("from mod1 import something")
_temp = __import__("mod1")
something = _temp.something
something()
| none | 1 | 1.741219 | 2 | |
bbbs/diary/migrations/0003_alter_diary_meeting_date.py | dangerousmonk/bigBrothers-bigSisters-backend | 0 | 6614324 | # Generated by Django 3.2.5 on 2021-08-13 18:40
import bbbs.common.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('diary', '0002_auto_20210812_2011'),
]
operations = [
migrations.AlterField(
model_name='diary',
name='meeting_date',
field=models.DateField(validators=[bbbs.common.validators.year_validator], verbose_name='meeting date'),
),
]
| # Generated by Django 3.2.5 on 2021-08-13 18:40
import bbbs.common.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('diary', '0002_auto_20210812_2011'),
]
operations = [
migrations.AlterField(
model_name='diary',
name='meeting_date',
field=models.DateField(validators=[bbbs.common.validators.year_validator], verbose_name='meeting date'),
),
]
| en | 0.846656 | # Generated by Django 3.2.5 on 2021-08-13 18:40 | 1.563956 | 2 |
app/views/dashboard/questions/__init__.py | Wern-rm/raton.by | 0 | 6614325 | <reponame>Wern-rm/raton.by
from app.views.dashboard.questions.index import questions
from app.views.dashboard.questions.activation import question_activated
from app.views.dashboard.questions.electron_activation import question_electron_activated
from app.views.dashboard.questions.items_activation import question_items_activated | from app.views.dashboard.questions.index import questions
from app.views.dashboard.questions.activation import question_activated
from app.views.dashboard.questions.electron_activation import question_electron_activated
from app.views.dashboard.questions.items_activation import question_items_activated | none | 1 | 1.110897 | 1 | |
challenge/slides/quarter-model/quarter-model.py | kjartan-at-tec/mr2023 | 0 | 6614326 | <reponame>kjartan-at-tec/mr2023<filename>challenge/slides/quarter-model/quarter-model.py<gh_stars>0
import numpy as np
from control import matlab as cm
# Parameters From https://ctms.engin.umich.edu/CTMS/index.php?example=Suspension§ion=SimulinkModeling
M1 = 2500 # Sprung mass
M1 = 0.2*M1 # Sprung mass
M2 = 320 # Unsprung mass
M2 = 0.2*M2 # Unsprung mass
b1 = 350 # Damping coeff
b1 = 8*b1 # Damping coeff
b2 = 15020 # Damping coeff
b2 = 0.5*b2 # Damping coeff
K1 = 80000 # Spring stiffness
K1 = 0.2*K1 # Spring stiffness
K2 = 200000 # Tire stiffness
Zr = 0.15 # Road input
# State space model
A = np.array([[0, 1, 0, 0],
[-(b1*b2)/(M1*M2), 0, (b1/M1)*(b1/M1 + b1/M2 + b2/M2)-K1/M1, -b1/M1],
[b2/M2, 0, -(b1/M1 + b1/M2 + b2/M2), 1],
[K2/M2, 0, -(K1/M1 + K1/M2 + K2/M2), 0]])
B = np.array([[0, 0],
[1/M1, b1*b2/(M1*M2)],
[0, -b2/M2],
[(1/M2+1/M1), -K2/M2]])
C = np.array([[1, 0, 0, 0],[0,0,1,0]])
D = np.array([[0,0],[0,0]])
ss_sys = cm.ss(A, B, C, D)
# Input signal
N = 240
tend = 6
tt = np.linspace(0, tend, N)
uw = np.zeros((2,N))
uw[1,40:70] = Zr*np.sin(np.linspace(0,np.pi,30))
uw[1,120:150] = -Zr*np.sin(np.linspace(0,np.pi,30))
yout, T, xout = cm.lsim(ss_sys, uw.T, tt)
kk = 10 # convert to dm for animation
x1 = kk * xout[::2,0]
x2 = x1 - kk*xout[::2,2]
w = uw[1,::2] * 10
T.shape=(len(T),1)
TT = T[::2]
dta = np.transpose(np.vstack((np.ravel(TT), x1, x2, w)))
np.savetxt('./quarter_model.dta', dta, delimiter=',', fmt='%8.4f')
| import numpy as np
from control import matlab as cm
# Parameters From https://ctms.engin.umich.edu/CTMS/index.php?example=Suspension§ion=SimulinkModeling
M1 = 2500 # Sprung mass
M1 = 0.2*M1 # Sprung mass
M2 = 320 # Unsprung mass
M2 = 0.2*M2 # Unsprung mass
b1 = 350 # Damping coeff
b1 = 8*b1 # Damping coeff
b2 = 15020 # Damping coeff
b2 = 0.5*b2 # Damping coeff
K1 = 80000 # Spring stiffness
K1 = 0.2*K1 # Spring stiffness
K2 = 200000 # Tire stiffness
Zr = 0.15 # Road input
# State space model
A = np.array([[0, 1, 0, 0],
[-(b1*b2)/(M1*M2), 0, (b1/M1)*(b1/M1 + b1/M2 + b2/M2)-K1/M1, -b1/M1],
[b2/M2, 0, -(b1/M1 + b1/M2 + b2/M2), 1],
[K2/M2, 0, -(K1/M1 + K1/M2 + K2/M2), 0]])
B = np.array([[0, 0],
[1/M1, b1*b2/(M1*M2)],
[0, -b2/M2],
[(1/M2+1/M1), -K2/M2]])
C = np.array([[1, 0, 0, 0],[0,0,1,0]])
D = np.array([[0,0],[0,0]])
ss_sys = cm.ss(A, B, C, D)
# Input signal
N = 240
tend = 6
tt = np.linspace(0, tend, N)
uw = np.zeros((2,N))
uw[1,40:70] = Zr*np.sin(np.linspace(0,np.pi,30))
uw[1,120:150] = -Zr*np.sin(np.linspace(0,np.pi,30))
yout, T, xout = cm.lsim(ss_sys, uw.T, tt)
kk = 10 # convert to dm for animation
x1 = kk * xout[::2,0]
x2 = x1 - kk*xout[::2,2]
w = uw[1,::2] * 10
T.shape=(len(T),1)
TT = T[::2]
dta = np.transpose(np.vstack((np.ravel(TT), x1, x2, w)))
np.savetxt('./quarter_model.dta', dta, delimiter=',', fmt='%8.4f') | en | 0.604496 | # Parameters From https://ctms.engin.umich.edu/CTMS/index.php?example=Suspension§ion=SimulinkModeling # Sprung mass # Sprung mass # Unsprung mass # Unsprung mass # Damping coeff # Damping coeff # Damping coeff # Damping coeff # Spring stiffness # Spring stiffness # Tire stiffness # Road input # State space model # Input signal # convert to dm for animation | 2.633816 | 3 |
globals.py | uuk0/mcpython-a-minecraft-clone-in-python | 2 | 6614327 | <reponame>uuk0/mcpython-a-minecraft-clone-in-python<gh_stars>1-10
window = None
model = None
player = None
inventoryhandler = None
local = "."
mods = []
modnames = []
seed = None
random = None
statehandler = None
State = None
TileState = None
NEXT_SPHERE_ID = 0
| window = None
model = None
player = None
inventoryhandler = None
local = "."
mods = []
modnames = []
seed = None
random = None
statehandler = None
State = None
TileState = None
NEXT_SPHERE_ID = 0 | none | 1 | 1.304428 | 1 | |
backend/Scripts/backend/api/models.py | makr11/FitCommit | 1 | 6614328 | from django.db import models
from django.contrib.auth.models import AbstractUser
from django.utils import timezone
from django.utils.translation import gettext_lazy as _
from django.contrib.auth.validators import UnicodeUsernameValidator
class Setup(models.Model):
name = models.CharField(max_length=30, primary_key=True)
value = models.CharField(max_length=100)
class CustomUser(AbstractUser):
username_validator = UnicodeUsernameValidator()
IDUser = models.CharField(max_length=5, null=True, blank=True)
phone = models.CharField(max_length=50, null=True, blank=True)
birth_date = models.DateField(null=True, blank=True)
address = models.CharField(max_length=70, null=True, blank=True)
city = models.CharField(max_length=50, null=True, blank=True)
username = models.CharField(
_('username'),
max_length=150,
unique=True,
help_text=_(
'Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.'),
validators=[username_validator],
error_messages={
'unique': _("A user with that username already exists."),
},
blank=True
)
password = models.CharField(_('password'), max_length=128, blank=True)
def __str__(self):
return self.first_name + ' ' + self.last_name
class Services(models.Model):
service = models.CharField(max_length=50)
def __str__(self):
return self.service
class Categories(models.Model):
category = models.CharField(max_length=50)
serviceID = models.ForeignKey(
Services, related_name='categories', on_delete=models.CASCADE)
def __str__(self):
return self.category
class Options(models.Model):
arrivals = models.IntegerField()
price = models.IntegerField()
duration = models.IntegerField()
categoryID = models.ForeignKey(
Categories, related_name='options', on_delete=models.CASCADE)
def __str__(self):
return str(self.arrivals)
class Records(models.Model):
userObj = models.ForeignKey(
CustomUser, related_name='user_records', on_delete=models.CASCADE, null=True)
serviceObj = models.ForeignKey(
Services, related_name='service_records', on_delete=models.CASCADE, null=True)
categoryObj = models.ForeignKey(
Categories, related_name='category_records', on_delete=models.CASCADE, null=True)
optionObj = models.ForeignKey(
Options, related_name='options_records', on_delete=models.CASCADE, null=True)
arrivals_left = models.IntegerField()
days_left = models.IntegerField(default=0)
active = models.BooleanField(default=1, blank=True)
price = models.IntegerField()
discount = models.IntegerField()
nett_price = models.IntegerField()
paid = models.BooleanField(default=0)
frozen = models.IntegerField(default=0)
freeze_started = models.DateField(blank=True, null=True)
freeze_ended = models.DateField(blank=True, null=True)
started = models.DateField(auto_now_add=True)
ends = models.DateField()
def is_active(self):
if self.arrivals_left == 0:
self.active = 0
self.save()
else:
self.active = 1
self.save()
def get_days_left(self):
now = timezone.now().date()
if self.ends > now:
days_left = self.ends - now
self.days_left = days_left.days
self.save()
else:
self.days_left = 0
self.active = False
self.save()
def is_frozen(self):
now = timezone.now().date()
if self.freeze_ended != None and self.freeze_ended < now:
self.freeze_started = None
self.freeze_ended = None
self.save()
@property
def user(self):
return self.userObj.first_name + ' ' + self.userObj.last_name
class Arrivals(models.Model):
userObj = models.ForeignKey(
CustomUser, related_name='user_arrivals', on_delete=models.CASCADE, null=True)
recordObj = models.ForeignKey(
Records, related_name='record_arrivals', on_delete=models.CASCADE, null=True)
arrival = models.DateTimeField()
| from django.db import models
from django.contrib.auth.models import AbstractUser
from django.utils import timezone
from django.utils.translation import gettext_lazy as _
from django.contrib.auth.validators import UnicodeUsernameValidator
class Setup(models.Model):
name = models.CharField(max_length=30, primary_key=True)
value = models.CharField(max_length=100)
class CustomUser(AbstractUser):
username_validator = UnicodeUsernameValidator()
IDUser = models.CharField(max_length=5, null=True, blank=True)
phone = models.CharField(max_length=50, null=True, blank=True)
birth_date = models.DateField(null=True, blank=True)
address = models.CharField(max_length=70, null=True, blank=True)
city = models.CharField(max_length=50, null=True, blank=True)
username = models.CharField(
_('username'),
max_length=150,
unique=True,
help_text=_(
'Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.'),
validators=[username_validator],
error_messages={
'unique': _("A user with that username already exists."),
},
blank=True
)
password = models.CharField(_('password'), max_length=128, blank=True)
def __str__(self):
return self.first_name + ' ' + self.last_name
class Services(models.Model):
service = models.CharField(max_length=50)
def __str__(self):
return self.service
class Categories(models.Model):
category = models.CharField(max_length=50)
serviceID = models.ForeignKey(
Services, related_name='categories', on_delete=models.CASCADE)
def __str__(self):
return self.category
class Options(models.Model):
arrivals = models.IntegerField()
price = models.IntegerField()
duration = models.IntegerField()
categoryID = models.ForeignKey(
Categories, related_name='options', on_delete=models.CASCADE)
def __str__(self):
return str(self.arrivals)
class Records(models.Model):
userObj = models.ForeignKey(
CustomUser, related_name='user_records', on_delete=models.CASCADE, null=True)
serviceObj = models.ForeignKey(
Services, related_name='service_records', on_delete=models.CASCADE, null=True)
categoryObj = models.ForeignKey(
Categories, related_name='category_records', on_delete=models.CASCADE, null=True)
optionObj = models.ForeignKey(
Options, related_name='options_records', on_delete=models.CASCADE, null=True)
arrivals_left = models.IntegerField()
days_left = models.IntegerField(default=0)
active = models.BooleanField(default=1, blank=True)
price = models.IntegerField()
discount = models.IntegerField()
nett_price = models.IntegerField()
paid = models.BooleanField(default=0)
frozen = models.IntegerField(default=0)
freeze_started = models.DateField(blank=True, null=True)
freeze_ended = models.DateField(blank=True, null=True)
started = models.DateField(auto_now_add=True)
ends = models.DateField()
def is_active(self):
if self.arrivals_left == 0:
self.active = 0
self.save()
else:
self.active = 1
self.save()
def get_days_left(self):
now = timezone.now().date()
if self.ends > now:
days_left = self.ends - now
self.days_left = days_left.days
self.save()
else:
self.days_left = 0
self.active = False
self.save()
def is_frozen(self):
now = timezone.now().date()
if self.freeze_ended != None and self.freeze_ended < now:
self.freeze_started = None
self.freeze_ended = None
self.save()
@property
def user(self):
return self.userObj.first_name + ' ' + self.userObj.last_name
class Arrivals(models.Model):
userObj = models.ForeignKey(
CustomUser, related_name='user_arrivals', on_delete=models.CASCADE, null=True)
recordObj = models.ForeignKey(
Records, related_name='record_arrivals', on_delete=models.CASCADE, null=True)
arrival = models.DateTimeField()
| none | 1 | 2.431582 | 2 | |
node_listener/demo/demo_weather.py | bkosciow/sensor_listener | 0 | 6614329 | from pprint import pprint
from node_listener.worker.openweather_worker import OpenweatherWorker
from node_listener.service.config import Config
config = Config('../../config.ini')
apikey = config["openweather"]["apikey"]
cities = {3103402: "Bielsko-Biała"}
w = OpenweatherWorker(cities, apikey, config["general"]["user_agent"])
pprint(w.execute())
| from pprint import pprint
from node_listener.worker.openweather_worker import OpenweatherWorker
from node_listener.service.config import Config
config = Config('../../config.ini')
apikey = config["openweather"]["apikey"]
cities = {3103402: "Bielsko-Biała"}
w = OpenweatherWorker(cities, apikey, config["general"]["user_agent"])
pprint(w.execute())
| none | 1 | 1.900738 | 2 | |
major_event_log/migrations/0002_auto_20180911_1613.py | unt-libraries/django-major-event-log | 0 | 6614330 | <reponame>unt-libraries/django-major-event-log
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('major_event_log', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='event',
name='contact_name',
field=models.CharField(help_text=b'Appears as the Reporting Agent', max_length=100),
),
migrations.AlterField(
model_name='event',
name='outcome',
field=models.CharField(max_length=80, choices=[(b'http://purl.org/NET/UNTL/vocabularies/eventOutcomes/#success', b'Success'), (b'http://purl.org/NET/UNTL/vocabularies/eventOutcomes/#failure', b'Failure')]),
),
]
| # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('major_event_log', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='event',
name='contact_name',
field=models.CharField(help_text=b'Appears as the Reporting Agent', max_length=100),
),
migrations.AlterField(
model_name='event',
name='outcome',
field=models.CharField(max_length=80, choices=[(b'http://purl.org/NET/UNTL/vocabularies/eventOutcomes/#success', b'Success'), (b'http://purl.org/NET/UNTL/vocabularies/eventOutcomes/#failure', b'Failure')]),
),
] | en | 0.375 | # -*- coding: utf-8 -*- #success', b'Success'), (b'http://purl.org/NET/UNTL/vocabularies/eventOutcomes/#failure', b'Failure')]), | 1.683153 | 2 |
videos/factories.py | mitodl/ocw-studio | 2 | 6614331 | """videos factories"""
import factory
from django.conf import settings
from factory.django import DjangoModelFactory
from factory.fuzzy import FuzzyChoice
from videos.constants import ALL_DESTINATIONS, VideoStatus
from videos.models import Video, VideoFile, VideoJob
from websites.factories import WebsiteFactory
class VideoFactory(DjangoModelFactory):
""" Factory for Video model"""
source_key = factory.Sequence(
lambda n: f"{settings.DRIVE_S3_UPLOAD_PREFIX}/{n}/file_{n}"
)
website = factory.SubFactory(WebsiteFactory)
status = FuzzyChoice(VideoStatus.ALL_STATUSES)
class Meta:
model = Video
class VideoFileFactory(DjangoModelFactory):
"""Factory for VideoFile model"""
video = factory.SubFactory(VideoFactory)
s3_key = factory.Sequence(
lambda n: f"{settings.VIDEO_S3_TRANSCODE_PREFIX}/{n}/file_{n}"
)
destination = FuzzyChoice(ALL_DESTINATIONS)
destination_id = factory.Faker("domain_word")
class Meta:
model = VideoFile
class VideoJobFactory(DjangoModelFactory):
"""Factory for VideoJob model"""
video = factory.SubFactory(VideoFactory)
job_id = factory.Faker("md5")
status = FuzzyChoice("ERROR")
class Meta:
model = VideoJob
| """videos factories"""
import factory
from django.conf import settings
from factory.django import DjangoModelFactory
from factory.fuzzy import FuzzyChoice
from videos.constants import ALL_DESTINATIONS, VideoStatus
from videos.models import Video, VideoFile, VideoJob
from websites.factories import WebsiteFactory
class VideoFactory(DjangoModelFactory):
""" Factory for Video model"""
source_key = factory.Sequence(
lambda n: f"{settings.DRIVE_S3_UPLOAD_PREFIX}/{n}/file_{n}"
)
website = factory.SubFactory(WebsiteFactory)
status = FuzzyChoice(VideoStatus.ALL_STATUSES)
class Meta:
model = Video
class VideoFileFactory(DjangoModelFactory):
"""Factory for VideoFile model"""
video = factory.SubFactory(VideoFactory)
s3_key = factory.Sequence(
lambda n: f"{settings.VIDEO_S3_TRANSCODE_PREFIX}/{n}/file_{n}"
)
destination = FuzzyChoice(ALL_DESTINATIONS)
destination_id = factory.Faker("domain_word")
class Meta:
model = VideoFile
class VideoJobFactory(DjangoModelFactory):
"""Factory for VideoJob model"""
video = factory.SubFactory(VideoFactory)
job_id = factory.Faker("md5")
status = FuzzyChoice("ERROR")
class Meta:
model = VideoJob
| en | 0.768992 | videos factories Factory for Video model Factory for VideoFile model Factory for VideoJob model | 2.265178 | 2 |
projectdiffview/gui.py | jdpatt/project-diff-view | 0 | 6614332 | # -*- coding: utf-8 -*-
################################################################################
## Form generated from reading UI file 'gui.ui'
##
## Created by: Qt User Interface Compiler version 5.15.0
##
## WARNING! All changes made in this file will be lost when recompiling UI file!
################################################################################
from PySide2.QtCore import (
QCoreApplication,
QDate,
QDateTime,
QMetaObject,
QObject,
QPoint,
QRect,
QSize,
Qt,
QTime,
QUrl,
)
from PySide2.QtGui import (
QBrush,
QColor,
QConicalGradient,
QCursor,
QFont,
QFontDatabase,
QIcon,
QKeySequence,
QLinearGradient,
QPainter,
QPalette,
QPixmap,
QRadialGradient,
)
from PySide2.QtWidgets import (
QAbstractItemView,
QAction,
QHBoxLayout,
QLabel,
QLineEdit,
QMenu,
QMenuBar,
QPushButton,
QSizePolicy,
QSpacerItem,
QStatusBar,
QTreeView,
QVBoxLayout,
QWidget,
)
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
if not MainWindow.objectName():
MainWindow.setObjectName("MainWindow")
MainWindow.resize(1200, 600)
MainWindow.setDocumentMode(True)
self.actionPreferences = QAction(MainWindow)
self.actionPreferences.setObjectName("actionPreferences")
self.actionExit = QAction(MainWindow)
self.actionExit.setObjectName("actionExit")
self.actionNew_Project = QAction(MainWindow)
self.actionNew_Project.setObjectName("actionNew_Project")
self.actionSave = QAction(MainWindow)
self.actionSave.setObjectName("actionSave")
self.actionOpen_Project = QAction(MainWindow)
self.actionOpen_Project.setObjectName("actionOpen_Project")
self.actionDocumentation = QAction(MainWindow)
self.actionDocumentation.setObjectName("actionDocumentation")
self.actionAbout = QAction(MainWindow)
self.actionAbout.setObjectName("actionAbout")
self.actionConsole_Visibility = QAction(MainWindow)
self.actionConsole_Visibility.setObjectName("actionConsole_Visibility")
self.actionConsole_Visibility.setCheckable(True)
self.actionConsole_Visibility.setChecked(True)
self.actionSave_As = QAction(MainWindow)
self.actionSave_As.setObjectName("actionSave_As")
self.actionSettings = QAction(MainWindow)
self.actionSettings.setObjectName("actionSettings")
self.centralwidget = QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.verticalLayout_3 = QVBoxLayout(self.centralwidget)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.horizontalLayout = QHBoxLayout()
self.horizontalLayout.setObjectName("horizontalLayout")
self.label = QLabel(self.centralwidget)
self.label.setObjectName("label")
self.horizontalLayout.addWidget(self.label)
self.directory_path = QLineEdit(self.centralwidget)
self.directory_path.setObjectName("directory_path")
self.horizontalLayout.addWidget(self.directory_path)
self.browse = QPushButton(self.centralwidget)
self.browse.setObjectName("browse")
self.horizontalLayout.addWidget(self.browse)
self.verticalLayout_3.addLayout(self.horizontalLayout)
self.horizontalLayout_5 = QHBoxLayout()
self.horizontalLayout_5.setObjectName("horizontalLayout_5")
self.verticalLayout = QVBoxLayout()
self.verticalLayout.setObjectName("verticalLayout")
self.template_tree = QTreeView(self.centralwidget)
self.template_tree.setObjectName("template_tree")
self.template_tree.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.template_tree.setUniformRowHeights(True)
self.template_tree.setSortingEnabled(True)
self.template_tree.header().setMinimumSectionSize(20)
self.template_tree.header().setDefaultSectionSize(150)
self.template_tree.header().setStretchLastSection(False)
self.verticalLayout.addWidget(self.template_tree)
self.horizontalLayout_3 = QHBoxLayout()
self.horizontalLayout_3.setObjectName("horizontalLayout_3")
self.label_2 = QLabel(self.centralwidget)
self.label_2.setObjectName("label_2")
self.horizontalLayout_3.addWidget(self.label_2)
self.template_version = QLineEdit(self.centralwidget)
self.template_version.setObjectName("template_version")
self.template_version.setReadOnly(True)
self.horizontalLayout_3.addWidget(self.template_version)
self.horizontalSpacer_2 = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_3.addItem(self.horizontalSpacer_2)
self.verticalLayout.addLayout(self.horizontalLayout_3)
self.horizontalLayout_5.addLayout(self.verticalLayout)
self.verticalLayout_2 = QVBoxLayout()
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.working_tree = QTreeView(self.centralwidget)
self.working_tree.setObjectName("working_tree")
self.working_tree.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.working_tree.setUniformRowHeights(True)
self.working_tree.setSortingEnabled(True)
self.working_tree.header().setMinimumSectionSize(20)
self.working_tree.header().setDefaultSectionSize(150)
self.working_tree.header().setStretchLastSection(False)
self.verticalLayout_2.addWidget(self.working_tree)
self.horizontalLayout_2 = QHBoxLayout()
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.label_3 = QLabel(self.centralwidget)
self.label_3.setObjectName("label_3")
self.horizontalLayout_2.addWidget(self.label_3)
self.directory_version = QLineEdit(self.centralwidget)
self.directory_version.setObjectName("directory_version")
self.directory_version.setReadOnly(True)
self.horizontalLayout_2.addWidget(self.directory_version)
self.horizontalSpacer_3 = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_2.addItem(self.horizontalSpacer_3)
self.verticalLayout_2.addLayout(self.horizontalLayout_2)
self.horizontalLayout_5.addLayout(self.verticalLayout_2)
self.verticalLayout_3.addLayout(self.horizontalLayout_5)
self.horizontalLayout_4 = QHBoxLayout()
self.horizontalLayout_4.setObjectName("horizontalLayout_4")
self.horizontalSpacer = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_4.addItem(self.horizontalSpacer)
self.cleanup_working = QPushButton(self.centralwidget)
self.cleanup_working.setObjectName("cleanup_working")
self.cleanup_working.setFlat(False)
self.horizontalLayout_4.addWidget(self.cleanup_working)
self.copy_template = QPushButton(self.centralwidget)
self.copy_template.setObjectName("copy_template")
self.horizontalLayout_4.addWidget(self.copy_template)
self.add_selected = QPushButton(self.centralwidget)
self.add_selected.setObjectName("add_selected")
self.horizontalLayout_4.addWidget(self.add_selected)
self.verticalLayout_3.addLayout(self.horizontalLayout_4)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QMenuBar(MainWindow)
self.menubar.setObjectName("menubar")
self.menubar.setGeometry(QRect(0, 0, 1200, 22))
self.menubar.setDefaultUp(False)
self.menuFile = QMenu(self.menubar)
self.menuFile.setObjectName("menuFile")
self.menuHelp = QMenu(self.menubar)
self.menuHelp.setObjectName("menuHelp")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.menubar.addAction(self.menuFile.menuAction())
self.menubar.addAction(self.menuHelp.menuAction())
self.menuFile.addAction(self.actionSettings)
self.menuFile.addAction(self.actionExit)
self.menuHelp.addAction(self.actionDocumentation)
self.menuHelp.addAction(self.actionAbout)
self.retranslateUi(MainWindow)
QMetaObject.connectSlotsByName(MainWindow)
# setupUi
def retranslateUi(self, MainWindow):
MainWindow.setWindowTitle(
QCoreApplication.translate("MainWindow", "projectdiffview", None)
)
self.actionPreferences.setText(
QCoreApplication.translate("MainWindow", "Preferences", None)
)
# if QT_CONFIG(shortcut)
self.actionPreferences.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+,", None)
)
# endif // QT_CONFIG(shortcut)
self.actionExit.setText(QCoreApplication.translate("MainWindow", "Exit", None))
# if QT_CONFIG(shortcut)
self.actionExit.setShortcut(QCoreApplication.translate("MainWindow", "Ctrl+Q", None))
# endif // QT_CONFIG(shortcut)
self.actionNew_Project.setText(
QCoreApplication.translate("MainWindow", "New Project", None)
)
# if QT_CONFIG(shortcut)
self.actionNew_Project.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+N", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSave.setText(QCoreApplication.translate("MainWindow", "Save", None))
# if QT_CONFIG(shortcut)
self.actionSave.setShortcut(QCoreApplication.translate("MainWindow", "Ctrl+S", None))
# endif // QT_CONFIG(shortcut)
self.actionOpen_Project.setText(QCoreApplication.translate("MainWindow", "Open", None))
# if QT_CONFIG(shortcut)
self.actionOpen_Project.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+O", None)
)
# endif // QT_CONFIG(shortcut)
self.actionDocumentation.setText(
QCoreApplication.translate("MainWindow", "Documentation", None)
)
self.actionAbout.setText(QCoreApplication.translate("MainWindow", "About", None))
self.actionConsole_Visibility.setText(
QCoreApplication.translate("MainWindow", "Console Visibility", None)
)
# if QT_CONFIG(shortcut)
self.actionConsole_Visibility.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+`", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSave_As.setText(QCoreApplication.translate("MainWindow", "Save As", None))
# if QT_CONFIG(shortcut)
self.actionSave_As.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+Shift+S", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSettings.setText(QCoreApplication.translate("MainWindow", "Settings", None))
self.label.setText(QCoreApplication.translate("MainWindow", "Project Directory", None))
self.browse.setText(QCoreApplication.translate("MainWindow", "Browse", None))
self.label_2.setText(QCoreApplication.translate("MainWindow", "Template Version:", None))
self.label_3.setText(QCoreApplication.translate("MainWindow", "Folder Version:", None))
self.cleanup_working.setText(
QCoreApplication.translate("MainWindow", "Cleanup Folder", None)
)
self.copy_template.setText(
QCoreApplication.translate("MainWindow", "Add All to Folder", None)
)
self.add_selected.setText(
QCoreApplication.translate("MainWindow", "Add Selected to Folder", None)
)
self.menuFile.setTitle(QCoreApplication.translate("MainWindow", "&File", None))
self.menuHelp.setTitle(QCoreApplication.translate("MainWindow", "&Help", None))
# retranslateUi
| # -*- coding: utf-8 -*-
################################################################################
## Form generated from reading UI file 'gui.ui'
##
## Created by: Qt User Interface Compiler version 5.15.0
##
## WARNING! All changes made in this file will be lost when recompiling UI file!
################################################################################
from PySide2.QtCore import (
QCoreApplication,
QDate,
QDateTime,
QMetaObject,
QObject,
QPoint,
QRect,
QSize,
Qt,
QTime,
QUrl,
)
from PySide2.QtGui import (
QBrush,
QColor,
QConicalGradient,
QCursor,
QFont,
QFontDatabase,
QIcon,
QKeySequence,
QLinearGradient,
QPainter,
QPalette,
QPixmap,
QRadialGradient,
)
from PySide2.QtWidgets import (
QAbstractItemView,
QAction,
QHBoxLayout,
QLabel,
QLineEdit,
QMenu,
QMenuBar,
QPushButton,
QSizePolicy,
QSpacerItem,
QStatusBar,
QTreeView,
QVBoxLayout,
QWidget,
)
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
if not MainWindow.objectName():
MainWindow.setObjectName("MainWindow")
MainWindow.resize(1200, 600)
MainWindow.setDocumentMode(True)
self.actionPreferences = QAction(MainWindow)
self.actionPreferences.setObjectName("actionPreferences")
self.actionExit = QAction(MainWindow)
self.actionExit.setObjectName("actionExit")
self.actionNew_Project = QAction(MainWindow)
self.actionNew_Project.setObjectName("actionNew_Project")
self.actionSave = QAction(MainWindow)
self.actionSave.setObjectName("actionSave")
self.actionOpen_Project = QAction(MainWindow)
self.actionOpen_Project.setObjectName("actionOpen_Project")
self.actionDocumentation = QAction(MainWindow)
self.actionDocumentation.setObjectName("actionDocumentation")
self.actionAbout = QAction(MainWindow)
self.actionAbout.setObjectName("actionAbout")
self.actionConsole_Visibility = QAction(MainWindow)
self.actionConsole_Visibility.setObjectName("actionConsole_Visibility")
self.actionConsole_Visibility.setCheckable(True)
self.actionConsole_Visibility.setChecked(True)
self.actionSave_As = QAction(MainWindow)
self.actionSave_As.setObjectName("actionSave_As")
self.actionSettings = QAction(MainWindow)
self.actionSettings.setObjectName("actionSettings")
self.centralwidget = QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.verticalLayout_3 = QVBoxLayout(self.centralwidget)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.horizontalLayout = QHBoxLayout()
self.horizontalLayout.setObjectName("horizontalLayout")
self.label = QLabel(self.centralwidget)
self.label.setObjectName("label")
self.horizontalLayout.addWidget(self.label)
self.directory_path = QLineEdit(self.centralwidget)
self.directory_path.setObjectName("directory_path")
self.horizontalLayout.addWidget(self.directory_path)
self.browse = QPushButton(self.centralwidget)
self.browse.setObjectName("browse")
self.horizontalLayout.addWidget(self.browse)
self.verticalLayout_3.addLayout(self.horizontalLayout)
self.horizontalLayout_5 = QHBoxLayout()
self.horizontalLayout_5.setObjectName("horizontalLayout_5")
self.verticalLayout = QVBoxLayout()
self.verticalLayout.setObjectName("verticalLayout")
self.template_tree = QTreeView(self.centralwidget)
self.template_tree.setObjectName("template_tree")
self.template_tree.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.template_tree.setUniformRowHeights(True)
self.template_tree.setSortingEnabled(True)
self.template_tree.header().setMinimumSectionSize(20)
self.template_tree.header().setDefaultSectionSize(150)
self.template_tree.header().setStretchLastSection(False)
self.verticalLayout.addWidget(self.template_tree)
self.horizontalLayout_3 = QHBoxLayout()
self.horizontalLayout_3.setObjectName("horizontalLayout_3")
self.label_2 = QLabel(self.centralwidget)
self.label_2.setObjectName("label_2")
self.horizontalLayout_3.addWidget(self.label_2)
self.template_version = QLineEdit(self.centralwidget)
self.template_version.setObjectName("template_version")
self.template_version.setReadOnly(True)
self.horizontalLayout_3.addWidget(self.template_version)
self.horizontalSpacer_2 = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_3.addItem(self.horizontalSpacer_2)
self.verticalLayout.addLayout(self.horizontalLayout_3)
self.horizontalLayout_5.addLayout(self.verticalLayout)
self.verticalLayout_2 = QVBoxLayout()
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.working_tree = QTreeView(self.centralwidget)
self.working_tree.setObjectName("working_tree")
self.working_tree.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.working_tree.setUniformRowHeights(True)
self.working_tree.setSortingEnabled(True)
self.working_tree.header().setMinimumSectionSize(20)
self.working_tree.header().setDefaultSectionSize(150)
self.working_tree.header().setStretchLastSection(False)
self.verticalLayout_2.addWidget(self.working_tree)
self.horizontalLayout_2 = QHBoxLayout()
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.label_3 = QLabel(self.centralwidget)
self.label_3.setObjectName("label_3")
self.horizontalLayout_2.addWidget(self.label_3)
self.directory_version = QLineEdit(self.centralwidget)
self.directory_version.setObjectName("directory_version")
self.directory_version.setReadOnly(True)
self.horizontalLayout_2.addWidget(self.directory_version)
self.horizontalSpacer_3 = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_2.addItem(self.horizontalSpacer_3)
self.verticalLayout_2.addLayout(self.horizontalLayout_2)
self.horizontalLayout_5.addLayout(self.verticalLayout_2)
self.verticalLayout_3.addLayout(self.horizontalLayout_5)
self.horizontalLayout_4 = QHBoxLayout()
self.horizontalLayout_4.setObjectName("horizontalLayout_4")
self.horizontalSpacer = QSpacerItem(40, 20, QSizePolicy.Expanding, QSizePolicy.Minimum)
self.horizontalLayout_4.addItem(self.horizontalSpacer)
self.cleanup_working = QPushButton(self.centralwidget)
self.cleanup_working.setObjectName("cleanup_working")
self.cleanup_working.setFlat(False)
self.horizontalLayout_4.addWidget(self.cleanup_working)
self.copy_template = QPushButton(self.centralwidget)
self.copy_template.setObjectName("copy_template")
self.horizontalLayout_4.addWidget(self.copy_template)
self.add_selected = QPushButton(self.centralwidget)
self.add_selected.setObjectName("add_selected")
self.horizontalLayout_4.addWidget(self.add_selected)
self.verticalLayout_3.addLayout(self.horizontalLayout_4)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QMenuBar(MainWindow)
self.menubar.setObjectName("menubar")
self.menubar.setGeometry(QRect(0, 0, 1200, 22))
self.menubar.setDefaultUp(False)
self.menuFile = QMenu(self.menubar)
self.menuFile.setObjectName("menuFile")
self.menuHelp = QMenu(self.menubar)
self.menuHelp.setObjectName("menuHelp")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.menubar.addAction(self.menuFile.menuAction())
self.menubar.addAction(self.menuHelp.menuAction())
self.menuFile.addAction(self.actionSettings)
self.menuFile.addAction(self.actionExit)
self.menuHelp.addAction(self.actionDocumentation)
self.menuHelp.addAction(self.actionAbout)
self.retranslateUi(MainWindow)
QMetaObject.connectSlotsByName(MainWindow)
# setupUi
def retranslateUi(self, MainWindow):
MainWindow.setWindowTitle(
QCoreApplication.translate("MainWindow", "projectdiffview", None)
)
self.actionPreferences.setText(
QCoreApplication.translate("MainWindow", "Preferences", None)
)
# if QT_CONFIG(shortcut)
self.actionPreferences.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+,", None)
)
# endif // QT_CONFIG(shortcut)
self.actionExit.setText(QCoreApplication.translate("MainWindow", "Exit", None))
# if QT_CONFIG(shortcut)
self.actionExit.setShortcut(QCoreApplication.translate("MainWindow", "Ctrl+Q", None))
# endif // QT_CONFIG(shortcut)
self.actionNew_Project.setText(
QCoreApplication.translate("MainWindow", "New Project", None)
)
# if QT_CONFIG(shortcut)
self.actionNew_Project.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+N", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSave.setText(QCoreApplication.translate("MainWindow", "Save", None))
# if QT_CONFIG(shortcut)
self.actionSave.setShortcut(QCoreApplication.translate("MainWindow", "Ctrl+S", None))
# endif // QT_CONFIG(shortcut)
self.actionOpen_Project.setText(QCoreApplication.translate("MainWindow", "Open", None))
# if QT_CONFIG(shortcut)
self.actionOpen_Project.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+O", None)
)
# endif // QT_CONFIG(shortcut)
self.actionDocumentation.setText(
QCoreApplication.translate("MainWindow", "Documentation", None)
)
self.actionAbout.setText(QCoreApplication.translate("MainWindow", "About", None))
self.actionConsole_Visibility.setText(
QCoreApplication.translate("MainWindow", "Console Visibility", None)
)
# if QT_CONFIG(shortcut)
self.actionConsole_Visibility.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+`", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSave_As.setText(QCoreApplication.translate("MainWindow", "Save As", None))
# if QT_CONFIG(shortcut)
self.actionSave_As.setShortcut(
QCoreApplication.translate("MainWindow", "Ctrl+Shift+S", None)
)
# endif // QT_CONFIG(shortcut)
self.actionSettings.setText(QCoreApplication.translate("MainWindow", "Settings", None))
self.label.setText(QCoreApplication.translate("MainWindow", "Project Directory", None))
self.browse.setText(QCoreApplication.translate("MainWindow", "Browse", None))
self.label_2.setText(QCoreApplication.translate("MainWindow", "Template Version:", None))
self.label_3.setText(QCoreApplication.translate("MainWindow", "Folder Version:", None))
self.cleanup_working.setText(
QCoreApplication.translate("MainWindow", "Cleanup Folder", None)
)
self.copy_template.setText(
QCoreApplication.translate("MainWindow", "Add All to Folder", None)
)
self.add_selected.setText(
QCoreApplication.translate("MainWindow", "Add Selected to Folder", None)
)
self.menuFile.setTitle(QCoreApplication.translate("MainWindow", "&File", None))
self.menuHelp.setTitle(QCoreApplication.translate("MainWindow", "&Help", None))
# retranslateUi
| de | 0.122805 | # -*- coding: utf-8 -*- ################################################################################ ## Form generated from reading UI file 'gui.ui' ## ## Created by: Qt User Interface Compiler version 5.15.0 ## ## WARNING! All changes made in this file will be lost when recompiling UI file! ################################################################################ # setupUi # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # if QT_CONFIG(shortcut) # endif // QT_CONFIG(shortcut) # retranslateUi | 1.468272 | 1 |
src/nn/exmple1.py | del680202/MachineLearning-memo | 4 | 6614333 | #!/usr/bin/env python
# encoding: utf-8
from neuralnetwork import *
# [(inputs, outputs)]
dataset = [
((0.3, 0.5), (0, 1))]
nn = NeuralNetwork()
hidden_layer = NeuronLayer(input_num=2, neuron_num=2, init_weights=[0.5, 0.3, 0.25, 0.6], bias=0.6)
output_layer = NeuronLayer(input_num=2, neuron_num=2, init_weights=[0.1, 0.25, 0.2, 0.7], bias=0.5)
nn.add_layer(hidden_layer)
nn.add_layer(output_layer)
nn.dump()
tracking = []
for i in range(2000):
nn.train(dataset)
tracking.append(nn.calculate_total_error(dataset))
#for (i, e) in enumerate(tracking):
# print "%sth square total error: %s" % (i+1, e)
print "NeuralNetwork 2-2-2, Except output:[0, 1], Real output:%s" % nn.get_output([0.3, 0.5])
nn2 = NeuralNetwork()
nn2.add_layer(NeuronLayer(input_num=2, neuron_num=5))
nn2.add_layer(NeuronLayer(input_num=5, neuron_num=5))
nn2.add_layer(NeuronLayer(input_num=5, neuron_num=2))
for i in range(2000):
nn2.train(dataset)
print "NeuralNetwork 2-5-5-2, Except output:[0, 1], Real output:%s" % nn2.get_output([0.3, 0.5])
# When model is too complex, it need more iterations to train
#nn3 = NeuralNetwork()
#nn3.add_layer(NeuronLayer(input_num=2, neuron_num=30))
#nn3.add_layer(NeuronLayer(input_num=30, neuron_num=2))
#for i in range(200000):
# nn3.train(dataset)
#print "NeuralNetwork 2-30-2, Except output:[0, 1], Real output:%s" % nn3.get_output([0.3, 0.5])
| #!/usr/bin/env python
# encoding: utf-8
from neuralnetwork import *
# [(inputs, outputs)]
dataset = [
((0.3, 0.5), (0, 1))]
nn = NeuralNetwork()
hidden_layer = NeuronLayer(input_num=2, neuron_num=2, init_weights=[0.5, 0.3, 0.25, 0.6], bias=0.6)
output_layer = NeuronLayer(input_num=2, neuron_num=2, init_weights=[0.1, 0.25, 0.2, 0.7], bias=0.5)
nn.add_layer(hidden_layer)
nn.add_layer(output_layer)
nn.dump()
tracking = []
for i in range(2000):
nn.train(dataset)
tracking.append(nn.calculate_total_error(dataset))
#for (i, e) in enumerate(tracking):
# print "%sth square total error: %s" % (i+1, e)
print "NeuralNetwork 2-2-2, Except output:[0, 1], Real output:%s" % nn.get_output([0.3, 0.5])
nn2 = NeuralNetwork()
nn2.add_layer(NeuronLayer(input_num=2, neuron_num=5))
nn2.add_layer(NeuronLayer(input_num=5, neuron_num=5))
nn2.add_layer(NeuronLayer(input_num=5, neuron_num=2))
for i in range(2000):
nn2.train(dataset)
print "NeuralNetwork 2-5-5-2, Except output:[0, 1], Real output:%s" % nn2.get_output([0.3, 0.5])
# When model is too complex, it need more iterations to train
#nn3 = NeuralNetwork()
#nn3.add_layer(NeuronLayer(input_num=2, neuron_num=30))
#nn3.add_layer(NeuronLayer(input_num=30, neuron_num=2))
#for i in range(200000):
# nn3.train(dataset)
#print "NeuralNetwork 2-30-2, Except output:[0, 1], Real output:%s" % nn3.get_output([0.3, 0.5])
| en | 0.389699 | #!/usr/bin/env python # encoding: utf-8 # [(inputs, outputs)] #for (i, e) in enumerate(tracking): # print "%sth square total error: %s" % (i+1, e) # When model is too complex, it need more iterations to train #nn3 = NeuralNetwork() #nn3.add_layer(NeuronLayer(input_num=2, neuron_num=30)) #nn3.add_layer(NeuronLayer(input_num=30, neuron_num=2)) #for i in range(200000): # nn3.train(dataset) #print "NeuralNetwork 2-30-2, Except output:[0, 1], Real output:%s" % nn3.get_output([0.3, 0.5]) | 3.426452 | 3 |
tests/test_one_line_logger.py | balosh-daniel/tuul | 0 | 6614334 | <filename>tests/test_one_line_logger.py
#!/usr/bin/env python
"""Tests for `tuul` package."""
import logging
import unittest
from unittest.mock import patch
import tuul
class TestOneLineLogger(unittest.TestCase):
def test_print_only_one_line(self):
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger()
logger.debug("d1\nd2\n")
logger.info("i1\ni2\n")
logger.error("e1\ne2\n")
try:
1 / 0
except ZeroDivisionError:
logger.exception("x1\nx2\n")
self.assertEqual(len(cm.output), 4, cm.output)
self.assertEqual(len(cm.output[0].splitlines()), 1, cm.output[0])
self.assertEqual(len(cm.output[1].splitlines()), 1, cm.output[0])
self.assertEqual(len(cm.output[2].splitlines()), 1, cm.output[0])
def test_control_logging_level(self):
with self.assertLogs(level="ERROR") as cm:
logger = tuul.one_line_logger.get_logger(logging_level=logging.ERROR)
logger.debug("d1\nd2\n")
logger.info("i1\ni2\n")
logger.error("e1\ne2\n")
try:
1 / 0
except ZeroDivisionError:
logger.exception("x1\nx2\n")
self.assertEqual(len(cm.output), 2, cm.output)
def test_set_aws_logger_level(self):
for name in ["boto", "urllib3", "s3transfer", "boto3", "botocore", "nose"]:
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger()
logger.debug("print me")
l1 = logging.getLogger(name)
l1.debug("I should not be printed")
l1.info("I should not be printed")
l1.critical("I should be printed")
self.assertEqual(2, len(cm.output), cm.output)
for name in ["boto", "urllib3", "s3transfer", "boto3", "botocore", "nose"]:
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger(aws_logging_level=logging.INFO)
logger.debug("print me")
l1 = logging.getLogger(name)
l1.debug("I should not be printed")
l1.info("I should be printed")
l1.critical("I should be printed")
self.assertEqual(3, len(cm.output), cm.output)
@staticmethod
def foo_raises(record):
raise RuntimeError("boom")
@patch.object(
tuul.one_line_logger.OneLineFormatter,
"_handle_non_exists_aws_request_id",
foo_raises,
)
def test_error_raised_in_formatter(self):
with self.assertLogs(level="INFO") as cm:
logger = tuul.one_line_logger.get_logger(logging_level=logging.DEBUG)
logger.info("i1\ni2\n")
self.assertEqual(len(cm.output), 1, cm.output)
self.assertTrue("MONITOR_THIS " in cm.output[0], cm.output[0])
| <filename>tests/test_one_line_logger.py
#!/usr/bin/env python
"""Tests for `tuul` package."""
import logging
import unittest
from unittest.mock import patch
import tuul
class TestOneLineLogger(unittest.TestCase):
def test_print_only_one_line(self):
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger()
logger.debug("d1\nd2\n")
logger.info("i1\ni2\n")
logger.error("e1\ne2\n")
try:
1 / 0
except ZeroDivisionError:
logger.exception("x1\nx2\n")
self.assertEqual(len(cm.output), 4, cm.output)
self.assertEqual(len(cm.output[0].splitlines()), 1, cm.output[0])
self.assertEqual(len(cm.output[1].splitlines()), 1, cm.output[0])
self.assertEqual(len(cm.output[2].splitlines()), 1, cm.output[0])
def test_control_logging_level(self):
with self.assertLogs(level="ERROR") as cm:
logger = tuul.one_line_logger.get_logger(logging_level=logging.ERROR)
logger.debug("d1\nd2\n")
logger.info("i1\ni2\n")
logger.error("e1\ne2\n")
try:
1 / 0
except ZeroDivisionError:
logger.exception("x1\nx2\n")
self.assertEqual(len(cm.output), 2, cm.output)
def test_set_aws_logger_level(self):
for name in ["boto", "urllib3", "s3transfer", "boto3", "botocore", "nose"]:
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger()
logger.debug("print me")
l1 = logging.getLogger(name)
l1.debug("I should not be printed")
l1.info("I should not be printed")
l1.critical("I should be printed")
self.assertEqual(2, len(cm.output), cm.output)
for name in ["boto", "urllib3", "s3transfer", "boto3", "botocore", "nose"]:
with self.assertLogs(level="DEBUG") as cm:
logger = tuul.one_line_logger.get_logger(aws_logging_level=logging.INFO)
logger.debug("print me")
l1 = logging.getLogger(name)
l1.debug("I should not be printed")
l1.info("I should be printed")
l1.critical("I should be printed")
self.assertEqual(3, len(cm.output), cm.output)
@staticmethod
def foo_raises(record):
raise RuntimeError("boom")
@patch.object(
tuul.one_line_logger.OneLineFormatter,
"_handle_non_exists_aws_request_id",
foo_raises,
)
def test_error_raised_in_formatter(self):
with self.assertLogs(level="INFO") as cm:
logger = tuul.one_line_logger.get_logger(logging_level=logging.DEBUG)
logger.info("i1\ni2\n")
self.assertEqual(len(cm.output), 1, cm.output)
self.assertTrue("MONITOR_THIS " in cm.output[0], cm.output[0])
| en | 0.288768 | #!/usr/bin/env python Tests for `tuul` package. | 2.878037 | 3 |
crownstone_core/packets/debug/PowerSamplesPacket.py | crownstone/crownstone-lib-python-core | 0 | 6614335 | <filename>crownstone_core/packets/debug/PowerSamplesPacket.py
from crownstone_core.protocol.BluenetTypes import PowerSamplesType
from crownstone_core.util.BufferReader import BufferReader
class PowerSamplesPacket:
def __init__(self, data):
self.samplesType = PowerSamplesType.UNSPECIFIED
self.index = 0 # uint8
self.count = 0 # uint16
self.timestamp = 0 # uint32
self.delayUs = 0 # uint16
self.sampleIntervalUs = 0 # uint16
self.reserved = 0 # 2 bytes
self.offset = 0 # int16
self.multiplier = 0.0 # float
self.samples = [] # int16 list
self.load(data)
def load(self, data):
"""
Parses data buffer to set member variables.
data : list of bytes
Raises exception when parsing fails.
"""
streamBuf = BufferReader(data)
samplesTypeVal = streamBuf.getUInt8()
self.samplesType = PowerSamplesType(samplesTypeVal) # Throws exception of value is not in enum
self.index = streamBuf.getUInt8()
self.count = streamBuf.getUInt16()
self.timestamp = streamBuf.getUInt32()
self.delayUs = streamBuf.getUInt16()
self.sampleIntervalUs = streamBuf.getUInt16()
streamBuf.skip(2)
self.offset = streamBuf.getInt16()
self.multiplier = streamBuf.getFloat()
self.samples = []
for i in range(0, self.count):
self.samples.append(streamBuf.getInt16())
def toString(self):
msg = "PowerSamplesPacket("
msg += "type=" + str(self.samplesType)
msg += " count=" + str(self.count)
msg += " timestamp=" + str(self.timestamp)
msg += " delayUs=" + str(self.delayUs)
msg += " sampleIntervalUs=" + str(self.sampleIntervalUs)
msg += " offset=" + str(self.offset)
msg += " multiplier=" + str(self.multiplier)
msg += " samples=" + str(self.samples)
msg += ")"
return msg
def __str__(self):
return self.toString() | <filename>crownstone_core/packets/debug/PowerSamplesPacket.py
from crownstone_core.protocol.BluenetTypes import PowerSamplesType
from crownstone_core.util.BufferReader import BufferReader
class PowerSamplesPacket:
def __init__(self, data):
self.samplesType = PowerSamplesType.UNSPECIFIED
self.index = 0 # uint8
self.count = 0 # uint16
self.timestamp = 0 # uint32
self.delayUs = 0 # uint16
self.sampleIntervalUs = 0 # uint16
self.reserved = 0 # 2 bytes
self.offset = 0 # int16
self.multiplier = 0.0 # float
self.samples = [] # int16 list
self.load(data)
def load(self, data):
"""
Parses data buffer to set member variables.
data : list of bytes
Raises exception when parsing fails.
"""
streamBuf = BufferReader(data)
samplesTypeVal = streamBuf.getUInt8()
self.samplesType = PowerSamplesType(samplesTypeVal) # Throws exception of value is not in enum
self.index = streamBuf.getUInt8()
self.count = streamBuf.getUInt16()
self.timestamp = streamBuf.getUInt32()
self.delayUs = streamBuf.getUInt16()
self.sampleIntervalUs = streamBuf.getUInt16()
streamBuf.skip(2)
self.offset = streamBuf.getInt16()
self.multiplier = streamBuf.getFloat()
self.samples = []
for i in range(0, self.count):
self.samples.append(streamBuf.getInt16())
def toString(self):
msg = "PowerSamplesPacket("
msg += "type=" + str(self.samplesType)
msg += " count=" + str(self.count)
msg += " timestamp=" + str(self.timestamp)
msg += " delayUs=" + str(self.delayUs)
msg += " sampleIntervalUs=" + str(self.sampleIntervalUs)
msg += " offset=" + str(self.offset)
msg += " multiplier=" + str(self.multiplier)
msg += " samples=" + str(self.samples)
msg += ")"
return msg
def __str__(self):
return self.toString() | en | 0.742306 | # uint8 # uint16 # uint32 # uint16 # uint16 # 2 bytes # int16 # float # int16 list Parses data buffer to set member variables. data : list of bytes Raises exception when parsing fails. # Throws exception of value is not in enum | 2.244307 | 2 |
Towers/main.py | avivgood/Reinforcement-learning-starters-template | 0 | 6614336 | <reponame>avivgood/Reinforcement-learning-starters-template
import asyncio
from Enums.gameLevel import GameLevel
from eventNotifier import _game_tick, _game_turn
from map import Map
async def main():
map_ = Map(GameLevel.EASY.value)
async def driver():
await asyncio.gather(_game_tick(), _game_turn(), main())
if __name__ == '__main__':
asyncio.run(driver())
| import asyncio
from Enums.gameLevel import GameLevel
from eventNotifier import _game_tick, _game_turn
from map import Map
async def main():
map_ = Map(GameLevel.EASY.value)
async def driver():
await asyncio.gather(_game_tick(), _game_turn(), main())
if __name__ == '__main__':
asyncio.run(driver()) | none | 1 | 2.60501 | 3 | |
HIV/params.py | jonathanhhb/bindery_demo | 0 | 6614337 | <reponame>jonathanhhb/bindery_demo
exp_name="HIV Rakai Simpler CoC"
nSims = 1
base_year=1960.5
| exp_name="HIV Rakai Simpler CoC"
nSims = 1
base_year=1960.5 | none | 1 | 0.892477 | 1 | |
semparse/attention.py | lukovnikov/semparse | 0 | 6614338 | import torch
import qelos as q
import numpy as np
import math
# region normal attention
class AttComp(torch.nn.Module):
""" computes attention scores """
def forward(self, qry, ctx, ctx_mask=None):
raise NotImplemented()
class SummComp(torch.nn.Module):
def forward(self, values, alphas):
raise NotImplemented()
class Attention(torch.nn.Module):
""" Computes phrase attention. For use with encoders and decoders from rnn.py """
def __init__(self, attcomp:AttComp=None, summcomp:SummComp=None, score_norm=torch.nn.Softmax(-1)):
"""
:param attcomp: used to compute attention scores
:param summcomp: used to compute summary
"""
super(Attention, self).__init__()
# self.prevatts = None # holds previous attention vectors
# self.prevatt_ptr = None # for every example, contains a list with pointers to indexes of prevatts
self.attcomp = attcomp if attcomp is not None else DotAttComp()
self.summcomp = summcomp if summcomp is not None else SumSummComp()
self.score_norm = score_norm
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:return:
"""
scores = self.attcomp(qry, ctx, ctx_mask=ctx_mask)
scores = scores + (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
alphas = self.score_norm(scores)
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
return alphas, summary, scores
class DotAttComp(AttComp):
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, dim) or (batsize, zeqlen, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask:
:return:
"""
if qry.dim() == 2:
ret = torch.einsum("bd,bsd->bs", [qry, ctx])
elif qry.dim() == 3:
ret = torch.einsum("bzd,bsd->bzs", [qry, ctx])
else:
raise q.SumTingWongException("qry has unsupported dimension: {}".format(qry.dim()))
return ret
class FwdAttComp(AttComp):
def __init__(self, qrydim=None, ctxdim=None, encdim=None, numlayers=1, dropout=0, **kw):
super(FwdAttComp, self).__init__(**kw)
layers = [torch.nn.Linear(qrydim + ctxdim, encdim)] \
+ [torch.nn.Linear(encdim, encdim) for _ in range(numlayers - 1)]
acts = [torch.nn.Tanh() for _ in range(len(layers))]
layers = [a for b in zip(layers, acts) for a in b]
layers.append(torch.nn.Dropout(dropout))
layers.append(torch.nn.Linear(encdim, 1))
self.mlp = torch.nn.Sequential(*layers)
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, qrydim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:return:
"""
inp = torch.cat([ctx, qry.unsqueeze(1).repeat(1, ctx.size(1), 1)], 2)
out = self.mlp(inp)
ret = out.squeeze(-1)
return ret
class SumSummComp(SummComp):
def forward(self, values, alphas):
summary = values * alphas.unsqueeze(2)
summary = summary.sum(1)
return summary
class BasicAttention(Attention):
def __init__(self, **kw):
attcomp = DotAttComp()
summcomp = SumSummComp()
super(BasicAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, **kw)
class FwdAttention(Attention):
def __init__(self, qrydim, ctxdim, encdim, dropout=0., **kw):
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout)
summcomp = SumSummComp()
super(FwdAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, **kw)
# endregion
# region Relative Attention
class VecComp(torch.nn.Module):
""" maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention """
def __init__(self, ctxdim, vecdim, **kw):
super(VecComp, self).__init__(**kw)
self.ctxdim, self.vecdim = ctxdim, vecdim
def forward(self, ctx):
"""
:param ctx: (batsize, seqlen, ctxdim)
:return: (batsize, seqlen, seqlen, vecdim)
"""
raise NotImplemented()
class FwdVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(FwdVecComp, self).__init__(ctxdim, vecdim, **kw)
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
def forward(self, ctx):
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
out = self.nonlin(out1.unsqueeze(1) + out2.unsqueeze(2))
return out
class ComboFwdVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(ComboFwdVecComp, self).__init__(ctxdim, vecdim, **kw)
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_mul = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_diff = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
def forward(self, ctx):
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
mul = ctx.unsqueeze(1) * ctx.unsqueeze(2) # (batsize, seqlen, seqlen, vecdim)
diff = ctx.unsqueeze(1) - ctx.unsqueeze(2)
outmul = self.lin_mul(mul)
outdiff = self.lin_diff(diff)
out = self.nonlin(out1.unsqueeze(1) + out2.unsqueeze(2) + outmul + outdiff)
return out
class BilinVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(BilinVecComp, self).__init__(ctxdim, vecdim, **kw)
self.W = torch.nn.Parameter(torch.Tensor(ctxdim, ctxdim, vecdim))
self.bias = torch.nn.Parameter(torch.Tensor(vecdim)) if bias else None
self.nonlin = torch.nn.Tanh()
self.reset_parameters()
def reset_parameters(self):
bound = 1 / math.sqrt(self.weight.size(1))
torch.init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
torch.init.uniform_(self.bias, -bound, bound)
def forward(self, ctx):
out = torch.einsum("bsi,bzj,ijk->bszk", ctx, ctx, self.W)
if self.bias is not None:
out = out + self.bias
out = self.nonlin(out)
return out
class BilinAndFwdComboVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(BilinAndFwdComboVecComp, self).__init__(ctxdim, vecdim, **kw)
self.W = torch.nn.Parameter(torch.Tensor(ctxdim, ctxdim, vecdim))
self.bias = torch.nn.Parameter(torch.Tensor(vecdim)) if bias else None
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_mul = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_diff = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
self.reset_parameters()
def reset_parameters(self):
bound = 1 / math.sqrt(self.weight.size(1))
torch.init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
torch.init.uniform_(self.bias, -bound, bound)
def forward(self, ctx):
out = torch.einsum("bsi,bzj,ijk->bszk", ctx, ctx, self.W)
if self.bias is not None:
out = out + self.bias
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
mul = ctx.unsqueeze(1) * ctx.unsqueeze(2) # (batsize, seqlen, seqlen, vecdim)
diff = ctx.unsqueeze(1) - ctx.unsqueeze(2)
outmul = self.lin_mul(mul)
outdiff = self.lin_diff(diff)
out = self.nonlin(out + out1.unsqueeze(1) + out2.unsqueeze(2) + outmul + outdiff)
return out
class RelAttention(torch.nn.Module):
def __init__(self, veccomp:VecComp=None, attcomp:AttComp=DotAttComp(), summcomp:SummComp=SumSummComp(), temperature=1., threshold=1e-6, **kw):
super(RelAttention, self).__init__(**kw)
self.threshold, self.temperature = threshold, temperature
self.veccomp = veccomp # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention
self.attcomp, self.summcomp = attcomp, summcomp
self.scorenorm = torch.nn.Softmax(-1)
self.prevatts = None # (batsize, seqlen)
self.relvecs = None # (batsize, seqlen, seqlen, vecdim)
self.prevatts_history = [] # will become decseqlen-sized list of prevatts (batsize, seqlen)
self.feed_prevatts_acc = None # decseqlen-sized list of prevatts (batsize, seqlen)
self.t = 0
self._bad_prevatts = False
def batch_reset(self):
self.prevatts = None
self.relvecs = None
self.prevatts_history = []
self.feed_prevatts_acc = None
self.t = 0
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, qdim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen)
:return:
"""
# initialize prevatts if None: init assigns all prob to first element --> first element of ctx must be a start token
if self.prevatts is None:
self.prevatts = torch.zeros_like(ctx[:, :, 0])
self.prevatts[:, 0] = 1.
# create and store relation vectors
self.relvecs = self.veccomp(ctx)
# get non-negligible part of relvecs # TODO: do sparse for more efficiency
# relvecs_idxs = torch.nonzero(self.prevatts > self.threshold)
# do attcomp with qry over relvecs
flatrelvecs = self.relvecs.view(self.relvecs.size(0), self.relvecs.size(1) * self.relvecs.size(2), self.relvecs.size(3)) # (batsize, seqlen*seqlen, vecdim)
flatrelatt_scores = self.attcomp(qry, flatrelvecs) # (batsize, seqlen * seqlen)
relatt_scores = flatrelatt_scores.view(self.relvecs.size(0), self.relvecs.size(1), self.relvecs.size(2)) # (batsize, seqlen, seqlen)
# apply ctx_mask before summary
relatt_scores = relatt_scores + (torch.log(ctx_mask.float().unsqueeze(1)) if ctx_mask is not None else 0)
relatt_alphas = self.scorenorm(relatt_scores) # (batsize, seqlen, seqlen)
alphas = torch.einsum("bsz,bs->bz", relatt_alphas, self.prevatts)
self.prevatts = alphas
if self._bad_prevatts is True:
self.prevatts = torch.zeros_like(ctx[:, :, 0])
self.prevatts[:, 2] = 1.
# saving history and using feed:
self.prevatts_history.append(self.prevatts)
if self.feed_prevatts_acc is not None:
self.prevatts = self.feed_prevatts_acc[self.t]
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
self.t += 1
return alphas, summary, relatt_scores
class BasicRelAttention(RelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = FwdVecComp(ctxdim, vecdim, bias=bias)
super(BasicRelAttention, self).__init__(veccomp, **kw)
class ComboRelAttention(RelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = ComboFwdVecComp(ctxdim, vecdim, bias=bias)
super(ComboRelAttention, self).__init__(veccomp, **kw)
class AbsRelAttention(torch.nn.Module):
"""
Attention mechanism consisting of first absolute attention, followed by a relative attention step
--> doesn't need prevatts
"""
def __init__(self, prevattcomp:AttComp=DotAttComp(), veccomp:VecComp=None, attcomp:AttComp=DotAttComp(), summcomp:SummComp=SumSummComp(), temperature=1., threshold=1e-6, **kw):
super(AbsRelAttention, self).__init__(**kw)
self.threshold, self.temperature = threshold, temperature
self.prevattcomp = prevattcomp
self.veccomp = veccomp # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention
self.attcomp, self.summcomp = attcomp, summcomp
self.scorenorm = torch.nn.Softmax(-1)
self.relvecs = None # (batsize, seqlen, seqlen, vecdim)
self.t = 0
def batch_reset(self):
self.relvecs = None
self.t = 0
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, qdim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen)
:return:
"""
if self.relvecs is None:
# create and store relation vectors
self.relvecs = self.veccomp(ctx)
prevatts = self.prevattcomp(qry, ctx, ctx_mask=ctx_mask)
prevatts += (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
prevatts = self.scorenorm(prevatts)
# get non-negligible part of relvecs # TODO: do sparse for more efficiency
# relvecs_idxs = torch.nonzero(self.prevatts > self.threshold)
# do attcomp with qry over relvecs
flatrelvecs = self.relvecs.view(self.relvecs.size(0), self.relvecs.size(1) * self.relvecs.size(2), self.relvecs.size(3)) # (batsize, seqlen*seqlen, vecdim)
flatrelatt_scores = self.attcomp(qry, flatrelvecs) # (batsize, seqlen * seqlen)
relatt_scores = flatrelatt_scores.view(self.relvecs.size(0), self.relvecs.size(1), self.relvecs.size(2)) # (batsize, seqlen, seqlen)
# apply ctx_mask before summary
relatt_scores = relatt_scores + (torch.log(ctx_mask.float().unsqueeze(1)) if ctx_mask is not None else 0)
relatt_alphas = self.scorenorm(relatt_scores) # (batsize, seqlen, seqlen)
alphas = torch.einsum("bsz,bs->bz", relatt_alphas, prevatts)
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
self.t += 1
return alphas, summary, relatt_scores
class BasicAbsRelAttention(AbsRelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = FwdVecComp(ctxdim, vecdim, bias=bias)
super(BasicAbsRelAttention, self).__init__(veccomp=veccomp, **kw)
class ComboAbsRelAttention(AbsRelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = ComboFwdVecComp(ctxdim, vecdim, bias=bias)
super(ComboAbsRelAttention, self).__init__(veccomp=veccomp, **kw)
def test_rel_attention(lr=0.):
qry = torch.randn(2, 5)
ctx = torch.randn(2, 3, 6)
ctx_mask = torch.tensor([
[1,1,0],
[1,1,1]
])
m = ComboRelAttention(6, 5)
y = m(qry, ctx, ctx_mask=ctx_mask)
print(y)
# endregion
# region Phrase Attention
# function with a custom backward for getting gradients to both parent and children in PhraseAttention
# forward is an elementwise min
# backward: - alphas always gets whole gradient
# - parent_alphas is increased when gradient > 0 else nothing
# (so parent's attention can only be increased here if the child needs to attend more to certain places)
# (if we equally tried to decrease parent's attentions here too, then would have conflicting signals from its children's attentions, which may not overlap)
# (decrease comes from overlap penalty and gradient on parent attention itself)
class ParentOverlapFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, parent_alphas, alphas):
ctx.save_for_backward(parent_alphas, alphas)
ret = torch.min(parent_alphas, alphas)
return ret
@staticmethod
def backward(ctx, grad_output):
gradzeros = torch.zeros_like(grad_output)
parent_grads = torch.max(gradzeros, grad_output)
return parent_grads, grad_output
def parent_overlap_f_parent_first(parent_alphas, alphas):
alphas = 1 - alphas
_z = torch.min(torch.tensor(1.0), parent_alphas / alphas)
z = parent_alphas - alphas * _z.detach()
return z
parent_overlap_f = ParentOverlapFunction.apply
# parent_overlap_f = parent_overlap_f_parent_first
def test_custom_f(lr=0):
x = torch.rand(5)
x.requires_grad = True
y = torch.rand(5)
y.requires_grad = True
z = parent_overlap_f(x, y)
l = z #z.sum()
l.backward(gradient=torch.tensor([-1,1,-1,1,1]).float())
print(x)
print(y)
print(z)
print(x.grad)
print(y.grad)
class PhraseAttention(Attention): # for depth-first decoding
""" Assumes masking by termination of tree structure assuming single root (which is also start token) """
def __init__(self, attcomp:AttComp=None, summcomp:SummComp=None, hard=False, **kw):
score_norm = torch.nn.Sigmoid()
super(PhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, score_norm=score_norm)
self.hard = hard
self.prevatts_probs = None # (batsize, declen_so_far, enclen)
if self.hard is True:
self.prevatts_samples = None
self.prevatts_mask = None
self.prevatt_ptr = None # for every example, keeps a list of pointers to positions in prevatts
# structure: batsize x stackdepth x numsiblings
# For every example, the stack contains groups of siblings.
# Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings)
self.prevatt_siblings = None # for every example, keeps a list of sets of pointers to groups of siblings
# structure: batsize x num_sibling_groups x num_siblings_in_group
# Mainly populated during forward (with a finalization step for top-level siblings in get_sibling_overlap).
# Consumed in get_sibling_overlap.
def batch_reset(self):
self.prevatts_probs, self.prevatt_ptr = None, None
self.prevatt_siblings = None
if self.hard is True:
self.prevatts_samples = None
self.prevatts_mask = None
def get_sibling_overlap(self): # called after all forwards are done
"""
Gets overlap in siblings based on current state of prevatts and prevatt_ptr.
Must be called after a batch and before batch reset.
"""
# finalize prevattr_ptr
for i, prevattr_ptr_e in enumerate(self.prevatt_ptr):
if len(prevattr_ptr_e) != 2: # must contain only the zero-group and top-level group
pass
# raise q.SumTingWongException()
while len(prevattr_ptr_e) > 0:
ptr_group = prevattr_ptr_e.pop()
if len(ptr_group) > 1:
pass
# self.prevatt_siblings[i].append(ptr_group) # don't add overlap of top-level siblings (we assume single top child, everything else is mask)
# generate ids by which to gather from prevatts
ids = torch.zeros(self.prevatts_probs.size(0), self.prevatts_probs.size(1), self.prevatts_probs.size(1),
dtype=torch.long, device=self.prevatts_probs.device)
maxnumsiblingses, maxnumsiblings = 0, 0
for eid, siblingses in enumerate(self.prevatt_siblings): # list of lists of ids in prevatts
maxnumsiblingses = max(maxnumsiblingses, len(siblingses))
for sgid, siblings in enumerate(siblingses): # list of ids in prevatts
maxnumsiblings = max(maxnumsiblings, len(siblings))
for sid, sibling in enumerate(siblings):
ids[eid, sgid, sid] = sibling
ids = ids[:, :maxnumsiblingses, :maxnumsiblings]
prevatts = self.prevatts_probs
idsmask= ((ids != 0).sum(2, keepdim=True) > 1).float()
# gather from prevatts
_ids = ids.contiguous().view(ids.size(0), -1).unsqueeze(-1).repeat(1, 1, prevatts.size(2))
prevatts_gathered = torch.gather(prevatts, 1, _ids)
prevatts_gathered = prevatts_gathered.view(prevatts.size(0), ids.size(1), ids.size(2), prevatts.size(2))
# compute overlaps
overlaps = prevatts_gathered.prod(2)
overlaps = overlaps * idsmask
overlaps = overlaps.sum(2).sum(1)
# overlaps = overlaps.mean(0)
return overlaps
def get_logprob_of_sampled_alphas(self):
if self.hard is False:
raise q.SumTingWongException("Use this only for RL on hard attention (must be in hard mode).")
probs = self.prevatts_probs * self.prevatts_samples + (1 - self.prevatts_probs) * (1 - self.prevatts_samples)
logprobs = torch.log(probs)
logprobs = logprobs * self.prevatts_mask # mask the logprobs
average_within_timestep = True
if average_within_timestep:
totals = self.prevatts_mask.sum(2) + 1e-6
logprobs = logprobs.sum(2) / totals
else:
logprobs = logprobs.mean(2)
return logprobs[:, 2:] # (batsize, seqlen) -- decoder mask should be applied on this later
def get_entropies_of_alpha_dists(self):
probs = self.prevatts_probs
dists = torch.distributions.Bernoulli(probs=probs)
entropies = dists.entropy()
entropies = entropies * self.prevatts_mask
average_within_timestep = True
if average_within_timestep:
totals = self.prevatts_mask.sum(2) + 1e-12
entropies = entropies.sum(2) / totals
else:
entropies = entropies.mean(2)
return entropies[:, 2:] # (batsize, seqlen) -- decoder mask should be applied on this later
def forward(self, qry, ctx, ctx_mask=None, values=None, prev_pushpop=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token:
-N ==> pop (N=how many to pop),
0 ==> nothing,
+N ==> push (N doesn't matter, always pushes one)
! push/pop happens AFTER the element
:return:
"""
# compute attention for token that we will produce next
# compute attention scores
scores = self.attcomp(qry, ctx, ctx_mask=ctx_mask)
# apply ctx mask to attention scores
scores = scores + (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
# normalize attention scores
alphas_probs = self.score_norm(scores) # sigmoid probs
if self.hard:
alphas_dist = torch.distributions.Bernoulli(probs=alphas_probs)
alphas_samples = alphas_dist.sample()
# constrain alphas to parent's alphas:
if self.prevatts_probs is None: # there is no history
# initialize prevatts (history)
self.prevatts_probs = torch.ones_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1) # means everything is attended to
if ctx_mask is not None:
self.prevatts_probs = self.prevatts_probs * ctx_mask.float().unsqueeze(1)
if self.hard is True:
self.prevatts_samples = self.prevatts_probs.clone().detach() #torch.ones_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1)
self.prevatts_mask = torch.zeros_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1)
# --> we assume the previous (first ever) attention, used to compute initial (current input) token attended over whole sequence
# initialize prevatt_ptr
self.prevatt_ptr = [[[0], []] for _ in range(len(prev_pushpop))]
# initialize prevatt_siblings
self.prevatt_siblings = [[] for _ in range(len(prev_pushpop))]
# update pointers to prevatt
k = self.prevatts_probs.size(1) - 1 # index of the last produced attention alphas (that were used for prev token)
for i in range(len(prev_pushpop)):
self.prevatt_ptr[i][-1].append(k) # make last token a sibling of the children of the same parent before it (if any)
if prev_pushpop[i].cpu().item() > 0: # PUSH: previous token requires children --> make stack deeper by one level
self.prevatt_ptr[i].append([])
elif prev_pushpop[i].cpu().item() < 0: # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too)
pp = prev_pushpop[i].cpu().item()
while pp < 0 and len(self.prevatt_ptr[i]) > 2:
siblings = self.prevatt_ptr[i].pop(-1) # pop the list from stack
if len(siblings) > 1: # if longer than 1, add to the list of siblings
self.prevatt_siblings[i].append(siblings)
pp += 1
else:
pass
# constrain alphas to parent's alphas
parent_ptr = [prevatt_ptr_e[-2][-1] for prevatt_ptr_e in self.prevatt_ptr]
parent_ptr = torch.tensor(parent_ptr).long().to(self.prevatts_probs.device)
if self.hard is True: # no backprop through alphas
# parent_alphas_probs = self.prevatts_probs.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
# .repeat(1, 1, self.prevatts_probs.size(-1))).squeeze(1)
parent_alphas_samples = self.prevatts_samples.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
.repeat(1, 1, self.prevatts_samples.size(-1))).squeeze(1)
alphas_samples = torch.min(parent_alphas_samples, alphas_samples)
# save parent-masked samples
self.prevatts_samples = torch.cat([self.prevatts_samples, alphas_samples.unsqueeze(1)], 1)
self.prevatts_mask = torch.cat([self.prevatts_mask, parent_alphas_samples.unsqueeze(1)], 1)
alphas = alphas_samples
else: # need to backprop differently
parent_alphas_probs = self.prevatts_probs.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
.repeat(1, 1, self.prevatts_probs.size(-1))).squeeze(1)
alphas_probs = parent_overlap_f(parent_alphas_probs, alphas_probs)
alphas = alphas_probs
# append current alpha probs to prevatts accumulator
self.prevatts_probs = torch.cat([self.prevatts_probs, alphas_probs.unsqueeze(1)], 1)
# compute summary
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
return alphas, summary, scores
class PhraseAttentionTeacher(Attention): # for depth-first decoding
""" Normal attention. Stores probs for the batch for use to supervise real phrase attention. """
""" Assumes masking by termination of tree structure assuming single root (which is also start token) """
def __init__(self, attcomp:AttComp=None, hard=True, **kw):
score_norm = torch.nn.Softmax(-1)
summcomp = SumSummComp()
super(PhraseAttentionTeacher, self).__init__(attcomp=attcomp, summcomp=summcomp, score_norm=score_norm)
self.prevatts_probs = None # (batsize, declen_so_far, enclen)
self.prevatt_ptr = None # for every example, keeps a list of pointers to positions in prevatts
self.prevatts_masks = None
# structure: batsize x stackdepth x numsiblings
# For every example, the stack contains groups of siblings.
# Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings)
self.record = False
self.hard = hard
def batch_reset(self):
self.prevatts_probs, self.prevatt_ptr = None, None
self.prevatts_masks = None
def get_phraseatt_supervision(self, hard=True):
"""
Propagates attentions in self.prevatts_probs from children to parents according to self.prevatt_ptr, this way
converting softmax attentions generated here to a supervision signal usable for sigmoid attention.
If hard, does argmax before propagating, else propagates probs.
"""
return self.prevatts_probs, self.prevatts_masks
def forward(self, qry, ctx, ctx_mask=None, values=None, prev_pushpop=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token:
-N ==> pop (N=how many to pop),
0 ==> nothing,
+N ==> push (N doesn't matter, always pushes one)
! push/pop happens AFTER the element
:return:
"""
alphas_probs, summary, scores = super(PhraseAttentionTeacher, self).forward(qry, ctx, ctx_mask=ctx_mask, values=values)
if self.record is True:
if self.prevatts_probs is None: # there is no history
# initialize prevatts (history)
self.prevatts_probs = torch.zeros_like(alphas_probs).unsqueeze(1)
# initialize prevatt masks
self.prevatts_masks = torch.ones_like(alphas_probs).unsqueeze(1)
if ctx_mask is not None:
self.prevatts_masks = self.prevatts_masks * ctx_mask.unsqueeze(1).float()
# initialize prevatt_ptr
self.prevatt_ptr = [[[]] for _ in range(len(prev_pushpop))]
# update pointers to prevatt
k = self.prevatts_probs.size(1) - 1 # index of the last produced attention alphas (that were used for prev token)
for i in range(len(prev_pushpop)): # iterate over all examples
self.prevatt_ptr[i][-1].append(k) # make last token a sibling of the children of the same parent before it (if any)
if prev_pushpop[i].cpu().item() > 0: # PUSH: previous token requires children --> make stack deeper by one level
self.prevatt_ptr[i].append([])
elif prev_pushpop[i].cpu().item() < 0: # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too)
pp = prev_pushpop[i].cpu().item()
while pp < 0 and len(self.prevatt_ptr[i]) > 1:
siblings = self.prevatt_ptr[i].pop(-1) # pop the list from stack
# add each of the sibling's attention probs to their parent and populate children's masks
parent = self.prevatt_ptr[i][-1][-1]
for sibling in siblings:
sibling_alphas = self.prevatts_probs[i, sibling]
self.prevatts_probs[i, parent] += sibling_alphas
self.prevatts_probs[i, parent].clamp_(0., 1.)
parent_alphas = self.prevatts_probs[i, parent]
for sibling in siblings:
self.prevatts_masks[i, sibling] = parent_alphas
pp += 1
else:
pass
# append current alpha probs to prevatts accumulator
if self.hard:
# _alphas_probs = torch.zeros_like(alphas_probs)\
# .scatter_(1, torch.argmax(alphas_probs, 1, True), 1.)
alphas_dist = torch.distributions.OneHotCategorical(probs=alphas_probs)
_alphas_probs = alphas_dist.sample()
else:
_alphas_probs = alphas_probs
self.prevatts_probs = torch.cat([self.prevatts_probs, _alphas_probs.unsqueeze(1)], 1)
self.prevatts_masks = torch.cat([self.prevatts_masks, torch.zeros_like(_alphas_probs.unsqueeze(1))], 1) # will be filled once siblings have been done
return alphas_probs, summary, scores
def test_phrase_attention(lr=0):
# simulate operation of attention
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = torch.tensor([[1,0,1,0,-1,-1], [1,1,1,1,-4,0]])
# pushpop = list(zip(*pushpop))
m = PhraseAttention(hard=True)
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
overlap = m.get_sibling_overlap()
pass
def test_phrase_attention_teacher(lr=0):
# simulate operation of attention
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = torch.tensor([[1,0,1,0,-1,-1], [1,1,1,1,-4,0]])
# pushpop = list(zip(*pushpop))
m = PhraseAttentionTeacher(hard=True)
m.record = True
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
print(m.prevatts_probs[0])
print(m.prevatts_masks[0])
print(m.prevatts_probs[1])
print(m.prevatts_masks[1])
overlap = m.get_phraseatt_supervision(hard=True)
pass
# endregion
# region components for phrase attention
class LSTMAttComp(AttComp):
def __init__(self, qrydim=None, ctxdim=None, encdim=None, dropout=0., numlayers=1, bidir=False, **kw):
super(LSTMAttComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMEncoder(qrydim+ctxdim, *encdims, bidir=bidir, dropout_in=dropout)
self.lin = torch.nn.Linear(encdim, 1)
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, qrydim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:return:
"""
inp = torch.cat([ctx, qry.unsqueeze(1).repeat(1, ctx.size(1), 1)], 2)
out = self.layers(inp, mask=ctx_mask)
ret = self.lin(out).squeeze(-1) # (batsize, seqlen)
return ret
class LSTMSummComp(SummComp):
def __init__(self, valdim=None, encdim=None, dropout=0., numlayers=1, **kw):
super(LSTMSummComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMCellEncoder(valdim, *encdims, bidir=False, dropout_in=dropout)
def forward(self, values, alphas):
_, out = self.layers(values, gate=alphas, ret_states=True)
out = out[:, 0]
skip = values * alphas.unsqueeze(-1)
skip = skip.sum(1)
out = out + skip
return out
class PooledLSTMSummComp(SummComp):
"""
Uses a bidirectional lstm encoder with skip connections to encode according to given mask, not updating the state if gate is zero.
If valdim != encdim * 2, uses a linear projection on the values.
After encoding, does max and mean pooling across time, weighted by the provided attention weights.
Best use only with hard attention alphas.
"""
def __init__(self, valdim=None, encdim=None, dropout=0., numlayers=1, **kw):
super(PooledLSTMSummComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMCellEncoder(valdim, *encdims, bidir=True, dropout_in=dropout)
self.skip_adapt = torch.nn.Linear(valdim, encdim*2) if valdim != encdim * 2 else lambda x: x
def forward(self, values, alphas):
"""
:param values: (batsize, seqlen, valdim)
:param alphas: (batsize, seqlen)
:return: (batsize, seqlen, encdim*2*2)
"""
topouts, out = self.layers(values, gate=alphas, ret_states=True)
skip_vals = self.skip_adapt(values)
rnnouts = topouts + skip_vals
rnnouts = rnnouts * alphas.unsqueeze(2)
meanpool = rnnouts.sum(1) / (alphas.unsqueeze(2).sum(1) + 1e-6)
maxpool = rnnouts#.clone()
maxpool.masked_fill_((1 - alphas).byte().unsqueeze(2), -np.infty)
maxpool = maxpool.max(1)[0]
maxpool = q.inf2zero(maxpool)
out = torch.cat([meanpool, maxpool], 1) # (batsize, encdim * 2 * 2)
return out
#
# out = out[:, 0]
#
# skip = values * alphas.unsqueeze(-1)
# skip = skip.sum(1)
#
# out = out + skip
# return out
def test_pooled_lstm_summ_comp(lr=0.):
vals = torch.randn(2, 5, 8)
vals.requires_grad = True
alphas = (torch.rand(2, 5) > 0.8).float()
print(alphas)
m = PooledLSTMSummComp(valdim=8, encdim=4)
out = m(vals, alphas)
print(out.size())
l = out.sum()
l.backward()
print(vals.grad)
class LSTMPhraseAttention(PhraseAttention):
def __init__(self, qrydim=None, ctxdim=None, valdim=None, encdim=None, dropout=0., numlayers=1, hard=False, **kw):
ctxdim = qrydim if ctxdim is None else ctxdim
valdim = ctxdim if valdim is None else valdim
encdim = ctxdim if encdim is None else encdim
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
summcomp = LSTMSummComp(valdim=valdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
super(LSTMPhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, hard=hard, **kw)
class PooledLSTMPhraseAttention(PhraseAttention):
def __init__(self, qrydim=None, ctxdim=None, valdim=None, encdim=None, dropout=0., numlayers=1, hard=False, **kw):
ctxdim = qrydim if ctxdim is None else ctxdim
valdim = ctxdim if valdim is None else valdim
encdim = ctxdim if encdim is None else encdim
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
summcomp = PooledLSTMSummComp(valdim=valdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
super(PooledLSTMPhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, hard=hard, **kw)
class PhraseAttentionDecoderCell(torch.nn.Module): # Luong-style decoder cell
""" Need to subclass this, implementing get_pushpop_from for specific vocabulary. Or specify mapping id2pushpop during construction. """
def __init__(self, emb=None, core=None, att:PhraseAttention=None, merge:q.rnn.DecCellMerge=q.rnn.ConcatDecCellMerge(),
out=None, feed_att=False, return_alphas=False, return_scores=False, return_other=False,
dropout=0, id2pushpop=None, **kw):
"""
Based on LuongCell, only change: support for prev_pushpop arg in forward --> passed to attention
:param emb:
:param core:
:param att:
:param merge:
:param out: if None, out_vec (after merge) is returned
:param feed_att:
:param h_hat_0:
:param id2pushpop: torch tensor mapping token ids to pushpop values
:param kw:
"""
super(PhraseAttentionDecoderCell, self).__init__(**kw)
self.emb, self.core, self.att, self.merge, self.out = emb, core, att, merge, out
self.feed_att = feed_att
self._outvec_tm1 = None
self.outvec_t0 = None
self.return_alphas = return_alphas
self.return_scores = return_scores
self.return_other = return_other
self._id2pushpop = id2pushpop # THIS LINE IS ADDED
self.dropout = torch.nn.Dropout(dropout)
def batch_reset(self):
self.outvec_t0 = None
self._outvec_tm1 = None
def forward(self, x_t, ctx=None, ctx_mask=None, **kw):
assert (ctx is not None)
embs = self.emb(x_t)
if q.issequence(embs) and len(embs) == 2:
embs, mask = embs
if self.feed_att:
if self._outvec_tm1 is None:
assert (self.outvec_t0 is not None) #"h_hat_0 must be set when feed_att=True"
self._outvec_tm1 = self.outvec_t0
core_inp = torch.cat([embs, self._outvec_tm1], 1)
else:
core_inp = embs
prev_pushpop = self.get_pushpop_from(x_t) # THIS LINE IS ADDED
core_out = self.core(core_inp)
alphas, summaries, scores = self.att(core_out, ctx, ctx_mask=ctx_mask, values=ctx, prev_pushpop=prev_pushpop) # THIS LINE IS CHANGED
out_vec = self.merge(core_out, summaries, core_inp)
out_vec = self.dropout(out_vec)
self._outvec_tm1 = out_vec # store outvec
ret = tuple()
if self.out is None:
ret += (out_vec,)
else:
_out_vec = self.out(out_vec)
ret += (_out_vec,)
if self.return_alphas:
ret += (alphas,)
if self.return_scores:
ret += (scores,)
if self.return_other:
ret += (embs, core_out, summaries)
return ret[0] if len(ret) == 1 else ret
def get_pushpop_from(self, x_t): # (batsize,) ids # THIS METHOD IS ADDED
""" Get pushpop from x_t: based on x_t, decides whether to push (>0), do nothing (0) or pop (<0) previous attentions """
if self._id2pushpop is not None:
return self._id2pushpop[x_t]
else:
raise NotImplemented()
def test_lstm_phrase_attention(lr=0):
m = LSTMPhraseAttention(4)
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = [[1,0,1,0,-1,-1], # output of last step will be "masked"
[1,1,1,1,-4,0]] # output of last two steps will be "masked"
pushpop = torch.tensor(pushpop)
# pushpop = list(zip(*pushpop))
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
overlap = m.get_sibling_overlap()
pass
# endregion
if __name__ == '__main__':
# q.argprun(test_custom_f)
# q.argprun(test_phrase_attention)
# q.argprun(test_phrase_attention_teacher)
# q.argprun(test_lstm_phrase_attention)
# q.argprun(test_pooled_lstm_summ_comp)
q.argprun(test_rel_attention) | import torch
import qelos as q
import numpy as np
import math
# region normal attention
class AttComp(torch.nn.Module):
""" computes attention scores """
def forward(self, qry, ctx, ctx_mask=None):
raise NotImplemented()
class SummComp(torch.nn.Module):
def forward(self, values, alphas):
raise NotImplemented()
class Attention(torch.nn.Module):
""" Computes phrase attention. For use with encoders and decoders from rnn.py """
def __init__(self, attcomp:AttComp=None, summcomp:SummComp=None, score_norm=torch.nn.Softmax(-1)):
"""
:param attcomp: used to compute attention scores
:param summcomp: used to compute summary
"""
super(Attention, self).__init__()
# self.prevatts = None # holds previous attention vectors
# self.prevatt_ptr = None # for every example, contains a list with pointers to indexes of prevatts
self.attcomp = attcomp if attcomp is not None else DotAttComp()
self.summcomp = summcomp if summcomp is not None else SumSummComp()
self.score_norm = score_norm
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:return:
"""
scores = self.attcomp(qry, ctx, ctx_mask=ctx_mask)
scores = scores + (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
alphas = self.score_norm(scores)
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
return alphas, summary, scores
class DotAttComp(AttComp):
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, dim) or (batsize, zeqlen, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask:
:return:
"""
if qry.dim() == 2:
ret = torch.einsum("bd,bsd->bs", [qry, ctx])
elif qry.dim() == 3:
ret = torch.einsum("bzd,bsd->bzs", [qry, ctx])
else:
raise q.SumTingWongException("qry has unsupported dimension: {}".format(qry.dim()))
return ret
class FwdAttComp(AttComp):
def __init__(self, qrydim=None, ctxdim=None, encdim=None, numlayers=1, dropout=0, **kw):
super(FwdAttComp, self).__init__(**kw)
layers = [torch.nn.Linear(qrydim + ctxdim, encdim)] \
+ [torch.nn.Linear(encdim, encdim) for _ in range(numlayers - 1)]
acts = [torch.nn.Tanh() for _ in range(len(layers))]
layers = [a for b in zip(layers, acts) for a in b]
layers.append(torch.nn.Dropout(dropout))
layers.append(torch.nn.Linear(encdim, 1))
self.mlp = torch.nn.Sequential(*layers)
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, qrydim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:return:
"""
inp = torch.cat([ctx, qry.unsqueeze(1).repeat(1, ctx.size(1), 1)], 2)
out = self.mlp(inp)
ret = out.squeeze(-1)
return ret
class SumSummComp(SummComp):
def forward(self, values, alphas):
summary = values * alphas.unsqueeze(2)
summary = summary.sum(1)
return summary
class BasicAttention(Attention):
def __init__(self, **kw):
attcomp = DotAttComp()
summcomp = SumSummComp()
super(BasicAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, **kw)
class FwdAttention(Attention):
def __init__(self, qrydim, ctxdim, encdim, dropout=0., **kw):
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout)
summcomp = SumSummComp()
super(FwdAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, **kw)
# endregion
# region Relative Attention
class VecComp(torch.nn.Module):
""" maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention """
def __init__(self, ctxdim, vecdim, **kw):
super(VecComp, self).__init__(**kw)
self.ctxdim, self.vecdim = ctxdim, vecdim
def forward(self, ctx):
"""
:param ctx: (batsize, seqlen, ctxdim)
:return: (batsize, seqlen, seqlen, vecdim)
"""
raise NotImplemented()
class FwdVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(FwdVecComp, self).__init__(ctxdim, vecdim, **kw)
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
def forward(self, ctx):
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
out = self.nonlin(out1.unsqueeze(1) + out2.unsqueeze(2))
return out
class ComboFwdVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(ComboFwdVecComp, self).__init__(ctxdim, vecdim, **kw)
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_mul = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_diff = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
def forward(self, ctx):
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
mul = ctx.unsqueeze(1) * ctx.unsqueeze(2) # (batsize, seqlen, seqlen, vecdim)
diff = ctx.unsqueeze(1) - ctx.unsqueeze(2)
outmul = self.lin_mul(mul)
outdiff = self.lin_diff(diff)
out = self.nonlin(out1.unsqueeze(1) + out2.unsqueeze(2) + outmul + outdiff)
return out
class BilinVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(BilinVecComp, self).__init__(ctxdim, vecdim, **kw)
self.W = torch.nn.Parameter(torch.Tensor(ctxdim, ctxdim, vecdim))
self.bias = torch.nn.Parameter(torch.Tensor(vecdim)) if bias else None
self.nonlin = torch.nn.Tanh()
self.reset_parameters()
def reset_parameters(self):
bound = 1 / math.sqrt(self.weight.size(1))
torch.init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
torch.init.uniform_(self.bias, -bound, bound)
def forward(self, ctx):
out = torch.einsum("bsi,bzj,ijk->bszk", ctx, ctx, self.W)
if self.bias is not None:
out = out + self.bias
out = self.nonlin(out)
return out
class BilinAndFwdComboVecComp(VecComp):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
super(BilinAndFwdComboVecComp, self).__init__(ctxdim, vecdim, **kw)
self.W = torch.nn.Parameter(torch.Tensor(ctxdim, ctxdim, vecdim))
self.bias = torch.nn.Parameter(torch.Tensor(vecdim)) if bias else None
self.lin1 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin2 = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_mul = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.lin_diff = torch.nn.Linear(ctxdim, vecdim, bias=bias)
self.nonlin = torch.nn.Tanh()
self.reset_parameters()
def reset_parameters(self):
bound = 1 / math.sqrt(self.weight.size(1))
torch.init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
torch.init.uniform_(self.bias, -bound, bound)
def forward(self, ctx):
out = torch.einsum("bsi,bzj,ijk->bszk", ctx, ctx, self.W)
if self.bias is not None:
out = out + self.bias
out1 = self.lin1(ctx)
out2 = self.lin2(ctx) # (batsize, seqlen, vecdim)
mul = ctx.unsqueeze(1) * ctx.unsqueeze(2) # (batsize, seqlen, seqlen, vecdim)
diff = ctx.unsqueeze(1) - ctx.unsqueeze(2)
outmul = self.lin_mul(mul)
outdiff = self.lin_diff(diff)
out = self.nonlin(out + out1.unsqueeze(1) + out2.unsqueeze(2) + outmul + outdiff)
return out
class RelAttention(torch.nn.Module):
def __init__(self, veccomp:VecComp=None, attcomp:AttComp=DotAttComp(), summcomp:SummComp=SumSummComp(), temperature=1., threshold=1e-6, **kw):
super(RelAttention, self).__init__(**kw)
self.threshold, self.temperature = threshold, temperature
self.veccomp = veccomp # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention
self.attcomp, self.summcomp = attcomp, summcomp
self.scorenorm = torch.nn.Softmax(-1)
self.prevatts = None # (batsize, seqlen)
self.relvecs = None # (batsize, seqlen, seqlen, vecdim)
self.prevatts_history = [] # will become decseqlen-sized list of prevatts (batsize, seqlen)
self.feed_prevatts_acc = None # decseqlen-sized list of prevatts (batsize, seqlen)
self.t = 0
self._bad_prevatts = False
def batch_reset(self):
self.prevatts = None
self.relvecs = None
self.prevatts_history = []
self.feed_prevatts_acc = None
self.t = 0
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, qdim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen)
:return:
"""
# initialize prevatts if None: init assigns all prob to first element --> first element of ctx must be a start token
if self.prevatts is None:
self.prevatts = torch.zeros_like(ctx[:, :, 0])
self.prevatts[:, 0] = 1.
# create and store relation vectors
self.relvecs = self.veccomp(ctx)
# get non-negligible part of relvecs # TODO: do sparse for more efficiency
# relvecs_idxs = torch.nonzero(self.prevatts > self.threshold)
# do attcomp with qry over relvecs
flatrelvecs = self.relvecs.view(self.relvecs.size(0), self.relvecs.size(1) * self.relvecs.size(2), self.relvecs.size(3)) # (batsize, seqlen*seqlen, vecdim)
flatrelatt_scores = self.attcomp(qry, flatrelvecs) # (batsize, seqlen * seqlen)
relatt_scores = flatrelatt_scores.view(self.relvecs.size(0), self.relvecs.size(1), self.relvecs.size(2)) # (batsize, seqlen, seqlen)
# apply ctx_mask before summary
relatt_scores = relatt_scores + (torch.log(ctx_mask.float().unsqueeze(1)) if ctx_mask is not None else 0)
relatt_alphas = self.scorenorm(relatt_scores) # (batsize, seqlen, seqlen)
alphas = torch.einsum("bsz,bs->bz", relatt_alphas, self.prevatts)
self.prevatts = alphas
if self._bad_prevatts is True:
self.prevatts = torch.zeros_like(ctx[:, :, 0])
self.prevatts[:, 2] = 1.
# saving history and using feed:
self.prevatts_history.append(self.prevatts)
if self.feed_prevatts_acc is not None:
self.prevatts = self.feed_prevatts_acc[self.t]
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
self.t += 1
return alphas, summary, relatt_scores
class BasicRelAttention(RelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = FwdVecComp(ctxdim, vecdim, bias=bias)
super(BasicRelAttention, self).__init__(veccomp, **kw)
class ComboRelAttention(RelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = ComboFwdVecComp(ctxdim, vecdim, bias=bias)
super(ComboRelAttention, self).__init__(veccomp, **kw)
class AbsRelAttention(torch.nn.Module):
"""
Attention mechanism consisting of first absolute attention, followed by a relative attention step
--> doesn't need prevatts
"""
def __init__(self, prevattcomp:AttComp=DotAttComp(), veccomp:VecComp=None, attcomp:AttComp=DotAttComp(), summcomp:SummComp=SumSummComp(), temperature=1., threshold=1e-6, **kw):
super(AbsRelAttention, self).__init__(**kw)
self.threshold, self.temperature = threshold, temperature
self.prevattcomp = prevattcomp
self.veccomp = veccomp # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention
self.attcomp, self.summcomp = attcomp, summcomp
self.scorenorm = torch.nn.Softmax(-1)
self.relvecs = None # (batsize, seqlen, seqlen, vecdim)
self.t = 0
def batch_reset(self):
self.relvecs = None
self.t = 0
def forward(self, qry, ctx, ctx_mask=None, values=None):
"""
:param qry: (batsize, qdim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen)
:return:
"""
if self.relvecs is None:
# create and store relation vectors
self.relvecs = self.veccomp(ctx)
prevatts = self.prevattcomp(qry, ctx, ctx_mask=ctx_mask)
prevatts += (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
prevatts = self.scorenorm(prevatts)
# get non-negligible part of relvecs # TODO: do sparse for more efficiency
# relvecs_idxs = torch.nonzero(self.prevatts > self.threshold)
# do attcomp with qry over relvecs
flatrelvecs = self.relvecs.view(self.relvecs.size(0), self.relvecs.size(1) * self.relvecs.size(2), self.relvecs.size(3)) # (batsize, seqlen*seqlen, vecdim)
flatrelatt_scores = self.attcomp(qry, flatrelvecs) # (batsize, seqlen * seqlen)
relatt_scores = flatrelatt_scores.view(self.relvecs.size(0), self.relvecs.size(1), self.relvecs.size(2)) # (batsize, seqlen, seqlen)
# apply ctx_mask before summary
relatt_scores = relatt_scores + (torch.log(ctx_mask.float().unsqueeze(1)) if ctx_mask is not None else 0)
relatt_alphas = self.scorenorm(relatt_scores) # (batsize, seqlen, seqlen)
alphas = torch.einsum("bsz,bs->bz", relatt_alphas, prevatts)
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
self.t += 1
return alphas, summary, relatt_scores
class BasicAbsRelAttention(AbsRelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = FwdVecComp(ctxdim, vecdim, bias=bias)
super(BasicAbsRelAttention, self).__init__(veccomp=veccomp, **kw)
class ComboAbsRelAttention(AbsRelAttention):
def __init__(self, ctxdim, vecdim, bias=True, **kw):
veccomp = ComboFwdVecComp(ctxdim, vecdim, bias=bias)
super(ComboAbsRelAttention, self).__init__(veccomp=veccomp, **kw)
def test_rel_attention(lr=0.):
qry = torch.randn(2, 5)
ctx = torch.randn(2, 3, 6)
ctx_mask = torch.tensor([
[1,1,0],
[1,1,1]
])
m = ComboRelAttention(6, 5)
y = m(qry, ctx, ctx_mask=ctx_mask)
print(y)
# endregion
# region Phrase Attention
# function with a custom backward for getting gradients to both parent and children in PhraseAttention
# forward is an elementwise min
# backward: - alphas always gets whole gradient
# - parent_alphas is increased when gradient > 0 else nothing
# (so parent's attention can only be increased here if the child needs to attend more to certain places)
# (if we equally tried to decrease parent's attentions here too, then would have conflicting signals from its children's attentions, which may not overlap)
# (decrease comes from overlap penalty and gradient on parent attention itself)
class ParentOverlapFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, parent_alphas, alphas):
ctx.save_for_backward(parent_alphas, alphas)
ret = torch.min(parent_alphas, alphas)
return ret
@staticmethod
def backward(ctx, grad_output):
gradzeros = torch.zeros_like(grad_output)
parent_grads = torch.max(gradzeros, grad_output)
return parent_grads, grad_output
def parent_overlap_f_parent_first(parent_alphas, alphas):
alphas = 1 - alphas
_z = torch.min(torch.tensor(1.0), parent_alphas / alphas)
z = parent_alphas - alphas * _z.detach()
return z
parent_overlap_f = ParentOverlapFunction.apply
# parent_overlap_f = parent_overlap_f_parent_first
def test_custom_f(lr=0):
x = torch.rand(5)
x.requires_grad = True
y = torch.rand(5)
y.requires_grad = True
z = parent_overlap_f(x, y)
l = z #z.sum()
l.backward(gradient=torch.tensor([-1,1,-1,1,1]).float())
print(x)
print(y)
print(z)
print(x.grad)
print(y.grad)
class PhraseAttention(Attention): # for depth-first decoding
""" Assumes masking by termination of tree structure assuming single root (which is also start token) """
def __init__(self, attcomp:AttComp=None, summcomp:SummComp=None, hard=False, **kw):
score_norm = torch.nn.Sigmoid()
super(PhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, score_norm=score_norm)
self.hard = hard
self.prevatts_probs = None # (batsize, declen_so_far, enclen)
if self.hard is True:
self.prevatts_samples = None
self.prevatts_mask = None
self.prevatt_ptr = None # for every example, keeps a list of pointers to positions in prevatts
# structure: batsize x stackdepth x numsiblings
# For every example, the stack contains groups of siblings.
# Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings)
self.prevatt_siblings = None # for every example, keeps a list of sets of pointers to groups of siblings
# structure: batsize x num_sibling_groups x num_siblings_in_group
# Mainly populated during forward (with a finalization step for top-level siblings in get_sibling_overlap).
# Consumed in get_sibling_overlap.
def batch_reset(self):
self.prevatts_probs, self.prevatt_ptr = None, None
self.prevatt_siblings = None
if self.hard is True:
self.prevatts_samples = None
self.prevatts_mask = None
def get_sibling_overlap(self): # called after all forwards are done
"""
Gets overlap in siblings based on current state of prevatts and prevatt_ptr.
Must be called after a batch and before batch reset.
"""
# finalize prevattr_ptr
for i, prevattr_ptr_e in enumerate(self.prevatt_ptr):
if len(prevattr_ptr_e) != 2: # must contain only the zero-group and top-level group
pass
# raise q.SumTingWongException()
while len(prevattr_ptr_e) > 0:
ptr_group = prevattr_ptr_e.pop()
if len(ptr_group) > 1:
pass
# self.prevatt_siblings[i].append(ptr_group) # don't add overlap of top-level siblings (we assume single top child, everything else is mask)
# generate ids by which to gather from prevatts
ids = torch.zeros(self.prevatts_probs.size(0), self.prevatts_probs.size(1), self.prevatts_probs.size(1),
dtype=torch.long, device=self.prevatts_probs.device)
maxnumsiblingses, maxnumsiblings = 0, 0
for eid, siblingses in enumerate(self.prevatt_siblings): # list of lists of ids in prevatts
maxnumsiblingses = max(maxnumsiblingses, len(siblingses))
for sgid, siblings in enumerate(siblingses): # list of ids in prevatts
maxnumsiblings = max(maxnumsiblings, len(siblings))
for sid, sibling in enumerate(siblings):
ids[eid, sgid, sid] = sibling
ids = ids[:, :maxnumsiblingses, :maxnumsiblings]
prevatts = self.prevatts_probs
idsmask= ((ids != 0).sum(2, keepdim=True) > 1).float()
# gather from prevatts
_ids = ids.contiguous().view(ids.size(0), -1).unsqueeze(-1).repeat(1, 1, prevatts.size(2))
prevatts_gathered = torch.gather(prevatts, 1, _ids)
prevatts_gathered = prevatts_gathered.view(prevatts.size(0), ids.size(1), ids.size(2), prevatts.size(2))
# compute overlaps
overlaps = prevatts_gathered.prod(2)
overlaps = overlaps * idsmask
overlaps = overlaps.sum(2).sum(1)
# overlaps = overlaps.mean(0)
return overlaps
def get_logprob_of_sampled_alphas(self):
if self.hard is False:
raise q.SumTingWongException("Use this only for RL on hard attention (must be in hard mode).")
probs = self.prevatts_probs * self.prevatts_samples + (1 - self.prevatts_probs) * (1 - self.prevatts_samples)
logprobs = torch.log(probs)
logprobs = logprobs * self.prevatts_mask # mask the logprobs
average_within_timestep = True
if average_within_timestep:
totals = self.prevatts_mask.sum(2) + 1e-6
logprobs = logprobs.sum(2) / totals
else:
logprobs = logprobs.mean(2)
return logprobs[:, 2:] # (batsize, seqlen) -- decoder mask should be applied on this later
def get_entropies_of_alpha_dists(self):
probs = self.prevatts_probs
dists = torch.distributions.Bernoulli(probs=probs)
entropies = dists.entropy()
entropies = entropies * self.prevatts_mask
average_within_timestep = True
if average_within_timestep:
totals = self.prevatts_mask.sum(2) + 1e-12
entropies = entropies.sum(2) / totals
else:
entropies = entropies.mean(2)
return entropies[:, 2:] # (batsize, seqlen) -- decoder mask should be applied on this later
def forward(self, qry, ctx, ctx_mask=None, values=None, prev_pushpop=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token:
-N ==> pop (N=how many to pop),
0 ==> nothing,
+N ==> push (N doesn't matter, always pushes one)
! push/pop happens AFTER the element
:return:
"""
# compute attention for token that we will produce next
# compute attention scores
scores = self.attcomp(qry, ctx, ctx_mask=ctx_mask)
# apply ctx mask to attention scores
scores = scores + (torch.log(ctx_mask.float()) if ctx_mask is not None else 0)
# normalize attention scores
alphas_probs = self.score_norm(scores) # sigmoid probs
if self.hard:
alphas_dist = torch.distributions.Bernoulli(probs=alphas_probs)
alphas_samples = alphas_dist.sample()
# constrain alphas to parent's alphas:
if self.prevatts_probs is None: # there is no history
# initialize prevatts (history)
self.prevatts_probs = torch.ones_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1) # means everything is attended to
if ctx_mask is not None:
self.prevatts_probs = self.prevatts_probs * ctx_mask.float().unsqueeze(1)
if self.hard is True:
self.prevatts_samples = self.prevatts_probs.clone().detach() #torch.ones_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1)
self.prevatts_mask = torch.zeros_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1)
# --> we assume the previous (first ever) attention, used to compute initial (current input) token attended over whole sequence
# initialize prevatt_ptr
self.prevatt_ptr = [[[0], []] for _ in range(len(prev_pushpop))]
# initialize prevatt_siblings
self.prevatt_siblings = [[] for _ in range(len(prev_pushpop))]
# update pointers to prevatt
k = self.prevatts_probs.size(1) - 1 # index of the last produced attention alphas (that were used for prev token)
for i in range(len(prev_pushpop)):
self.prevatt_ptr[i][-1].append(k) # make last token a sibling of the children of the same parent before it (if any)
if prev_pushpop[i].cpu().item() > 0: # PUSH: previous token requires children --> make stack deeper by one level
self.prevatt_ptr[i].append([])
elif prev_pushpop[i].cpu().item() < 0: # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too)
pp = prev_pushpop[i].cpu().item()
while pp < 0 and len(self.prevatt_ptr[i]) > 2:
siblings = self.prevatt_ptr[i].pop(-1) # pop the list from stack
if len(siblings) > 1: # if longer than 1, add to the list of siblings
self.prevatt_siblings[i].append(siblings)
pp += 1
else:
pass
# constrain alphas to parent's alphas
parent_ptr = [prevatt_ptr_e[-2][-1] for prevatt_ptr_e in self.prevatt_ptr]
parent_ptr = torch.tensor(parent_ptr).long().to(self.prevatts_probs.device)
if self.hard is True: # no backprop through alphas
# parent_alphas_probs = self.prevatts_probs.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
# .repeat(1, 1, self.prevatts_probs.size(-1))).squeeze(1)
parent_alphas_samples = self.prevatts_samples.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
.repeat(1, 1, self.prevatts_samples.size(-1))).squeeze(1)
alphas_samples = torch.min(parent_alphas_samples, alphas_samples)
# save parent-masked samples
self.prevatts_samples = torch.cat([self.prevatts_samples, alphas_samples.unsqueeze(1)], 1)
self.prevatts_mask = torch.cat([self.prevatts_mask, parent_alphas_samples.unsqueeze(1)], 1)
alphas = alphas_samples
else: # need to backprop differently
parent_alphas_probs = self.prevatts_probs.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1)
.repeat(1, 1, self.prevatts_probs.size(-1))).squeeze(1)
alphas_probs = parent_overlap_f(parent_alphas_probs, alphas_probs)
alphas = alphas_probs
# append current alpha probs to prevatts accumulator
self.prevatts_probs = torch.cat([self.prevatts_probs, alphas_probs.unsqueeze(1)], 1)
# compute summary
values = ctx if values is None else values
summary = self.summcomp(values, alphas)
return alphas, summary, scores
class PhraseAttentionTeacher(Attention): # for depth-first decoding
""" Normal attention. Stores probs for the batch for use to supervise real phrase attention. """
""" Assumes masking by termination of tree structure assuming single root (which is also start token) """
def __init__(self, attcomp:AttComp=None, hard=True, **kw):
score_norm = torch.nn.Softmax(-1)
summcomp = SumSummComp()
super(PhraseAttentionTeacher, self).__init__(attcomp=attcomp, summcomp=summcomp, score_norm=score_norm)
self.prevatts_probs = None # (batsize, declen_so_far, enclen)
self.prevatt_ptr = None # for every example, keeps a list of pointers to positions in prevatts
self.prevatts_masks = None
# structure: batsize x stackdepth x numsiblings
# For every example, the stack contains groups of siblings.
# Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings)
self.record = False
self.hard = hard
def batch_reset(self):
self.prevatts_probs, self.prevatt_ptr = None, None
self.prevatts_masks = None
def get_phraseatt_supervision(self, hard=True):
"""
Propagates attentions in self.prevatts_probs from children to parents according to self.prevatt_ptr, this way
converting softmax attentions generated here to a supervision signal usable for sigmoid attention.
If hard, does argmax before propagating, else propagates probs.
"""
return self.prevatts_probs, self.prevatts_masks
def forward(self, qry, ctx, ctx_mask=None, values=None, prev_pushpop=None):
"""
:param qry: (batsize, dim)
:param ctx: (batsize, seqlen, dim)
:param ctx_mask: (batsize, seqlen)
:param values: (batsize, seqlen, dim)
:param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token:
-N ==> pop (N=how many to pop),
0 ==> nothing,
+N ==> push (N doesn't matter, always pushes one)
! push/pop happens AFTER the element
:return:
"""
alphas_probs, summary, scores = super(PhraseAttentionTeacher, self).forward(qry, ctx, ctx_mask=ctx_mask, values=values)
if self.record is True:
if self.prevatts_probs is None: # there is no history
# initialize prevatts (history)
self.prevatts_probs = torch.zeros_like(alphas_probs).unsqueeze(1)
# initialize prevatt masks
self.prevatts_masks = torch.ones_like(alphas_probs).unsqueeze(1)
if ctx_mask is not None:
self.prevatts_masks = self.prevatts_masks * ctx_mask.unsqueeze(1).float()
# initialize prevatt_ptr
self.prevatt_ptr = [[[]] for _ in range(len(prev_pushpop))]
# update pointers to prevatt
k = self.prevatts_probs.size(1) - 1 # index of the last produced attention alphas (that were used for prev token)
for i in range(len(prev_pushpop)): # iterate over all examples
self.prevatt_ptr[i][-1].append(k) # make last token a sibling of the children of the same parent before it (if any)
if prev_pushpop[i].cpu().item() > 0: # PUSH: previous token requires children --> make stack deeper by one level
self.prevatt_ptr[i].append([])
elif prev_pushpop[i].cpu().item() < 0: # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too)
pp = prev_pushpop[i].cpu().item()
while pp < 0 and len(self.prevatt_ptr[i]) > 1:
siblings = self.prevatt_ptr[i].pop(-1) # pop the list from stack
# add each of the sibling's attention probs to their parent and populate children's masks
parent = self.prevatt_ptr[i][-1][-1]
for sibling in siblings:
sibling_alphas = self.prevatts_probs[i, sibling]
self.prevatts_probs[i, parent] += sibling_alphas
self.prevatts_probs[i, parent].clamp_(0., 1.)
parent_alphas = self.prevatts_probs[i, parent]
for sibling in siblings:
self.prevatts_masks[i, sibling] = parent_alphas
pp += 1
else:
pass
# append current alpha probs to prevatts accumulator
if self.hard:
# _alphas_probs = torch.zeros_like(alphas_probs)\
# .scatter_(1, torch.argmax(alphas_probs, 1, True), 1.)
alphas_dist = torch.distributions.OneHotCategorical(probs=alphas_probs)
_alphas_probs = alphas_dist.sample()
else:
_alphas_probs = alphas_probs
self.prevatts_probs = torch.cat([self.prevatts_probs, _alphas_probs.unsqueeze(1)], 1)
self.prevatts_masks = torch.cat([self.prevatts_masks, torch.zeros_like(_alphas_probs.unsqueeze(1))], 1) # will be filled once siblings have been done
return alphas_probs, summary, scores
def test_phrase_attention(lr=0):
# simulate operation of attention
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = torch.tensor([[1,0,1,0,-1,-1], [1,1,1,1,-4,0]])
# pushpop = list(zip(*pushpop))
m = PhraseAttention(hard=True)
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
overlap = m.get_sibling_overlap()
pass
def test_phrase_attention_teacher(lr=0):
# simulate operation of attention
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = torch.tensor([[1,0,1,0,-1,-1], [1,1,1,1,-4,0]])
# pushpop = list(zip(*pushpop))
m = PhraseAttentionTeacher(hard=True)
m.record = True
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
print(m.prevatts_probs[0])
print(m.prevatts_masks[0])
print(m.prevatts_probs[1])
print(m.prevatts_masks[1])
overlap = m.get_phraseatt_supervision(hard=True)
pass
# endregion
# region components for phrase attention
class LSTMAttComp(AttComp):
def __init__(self, qrydim=None, ctxdim=None, encdim=None, dropout=0., numlayers=1, bidir=False, **kw):
super(LSTMAttComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMEncoder(qrydim+ctxdim, *encdims, bidir=bidir, dropout_in=dropout)
self.lin = torch.nn.Linear(encdim, 1)
def forward(self, qry, ctx, ctx_mask=None):
"""
:param qry: (batsize, qrydim)
:param ctx: (batsize, seqlen, ctxdim)
:param ctx_mask: (batsize, seqlen)
:return:
"""
inp = torch.cat([ctx, qry.unsqueeze(1).repeat(1, ctx.size(1), 1)], 2)
out = self.layers(inp, mask=ctx_mask)
ret = self.lin(out).squeeze(-1) # (batsize, seqlen)
return ret
class LSTMSummComp(SummComp):
def __init__(self, valdim=None, encdim=None, dropout=0., numlayers=1, **kw):
super(LSTMSummComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMCellEncoder(valdim, *encdims, bidir=False, dropout_in=dropout)
def forward(self, values, alphas):
_, out = self.layers(values, gate=alphas, ret_states=True)
out = out[:, 0]
skip = values * alphas.unsqueeze(-1)
skip = skip.sum(1)
out = out + skip
return out
class PooledLSTMSummComp(SummComp):
"""
Uses a bidirectional lstm encoder with skip connections to encode according to given mask, not updating the state if gate is zero.
If valdim != encdim * 2, uses a linear projection on the values.
After encoding, does max and mean pooling across time, weighted by the provided attention weights.
Best use only with hard attention alphas.
"""
def __init__(self, valdim=None, encdim=None, dropout=0., numlayers=1, **kw):
super(PooledLSTMSummComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMCellEncoder(valdim, *encdims, bidir=True, dropout_in=dropout)
self.skip_adapt = torch.nn.Linear(valdim, encdim*2) if valdim != encdim * 2 else lambda x: x
def forward(self, values, alphas):
"""
:param values: (batsize, seqlen, valdim)
:param alphas: (batsize, seqlen)
:return: (batsize, seqlen, encdim*2*2)
"""
topouts, out = self.layers(values, gate=alphas, ret_states=True)
skip_vals = self.skip_adapt(values)
rnnouts = topouts + skip_vals
rnnouts = rnnouts * alphas.unsqueeze(2)
meanpool = rnnouts.sum(1) / (alphas.unsqueeze(2).sum(1) + 1e-6)
maxpool = rnnouts#.clone()
maxpool.masked_fill_((1 - alphas).byte().unsqueeze(2), -np.infty)
maxpool = maxpool.max(1)[0]
maxpool = q.inf2zero(maxpool)
out = torch.cat([meanpool, maxpool], 1) # (batsize, encdim * 2 * 2)
return out
#
# out = out[:, 0]
#
# skip = values * alphas.unsqueeze(-1)
# skip = skip.sum(1)
#
# out = out + skip
# return out
def test_pooled_lstm_summ_comp(lr=0.):
vals = torch.randn(2, 5, 8)
vals.requires_grad = True
alphas = (torch.rand(2, 5) > 0.8).float()
print(alphas)
m = PooledLSTMSummComp(valdim=8, encdim=4)
out = m(vals, alphas)
print(out.size())
l = out.sum()
l.backward()
print(vals.grad)
class LSTMPhraseAttention(PhraseAttention):
def __init__(self, qrydim=None, ctxdim=None, valdim=None, encdim=None, dropout=0., numlayers=1, hard=False, **kw):
ctxdim = qrydim if ctxdim is None else ctxdim
valdim = ctxdim if valdim is None else valdim
encdim = ctxdim if encdim is None else encdim
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
summcomp = LSTMSummComp(valdim=valdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
super(LSTMPhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, hard=hard, **kw)
class PooledLSTMPhraseAttention(PhraseAttention):
def __init__(self, qrydim=None, ctxdim=None, valdim=None, encdim=None, dropout=0., numlayers=1, hard=False, **kw):
ctxdim = qrydim if ctxdim is None else ctxdim
valdim = ctxdim if valdim is None else valdim
encdim = ctxdim if encdim is None else encdim
attcomp = FwdAttComp(qrydim=qrydim, ctxdim=ctxdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
summcomp = PooledLSTMSummComp(valdim=valdim, encdim=encdim, dropout=dropout, numlayers=numlayers)
super(PooledLSTMPhraseAttention, self).__init__(attcomp=attcomp, summcomp=summcomp, hard=hard, **kw)
class PhraseAttentionDecoderCell(torch.nn.Module): # Luong-style decoder cell
""" Need to subclass this, implementing get_pushpop_from for specific vocabulary. Or specify mapping id2pushpop during construction. """
def __init__(self, emb=None, core=None, att:PhraseAttention=None, merge:q.rnn.DecCellMerge=q.rnn.ConcatDecCellMerge(),
out=None, feed_att=False, return_alphas=False, return_scores=False, return_other=False,
dropout=0, id2pushpop=None, **kw):
"""
Based on LuongCell, only change: support for prev_pushpop arg in forward --> passed to attention
:param emb:
:param core:
:param att:
:param merge:
:param out: if None, out_vec (after merge) is returned
:param feed_att:
:param h_hat_0:
:param id2pushpop: torch tensor mapping token ids to pushpop values
:param kw:
"""
super(PhraseAttentionDecoderCell, self).__init__(**kw)
self.emb, self.core, self.att, self.merge, self.out = emb, core, att, merge, out
self.feed_att = feed_att
self._outvec_tm1 = None
self.outvec_t0 = None
self.return_alphas = return_alphas
self.return_scores = return_scores
self.return_other = return_other
self._id2pushpop = id2pushpop # THIS LINE IS ADDED
self.dropout = torch.nn.Dropout(dropout)
def batch_reset(self):
self.outvec_t0 = None
self._outvec_tm1 = None
def forward(self, x_t, ctx=None, ctx_mask=None, **kw):
assert (ctx is not None)
embs = self.emb(x_t)
if q.issequence(embs) and len(embs) == 2:
embs, mask = embs
if self.feed_att:
if self._outvec_tm1 is None:
assert (self.outvec_t0 is not None) #"h_hat_0 must be set when feed_att=True"
self._outvec_tm1 = self.outvec_t0
core_inp = torch.cat([embs, self._outvec_tm1], 1)
else:
core_inp = embs
prev_pushpop = self.get_pushpop_from(x_t) # THIS LINE IS ADDED
core_out = self.core(core_inp)
alphas, summaries, scores = self.att(core_out, ctx, ctx_mask=ctx_mask, values=ctx, prev_pushpop=prev_pushpop) # THIS LINE IS CHANGED
out_vec = self.merge(core_out, summaries, core_inp)
out_vec = self.dropout(out_vec)
self._outvec_tm1 = out_vec # store outvec
ret = tuple()
if self.out is None:
ret += (out_vec,)
else:
_out_vec = self.out(out_vec)
ret += (_out_vec,)
if self.return_alphas:
ret += (alphas,)
if self.return_scores:
ret += (scores,)
if self.return_other:
ret += (embs, core_out, summaries)
return ret[0] if len(ret) == 1 else ret
def get_pushpop_from(self, x_t): # (batsize,) ids # THIS METHOD IS ADDED
""" Get pushpop from x_t: based on x_t, decides whether to push (>0), do nothing (0) or pop (<0) previous attentions """
if self._id2pushpop is not None:
return self._id2pushpop[x_t]
else:
raise NotImplemented()
def test_lstm_phrase_attention(lr=0):
m = LSTMPhraseAttention(4)
ctx = torch.randn(2, 5, 4)
qrys = torch.randn(2, 6, 4)
ctx_mask = torch.tensor([[1,1,1,1,1],[1,1,1,0,0]])
pushpop = [[1,0,1,0,-1,-1], # output of last step will be "masked"
[1,1,1,1,-4,0]] # output of last two steps will be "masked"
pushpop = torch.tensor(pushpop)
# pushpop = list(zip(*pushpop))
for i in range(qrys.size(1)):
alphas, summary, scores = m(qrys[:, i], ctx, ctx_mask=ctx_mask, prev_pushpop=pushpop[:, i])
overlap = m.get_sibling_overlap()
pass
# endregion
if __name__ == '__main__':
# q.argprun(test_custom_f)
# q.argprun(test_phrase_attention)
# q.argprun(test_phrase_attention_teacher)
# q.argprun(test_lstm_phrase_attention)
# q.argprun(test_pooled_lstm_summ_comp)
q.argprun(test_rel_attention) | en | 0.730075 | # region normal attention computes attention scores Computes phrase attention. For use with encoders and decoders from rnn.py :param attcomp: used to compute attention scores :param summcomp: used to compute summary # self.prevatts = None # holds previous attention vectors # self.prevatt_ptr = None # for every example, contains a list with pointers to indexes of prevatts :param qry: (batsize, dim) :param ctx: (batsize, seqlen, dim) :param ctx_mask: (batsize, seqlen) :param values: (batsize, seqlen, dim) :return: :param qry: (batsize, dim) or (batsize, zeqlen, dim) :param ctx: (batsize, seqlen, dim) :param ctx_mask: :return: :param qry: (batsize, qrydim) :param ctx: (batsize, seqlen, ctxdim) :param ctx_mask: (batsize, seqlen) :return: # endregion # region Relative Attention maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention :param ctx: (batsize, seqlen, ctxdim) :return: (batsize, seqlen, seqlen, vecdim) # (batsize, seqlen, vecdim) # (batsize, seqlen, vecdim) # (batsize, seqlen, seqlen, vecdim) # (batsize, seqlen, vecdim) # (batsize, seqlen, seqlen, vecdim) # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention # (batsize, seqlen) # (batsize, seqlen, seqlen, vecdim) # will become decseqlen-sized list of prevatts (batsize, seqlen) # decseqlen-sized list of prevatts (batsize, seqlen) :param qry: (batsize, qdim) :param ctx: (batsize, seqlen, ctxdim) :param ctx_mask: (batsize, seqlen) :param values: (batsize, seqlen) :return: # initialize prevatts if None: init assigns all prob to first element --> first element of ctx must be a start token # create and store relation vectors # get non-negligible part of relvecs # TODO: do sparse for more efficiency # relvecs_idxs = torch.nonzero(self.prevatts > self.threshold) # do attcomp with qry over relvecs # (batsize, seqlen*seqlen, vecdim) # (batsize, seqlen * seqlen) # (batsize, seqlen, seqlen) # apply ctx_mask before summary # (batsize, seqlen, seqlen) # saving history and using feed: Attention mechanism consisting of first absolute attention, followed by a relative attention step --> doesn't need prevatts # maps ctx~(batsize, seqlen, ctxdim) to relvecs~(batsize, seqlen, seqlen, vecdim) ~~ self-attention # (batsize, seqlen, seqlen, vecdim) :param qry: (batsize, qdim) :param ctx: (batsize, seqlen, ctxdim) :param ctx_mask: (batsize, seqlen) :param values: (batsize, seqlen) :return: # create and store relation vectors # get non-negligible part of relvecs # TODO: do sparse for more efficiency # relvecs_idxs = torch.nonzero(self.prevatts > self.threshold) # do attcomp with qry over relvecs # (batsize, seqlen*seqlen, vecdim) # (batsize, seqlen * seqlen) # (batsize, seqlen, seqlen) # apply ctx_mask before summary # (batsize, seqlen, seqlen) # endregion # region Phrase Attention # function with a custom backward for getting gradients to both parent and children in PhraseAttention # forward is an elementwise min # backward: - alphas always gets whole gradient # - parent_alphas is increased when gradient > 0 else nothing # (so parent's attention can only be increased here if the child needs to attend more to certain places) # (if we equally tried to decrease parent's attentions here too, then would have conflicting signals from its children's attentions, which may not overlap) # (decrease comes from overlap penalty and gradient on parent attention itself) # parent_overlap_f = parent_overlap_f_parent_first #z.sum() # for depth-first decoding Assumes masking by termination of tree structure assuming single root (which is also start token) # (batsize, declen_so_far, enclen) # for every example, keeps a list of pointers to positions in prevatts # structure: batsize x stackdepth x numsiblings # For every example, the stack contains groups of siblings. # Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings) # for every example, keeps a list of sets of pointers to groups of siblings # structure: batsize x num_sibling_groups x num_siblings_in_group # Mainly populated during forward (with a finalization step for top-level siblings in get_sibling_overlap). # Consumed in get_sibling_overlap. # called after all forwards are done Gets overlap in siblings based on current state of prevatts and prevatt_ptr. Must be called after a batch and before batch reset. # finalize prevattr_ptr # must contain only the zero-group and top-level group # raise q.SumTingWongException() # self.prevatt_siblings[i].append(ptr_group) # don't add overlap of top-level siblings (we assume single top child, everything else is mask) # generate ids by which to gather from prevatts # list of lists of ids in prevatts # list of ids in prevatts # gather from prevatts # compute overlaps # overlaps = overlaps.mean(0) # mask the logprobs # (batsize, seqlen) -- decoder mask should be applied on this later # (batsize, seqlen) -- decoder mask should be applied on this later :param qry: (batsize, dim) :param ctx: (batsize, seqlen, dim) :param ctx_mask: (batsize, seqlen) :param values: (batsize, seqlen, dim) :param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token: -N ==> pop (N=how many to pop), 0 ==> nothing, +N ==> push (N doesn't matter, always pushes one) ! push/pop happens AFTER the element :return: # compute attention for token that we will produce next # compute attention scores # apply ctx mask to attention scores # normalize attention scores # sigmoid probs # constrain alphas to parent's alphas: # there is no history # initialize prevatts (history) # means everything is attended to #torch.ones_like(alphas_probs).unsqueeze(1).repeat(1, 2, 1) # --> we assume the previous (first ever) attention, used to compute initial (current input) token attended over whole sequence # initialize prevatt_ptr # initialize prevatt_siblings # update pointers to prevatt # index of the last produced attention alphas (that were used for prev token) # make last token a sibling of the children of the same parent before it (if any) # PUSH: previous token requires children --> make stack deeper by one level # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too) # pop the list from stack # if longer than 1, add to the list of siblings # constrain alphas to parent's alphas # no backprop through alphas # parent_alphas_probs = self.prevatts_probs.gather(1, parent_ptr.unsqueeze(-1).unsqueeze(-1) # .repeat(1, 1, self.prevatts_probs.size(-1))).squeeze(1) # save parent-masked samples # need to backprop differently # append current alpha probs to prevatts accumulator # compute summary # for depth-first decoding Normal attention. Stores probs for the batch for use to supervise real phrase attention. Assumes masking by termination of tree structure assuming single root (which is also start token) # (batsize, declen_so_far, enclen) # for every example, keeps a list of pointers to positions in prevatts # structure: batsize x stackdepth x numsiblings # For every example, the stack contains groups of siblings. # Could have had just batsize x stackdepth, but need to remember siblings for sibling overlap penalty (see prevatt_siblings) Propagates attentions in self.prevatts_probs from children to parents according to self.prevatt_ptr, this way converting softmax attentions generated here to a supervision signal usable for sigmoid attention. If hard, does argmax before propagating, else propagates probs. :param qry: (batsize, dim) :param ctx: (batsize, seqlen, dim) :param ctx_mask: (batsize, seqlen) :param values: (batsize, seqlen, dim) :param prev_pushpop: (batsize,) - whether PREVIOUS token was a push or pop (or do-nothing) token: -N ==> pop (N=how many to pop), 0 ==> nothing, +N ==> push (N doesn't matter, always pushes one) ! push/pop happens AFTER the element :return: # there is no history # initialize prevatts (history) # initialize prevatt masks # initialize prevatt_ptr # update pointers to prevatt # index of the last produced attention alphas (that were used for prev token) # iterate over all examples # make last token a sibling of the children of the same parent before it (if any) # PUSH: previous token requires children --> make stack deeper by one level # POP: previous token was last in its row of siblings (and possibly terminates upwards levels too) # pop the list from stack # add each of the sibling's attention probs to their parent and populate children's masks # append current alpha probs to prevatts accumulator # _alphas_probs = torch.zeros_like(alphas_probs)\ # .scatter_(1, torch.argmax(alphas_probs, 1, True), 1.) # will be filled once siblings have been done # simulate operation of attention # pushpop = list(zip(*pushpop)) # simulate operation of attention # pushpop = list(zip(*pushpop)) # endregion # region components for phrase attention :param qry: (batsize, qrydim) :param ctx: (batsize, seqlen, ctxdim) :param ctx_mask: (batsize, seqlen) :return: # (batsize, seqlen) Uses a bidirectional lstm encoder with skip connections to encode according to given mask, not updating the state if gate is zero. If valdim != encdim * 2, uses a linear projection on the values. After encoding, does max and mean pooling across time, weighted by the provided attention weights. Best use only with hard attention alphas. :param values: (batsize, seqlen, valdim) :param alphas: (batsize, seqlen) :return: (batsize, seqlen, encdim*2*2) #.clone() # (batsize, encdim * 2 * 2) # # out = out[:, 0] # # skip = values * alphas.unsqueeze(-1) # skip = skip.sum(1) # # out = out + skip # return out # Luong-style decoder cell Need to subclass this, implementing get_pushpop_from for specific vocabulary. Or specify mapping id2pushpop during construction. Based on LuongCell, only change: support for prev_pushpop arg in forward --> passed to attention :param emb: :param core: :param att: :param merge: :param out: if None, out_vec (after merge) is returned :param feed_att: :param h_hat_0: :param id2pushpop: torch tensor mapping token ids to pushpop values :param kw: # THIS LINE IS ADDED #"h_hat_0 must be set when feed_att=True" # THIS LINE IS ADDED # THIS LINE IS CHANGED # store outvec # (batsize,) ids # THIS METHOD IS ADDED Get pushpop from x_t: based on x_t, decides whether to push (>0), do nothing (0) or pop (<0) previous attentions # output of last step will be "masked" # output of last two steps will be "masked" # pushpop = list(zip(*pushpop)) # endregion # q.argprun(test_custom_f) # q.argprun(test_phrase_attention) # q.argprun(test_phrase_attention_teacher) # q.argprun(test_lstm_phrase_attention) # q.argprun(test_pooled_lstm_summ_comp) | 2.883658 | 3 |
lib/config.py | lee-kode/hapaas | 0 | 6614339 | <filename>lib/config.py
import ConfigParser
import os
CONFIG_FILE = '/etc/hapaas/config.ini'
def get_config():
cfg_file = os.getenv('HAPAAS_CONFIG', CONFIG_FILE)
cfg = ConfigParser.RawConfigParser(allow_no_value=True)
cfg.read(cfg_file)
return cfg
| <filename>lib/config.py
import ConfigParser
import os
CONFIG_FILE = '/etc/hapaas/config.ini'
def get_config():
cfg_file = os.getenv('HAPAAS_CONFIG', CONFIG_FILE)
cfg = ConfigParser.RawConfigParser(allow_no_value=True)
cfg.read(cfg_file)
return cfg
| none | 1 | 2.222972 | 2 | |
organize/filters/lastmodified.py | ytzhangFTD/organize | 1 | 6614340 | <gh_stars>1-10
from datetime import datetime, timedelta
from typing import Union
from fs.base import FS
from schema import Optional, Or
from .filter import Filter, FilterResult
from .utils import age_condition_applies
class LastModified(Filter):
"""Matches files by last modified date
Args:
years (int): specify number of years
months (int): specify number of months
weeks (float): specify number of weeks
days (float): specify number of days
hours (float): specify number of hours
minutes (float): specify number of minutes
seconds (float): specify number of seconds
mode (str):
either 'older' or 'newer'. 'older' matches files / folders last modified before
the given time, 'newer' matches files / folders last modified within the given
time. (default = 'older')
Returns:
{lastmodified}: The datetime the files / folders was lastmodified.
"""
name = "lastmodified"
schema_support_instance_without_args = True
arg_schema = {
Optional("mode"): Or("older", "newer"),
Optional("years"): int,
Optional("months"): int,
Optional("weeks"): int,
Optional("days"): int,
Optional("hours"): int,
Optional("minutes"): int,
Optional("seconds"): int,
}
def __init__(
self,
years=0,
months=0,
weeks=0,
days=0,
hours=0,
minutes=0,
seconds=0,
mode="older",
):
self.age = timedelta(
weeks=52 * years + 4 * months + weeks, # quick and a bit dirty
days=days,
hours=hours,
minutes=minutes,
seconds=seconds,
)
self.mode = mode.strip().lower()
if self.mode not in ("older", "newer"):
raise ValueError("Unknown option for 'mode': must be 'older' or 'newer'.")
def matches_lastmodified_time(self, lastmodified: Union[None, datetime]):
match = True
if self.age.total_seconds():
if not lastmodified:
match = False
else:
match = age_condition_applies(
dt=lastmodified,
age=self.age,
mode=self.mode,
reference=datetime.now(),
)
return match
def pipeline(self, args: dict) -> FilterResult:
fs = args["fs"] # type: FS
fs_path = args["fs_path"]
modified = fs.getmodified(fs_path)
if modified:
modified = modified.astimezone()
match = self.matches_lastmodified_time(modified)
return FilterResult(
matches=match,
updates={self.get_name(): modified},
)
def __str__(self):
return "[LastModified] All files / folders last modified %s than %s" % (
self._mode,
self.timedelta,
)
| from datetime import datetime, timedelta
from typing import Union
from fs.base import FS
from schema import Optional, Or
from .filter import Filter, FilterResult
from .utils import age_condition_applies
class LastModified(Filter):
"""Matches files by last modified date
Args:
years (int): specify number of years
months (int): specify number of months
weeks (float): specify number of weeks
days (float): specify number of days
hours (float): specify number of hours
minutes (float): specify number of minutes
seconds (float): specify number of seconds
mode (str):
either 'older' or 'newer'. 'older' matches files / folders last modified before
the given time, 'newer' matches files / folders last modified within the given
time. (default = 'older')
Returns:
{lastmodified}: The datetime the files / folders was lastmodified.
"""
name = "lastmodified"
schema_support_instance_without_args = True
arg_schema = {
Optional("mode"): Or("older", "newer"),
Optional("years"): int,
Optional("months"): int,
Optional("weeks"): int,
Optional("days"): int,
Optional("hours"): int,
Optional("minutes"): int,
Optional("seconds"): int,
}
def __init__(
self,
years=0,
months=0,
weeks=0,
days=0,
hours=0,
minutes=0,
seconds=0,
mode="older",
):
self.age = timedelta(
weeks=52 * years + 4 * months + weeks, # quick and a bit dirty
days=days,
hours=hours,
minutes=minutes,
seconds=seconds,
)
self.mode = mode.strip().lower()
if self.mode not in ("older", "newer"):
raise ValueError("Unknown option for 'mode': must be 'older' or 'newer'.")
def matches_lastmodified_time(self, lastmodified: Union[None, datetime]):
match = True
if self.age.total_seconds():
if not lastmodified:
match = False
else:
match = age_condition_applies(
dt=lastmodified,
age=self.age,
mode=self.mode,
reference=datetime.now(),
)
return match
def pipeline(self, args: dict) -> FilterResult:
fs = args["fs"] # type: FS
fs_path = args["fs_path"]
modified = fs.getmodified(fs_path)
if modified:
modified = modified.astimezone()
match = self.matches_lastmodified_time(modified)
return FilterResult(
matches=match,
updates={self.get_name(): modified},
)
def __str__(self):
return "[LastModified] All files / folders last modified %s than %s" % (
self._mode,
self.timedelta,
) | en | 0.386335 | Matches files by last modified date Args: years (int): specify number of years months (int): specify number of months weeks (float): specify number of weeks days (float): specify number of days hours (float): specify number of hours minutes (float): specify number of minutes seconds (float): specify number of seconds mode (str): either 'older' or 'newer'. 'older' matches files / folders last modified before the given time, 'newer' matches files / folders last modified within the given time. (default = 'older') Returns: {lastmodified}: The datetime the files / folders was lastmodified. # quick and a bit dirty # type: FS | 2.982057 | 3 |
IPP/desempacotamento.py | juarezhenriquelisboa/Python | 1 | 6614341 | def imprime_maior(mensagem, *numeros):
maior = None
for e in numeros:
if maior == None or maior < e:
maior = e
print(mensagem, maior)
imprime_maior("Maior:", 5,4,3,1)
imprime_maior("Max:", *[1, 7, 9])
| def imprime_maior(mensagem, *numeros):
maior = None
for e in numeros:
if maior == None or maior < e:
maior = e
print(mensagem, maior)
imprime_maior("Maior:", 5,4,3,1)
imprime_maior("Max:", *[1, 7, 9])
| none | 1 | 3.571095 | 4 | |
fish-wf/scripts/fish_sphere_boxplot.py | jfear/larval_gonad | 1 | 6614342 | <reponame>jfear/larval_gonad
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from scipy.stats import ttest_rel
from larval_gonad import plotting
def main():
plt.style.use(["1c", "science_base"])
width = plt.rcParams["figure.figsize"][0]
plt.rcParams["figure.figsize"] = (width, width)
sphere = pd.read_csv(snakemake.input[0])
ax = sns.boxplot(
"chrom",
"um3",
data=sphere.melt(var_name="chrom", value_name="um3"),
palette=snakemake.params.colors,
notch=True
)
# Clean up plot
ax.set(ylabel=r"$\Psi$", xlabel="")
sns.despine(ax=ax)
# Test that not significant
pval = np.round(ttest_rel(sphere["X"], sphere["2L"])[1], 3)
if pval <= 0.05:
# Extend axis and add NS.
_max = sphere.max().max() + 0.05
ax.set_ylim(None, _max)
ax.text(0.5, 0.99, f"p = {pval}", transform=ax.transAxes, va="top", ha="center")
l = plt.Line2D([0.3, 0.7], [0.94, 0.94], transform=ax.transAxes, color="k", lw=0.8, ls="-")
ax.add_line(l)
plt.savefig(snakemake.output[0])
if __name__ == "__main__":
if os.getenv("SNAKE_DEBUG", False):
from larval_gonad.debug import snakemake_debug
snakemake = snakemake_debug(
workdir="fish-wf",
input="../data/external/miriam/oligopaint_sphere.csv",
params=dict(colors=["red", "grey"]),
)
main()
| import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from scipy.stats import ttest_rel
from larval_gonad import plotting
def main():
plt.style.use(["1c", "science_base"])
width = plt.rcParams["figure.figsize"][0]
plt.rcParams["figure.figsize"] = (width, width)
sphere = pd.read_csv(snakemake.input[0])
ax = sns.boxplot(
"chrom",
"um3",
data=sphere.melt(var_name="chrom", value_name="um3"),
palette=snakemake.params.colors,
notch=True
)
# Clean up plot
ax.set(ylabel=r"$\Psi$", xlabel="")
sns.despine(ax=ax)
# Test that not significant
pval = np.round(ttest_rel(sphere["X"], sphere["2L"])[1], 3)
if pval <= 0.05:
# Extend axis and add NS.
_max = sphere.max().max() + 0.05
ax.set_ylim(None, _max)
ax.text(0.5, 0.99, f"p = {pval}", transform=ax.transAxes, va="top", ha="center")
l = plt.Line2D([0.3, 0.7], [0.94, 0.94], transform=ax.transAxes, color="k", lw=0.8, ls="-")
ax.add_line(l)
plt.savefig(snakemake.output[0])
if __name__ == "__main__":
if os.getenv("SNAKE_DEBUG", False):
from larval_gonad.debug import snakemake_debug
snakemake = snakemake_debug(
workdir="fish-wf",
input="../data/external/miriam/oligopaint_sphere.csv",
params=dict(colors=["red", "grey"]),
)
main() | en | 0.792041 | # Clean up plot # Test that not significant # Extend axis and add NS. | 2.481744 | 2 |
01_Language/05_Python/study/lesson_02/10.类型转换.py | cliff363825/TwentyFour | 3 | 6614343 | <gh_stars>1-10
# 类型转换四个函数 int() float() str() bool()
# int() 可以用来将其他的对象转换为整型
# 规则：
# 布尔值：True -> 1 False -> 0
# 浮点数：直接取整，省略小数点后的内容
# 字符串：合法的整数字符串，直接转换为对应的数字
# 如果不是一个合法的整数字符串，则报错 ValueError: invalid literal for int() with base 10: '11.5'
# 对于其他不可转换为整型的对象，直接抛出异常 ValueError
# float() 和 int()基本一致，不同的是它会将对象转换为浮点数
# str() 可以将对象转换为字符串
# True -> 'True'
# False -> 'False'
# 123 -> '123'
# 。。。
# bool() 可以将对象转换为布尔值，任何对象都可以转换为布尔值
# 规则：对于所有表示空性的对象都会转换为False，其余的转换为True
# 哪些表示的空性：0 、 None 、 '' 。。。
a = True
# 调用int()来将a转换为整型
# int()函数不会对原来的变量产生影响，他是对象转换为指定的类型并将其作为返回值返回
# 如果希望修改原来的变量，则需要对变量进行重新赋值
a = int(a)
a = False
a = int(a)
a = '123'
a = int(a)
a = 11.6
a = int(a)
a = '11.5'
# a = int(a)
a = None
# a = int(a)
a = 1
a = float(a)
a = False
a = float(a)
a = 123
a = str(a)
a = None
a = bool(a)
print('a =', a)
print('a的类型是', type(a))
# b = 456
# print('hello'+str(b))
| # 类型转换四个函数 int() float() str() bool()
# int() 可以用来将其他的对象转换为整型
# 规则：
# 布尔值：True -> 1 False -> 0
# 浮点数：直接取整，省略小数点后的内容
# 字符串：合法的整数字符串，直接转换为对应的数字
# 如果不是一个合法的整数字符串，则报错 ValueError: invalid literal for int() with base 10: '11.5'
# 对于其他不可转换为整型的对象，直接抛出异常 ValueError
# float() 和 int()基本一致，不同的是它会将对象转换为浮点数
# str() 可以将对象转换为字符串
# True -> 'True'
# False -> 'False'
# 123 -> '123'
# 。。。
# bool() 可以将对象转换为布尔值，任何对象都可以转换为布尔值
# 规则：对于所有表示空性的对象都会转换为False，其余的转换为True
# 哪些表示的空性：0 、 None 、 '' 。。。
a = True
# 调用int()来将a转换为整型
# int()函数不会对原来的变量产生影响，他是对象转换为指定的类型并将其作为返回值返回
# 如果希望修改原来的变量，则需要对变量进行重新赋值
a = int(a)
a = False
a = int(a)
a = '123'
a = int(a)
a = 11.6
a = int(a)
a = '11.5'
# a = int(a)
a = None
# a = int(a)
a = 1
a = float(a)
a = False
a = float(a)
a = 123
a = str(a)
a = None
a = bool(a)
print('a =', a)
print('a的类型是', type(a))
# b = 456
# print('hello'+str(b)) | zh | 0.948916 | # 类型转换四个函数 int() float() str() bool() # int() 可以用来将其他的对象转换为整型 # 规则： # 布尔值：True -> 1 False -> 0 # 浮点数：直接取整，省略小数点后的内容 # 字符串：合法的整数字符串，直接转换为对应的数字 # 如果不是一个合法的整数字符串，则报错 ValueError: invalid literal for int() with base 10: '11.5' # 对于其他不可转换为整型的对象，直接抛出异常 ValueError # float() 和 int()基本一致，不同的是它会将对象转换为浮点数 # str() 可以将对象转换为字符串 # True -> 'True' # False -> 'False' # 123 -> '123' # 。。。 # bool() 可以将对象转换为布尔值，任何对象都可以转换为布尔值 # 规则：对于所有表示空性的对象都会转换为False，其余的转换为True # 哪些表示的空性：0 、 None 、 '' 。。。 # 调用int()来将a转换为整型 # int()函数不会对原来的变量产生影响，他是对象转换为指定的类型并将其作为返回值返回 # 如果希望修改原来的变量，则需要对变量进行重新赋值 # a = int(a) # a = int(a) # b = 456 # print('hello'+str(b)) | 4.383765 | 4 |
DB.py | YashSingh2006/DATA-VISUALISATION | 0 | 6614344 | import pandas as pd
import plotly.express as ps
df = pd.read_csv("Copy+of+data+-+data.csv")
fig = ps.scatter(df,x = "date", y = "cases", color = "country")
fig.show() | import pandas as pd
import plotly.express as ps
df = pd.read_csv("Copy+of+data+-+data.csv")
fig = ps.scatter(df,x = "date", y = "cases", color = "country")
fig.show() | none | 1 | 3.338706 | 3 | |
deploy/deploy.py | chud0/is_workday | 0 | 6614345 | <reponame>chud0/is_workday
import logging
from pathlib import Path
import paramiko
from plumbum import local
from plumbum.machines.paramiko_machine import ParamikoMachine
BASE_DIR = Path(__file__).parent.parent
logging.basicConfig(format='%(levelname)-8s %(asctime)s %(message)s', level=logging.INFO)
logger = logging.getLogger('deploy')
if __name__ == '__main__':
host = local.env['DEPLOY_HOST']
user = local.env['DEPLOY_USER']
remote_dir = local.env['DEPLOY_DIR']
deploy_key = local.env['DEPLOY_KEY']
deploy_key_file = BASE_DIR / 'tmp'
deploy_key_file.write_text('\n'.join(deploy_key.split('|')))
try:
with ParamikoMachine(
host,
user=user,
keyfile=str(deploy_key_file),
load_system_host_keys=False,
missing_host_policy=paramiko.AutoAddPolicy(),
) as rem:
rem.upload(BASE_DIR / 'deploy' / 'docker-compose.yml', f'{remote_dir}/docker-compose.yml')
logger.info('Copy %s', 'docker-compose.yml')
rem.upload(BASE_DIR / 'deploy' / 'nginx.conf', f'{remote_dir}/nginx.conf')
logger.info('Copy %s', 'nginx.conf')
docker_compose = rem['docker-compose']
docker_compose['down', '--rmi', 'all']()
logger.info('docker-compose stop')
docker_compose['up', '-d']()
logger.info('docker-compose up')
finally:
deploy_key_file.unlink()
| import logging
from pathlib import Path
import paramiko
from plumbum import local
from plumbum.machines.paramiko_machine import ParamikoMachine
BASE_DIR = Path(__file__).parent.parent
logging.basicConfig(format='%(levelname)-8s %(asctime)s %(message)s', level=logging.INFO)
logger = logging.getLogger('deploy')
if __name__ == '__main__':
host = local.env['DEPLOY_HOST']
user = local.env['DEPLOY_USER']
remote_dir = local.env['DEPLOY_DIR']
deploy_key = local.env['DEPLOY_KEY']
deploy_key_file = BASE_DIR / 'tmp'
deploy_key_file.write_text('\n'.join(deploy_key.split('|')))
try:
with ParamikoMachine(
host,
user=user,
keyfile=str(deploy_key_file),
load_system_host_keys=False,
missing_host_policy=paramiko.AutoAddPolicy(),
) as rem:
rem.upload(BASE_DIR / 'deploy' / 'docker-compose.yml', f'{remote_dir}/docker-compose.yml')
logger.info('Copy %s', 'docker-compose.yml')
rem.upload(BASE_DIR / 'deploy' / 'nginx.conf', f'{remote_dir}/nginx.conf')
logger.info('Copy %s', 'nginx.conf')
docker_compose = rem['docker-compose']
docker_compose['down', '--rmi', 'all']()
logger.info('docker-compose stop')
docker_compose['up', '-d']()
logger.info('docker-compose up')
finally:
deploy_key_file.unlink() | none | 1 | 2.096409 | 2 | |
app/blueprints/gallery/routes.py | reynoldsjs/Final-Project | 0 | 6614346 | import os
from . import bp as gallery
from flask import render_template, url_for
@gallery.route('/gallery')
def gallery():
# return render_template('gallery.html')
# def photos():
# basedir = os.path.abspath(os.path.dirname(__file__))
pics = os.listdir('app/static/gallery')
return render_template("gallery.html", pics=pics)
| import os
from . import bp as gallery
from flask import render_template, url_for
@gallery.route('/gallery')
def gallery():
# return render_template('gallery.html')
# def photos():
# basedir = os.path.abspath(os.path.dirname(__file__))
pics = os.listdir('app/static/gallery')
return render_template("gallery.html", pics=pics)
| en | 0.20987 | # return render_template('gallery.html') # def photos(): # basedir = os.path.abspath(os.path.dirname(__file__)) | 2.230018 | 2 |
examples/demo1.py | rid-dim/pySafe | 9 | 6614347 | # Demo 1
# Demonstrates some basic functionality of the library and serves as a reminder of what needs to be cleaned up
# This file will be done when there is no weird variables or boilerplate
import safenet
import time
# try it with and without the logger enabled.
# check config.py for default settings. check log_util.setup_logger() for kw arguments
# if you want to intercept the messages, you can inject your own handlers.
safenet.setup_logger()
with open('creds.txt') as f: # For simplicity, my credentials are in a simple file called creds.txt.
creds=f.readlines()[0].strip().split()
usrnm,psw = creds[0],creds[1]
# Logging in is easy!
myAuth=safenet.Authenticator()
#myAuth.login(usrnm,psw,myAuth.pointer, o_cb=myAuth.login_cb) # works, as it goes through user data
myAuth.login(usrnm,psw,None, o_cb=myAuth.login_cb)
# Necessary to avoid issues with threading ..
safenet.log.info('sleeping until login cb')
while myAuth.handle is None:
time.sleep(0.1)
#myAuth.auth_account_info(myAuth.handle, myAuth.pointer, o_cb=myAuth.info_cb) # this way uses userdata
myAuth.account_info()
# Necessary to avoid issues with threading ..
safenet.log.info('sleeping until info cb')
while myAuth._info is None:
time.sleep(0.1)
myAuth.auth_registered_apps(myAuth.handle, myAuth.pointer, o_cb=myAuth.registered_apps_cb)
safenet.log.info('sleeping until apps cb')
while myAuth._apps is None:
time.sleep(0.1)
#myApp=safenet.App()
#I = safenet.ImmutableData()
#I.idata_new_self_encryptor() | # Demo 1
# Demonstrates some basic functionality of the library and serves as a reminder of what needs to be cleaned up
# This file will be done when there is no weird variables or boilerplate
import safenet
import time
# try it with and without the logger enabled.
# check config.py for default settings. check log_util.setup_logger() for kw arguments
# if you want to intercept the messages, you can inject your own handlers.
safenet.setup_logger()
with open('creds.txt') as f: # For simplicity, my credentials are in a simple file called creds.txt.
creds=f.readlines()[0].strip().split()
usrnm,psw = creds[0],creds[1]
# Logging in is easy!
myAuth=safenet.Authenticator()
#myAuth.login(usrnm,psw,myAuth.pointer, o_cb=myAuth.login_cb) # works, as it goes through user data
myAuth.login(usrnm,psw,None, o_cb=myAuth.login_cb)
# Necessary to avoid issues with threading ..
safenet.log.info('sleeping until login cb')
while myAuth.handle is None:
time.sleep(0.1)
#myAuth.auth_account_info(myAuth.handle, myAuth.pointer, o_cb=myAuth.info_cb) # this way uses userdata
myAuth.account_info()
# Necessary to avoid issues with threading ..
safenet.log.info('sleeping until info cb')
while myAuth._info is None:
time.sleep(0.1)
myAuth.auth_registered_apps(myAuth.handle, myAuth.pointer, o_cb=myAuth.registered_apps_cb)
safenet.log.info('sleeping until apps cb')
while myAuth._apps is None:
time.sleep(0.1)
#myApp=safenet.App()
#I = safenet.ImmutableData()
#I.idata_new_self_encryptor() | en | 0.812471 | # Demo 1 # Demonstrates some basic functionality of the library and serves as a reminder of what needs to be cleaned up # This file will be done when there is no weird variables or boilerplate # try it with and without the logger enabled. # check config.py for default settings. check log_util.setup_logger() for kw arguments # if you want to intercept the messages, you can inject your own handlers. # For simplicity, my credentials are in a simple file called creds.txt. # Logging in is easy! #myAuth.login(usrnm,psw,myAuth.pointer, o_cb=myAuth.login_cb) # works, as it goes through user data # Necessary to avoid issues with threading .. #myAuth.auth_account_info(myAuth.handle, myAuth.pointer, o_cb=myAuth.info_cb) # this way uses userdata # Necessary to avoid issues with threading .. #myApp=safenet.App() #I = safenet.ImmutableData() #I.idata_new_self_encryptor() | 2.165715 | 2 |
localgraphclustering/capacity_releasing_diffusion.py | vishalbelsare/LocalGraphClustering | 106 | 6614348 | from typing import *
import numpy as np
from .cpp import *
from .GraphLocal import GraphLocal
def capacity_releasing_diffusion(G,ref_nodes,
U: int = 3,
h: int = 10,
w: int = 2,
iterations: int = 20):
"""
Description
-----------
Algorithm Capacity Releasing Diffusion for local graph clustering. This algorithm uses
a flow based method to push excess flow out of nodes. The algorithm is in worst-case
faster and stays more local than classical spectral diffusion processes.
For more details please refere to: <NAME>, <NAME>, <NAME>, <NAME>
and <NAME>. Capacity Releasing Diffusion for Speed and Locality. ICML 2017.
arXiv link: https://arxiv.org/abs/1706.05826
Parameters (mandatory)
----------------------
G: GraphLocal
ref_nodes: Sequence[int]
A sequence of reference nodes, i.e., nodes of interest around which
we are looking for a target cluster.
Parameters (optional)
--------------------
U: integer
default == 3
The maximum flow that can be send out of a node for the push/relabel algorithm.
h: integer
defaul == 10
The maximum flow that an edge can handle.
w: integer
default == 2
Multiplicative factor for increasing the capacity of the nodes at each iteration.
iterations: integer
default = 20
Maximum number of iterations of Capacity Releasing Diffusion Algorithm.
Returns
-------
It returns in a list of length 2 with the following:
output 0: list
Stores indices of the best clusters found by the last called rounding procedure.
output 1: float
Stores the value of the best conductance found by the last called rounding procedure.
Printing statements (warnings)
------------------------------
Too much excess: Means that push/relabel cannot push the excess flow out of the nodes.
This might indicate that a cluster has been found. In this case the best
cluster in terms of conductance is returned.
Too much flow: Means that the algorithm has touched about a third of the whole given graph.
The algorithm is terminated in this case and the best cluster in terms of
conductance is returned.
"""
n = G.adjacency_matrix.shape[0]
actual_xids = capacity_releasing_diffusion_cpp(n,G.ai,G.aj,np.float64(G.adjacency_matrix.data),
U,h,w,iterations,ref_nodes)
return [actual_xids, G.compute_conductance(actual_xids)] | from typing import *
import numpy as np
from .cpp import *
from .GraphLocal import GraphLocal
def capacity_releasing_diffusion(G,ref_nodes,
U: int = 3,
h: int = 10,
w: int = 2,
iterations: int = 20):
"""
Description
-----------
Algorithm Capacity Releasing Diffusion for local graph clustering. This algorithm uses
a flow based method to push excess flow out of nodes. The algorithm is in worst-case
faster and stays more local than classical spectral diffusion processes.
For more details please refere to: <NAME>, <NAME>, <NAME>, <NAME>
and <NAME>. Capacity Releasing Diffusion for Speed and Locality. ICML 2017.
arXiv link: https://arxiv.org/abs/1706.05826
Parameters (mandatory)
----------------------
G: GraphLocal
ref_nodes: Sequence[int]
A sequence of reference nodes, i.e., nodes of interest around which
we are looking for a target cluster.
Parameters (optional)
--------------------
U: integer
default == 3
The maximum flow that can be send out of a node for the push/relabel algorithm.
h: integer
defaul == 10
The maximum flow that an edge can handle.
w: integer
default == 2
Multiplicative factor for increasing the capacity of the nodes at each iteration.
iterations: integer
default = 20
Maximum number of iterations of Capacity Releasing Diffusion Algorithm.
Returns
-------
It returns in a list of length 2 with the following:
output 0: list
Stores indices of the best clusters found by the last called rounding procedure.
output 1: float
Stores the value of the best conductance found by the last called rounding procedure.
Printing statements (warnings)
------------------------------
Too much excess: Means that push/relabel cannot push the excess flow out of the nodes.
This might indicate that a cluster has been found. In this case the best
cluster in terms of conductance is returned.
Too much flow: Means that the algorithm has touched about a third of the whole given graph.
The algorithm is terminated in this case and the best cluster in terms of
conductance is returned.
"""
n = G.adjacency_matrix.shape[0]
actual_xids = capacity_releasing_diffusion_cpp(n,G.ai,G.aj,np.float64(G.adjacency_matrix.data),
U,h,w,iterations,ref_nodes)
return [actual_xids, G.compute_conductance(actual_xids)] | en | 0.847586 | Description ----------- Algorithm Capacity Releasing Diffusion for local graph clustering. This algorithm uses a flow based method to push excess flow out of nodes. The algorithm is in worst-case faster and stays more local than classical spectral diffusion processes. For more details please refere to: <NAME>, <NAME>, <NAME>, <NAME> and <NAME>. Capacity Releasing Diffusion for Speed and Locality. ICML 2017. arXiv link: https://arxiv.org/abs/1706.05826 Parameters (mandatory) ---------------------- G: GraphLocal ref_nodes: Sequence[int] A sequence of reference nodes, i.e., nodes of interest around which we are looking for a target cluster. Parameters (optional) -------------------- U: integer default == 3 The maximum flow that can be send out of a node for the push/relabel algorithm. h: integer defaul == 10 The maximum flow that an edge can handle. w: integer default == 2 Multiplicative factor for increasing the capacity of the nodes at each iteration. iterations: integer default = 20 Maximum number of iterations of Capacity Releasing Diffusion Algorithm. Returns ------- It returns in a list of length 2 with the following: output 0: list Stores indices of the best clusters found by the last called rounding procedure. output 1: float Stores the value of the best conductance found by the last called rounding procedure. Printing statements (warnings) ------------------------------ Too much excess: Means that push/relabel cannot push the excess flow out of the nodes. This might indicate that a cluster has been found. In this case the best cluster in terms of conductance is returned. Too much flow: Means that the algorithm has touched about a third of the whole given graph. The algorithm is terminated in this case and the best cluster in terms of conductance is returned. | 3.232136 | 3 |
src/kafka_core/kafka_util.py | bkatwal/distributed-kafka-consumer-python | 2 | 6614349 | <gh_stars>1-10
from kafka import KafkaConsumer
TWO_MINUTES = 2
def is_end_offset_none(end_offsets: dict, start_offsets: dict) -> bool:
"""
Utility function to check if the partition that has start offset has end offset too.
:param end_offsets: topic partition and end offsets
:param start_offsets:topic partition and start offsets
:return: True/False
"""
if len(end_offsets) == 0:
return True
for tp, offset in end_offsets.items():
if offset is None and start_offsets[tp] is not None:
return True
return False
def is_all_end_offset_found(end_offsets: dict, start_offsets: dict) -> bool:
"""
Utility function to check if the partition that has start offset has end offset too.
:param end_offsets: topic partition and end offsets
:param start_offsets:topic partition and start offsets
:return: True/False
"""
if len(end_offsets) == 0:
return False
for tp, offset in end_offsets.items():
if offset is None and start_offsets[tp] is not None:
return False
return True
def get_start_end_offsets(start_timestamp: int, end_timestamp: int,
topic_partitions: set, consumer: KafkaConsumer):
"""
Get start and end offset for all the partitions based on the given start and end timestamp
:param start_timestamp: start timestamp in epoch time millis
:param end_timestamp: end timestamp in epoch time millis
:param topic_partitions: topic partition set
:param consumer: kafka consumer
:return: tuple of start offsets and end offsets for each partition
"""
tp_start_timestamps: dict = {}
for tp in topic_partitions:
tp_start_timestamps[tp] = start_timestamp
start_offsets = consumer.offsets_for_times(tp_start_timestamps)
end_offsets = {}
# go back 2 minute and keep checking if there are end offsets in partition
tp_end_timestamps: dict = {}
while not is_all_end_offset_found(start_offsets=start_offsets, end_offsets=end_offsets):
for tp in topic_partitions:
# seek previous offset from a partition only if the offset is not found
if len(end_offsets) == 0 or (end_offsets[tp] is None and start_offsets[tp] is not
None):
tp_end_timestamps[tp] = end_timestamp
end_offsets = consumer.offsets_for_times(tp_end_timestamps)
end_timestamp = end_timestamp - (TWO_MINUTES * 60 * 1000)
return start_offsets, end_offsets
| from kafka import KafkaConsumer
TWO_MINUTES = 2
def is_end_offset_none(end_offsets: dict, start_offsets: dict) -> bool:
"""
Utility function to check if the partition that has start offset has end offset too.
:param end_offsets: topic partition and end offsets
:param start_offsets:topic partition and start offsets
:return: True/False
"""
if len(end_offsets) == 0:
return True
for tp, offset in end_offsets.items():
if offset is None and start_offsets[tp] is not None:
return True
return False
def is_all_end_offset_found(end_offsets: dict, start_offsets: dict) -> bool:
"""
Utility function to check if the partition that has start offset has end offset too.
:param end_offsets: topic partition and end offsets
:param start_offsets:topic partition and start offsets
:return: True/False
"""
if len(end_offsets) == 0:
return False
for tp, offset in end_offsets.items():
if offset is None and start_offsets[tp] is not None:
return False
return True
def get_start_end_offsets(start_timestamp: int, end_timestamp: int,
topic_partitions: set, consumer: KafkaConsumer):
"""
Get start and end offset for all the partitions based on the given start and end timestamp
:param start_timestamp: start timestamp in epoch time millis
:param end_timestamp: end timestamp in epoch time millis
:param topic_partitions: topic partition set
:param consumer: kafka consumer
:return: tuple of start offsets and end offsets for each partition
"""
tp_start_timestamps: dict = {}
for tp in topic_partitions:
tp_start_timestamps[tp] = start_timestamp
start_offsets = consumer.offsets_for_times(tp_start_timestamps)
end_offsets = {}
# go back 2 minute and keep checking if there are end offsets in partition
tp_end_timestamps: dict = {}
while not is_all_end_offset_found(start_offsets=start_offsets, end_offsets=end_offsets):
for tp in topic_partitions:
# seek previous offset from a partition only if the offset is not found
if len(end_offsets) == 0 or (end_offsets[tp] is None and start_offsets[tp] is not
None):
tp_end_timestamps[tp] = end_timestamp
end_offsets = consumer.offsets_for_times(tp_end_timestamps)
end_timestamp = end_timestamp - (TWO_MINUTES * 60 * 1000)
return start_offsets, end_offsets | en | 0.826563 | Utility function to check if the partition that has start offset has end offset too. :param end_offsets: topic partition and end offsets :param start_offsets:topic partition and start offsets :return: True/False Utility function to check if the partition that has start offset has end offset too. :param end_offsets: topic partition and end offsets :param start_offsets:topic partition and start offsets :return: True/False Get start and end offset for all the partitions based on the given start and end timestamp :param start_timestamp: start timestamp in epoch time millis :param end_timestamp: end timestamp in epoch time millis :param topic_partitions: topic partition set :param consumer: kafka consumer :return: tuple of start offsets and end offsets for each partition # go back 2 minute and keep checking if there are end offsets in partition # seek previous offset from a partition only if the offset is not found | 3.158036 | 3 |
bugal/base/models/shift_types.py | aquitania99/bugal-app | 0 | 6614350 | # Django
from django.db import models
class ShiftType(models.Model):
"""Shift Types model.
List of Shift types, used by users and clients.
"""
name = models.CharField('shift type', max_length=100)
def __str__(self):
return self.name
| # Django
from django.db import models
class ShiftType(models.Model):
"""Shift Types model.
List of Shift types, used by users and clients.
"""
name = models.CharField('shift type', max_length=100)
def __str__(self):
return self.name
| en | 0.863859 | # Django Shift Types model. List of Shift types, used by users and clients. | 2.429767 | 2 |