fumiyau/python_no_comments_test10000 · Datasets at Hugging Face

f71970c5e8e3cf8ece041b681a8768c3ae4e47b1

1,079

py

Python

codenerix_pos/admin.py

centrologic/django-codenerix-pos

449c54971c510aba2326797ab7aaf3a0b5f6c3ab

[ "Apache-2.0" ]

3

2017-07-19T15:24:26.000Z

2017-12-22T01:35:28.000Z

codenerix_pos/admin.py

centrologic/django-codenerix-pos

449c54971c510aba2326797ab7aaf3a0b5f6c3ab

[ "Apache-2.0" ]

null

codenerix_pos/admin.py

centrologic/django-codenerix-pos

449c54971c510aba2326797ab7aaf3a0b5f6c3ab

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # django-codenerix-pos # # Codenerix GNU # # Project URL : http://www.codenerix.com # # 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. from django.contrib import admin from .models import POS, POSSlot, POSPlant, POSZone, POSProduct, POSHardware, POSOperator, POSLog, POSGroupProduct admin.site.register(POSPlant) admin.site.register(POSZone) admin.site.register(POSHardware) admin.site.register(POSGroupProduct) admin.site.register(POS) admin.site.register(POSSlot) admin.site.register(POSProduct) admin.site.register(POSOperator) admin.site.register(POSLog)

31.735294

114

0.776645

from django.contrib import admin from .models import POS, POSSlot, POSPlant, POSZone, POSProduct, POSHardware, POSOperator, POSLog, POSGroupProduct admin.site.register(POSPlant) admin.site.register(POSZone) admin.site.register(POSHardware) admin.site.register(POSGroupProduct) admin.site.register(POS) admin.site.register(POSSlot) admin.site.register(POSProduct) admin.site.register(POSOperator) admin.site.register(POSLog)

true

f71970cd94b344b2aafb1578c85a08cee5366ca7

20,090

py

Python

script.py

lawlie8/Mr.Hyde

c3d2c04de6343580b4b14cbd2319737ed0b3a73e

[ "MIT" ]

3

2020-06-04T10:02:35.000Z

2020-06-05T11:44:02.000Z

script.py

lawlie8/Mr.Hyde

c3d2c04de6343580b4b14cbd2319737ed0b3a73e

[ "MIT" ]

null

script.py

lawlie8/Mr.Hyde

c3d2c04de6343580b4b14cbd2319737ed0b3a73e

[ "MIT" ]

null

#!/usr/bin/python3 import tkinter as tk from tkinter import * from tkinter.ttk import * from Crypto import Random from Crypto.Cipher import AES from Crypto.Hash import SHA256 import os import os.path from os import listdir from os.path import isfile, join #import time import ctypes #import threading import hashlib import random import binascii #add admin to read write C: Files try: def is_admin(): try: return ctypes.windll.shell32.IsUserAnAdmin() except: return False if is_admin(): def initilise(): global check_file check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() os.system('mkdir .hydefiles') ctypes.windll.shcore.SetProcessDpiAwareness(1) global label,window window = tk.Tk() #creates window window.tk.call('tk', 'scaling', 2.0) window.geometry("1050x380") window.resizable(width=False,height=False) window.title("Mr. Hyde") try: window.iconbitmap('hyde.ico') except: pass window.configure(bg='#333338') label = tk.Label(text = "Mr. Hyde" ,fg="#d6d6c2",bg="#333338") label.place(relx=.5,rely=.5,anchor="c") def clear_label(): label.place_forget() label1 = tk.Label(text = "Encrypt Your Files with 'AES-256'" ,fg="white",bg="#333338") label1.pack() open_files_button = tk.Button(text='Select Files',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=open_files,master=window) open_files_button.pack(anchor='nw',pady=50,padx=10,side='left') password_entry = tk.Entry(width=15,show='*') password_entry.place(x=600,y=82,height=42) encrypt_button = tk.Button(text='Encrypt',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=lambda:encrypt_section(password_entry,select_files_label),master=window) encrypt_button.place(x=910,y=82) decrypt_button = tk.Button(text='Decrypt',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=lambda:decrypt_section(password_entry,select_files_label),master=window) decrypt_button.place(x=770,y=82) password_label = tk.Label(text='Password',fg='#d6d6c2',bg='#333338') password_label.place(x=500,y=85) select_files_label = tk.Label(text='files not selected',fg='#d6d6c2',bg='#333338') select_files_label.place(x=460,y=220) setting_button = tk.Button(text='setting',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=5,fg='white',bg='#338237',command=lambda :setting_window(check_file))#,master=window) setting_button.place(x=980,y=12,height=30) def open_files():#selects files to encrypt decrypt from tkinter import filedialog global window_filename,enc_file_list,mylist,enc_file_scroll,file_to_encrypt_label enc_file_list = [] window_filename = filedialog.askopenfilenames(initialdir = "/",title = "Select file",filetypes = (("all files","*.*"),("enc files","*.enc"),("jpeg files","*.jpg"))) enc_file_list.append(window_filename) file_to_encrypt_label = tk.Label(text='Files to Encrypt',justify='left',fg='#d6d6c2',bg='#333338') enc_file_scroll = tk.Scrollbar(window,width=16,elementborderwidth=0,highlightcolor='green',bg='green',bd=0,activebackground='green') mylist = Listbox(window,width='90',height='7',yscrollcommand=enc_file_scroll.set,bg='green',bd=0,fg='#d6d6c2') if window_filename: for i in window_filename: mylist.insert(END,' ' + i) mylist.place(x='65',y='160') enc_file_scroll.place(x='975',y='160',height=185)#anchor='w',fill='y',side='right',pady=50,padx=20) file_to_encrypt_label.place(x=70,y=125) enc_file_scroll.config(command=mylist.yview) mainloop() def setting_window(check_file):#setting window code global default_password_entry,setting check_file = open('hyde.law','r+') check_file_lines = check_file.readlines() setting_flag = check_file_lines[0] if setting_flag == 'setting_window_off': setting = tk.Tk() setting.tk.call('tk', 'scaling', 2.0) setting.geometry("570x300") setting.resizable(width=False,height=False) setting.title('Setting') setting.configure(bg='#333338') try: setting.iconbitmap('setting.ico') except: pass setting_info_label = tk.Label(bg='#333338',fg='#d6d6c2',text='Mr.Hyde uses AES-256 bit Encryption algorithm \n Users be advised',master=setting) setting_info_label.pack() default_password_label = tk.Label(bg='#333338',fg='#d6d6c2',text='Default password',master=setting) default_password_label.pack(anchor='w',padx='10',pady='30')#place(x=30,y=80) default_password_entry = tk.Entry(width='15',show='*',master=setting) default_password_entry.place(x=140,y=80) default_password_warning = tk.Label(bg='white',fg='red',text='1:The use of default password is not recommended. Remember password instead. \n 2: If you decide to use default password,\n there is no need to set a password in the main window.',master=setting) default_password_warning.place(x=10,y=150) set_default_password = tk.Button(text='set password',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=13,fg='white',bg='#338237',command=set_default_password_section,master=setting)#,master=window) set_default_password.place(x=280,y=80,height=25) check_file = open('hyde.law','w+') check_file.write('setting_window_on') check_file.close() def on_closing(): #jugad pe jugad check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() setting.destroy() def close_everything(): window.destroy() setting.destroy() setting.protocol('WM_DELETE_WINDOW',on_closing) window.protocol('WM_DELETE_WINDOW',close_everything) def get_default_password_section(default_password_file_list):#does what the function name says global default_key default_key = '' for file,sun in zip(default_password_file_list,range(0,8)): file_extract = open(file,'r+') file_extract = file_extract.readlines() bun= sun * 8 default_key = default_key + file_extract[0][bun:bun+8] return default_key #print(key) def set_default_password_section():#sets default password os.system('mkdir .hydefiles') global default_password_file_list default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] default_password = default_password_entry.get() if default_password != '': salt_value = '' hex_list = ['a','b','c','d','e','f','1','2','3','4','5','6','7','8','9','0'] for salt_char in range(0,8): salt_value += random.choice(hex_list) #salt_value += salt_value #print(salt_value) salt_file = open('.hydefiles/default_salt.law','w+') salt_file.write(salt_value) salt_file.close() default_password = str(default_password+salt_value) default_key = hashlib.sha256(default_password.encode('utf-8')).hexdigest() #print(default_key) #print(default_password) for file,sun in zip(default_password_file_list,range(0,8)): seti = '' for bill in range(0,65): seti = random.choice(hex_list) + seti #print(seti) seti2 = seti #for sun in range(0,8): if sun == 0: bun = sun * 7 seti = default_key[bun:bun+8] + seti2[9:65] default_password_file = open(file,'w+') default_password_file.write(seti) default_password_file.close() #print(seti) else: bun2 = sun * 8 seti = seti2[0:bun2]+ default_key[bun2:bun2+8] + seti2[bun2+9:65] #print(seti) default_password_file = open(file,'w+') default_password_file.write(seti) default_password_file.close() #print('setting default password') else: try: import shutil shutil.rmtree('.hydefiles') MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Blank Password Not Allowed','Error', 0) except: pass check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() setting.destroy() get_default_password_section(default_password_file_list) def encrypt_section(password_entry,select_files_label):#Encrypt Section try: default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] #IV = 16 * '\x00' #mode = AES.MODE_CBC password_entry_for_encryption = password_entry.get() def pad(s): return s + b"\0" * (AES.block_size - len(s) % AES.block_size) if password_entry_for_encryption == '': key = get_default_password_section(default_password_file_list) check_sum_key = key.lower() key = binascii.unhexlify(key) #print(key) else: hex_list = ['a','b','c','d','e','f','1','2','3','4','5','6','7','8','9','0'] salt_value = '' for salt_char in range(0,8): salt_value += random.choice(hex_list) password_entry_for_encryption = password_entry_for_encryption + salt_value not_defalt_salt = open('.hydefiles/salt.law','a') key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).digest() check_sum_key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).hexdigest() not_defalt_salt.write(check_sum_key[30:36]+'---'+salt_value+'\n') not_defalt_salt.close() hex_list = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f'] #print(a) num = check_sum_key[30:36] ''' for hex_i in check_sum_key: for hex_b in hex_list: if hex_i == hex_b: num += hex_list.index(hex_b)# * check_sum_key.index(hex_i) ''' #print(str(num)) enc_counter = 0 progress = Progressbar(window,orient=HORIZONTAL,length=926,mode='determinate') progress.place(anchor='w',x=65,y=360) prog = 100 / len(window_filename) for file_to_encrypt in window_filename: if file_to_encrypt.endswith('.enc'): enc_counter+=1 else: progress['value'] = prog window.update_idletasks() prog = prog + prog fh = open(file_to_encrypt,'rb') message = fh.read() fh.close() message = pad(message) iv = Random.new().read(AES.block_size) cipher = AES.new(key,AES.MODE_CBC,iv) encrypted_text = iv + cipher.encrypt(message) fh = open(file_to_encrypt + str(num) + '.enc','wb') fh.write(encrypted_text) fh.close() os.remove(file_to_encrypt) #print(file_to_encrypt) if enc_counter !=0: MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Already encrypted', 'Error', 0) mylist.delete(0,END) enc_file_scroll.place_forget() mylist.place_forget() progress.place_forget() file_to_encrypt_label.place_forget() password_entry.delete(0,END) MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Selected Files Encrypted','Success', 0) #window_filename = {} except: progress.place_forget() try: mylist.place_forget() except: pass MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Select Files First', 'Error', 0) password_entry.delete(0,END) finally: pass def decrypt_section(password_entry,select_files_label):#Decrypt section try: def unpad(s): return s[:-ord(s[len(s)-1:])] default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] password_entry_for_encryption = password_entry.get() if password_entry_for_encryption == '': key = get_default_password_section(default_password_file_list) check_sum_key = key.lower() key = binascii.unhexlify(key) #print(key) else: not_defalt_salt = open('.hydefiles/salt.law','r+') salt_lines = not_defalt_salt.readlines() #salt_lines = salt_lines[0].strip(' ') #print(salt_lines) for check_salt_value in salt_lines: for file_to_check in window_filename: if str(check_salt_value[0:6]) == str(file_to_check[-10:-4]): #print('here') salt_lines = check_salt_value[-9:-1] #print(salt_lines) #print(check_salt_value) #ashdkjahsdjh = input("inpput here") #print(str(file_to_check[-10:-4])+'---'+check_salt_value[0:6]) password_entry_for_encryption2 = password_entry_for_encryption password_entry_for_encryption = password_entry_for_encryption + salt_lines key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).digest() check_sum_key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).hexdigest() hex_list = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f'] #print(a) num = str(check_sum_key[30:36]).strip(' ') #print(num+'---'+salt_lines) #asdasdasrca = input('here2') ''' for hex_i in check_sum_key: for hex_b in hex_list: if hex_i == hex_b: num += hex_list.index(hex_b) ''' invalid_counter = 0 progress = Progressbar(window,orient=HORIZONTAL,length=926,mode='determinate') progress.place(anchor='w',x=65,y=360) prog = 100 / len(window_filename) for file_to_decrypt in window_filename: #print(str(file_to_decrypt[-10:-4])) if num == str(file_to_decrypt[-10:-4]): progress['value'] = prog window.update_idletasks() prog = prog + prog fd = open(file_to_decrypt,'rb') message = fd.read() fd.close() iv = message[:AES.block_size] cipher = AES.new(key,AES.MODE_CBC,iv) plaintext = cipher.decrypt(message[AES.block_size:]) write_message = plaintext.rstrip(b"\0") remove_file = file_to_decrypt file_to_decrypt = file_to_decrypt[0:-10] fd = open(file_to_decrypt,'wb') fd.write(write_message) fd.close() os.remove(remove_file) else: #print('key_invalid') invalid_counter +=1 #entry1.delete(0,tk.END) if invalid_counter != 0 : MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Invalid key used for '+str(invalid_counter)+' files','Error', 0) progress.place_forget() password_entry.delete(0,END) else: file_to_encrypt_label.place_forget() enc_file_scroll.place_forget() mylist.place_forget() progress.place_forget() password_entry.delete(0,END) MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Selected Files Decrypted','Success', 0) except: progress.place_forget() try: mylist.place_forget() except: pass MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Select Files First', 'Error', 0) password_entry.delete(0,END) finally: pass #window_filename = {} #print(window_filename) initilise() #initialise window label.after(3000,clear_label) #app_name label intro window.mainloop() else: ctypes.windll.shell32.ShellExecuteW(None, "runas", sys.executable, __file__, None, 1) is_admin() except IOError as e: error_file = open('error.log','a+') error_file.write(e+'\n') error_file.close()

49.482759

318

0.541563

import tkinter as tk from tkinter import * from tkinter.ttk import * from Crypto import Random from Crypto.Cipher import AES from Crypto.Hash import SHA256 import os import os.path from os import listdir from os.path import isfile, join import ctypes import hashlib import random import binascii try: def is_admin(): try: return ctypes.windll.shell32.IsUserAnAdmin() except: return False if is_admin(): def initilise(): global check_file check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() os.system('mkdir .hydefiles') ctypes.windll.shcore.SetProcessDpiAwareness(1) global label,window window = tk.Tk() window.tk.call('tk', 'scaling', 2.0) window.geometry("1050x380") window.resizable(width=False,height=False) window.title("Mr. Hyde") try: window.iconbitmap('hyde.ico') except: pass window.configure(bg='#333338') label = tk.Label(text = "Mr. Hyde" ,fg="#d6d6c2",bg="#333338") label.place(relx=.5,rely=.5,anchor="c") def clear_label(): label.place_forget() label1 = tk.Label(text = "Encrypt Your Files with 'AES-256'" ,fg="white",bg="#333338") label1.pack() open_files_button = tk.Button(text='Select Files',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=open_files,master=window) open_files_button.pack(anchor='nw',pady=50,padx=10,side='left') password_entry = tk.Entry(width=15,show='*') password_entry.place(x=600,y=82,height=42) encrypt_button = tk.Button(text='Encrypt',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=lambda:encrypt_section(password_entry,select_files_label),master=window) encrypt_button.place(x=910,y=82) decrypt_button = tk.Button(text='Decrypt',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=12,fg='white',bg='#338237',command=lambda:decrypt_section(password_entry,select_files_label),master=window) decrypt_button.place(x=770,y=82) password_label = tk.Label(text='Password',fg='#d6d6c2',bg='#333338') password_label.place(x=500,y=85) select_files_label = tk.Label(text='files not selected',fg='#d6d6c2',bg='#333338') select_files_label.place(x=460,y=220) setting_button = tk.Button(text='setting',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=5,fg='white',bg='#338237',command=lambda :setting_window(check_file)) setting_button.place(x=980,y=12,height=30) def open_files(): from tkinter import filedialog global window_filename,enc_file_list,mylist,enc_file_scroll,file_to_encrypt_label enc_file_list = [] window_filename = filedialog.askopenfilenames(initialdir = "/",title = "Select file",filetypes = (("all files","*.*"),("enc files","*.enc"),("jpeg files","*.jpg"))) enc_file_list.append(window_filename) file_to_encrypt_label = tk.Label(text='Files to Encrypt',justify='left',fg='#d6d6c2',bg='#333338') enc_file_scroll = tk.Scrollbar(window,width=16,elementborderwidth=0,highlightcolor='green',bg='green',bd=0,activebackground='green') mylist = Listbox(window,width='90',height='7',yscrollcommand=enc_file_scroll.set,bg='green',bd=0,fg='#d6d6c2') if window_filename: for i in window_filename: mylist.insert(END,' ' + i) mylist.place(x='65',y='160') enc_file_scroll.place(x='975',y='160',height=185) file_to_encrypt_label.place(x=70,y=125) enc_file_scroll.config(command=mylist.yview) mainloop() def setting_window(check_file): global default_password_entry,setting check_file = open('hyde.law','r+') check_file_lines = check_file.readlines() setting_flag = check_file_lines[0] if setting_flag == 'setting_window_off': setting = tk.Tk() setting.tk.call('tk', 'scaling', 2.0) setting.geometry("570x300") setting.resizable(width=False,height=False) setting.title('Setting') setting.configure(bg='#333338') try: setting.iconbitmap('setting.ico') except: pass setting_info_label = tk.Label(bg='#333338',fg='#d6d6c2',text='Mr.Hyde uses AES-256 bit Encryption algorithm \n Users be advised',master=setting) setting_info_label.pack() default_password_label = tk.Label(bg='#333338',fg='#d6d6c2',text='Default password',master=setting) default_password_label.pack(anchor='w',padx='10',pady='30') default_password_entry = tk.Entry(width='15',show='*',master=setting) default_password_entry.place(x=140,y=80) default_password_warning = tk.Label(bg='white',fg='red',text='1:The use of default password is not recommended. Remember password instead. \n 2: If you decide to use default password,\n there is no need to set a password in the main window.',master=setting) default_password_warning.place(x=10,y=150) set_default_password = tk.Button(text='set password',activebackground='black',highlightcolor='black',bd=1,relief='flat',height=0,width=13,fg='white',bg='#338237',command=set_default_password_section,master=setting) set_default_password.place(x=280,y=80,height=25) check_file = open('hyde.law','w+') check_file.write('setting_window_on') check_file.close() def on_closing(): check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() setting.destroy() def close_everything(): window.destroy() setting.destroy() setting.protocol('WM_DELETE_WINDOW',on_closing) window.protocol('WM_DELETE_WINDOW',close_everything) def get_default_password_section(default_password_file_list): global default_key default_key = '' for file,sun in zip(default_password_file_list,range(0,8)): file_extract = open(file,'r+') file_extract = file_extract.readlines() bun= sun * 8 default_key = default_key + file_extract[0][bun:bun+8] return default_key def set_default_password_section(): os.system('mkdir .hydefiles') global default_password_file_list default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] default_password = default_password_entry.get() if default_password != '': salt_value = '' hex_list = ['a','b','c','d','e','f','1','2','3','4','5','6','7','8','9','0'] for salt_char in range(0,8): salt_value += random.choice(hex_list) salt_file = open('.hydefiles/default_salt.law','w+') salt_file.write(salt_value) salt_file.close() default_password = str(default_password+salt_value) default_key = hashlib.sha256(default_password.encode('utf-8')).hexdigest() for file,sun in zip(default_password_file_list,range(0,8)): seti = '' for bill in range(0,65): seti = random.choice(hex_list) + seti seti2 = seti if sun == 0: bun = sun * 7 seti = default_key[bun:bun+8] + seti2[9:65] default_password_file = open(file,'w+') default_password_file.write(seti) default_password_file.close() else: bun2 = sun * 8 seti = seti2[0:bun2]+ default_key[bun2:bun2+8] + seti2[bun2+9:65] default_password_file = open(file,'w+') default_password_file.write(seti) default_password_file.close() else: try: import shutil shutil.rmtree('.hydefiles') MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Blank Password Not Allowed','Error', 0) except: pass check_file = open('hyde.law','w+') check_file.write('setting_window_off') check_file.close() setting.destroy() get_default_password_section(default_password_file_list) def encrypt_section(password_entry,select_files_label): try: default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] password_entry_for_encryption = password_entry.get() def pad(s): return s + b"\0" * (AES.block_size - len(s) % AES.block_size) if password_entry_for_encryption == '': key = get_default_password_section(default_password_file_list) check_sum_key = key.lower() key = binascii.unhexlify(key) else: hex_list = ['a','b','c','d','e','f','1','2','3','4','5','6','7','8','9','0'] salt_value = '' for salt_char in range(0,8): salt_value += random.choice(hex_list) password_entry_for_encryption = password_entry_for_encryption + salt_value not_defalt_salt = open('.hydefiles/salt.law','a') key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).digest() check_sum_key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).hexdigest() not_defalt_salt.write(check_sum_key[30:36]+'---'+salt_value+'\n') not_defalt_salt.close() hex_list = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f'] num = check_sum_key[30:36] enc_counter = 0 progress = Progressbar(window,orient=HORIZONTAL,length=926,mode='determinate') progress.place(anchor='w',x=65,y=360) prog = 100 / len(window_filename) for file_to_encrypt in window_filename: if file_to_encrypt.endswith('.enc'): enc_counter+=1 else: progress['value'] = prog window.update_idletasks() prog = prog + prog fh = open(file_to_encrypt,'rb') message = fh.read() fh.close() message = pad(message) iv = Random.new().read(AES.block_size) cipher = AES.new(key,AES.MODE_CBC,iv) encrypted_text = iv + cipher.encrypt(message) fh = open(file_to_encrypt + str(num) + '.enc','wb') fh.write(encrypted_text) fh.close() os.remove(file_to_encrypt) if enc_counter !=0: MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Already encrypted', 'Error', 0) mylist.delete(0,END) enc_file_scroll.place_forget() mylist.place_forget() progress.place_forget() file_to_encrypt_label.place_forget() password_entry.delete(0,END) MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Selected Files Encrypted','Success', 0) except: progress.place_forget() try: mylist.place_forget() except: pass MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Select Files First', 'Error', 0) password_entry.delete(0,END) finally: pass def decrypt_section(password_entry,select_files_label): try: def unpad(s): return s[:-ord(s[len(s)-1:])] default_password_file_list = ['.hydefiles/0okq7sgzt00emuwr.law','.hydefiles/dz5a0l17zehztni8.law','.hydefiles/uv8wbbi1zylip4v6.law','.hydefiles/0pk588qx1m1m5bf2.law','.hydefiles/nzlcnrcv88rrnghh.law','.hydefiles/kcf609aheo3rksm4.law','.hydefiles/q05y5cmdos60n58s.law','.hydefiles/5kcsxvpb5srx24vz.law'] password_entry_for_encryption = password_entry.get() if password_entry_for_encryption == '': key = get_default_password_section(default_password_file_list) check_sum_key = key.lower() key = binascii.unhexlify(key) else: not_defalt_salt = open('.hydefiles/salt.law','r+') salt_lines = not_defalt_salt.readlines() for check_salt_value in salt_lines: for file_to_check in window_filename: if str(check_salt_value[0:6]) == str(file_to_check[-10:-4]): salt_lines = check_salt_value[-9:-1] password_entry_for_encryption2 = password_entry_for_encryption password_entry_for_encryption = password_entry_for_encryption + salt_lines key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).digest() check_sum_key = hashlib.sha256(password_entry_for_encryption.encode('utf-8')).hexdigest() hex_list = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f'] num = str(check_sum_key[30:36]).strip(' ') invalid_counter = 0 progress = Progressbar(window,orient=HORIZONTAL,length=926,mode='determinate') progress.place(anchor='w',x=65,y=360) prog = 100 / len(window_filename) for file_to_decrypt in window_filename: if num == str(file_to_decrypt[-10:-4]): progress['value'] = prog window.update_idletasks() prog = prog + prog fd = open(file_to_decrypt,'rb') message = fd.read() fd.close() iv = message[:AES.block_size] cipher = AES.new(key,AES.MODE_CBC,iv) plaintext = cipher.decrypt(message[AES.block_size:]) write_message = plaintext.rstrip(b"\0") remove_file = file_to_decrypt file_to_decrypt = file_to_decrypt[0:-10] fd = open(file_to_decrypt,'wb') fd.write(write_message) fd.close() os.remove(remove_file) else: invalid_counter +=1 if invalid_counter != 0 : MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Invalid key used for '+str(invalid_counter)+' files','Error', 0) progress.place_forget() password_entry.delete(0,END) else: file_to_encrypt_label.place_forget() enc_file_scroll.place_forget() mylist.place_forget() progress.place_forget() password_entry.delete(0,END) MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Selected Files Decrypted','Success', 0) except: progress.place_forget() try: mylist.place_forget() except: pass MessageBox = ctypes.windll.user32.MessageBoxW MessageBox(None, 'Select Files First', 'Error', 0) password_entry.delete(0,END) finally: pass initilise() label.after(3000,clear_label) window.mainloop() else: ctypes.windll.shell32.ShellExecuteW(None, "runas", sys.executable, __file__, None, 1) is_admin() except IOError as e: error_file = open('error.log','a+') error_file.write(e+'\n') error_file.close()

true

f719721f94c03312a66a5dd67b5e3f239bdd431b

1,925

py

Python

setup.py

unclemedia0/phasiakon

fe6cef9b8c3d8f7da0a9ef3b18f9c2ea0ec08dc0

[ "MIT" ]

null

setup.py

unclemedia0/phasiakon

fe6cef9b8c3d8f7da0a9ef3b18f9c2ea0ec08dc0

[ "MIT" ]

null

setup.py

unclemedia0/phasiakon

fe6cef9b8c3d8f7da0a9ef3b18f9c2ea0ec08dc0

[ "MIT" ]

null

from distutils.core import setup try: with open("README.md","r") as fh: long_description = fh.read() except: long_description = 'Taxation7% by UncleMedia' setup( name = 'phasiakon', # How you named your package folder (MyLib) packages = ['phasiakon'], # Chose the same as "name" version = '0.1', # Start with a small number and increase it with every change you make license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository description = 'Taxation7% by UncleMedia', # Give a short description about your library long_description=long_description, long_description_content_type = "text/markdown", author = 'UncleMedia', # Type in your name author_email = 'unclemedia0@gmail.com', # Type in your E-Mail url = 'https://github.com/unclemedia0/phasiakon', # Provide either the link to your github or to your website download_url = 'https://github.com/unclemedia0/phasiakon/archive/v_01.tar.gz', # I explain this later on keywords = ['phasiakon', 'Hmong', 'UncleMedia'], # Keywords that define your package best classifiers=[ 'Development Status :: 3 - Alpha', # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package 'Intended Audience :: Developers', # Define that your audience are developers 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: MIT License', # Again, pick a license 'Programming Language :: Python :: 3', #Specify which pyhton versions that you want to support 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', ], )

55

147

0.660779

from distutils.core import setup try: with open("README.md","r") as fh: long_description = fh.read() except: long_description = 'Taxation7% by UncleMedia' setup( name = 'phasiakon', packages = ['phasiakon'], version = '0.1', license='MIT', description = 'Taxation7% by UncleMedia', long_description=long_description, long_description_content_type = "text/markdown", author = 'UncleMedia', author_email = 'unclemedia0@gmail.com', url = 'https://github.com/unclemedia0/phasiakon', download_url = 'https://github.com/unclemedia0/phasiakon/archive/v_01.tar.gz', keywords = ['phasiakon', 'Hmong', 'UncleMedia'], classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', ], )

true

f719733841763fd63a92a93738e9a161aaffbe6e

4,753

py

Python

momus/VHRED/split-examples-by-token.py

ourDirection/ourDirection

b99ed67a8cc0fe5016e03fe3b5ad083b7f8bbdc0

[ "Apache-2.0" ]

null

momus/VHRED/split-examples-by-token.py

ourDirection/ourDirection

b99ed67a8cc0fe5016e03fe3b5ad083b7f8bbdc0

[ "Apache-2.0" ]

null

momus/VHRED/split-examples-by-token.py

ourDirection/ourDirection

b99ed67a8cc0fe5016e03fe3b5ad083b7f8bbdc0

[ "Apache-2.0" ]

null

""" Takes as input a binarized dialogue corpus, splits the examples by a certain token and shuffles it Example run: python split-examples-by-token.py Training.dialogues.pkl 2 Training_SplitByDialogues.dialogues --join_last_two_examples @author Iulian Vlad Serban """ import collections import numpy import math import operator import os import sys import logging import cPickle from collections import Counter logging.basicConfig(level=logging.INFO) logger = logging.getLogger('text2dict') def safe_pickle(obj, filename): if os.path.isfile(filename): logger.info("Overwriting %s." % filename) else: logger.info("Saving to %s." % filename) with open(filename, 'wb') as f: cPickle.dump(obj, f, protocol=cPickle.HIGHEST_PROTOCOL) # Thanks to Emile on Stackoverflow: # http://stackoverflow.com/questions/4322705/split-a-list-into-nested-lists-on-a-value def _itersplit(l, splitters): current = [] for item in l: if item in splitters: yield current current = [] else: current.append(item) yield current def magicsplit(l, *splitters): return [subl for subl in _itersplit(l, splitters) if subl] import argparse parser = argparse.ArgumentParser() parser.add_argument("input", type=str, help="Binarized dialogue corpus (pkl file)") parser.add_argument("token_id", type=int, help="Token index to split examples by (e.g. to split by end-of-dialogue set this to 2)") parser.add_argument("consecutive_examples_to_merge", type=int, default='1', help="After splitting these number of examples will be merged.") parser.add_argument("--join_last_two_examples", action="store_true", default=False, help="If on, will join the last two splits generated from each example. This is useful to handle empty or very short last samples") parser.add_argument("output", type=str, help="Filename of processed binarized dialogue corpus (pkl file)") args = parser.parse_args() if not os.path.isfile(args.input): raise Exception("Input file not found!") logger.info("Loading dialogue corpus") data = cPickle.load(open(args.input, 'r')) data_len = len(data) logger.info('Corpus loaded... Data len is %d' % data_len) # Count number of tokens tokens_count = 0 for i in range(data_len): tokens_count += len(data[i]) logger.info('Tokens count %d' % tokens_count) logger.info("Splitting corpus examples by token id... ") processed_binarized_corpus = [] for i in range(data_len): logger.info(' Example %d ' % i) new_examples = magicsplit(data[i], int(args.token_id)) # If option is specified, we append the last new example to the second last one if args.join_last_two_examples and len(new_examples) > 1: new_examples[len(new_examples)-2] += new_examples[len(new_examples)-1] del new_examples[len(new_examples)-1] # Simpler version of the two for loops, which does not allow merging together samples #for new_example in new_examples: # processed_binarized_corpus.append(new_example + [int(args.token_id)]) s = int(math.floor(len(new_examples) / args.consecutive_examples_to_merge)) for j in range(1, s): start_index = j*args.consecutive_examples_to_merge merged_example = [] for k in reversed(range(args.consecutive_examples_to_merge)): merged_example += new_examples[start_index-k-1] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) if s > 0: merged_example = [] for k in range((s-1)*args.consecutive_examples_to_merge, len(new_examples)): merged_example += new_examples[k] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) else: merged_example = [] for k in range(len(new_examples)): merged_example += new_examples[k] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) logger.info('New data len is %d' % len(processed_binarized_corpus)) # Count number of tokens processed_tokens_count = 0 for i in range(len(processed_binarized_corpus)): processed_tokens_count += len(processed_binarized_corpus[i]) logger.info('New tokens count %d' % processed_tokens_count) # When splitting by end-of-utterance token </s>, there are some instances with multiple </s> at the end of each example. Our splitting method will effectively remove these, but it is not of any concern to us. # assert(processed_tokens_count == tokens_count) logger.info("Reshuffling corpus.") rng = numpy.random.RandomState(13248) rng.shuffle(processed_binarized_corpus) logger.info("Saving corpus.") safe_pickle(processed_binarized_corpus, args.output + ".pkl") logger.info("Corpus saved. All done!")

35.736842

208

0.722281

import collections import numpy import math import operator import os import sys import logging import cPickle from collections import Counter logging.basicConfig(level=logging.INFO) logger = logging.getLogger('text2dict') def safe_pickle(obj, filename): if os.path.isfile(filename): logger.info("Overwriting %s." % filename) else: logger.info("Saving to %s." % filename) with open(filename, 'wb') as f: cPickle.dump(obj, f, protocol=cPickle.HIGHEST_PROTOCOL) def _itersplit(l, splitters): current = [] for item in l: if item in splitters: yield current current = [] else: current.append(item) yield current def magicsplit(l, *splitters): return [subl for subl in _itersplit(l, splitters) if subl] import argparse parser = argparse.ArgumentParser() parser.add_argument("input", type=str, help="Binarized dialogue corpus (pkl file)") parser.add_argument("token_id", type=int, help="Token index to split examples by (e.g. to split by end-of-dialogue set this to 2)") parser.add_argument("consecutive_examples_to_merge", type=int, default='1', help="After splitting these number of examples will be merged.") parser.add_argument("--join_last_two_examples", action="store_true", default=False, help="If on, will join the last two splits generated from each example. This is useful to handle empty or very short last samples") parser.add_argument("output", type=str, help="Filename of processed binarized dialogue corpus (pkl file)") args = parser.parse_args() if not os.path.isfile(args.input): raise Exception("Input file not found!") logger.info("Loading dialogue corpus") data = cPickle.load(open(args.input, 'r')) data_len = len(data) logger.info('Corpus loaded... Data len is %d' % data_len) tokens_count = 0 for i in range(data_len): tokens_count += len(data[i]) logger.info('Tokens count %d' % tokens_count) logger.info("Splitting corpus examples by token id... ") processed_binarized_corpus = [] for i in range(data_len): logger.info(' Example %d ' % i) new_examples = magicsplit(data[i], int(args.token_id)) if args.join_last_two_examples and len(new_examples) > 1: new_examples[len(new_examples)-2] += new_examples[len(new_examples)-1] del new_examples[len(new_examples)-1] s = int(math.floor(len(new_examples) / args.consecutive_examples_to_merge)) for j in range(1, s): start_index = j*args.consecutive_examples_to_merge merged_example = [] for k in reversed(range(args.consecutive_examples_to_merge)): merged_example += new_examples[start_index-k-1] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) if s > 0: merged_example = [] for k in range((s-1)*args.consecutive_examples_to_merge, len(new_examples)): merged_example += new_examples[k] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) else: merged_example = [] for k in range(len(new_examples)): merged_example += new_examples[k] + [int(args.token_id)] processed_binarized_corpus.append(merged_example) logger.info('New data len is %d' % len(processed_binarized_corpus)) processed_tokens_count = 0 for i in range(len(processed_binarized_corpus)): processed_tokens_count += len(processed_binarized_corpus[i]) logger.info('New tokens count %d' % processed_tokens_count) logger.info("Reshuffling corpus.") rng = numpy.random.RandomState(13248) rng.shuffle(processed_binarized_corpus) logger.info("Saving corpus.") safe_pickle(processed_binarized_corpus, args.output + ".pkl") logger.info("Corpus saved. All done!")

true

f719736b5b137de7082002cec486dbcda1835bae

1,497

py

Python

tapis_cli/commands/taccapis/v2/systems/roles_show.py

shwetagopaul92/tapis-cli-ng

6f424b8352c0d034d4f5547fac21d5c8dd097a7f

[ "BSD-3-Clause" ]

null

tapis_cli/commands/taccapis/v2/systems/roles_show.py

shwetagopaul92/tapis-cli-ng

6f424b8352c0d034d4f5547fac21d5c8dd097a7f

[ "BSD-3-Clause" ]

null

tapis_cli/commands/taccapis/v2/systems/roles_show.py

shwetagopaul92/tapis-cli-ng

6f424b8352c0d034d4f5547fac21d5c8dd097a7f

[ "BSD-3-Clause" ]

null

from agavepy.agave import AgaveError from tapis_cli.display import Verbosity from tapis_cli.clients.services.mixins import ServiceIdentifier, Username from . import API_NAME, SERVICE_VERSION from .models import SystemRole from .formatters import SystemsFormatOne __all__ = ['SystemsRolesShow'] class SystemsRolesShow(SystemsFormatOne, ServiceIdentifier, Username): """Show role on a System for a User """ VERBOSITY = Verbosity.BRIEF EXTRA_VERBOSITY = Verbosity.RECORD def get_parser(self, prog_name): parser = super(SystemsRolesShow, self).get_parser(prog_name) parser = ServiceIdentifier.extend_parser(self, parser) parser = Username.extend_parser(self, parser) return parser def take_action(self, parsed_args): parsed_args = self.preprocess_args(parsed_args) self.requests_client.setup(API_NAME, SERVICE_VERSION) self.update_payload(parsed_args) headers = self.render_headers(SystemRole, parsed_args) try: rec = self.tapis_client.systems.getRoleForUser( systemId=parsed_args.identifier, username=parsed_args.username) except Exception: rec = { 'username': parsed_args.username, 'role': None, '_links': [] } data = [] for key in headers: val = self.render_value(rec.get(key, None)) data.append(val) return (tuple(headers), tuple(data))

33.266667

79

0.669339

from agavepy.agave import AgaveError from tapis_cli.display import Verbosity from tapis_cli.clients.services.mixins import ServiceIdentifier, Username from . import API_NAME, SERVICE_VERSION from .models import SystemRole from .formatters import SystemsFormatOne __all__ = ['SystemsRolesShow'] class SystemsRolesShow(SystemsFormatOne, ServiceIdentifier, Username): VERBOSITY = Verbosity.BRIEF EXTRA_VERBOSITY = Verbosity.RECORD def get_parser(self, prog_name): parser = super(SystemsRolesShow, self).get_parser(prog_name) parser = ServiceIdentifier.extend_parser(self, parser) parser = Username.extend_parser(self, parser) return parser def take_action(self, parsed_args): parsed_args = self.preprocess_args(parsed_args) self.requests_client.setup(API_NAME, SERVICE_VERSION) self.update_payload(parsed_args) headers = self.render_headers(SystemRole, parsed_args) try: rec = self.tapis_client.systems.getRoleForUser( systemId=parsed_args.identifier, username=parsed_args.username) except Exception: rec = { 'username': parsed_args.username, 'role': None, '_links': [] } data = [] for key in headers: val = self.render_value(rec.get(key, None)) data.append(val) return (tuple(headers), tuple(data))

true

f719761c09d3fa035769e8bee81a2d948a8ad1b9

255

py

Python

tests/test_example.py

skylifewww/handball

853190e44037086b7749cb8f62d9df6577b379fd

[ "MIT" ]

null

tests/test_example.py

skylifewww/handball

853190e44037086b7749cb8f62d9df6577b379fd

[ "MIT" ]

null

tests/test_example.py

skylifewww/handball

853190e44037086b7749cb8f62d9df6577b379fd

[ "MIT" ]

null

from handball.core.test import TestCase from handball.users.factories import UserFactory class TestExample(TestCase): def test_example(self): UserFactory() resp = self.client.get('/') self.assertEqual(resp.status_code, 200)

23.181818

48

0.709804

from handball.core.test import TestCase from handball.users.factories import UserFactory class TestExample(TestCase): def test_example(self): UserFactory() resp = self.client.get('/') self.assertEqual(resp.status_code, 200)

true

f719771bdcfb47ab5315aba6e6e1b06f312f1af0

792

py

Python

samples/client/petstore/python-experimental/test/test_parent.py

MalcolmScoffable/openapi-generator

73605a0c0e0c825286c95123c63678ba75b44d5c

[ "Apache-2.0" ]

4

2020-07-24T07:02:57.000Z

2022-01-08T17:37:38.000Z

samples/client/petstore/python-experimental/test/test_parent.py

MalcolmScoffable/openapi-generator

73605a0c0e0c825286c95123c63678ba75b44d5c

[ "Apache-2.0" ]

7

2021-05-12T00:00:20.000Z

2022-02-27T11:23:35.000Z

samples/client/petstore/python-experimental/test/test_parent.py

MalcolmScoffable/openapi-generator

73605a0c0e0c825286c95123c63678ba75b44d5c

[ "Apache-2.0" ]

2

2020-04-24T15:18:41.000Z

2021-12-07T09:39:40.000Z

# coding: utf-8 """ OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import petstore_api class TestParent(unittest.TestCase): """Parent unit test stubs""" def setUp(self): pass def tearDown(self): pass def testParent(self): """Test Parent""" # FIXME: construct object with mandatory attributes with example values # model = petstore_api.Parent() # noqa: E501 pass if __name__ == '__main__': unittest.main()

20.842105

174

0.667929

from __future__ import absolute_import import unittest import petstore_api class TestParent(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testParent(self): s if __name__ == '__main__': unittest.main()

true

f71977e9459670b106619a17c0921c378ddd8285

166

py

Python

tests/model_control/detailed/transf_Quantization/model_control_one_enabled_Quantization_LinearTrend_Seasonal_Hour_SVR.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

null

tests/model_control/detailed/transf_Quantization/model_control_one_enabled_Quantization_LinearTrend_Seasonal_Hour_SVR.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

1

2019-11-30T23:39:38.000Z

2019-12-01T04:34:35.000Z

tests/model_control/detailed/transf_Quantization/model_control_one_enabled_Quantization_LinearTrend_Seasonal_Hour_SVR.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

null

import pyaf.tests.model_control.test_ozone_custom_models_enabled as testmod testmod.build_model( ['Quantization'] , ['LinearTrend'] , ['Seasonal_Hour'] , ['SVR'] );

41.5

88

0.759036

import pyaf.tests.model_control.test_ozone_custom_models_enabled as testmod testmod.build_model( ['Quantization'] , ['LinearTrend'] , ['Seasonal_Hour'] , ['SVR'] );

true

f71979aaf903ff6353153012c1f5c64b155b2d5a

6,406

py

Python

libs/python/multicore_sorted/drafts/py_merge/multicore_sorted.py

denis-ryzhkov/antiques

6a67bf606c1b49cc413df26bfdf00d392b605f88

[ "MIT" ]

null

libs/python/multicore_sorted/drafts/py_merge/multicore_sorted.py

denis-ryzhkov/antiques

6a67bf606c1b49cc413df26bfdf00d392b605f88

[ "MIT" ]

null

libs/python/multicore_sorted/drafts/py_merge/multicore_sorted.py

denis-ryzhkov/antiques

6a67bf606c1b49cc413df26bfdf00d392b605f88

[ "MIT" ]

null

""" >>> DRAFT "py_merge"! <<< Builtin "sorted()" function, but using all CPU cores available for speedup! It supports all kwargs of "sorted()": "cmp", "key" and "reverse", however items of "iterable" and all of these kwargs should be picklable: https://docs.python.org/2/library/pickle.html#what-can-be-pickled-and-unpickled Under the hood it uses map-reduce via "multiprocessing.Pool().map()" with builtin "sorted()" and then merges sorted chunks as in merge-sort. "processes" kwarg allows to set number of processes different from "cpu_count()". Usage: pip install multicore_sorted cat <<END >test.py from multicore_sorted import multicore_sorted in_data = [1, 5, 2, 4, 3] out_data = [1, 2, 3, 4, 5] def cmp(a, b): return b - a def key(a): return -a if __name__ == '__main__': assert multicore_sorted(in_data) == sorted(in_data) == out_data # But N times faster, given Big data and N CPU cores! assert ( multicore_sorted(in_data, cmp=cmp) == multicore_sorted(in_data, key=key) == multicore_sorted(in_data, reverse=True) == list(reversed(out_data)) ) print('OK') END python test.py drafts/py_merge/multicore_sorted version 0.1.0 Copyright (C) 2014 by Denis Ryzhkov <denisr@denisr.com> MIT License, see http://opensource.org/licenses/MIT """ #### export __all__ = ['multicore_sorted'] #### import from bn import Bn from functools import cmp_to_key from multiprocessing import cpu_count, Pool #### multicore_sorted def multicore_sorted(iterable, **kwargs): bn = Bn() #### processes bn('processes') processes = kwargs.pop('processes', None) if processes is None: try: processes = cpu_count() # Yes, "Pool()" does the same, but we need "processes" before calling "Pool()". except NotImplementedError: processes = 1 if processes < 2: return sorted(iterable, **kwargs) # No need for multiprocessing if less than 2 processes! # It is tempting to do the same for small enough "len(iterable)", # but then the code below would be not efficient for generators having no "__len__". #### chunks bn('chunks') chunks = [[] for _ in xrange(processes)] # "[[]] * processes" would have created N links to the same list, # while we need separate lists. for i, item in enumerate(iterable): # Efficient even if "iterable" is a generator. chunks[i % processes].append(item) # Round-robin chunking. chunks = [ # Packing for "picklable_sorted" below. (chunk, kwargs) # "chunk" here is just a ref to one of big lists created above. So it is efficient. for chunk in chunks ] #### map-reduce bn('pool') pool = Pool(processes=processes) # No "maxtasksperchild" - the pool will be GC-ed after the sort. bn('map') chunks = pool.map(picklable_sorted, chunks) #bn('pool') #pool.close() # Test! #bn('merge_sorted') result = merge_sorted(chunks, **kwargs) # Alas "heapq.merge()" does not support "key=lambda", etc. #bn('test_import') #from itertools import chain #bn('test_timsort') #result = sorted(chain(*chunks), **kwargs) print(bn) return result #### picklable_sorted def picklable_sorted(chunk): # "Pool().map()" does not support additional kwargs like "key=lambda" for the "func". # Natural closure inside "multicore_sorted" is not picklable. # This is a picklable single-argument workaround. chunk, kwargs = chunk # Unpacking via efficient refs. #print((chunk, kwargs)) return sorted(chunk, **kwargs) #### merge_sorted def merge_sorted(chunks, cmp=None, key=None, reverse=False): #bn = Bn() #bn('init') #### K - combined key. if cmp: cmp_key = cmp_to_key(cmp) K = (lambda a: cmp_key(key(a))) if key else cmp_key elif key: K = key else: K = lambda a: a # NOTE: "reverse" is processed below. #### init chunks = [iter(chunk) for chunk in chunks] # Prepare to fetch from each chunk. items = [chunk.next() for chunk in chunks] # Fetch first item from each chunk. Should be no empty chunks here. skip_me = object() # Unique marker. result = [] while True: min_item = min_key = min_index = None #### Find "min". #bn('min') for chunk_index, item in enumerate(items): # Bultin "min()" does not fit, even with its "key" kwarg. if item is not skip_me and ( min_index is None or # First not "skip_me" chunk becomes "min" chunk. not reverse and K(item) < min_key or # Default case "reverse=False" should be the first one. reverse and K(item) > min_key # Attempt to use "not <" would lead to extra computations below on "==". ): min_item = item min_key = K(item) min_index = chunk_index if min_index is None: # All chunks are "skip_me". break #bn('append') result.append(min_item) #### Fetch next item instead of "min". #bn('fetch') try: items[min_index] = chunks[min_index].next() except StopIteration: items[min_index] = skip_me #print(bn) return result #### tests def cmp(a, b): return b - a def key(a): return -a def tests(): from random import randint in_data = [randint(-100, 100) for _ in xrange(4 * 10**6)] out_data = sorted(in_data) reversed_out_data = list(reversed(out_data)) bn = Bn() bn('sorted') assert sorted(in_data) == out_data bn('multicore_sorted') assert multicore_sorted(in_data) == out_data print(bn) #""" assert multicore_sorted(in_data) == sorted(in_data) == out_data assert multicore_sorted(in_data, cmp=cmp) == reversed_out_data assert multicore_sorted(in_data, key=key) == reversed_out_data assert multicore_sorted(in_data, reverse=True) == reversed_out_data assert multicore_sorted(in_data, cmp=cmp, key=key) == out_data assert multicore_sorted(in_data, cmp=cmp, reverse=True) == out_data assert multicore_sorted(in_data, key=key, reverse=True) == out_data assert multicore_sorted(in_data, cmp=cmp, key=key, reverse=True) == reversed_out_data #""" print('OK') if __name__ == '__main__': tests()

28.471111

118

0.635498

ted'] nctools import cmp_to_key from multiprocessing import cpu_count, Pool = kwargs.pop('processes', None) if processes is None: try: processes = cpu_count() except NotImplementedError: processes = 1 if processes < 2: return sorted(iterable, **kwargs) ks = [[] for _ in xrange(processes)] for i, item in enumerate(iterable): chunks[i % processes].append(item) chunks = [ (chunk, kwargs) for chunk in chunks ] ses=processes) bn('map') chunks = pool.map(picklable_sorted, chunks) result = merge_sorted(chunks, **kwargs) print(bn) return result kwargs = chunk return sorted(chunk, **kwargs) , reverse=False): (lambda a: cmp_key(key(a))) if key else cmp_key elif key: K = key else: K = lambda a: a chunk) for chunk in chunks] items = [chunk.next() for chunk in chunks] skip_me = object() result = [] while True: min_item = min_key = min_index = None n enumerate(items): if item is not skip_me and ( min_index is None or not reverse and K(item) < min_key or reverse and K(item) > min_key ): min_item = item min_key = K(item) min_index = chunk_index if min_index is None: break result.append(min_item) : items[min_index] = skip_me return result urn b - a def key(a): return -a def tests(): from random import randint in_data = [randint(-100, 100) for _ in xrange(4 * 10**6)] out_data = sorted(in_data) reversed_out_data = list(reversed(out_data)) bn = Bn() bn('sorted') assert sorted(in_data) == out_data bn('multicore_sorted') assert multicore_sorted(in_data) == out_data print(bn) assert multicore_sorted(in_data) == sorted(in_data) == out_data assert multicore_sorted(in_data, cmp=cmp) == reversed_out_data assert multicore_sorted(in_data, key=key) == reversed_out_data assert multicore_sorted(in_data, reverse=True) == reversed_out_data assert multicore_sorted(in_data, cmp=cmp, key=key) == out_data assert multicore_sorted(in_data, cmp=cmp, reverse=True) == out_data assert multicore_sorted(in_data, key=key, reverse=True) == out_data assert multicore_sorted(in_data, cmp=cmp, key=key, reverse=True) == reversed_out_data #""" print('OK') if __name__ == '__main__': tests()

true

f7197a79112a1c5cebafd40d2898d9834ee03a99

15,162

py

Python

test/simulator_tests/birth_death_simulator_test.py

YosefLab/SingleCellLineageTracing

d9133fc80c8314e7935fde037dd86111cac47447

[ "MIT" ]

52

2019-05-14T02:06:24.000Z

2022-03-27T05:22:56.000Z

test/simulator_tests/birth_death_simulator_test.py

sbradford2/Cassiopeia

010072b307f7eadbf10dc4af8b2165e48f1736a7

[ "MIT" ]

88

2019-06-07T15:07:45.000Z

2022-03-22T14:40:03.000Z

test/simulator_tests/birth_death_simulator_test.py

sbradford2/Cassiopeia

010072b307f7eadbf10dc4af8b2165e48f1736a7

[ "MIT" ]

17

2019-05-17T00:46:16.000Z

2022-03-25T00:39:18.000Z

import unittest import networkx as nx import numpy as np from typing import List, Tuple from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import TreeSimulatorError from cassiopeia.simulator.BirthDeathFitnessSimulator import ( BirthDeathFitnessSimulator, ) import cassiopeia.data.utilities as utilities def extract_tree_statistics( tree: CassiopeiaTree, ) -> Tuple[List[float], int, bool]: """A helper function for testing simulated trees. Outputs the total lived time for each extant lineage, the number of extant lineages, and whether the tree has the expected node degrees (to ensure unifurcations were collapsed). Args: tree: The tree to test Returns: The total time lived for each leaf, the number of leaves, and if the degrees only have degree 0 or 2 """ times = [] out_degrees = [] for i in tree.nodes: if tree.is_leaf(i): times.append(tree.get_time(i)) out_degrees.append(len(tree.children(i))) out_degrees.pop(0) correct_degrees = all(x == 2 or x == 0 for x in out_degrees) return times, len(times), correct_degrees class BirthDeathSimulatorTest(unittest.TestCase): def test_bad_waiting_distributions(self): """Ensures errors when invalid distributions are given.""" with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: -1, 1, experiment_time=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator(lambda _: 0, 1, num_extant=4) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: -1, num_extant=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 0, experiment_time=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 0, mutation_distribution=lambda: -1, fitness_distribution=lambda: 1, experiment_time=1, ) tree = bd_sim.simulate_tree() def test_bad_stopping_conditions(self): """Ensures errors when an invalid stopping conditions are given.""" with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, lambda: 2) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=0.5 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=-1 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=0 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, experiment_time=-1 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, experiment_time=0 ) def test_dead_at_start(self): """Ensures errors in base case where all lineages die on first event.""" with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 2, 1, lambda: 1, num_extant=4 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 2, 1, lambda: 1, experiment_time=4 ) tree = bd_sim.simulate_tree() def test_dead_before_end(self): """Ensures errors when all lineages die before stopping condition.""" birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(0.6) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, random_seed=5 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=2, random_seed=5 ) tree = bd_sim.simulate_tree() def test_single_lineage(self): """Tests base case that stopping conditions work before divisions.""" bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, num_extant=1) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertEqual(results[1], 1) self.assertEqual(tree.get_branch_length("0", "1"), 1.0) self.assertEqual(results[0], [1]) bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, experiment_time=1) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertEqual(results[1], 1) self.assertEqual(tree.get_branch_length("0", "1"), 1.0) self.assertEqual(results[0], [1]) def test_constant_yule(self): """Tests small case without death with constant waiting times.""" bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, num_extant=32) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 6) self.assertEqual(results[1], 32) self.assertTrue(results[2]) bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, experiment_time=6) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 6) self.assertEqual(results[1], 32) self.assertTrue(results[2]) def test_nonconstant_yule(self): """Tests case without death with variable waiting times.""" birth_wd = lambda scale: np.random.exponential(scale) bd_sim = BirthDeathFitnessSimulator( birth_wd, 1, num_extant=16, random_seed=54 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 16) self.assertTrue(results[2]) self.assertEqual(max([int(i) for i in tree.nodes]), 31) bd_sim = BirthDeathFitnessSimulator( birth_wd, 1, experiment_time=2, random_seed=54 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 2) self.assertTrue(results[2]) def test_nonconstant_birth_death(self): """Tests case with with variable birth and death waiting times. Also, tests pruning dead lineages and unifurcation collapsing.""" birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, random_seed=1234 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) self.assertNotIn("9", tree.nodes) self.assertNotIn("2", tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=2, random_seed=1234 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 2)) self.assertTrue(results[2]) self.assertNotIn("9", tree.nodes) self.assertNotIn("2", tree.nodes) def test_nonconstant_birth_death_no_unifurcation_collapsing(self): """Tests case with with variable birth and death waiting times. Checks that unifurcations are not collapsed.""" birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, collapse_unifurcations=False, random_seed=12, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertFalse(results[2]) self.assertNotIn("3", tree.nodes) self.assertIn("2", tree.nodes) self.assertIn("6", tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=1.3, collapse_unifurcations=False, random_seed=12, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 1.3)) self.assertFalse(results[2]) self.assertNotIn("3", tree.nodes) self.assertIn("2", tree.nodes) self.assertIn("6", tree.nodes) def test_nonconstant_birth_death_both_stopping_conditions(self): """Tests case with with variable birth and death waiting times. Checks that using both stopping conditions works fine.""" birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, experiment_time=2, random_seed=17, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertTrue(all(x > 1 for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, experiment_time=1, random_seed=17, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 1)) self.assertEqual(results[1], 3) self.assertTrue(results[2]) def test_nonconstant_yule_with_predictable_fitness(self): """Tests case with birth and death with constant fitness.""" def check_fitness_values_as_expected(tree: nx.DiGraph): """Checks if the fitness value stored at each node is what we expect given deterministic fitness evolution""" tree = tree.copy() for u, v in tree.edges: tree[u][v]["val"] = 1 tree.nodes["0"]["depth"] = 0 for u, v in nx.dfs_edges(tree, source="0"): tree.nodes[v]["depth"] = ( tree.nodes[u]["depth"] + tree[u][v]["val"] ) leaves = [n for n in tree if tree.out_degree(n) == 0] for i in tree.nodes: if i in leaves: self.assertTrue( np.isclose( tree.nodes[i]["birth_scale"], 0.5 * 0.98 ** (2 * (tree.nodes[i]["depth"] - 1)), ) ) else: self.assertTrue( np.isclose( tree.nodes[i]["birth_scale"], 0.5 * 0.98 ** (2 * tree.nodes[i]["depth"]), ) ) birth_wd = lambda scale: np.random.exponential(scale) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, mutation_distribution=lambda: 2, fitness_distribution=lambda: 1, fitness_base=0.98, num_extant=8, random_seed=1234, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) check_fitness_values_as_expected(tree.get_tree_topology()) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, mutation_distribution=lambda: 2, fitness_distribution=lambda: 1, fitness_base=0.98, experiment_time=0.6, random_seed=1234, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 0.6)) self.assertTrue(results[2]) check_fitness_values_as_expected(tree.get_tree_topology()) def test_nonconstant_birth_death_with_variable_fitness(self): """Tests a case with variable birth and death waiting times, as well as variable fitness evolution. Also tests pruning and collapsing.""" birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(0.6) mut_dist = lambda: 1 if np.random.uniform() < 0.2 else 0 fit_dist = lambda: np.random.uniform(-1, 1) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, mut_dist, fit_dist, 1.5, num_extant=8, random_seed=12364, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) self.assertNotIn(2, tree.nodes) self.assertNotIn(3, tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, mut_dist, fit_dist, 1.5, experiment_time=3, random_seed=12364, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 3)) self.assertTrue(results[2]) self.assertNotIn(2, tree.nodes) self.assertNotIn(3, tree.nodes) if __name__ == "__main__": unittest.main()

36.186158

80

0.595502

import unittest import networkx as nx import numpy as np from typing import List, Tuple from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import TreeSimulatorError from cassiopeia.simulator.BirthDeathFitnessSimulator import ( BirthDeathFitnessSimulator, ) import cassiopeia.data.utilities as utilities def extract_tree_statistics( tree: CassiopeiaTree, ) -> Tuple[List[float], int, bool]: times = [] out_degrees = [] for i in tree.nodes: if tree.is_leaf(i): times.append(tree.get_time(i)) out_degrees.append(len(tree.children(i))) out_degrees.pop(0) correct_degrees = all(x == 2 or x == 0 for x in out_degrees) return times, len(times), correct_degrees class BirthDeathSimulatorTest(unittest.TestCase): def test_bad_waiting_distributions(self): with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: -1, 1, experiment_time=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator(lambda _: 0, 1, num_extant=4) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: -1, num_extant=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 0, experiment_time=1 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 0, mutation_distribution=lambda: -1, fitness_distribution=lambda: 1, experiment_time=1, ) tree = bd_sim.simulate_tree() def test_bad_stopping_conditions(self): with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, lambda: 2) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=0.5 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=-1 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, num_extant=0 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, experiment_time=-1 ) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 1, 1, lambda: 2, experiment_time=0 ) def test_dead_at_start(self): with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 2, 1, lambda: 1, num_extant=4 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( lambda _: 2, 1, lambda: 1, experiment_time=4 ) tree = bd_sim.simulate_tree() def test_dead_before_end(self): birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(0.6) with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, random_seed=5 ) tree = bd_sim.simulate_tree() with self.assertRaises(TreeSimulatorError): bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=2, random_seed=5 ) tree = bd_sim.simulate_tree() def test_single_lineage(self): bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, num_extant=1) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertEqual(results[1], 1) self.assertEqual(tree.get_branch_length("0", "1"), 1.0) self.assertEqual(results[0], [1]) bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, experiment_time=1) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertEqual(results[1], 1) self.assertEqual(tree.get_branch_length("0", "1"), 1.0) self.assertEqual(results[0], [1]) def test_constant_yule(self): bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, num_extant=32) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 6) self.assertEqual(results[1], 32) self.assertTrue(results[2]) bd_sim = BirthDeathFitnessSimulator(lambda _: 1, 1, experiment_time=6) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 6) self.assertEqual(results[1], 32) self.assertTrue(results[2]) def test_nonconstant_yule(self): birth_wd = lambda scale: np.random.exponential(scale) bd_sim = BirthDeathFitnessSimulator( birth_wd, 1, num_extant=16, random_seed=54 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 16) self.assertTrue(results[2]) self.assertEqual(max([int(i) for i in tree.nodes]), 31) bd_sim = BirthDeathFitnessSimulator( birth_wd, 1, experiment_time=2, random_seed=54 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertEqual(i, 2) self.assertTrue(results[2]) def test_nonconstant_birth_death(self): birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, random_seed=1234 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) self.assertNotIn("9", tree.nodes) self.assertNotIn("2", tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=2, random_seed=1234 ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 2)) self.assertTrue(results[2]) self.assertNotIn("9", tree.nodes) self.assertNotIn("2", tree.nodes) def test_nonconstant_birth_death_no_unifurcation_collapsing(self): birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, collapse_unifurcations=False, random_seed=12, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertFalse(results[2]) self.assertNotIn("3", tree.nodes) self.assertIn("2", tree.nodes) self.assertIn("6", tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, experiment_time=1.3, collapse_unifurcations=False, random_seed=12, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 1.3)) self.assertFalse(results[2]) self.assertNotIn("3", tree.nodes) self.assertIn("2", tree.nodes) self.assertIn("6", tree.nodes) def test_nonconstant_birth_death_both_stopping_conditions(self): birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(1.5) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, experiment_time=2, random_seed=17, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertTrue(all(x > 1 for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, num_extant=8, experiment_time=1, random_seed=17, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 1)) self.assertEqual(results[1], 3) self.assertTrue(results[2]) def test_nonconstant_yule_with_predictable_fitness(self): def check_fitness_values_as_expected(tree: nx.DiGraph): tree = tree.copy() for u, v in tree.edges: tree[u][v]["val"] = 1 tree.nodes["0"]["depth"] = 0 for u, v in nx.dfs_edges(tree, source="0"): tree.nodes[v]["depth"] = ( tree.nodes[u]["depth"] + tree[u][v]["val"] ) leaves = [n for n in tree if tree.out_degree(n) == 0] for i in tree.nodes: if i in leaves: self.assertTrue( np.isclose( tree.nodes[i]["birth_scale"], 0.5 * 0.98 ** (2 * (tree.nodes[i]["depth"] - 1)), ) ) else: self.assertTrue( np.isclose( tree.nodes[i]["birth_scale"], 0.5 * 0.98 ** (2 * tree.nodes[i]["depth"]), ) ) birth_wd = lambda scale: np.random.exponential(scale) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, mutation_distribution=lambda: 2, fitness_distribution=lambda: 1, fitness_base=0.98, num_extant=8, random_seed=1234, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) check_fitness_values_as_expected(tree.get_tree_topology()) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, mutation_distribution=lambda: 2, fitness_distribution=lambda: 1, fitness_base=0.98, experiment_time=0.6, random_seed=1234, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 0.6)) self.assertTrue(results[2]) check_fitness_values_as_expected(tree.get_tree_topology()) def test_nonconstant_birth_death_with_variable_fitness(self): birth_wd = lambda scale: np.random.exponential(scale) death_wd = lambda: np.random.exponential(0.6) mut_dist = lambda: 1 if np.random.uniform() < 0.2 else 0 fit_dist = lambda: np.random.uniform(-1, 1) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, mut_dist, fit_dist, 1.5, num_extant=8, random_seed=12364, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) self.assertTrue(all(np.isclose(x, results[0][0]) for x in results[0])) self.assertEqual(results[1], 8) self.assertTrue(results[2]) self.assertNotIn(2, tree.nodes) self.assertNotIn(3, tree.nodes) bd_sim = BirthDeathFitnessSimulator( birth_wd, 0.5, death_wd, mut_dist, fit_dist, 1.5, experiment_time=3, random_seed=12364, ) tree = bd_sim.simulate_tree() results = extract_tree_statistics(tree) for i in results[0]: self.assertTrue(np.isclose(i, 3)) self.assertTrue(results[2]) self.assertNotIn(2, tree.nodes) self.assertNotIn(3, tree.nodes) if __name__ == "__main__": unittest.main()

true

f7197adb438e0099947d4309aa51de3f15e7c419

2,615

py

Python

src/sparkload.py

jbalint/spark

caccf1cd9122dd4a7dc0f26a57ee4a649056aa6f

[ "CNRI-Jython" ]

1

2015-05-21T20:00:12.000Z

src/sparkload.py

jbalint/spark

caccf1cd9122dd4a7dc0f26a57ee4a649056aa6f

[ "CNRI-Jython" ]

null

src/sparkload.py

jbalint/spark

caccf1cd9122dd4a7dc0f26a57ee4a649056aa6f

[ "CNRI-Jython" ]

null

#!/usr/bin/env jython #*****************************************************************************# #* Copyright (c) 2004-2008, SRI International. *# #* 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 SRI International 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 *# #* OWNER 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. *# #*****************************************************************************# #* "$Revision:: 26 $" *# #* "$HeadURL:: https://svn.ai.sri.com/projects/spark/trunk/spark/src/spar#$" *# #*****************************************************************************# import sys from spark.internal.version import * from spark.main import main

70.675676

80

0.518164

al.version import * from spark.main import main

true

f7197b8026a171a1b01fc519f2c5d4c23b3f4e4d

7,153

py

Python

tests/test_other_scripts.py

vaibhavad/ParlAI

8960fab4cb7b7063df6023d8734adc8881dfed6e

[ "MIT" ]

2

2017-09-20T21:49:51.000Z

2018-08-12T06:58:10.000Z

tests/test_other_scripts.py

vaibhavad/ParlAI

8960fab4cb7b7063df6023d8734adc8881dfed6e

[ "MIT" ]

1

2021-01-22T08:11:01.000Z

tests/test_other_scripts.py

vaibhavad/ParlAI

8960fab4cb7b7063df6023d8734adc8881dfed6e

[ "MIT" ]

1

2021-01-07T11:45:03.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Catch all for a number of "other" scripts. """ import os import unittest import parlai.utils.testing as testing_utils class TestConvertToParlaiFormat(unittest.TestCase): def test_convert(self): from parlai.scripts.convert_data_to_parlai_format import ( ConvertDataToParlaiFormat, ) with testing_utils.tempdir() as tmpdir: fn = os.path.join(tmpdir, 'parlai.txt') ConvertDataToParlaiFormat.main( task='integration_tests:nocandidate', outfile=fn ) with open(fn) as f: assert ( f.readline() == 'text:4 1 3 2\tlabels:4 1 3 2\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:3 0 4 1\tlabels:3 0 4 1\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:5 1 6 3\tlabels:5 1 6 3\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:4 5 6 2\tlabels:4 5 6 2\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:0 5 3 1\tlabels:0 5 3 1\tepisode_done:True\n' ) assert f.readline() == '\n' class TestVerifyData(unittest.TestCase): def test_verify_data(self): from parlai.scripts.verify_data import VerifyData report = VerifyData.main(task='integration_tests') assert report['did_not_return_message'] == 0 assert report['empty_string_label_candidates'] == 0 assert report['exs'] == 500 assert report['label_candidates_with_missing_label'] == 0 assert report['missing_label_candidates'] == 0 assert report['missing_labels'] == 0 assert report['missing_text'] == 0 class TestVacuum(unittest.TestCase): def test_vacuum(self): with testing_utils.tempdir() as tmpdir: from parlai.scripts.vacuum import Vacuum model_file = os.path.join(tmpdir, 'model') valid, test = testing_utils.train_model( { 'task': 'integration_tests', 'optimizer': 'adam', 'learningrate': 0.01, 'model_file': model_file, 'num_epochs': 0.05, 'skip_generation': True, 'batchsize': 8, # TODO: switch to test_agents/unigram 'model': 'transformer/generator', 'ffn_size': 32, 'embedding_size': 32, 'n_layers': 1, } ) size_before = os.stat(model_file).st_size Vacuum.main(model_file=model_file) size_after = os.stat(model_file).st_size assert size_after < size_before assert os.path.exists(model_file + '.unvacuumed') valid2, test2 = testing_utils.eval_model( {'task': 'integration_tests', 'model_file': model_file, 'batchsize': 8} ) for key in ['loss', 'exs', 'ppl', 'token_acc']: assert valid2[key] == valid[key], f"{key} score doesn't match" assert test2[key] == test[key], f"{key} score doesn't match" class TestDetectOffensive(unittest.TestCase): def test_offensive(self): from parlai.scripts.detect_offensive_language import DetectOffensive report = DetectOffensive.main( task='babi:task1k:10', datatype='valid', safety='string_matcher' ) assert report['string_offenses%'] == 0 assert report['word_offenses'] == 0 assert report['exs'] == 100 class TestParty(unittest.TestCase): def test_party(self): from parlai.scripts.party import Party Party.main(seconds=0.01) class TestDataStats(unittest.TestCase): def test_simple(self): from parlai.scripts.data_stats import DataStats report = DataStats.main(task='integration_tests') assert report['both/avg_utterance_length'] == 4 assert report['input/avg_utterance_length'] == 4 assert report['labels/avg_utterance_length'] == 4 assert report['both/tokens'] == 4000 assert report['input/tokens'] == 2000 assert report['labels/tokens'] == 2000 assert report['both/unique_tokens'] == 7 assert report['input/unique_tokens'] == 7 assert report['labels/unique_tokens'] == 7 assert report['both/unique_utterances'] == 500 assert report['input/unique_utterances'] == 500 assert report['labels/unique_utterances'] == 500 assert report['both/utterances'] == 1000 assert report['input/utterances'] == 500 assert report['labels/utterances'] == 500 class TestProfileTrain(unittest.TestCase): """ Test profile_train doesn't crash. """ def test_cprofile(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) def test_torch(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', torch=True, model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) @testing_utils.skipUnlessGPU def test_torch_cuda(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', torch_cuda=True, model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) class TestTokenStats(unittest.TestCase): def test_token_stats(self): from parlai.scripts.token_stats import TokenStats from parlai.core.metrics import dict_report results = dict_report(TokenStats.main(task='integration_tests:multiturn')) assert results == { 'exs': 2000, 'max': 16, 'mean': 7.5, 'min': 1, 'p01': 1, 'p05': 1, 'p10': 1, 'p25': 4, 'p50': 7.5, 'p75': 11.5, 'p90': 16, 'p95': 16, 'p99': 16, 'p@128': 1, }

35.063725

87

0.562282

import os import unittest import parlai.utils.testing as testing_utils class TestConvertToParlaiFormat(unittest.TestCase): def test_convert(self): from parlai.scripts.convert_data_to_parlai_format import ( ConvertDataToParlaiFormat, ) with testing_utils.tempdir() as tmpdir: fn = os.path.join(tmpdir, 'parlai.txt') ConvertDataToParlaiFormat.main( task='integration_tests:nocandidate', outfile=fn ) with open(fn) as f: assert ( f.readline() == 'text:4 1 3 2\tlabels:4 1 3 2\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:3 0 4 1\tlabels:3 0 4 1\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:5 1 6 3\tlabels:5 1 6 3\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:4 5 6 2\tlabels:4 5 6 2\tepisode_done:True\n' ) assert f.readline() == '\n' assert ( f.readline() == 'text:0 5 3 1\tlabels:0 5 3 1\tepisode_done:True\n' ) assert f.readline() == '\n' class TestVerifyData(unittest.TestCase): def test_verify_data(self): from parlai.scripts.verify_data import VerifyData report = VerifyData.main(task='integration_tests') assert report['did_not_return_message'] == 0 assert report['empty_string_label_candidates'] == 0 assert report['exs'] == 500 assert report['label_candidates_with_missing_label'] == 0 assert report['missing_label_candidates'] == 0 assert report['missing_labels'] == 0 assert report['missing_text'] == 0 class TestVacuum(unittest.TestCase): def test_vacuum(self): with testing_utils.tempdir() as tmpdir: from parlai.scripts.vacuum import Vacuum model_file = os.path.join(tmpdir, 'model') valid, test = testing_utils.train_model( { 'task': 'integration_tests', 'optimizer': 'adam', 'learningrate': 0.01, 'model_file': model_file, 'num_epochs': 0.05, 'skip_generation': True, 'batchsize': 8, 'model': 'transformer/generator', 'ffn_size': 32, 'embedding_size': 32, 'n_layers': 1, } ) size_before = os.stat(model_file).st_size Vacuum.main(model_file=model_file) size_after = os.stat(model_file).st_size assert size_after < size_before assert os.path.exists(model_file + '.unvacuumed') valid2, test2 = testing_utils.eval_model( {'task': 'integration_tests', 'model_file': model_file, 'batchsize': 8} ) for key in ['loss', 'exs', 'ppl', 'token_acc']: assert valid2[key] == valid[key], f"{key} score doesn't match" assert test2[key] == test[key], f"{key} score doesn't match" class TestDetectOffensive(unittest.TestCase): def test_offensive(self): from parlai.scripts.detect_offensive_language import DetectOffensive report = DetectOffensive.main( task='babi:task1k:10', datatype='valid', safety='string_matcher' ) assert report['string_offenses%'] == 0 assert report['word_offenses'] == 0 assert report['exs'] == 100 class TestParty(unittest.TestCase): def test_party(self): from parlai.scripts.party import Party Party.main(seconds=0.01) class TestDataStats(unittest.TestCase): def test_simple(self): from parlai.scripts.data_stats import DataStats report = DataStats.main(task='integration_tests') assert report['both/avg_utterance_length'] == 4 assert report['input/avg_utterance_length'] == 4 assert report['labels/avg_utterance_length'] == 4 assert report['both/tokens'] == 4000 assert report['input/tokens'] == 2000 assert report['labels/tokens'] == 2000 assert report['both/unique_tokens'] == 7 assert report['input/unique_tokens'] == 7 assert report['labels/unique_tokens'] == 7 assert report['both/unique_utterances'] == 500 assert report['input/unique_utterances'] == 500 assert report['labels/unique_utterances'] == 500 assert report['both/utterances'] == 1000 assert report['input/utterances'] == 500 assert report['labels/utterances'] == 500 class TestProfileTrain(unittest.TestCase): def test_cprofile(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) def test_torch(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', torch=True, model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) @testing_utils.skipUnlessGPU def test_torch_cuda(self): from parlai.scripts.profile_train import ProfileTrain with testing_utils.tempdir() as tmpdir: ProfileTrain.main( task='integration_tests:overfit', model='test_agents/unigram', torch_cuda=True, model_file=os.path.join(tmpdir, 'model'), skip_generation=True, ) class TestTokenStats(unittest.TestCase): def test_token_stats(self): from parlai.scripts.token_stats import TokenStats from parlai.core.metrics import dict_report results = dict_report(TokenStats.main(task='integration_tests:multiturn')) assert results == { 'exs': 2000, 'max': 16, 'mean': 7.5, 'min': 1, 'p01': 1, 'p05': 1, 'p10': 1, 'p25': 4, 'p50': 7.5, 'p75': 11.5, 'p90': 16, 'p95': 16, 'p99': 16, 'p@128': 1, }

true

f7197c8fd871714cbb61cb9b004d8a0b6f5dd33a

1,297

py

Python

sr700api/utils.py

AlexGS74/sr700api

22fc79c0e02ef66f4ef92f9c8b4a56c04fe09c4a

[ "MIT" ]

5

2017-10-15T21:58:55.000Z

2020-09-02T05:12:32.000Z

sr700api/utils.py

AlexGS74/sr700api

22fc79c0e02ef66f4ef92f9c8b4a56c04fe09c4a

[ "MIT" ]

null

sr700api/utils.py

AlexGS74/sr700api

22fc79c0e02ef66f4ef92f9c8b4a56c04fe09c4a

[ "MIT" ]

1

2018-08-25T23:27:53.000Z

""" MIT License Copyright (c) 2017 int3ll3ct.ly@gmail.com Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ def f_to_c(deg_f): "utility to convert degrees fahrenheit to celsius" return (deg_f - 32.0)/1.8 def c_to_f(deg_c): "utility to convert degrees celsius to fahrenheit" return deg_c * 1.8 + 32.0

40.53125

78

0.781033

def f_to_c(deg_f): return (deg_f - 32.0)/1.8 def c_to_f(deg_c): return deg_c * 1.8 + 32.0

true

f7197ce5cfde61155bd0c88a6aa247110b8af814

10,671

py

Python

tests/test_camera.py

netmanchris/abodepy

cd7b5527cc2becd12763d949057fe0184e0395d2

[ "MIT" ]

null

tests/test_camera.py

netmanchris/abodepy

cd7b5527cc2becd12763d949057fe0184e0395d2

[ "MIT" ]

null

tests/test_camera.py

netmanchris/abodepy

cd7b5527cc2becd12763d949057fe0184e0395d2

[ "MIT" ]

null

"""Test the Abode camera class.""" import os import unittest import requests_mock import abodepy import abodepy.helpers.constants as CONST import tests.mock as MOCK import tests.mock.devices.ir_camera as IRCAMERA import tests.mock.login as LOGIN import tests.mock.oauth_claims as OAUTH_CLAIMS import tests.mock.logout as LOGOUT import tests.mock.panel as PANEL USERNAME = 'foobar' PASSWORD = 'deadbeef' class TestCamera(unittest.TestCase): """Test the AbodePy camera.""" def setUp(self): """Set up Abode module.""" self.abode = abodepy.Abode(username=USERNAME, password=PASSWORD, disable_cache=True) def tearDown(self): """Clean up after test.""" self.abode = None @requests_mock.mock() def tests_camera_properties(self, m): """Tests that camera properties work as expected.""" # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) self.assertFalse(device.battery_low) self.assertFalse(device.no_response) # Set up our direct device get url device_url = str.replace(CONST.DEVICE_URL, '$DEVID$', IRCAMERA.DEVICE_ID) # Change device properties m.get(device_url, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_OFFLINE, low_battery=True, no_response=True)) # Refesh device and test changes device.refresh() self.assertEqual(device.status, CONST.STATUS_OFFLINE) self.assertTrue(device.battery_low) self.assertTrue(device.no_response) @requests_mock.mock() def tests_camera_capture(self, m): """Tests that camera devices capture new images.""" # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up capture url response url = str.replace(CONST.CAMS_ID_CAPTURE_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.put(url, text=MOCK.generic_response_ok()) # Capture the image self.assertTrue(device.capture()) # Change response m.put(url, text=IRCAMERA.get_capture_timeout(), status_code=600) # Capture the image with failure self.assertFalse(device.capture()) @requests_mock.mock() def tests_camera_image_update(self, m): """Tests that camera devices update correctly via timeline request.""" # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up timeline response url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID) + ']') # Set up our file path response file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302, headers={'Location': IRCAMERA.LOCATION_HEADER}) # Refresh the image self.assertTrue(device.refresh_image()) # Verify the image location self.assertEqual(device.image_url, IRCAMERA.LOCATION_HEADER) # Test that a bad file_path response header results in an exception file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302) with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that a bad file_path response code results in an exception file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=200, headers={'Location': IRCAMERA.LOCATION_HEADER}) with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that an an empty timeline event throws exception url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID, file_path='') + ']') with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that an unexpected timeline event throws exception url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID, event_code='1234') + ']') with self.assertRaises(abodepy.AbodeException): device.refresh_image() @requests_mock.mock() def tests_camera_no_image_update(self, m): """Tests that camera updates correctly with no timeline events.""" # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up timeline response url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[]') # Refresh the image self.assertFalse(device.refresh_image()) self.assertIsNone(device.image_url) @requests_mock.mock() def tests_camera_image_write(self, m): """Tests that camera images will write to a file.""" # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up timeline response url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID) + ']') # Set up our file path response file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302, headers={'Location': IRCAMERA.LOCATION_HEADER}) # Set up our image response image_response = "this is a beautiful jpeg image" m.get(IRCAMERA.LOCATION_HEADER, text=image_response) # Refresh the image path = "test.jpg" self.assertTrue(device.image_to_file(path, get_image=True)) # Test the file written and cleanup image_data = open(path, 'r').read() self.assertTrue(image_response, image_data) os.remove(path) # Test that bad response returns False m.get(IRCAMERA.LOCATION_HEADER, status_code=400) with self.assertRaises(abodepy.AbodeException): device.image_to_file(path, get_image=True) # Test that the image fails to update returns False m.get(url, text='[]') self.assertFalse(device.image_to_file(path, get_image=True))

37.181185

79

0.607722

import os import unittest import requests_mock import abodepy import abodepy.helpers.constants as CONST import tests.mock as MOCK import tests.mock.devices.ir_camera as IRCAMERA import tests.mock.login as LOGIN import tests.mock.oauth_claims as OAUTH_CLAIMS import tests.mock.logout as LOGOUT import tests.mock.panel as PANEL USERNAME = 'foobar' PASSWORD = 'deadbeef' class TestCamera(unittest.TestCase): def setUp(self): self.abode = abodepy.Abode(username=USERNAME, password=PASSWORD, disable_cache=True) def tearDown(self): self.abode = None @requests_mock.mock() def tests_camera_properties(self, m): m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) self.assertFalse(device.battery_low) self.assertFalse(device.no_response) # Set up our direct device get url device_url = str.replace(CONST.DEVICE_URL, '$DEVID$', IRCAMERA.DEVICE_ID) # Change device properties m.get(device_url, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_OFFLINE, low_battery=True, no_response=True)) # Refesh device and test changes device.refresh() self.assertEqual(device.status, CONST.STATUS_OFFLINE) self.assertTrue(device.battery_low) self.assertTrue(device.no_response) @requests_mock.mock() def tests_camera_capture(self, m): # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) self.abode.logout() device = self.abode.get_device(IRCAMERA.DEVICE_ID) self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) url = str.replace(CONST.CAMS_ID_CAPTURE_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.put(url, text=MOCK.generic_response_ok()) self.assertTrue(device.capture()) m.put(url, text=IRCAMERA.get_capture_timeout(), status_code=600) self.assertFalse(device.capture()) @requests_mock.mock() def tests_camera_image_update(self, m): m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up timeline response url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID) + ']') # Set up our file path response file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302, headers={'Location': IRCAMERA.LOCATION_HEADER}) # Refresh the image self.assertTrue(device.refresh_image()) # Verify the image location self.assertEqual(device.image_url, IRCAMERA.LOCATION_HEADER) # Test that a bad file_path response header results in an exception file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302) with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that a bad file_path response code results in an exception file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=200, headers={'Location': IRCAMERA.LOCATION_HEADER}) with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that an an empty timeline event throws exception url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID, file_path='') + ']') with self.assertRaises(abodepy.AbodeException): device.refresh_image() # Test that an unexpected timeline event throws exception url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID, event_code='1234') + ']') with self.assertRaises(abodepy.AbodeException): device.refresh_image() @requests_mock.mock() def tests_camera_no_image_update(self, m): # Set up URL's m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) self.abode.logout() device = self.abode.get_device(IRCAMERA.DEVICE_ID) self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[]') self.assertFalse(device.refresh_image()) self.assertIsNone(device.image_url) @requests_mock.mock() def tests_camera_image_write(self, m): m.post(CONST.LOGIN_URL, text=LOGIN.post_response_ok()) m.get(CONST.OAUTH_TOKEN_URL, text=OAUTH_CLAIMS.get_response_ok()) m.post(CONST.LOGOUT_URL, text=LOGOUT.post_response_ok()) m.get(CONST.PANEL_URL, text=PANEL.get_response_ok(mode=CONST.MODE_STANDBY)) m.get(CONST.DEVICES_URL, text=IRCAMERA.device(devid=IRCAMERA.DEVICE_ID, status=CONST.STATUS_ONLINE, low_battery=False, no_response=False)) # Logout to reset everything self.abode.logout() # Get our camera device = self.abode.get_device(IRCAMERA.DEVICE_ID) # Test that we have our device self.assertIsNotNone(device) self.assertEqual(device.status, CONST.STATUS_ONLINE) # Set up timeline response url = str.replace(CONST.TIMELINE_IMAGES_ID_URL, '$DEVID$', IRCAMERA.DEVICE_ID) m.get(url, text='[' + IRCAMERA.timeline_event(IRCAMERA.DEVICE_ID) + ']') # Set up our file path response file_path = CONST.BASE_URL + IRCAMERA.FILE_PATH m.head(file_path, status_code=302, headers={'Location': IRCAMERA.LOCATION_HEADER}) # Set up our image response image_response = "this is a beautiful jpeg image" m.get(IRCAMERA.LOCATION_HEADER, text=image_response) # Refresh the image path = "test.jpg" self.assertTrue(device.image_to_file(path, get_image=True)) # Test the file written and cleanup image_data = open(path, 'r').read() self.assertTrue(image_response, image_data) os.remove(path) # Test that bad response returns False m.get(IRCAMERA.LOCATION_HEADER, status_code=400) with self.assertRaises(abodepy.AbodeException): device.image_to_file(path, get_image=True) # Test that the image fails to update returns False m.get(url, text='[]') self.assertFalse(device.image_to_file(path, get_image=True))

true

f7197da9b6e226e3c5a5e47bd5f775747c208e82

13,187

py

Python

kws_streaming/train/train.py

ssccutyy/KWS-Transformer

7ae6d2e8fce1a293d88eedc0dbfacae726151a08

[ "Apache-2.0" ]

1

2022-03-13T07:52:15.000Z

kws_streaming/train/train.py

ssccutyy/KWS-Transformer

7ae6d2e8fce1a293d88eedc0dbfacae726151a08

[ "Apache-2.0" ]

null

kws_streaming/train/train.py

ssccutyy/KWS-Transformer

7ae6d2e8fce1a293d88eedc0dbfacae726151a08

[ "Apache-2.0" ]

1

2022-03-11T12:33:27.000Z

# coding=utf-8 # Copyright (c) 2021, Arm Limited and Contributors. # SPDX-License-Identifier: Apache-2.0 # Copyright 2021 The Google Research Authors. # # 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. """Train utility functions, based on tensorflow/examples/speech_commands. It consists of several steps: 1. Creates model. 2. Reads data 3. Trains model 4. Select the best model and evaluates it """ import json from types import SimpleNamespace import os.path import pprint from absl import logging import numpy as np import tensorflow.compat.v1 as tf import tensorflow_addons as tfa import kws_streaming.data.input_data as input_data from kws_streaming.models import models from kws_streaming.models import utils import math from transformers import AdamWeightDecay from kws_streaming.models import model_flags def train(flags): """Model training.""" flags.training = True # Set the verbosity based on flags (default is INFO, so we see all messages) logging.set_verbosity(flags.verbosity) # Start a new TensorFlow session. tf.reset_default_graph() config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) tf.keras.backend.set_session(sess) audio_processor = input_data.AudioProcessor(flags) time_shift_samples = int((flags.time_shift_ms * flags.sample_rate) / 1000) # Figure out the learning rates for each training phase. Since it's often # effective to have high learning rates at the start of training, followed by # lower levels towards the end, the number of steps and learning rates can be # specified as comma-separated lists to define the rate at each stage. For # example --how_many_training_steps=10000,3000 --learning_rate=0.001,0.0001 # will run 13,000 training loops in total, with a rate of 0.001 for the first # 10,000, and 0.0001 for the final 3,000. training_steps_list = list(map(int, flags.how_many_training_steps.split(','))) learning_rates_list = list(map(float, flags.learning_rate.split(','))) if len(training_steps_list) != len(learning_rates_list): raise Exception( '--how_many_training_steps and --learning_rate must be equal length ' 'lists, but are %d and %d long instead' % (len(training_steps_list), len(learning_rates_list))) logging.info(flags) model = models.MODELS[flags.model_name](flags) if flags.distill_teacher_json: with open(flags.distill_teacher_json, 'r') as f: teacher_flags = json.load(f, object_hook=lambda d: SimpleNamespace( **{ k: v for k, v in flags.__dict__.items() if not k in d }, **d)) teacher_base = models.MODELS[teacher_flags.model_name](teacher_flags) hard_labels = tf.keras.layers.Lambda(lambda logits: tf.one_hot(tf.math.argmax(logits, axis=-1), depth=flags.label_count)) teacher = tf.keras.models.Sequential([teacher_base, hard_labels]) teacher_base.trainable = False teacher.trainable = False else: teacher = None teacher_flags = None base_model = model logging.info(model.summary()) # save model summary utils.save_model_summary(model, flags.train_dir) # save model and data flags with open(os.path.join(flags.train_dir, 'flags.txt'), 'wt') as f: pprint.pprint(flags, stream=f) loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=flags.label_smoothing) metrics = ['accuracy'] if flags.optimizer == 'adam': optimizer = tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon) elif flags.optimizer == 'momentum': optimizer = tf.keras.optimizers.SGD(momentum=0.9) elif flags.optimizer == 'novograd': optimizer = tfa.optimizers.NovoGrad( lr=0.05, beta_1=flags.novograd_beta_1, beta_2=flags.novograd_beta_2, weight_decay=flags.novograd_weight_decay, grad_averaging=bool(flags.novograd_grad_averaging)) elif flags.optimizer == 'adamw': # Exclude some layers for weight decay exclude = ["pos_emb", "class_emb", "layer_normalization", "bias"] optimizer = AdamWeightDecay(learning_rate=0.05, weight_decay_rate=flags.l2_weight_decay, exclude_from_weight_decay=exclude) else: raise ValueError('Unsupported optimizer:%s' % flags.optimizer) loss_weights = [ 0.5, 0.5, 0.0 ] if teacher else [ 1. ] # equally weight losses form label and teacher, ignore ensemble output model.compile(optimizer=optimizer, loss=loss, loss_weights=loss_weights, metrics=metrics) train_writer = tf.summary.FileWriter(flags.summaries_dir + '/train', sess.graph) validation_writer = tf.summary.FileWriter(flags.summaries_dir + '/validation') sess.run(tf.global_variables_initializer()) if flags.start_checkpoint: model.load_weights(flags.start_checkpoint).expect_partial() logging.info('Weights loaded from %s', flags.start_checkpoint) if teacher_flags and teacher_flags.start_checkpoint: # Load weights into teacher base as this is the actual model that was saved, teacher includes hard label head teacher_base.load_weights(teacher_flags.start_checkpoint).assert_existing_objects_matched() logging.info('Distillation teacher weights loaded from %s', teacher_flags.start_checkpoint) start_step = 0 logging.info('Training from step: %d ', start_step) # Save graph.pbtxt. tf.train.write_graph(sess.graph_def, flags.train_dir, 'graph.pbtxt') # Save list of words. with tf.io.gfile.GFile(os.path.join(flags.train_dir, 'labels.txt'), 'w') as f: f.write('\n'.join(audio_processor.words_list)) best_accuracy = 0.0 # prepare parameters for exp learning rate decay training_steps_max = np.sum(training_steps_list) lr_init = learning_rates_list[0] exp_rate = -np.log(learning_rates_list[-1] / lr_init)/training_steps_max mode = 'training' if flags.lr_schedule == 'cosine': # Currently, no restarts are performed, so it is just a cosine decay over the entire # training process. I think this is how DeiT does it. lr_init = lr_init * flags.batch_size / 512 num_train = audio_processor.set_size(mode) warmup_steps = int((num_train / flags.batch_size) * flags.warmup_epochs) first_decay_steps=training_steps_max # Training loop. for training_step in range(start_step, training_steps_max + 1): if training_step > 0: offset = (training_step - 1) * flags.batch_size if flags.pick_deterministically else 0 # Pull the audio samples we'll use for training. train_fingerprints, train_ground_truth = audio_processor.get_data( flags.batch_size, offset, flags, flags.background_frequency, flags.background_volume, time_shift_samples, mode, flags.resample, flags.volume_resample, sess) if flags.lr_schedule == 'exp': learning_rate_value = lr_init * np.exp(-exp_rate * training_step) elif flags.lr_schedule == 'linear': # Figure out what the current learning rate is. training_steps_sum = 0 for i in range(len(training_steps_list)): training_steps_sum += training_steps_list[i] if training_step <= training_steps_sum: learning_rate_value = learning_rates_list[i] break elif flags.lr_schedule == 'cosine': learning_rate_value = lr_init * min(1, float(training_step) / max(1, warmup_steps)) * (math.cos(math.pi * training_step / training_steps_max) + 1) / 2. else: raise ValueError('Wrong lr_schedule: %s' % flags.lr_schedule) tf.keras.backend.set_value(model.optimizer.learning_rate, learning_rate_value) one_hot_labels = tf.keras.utils.to_categorical(train_ground_truth, num_classes=flags.label_count) if teacher: teacher_labels = teacher.predict_on_batch(train_fingerprints) one_hot_labels = [ one_hot_labels, teacher_labels, one_hot_labels ] # third is for the ensemble output, gradient is unused result = model.train_on_batch(train_fingerprints, one_hot_labels) if teacher: loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result differences = (teacher_labels != one_hot_labels).astype(dtype=int).sum() logging.info( 'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f, teacher acc %.2f%% (%d diff), teacher cross entropy %f, ensemble acc %.2f%%', *(training_step, learning_rate_value, acc_label * 100, loss_total, acc_teacher * 100, differences, loss_teacher, acc_ensemble * 100)) summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label), tf.Summary.Value(tag='teacher_accuracy', simple_value=acc_teacher), tf.Summary.Value(tag='ensemble_accuracy', simple_value=acc_ensemble), ]) else: loss_label, acc_label = result logging.info( 'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f', *(training_step, learning_rate_value, acc_label * 100, loss_label)) summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label), ]) train_writer.add_summary(summary, training_step) is_last_step = (training_step == training_steps_max) if (training_step % flags.eval_step_interval) == 0 or is_last_step: set_size = audio_processor.set_size('validation') set_size = int(set_size / flags.batch_size) * flags.batch_size total_accuracy = 0.0 count = 0.0 for i in range(0, set_size, flags.batch_size): validation_fingerprints, validation_ground_truth = audio_processor.get_data( flags.batch_size, i, flags, 0.0, 0.0, 0, 'validation', 0.0, 0.0, sess) one_hot_labels = tf.keras.utils.to_categorical(validation_ground_truth, num_classes=flags.label_count) if teacher: one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ] # Run a validation step and capture training summaries for TensorBoard # with the `merged` op. result = model.test_on_batch(validation_fingerprints, one_hot_labels) if teacher: loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_ensemble), tf.Summary.Value(tag='label_head_accuracy', simple_value=acc_label), tf.Summary.Value(tag='distill_head_accuracy', simple_value=acc_teacher), ]) accuracy = acc_ensemble else: loss_label, acc_label = result summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label),]) accuracy = acc_label validation_writer.add_summary(summary, training_step) total_accuracy += accuracy count = count + 1.0 total_accuracy = total_accuracy / count logging.info('Step %d: Validation accuracy = %.2f%% (N=%d)', *(training_step, total_accuracy * 100, set_size)) # Save the model checkpoint when validation accuracy improves if total_accuracy >= best_accuracy: best_accuracy = total_accuracy # overwrite the best model weights model.save_weights(flags.train_dir + 'best_weights') logging.info('So far the best validation accuracy is %.2f%%', (best_accuracy * 100)) tf.keras.backend.set_learning_phase(0) set_size = audio_processor.set_size('testing') set_size = int(set_size / flags.batch_size) * flags.batch_size logging.info('set_size=%d', set_size) total_accuracy = 0.0 count = 0.0 for i in range(0, set_size, flags.batch_size): test_fingerprints, test_ground_truth = audio_processor.get_data( flags.batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0, sess) one_hot_labels = tf.keras.utils.to_categorical(test_ground_truth, num_classes=flags.label_count) if teacher: one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ] result = model.test_on_batch(test_fingerprints, one_hot_labels) total_accuracy += result[-1] if teacher else result[1] count = count + 1.0 total_accuracy = total_accuracy / count logging.info('Final test accuracy = %.2f%% (N=%d)', *(total_accuracy * 100, set_size)) with open(os.path.join(flags.train_dir, 'accuracy_last.txt'), 'wt') as fd: fd.write(str(total_accuracy * 100)) model.save_weights(flags.train_dir + 'last_weights') if __name__ == '__main__': flags = model_flags.update_flags(None) train(flags)

42.401929

159

0.705543

import json from types import SimpleNamespace import os.path import pprint from absl import logging import numpy as np import tensorflow.compat.v1 as tf import tensorflow_addons as tfa import kws_streaming.data.input_data as input_data from kws_streaming.models import models from kws_streaming.models import utils import math from transformers import AdamWeightDecay from kws_streaming.models import model_flags def train(flags): flags.training = True logging.set_verbosity(flags.verbosity) tf.reset_default_graph() config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) tf.keras.backend.set_session(sess) audio_processor = input_data.AudioProcessor(flags) time_shift_samples = int((flags.time_shift_ms * flags.sample_rate) / 1000) # effective to have high learning rates at the start of training, followed by # lower levels towards the end, the number of steps and learning rates can be # specified as comma-separated lists to define the rate at each stage. For # example --how_many_training_steps=10000,3000 --learning_rate=0.001,0.0001 # will run 13,000 training loops in total, with a rate of 0.001 for the first # 10,000, and 0.0001 for the final 3,000. training_steps_list = list(map(int, flags.how_many_training_steps.split(','))) learning_rates_list = list(map(float, flags.learning_rate.split(','))) if len(training_steps_list) != len(learning_rates_list): raise Exception( '--how_many_training_steps and --learning_rate must be equal length ' 'lists, but are %d and %d long instead' % (len(training_steps_list), len(learning_rates_list))) logging.info(flags) model = models.MODELS[flags.model_name](flags) if flags.distill_teacher_json: with open(flags.distill_teacher_json, 'r') as f: teacher_flags = json.load(f, object_hook=lambda d: SimpleNamespace( **{ k: v for k, v in flags.__dict__.items() if not k in d }, **d)) teacher_base = models.MODELS[teacher_flags.model_name](teacher_flags) hard_labels = tf.keras.layers.Lambda(lambda logits: tf.one_hot(tf.math.argmax(logits, axis=-1), depth=flags.label_count)) teacher = tf.keras.models.Sequential([teacher_base, hard_labels]) teacher_base.trainable = False teacher.trainable = False else: teacher = None teacher_flags = None base_model = model logging.info(model.summary()) # save model summary utils.save_model_summary(model, flags.train_dir) # save model and data flags with open(os.path.join(flags.train_dir, 'flags.txt'), 'wt') as f: pprint.pprint(flags, stream=f) loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=flags.label_smoothing) metrics = ['accuracy'] if flags.optimizer == 'adam': optimizer = tf.keras.optimizers.Adam(epsilon=flags.optimizer_epsilon) elif flags.optimizer == 'momentum': optimizer = tf.keras.optimizers.SGD(momentum=0.9) elif flags.optimizer == 'novograd': optimizer = tfa.optimizers.NovoGrad( lr=0.05, beta_1=flags.novograd_beta_1, beta_2=flags.novograd_beta_2, weight_decay=flags.novograd_weight_decay, grad_averaging=bool(flags.novograd_grad_averaging)) elif flags.optimizer == 'adamw': # Exclude some layers for weight decay exclude = ["pos_emb", "class_emb", "layer_normalization", "bias"] optimizer = AdamWeightDecay(learning_rate=0.05, weight_decay_rate=flags.l2_weight_decay, exclude_from_weight_decay=exclude) else: raise ValueError('Unsupported optimizer:%s' % flags.optimizer) loss_weights = [ 0.5, 0.5, 0.0 ] if teacher else [ 1. ] # equally weight losses form label and teacher, ignore ensemble output model.compile(optimizer=optimizer, loss=loss, loss_weights=loss_weights, metrics=metrics) train_writer = tf.summary.FileWriter(flags.summaries_dir + '/train', sess.graph) validation_writer = tf.summary.FileWriter(flags.summaries_dir + '/validation') sess.run(tf.global_variables_initializer()) if flags.start_checkpoint: model.load_weights(flags.start_checkpoint).expect_partial() logging.info('Weights loaded from %s', flags.start_checkpoint) if teacher_flags and teacher_flags.start_checkpoint: # Load weights into teacher base as this is the actual model that was saved, teacher includes hard label head teacher_base.load_weights(teacher_flags.start_checkpoint).assert_existing_objects_matched() logging.info('Distillation teacher weights loaded from %s', teacher_flags.start_checkpoint) start_step = 0 logging.info('Training from step: %d ', start_step) # Save graph.pbtxt. tf.train.write_graph(sess.graph_def, flags.train_dir, 'graph.pbtxt') # Save list of words. with tf.io.gfile.GFile(os.path.join(flags.train_dir, 'labels.txt'), 'w') as f: f.write('\n'.join(audio_processor.words_list)) best_accuracy = 0.0 # prepare parameters for exp learning rate decay training_steps_max = np.sum(training_steps_list) lr_init = learning_rates_list[0] exp_rate = -np.log(learning_rates_list[-1] / lr_init)/training_steps_max mode = 'training' if flags.lr_schedule == 'cosine': # Currently, no restarts are performed, so it is just a cosine decay over the entire # training process. I think this is how DeiT does it. lr_init = lr_init * flags.batch_size / 512 num_train = audio_processor.set_size(mode) warmup_steps = int((num_train / flags.batch_size) * flags.warmup_epochs) first_decay_steps=training_steps_max # Training loop. for training_step in range(start_step, training_steps_max + 1): if training_step > 0: offset = (training_step - 1) * flags.batch_size if flags.pick_deterministically else 0 # Pull the audio samples we'll use for training. train_fingerprints, train_ground_truth = audio_processor.get_data( flags.batch_size, offset, flags, flags.background_frequency, flags.background_volume, time_shift_samples, mode, flags.resample, flags.volume_resample, sess) if flags.lr_schedule == 'exp': learning_rate_value = lr_init * np.exp(-exp_rate * training_step) elif flags.lr_schedule == 'linear': training_steps_sum = 0 for i in range(len(training_steps_list)): training_steps_sum += training_steps_list[i] if training_step <= training_steps_sum: learning_rate_value = learning_rates_list[i] break elif flags.lr_schedule == 'cosine': learning_rate_value = lr_init * min(1, float(training_step) / max(1, warmup_steps)) * (math.cos(math.pi * training_step / training_steps_max) + 1) / 2. else: raise ValueError('Wrong lr_schedule: %s' % flags.lr_schedule) tf.keras.backend.set_value(model.optimizer.learning_rate, learning_rate_value) one_hot_labels = tf.keras.utils.to_categorical(train_ground_truth, num_classes=flags.label_count) if teacher: teacher_labels = teacher.predict_on_batch(train_fingerprints) one_hot_labels = [ one_hot_labels, teacher_labels, one_hot_labels ] result = model.train_on_batch(train_fingerprints, one_hot_labels) if teacher: loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result differences = (teacher_labels != one_hot_labels).astype(dtype=int).sum() logging.info( 'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f, teacher acc %.2f%% (%d diff), teacher cross entropy %f, ensemble acc %.2f%%', *(training_step, learning_rate_value, acc_label * 100, loss_total, acc_teacher * 100, differences, loss_teacher, acc_ensemble * 100)) summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label), tf.Summary.Value(tag='teacher_accuracy', simple_value=acc_teacher), tf.Summary.Value(tag='ensemble_accuracy', simple_value=acc_ensemble), ]) else: loss_label, acc_label = result logging.info( 'Step #%d: rate %f, accuracy %.2f%%, cross entropy %f', *(training_step, learning_rate_value, acc_label * 100, loss_label)) summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label), ]) train_writer.add_summary(summary, training_step) is_last_step = (training_step == training_steps_max) if (training_step % flags.eval_step_interval) == 0 or is_last_step: set_size = audio_processor.set_size('validation') set_size = int(set_size / flags.batch_size) * flags.batch_size total_accuracy = 0.0 count = 0.0 for i in range(0, set_size, flags.batch_size): validation_fingerprints, validation_ground_truth = audio_processor.get_data( flags.batch_size, i, flags, 0.0, 0.0, 0, 'validation', 0.0, 0.0, sess) one_hot_labels = tf.keras.utils.to_categorical(validation_ground_truth, num_classes=flags.label_count) if teacher: one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ] result = model.test_on_batch(validation_fingerprints, one_hot_labels) if teacher: loss_total, loss_label, loss_teacher, loss_average, acc_label, acc_teacher, acc_ensemble = result summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_ensemble), tf.Summary.Value(tag='label_head_accuracy', simple_value=acc_label), tf.Summary.Value(tag='distill_head_accuracy', simple_value=acc_teacher), ]) accuracy = acc_ensemble else: loss_label, acc_label = result summary = tf.Summary(value=[ tf.Summary.Value(tag='accuracy', simple_value=acc_label),]) accuracy = acc_label validation_writer.add_summary(summary, training_step) total_accuracy += accuracy count = count + 1.0 total_accuracy = total_accuracy / count logging.info('Step %d: Validation accuracy = %.2f%% (N=%d)', *(training_step, total_accuracy * 100, set_size)) if total_accuracy >= best_accuracy: best_accuracy = total_accuracy model.save_weights(flags.train_dir + 'best_weights') logging.info('So far the best validation accuracy is %.2f%%', (best_accuracy * 100)) tf.keras.backend.set_learning_phase(0) set_size = audio_processor.set_size('testing') set_size = int(set_size / flags.batch_size) * flags.batch_size logging.info('set_size=%d', set_size) total_accuracy = 0.0 count = 0.0 for i in range(0, set_size, flags.batch_size): test_fingerprints, test_ground_truth = audio_processor.get_data( flags.batch_size, i, flags, 0.0, 0.0, 0, 'testing', 0.0, 0.0, sess) one_hot_labels = tf.keras.utils.to_categorical(test_ground_truth, num_classes=flags.label_count) if teacher: one_hot_labels = [ one_hot_labels, one_hot_labels, one_hot_labels ] result = model.test_on_batch(test_fingerprints, one_hot_labels) total_accuracy += result[-1] if teacher else result[1] count = count + 1.0 total_accuracy = total_accuracy / count logging.info('Final test accuracy = %.2f%% (N=%d)', *(total_accuracy * 100, set_size)) with open(os.path.join(flags.train_dir, 'accuracy_last.txt'), 'wt') as fd: fd.write(str(total_accuracy * 100)) model.save_weights(flags.train_dir + 'last_weights') if __name__ == '__main__': flags = model_flags.update_flags(None) train(flags)

true

f7197ec8accb7480f7e6eca284267bccdb20df57

5,965

py

Python

test/functional/rpc_users.py

mrmikeo/GAU-Core

6f56bb73d0736a4245c22391314d6ba55de0e0d8

[ "MIT" ]

2

2020-08-25T18:02:32.000Z

2021-08-23T09:40:41.000Z

test/functional/rpc_users.py

mrmikeo/GAU-Core

6f56bb73d0736a4245c22391314d6ba55de0e0d8

[ "MIT" ]

null

test/functional/rpc_users.py

mrmikeo/GAU-Core

6f56bb73d0736a4245c22391314d6ba55de0e0d8

[ "MIT" ]

2

2020-08-06T20:56:42.000Z

2020-11-23T03:11:17.000Z

#!/usr/bin/env python3 # Copyright (c) 2015-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test multiple RPC users.""" from test_framework.test_framework import BitcoinTestFramework from test_framework.util import str_to_b64str, assert_equal import os import http.client import urllib.parse class HTTPBasicsTest (BitcoinTestFramework): def set_test_params(self): self.num_nodes = 2 def setup_chain(self): super().setup_chain() #Append rpcauth to bitcoin.conf before initialization rpcauth = "rpcauth=rt:93648e835a54c573682c2eb19f882535$7681e9c5b74bdd85e78166031d2058e1069b3ed7ed967c93fc63abba06f31144" rpcauth2 = "rpcauth=rt2:f8607b1a88861fac29dfccf9b52ff9f$ff36a0c23c8c62b4846112e50fa888416e94c17bfd4c42f88fd8f55ec6a3137e" rpcuser = "rpcuser=rpcuser�" rpcpassword = "rpcpassword=rpcpassword�" with open(os.path.join(self.options.tmpdir+"/node0", "gauntlet.conf"), 'a', encoding='utf8') as f: f.write(rpcauth+"\n") f.write(rpcauth2+"\n") with open(os.path.join(self.options.tmpdir+"/node1", "gauntlet.conf"), 'a', encoding='utf8') as f: f.write(rpcuser+"\n") f.write(rpcpassword+"\n") def run_test(self): ################################################## # Check correctness of the rpcauth config option # ################################################## url = urllib.parse.urlparse(self.nodes[0].url) #Old authpair authpair = url.username + ':' + url.password #New authpair generated via share/rpcuser tool password = "cA773lm788buwYe4g4WT+05pKyNruVKjQ25x3n0DQcM=" #Second authpair with different username password2 = "8/F3uMDw4KSEbw96U3CA1C4X05dkHDN2BPFjTgZW4KI=" authpairnew = "rt:"+password headers = {"Authorization": "Basic " + str_to_b64str(authpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 200) conn.close() #Use new authpair to confirm both work headers = {"Authorization": "Basic " + str_to_b64str(authpairnew)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 200) conn.close() #Wrong login name with rt's password authpairnew = "rtwrong:"+password headers = {"Authorization": "Basic " + str_to_b64str(authpairnew)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() #Wrong password for rt authpairnew = "rt:"+password+"wrong" headers = {"Authorization": "Basic " + str_to_b64str(authpairnew)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() #Correct for rt2 authpairnew = "rt2:"+password2 headers = {"Authorization": "Basic " + str_to_b64str(authpairnew)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 200) conn.close() #Wrong password for rt2 authpairnew = "rt2:"+password2+"wrong" headers = {"Authorization": "Basic " + str_to_b64str(authpairnew)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() ############################################################### # Check correctness of the rpcuser/rpcpassword config options # ############################################################### url = urllib.parse.urlparse(self.nodes[1].url) # rpcuser and rpcpassword authpair rpcuserauthpair = "rpcuser�:rpcpassword�" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 200) conn.close() #Wrong login name with rpcuser's password rpcuserauthpair = "rpcuserwrong:rpcpassword" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() #Wrong password for rpcuser rpcuserauthpair = "rpcuser:rpcpasswordwrong" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() if __name__ == '__main__': HTTPBasicsTest ().main ()

38.733766

129

0.61425

from test_framework.test_framework import BitcoinTestFramework from test_framework.util import str_to_b64str, assert_equal import os import http.client import urllib.parse class HTTPBasicsTest (BitcoinTestFramework): def set_test_params(self): self.num_nodes = 2 def setup_chain(self): super().setup_chain() rpcauth = "rpcauth=rt:93648e835a54c573682c2eb19f882535$7681e9c5b74bdd85e78166031d2058e1069b3ed7ed967c93fc63abba06f31144" rpcauth2 = "rpcauth=rt2:f8607b1a88861fac29dfccf9b52ff9f$ff36a0c23c8c62b4846112e50fa888416e94c17bfd4c42f88fd8f55ec6a3137e" rpcuser = "rpcuser=rpcuser�" rpcpassword = "rpcpassword=rpcpassword�" with open(os.path.join(self.options.tmpdir+"/node0", "gauntlet.conf"), 'a', encoding='utf8') as f: f.write(rpcauth+"\n") f.write(rpcauth2+"\n") with open(os.path.join(self.options.tmpdir+"/node1", "gauntlet.conf"), 'a', encoding='utf8') as f: f.write(rpcuser+"\n") f.write(rpcpassword+"\n") def run_test(self): () conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() ############################################################### # Check correctness of the rpcuser/rpcpassword config options # ############################################################### url = urllib.parse.urlparse(self.nodes[1].url) # rpcuser and rpcpassword authpair rpcuserauthpair = "rpcuser�:rpcpassword�" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 200) conn.close() #Wrong login name with rpcuser's password rpcuserauthpair = "rpcuserwrong:rpcpassword" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() rpcuserauthpair = "rpcuser:rpcpasswordwrong" headers = {"Authorization": "Basic " + str_to_b64str(rpcuserauthpair)} conn = http.client.HTTPConnection(url.hostname, url.port) conn.connect() conn.request('POST', '/', '{"method": "getbestblockhash"}', headers) resp = conn.getresponse() assert_equal(resp.status, 401) conn.close() if __name__ == '__main__': HTTPBasicsTest ().main ()

true

f719804da78f16f6af3489ac457e49300a75a6b2

1,916

py

Python

do_flask_mail.py

penglee87/lpython

3a53322ccdebf83d6b358386518cf81712433c9e

[ "bzip2-1.0.6" ]

null

do_flask_mail.py

penglee87/lpython

3a53322ccdebf83d6b358386518cf81712433c9e

[ "bzip2-1.0.6" ]

null

do_flask_mail.py

penglee87/lpython

3a53322ccdebf83d6b358386518cf81712433c9e

[ "bzip2-1.0.6" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from flask import Flask from flask_mail import Mail from flask_mail import Message import os #测试成功,部分参数作用不明 app = Flask(__name__) app.config['MAIL_SERVER'] = 'smtp.163.com' app.config['MAIL_PORT'] = 25 app.config['MAIL_USE_TLS'] = True app.config['MAIL_USERNAME'] = 'penglee87@163.com' app.config['MAIL_PASSWORD'] = '******' app.config['FLASKY_MAIL_SUBJECT_PREFIX'] = '[Flasky]' #邮件主题 #app.config['FLASKY_MAIL_SENDER'] = 'penglee87@163.com' #app.config['FLASKY_ADMIN'] = 'penglee87@163.com' mail = Mail(app) """ app.config['MAIL_USERNAME'] = os.environ.get('MAIL_USERNAME') app.config['MAIL_PASSWORD'] = os.environ.get('MAIL_PASSWORD') app.config['FLASKY_MAIL_SUBJECT_PREFIX'] = '[Flasky]' app.config['FLASKY_MAIL_SENDER'] = 'Flasky Admin <flasky@example.com>' app.config['FLASKY_ADMIN'] = os.environ.get('FLASKY_ADMIN') """ @app.route("/") def index(): #Message(主题,发件人,收件人) msg = Message("Hello", sender="penglee87@163.com", recipients=["lipeng@163.com"]) msg.body = "testing" msg.html = "<b>testing</b>" mail.send(msg) return '<h1>Hello World!</h1>' if __name__ == '__main__': app.run(debug=True) """ msg = Message("Hello", sender="penglee87@163.com", recipients=["lipeng@163.com"]) msg.body = "testing" msg.html = "<b>testing</b>" mail.send(msg) if __name__ == '__main__': mail.send(msg) pip install --no-deps lamson chardet flask-mail set MAIL_USERNAME=penglee87@163.com set MAIL_PASSWORD=****** set FLASKY_ADMIN=penglee87@163.com >>> from flask.ext.mail import Message >>> from hello import mail >>> msg = Message('test subject', sender='penglee1206@gmail.com',recipients=['380517767@qq.com']) >>> msg.body = 'text body' >>> msg.html = '<b>HTML</b> body' >>> with app.app_context(): ... mail.send(msg) """

26.246575

97

0.647182

from flask import Flask from flask_mail import Mail from flask_mail import Message import os app = Flask(__name__) app.config['MAIL_SERVER'] = 'smtp.163.com' app.config['MAIL_PORT'] = 25 app.config['MAIL_USE_TLS'] = True app.config['MAIL_USERNAME'] = 'penglee87@163.com' app.config['MAIL_PASSWORD'] = '******' app.config['FLASKY_MAIL_SUBJECT_PREFIX'] = '[Flasky]' mail = Mail(app) @app.route("/") def index(): msg = Message("Hello", sender="penglee87@163.com", recipients=["lipeng@163.com"]) msg.body = "testing" msg.html = "<b>testing</b>" mail.send(msg) return '<h1>Hello World!</h1>' if __name__ == '__main__': app.run(debug=True)

true

f71982541576d139123ce5e181dca42523d11d05

459

py

Python

blog/search_indexes.py

GITliyanfeng/blog-django

a804702026a2d58664ec83a993116e17b89e9e8e

[ "MIT" ]

2

2019-03-14T12:35:36.000Z

2019-03-14T12:35:38.000Z

blog/search_indexes.py

GITliyanfeng/blog-django

a804702026a2d58664ec83a993116e17b89e9e8e

[ "MIT" ]

null

blog/search_indexes.py

GITliyanfeng/blog-django

a804702026a2d58664ec83a993116e17b89e9e8e

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2019/3/19 0019 16:25 # @Author : __Yanfeng # @Site : # @File : search_indexes.py # @Software: PyCharm from haystack import indexes from .models import Post class PostIndex(indexes.SearchIndex, indexes.Indexable): text = indexes.CharField(document=True, use_template=True) def get_model(self): return Post def index_queryset(self, using=None): return self.get_model().latest_posts()

24.157895

62

0.67756

from haystack import indexes from .models import Post class PostIndex(indexes.SearchIndex, indexes.Indexable): text = indexes.CharField(document=True, use_template=True) def get_model(self): return Post def index_queryset(self, using=None): return self.get_model().latest_posts()

true

f7198316dcf1fee5ef6b1b5530246a472718064a

109

py

Python

rest_framework_security/deny_repeat_password/__init__.py

RubenEu/django-rest-framework-security

638cf271c51a5bafd434a6b6a9c25a7c4849b485

[ "MIT" ]

7

2020-09-01T09:55:25.000Z

2021-11-04T06:59:04.000Z

rest_framework_security/deny_repeat_password/__init__.py

RubenEu/django-rest-framework-security

638cf271c51a5bafd434a6b6a9c25a7c4849b485

[ "MIT" ]

32

2020-10-28T17:09:18.000Z

2022-03-12T00:55:09.000Z

rest_framework_security/deny_repeat_password/__init__.py

RubenEu/django-rest-framework-security

638cf271c51a5bafd434a6b6a9c25a7c4849b485

[ "MIT" ]

2

2020-12-18T01:26:53.000Z

2021-11-04T06:59:07.000Z

default_app_config = ( "rest_framework_security.deny_repeat_password.apps.DenyRepeatPasswordAppConfig" )

27.25

83

0.844037

default_app_config = ( "rest_framework_security.deny_repeat_password.apps.DenyRepeatPasswordAppConfig" )

true

f71983d5a0a270119c6b7c7701a902ea4892f18a

20,123

py

Python

obstools/scripts/atacr_clean_spectra.py

paudetseis/OBStools

c6c02d8864c25a14f22d1fae17ff5ad911b9ff00

[ "MIT" ]

1

2019-12-05T04:32:38.000Z

obstools/scripts/atacr_clean_spectra.py

paudetseis/OBStools

c6c02d8864c25a14f22d1fae17ff5ad911b9ff00

[ "MIT" ]

2

2019-12-04T02:06:45.000Z

2019-12-06T22:20:19.000Z

obstools/scripts/atacr_clean_spectra.py

paudetseis/OBStools

c6c02d8864c25a14f22d1fae17ff5ad911b9ff00

[ "MIT" ]

1

2020-02-25T16:51:35.000Z

#!/usr/bin/env python # Copyright 2019 Pascal Audet & Helen Janiszewski # # This file is part of OBStools. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Import modules and functions import numpy as np import pickle import stdb from obstools.atacr import StaNoise, Power, Cross, Rotation from obstools.atacr import utils, plotting from pathlib import Path from argparse import ArgumentParser from os.path import exists as exist from obspy import UTCDateTime from numpy import nan def get_cleanspec_arguments(argv=None): """ Get Options from :class:`~optparse.OptionParser` objects. Calling options for the script `obs_clean_spectra.py` that accompany this package. """ parser = ArgumentParser( usage="%(prog)s [options] <indb>", description="Script used " "to extract daily spectra calculated from " + "`obs_daily_spectra.py` and flag days for outlier " + "PSDs and calculate spectral averages of the " + "corresponding Fourier transforms over the entire " + "time period specified. The stations are processed " + "one by one and the data are stored to disk.") parser.add_argument( "indb", help="Station Database to process from.", type=str) # General Settings parser.add_argument( "--keys", action="store", type=str, dest="stkeys", default="", help="Specify a comma separated list of station " + "keys for which to perform the analysis. These must " + "be contained within the station database. Partial " + "keys will be used to match against those in the " + "dictionary. For instance, providing IU will match " + "with all stations in the IU network. " + "[Default processes all stations in the database]") parser.add_argument( "-O", "--overwrite", action="store_true", dest="ovr", default=False, help="Force the overwriting of pre-existing data. " + "[Default False]") # Event Selection Criteria DaysGroup = parser.add_argument_group( title="Time Search Settings", description="Time settings associated with " + "searching for day-long seismograms") DaysGroup.add_argument( "--start", action="store", type=str, dest="startT", default="", help="Specify a UTCDateTime compatible string " + "representing the start day for the data search. " + "This will override any station start times. " + "[Default start date of each station in database]") DaysGroup.add_argument( "--end", action="store", type=str, dest="endT", default="", help="Specify a UTCDateTime compatible string " + "representing the start time for the data search. " + "This will override any station end times. " + "[Default end date of each station in database]") # Constants Settings ConstGroup = parser.add_argument_group( title='Parameter Settings', description="Miscellaneous default values " + "and settings") ConstGroup.add_argument( "--freq-band", action="store", type=str, dest="pd", default=None, help="Specify comma-separated frequency limits " + "(float, in Hz) over which to calculate spectral " + "features used in flagging the days/windows. " + "[Default 0.004,2.0]") ConstGroup.add_argument( "--tolerance", action="store", type=float, dest="tol", default=1.5, help="Specify parameter for tolerance threshold. " + "If spectrum > std*tol, window is flagged as bad. " + "[Default 1.5]") ConstGroup.add_argument( "--alpha", action="store", type=float, dest="alpha", default=0.05, help="Confidence level for f-test, for iterative " + "flagging of windows. [Default 0.05, or 95 percent confidence]") # Constants Settings FigureGroup = parser.add_argument_group( title='Figure Settings', description="Flags for plotting figures") FigureGroup.add_argument( "--figQC", action="store_true", dest="fig_QC", default=False, help="Plot Quality-Control figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--debug", action="store_true", dest="debug", default=False, help="Plot intermediate steps for debugging. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figAverage", action="store_true", dest="fig_average", default=False, help="Plot daily average figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figCoh", action="store_true", dest="fig_coh_ph", default=False, help="Plot Coherence and Phase figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figCross", action="store_true", dest="fig_av_cross", default=False, help="Plot cross-spectra figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--save-fig", action="store_true", dest="saveplot", default=False, help="Set this option if you wish to save the figure(s). [Default " + "does not save figure]") FigureGroup.add_argument( "--format", action="store", type=str, dest="form", default="png", help="Specify format of figure. Can be any one of the valid" + "matplotlib formats: 'png', 'jpg', 'eps', 'pdf'. [Default 'png']") args = parser.parse_args(argv) # Check inputs if not exist(args.indb): parser.error("Input file " + args.indb + " does not exist") # create station key list if len(args.stkeys) > 0: args.stkeys = args.stkeys.split(',') # construct start time if len(args.startT) > 0: try: args.startT = UTCDateTime(args.startT) except Exception: parser.error( "Error: Cannot construct UTCDateTime from start time: " + args.startT) else: args.startT = None # construct end time if len(args.endT) > 0: try: args.endT = UTCDateTime(args.endT) except Exception: parser.error( "Error: Cannot construct UTCDateTime from end time: " + args.endT) else: args.endT = None if args.pd is None: args.pd = [0.004, 2.0] else: args.pd = [float(val) for val in args.pd.split(',')] args.pd = sorted(args.pd) if (len(args.pd)) != 2: raise(Exception( "Error: --freq-band should contain 2 " + "comma-separated floats")) return args def main(args=None): if args is None: # Run Input Parser args = get_cleanspec_arguments() # Load Database # stdb>0.1.3 try: db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys) # stdb=0.1.3 except Exception: db = stdb.io.load_db(fname=args.indb) # Construct station key loop allkeys = db.keys() sorted(allkeys) # Extract key subset if len(args.stkeys) > 0: stkeys = [] for skey in args.stkeys: stkeys.extend([s for s in allkeys if skey in s]) else: stkeys = db.keys() sorted(stkeys) # Loop over station keys for stkey in list(stkeys): # Extract station information from dictionary sta = db[stkey] # Path where spectra are located specpath = Path('SPECTRA') / stkey if not specpath.is_dir(): raise(Exception( "Path to " + str(specpath) + " doesn`t exist - aborting")) # Path where average spectra will be saved avstpath = Path('AVG_STA') / stkey if not avstpath.is_dir(): print("Path to "+str(avstpath)+" doesn`t exist - creating it") avstpath.mkdir(parents=True) # Path where plots will be saved if args.saveplot: plotpath = avstpath / 'PLOTS' if not plotpath.is_dir(): plotpath.mkdir(parents=True) else: plotpath = False # Get catalogue search start time if args.startT is None: tstart = sta.startdate else: tstart = args.startT # Get catalogue search end time if args.endT is None: tend = sta.enddate else: tend = args.endT if tstart > sta.enddate or tend < sta.startdate: continue # Temporary print locations tlocs = sta.location if len(tlocs) == 0: tlocs = [''] for il in range(0, len(tlocs)): if len(tlocs[il]) == 0: tlocs[il] = "--" sta.location = tlocs # Update Display print("\n|===============================================|") print("|===============================================|") print("| {0:>8s} |".format( sta.station)) print("|===============================================|") print("|===============================================|") print("| Station: {0:>2s}.{1:5s} |".format( sta.network, sta.station)) print("| Channel: {0:2s}; Locations: {1:15s} |".format( sta.channel, ",".join(tlocs))) print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format( sta.longitude, sta.latitude)) print("| Start time: {0:19s} |".format( sta.startdate.strftime("%Y-%m-%d %H:%M:%S"))) print("| End time: {0:19s} |".format( sta.enddate.strftime("%Y-%m-%d %H:%M:%S"))) print("|-----------------------------------------------|") # Filename for output average spectra dstart = str(tstart.year).zfill(4)+'.'+str(tstart.julday).zfill(3)+'-' dend = str(tend.year).zfill(4)+'.'+str(tend.julday).zfill(3)+'.' fileavst = avstpath / (dstart+dend+'avg_sta.pkl') if fileavst.exists(): if not args.ovr: print("* -> file "+str(fileavst)+" exists - continuing") continue # Containers for power and cross spectra coh_all = [] ph_all = [] coh_12_all = [] coh_1Z_all = [] coh_1P_all = [] coh_2Z_all = [] coh_2P_all = [] coh_ZP_all = [] ph_12_all = [] ph_1Z_all = [] ph_1P_all = [] ph_2Z_all = [] ph_2P_all = [] ph_ZP_all = [] ad_12_all = [] ad_1Z_all = [] ad_1P_all = [] ad_2Z_all = [] ad_2P_all = [] ad_ZP_all = [] nwins = [] t1 = tstart # Initialize StaNoise object stanoise = StaNoise() # Loop through each day withing time range while t1 < tend: year = str(t1.year).zfill(4) jday = str(t1.julday).zfill(3) tstamp = year+'.'+jday+'.' filespec = specpath / (tstamp + 'spectra.pkl') # Load file if it exists if filespec.exists(): print("\n"+"*"*60) print('* Calculating noise spectra for key ' + stkey+' and day '+year+'.'+jday) print("* -> file "+str(filespec)+" found - loading") file = open(filespec, 'rb') daynoise = pickle.load(file) file.close() stanoise += daynoise else: t1 += 3600.*24. continue coh_all.append(daynoise.rotation.coh) ph_all.append(daynoise.rotation.ph) # Coherence coh_12_all.append( utils.smooth( utils.coherence( daynoise.cross.c12, daynoise.power.c11, daynoise.power.c22), 50)) coh_1Z_all.append( utils.smooth( utils.coherence( daynoise.cross.c1Z, daynoise.power.c11, daynoise.power.cZZ), 50)) coh_1P_all.append( utils.smooth( utils.coherence( daynoise.cross.c1P, daynoise.power.c11, daynoise.power.cPP), 50)) coh_2Z_all.append( utils.smooth( utils.coherence( daynoise.cross.c2Z, daynoise.power.c22, daynoise.power.cZZ), 50)) coh_2P_all.append( utils.smooth( utils.coherence( daynoise.cross.c2P, daynoise.power.c22, daynoise.power.cPP), 50)) coh_ZP_all.append( utils.smooth( utils.coherence( daynoise.cross.cZP, daynoise.power.cZZ, daynoise.power.cPP), 50)) # Phase try: ph_12_all.append( 180./np.pi*utils.phase(daynoise.cross.c12)) except Exception: ph_12_all.append(None) try: ph_1Z_all.append( 180./np.pi*utils.phase(daynoise.cross.c1Z)) except Exception: ph_1Z_all.append(None) try: ph_1P_all.append( 180./np.pi*utils.phase(daynoise.cross.c1P)) except Exception: ph_1P_all.append(None) try: ph_2Z_all.append( 180./np.pi*utils.phase(daynoise.cross.c2Z)) except Exception: ph_2Z_all.append(None) try: ph_2P_all.append( 180./np.pi*utils.phase(daynoise.cross.c2P)) except Exception: ph_2P_all.append(None) try: ph_ZP_all.append( 180./np.pi*utils.phase(daynoise.cross.cZP)) except Exception: ph_ZP_all.append(None) # Admittance ad_12_all.append(utils.smooth(utils.admittance( daynoise.cross.c12, daynoise.power.c11), 50)) ad_1Z_all.append(utils.smooth(utils.admittance( daynoise.cross.c1Z, daynoise.power.c11), 50)) ad_1P_all.append(utils.smooth(utils.admittance( daynoise.cross.c1P, daynoise.power.c11), 50)) ad_2Z_all.append(utils.smooth(utils.admittance( daynoise.cross.c2Z, daynoise.power.c22), 50)) ad_2P_all.append(utils.smooth(utils.admittance( daynoise.cross.c2P, daynoise.power.c22), 50)) ad_ZP_all.append(utils.smooth(utils.admittance( daynoise.cross.cZP, daynoise.power.cZZ), 50)) t1 += 3600.*24. # Convert to numpy arrays coh_all = np.array(coh_all) ph_all = np.array(ph_all) coh_12_all = np.array(coh_12_all) coh_1Z_all = np.array(coh_1Z_all) coh_1P_all = np.array(coh_1P_all) coh_2Z_all = np.array(coh_2Z_all) coh_2P_all = np.array(coh_2P_all) coh_ZP_all = np.array(coh_ZP_all) ph_12_all = np.array(ph_12_all) ph_1Z_all = np.array(ph_1Z_all) ph_1P_all = np.array(ph_1P_all) ph_2Z_all = np.array(ph_2Z_all) ph_2P_all = np.array(ph_2P_all) ph_ZP_all = np.array(ph_ZP_all) ad_12_all = np.array(ad_12_all) ad_1Z_all = np.array(ad_1Z_all) ad_1P_all = np.array(ad_1P_all) ad_2Z_all = np.array(ad_2Z_all) ad_2P_all = np.array(ad_2P_all) ad_ZP_all = np.array(ad_ZP_all) # Store transfer functions as objects for plotting coh = Cross(coh_12_all, coh_1Z_all, coh_1P_all, coh_2Z_all, coh_2P_all, coh_ZP_all) ph = Cross(ph_12_all, ph_1Z_all, ph_1P_all, ph_2Z_all, ph_2P_all, ph_ZP_all) ad = Cross(ad_12_all, ad_1Z_all, ad_1P_all, ad_2Z_all, ad_2P_all, ad_ZP_all) # Quality control to identify outliers stanoise.QC_sta_spectra(pd=args.pd, tol=args.tol, alpha=args.alpha, fig_QC=args.fig_QC, debug=args.debug, save=plotpath, form=args.form) # Average spectra for good days stanoise.average_sta_spectra( fig_average=args.fig_average, save=plotpath, form=args.form) if args.fig_av_cross: fname = stkey + '.' + 'av_coherence' plot = plotting.fig_av_cross( stanoise.f, coh, stanoise.gooddays, 'Coherence', stanoise.ncomp, key=stkey, lw=0.5) # if plotpath.is_dir(): if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() fname = stkey + '.' + 'av_admittance' plot = plotting.fig_av_cross( stanoise.f, ad, stanoise.gooddays, 'Admittance', stanoise.ncomp, key=stkey, lw=0.5) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() fname = stkey + '.' + 'av_phase' plot = plotting.fig_av_cross( stanoise.f, ph, stanoise.gooddays, 'Phase', stanoise.ncomp, key=stkey, marker=',', lw=0) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() if args.fig_coh_ph and stanoise.direc is not None: fname = stkey + '.' + 'coh_ph' plot = plotting.fig_coh_ph(coh_all, ph_all, stanoise.direc) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() # Save to file stanoise.save(fileavst) if __name__ == "__main__": # Run main program main()

34.28109

79

0.533718

import numpy as np import pickle import stdb from obstools.atacr import StaNoise, Power, Cross, Rotation from obstools.atacr import utils, plotting from pathlib import Path from argparse import ArgumentParser from os.path import exists as exist from obspy import UTCDateTime from numpy import nan def get_cleanspec_arguments(argv=None): parser = ArgumentParser( usage="%(prog)s [options] <indb>", description="Script used " "to extract daily spectra calculated from " + "`obs_daily_spectra.py` and flag days for outlier " + "PSDs and calculate spectral averages of the " + "corresponding Fourier transforms over the entire " + "time period specified. The stations are processed " + "one by one and the data are stored to disk.") parser.add_argument( "indb", help="Station Database to process from.", type=str) parser.add_argument( "--keys", action="store", type=str, dest="stkeys", default="", help="Specify a comma separated list of station " + "keys for which to perform the analysis. These must " + "be contained within the station database. Partial " + "keys will be used to match against those in the " + "dictionary. For instance, providing IU will match " + "with all stations in the IU network. " + "[Default processes all stations in the database]") parser.add_argument( "-O", "--overwrite", action="store_true", dest="ovr", default=False, help="Force the overwriting of pre-existing data. " + "[Default False]") DaysGroup = parser.add_argument_group( title="Time Search Settings", description="Time settings associated with " + "searching for day-long seismograms") DaysGroup.add_argument( "--start", action="store", type=str, dest="startT", default="", help="Specify a UTCDateTime compatible string " + "representing the start day for the data search. " + "This will override any station start times. " + "[Default start date of each station in database]") DaysGroup.add_argument( "--end", action="store", type=str, dest="endT", default="", help="Specify a UTCDateTime compatible string " + "representing the start time for the data search. " + "This will override any station end times. " + "[Default end date of each station in database]") ConstGroup = parser.add_argument_group( title='Parameter Settings', description="Miscellaneous default values " + "and settings") ConstGroup.add_argument( "--freq-band", action="store", type=str, dest="pd", default=None, help="Specify comma-separated frequency limits " + "(float, in Hz) over which to calculate spectral " + "features used in flagging the days/windows. " + "[Default 0.004,2.0]") ConstGroup.add_argument( "--tolerance", action="store", type=float, dest="tol", default=1.5, help="Specify parameter for tolerance threshold. " + "If spectrum > std*tol, window is flagged as bad. " + "[Default 1.5]") ConstGroup.add_argument( "--alpha", action="store", type=float, dest="alpha", default=0.05, help="Confidence level for f-test, for iterative " + "flagging of windows. [Default 0.05, or 95 percent confidence]") FigureGroup = parser.add_argument_group( title='Figure Settings', description="Flags for plotting figures") FigureGroup.add_argument( "--figQC", action="store_true", dest="fig_QC", default=False, help="Plot Quality-Control figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--debug", action="store_true", dest="debug", default=False, help="Plot intermediate steps for debugging. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figAverage", action="store_true", dest="fig_average", default=False, help="Plot daily average figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figCoh", action="store_true", dest="fig_coh_ph", default=False, help="Plot Coherence and Phase figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--figCross", action="store_true", dest="fig_av_cross", default=False, help="Plot cross-spectra figure. " + "[Default does not plot figure]") FigureGroup.add_argument( "--save-fig", action="store_true", dest="saveplot", default=False, help="Set this option if you wish to save the figure(s). [Default " + "does not save figure]") FigureGroup.add_argument( "--format", action="store", type=str, dest="form", default="png", help="Specify format of figure. Can be any one of the valid" + "matplotlib formats: 'png', 'jpg', 'eps', 'pdf'. [Default 'png']") args = parser.parse_args(argv) if not exist(args.indb): parser.error("Input file " + args.indb + " does not exist") if len(args.stkeys) > 0: args.stkeys = args.stkeys.split(',') if len(args.startT) > 0: try: args.startT = UTCDateTime(args.startT) except Exception: parser.error( "Error: Cannot construct UTCDateTime from start time: " + args.startT) else: args.startT = None if len(args.endT) > 0: try: args.endT = UTCDateTime(args.endT) except Exception: parser.error( "Error: Cannot construct UTCDateTime from end time: " + args.endT) else: args.endT = None if args.pd is None: args.pd = [0.004, 2.0] else: args.pd = [float(val) for val in args.pd.split(',')] args.pd = sorted(args.pd) if (len(args.pd)) != 2: raise(Exception( "Error: --freq-band should contain 2 " + "comma-separated floats")) return args def main(args=None): if args is None: args = get_cleanspec_arguments() try: db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys) except Exception: db = stdb.io.load_db(fname=args.indb) allkeys = db.keys() sorted(allkeys) if len(args.stkeys) > 0: stkeys = [] for skey in args.stkeys: stkeys.extend([s for s in allkeys if skey in s]) else: stkeys = db.keys() sorted(stkeys) for stkey in list(stkeys): sta = db[stkey] specpath = Path('SPECTRA') / stkey if not specpath.is_dir(): raise(Exception( "Path to " + str(specpath) + " doesn`t exist - aborting")) avstpath = Path('AVG_STA') / stkey if not avstpath.is_dir(): print("Path to "+str(avstpath)+" doesn`t exist - creating it") avstpath.mkdir(parents=True) if args.saveplot: plotpath = avstpath / 'PLOTS' if not plotpath.is_dir(): plotpath.mkdir(parents=True) else: plotpath = False if args.startT is None: tstart = sta.startdate else: tstart = args.startT if args.endT is None: tend = sta.enddate else: tend = args.endT if tstart > sta.enddate or tend < sta.startdate: continue tlocs = sta.location if len(tlocs) == 0: tlocs = [''] for il in range(0, len(tlocs)): if len(tlocs[il]) == 0: tlocs[il] = "--" sta.location = tlocs print("\n|===============================================|") print("|===============================================|") print("| {0:>8s} |".format( sta.station)) print("|===============================================|") print("|===============================================|") print("| Station: {0:>2s}.{1:5s} |".format( sta.network, sta.station)) print("| Channel: {0:2s}; Locations: {1:15s} |".format( sta.channel, ",".join(tlocs))) print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format( sta.longitude, sta.latitude)) print("| Start time: {0:19s} |".format( sta.startdate.strftime("%Y-%m-%d %H:%M:%S"))) print("| End time: {0:19s} |".format( sta.enddate.strftime("%Y-%m-%d %H:%M:%S"))) print("|-----------------------------------------------|") dstart = str(tstart.year).zfill(4)+'.'+str(tstart.julday).zfill(3)+'-' dend = str(tend.year).zfill(4)+'.'+str(tend.julday).zfill(3)+'.' fileavst = avstpath / (dstart+dend+'avg_sta.pkl') if fileavst.exists(): if not args.ovr: print("* -> file "+str(fileavst)+" exists - continuing") continue coh_all = [] ph_all = [] coh_12_all = [] coh_1Z_all = [] coh_1P_all = [] coh_2Z_all = [] coh_2P_all = [] coh_ZP_all = [] ph_12_all = [] ph_1Z_all = [] ph_1P_all = [] ph_2Z_all = [] ph_2P_all = [] ph_ZP_all = [] ad_12_all = [] ad_1Z_all = [] ad_1P_all = [] ad_2Z_all = [] ad_2P_all = [] ad_ZP_all = [] nwins = [] t1 = tstart stanoise = StaNoise() while t1 < tend: year = str(t1.year).zfill(4) jday = str(t1.julday).zfill(3) tstamp = year+'.'+jday+'.' filespec = specpath / (tstamp + 'spectra.pkl') if filespec.exists(): print("\n"+"*"*60) print('* Calculating noise spectra for key ' + stkey+' and day '+year+'.'+jday) print("* -> file "+str(filespec)+" found - loading") file = open(filespec, 'rb') daynoise = pickle.load(file) file.close() stanoise += daynoise else: t1 += 3600.*24. continue coh_all.append(daynoise.rotation.coh) ph_all.append(daynoise.rotation.ph) coh_12_all.append( utils.smooth( utils.coherence( daynoise.cross.c12, daynoise.power.c11, daynoise.power.c22), 50)) coh_1Z_all.append( utils.smooth( utils.coherence( daynoise.cross.c1Z, daynoise.power.c11, daynoise.power.cZZ), 50)) coh_1P_all.append( utils.smooth( utils.coherence( daynoise.cross.c1P, daynoise.power.c11, daynoise.power.cPP), 50)) coh_2Z_all.append( utils.smooth( utils.coherence( daynoise.cross.c2Z, daynoise.power.c22, daynoise.power.cZZ), 50)) coh_2P_all.append( utils.smooth( utils.coherence( daynoise.cross.c2P, daynoise.power.c22, daynoise.power.cPP), 50)) coh_ZP_all.append( utils.smooth( utils.coherence( daynoise.cross.cZP, daynoise.power.cZZ, daynoise.power.cPP), 50)) try: ph_12_all.append( 180./np.pi*utils.phase(daynoise.cross.c12)) except Exception: ph_12_all.append(None) try: ph_1Z_all.append( 180./np.pi*utils.phase(daynoise.cross.c1Z)) except Exception: ph_1Z_all.append(None) try: ph_1P_all.append( 180./np.pi*utils.phase(daynoise.cross.c1P)) except Exception: ph_1P_all.append(None) try: ph_2Z_all.append( 180./np.pi*utils.phase(daynoise.cross.c2Z)) except Exception: ph_2Z_all.append(None) try: ph_2P_all.append( 180./np.pi*utils.phase(daynoise.cross.c2P)) except Exception: ph_2P_all.append(None) try: ph_ZP_all.append( 180./np.pi*utils.phase(daynoise.cross.cZP)) except Exception: ph_ZP_all.append(None) ad_12_all.append(utils.smooth(utils.admittance( daynoise.cross.c12, daynoise.power.c11), 50)) ad_1Z_all.append(utils.smooth(utils.admittance( daynoise.cross.c1Z, daynoise.power.c11), 50)) ad_1P_all.append(utils.smooth(utils.admittance( daynoise.cross.c1P, daynoise.power.c11), 50)) ad_2Z_all.append(utils.smooth(utils.admittance( daynoise.cross.c2Z, daynoise.power.c22), 50)) ad_2P_all.append(utils.smooth(utils.admittance( daynoise.cross.c2P, daynoise.power.c22), 50)) ad_ZP_all.append(utils.smooth(utils.admittance( daynoise.cross.cZP, daynoise.power.cZZ), 50)) t1 += 3600.*24. coh_all = np.array(coh_all) ph_all = np.array(ph_all) coh_12_all = np.array(coh_12_all) coh_1Z_all = np.array(coh_1Z_all) coh_1P_all = np.array(coh_1P_all) coh_2Z_all = np.array(coh_2Z_all) coh_2P_all = np.array(coh_2P_all) coh_ZP_all = np.array(coh_ZP_all) ph_12_all = np.array(ph_12_all) ph_1Z_all = np.array(ph_1Z_all) ph_1P_all = np.array(ph_1P_all) ph_2Z_all = np.array(ph_2Z_all) ph_2P_all = np.array(ph_2P_all) ph_ZP_all = np.array(ph_ZP_all) ad_12_all = np.array(ad_12_all) ad_1Z_all = np.array(ad_1Z_all) ad_1P_all = np.array(ad_1P_all) ad_2Z_all = np.array(ad_2Z_all) ad_2P_all = np.array(ad_2P_all) ad_ZP_all = np.array(ad_ZP_all) coh = Cross(coh_12_all, coh_1Z_all, coh_1P_all, coh_2Z_all, coh_2P_all, coh_ZP_all) ph = Cross(ph_12_all, ph_1Z_all, ph_1P_all, ph_2Z_all, ph_2P_all, ph_ZP_all) ad = Cross(ad_12_all, ad_1Z_all, ad_1P_all, ad_2Z_all, ad_2P_all, ad_ZP_all) stanoise.QC_sta_spectra(pd=args.pd, tol=args.tol, alpha=args.alpha, fig_QC=args.fig_QC, debug=args.debug, save=plotpath, form=args.form) stanoise.average_sta_spectra( fig_average=args.fig_average, save=plotpath, form=args.form) if args.fig_av_cross: fname = stkey + '.' + 'av_coherence' plot = plotting.fig_av_cross( stanoise.f, coh, stanoise.gooddays, 'Coherence', stanoise.ncomp, key=stkey, lw=0.5) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() fname = stkey + '.' + 'av_admittance' plot = plotting.fig_av_cross( stanoise.f, ad, stanoise.gooddays, 'Admittance', stanoise.ncomp, key=stkey, lw=0.5) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() fname = stkey + '.' + 'av_phase' plot = plotting.fig_av_cross( stanoise.f, ph, stanoise.gooddays, 'Phase', stanoise.ncomp, key=stkey, marker=',', lw=0) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() if args.fig_coh_ph and stanoise.direc is not None: fname = stkey + '.' + 'coh_ph' plot = plotting.fig_coh_ph(coh_all, ph_all, stanoise.direc) if plotpath: plot.savefig( str(plotpath / (fname + '.' + args.form)), dpi=300, bbox_inches='tight', format=args.form) else: plot.show() stanoise.save(fileavst) if __name__ == "__main__": main()

true

f7198466f423c197e1cd92a6791f6a97eeca93b9

2,362

py

Python

tests/demos/test_demos.py

Nicolinho/RLBench

3014e872f518d5439e73e057e2251dee1f9df481

[ "BSD-3-Clause" ]

619

2019-09-26T23:15:57.000Z

2022-03-15T23:46:48.000Z

tests/demos/test_demos.py

Nicolinho/RLBench

3014e872f518d5439e73e057e2251dee1f9df481

[ "BSD-3-Clause" ]

147

2019-09-27T02:22:45.000Z

2022-03-30T08:37:43.000Z

tests/demos/test_demos.py

Nicolinho/RLBench

3014e872f518d5439e73e057e2251dee1f9df481

[ "BSD-3-Clause" ]

142

2019-09-27T03:43:12.000Z

2022-03-13T19:00:18.000Z

import unittest import rlbench.backend.task as task import os from rlbench.backend.utils import task_file_to_task_class from pyrep import PyRep from pyrep.robots.arms.panda import Panda from pyrep.robots.end_effectors.panda_gripper import PandaGripper from rlbench.backend.const import TTT_FILE from tools.task_validator import task_smoke from rlbench.observation_config import ObservationConfig from rlbench.backend.scene import Scene from rlbench.backend.robot import Robot TASKS = [t for t in os.listdir(task.TASKS_PATH) if t != '__init__.py' and t.endswith('.py')] DIR_PATH = os.path.dirname(os.path.abspath(__file__)) # Task does work, but fails demos often. These should eventually be improved. FLAKY_TASKS = ['put_all_groceries_in_cupboard'] class TestTasks(unittest.TestCase): """Tests all of the tasks via the task_validator tool. Given that unit tests shouldn't take forever to run, we only limit each validation run to 1 variation. In practice, a newly created task should be validated for all variations. Despite this, the test still takes a while to run. """ def test_run_task_validator(self): for task_file in TASKS: test_name = task_file.split('.py')[0] with self.subTest(task=test_name): if test_name in FLAKY_TASKS: self.skipTest('Flaky task.') sim = PyRep() ttt_file = os.path.join( DIR_PATH, '..', '..', 'rlbench', TTT_FILE) sim.launch(ttt_file, headless=True) sim.step_ui() sim.set_simulation_timestep(50.0) sim.step_ui() sim.start() robot = Robot(Panda(), PandaGripper()) obs = ObservationConfig() obs.set_all(False) scene = Scene(sim, robot, obs) sim.start() task_class = task_file_to_task_class(task_file) active_task = task_class(sim, robot) try: task_smoke(active_task, scene, variation=-1, max_variations=2, success=0.25) except Exception as e: sim.stop() sim.shutdown() raise e sim.stop() sim.shutdown()

38.096774

78

0.610076

import unittest import rlbench.backend.task as task import os from rlbench.backend.utils import task_file_to_task_class from pyrep import PyRep from pyrep.robots.arms.panda import Panda from pyrep.robots.end_effectors.panda_gripper import PandaGripper from rlbench.backend.const import TTT_FILE from tools.task_validator import task_smoke from rlbench.observation_config import ObservationConfig from rlbench.backend.scene import Scene from rlbench.backend.robot import Robot TASKS = [t for t in os.listdir(task.TASKS_PATH) if t != '__init__.py' and t.endswith('.py')] DIR_PATH = os.path.dirname(os.path.abspath(__file__)) FLAKY_TASKS = ['put_all_groceries_in_cupboard'] class TestTasks(unittest.TestCase): def test_run_task_validator(self): for task_file in TASKS: test_name = task_file.split('.py')[0] with self.subTest(task=test_name): if test_name in FLAKY_TASKS: self.skipTest('Flaky task.') sim = PyRep() ttt_file = os.path.join( DIR_PATH, '..', '..', 'rlbench', TTT_FILE) sim.launch(ttt_file, headless=True) sim.step_ui() sim.set_simulation_timestep(50.0) sim.step_ui() sim.start() robot = Robot(Panda(), PandaGripper()) obs = ObservationConfig() obs.set_all(False) scene = Scene(sim, robot, obs) sim.start() task_class = task_file_to_task_class(task_file) active_task = task_class(sim, robot) try: task_smoke(active_task, scene, variation=-1, max_variations=2, success=0.25) except Exception as e: sim.stop() sim.shutdown() raise e sim.stop() sim.shutdown()

true

f719867e8b00abb554a28d0fafbc160c9ea3d04e

3,652

py

Python

nova/openstack/common/excutils.py

bopopescu/nova_audit

1cd2901802f82d39411adfa04cf2f432ff3bf280

[ "Apache-2.0" ]

1

2020-02-21T19:19:11.000Z

nova/openstack/common/excutils.py

bopopescu/nova_audit

1cd2901802f82d39411adfa04cf2f432ff3bf280

[ "Apache-2.0" ]

null

nova/openstack/common/excutils.py

bopopescu/nova_audit

1cd2901802f82d39411adfa04cf2f432ff3bf280

[ "Apache-2.0" ]

1

2020-07-24T09:15:58.000Z

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2011 OpenStack Foundation. # Copyright 2012, Red Hat, Inc. # # 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. """ Exception related utilities. """ import logging import sys import time import traceback from nova.openstack.common.gettextutils import _ class save_and_reraise_exception(object): """Save current exception, run some code and then re-raise. In some cases the exception context can be cleared, resulting in None being attempted to be re-raised after an exception handler is run. This can happen when eventlet switches greenthreads or when running an exception handler, code raises and catches an exception. In both cases the exception context will be cleared. To work around this, we save the exception state, run handler code, and then re-raise the original exception. If another exception occurs, the saved exception is logged and the new exception is re-raised. In some cases the caller may not want to re-raise the exception, and for those circumstances this context provides a reraise flag that can be used to suppress the exception. For example: except Exception: with save_and_reraise_exception() as ctxt: decide_if_need_reraise() if not should_be_reraised: ctxt.reraise = False """ def __init__(self): self.reraise = True def __enter__(self): self.type_, self.value, self.tb, = sys.exc_info() return self def __exit__(self, exc_type, exc_val, exc_tb): if exc_type is not None: logging.error(_('Original exception being dropped: %s'), traceback.format_exception(self.type_, self.value, self.tb)) return False if self.reraise: raise self.type_, self.value, self.tb def forever_retry_uncaught_exceptions(infunc): def inner_func(*args, **kwargs): last_log_time = 0 last_exc_message = None exc_count = 0 while True: try: return infunc(*args, **kwargs) except Exception as exc: if exc.message == last_exc_message: exc_count += 1 else: exc_count = 1 # Do not log any more frequently than once a minute unless # the exception message changes cur_time = int(time.time()) if (cur_time - last_log_time > 60 or exc.message != last_exc_message): logging.exception( _('Unexpected exception occurred %d time(s)... ' 'retrying.') % exc_count) last_log_time = cur_time last_exc_message = exc.message exc_count = 0 # This should be a very rare event. In case it isn't, do # a sleep. time.sleep(1) return inner_func

36.888889

78

0.61172

""" Exception related utilities. """ import logging import sys import time import traceback from nova.openstack.common.gettextutils import _ class save_and_reraise_exception(object): """Save current exception, run some code and then re-raise. In some cases the exception context can be cleared, resulting in None being attempted to be re-raised after an exception handler is run. This can happen when eventlet switches greenthreads or when running an exception handler, code raises and catches an exception. In both cases the exception context will be cleared. To work around this, we save the exception state, run handler code, and then re-raise the original exception. If another exception occurs, the saved exception is logged and the new exception is re-raised. In some cases the caller may not want to re-raise the exception, and for those circumstances this context provides a reraise flag that can be used to suppress the exception. For example: except Exception: with save_and_reraise_exception() as ctxt: decide_if_need_reraise() if not should_be_reraised: ctxt.reraise = False """ def __init__(self): self.reraise = True def __enter__(self): self.type_, self.value, self.tb, = sys.exc_info() return self def __exit__(self, exc_type, exc_val, exc_tb): if exc_type is not None: logging.error(_('Original exception being dropped: %s'), traceback.format_exception(self.type_, self.value, self.tb)) return False if self.reraise: raise self.type_, self.value, self.tb def forever_retry_uncaught_exceptions(infunc): def inner_func(*args, **kwargs): last_log_time = 0 last_exc_message = None exc_count = 0 while True: try: return infunc(*args, **kwargs) except Exception as exc: if exc.message == last_exc_message: exc_count += 1 else: exc_count = 1 cur_time = int(time.time()) if (cur_time - last_log_time > 60 or exc.message != last_exc_message): logging.exception( _('Unexpected exception occurred %d time(s)... ' 'retrying.') % exc_count) last_log_time = cur_time last_exc_message = exc.message exc_count = 0 # a sleep. time.sleep(1) return inner_func

false

true

f71986b928e02b3c1c5322f3668bc41a49a8abc1

7,013

py

Python

GHC2018/process.py

purrcat259/n-n-hashcode

98a1c443e6112903bc29a858bc18476a6635d460

[ "MIT" ]

null

GHC2018/process.py

purrcat259/n-n-hashcode

98a1c443e6112903bc29a858bc18476a6635d460

[ "MIT" ]

null

GHC2018/process.py

purrcat259/n-n-hashcode

98a1c443e6112903bc29a858bc18476a6635d460

[ "MIT" ]

null

from GHC2018.input import Input from GHC2018.models.Car import Car from GHC2018.models.Route import Route from GHC2018.models.ride import calculate_distance from tqdm import tqdm class Process: def __init__(self, input_data, debug=True): self.input_data = input_data self.debug = debug self.current_time = 0 # self.get_routes() def initialise_cars(self): cars = [] for i in range(0, self.input_data.vehicle_count): car = Car(i, 0, 0) cars.append(car) self.cars = cars def debug_print(self, message): if self.debug: print(message) def run(self): self.initialise_cars() self.rides = self.input_data.rides sim_range = range(0, self.input_data.sim_steps) if not self.debug: sim_range = tqdm(sim_range) for i in sim_range: self.debug_print('--- STEP {}/{} ---'.format(i, self.input_data.sim_steps)) self.current_time = i # if cars are at their destination, end the ride self.end_rides() # schedule any cars that are not assigned a ride self.schedule_rides() # move any cars self.move_cars() self.debug_print('SIMULATION ENDED') print('{} rides completed. {} rides left unfinished.'.format( len(self.get_completed_rides()), len(self.rides) - len(self.get_completed_rides())) ) self.output_file() def output_file(self): output_file_path = self.input_data.file_path.replace('.in', '.out') car_rides = {} for ride in self.get_completed_rides(): if ride.assigned_car in car_rides.keys(): car_rides[ride.assigned_car].append(ride.ride_id) else: car_rides[ride.assigned_car] = [ride.ride_id] with open(output_file_path, 'w') as output_file: for car, rides in car_rides.items(): output_string = str(len(rides)) for ride_id in rides: output_string += ' {}'.format(ride_id) output_file.write(output_string + '\n') def end_rides(self): self.debug_print('Checking if cars have arrived') completed_cars = [ car for car in self.get_assigned_cars() if car.is_at_destination() ] self.debug_print('{} cars completed their ride this turn'.format(len(completed_cars))) for car in completed_cars: self.debug_print('Car {} has completed their ride'.format(car.car_id)) car.complete_ride() if car.assigned_route_completed(): car.complete_route() self.debug_print('{}/{} rides completed'.format( len(self.get_completed_rides()), len(self.rides) )) def get_completed_rides(self): return [ride for ride in self.input_data.rides if ride.completed] def schedule_rides(self): unassigned_cars = self.get_unassigned_cars() self.debug_print('Scheduling {} cars'.format(len(unassigned_cars))) unassigned_rides = self.get_unassigned_rides() if len(unassigned_rides) == 0: return for car in unassigned_cars: # next_ride = unassigned_rides.pop(0) unassigned_rides = self.get_unassigned_rides() next_ride = self.get_next_ride(car, unassigned_rides, self.current_time) rides_for_route = [next_ride] route = Route(rides_for_route) self.debug_print('Assigned route with ride IDs {} to car: {}'.format( route.get_route_ride_ids(), car.car_id )) car.assign_route(route) def get_closest_ride_to_car(self, car, rides): closest_ride = rides[0] closest_distance = calculate_distance(car.row, closest_ride.row_start, car.col, closest_ride.col_start) for i in range(1, len(rides)): ride = rides[i] next_closest_distance = calculate_distance(car.row, ride.row_start, car.col, ride.col_start) if next_closest_distance < closest_distance: closest_ride = ride closest_distance = next_closest_distance return rides.pop(rides.index(closest_ride)) def move_cars(self): for car in self.get_assigned_cars(): self.debug_print('Moving car with ID: {}'.format(car.car_id)) car.move_towards_destination() def get_assigned_cars(self): return [car for car in self.cars if car.assigned_route is not None] def get_unassigned_cars(self): return [car for car in self.cars if car.assigned_route is None] def get_unassigned_rides(self): return [ride for ride in self.input_data.rides if ride.assigned_car is None] def set_next_routes(self, route, routes): for t_route in routes: if not t_route is route: wait = t_route.ordered_rides[0].earliest_start -route.ordered_rides[-1].latest_finish if wait >= 0: route.next_routes.append({'route':t_route, 'wait_time': wait}) def add_to_route(self, ride, next_ride, routes): for route in routes: start_ride = route.ordered_rides[0] end_ride = route.ordered_rides[-1] if start_ride is next_ride: route.ordered_rides.insert(0, ride) return routes elif end_ride is ride: route.ordered_rides.insert(-1, next_ride) return routes routes.append(Route([ride, next_ride])) return routes def get_next_ride(self, car, rides, actual_start_time): # unassigned_rides = deepcopy(self.get_unassigned_rides()) best_ride = None waiting = 0 for unassigned_ride in rides: distance_to_next_ride = calculate_distance(car.row, unassigned_ride.row_start, car.col, unassigned_ride.col_start) time_to_new_start = actual_start_time + distance_to_next_ride if time_to_new_start + unassigned_ride.distance <= unassigned_ride.latest_finish: temp_waiting = max(unassigned_ride.earliest_start - (time_to_new_start + unassigned_ride.distance), 0) if best_ride is None or waiting > temp_waiting: waiting = temp_waiting best_ride = unassigned_ride if waiting == 0: return best_ride return best_ride if __name__ == '__main__': file_names = [ 'a_example.in', 'b_should_be_easy.in', 'c_no_hurry.in', 'd_metropolis.in', 'e_high_bonus.in' ] for file_name in file_names: print('Running: {}\n'.format(file_name)) input_parser = Input(file_name) input_parser.read_file() p = Process(input_data=input_parser, debug=False) p.run()

39.178771

126

0.610866

from GHC2018.input import Input from GHC2018.models.Car import Car from GHC2018.models.Route import Route from GHC2018.models.ride import calculate_distance from tqdm import tqdm class Process: def __init__(self, input_data, debug=True): self.input_data = input_data self.debug = debug self.current_time = 0 def initialise_cars(self): cars = [] for i in range(0, self.input_data.vehicle_count): car = Car(i, 0, 0) cars.append(car) self.cars = cars def debug_print(self, message): if self.debug: print(message) def run(self): self.initialise_cars() self.rides = self.input_data.rides sim_range = range(0, self.input_data.sim_steps) if not self.debug: sim_range = tqdm(sim_range) for i in sim_range: self.debug_print('--- STEP {}/{} ---'.format(i, self.input_data.sim_steps)) self.current_time = i self.end_rides() self.schedule_rides() self.move_cars() self.debug_print('SIMULATION ENDED') print('{} rides completed. {} rides left unfinished.'.format( len(self.get_completed_rides()), len(self.rides) - len(self.get_completed_rides())) ) self.output_file() def output_file(self): output_file_path = self.input_data.file_path.replace('.in', '.out') car_rides = {} for ride in self.get_completed_rides(): if ride.assigned_car in car_rides.keys(): car_rides[ride.assigned_car].append(ride.ride_id) else: car_rides[ride.assigned_car] = [ride.ride_id] with open(output_file_path, 'w') as output_file: for car, rides in car_rides.items(): output_string = str(len(rides)) for ride_id in rides: output_string += ' {}'.format(ride_id) output_file.write(output_string + '\n') def end_rides(self): self.debug_print('Checking if cars have arrived') completed_cars = [ car for car in self.get_assigned_cars() if car.is_at_destination() ] self.debug_print('{} cars completed their ride this turn'.format(len(completed_cars))) for car in completed_cars: self.debug_print('Car {} has completed their ride'.format(car.car_id)) car.complete_ride() if car.assigned_route_completed(): car.complete_route() self.debug_print('{}/{} rides completed'.format( len(self.get_completed_rides()), len(self.rides) )) def get_completed_rides(self): return [ride for ride in self.input_data.rides if ride.completed] def schedule_rides(self): unassigned_cars = self.get_unassigned_cars() self.debug_print('Scheduling {} cars'.format(len(unassigned_cars))) unassigned_rides = self.get_unassigned_rides() if len(unassigned_rides) == 0: return for car in unassigned_cars: unassigned_rides = self.get_unassigned_rides() next_ride = self.get_next_ride(car, unassigned_rides, self.current_time) rides_for_route = [next_ride] route = Route(rides_for_route) self.debug_print('Assigned route with ride IDs {} to car: {}'.format( route.get_route_ride_ids(), car.car_id )) car.assign_route(route) def get_closest_ride_to_car(self, car, rides): closest_ride = rides[0] closest_distance = calculate_distance(car.row, closest_ride.row_start, car.col, closest_ride.col_start) for i in range(1, len(rides)): ride = rides[i] next_closest_distance = calculate_distance(car.row, ride.row_start, car.col, ride.col_start) if next_closest_distance < closest_distance: closest_ride = ride closest_distance = next_closest_distance return rides.pop(rides.index(closest_ride)) def move_cars(self): for car in self.get_assigned_cars(): self.debug_print('Moving car with ID: {}'.format(car.car_id)) car.move_towards_destination() def get_assigned_cars(self): return [car for car in self.cars if car.assigned_route is not None] def get_unassigned_cars(self): return [car for car in self.cars if car.assigned_route is None] def get_unassigned_rides(self): return [ride for ride in self.input_data.rides if ride.assigned_car is None] def set_next_routes(self, route, routes): for t_route in routes: if not t_route is route: wait = t_route.ordered_rides[0].earliest_start -route.ordered_rides[-1].latest_finish if wait >= 0: route.next_routes.append({'route':t_route, 'wait_time': wait}) def add_to_route(self, ride, next_ride, routes): for route in routes: start_ride = route.ordered_rides[0] end_ride = route.ordered_rides[-1] if start_ride is next_ride: route.ordered_rides.insert(0, ride) return routes elif end_ride is ride: route.ordered_rides.insert(-1, next_ride) return routes routes.append(Route([ride, next_ride])) return routes def get_next_ride(self, car, rides, actual_start_time): best_ride = None waiting = 0 for unassigned_ride in rides: distance_to_next_ride = calculate_distance(car.row, unassigned_ride.row_start, car.col, unassigned_ride.col_start) time_to_new_start = actual_start_time + distance_to_next_ride if time_to_new_start + unassigned_ride.distance <= unassigned_ride.latest_finish: temp_waiting = max(unassigned_ride.earliest_start - (time_to_new_start + unassigned_ride.distance), 0) if best_ride is None or waiting > temp_waiting: waiting = temp_waiting best_ride = unassigned_ride if waiting == 0: return best_ride return best_ride if __name__ == '__main__': file_names = [ 'a_example.in', 'b_should_be_easy.in', 'c_no_hurry.in', 'd_metropolis.in', 'e_high_bonus.in' ] for file_name in file_names: print('Running: {}\n'.format(file_name)) input_parser = Input(file_name) input_parser.read_file() p = Process(input_data=input_parser, debug=False) p.run()

true

f719878d7cf2f176cf391bedf04e4b2cfa47cc02

1,701

py

Python

app/core/migrations/0001_initial.py

SirEric-A/recipe-app-api

05a767fcb87f2ca47918698930d10f6e21654576

[ "MIT" ]

null

app/core/migrations/0001_initial.py

SirEric-A/recipe-app-api

05a767fcb87f2ca47918698930d10f6e21654576

[ "MIT" ]

null

app/core/migrations/0001_initial.py

SirEric-A/recipe-app-api

05a767fcb87f2ca47918698930d10f6e21654576

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-06-18 21:50 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

50.029412

266

0.637272

from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

true

f71987f0e511820af63a6cf60ad703869664ef65

4,832

py

Python

.ycm_extra_conf.py

bigt1234/objectpool

dab515f71c12f8df22686053043f7e2c4c929354

[ "Zlib" ]

66

2016-11-07T01:00:46.000Z

2022-03-13T01:25:54.000Z

.ycm_extra_conf.py

bigt1234/objectpool

dab515f71c12f8df22686053043f7e2c4c929354

[ "Zlib" ]

1

2020-11-26T12:08:53.000Z

2021-09-24T01:06:49.000Z

.ycm_extra_conf.py

bigt1234/objectpool

dab515f71c12f8df22686053043f7e2c4c929354

[ "Zlib" ]

19

2016-07-18T07:58:11.000Z

2022-03-13T01:24:07.000Z

#!/usr/bin/env python # # Copyright (C) 2014 Google Inc. # # This file is part of YouCompleteMe. # # YouCompleteMe is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # YouCompleteMe is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with YouCompleteMe. If not, see <http://www.gnu.org/licenses/>. import os import ycm_core # These are the compilation flags that will be used in case there's no # compilation database set (by default, one is not set). # CHANGE THIS LIST OF FLAGS. YES, THIS IS THE DROID YOU HAVE BEEN LOOKING FOR. flags = [ '-Wall', '-Wextra', '-Werror', '-fexceptions', '-DNDEBUG', # THIS IS IMPORTANT! Without a "-std=<something>" flag, clang won't know which # language to use when compiling headers. So it will guess. Badly. So C++ # headers will be compiled as C headers. You don't want that so ALWAYS specify # a "-std=<something>". # For a C project, you would set this to something like 'c99' instead of # 'c++11'. '-std=c++11', # ...and the same thing goes for the magic -x option which specifies the # language that the files to be compiled are written in. This is mostly # relevant for c++ headers. # For a C project, you would set this to 'c' instead of 'c++'. '-x', 'c++', '-isystem', '/usr/include', '-isystem', '/usr/local/include', '-I', 'src', '-I', 'thirdparty/nonius', '-I', 'thirdparty/Catch', ] # Set this to the absolute path to the folder (NOT the file!) containing the # compile_commands.json file to use that instead of 'flags'. See here for # more details: http://clang.llvm.org/docs/JSONCompilationDatabase.html # # Most projects will NOT need to set this to anything; you can just change the # 'flags' list of compilation flags. compilation_database_folder = '' if os.path.exists( compilation_database_folder ): database = ycm_core.CompilationDatabase( compilation_database_folder ) else: database = None SOURCE_EXTENSIONS = [ '.cpp', '.cxx', '.cc', '.c', '.m', '.mm' ] def DirectoryOfThisScript(): return os.path.dirname( os.path.abspath( __file__ ) ) def MakeRelativePathsInFlagsAbsolute( flags, working_directory ): if not working_directory: return list( flags ) new_flags = [] make_next_absolute = False path_flags = [ '-isystem', '-I', '-iquote', '--sysroot=' ] for flag in flags: new_flag = flag if make_next_absolute: make_next_absolute = False if not flag.startswith( '/' ): new_flag = os.path.join( working_directory, flag ) for path_flag in path_flags: if flag == path_flag: make_next_absolute = True break if flag.startswith( path_flag ): path = flag[ len( path_flag ): ] new_flag = path_flag + os.path.join( working_directory, path ) break if new_flag: new_flags.append( new_flag ) return new_flags def IsHeaderFile( filename ): extension = os.path.splitext( filename )[ 1 ] return extension in ['.h', '.hxx', '.hpp', '.hh', '.h++'] def GetCompilationInfoForFile( filename ): # The compilation_commands.json file generated by CMake does not have entries # for header files. So we do our best by asking the db for flags for a # corresponding source file, if any. If one exists, the flags for that file # should be good enough. if IsHeaderFile( filename ): basename = os.path.splitext( filename )[ 0 ] for extension in SOURCE_EXTENSIONS: replacement_file = basename + extension if os.path.exists( replacement_file ): compilation_info = database.GetCompilationInfoForFile( replacement_file ) if compilation_info.compiler_flags_: return compilation_info return None return database.GetCompilationInfoForFile( filename ) # This is the entry point; this function is called by ycmd to produce flags for # a file. def FlagsForFile( filename, **kwargs ): if database: # Bear in mind that compilation_info.compiler_flags_ does NOT return a # python list, but a "list-like" StringVec object compilation_info = GetCompilationInfoForFile( filename ) if not compilation_info: return None final_flags = MakeRelativePathsInFlagsAbsolute( compilation_info.compiler_flags_, compilation_info.compiler_working_dir_ ) else: relative_to = DirectoryOfThisScript() final_flags = MakeRelativePathsInFlagsAbsolute( flags, relative_to ) return { 'flags': final_flags, 'do_cache': True }

32.648649

79

0.708609

import os import ycm_core # compilation database set (by default, one is not set). # CHANGE THIS LIST OF FLAGS. YES, THIS IS THE DROID YOU HAVE BEEN LOOKING FOR. flags = [ '-Wall', '-Wextra', '-Werror', '-fexceptions', '-DNDEBUG', # THIS IS IMPORTANT! Without a "-std=<something>" flag, clang won't know which # a "-std=<something>". # For a C project, you would set this to something like 'c99' instead of # 'c++11'. '-std=c++11', # ...and the same thing goes for the magic -x option which specifies the # language that the files to be compiled are written in. This is mostly # relevant for c++ headers. # For a C project, you would set this to 'c' instead of 'c++'. '-x', 'c++', '-isystem', '/usr/include', '-isystem', '/usr/local/include', '-I', 'src', '-I', 'thirdparty/nonius', '-I', 'thirdparty/Catch', ] # Set this to the absolute path to the folder (NOT the file!) containing the # compile_commands.json file to use that instead of 'flags'. See here for # more details: http://clang.llvm.org/docs/JSONCompilationDatabase.html # # Most projects will NOT need to set this to anything; you can just change the # 'flags' list of compilation flags. compilation_database_folder = '' if os.path.exists( compilation_database_folder ): database = ycm_core.CompilationDatabase( compilation_database_folder ) else: database = None SOURCE_EXTENSIONS = [ '.cpp', '.cxx', '.cc', '.c', '.m', '.mm' ] def DirectoryOfThisScript(): return os.path.dirname( os.path.abspath( __file__ ) ) def MakeRelativePathsInFlagsAbsolute( flags, working_directory ): if not working_directory: return list( flags ) new_flags = [] make_next_absolute = False path_flags = [ '-isystem', '-I', '-iquote', '--sysroot=' ] for flag in flags: new_flag = flag if make_next_absolute: make_next_absolute = False if not flag.startswith( '/' ): new_flag = os.path.join( working_directory, flag ) for path_flag in path_flags: if flag == path_flag: make_next_absolute = True break if flag.startswith( path_flag ): path = flag[ len( path_flag ): ] new_flag = path_flag + os.path.join( working_directory, path ) break if new_flag: new_flags.append( new_flag ) return new_flags def IsHeaderFile( filename ): extension = os.path.splitext( filename )[ 1 ] return extension in ['.h', '.hxx', '.hpp', '.hh', '.h++'] def GetCompilationInfoForFile( filename ): # The compilation_commands.json file generated by CMake does not have entries # for header files. So we do our best by asking the db for flags for a # corresponding source file, if any. If one exists, the flags for that file # should be good enough. if IsHeaderFile( filename ): basename = os.path.splitext( filename )[ 0 ] for extension in SOURCE_EXTENSIONS: replacement_file = basename + extension if os.path.exists( replacement_file ): compilation_info = database.GetCompilationInfoForFile( replacement_file ) if compilation_info.compiler_flags_: return compilation_info return None return database.GetCompilationInfoForFile( filename ) # This is the entry point; this function is called by ycmd to produce flags for # a file. def FlagsForFile( filename, **kwargs ): if database: # Bear in mind that compilation_info.compiler_flags_ does NOT return a # python list, but a "list-like" StringVec object compilation_info = GetCompilationInfoForFile( filename ) if not compilation_info: return None final_flags = MakeRelativePathsInFlagsAbsolute( compilation_info.compiler_flags_, compilation_info.compiler_working_dir_ ) else: relative_to = DirectoryOfThisScript() final_flags = MakeRelativePathsInFlagsAbsolute( flags, relative_to ) return { 'flags': final_flags, 'do_cache': True }

true

f71988e1a677b3eb305af40560a0785370f713df

14,327

py

Python

oqupy/backends/tempo_backend.py

tempoCollaboration/OQuPy

a389a161991a59259e5df47d8e0f405fcac75fe5

[ "Apache-2.0" ]

13

2022-02-15T12:33:17.000Z

2022-03-31T10:01:57.000Z

oqupy/backends/tempo_backend.py

tempoCollaboration/OQuPy

a389a161991a59259e5df47d8e0f405fcac75fe5

[ "Apache-2.0" ]

11

2022-02-16T07:35:46.000Z

2022-03-24T18:22:12.000Z

oqupy/backends/tempo_backend.py

tempoCollaboration/OQuPy

a389a161991a59259e5df47d8e0f405fcac75fe5

[ "Apache-2.0" ]

2

2022-02-17T01:23:55.000Z

2022-02-17T08:51:57.000Z

# Copyright 2020 The TEMPO Collaboration # # 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. """ Module for tempo and mean-field tempo backend. """ from typing import Callable, Dict, Optional, Tuple from copy import copy from numpy import ndarray, moveaxis, dot from oqupy import operators from oqupy.config import TEMPO_BACKEND_CONFIG from oqupy.backends import node_array as na from oqupy.util import create_delta class BaseTempoBackend: """ Backend class for TEMPO. Parameters ---------- initial_state: ndarray The initial density matrix (as a vector). influence: callable(int) -> ndarray Callable that takes an integer `step` and returns the influence super operator of that `step`. unitary_transform: ndarray Unitary that transforms the coupling operator into a diagonal form. sum_north: ndarray The summing vector for the north legs. sum_west: ndarray The summing vector for the west legs. dkmax: int Number of influences to include. If ``dkmax == None`` then all influences are included. epsrel: float Maximal relative SVD truncation error. """ def __init__( self, initial_state: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Optional[Dict] = None): """Create a TempoBackend object. """ self._initial_state = initial_state self._influence = influence self._unitary_transform = unitary_transform self._sum_north = sum_north self._sum_west = sum_west self._dkmax = dkmax self._epsrel = epsrel self._step = None self._state = None self._config = TEMPO_BACKEND_CONFIG if config is None else config self._mps = None self._mpo = None self._super_u = None self._super_u_dagg = None self._sum_north_na = None @property def step(self) -> int: """The current step in the TEMPO computation. """ return self._step def _initialize_mps_mpo(self) : """ToDo""" self._initial_state = copy(self._initial_state).reshape(-1) self._super_u = operators.left_right_super( self._unitary_transform, self._unitary_transform.conjugate().T) self._super_u_dagg = operators.left_right_super( self._unitary_transform.conjugate().T, self._unitary_transform) self._sum_north_na = na.NodeArray([self._sum_north], left=False, right=False, name="Sum north") influences = [] if self._dkmax is None: dkmax_pre_compute = 1 else: dkmax_pre_compute = self._dkmax + 1 for i in range(dkmax_pre_compute): infl = self._influence(i) infl_four_legs = create_delta(infl, [1, 0, 0, 1]) if i == 0: tmp = dot(moveaxis(infl_four_legs, 1, -1), self._super_u_dagg) tmp = moveaxis(tmp, -1, 1) tmp = dot(tmp, self._super_u.T) infl_four_legs = tmp influences.append(infl_four_legs) self._mps = na.NodeArray([self._initial_state], left=False, right=False, name="Thee MPS") self._mpo = na.NodeArray(list(reversed(influences)), left=True, right=True, name="Thee Time Evolving MPO") def _compute_system_step(self, current_step, prop_1, prop_2) -> ndarray: """ Takes a step in the TEMPO tensor network computation. For example, for at step 4, we start with: A ... self._mps B ... self._mpo w ... self._sum_west n ... self._sum_north_array p1 ... prop_1 p2 ... prop_2 n n n n | | | | | | | | | w~~ ~~B~~B~~B~~B~~ ~~p2 | | | | p1 | | | | A~~A~~A~~A return: step = 4 state = contraction of A,B,w,n,p1 effects: self._mpo will grow to the left with the next influence functional self._mps will be contraction of A,B,w,p1,p2 Returns ------- step: int The current step count. state: ndarray Density matrix at the current step. """ prop_1_na = na.NodeArray([prop_1.T], left=False, right=False, name="first half-step") prop_2_na = na.NodeArray([prop_2.T], left=True, right=False, name="second half-step") if self._dkmax is None: mpo = self._mpo.copy() infl = self._influence(len(mpo)) infl_four_legs = create_delta(infl, [1, 0, 0, 1]) infl_na = na.NodeArray([infl_four_legs], left=True, right=True) self._mpo = na.join(infl_na, self._mpo, name="The Time Evolving MPO", copy=False) elif current_step <= self._dkmax: _, mpo = na.split(self._mpo, int(0 - current_step), copy=True) else: # current_step > self._dkmax mpo = self._mpo.copy() infl = self._influence(self._dkmax-current_step) if infl is not None: infl_four_legs = create_delta(infl, [1, 0, 0, 1]) infl_na = na.NodeArray([infl_four_legs], left=True, right=True) _, mpo = na.split(self._mpo, index=1, copy=True) mpo = na.join(infl_na, mpo, name="Thee Time Evolving MPO", copy=False) mpo.name = "temporary MPO" mpo.apply_vector(self._sum_west, left=True) self._mps.zip_up(prop_1_na, axes=[(0,0)], left_index=-1, right_index=-1, direction="left", max_singular_values=None, max_truncation_err=self._epsrel, relative=True, copy=False) if len(self._mps) != len(mpo): self._mps.contract(self._sum_north_na, axes=[(0,0)], left_index=0, right_index=0, direction="right", copy=True) self._mps.zip_up(mpo, axes=[(0, 0)], left_index=0, right_index=-1, direction="right", max_singular_values=None, max_truncation_err=self._epsrel, relative=True, copy=False) self._mps.svd_sweep(from_index=-1, to_index=0, max_singular_values=None, max_truncation_err=self._epsrel, relative=True) self._mps = na.join(self._mps, prop_2_na, copy=False, name=f"The MPS ({current_step})") tmp_mps = self._mps.copy() for _ in range(len(tmp_mps)-1): tmp_mps.contract(self._sum_north_na, axes=[(0,0)], left_index=0, right_index=0, direction="right", copy=True) assert len(tmp_mps) == 1 assert not tmp_mps.left assert not tmp_mps.right assert tmp_mps.rank == 1 state = tmp_mps.nodes[0].get_tensor() return state class TempoBackend(BaseTempoBackend): """ ToDo """ def __init__( self, initial_state: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, propagators: Callable[[int], Tuple[ndarray, ndarray]], sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Optional[Dict] = None): """Create a TempoBackend object. """ super().__init__( initial_state, influence, unitary_transform, sum_north, sum_west, dkmax, epsrel, config) self._propagators = propagators def initialize(self)-> Tuple[int, ndarray]: """ ToDo """ self._step = 0 self._initialize_mps_mpo() self._state = self._initial_state return self._step, copy(self._state) def compute_step(self) -> Tuple[int, ndarray]: """ ToDo """ self._step += 1 prop_1, prop_2 = self._propagators(self._step-1) self._state = self._compute_system_step(self._step, prop_1, prop_2) return self._step, copy(self._state) class TempoWithFieldBackend(BaseTempoBackend): """ backend for tensor network tempo with coherent field evolution. Note the only difference from TensorNetworkTempoBackend in the signature is the addition of the initial_field and compute_field parameters, and the change of the propagator signature. Parameters ---------- initial_state: ndarray The initial density matrix (as a vector). initial_field: complex The initial field value. influence: callable(int) -> ndarray Callable that takes an integer `step` and returns the influence super operator of that `step`. unitary_transform: ndarray Unitary that transforms the coupling operator into a diagonal form. propagators: callable(int, ndarray, complex) -> ndarray, ndarray Callable that takes an integer `step`, an ndarray `state` and a complex `field` and returns the first and second half of the system propagator of that `step`. compute_field: callable(int, ndarray, complex, ndarray) -> complex Callable that takes an integer `step`, a complex `field` (the current value of the field) and two ndarrays for (respectively) the current and next density matrix as vectors, and returns the next field value. sum_north: ndarray The summing vector for the north legs. sum_west: ndarray The summing vector for the west legs. dkmax: int Number of influences to include. If ``dkmax == -1`` then all influences are included. epsrel: float Maximal relative SVD truncation error. """ def __init__( self, initial_state: ndarray, initial_field: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, propagators: Callable[[int, ndarray, complex], Tuple[ndarray, ndarray]], compute_field: Callable[[float, ndarray, complex], complex], sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Dict): # Field specific variables self._initial_field = initial_field self._compute_field = compute_field self._field = initial_field self._propagators = propagators """Create a TempoWithFieldBackend object. """ super().__init__(initial_state, influence, unitary_transform, sum_north, sum_west, dkmax, epsrel, config) def initialize(self) -> Tuple[int, ndarray, complex]: """See BaseBackend.initialize() for main docstring.""" self._step = 0 self._initialize_mps_mpo() self._state = self._initial_state self._field = self._initial_field return self._step, copy(self._state), self._field def compute_step(self) -> Tuple[int, ndarray, complex]: """ ToDo """ current_step = self._step next_step = current_step + 1 current_state = copy(self._state) current_field = self._field prop_1, prop_2 = self._propagators(current_step, current_state, current_field) next_state = self._compute_system_step(next_step, prop_1, prop_2) next_field = self._compute_field(current_step, current_state, current_field, next_state) self._state = next_state self._field = next_field self._step = next_step return self._step, copy(self._state), self._field

35.8175

79

0.520346

from typing import Callable, Dict, Optional, Tuple from copy import copy from numpy import ndarray, moveaxis, dot from oqupy import operators from oqupy.config import TEMPO_BACKEND_CONFIG from oqupy.backends import node_array as na from oqupy.util import create_delta class BaseTempoBackend: def __init__( self, initial_state: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Optional[Dict] = None): self._initial_state = initial_state self._influence = influence self._unitary_transform = unitary_transform self._sum_north = sum_north self._sum_west = sum_west self._dkmax = dkmax self._epsrel = epsrel self._step = None self._state = None self._config = TEMPO_BACKEND_CONFIG if config is None else config self._mps = None self._mpo = None self._super_u = None self._super_u_dagg = None self._sum_north_na = None @property def step(self) -> int: return self._step def _initialize_mps_mpo(self) : self._initial_state = copy(self._initial_state).reshape(-1) self._super_u = operators.left_right_super( self._unitary_transform, self._unitary_transform.conjugate().T) self._super_u_dagg = operators.left_right_super( self._unitary_transform.conjugate().T, self._unitary_transform) self._sum_north_na = na.NodeArray([self._sum_north], left=False, right=False, name="Sum north") influences = [] if self._dkmax is None: dkmax_pre_compute = 1 else: dkmax_pre_compute = self._dkmax + 1 for i in range(dkmax_pre_compute): infl = self._influence(i) infl_four_legs = create_delta(infl, [1, 0, 0, 1]) if i == 0: tmp = dot(moveaxis(infl_four_legs, 1, -1), self._super_u_dagg) tmp = moveaxis(tmp, -1, 1) tmp = dot(tmp, self._super_u.T) infl_four_legs = tmp influences.append(infl_four_legs) self._mps = na.NodeArray([self._initial_state], left=False, right=False, name="Thee MPS") self._mpo = na.NodeArray(list(reversed(influences)), left=True, right=True, name="Thee Time Evolving MPO") def _compute_system_step(self, current_step, prop_1, prop_2) -> ndarray: prop_1_na = na.NodeArray([prop_1.T], left=False, right=False, name="first half-step") prop_2_na = na.NodeArray([prop_2.T], left=True, right=False, name="second half-step") if self._dkmax is None: mpo = self._mpo.copy() infl = self._influence(len(mpo)) infl_four_legs = create_delta(infl, [1, 0, 0, 1]) infl_na = na.NodeArray([infl_four_legs], left=True, right=True) self._mpo = na.join(infl_na, self._mpo, name="The Time Evolving MPO", copy=False) elif current_step <= self._dkmax: _, mpo = na.split(self._mpo, int(0 - current_step), copy=True) else: mpo = self._mpo.copy() infl = self._influence(self._dkmax-current_step) if infl is not None: infl_four_legs = create_delta(infl, [1, 0, 0, 1]) infl_na = na.NodeArray([infl_four_legs], left=True, right=True) _, mpo = na.split(self._mpo, index=1, copy=True) mpo = na.join(infl_na, mpo, name="Thee Time Evolving MPO", copy=False) mpo.name = "temporary MPO" mpo.apply_vector(self._sum_west, left=True) self._mps.zip_up(prop_1_na, axes=[(0,0)], left_index=-1, right_index=-1, direction="left", max_singular_values=None, max_truncation_err=self._epsrel, relative=True, copy=False) if len(self._mps) != len(mpo): self._mps.contract(self._sum_north_na, axes=[(0,0)], left_index=0, right_index=0, direction="right", copy=True) self._mps.zip_up(mpo, axes=[(0, 0)], left_index=0, right_index=-1, direction="right", max_singular_values=None, max_truncation_err=self._epsrel, relative=True, copy=False) self._mps.svd_sweep(from_index=-1, to_index=0, max_singular_values=None, max_truncation_err=self._epsrel, relative=True) self._mps = na.join(self._mps, prop_2_na, copy=False, name=f"The MPS ({current_step})") tmp_mps = self._mps.copy() for _ in range(len(tmp_mps)-1): tmp_mps.contract(self._sum_north_na, axes=[(0,0)], left_index=0, right_index=0, direction="right", copy=True) assert len(tmp_mps) == 1 assert not tmp_mps.left assert not tmp_mps.right assert tmp_mps.rank == 1 state = tmp_mps.nodes[0].get_tensor() return state class TempoBackend(BaseTempoBackend): def __init__( self, initial_state: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, propagators: Callable[[int], Tuple[ndarray, ndarray]], sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Optional[Dict] = None): super().__init__( initial_state, influence, unitary_transform, sum_north, sum_west, dkmax, epsrel, config) self._propagators = propagators def initialize(self)-> Tuple[int, ndarray]: self._step = 0 self._initialize_mps_mpo() self._state = self._initial_state return self._step, copy(self._state) def compute_step(self) -> Tuple[int, ndarray]: self._step += 1 prop_1, prop_2 = self._propagators(self._step-1) self._state = self._compute_system_step(self._step, prop_1, prop_2) return self._step, copy(self._state) class TempoWithFieldBackend(BaseTempoBackend): def __init__( self, initial_state: ndarray, initial_field: ndarray, influence: Callable[[int], ndarray], unitary_transform: ndarray, propagators: Callable[[int, ndarray, complex], Tuple[ndarray, ndarray]], compute_field: Callable[[float, ndarray, complex], complex], sum_north: ndarray, sum_west: ndarray, dkmax: int, epsrel: float, config: Dict): self._initial_field = initial_field self._compute_field = compute_field self._field = initial_field self._propagators = propagators super().__init__(initial_state, influence, unitary_transform, sum_north, sum_west, dkmax, epsrel, config) def initialize(self) -> Tuple[int, ndarray, complex]: self._step = 0 self._initialize_mps_mpo() self._state = self._initial_state self._field = self._initial_field return self._step, copy(self._state), self._field def compute_step(self) -> Tuple[int, ndarray, complex]: current_step = self._step next_step = current_step + 1 current_state = copy(self._state) current_field = self._field prop_1, prop_2 = self._propagators(current_step, current_state, current_field) next_state = self._compute_system_step(next_step, prop_1, prop_2) next_field = self._compute_field(current_step, current_state, current_field, next_state) self._state = next_state self._field = next_field self._step = next_step return self._step, copy(self._state), self._field

true

f71988f8e6cbe49da433af143788c3ecc8e82b65

446

py

Python

setup.py

Moomoo-pls/NLP_Game_of_Life

afe6bb6ccd4a83b6ffeccc8ac257872251bd39bb

[ "MIT" ]

null

setup.py

Moomoo-pls/NLP_Game_of_Life

afe6bb6ccd4a83b6ffeccc8ac257872251bd39bb

[ "MIT" ]

null

setup.py

Moomoo-pls/NLP_Game_of_Life

afe6bb6ccd4a83b6ffeccc8ac257872251bd39bb

[ "MIT" ]

null

import setuptools setuptools.setup( name="Moo_NLP_Game_of_Life", version="1.0.0", author="Stephen Moo-Young", author_email="mooyoung12@gmail.com", description="Game of Life for the take home coding challenge", url="https://github.com/Moomoo-pls/NLP_Game_of_Life", packages=setuptools.find_packages(), entry_points={ 'console_scripts':[ 'game-of-life=Game_of_Life.main:main', ] }, )

27.875

66

0.663677

import setuptools setuptools.setup( name="Moo_NLP_Game_of_Life", version="1.0.0", author="Stephen Moo-Young", author_email="mooyoung12@gmail.com", description="Game of Life for the take home coding challenge", url="https://github.com/Moomoo-pls/NLP_Game_of_Life", packages=setuptools.find_packages(), entry_points={ 'console_scripts':[ 'game-of-life=Game_of_Life.main:main', ] }, )

true

f719891884a715f4ed60d4d29e0a80d1b2c17515

8,422

py

Python

2_data_collection/CIFAR_10/vgg16_CIFAR10.py

j-chan-hkust/deep_testing_of_advanced_learning_systems

ec535e2b4dc489d407b664a138d3f5262b71d21e

[ "MIT" ]

null

2_data_collection/CIFAR_10/vgg16_CIFAR10.py

j-chan-hkust/deep_testing_of_advanced_learning_systems

ec535e2b4dc489d407b664a138d3f5262b71d21e

[ "MIT" ]

null

2_data_collection/CIFAR_10/vgg16_CIFAR10.py

j-chan-hkust/deep_testing_of_advanced_learning_systems

ec535e2b4dc489d407b664a138d3f5262b71d21e

[ "MIT" ]

null

from __future__ import print_function import keras from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras import optimizers import numpy as np from keras.layers.core import Lambda from keras import backend as K from keras import regularizers class cifar10vgg: def __init__(self,train=True): self.num_classes = 10 self.weight_decay = 0.0005 self.x_shape = [32,32,3] self.model = self.build_model() if train: self.model = self.train(self.model) else: self.model.load_weights('cifar10vgg.h5') def build_model(self): # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper. model = Sequential() weight_decay = self.weight_decay model.add(Conv2D(64, (3, 3), padding='same', input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(self.num_classes)) model.add(Activation('softmax')) return model def normalize(self,X_train,X_test): #this function normalize inputs for zero mean and unit variance # it is used when training a model. # Input: training set and test set # Output: normalized training set and test set according to the trianing set statistics. mean = np.mean(X_train,axis=(0,1,2,3)) std = np.std(X_train, axis=(0, 1, 2, 3)) X_train = (X_train-mean)/(std+1e-7) X_test = (X_test-mean)/(std+1e-7) return X_train, X_test def normalize_production(self,x): #this function is used to normalize instances in production according to saved training set statistics # Input: X - a training set # Output X - a normalized training set according to normalization constants. #these values produced during first training and are general for the standard cifar10 training set normalization mean = 120.707 std = 64.15 return (x-mean)/(std+1e-7) def predict(self,x,normalize=True,batch_size=50): if normalize: x = self.normalize_production(x) return self.model.predict(x,batch_size) def train(self,model): model.load_weights("cifar10vgg.h5") #training parameters batch_size = 128 maxepoches = 250 learning_rate = 0.01 lr_decay = 1e-6 lr_drop = 20 # The data, shuffled and split between train and test sets: (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train, x_test = self.normalize(x_train, x_test) y_train = keras.utils.to_categorical(y_train, self.num_classes) y_test = keras.utils.to_categorical(y_test, self.num_classes) def lr_scheduler(epoch): return learning_rate * (0.5 ** (epoch // lr_drop)) reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler) #data augmentation datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180) width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) height_shift_range=0.1, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=False) # randomly flip images # (std, mean, and principal components if ZCA whitening is applied). datagen.fit(x_train) #optimization details sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy']) # training process in a for loop with learning rate drop every 25 epoches. historytemp = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), steps_per_epoch=x_train.shape[0] // batch_size, epochs=maxepoches, validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2) model.save_weights('cifar10vgg.h5') return model if __name__ == '__main__': (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) model = cifar10vgg() predicted_x = model.predict(x_test) residuals = np.argmax(predicted_x,1)!=np.argmax(y_test,1) loss = sum(residuals)/len(residuals) print("the validation 0/1 loss is: ",loss)

39.172093

120

0.65412

from __future__ import print_function import keras from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras import optimizers import numpy as np from keras.layers.core import Lambda from keras import backend as K from keras import regularizers class cifar10vgg: def __init__(self,train=True): self.num_classes = 10 self.weight_decay = 0.0005 self.x_shape = [32,32,3] self.model = self.build_model() if train: self.model = self.train(self.model) else: self.model.load_weights('cifar10vgg.h5') def build_model(self): model = Sequential() weight_decay = self.weight_decay model.add(Conv2D(64, (3, 3), padding='same', input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(self.num_classes)) model.add(Activation('softmax')) return model def normalize(self,X_train,X_test): mean = np.mean(X_train,axis=(0,1,2,3)) std = np.std(X_train, axis=(0, 1, 2, 3)) X_train = (X_train-mean)/(std+1e-7) X_test = (X_test-mean)/(std+1e-7) return X_train, X_test def normalize_production(self,x): mean = 120.707 std = 64.15 return (x-mean)/(std+1e-7) def predict(self,x,normalize=True,batch_size=50): if normalize: x = self.normalize_production(x) return self.model.predict(x,batch_size) def train(self,model): model.load_weights("cifar10vgg.h5") batch_size = 128 maxepoches = 250 learning_rate = 0.01 lr_decay = 1e-6 lr_drop = 20 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train, x_test = self.normalize(x_train, x_test) y_train = keras.utils.to_categorical(y_train, self.num_classes) y_test = keras.utils.to_categorical(y_test, self.num_classes) def lr_scheduler(epoch): return learning_rate * (0.5 ** (epoch // lr_drop)) reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler) datagen = ImageDataGenerator( featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=15, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False) datagen.fit(x_train) sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy']) historytemp = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), steps_per_epoch=x_train.shape[0] // batch_size, epochs=maxepoches, validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2) model.save_weights('cifar10vgg.h5') return model if __name__ == '__main__': (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) model = cifar10vgg() predicted_x = model.predict(x_test) residuals = np.argmax(predicted_x,1)!=np.argmax(y_test,1) loss = sum(residuals)/len(residuals) print("the validation 0/1 loss is: ",loss)

true

f719892d08f0cb15a072c2fb5acf64d76d3bd3a3

31,288

py

Python

scraps/forcefield_v2.py

kul-group/MAZE-sim

0f85e74bf93f9242a73bcfaa20a593ae966f38fa

[ "MIT" ]

13

2021-03-10T18:40:32.000Z

2022-03-21T20:40:57.000Z

scraps/forcefield_v2.py

kul-group/MAZE-sim

0f85e74bf93f9242a73bcfaa20a593ae966f38fa

[ "MIT" ]

27

2021-01-28T23:18:44.000Z

2021-05-06T19:33:09.000Z

scraps/forcefield_v2.py

kul-group/MAZE-sim

0f85e74bf93f9242a73bcfaa20a593ae966f38fa

[ "MIT" ]

4

2021-03-19T20:46:15.000Z

2022-03-21T20:40:59.000Z

from maze.extra_framework_maker import ExtraFrameworkMaker, ExtraFrameworkAnalyzer from maze.io_zeolite import read_vasp from maze.zeolite import PerfectZeolite, Zeolite from ase.neighborlist import natural_cutoffs, NeighborList import os from pathlib import Path from ase.io import write, read, gromacs, proteindatabank from ase.visualize import view import copy import shutil from glob import glob from ase.constraints import FixAtoms from simtk.openmm.app import * from simtk.openmm import * from simtk.unit import * from sys import stdout from ase.geometry.analysis import Analysis import numpy as np from itertools import permutations from lxml import etree from contextlib import closing from collections import OrderedDict from scipy.optimize import least_squares, minimize import matplotlib.pyplot as plt from statistics import mode import pickle import time from ase.data import atomic_masses, atomic_numbers def get_EF_atom_indices(atoms): """ for index tracking, to ensure we are comparing the DFT and FF forces on the same EF atoms after before and after scooping out the smaller cluster. alse used for recentering the cluster based on the EF-O atom """ TM_list = ['Pt', 'Cu', 'Co', 'Pd', 'Fe', 'Cr', 'Rh', 'Ru'] index_EF_TM = [a.index for a in atoms if a.symbol in TM_list] index_Al = [a.index for a in atoms if a.symbol == 'Al'] nl = NeighborList(natural_cutoffs(atoms), bothways=True, self_interaction=False) nl.update(atoms) Al_neigh_list = np.concatenate((nl.get_neighbors(index_Al[0])[0], nl.get_neighbors(index_Al[1])[0])) Al_neigh_list = [x for x in Al_neigh_list if atoms[x].symbol == 'O'] TM_neigh_list = np.concatenate((nl.get_neighbors(index_EF_TM[0])[0], nl.get_neighbors(index_EF_TM[1])[0])) centering_o = [[x for x in TM_neigh_list if list(TM_neigh_list).count(x) > 1 and x not in Al_neigh_list][0]] return index_EF_TM + centering_o def get_capped_cluster(atoms, folder_path, file_name, save_traj, EF_O_index): """ #TODO: check whether capping is necessary Inconsistent capping (remove all caps for now, does not need this cluster to be physical) Possible fix: change mult in neighbor list Extract smaller cluster containing the extra-framework atoms and cap all the O. Then the capped cluster is moved to the center of the cell to avoid boundary issue. Save cluster in both .traj file and .pdb format. :param atoms: :param folder_path: :param file_name: :param save_traj: if True, save clusters into .traj as well, for later comparison and trouble shooting :param EF_O_index: if not none, will use this value, else, will find the index using Extraframework code :return: 1. EF-cluster including 13 atoms, index of the EF atoms in original zeolite, index of the EF atoms in the current cluster (the later two output index lists share the ordering) """ EFMaker = ExtraFrameworkAnalyzer(atoms) cluster = atoms[[index for index in EFMaker.get_extraframework_cluster(EF_O_index)]] cluster_EF_index = get_EF_atom_indices(cluster) centering_pos = cluster.get_positions()[cluster_EF_index[-1]] recentered_cluster = EFMaker.recentering_atoms(cluster, centering_pos)[0] # FIXME: recentering doesn't work well for very small unit cells. eg. SOD # cluster = Zeolite(cluster).cap_atoms() proteindatabank.write_proteindatabank(folder_path + '/%s.pdb' % file_name, recentered_cluster) if save_traj is True: write(folder_path + '/%s.traj' % file_name, recentered_cluster) return cluster, EFMaker.get_extraframework_cluster(EF_O_index), cluster_EF_index def label_pdb(folder_path, file_name, del_unlabeled_pdb): """ Relabeling the Atom name in proteindatabank file. (required step for openMM) The same atom type connecting to different neighboring types are treated differently due to differences in their chemical environments, and is therefore named separately. :param folder_path: :param file_name: :param del_unlabeled_pdb: """ filein = open(folder_path + '/%s.pdb' % file_name, 'r') fileout = open(folder_path + '/%s_labeled.pdb' % file_name, 'w') name_list = [] for line in filein.readlines(): if line.startswith('ATOM') or line.startswith('HETATM'): name = line[12:16].strip() name_list.append(name) name = name + str(name_list.count(name)) name = name.rjust(4) line = line.replace(line[12:16], name, 1) # only replacing the first occurrence of line[12:16], atomic symbols are maintained fileout.writelines(line) filein.close() fileout.close() if del_unlabeled_pdb is True: os.remove(folder_path + '/%s.pdb' % file_name) def get_bonds(cluster, mult=1, excluded_index=None, excluded_pair=None): """ Using ase.geometry.analysis.Analysis to get all bonds, then remove the repeated ones. Function also allows removing certain bonding pair defined by user (excluded_pair). Or removing pairs including certain atomic indices (excluded_index). :param cluster: :param mult: :param excluded_index: list of integers :param excluded_pair: list of lists :return: full bonding list, shortened list. If both excluded_index and excluded_pair are None, bonding list == shortened list """ if excluded_index is None: excluded_index = [] if excluded_pair is None: excluded_pair = [] nl = NeighborList(natural_cutoffs(cluster, mult=mult), bothways=True, self_interaction=False) nl.update(cluster) bond_list, shortened_list = [], [] for count, indices in enumerate(Analysis(cluster, nl=nl).all_bonds[0]): for index in indices: if [count, index] not in bond_list and [index, count] not in bond_list: bond_list.append([count, index]) for bond in bond_list: if all(single_index not in bond for single_index in excluded_index) and \ all(tuple(bond) not in list(permutations(pair)) for pair in excluded_pair): shortened_list.append(bond) return bond_list, shortened_list def get_angles(cluster, mult=1, excluded_index=None, excluded_pair=None): """ #TODO: consider combining get_bonds and get_angles function ase.geometry.analysis.Analysis.unique_angles function does not work, return all angles. three-body interactions. :param excluded_pair: excluding all [particle1, particle2, particle3] lists involving the excluded pair """ if excluded_index is None: excluded_index = [] if excluded_pair is None: excluded_pair = [] nl = NeighborList(natural_cutoffs(cluster, mult=mult), bothways=True, self_interaction=False) nl.update(cluster) angle_list, shortened_list = [], [] for count, indices in enumerate(Analysis(cluster, nl=nl).all_angles[0]): for index in indices: if all(list(val) not in angle_list for val in list(permutations([count, index[0], index[1]]))): angle_list.append([count, index[0], index[1]]) for angle in angle_list: if all(single_index not in angle for single_index in excluded_index) and \ all(list(value) not in excluded_pair for value in list(permutations(angle, 2))): shortened_list.append(angle) return angle_list, shortened_list def write_xml(atoms, bonds, save_as): # on-the-fly generation of force field xml file, matching atoms and bonds with pdb file root = etree.Element('ForceField') xml_section = etree.SubElement(root, "AtomTypes") for atom in atoms: element_type = ''.join(filter(lambda x: not x.isdigit(), atom.name)) # properties = {'name': atom.name, 'class': atom.name, 'element': element_type, 'mass': str(atomic_mass)} if element_type == 'Cu' or atom.name == 'O9': atomic_mass = atomic_masses[atomic_numbers[element_type]] else: atomic_mass = 0.0 properties = {'name': atom.name, 'class': atom.name, 'element': element_type, 'mass': str(atomic_mass)} etree.SubElement(xml_section, 'Type', **properties) xml_section = etree.SubElement(root, 'Residues') xml_residue = etree.SubElement(xml_section, 'Residue', name='MOL') for atom in atoms: etree.SubElement(xml_residue, 'Atom', name=atom.name, type=atom.name) for bond in bonds: etree.SubElement(xml_residue, 'Bond', atomName1=bond[0].name, atomName2=bond[1].name) tree = etree.ElementTree(root) xml = etree.tostring(tree, pretty_print=True).decode('utf-8') with closing(open(save_as, 'w')) as f: f.write(xml) def check_atom_types(cluster, index): """ assign atom types, same element connected to different neighbors are assigned into different classes. For example, extra-framework O (in Cu-O-Cu) is in a different class from framework O (Si-O-Si). Each class assignment is unique (each atom belongs to one class and one class only). O_EF: extra-framework O O-Cu: framework O, connecting to one T-site(Al) and Cu O-H: framework O, connecting to one T-site(Al) and H (capping) """ nl = NeighborList(natural_cutoffs(cluster), bothways=True, self_interaction=False) nl.update(cluster) class_Al = [atom.index for atom in cluster if atom.symbol == 'Al'] class_Cu = [atom.index for atom in cluster if atom.symbol == 'Cu'] class_H = [atom.index for atom in cluster if atom.symbol == 'H'] class_O_EF = [get_EF_atom_indices(cluster)[-1]] class_O_Cu = [atom.index for atom in cluster if atom.symbol == 'O' and atom.index not in class_O_EF and all(val not in class_H for val in nl.get_neighbors(atom.index)[0])] class_O_H = [atom.index for atom in cluster if atom.symbol == 'O' and atom.index not in class_O_EF + class_O_Cu] if index in class_Al: return 'Al' if index in class_Cu: return 'Cu' if index in class_H: return 'H' if index in class_O_EF: return 'O-EF' if index in class_O_Cu: return 'O-Cu' if index in class_O_H: return 'O-H' else: return 'None' def get_property_types(cluster, property_list): """ assign all bonding pairs or angles into different types based on differences in atom types. For example, O(extra-framework)-Cu is different from O(framework)-Cu. :param property_list: bond or angle index list of the cluster of interests :return type_dict: return a dictionary of all unique bond-pairs or angle types, with "keys" being integers starting from 0, and "values" being a list of two atom types string for bonds or three atom types string for angles. eg. {0: [AtomClass1, AtomClass2], 1: [AtomClass1, AtomClass3], ...} for bonds Note: Bond types such as [AtomClass1, AtomClass2] and [AtomClass2, AtomClass1] are considered the same. Same rules also apply for angles. :return whole_type_list: return the entire list of bond or angle types assignment of the input. len(whole_type_list) = len(my_list) """ type_dict, repeated_list, whole_type_list, count = {}, [], [], 0 for items in property_list: my_list = [] for val in items: my_list.append(check_atom_types(cluster, val)) whole_type_list.append(my_list) if all(list(pair) not in repeated_list for pair in list(permutations(my_list))): repeated_list.append(my_list) type_dict[count] = my_list count += 1 return type_dict, whole_type_list def _get_index_dict(type_dict, whole_type_list, index_list): """ assign bond pairs or angles indices into different bond or angle types, all the pairs or angles within the same types will share the same set of force field parameters. :param type_dict: :param whole_type_list: :param index_list: :return index_dict: return a dictionary of all bond-pairs or angle indices for each unique bond or angle type, using the the same keys as type_dict. """ index_dict = {} for key, value in type_dict.items(): temp_list = [] for count, items in enumerate(whole_type_list): if any(list(pair) == value for pair in list(permutations(items))): temp_list.append(index_list[count]) index_dict[key] = temp_list return index_dict def get_type_index_pair(type_dict, whole_type_list, index_list): """ write bond_type and bond_index into a single dictionary; can use tuples as dictionary key, not lists :param type_dict: :param whole_type_list: :param index_list: """ bond_index_dict = _get_index_dict(type_dict, whole_type_list, index_list) type_index_dict = {} for key, value in type_dict.items(): type_index_dict[tuple(value)] = bond_index_dict[key] return type_index_dict def pretty_print(my_dict): """ for better visualization of the bond (or angle) types and bond (or angle) indices that belong to certain types. """ for key, value in my_dict.items(): print(key, '-->', value) def shorten_index_list_by_types(type_index_dict, exclude_atom_type=None, exclude_property_type=None, include_property_type=None, case=0): """ allow excluding certain property types or only including certain types """ if exclude_atom_type is not None and exclude_property_type is None: case = 1 if exclude_property_type is not None and exclude_atom_type is None: case = 2 if exclude_property_type is not None and exclude_atom_type is not None: case = 3 if include_property_type is not None: case = 4 shortened_list = [] for type_list, index_list in type_index_dict.items(): if case == 1 and all(single_type not in type_list for single_type in exclude_atom_type): shortened_list.extend(index_list) elif case == 2 and all(list(value) not in exclude_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) elif case == 3 and all(single_type not in type_list for single_type in exclude_atom_type) and \ all(list(value) not in exclude_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) elif case == 4 and any(list(value) in include_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) return shortened_list def set_up_openMM_system(folder_path, cluster_tag_number, shortened_bond_list): """ Feed pdb topology file and xml force field file into openMM, generate a system for the MD simulation/force calculation. :param folder_path: :param cluster_tag_number: :param shortened_bond_list: :return pdb: :return system: """ pdb = PDBFile(folder_path + '/cluster_%s_labeled.pdb' % cluster_tag_number) atoms = list(pdb.topology.atoms()) for index in shortened_bond_list: pdb.topology.addBond(atoms[index[0]], atoms[index[1]]) bonds = list(pdb.topology.bonds()) write_xml(atoms, bonds, folder_path + '/forcefield.xml') FF = ForceField(folder_path + '/forcefield.xml') system = FF.createSystem(pdb.topology) return pdb, system def custom_openMM_force_object(system, bond_list, bond_type_index_dict, bond_param_dict, angle_list=None, angle_type_index_dict=None, angle_param_dict=None): """ #todo: add argument allowing this custom function to be fed in as an input (more flexible used-designed ff) :param bond_list: list to be included into force field :param angle_list: :param bond_type_index_dict: {(type): [index], ...} :param angle_type_index_dict: :param bond_param_dict: {(type): [param], ...} Note: parameters here uses the standard units, kJ, nm, ... :param angle_param_dict: :return system: openMM system with custom forces added onto it """ force = CustomBondForce("D*(1-exp(-alpha*(r-r0)))^2") # Morse bond force.addPerBondParameter("D") force.addPerBondParameter("alpha") force.addPerBondParameter("r0") force.setUsesPeriodicBoundaryConditions(periodic=True) for bond in bond_list: for my_type, my_index in bond_type_index_dict.items(): if any(list(val) in my_index for val in list(permutations(bond))): try: force.addBond(int(bond[0]), int(bond[1]), bond_param_dict.get(my_type)) except: my_type = tuple(reversed(my_type)) force.addBond(int(bond[0]), int(bond[1]), bond_param_dict.get(my_type)) # note: consider updating the info_dict to make it order insensitive system.addForce(force) force = HarmonicAngleForce() # Harmonic angle force.setUsesPeriodicBoundaryConditions(periodic=True) # adding periodic conditions for angle in angle_list: for my_type, my_index in angle_type_index_dict.items(): if any(list(val) in my_index for val in list(permutations(angle))): type_tag = [tuple(val) for val in list(angle_param_dict.keys()) if val in list(permutations(my_type))] force.addAngle(int(angle[0]), int(angle[1]), int(angle[2]), *angle_param_dict.get(type_tag[0])) system.addForce(force) # assert(system.usesPeriodicBoundaryConditions() == True) return system def get_openMM_forces(pdb, system, bond_list, bond_type_index_dict, bond_param_dict, angle_list=None, angle_type_index_dict=None, angle_param_dict=None): """ forces for a single configuration use numb to keep track of individual configurations integrator used for advancing the equations of motion in MD doesn't matter what we pick here since we only need the forces on the initial structure, but do need to have it :return: forces values on atoms in units of eV/A """ system = custom_openMM_force_object(system, bond_list, bond_type_index_dict, bond_param_dict, angle_list, angle_type_index_dict, angle_param_dict) integrator = LangevinMiddleIntegrator(3 * kelvin, 1 / picosecond, 0.4 * picoseconds) # randomly picked simulation = Simulation(pdb.topology, system, integrator) simulation.context.setPositions(pdb.positions) state = simulation.context.getState(getForces=True) forces = np.array(state.getForces(asNumpy=True)) * 1.0364e-2 * 0.1 # convert forces from kJ/nm mol to eV/A return forces # NOTE: section below deals with multiple input structures for force field training def get_EF_O_index(traj): """ get the mode of EF_O, and use that to extract the EF cluster for the force field training all EF atoms should have the same indices regardless of there is binds on the zeolite, as long as the zeolite framework is the same - (all EF atoms, aka. Cu-O-Cu insertion follows the same procedures) :param traj: traj of configurations containing all atoms, including both the zeolite backbone and EF atoms """ EF_O_index_list = [] for atoms in traj: try: EFAnalyzer = ExtraFrameworkAnalyzer(atoms) EF_O_index_list.append(EFAnalyzer.get_extraframework_cluster()[-1]) except: ... return mode(tuple(EF_O_index_list)) def prep_topologies(folder_path, sample_zeolite, traj_name=None, save_traj=False, del_unlabeled_pdb=False, show_all=False): """ :param folder_path: :param sample_zeolite: :param traj_name: :param save_traj: :param del_unlabeled_pdb: :param show_all: """ if traj_name is not None: traj = read(folder_path + '/%s.traj' % traj_name, ':') output_dir = os.path.join(folder_path, traj_name) else: traj = read(folder_path + '/%s.traj' % sample_zeolite, ':') output_dir = os.path.join(folder_path, sample_zeolite) Path(output_dir).mkdir(parents=True, exist_ok=True) cluster_traj, EF_O_index, EF_atoms_index, cluster_EF_index = [], get_EF_O_index(traj[0:100]), [], [] for count, atoms in enumerate(traj): try: cluster, EF_atoms_index, cluster_EF_index = get_capped_cluster(atoms, output_dir, 'cluster_' + str(count), save_traj, [EF_O_index]) label_pdb(output_dir, 'cluster_%s' % str(count), del_unlabeled_pdb) cluster_traj.append(cluster) print(sample_zeolite, count) except: print(sample_zeolite, count, 'failed!') if show_all is True: view(cluster_traj) return EF_atoms_index, cluster_EF_index def reformat_inputs(bond_param_dict, angle_param_dict): """ reformat input dict into lists :return bond_type: List[List[str]] eg. ['Cu', 'O'] :return angle_type: List[List[str]] eg. ['Cu', 'O', 'Cu'] :return param_list: List[float], extend all parameters into a single list, since scipy.optimize.minimize can only take an 1D array as initial guess parameter """ bond_type, angle_type, param_list = [], [], [] for types, indices in bond_param_dict.items(): bond_type.append(list(types)) param_list.extend([val for val in np.array(indices)]) for types, indices in angle_param_dict.items(): angle_type.append(list(types)) param_list.extend([val for val in np.array(indices)]) return bond_type, angle_type, param_list def get_required_objects_for_ff(folder_path, cluster_tag_number, included_bond_type, included_angle_type, bond_type_index_dict, angle_type_index_dict): """ To reduce computational cost, objects such as pdb, system, shortened_bond_list, bond_type_index_dict are kept fixed for each configuration during the optimization (only run once). """ shortened_bond_list = shorten_index_list_by_types(bond_type_index_dict, include_property_type=included_bond_type) shortened_angle_list = shorten_index_list_by_types(angle_type_index_dict, include_property_type=included_angle_type) pdb, system = set_up_openMM_system(folder_path, cluster_tag_number, shortened_bond_list) return pdb, system, shortened_bond_list, shortened_angle_list def get_FF_forces(param, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): """ openMM forces for multiple configuration based on the same set of parameters """ bond_param_dict, angle_param_dict, number_of_bond_param = {}, {}, 0 for count, (types, indices) in enumerate(ini_bond_param_dict.items()): bond_param_dict[types] = list(param[count * len(indices):(count + 1) * len(indices)]) number_of_bond_param += len(indices) for count, (types, indices) in enumerate(ini_angle_param_dict.items()): angle_param_dict[types] = list( param[count * len(indices) + number_of_bond_param:(count + 1) * len(indices) + number_of_bond_param]) predicted_f = [] my_dict = copy.deepcopy(info_dict) for config_tag, info_list in my_dict.items(): ff_forces = get_openMM_forces(info_list[0], info_list[1], info_list[2], bond_type_index_dict, bond_param_dict, info_list[3], angle_type_index_dict, angle_param_dict)[EF_index] predicted_f.append([force_list for force_list in ff_forces]) return predicted_f def get_DFT_forces_single(atoms, atom_index): """ reference DFT forces on single atoms """ f_vec = atoms.calc.results['forces'][atom_index] # self.atoms.get_forces()[atom_index] f_mag = np.linalg.norm(f_vec) return f_vec def get_residue(param, info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): """ optimize force field parameters by minimizing this loss function (MSE), weighted by DFT electronic energies k (Boltzmann's constant) = 8.617e-5 eV/K T = 298 K """ predicted_f = get_FF_forces(param, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index) residue = np.reshape(np.array(np.reshape(predicted_f, [-1, 3])) - np.array(np.reshape(DFT_f, [-1, 3])), -1) weighted_residue = residue * weights # 39 number of atoms print(np.mean(weighted_residue ** 2)) return np.mean(weighted_residue ** 2) def get_fitting_parameters(initial_param, info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): # todo: more flexible bond reformating and feeding bounds = ((-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (0, np.pi), (-np.Inf, np.Inf), (0, np.pi), (-np.Inf, np.Inf), (0, np.pi), (-np.Inf, np.Inf)) res = minimize(get_residue, initial_param, method='Powell', bounds=bounds, options={'ftol': 0.01, 'maxiter': 1000}, args=(info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index)) print(res.success) return res def make_parity_plot(ff_forces, dft_forces, atom_name): """ plot FF forces vs. DFT forces """ plt.figure() fig, ax = plt.subplots() plt.plot(dft_forces, ff_forces, 'o') plt.xlabel('DFT_force', fontsize=18) plt.ylabel('FF_force', fontsize=18) lims = [np.min([ax.get_xlim(), ax.get_ylim()]), np.max([ax.get_xlim(), ax.get_ylim()])] ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0) ax.set_aspect('equal') ax.set_xlim(lims) ax.set_ylim(lims) plt.title('Force fitting on %s' % atom_name, fontsize=18) plt.show() def func(): tic = time.perf_counter() zeolite = 'SOD' folder_path, sample_zeolite, traj_name = '/Users/jiaweiguo/Box/openMM_FF', zeolite, zeolite + '_md' # prep_topologies(folder_path, sample_zeolite, traj_name, del_unlabeled_pdb=True) """ ini_bond_param_dict = {('O-Cu', 'Cu'): [1.2, 4, 0.3], ('O-EF', 'Cu'): [1.2, 4, 0.2], ('Al', 'Cu'): [1.2, 4, 0.4]} ini_angle_param_dict = {('Cu', 'O-EF', 'Cu'): [2.3, 10], ('O-Cu', 'Cu', 'O-EF'): [2.3, 10], ('Al', 'Cu', 'O-EF'): [2.3, 10]} """ ini_bond_param_dict = {('O-Cu', 'Cu'): [60.097, 2.267, 0.228], ('O-EF', 'Cu'): [4405.247, 4.163, 0.177], ('Al', 'Cu'): [-2.656, 4.608, 0.413]} ini_angle_param_dict = {('Cu', 'O-EF', 'Cu'): [2.458, 16.552], ('O-Cu', 'Cu', 'O-EF'): [3.266, 4.136], ('Al', 'Cu', 'O-EF'): [1.925, 1.673]} included_bond_type, included_angle_type, ini_param = reformat_inputs(ini_bond_param_dict, ini_angle_param_dict) # set up type_index_dict using a single set of data #fixme: randomly pick several initial clusters to built dict cluster = read(os.path.join(folder_path, traj_name) + '/cluster_0_labeled.pdb', '0') bond_index_list, shortened_bond_index_list = get_bonds(cluster, mult=2) bond_type_dict, whole_bond_type_list = get_property_types(cluster, bond_index_list) angle_index_list, shortened_angle_index_list = get_angles(cluster, mult=2) angle_type_dict, whole_angle_type_list = get_property_types(cluster, angle_index_list) bond_type_index_dict = get_type_index_pair(bond_type_dict, whole_bond_type_list, bond_index_list) angle_type_index_dict = get_type_index_pair(angle_type_dict, whole_angle_type_list, angle_index_list) numb_skip = 2000 info_dict, output_path = {}, os.path.join(folder_path, traj_name) files = [files for files in os.listdir(os.path.join(folder_path, traj_name)) if '.pdb' in files] for cluster_tag_number in np.arange(0, len(files), numb_skip): cluster_tag_number = int(cluster_tag_number) pdb, system, shortened_bond_list, shortened_angle_list = \ get_required_objects_for_ff(output_path, cluster_tag_number, included_bond_type, included_angle_type, bond_type_index_dict, angle_type_index_dict) info_dict[cluster_tag_number] = [pdb, system, shortened_bond_list, shortened_angle_list] print(cluster_tag_number) with open(output_path + '/info_dict_%s.pickle' % numb_skip, 'wb') as f: pickle.dump(info_dict, f) with open(folder_path + '/EF_index_dict.pickle', 'rb') as f: EF_index_dict = pickle.load(f) traj = read(folder_path + '/%s.traj' % traj_name, '0::%s' % numb_skip) DFT_f = [] for atoms in traj: DFT_f.append([get_DFT_forces_single(atoms, atom_index=val) for val in EF_index_dict.get(zeolite)[-3:]]) print(np.array(DFT_f).shape) ref_E = read(folder_path + '/%s.traj' % traj_name, '-1').calc.results['energy'] DFT_E = [] for atoms in traj: DFT_E.append(atoms.calc.results['energy']) with open(os.path.join(folder_path, traj_name) + '/info_dict_%s.pickle' % numb_skip, 'rb') as f: info_dict = pickle.load(f) with open(folder_path + '/cluster_EF_index_dict.pickle', 'rb') as f: cluster_EF_index_dict = pickle.load(f) my_dict = copy.deepcopy(info_dict) # important, need to keep openMM "systems" fixed weights = [] for value in np.exp(-(np.array(DFT_E) - ref_E) / len(traj[0]) / (8.617e-5 * 298)): weights.extend([value, value, value, value, value, value, value, value, value]) res = get_fitting_parameters(ini_param, my_dict, DFT_f, np.array(weights), ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, cluster_EF_index_dict.get(zeolite)) print([np.around(float(val), decimals=3) for val in res.x]) FF_f = get_FF_forces(res.x, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, cluster_EF_index_dict.get(zeolite)) make_parity_plot(np.array(np.reshape(FF_f, [-1, 3])), np.array(np.reshape(DFT_f, [-1, 3])), 'Cu-O-Cu') force_dict = {'FF': np.array(np.reshape(FF_f, [-1, 3])), 'DFT': np.array(np.reshape(DFT_f, [-1, 3]))} with open(output_path + '/forces_%s.pickle' % numb_skip, 'wb') as f: pickle.dump(force_dict, f) toc = time.perf_counter() print(f"Program terminated in {toc - tic:0.4f} seconds") if __name__ == '__main__': # func() """ weighting factor for the loss function zeolite = 'SOD' folder_path, traj_name, numb_skip = '/Users/jiaweiguo/Box/openMM_FF', zeolite + '_md', 2000 traj = read(folder_path + '/%s.traj' % traj_name, '0::%s' % numb_skip) ref_E = read(folder_path + '/%s.traj' % traj_name, '-1').calc.results['energy'] DFT_E = [] for atoms in traj: DFT_E.append(atoms.calc.results['energy']) weight = np.exp(-(np.array(DFT_E) - ref_E) / len(traj[0]) / (8.617e-5 * 298)) plt.plot(DFT_E, weight, 'o') plt.xlabel('DFT electronic energies (eV)', fontsize=16) plt.ylabel('Boltzmann weighting', fontsize=16) plt.show() """

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from maze.extra_framework_maker import ExtraFrameworkMaker, ExtraFrameworkAnalyzer from maze.io_zeolite import read_vasp from maze.zeolite import PerfectZeolite, Zeolite from ase.neighborlist import natural_cutoffs, NeighborList import os from pathlib import Path from ase.io import write, read, gromacs, proteindatabank from ase.visualize import view import copy import shutil from glob import glob from ase.constraints import FixAtoms from simtk.openmm.app import * from simtk.openmm import * from simtk.unit import * from sys import stdout from ase.geometry.analysis import Analysis import numpy as np from itertools import permutations from lxml import etree from contextlib import closing from collections import OrderedDict from scipy.optimize import least_squares, minimize import matplotlib.pyplot as plt from statistics import mode import pickle import time from ase.data import atomic_masses, atomic_numbers def get_EF_atom_indices(atoms): TM_list = ['Pt', 'Cu', 'Co', 'Pd', 'Fe', 'Cr', 'Rh', 'Ru'] index_EF_TM = [a.index for a in atoms if a.symbol in TM_list] index_Al = [a.index for a in atoms if a.symbol == 'Al'] nl = NeighborList(natural_cutoffs(atoms), bothways=True, self_interaction=False) nl.update(atoms) Al_neigh_list = np.concatenate((nl.get_neighbors(index_Al[0])[0], nl.get_neighbors(index_Al[1])[0])) Al_neigh_list = [x for x in Al_neigh_list if atoms[x].symbol == 'O'] TM_neigh_list = np.concatenate((nl.get_neighbors(index_EF_TM[0])[0], nl.get_neighbors(index_EF_TM[1])[0])) centering_o = [[x for x in TM_neigh_list if list(TM_neigh_list).count(x) > 1 and x not in Al_neigh_list][0]] return index_EF_TM + centering_o def get_capped_cluster(atoms, folder_path, file_name, save_traj, EF_O_index): EFMaker = ExtraFrameworkAnalyzer(atoms) cluster = atoms[[index for index in EFMaker.get_extraframework_cluster(EF_O_index)]] cluster_EF_index = get_EF_atom_indices(cluster) centering_pos = cluster.get_positions()[cluster_EF_index[-1]] recentered_cluster = EFMaker.recentering_atoms(cluster, centering_pos)[0] # cluster = Zeolite(cluster).cap_atoms() proteindatabank.write_proteindatabank(folder_path + '/%s.pdb' % file_name, recentered_cluster) if save_traj is True: write(folder_path + '/%s.traj' % file_name, recentered_cluster) return cluster, EFMaker.get_extraframework_cluster(EF_O_index), cluster_EF_index def label_pdb(folder_path, file_name, del_unlabeled_pdb): filein = open(folder_path + '/%s.pdb' % file_name, 'r') fileout = open(folder_path + '/%s_labeled.pdb' % file_name, 'w') name_list = [] for line in filein.readlines(): if line.startswith('ATOM') or line.startswith('HETATM'): name = line[12:16].strip() name_list.append(name) name = name + str(name_list.count(name)) name = name.rjust(4) line = line.replace(line[12:16], name, 1) # only replacing the first occurrence of line[12:16], atomic symbols are maintained fileout.writelines(line) filein.close() fileout.close() if del_unlabeled_pdb is True: os.remove(folder_path + '/%s.pdb' % file_name) def get_bonds(cluster, mult=1, excluded_index=None, excluded_pair=None): if excluded_index is None: excluded_index = [] if excluded_pair is None: excluded_pair = [] nl = NeighborList(natural_cutoffs(cluster, mult=mult), bothways=True, self_interaction=False) nl.update(cluster) bond_list, shortened_list = [], [] for count, indices in enumerate(Analysis(cluster, nl=nl).all_bonds[0]): for index in indices: if [count, index] not in bond_list and [index, count] not in bond_list: bond_list.append([count, index]) for bond in bond_list: if all(single_index not in bond for single_index in excluded_index) and \ all(tuple(bond) not in list(permutations(pair)) for pair in excluded_pair): shortened_list.append(bond) return bond_list, shortened_list def get_angles(cluster, mult=1, excluded_index=None, excluded_pair=None): if excluded_index is None: excluded_index = [] if excluded_pair is None: excluded_pair = [] nl = NeighborList(natural_cutoffs(cluster, mult=mult), bothways=True, self_interaction=False) nl.update(cluster) angle_list, shortened_list = [], [] for count, indices in enumerate(Analysis(cluster, nl=nl).all_angles[0]): for index in indices: if all(list(val) not in angle_list for val in list(permutations([count, index[0], index[1]]))): angle_list.append([count, index[0], index[1]]) for angle in angle_list: if all(single_index not in angle for single_index in excluded_index) and \ all(list(value) not in excluded_pair for value in list(permutations(angle, 2))): shortened_list.append(angle) return angle_list, shortened_list def write_xml(atoms, bonds, save_as): # on-the-fly generation of force field xml file, matching atoms and bonds with pdb file root = etree.Element('ForceField') xml_section = etree.SubElement(root, "AtomTypes") for atom in atoms: element_type = ''.join(filter(lambda x: not x.isdigit(), atom.name)) # properties = {'name': atom.name, 'class': atom.name, 'element': element_type, 'mass': str(atomic_mass)} if element_type == 'Cu' or atom.name == 'O9': atomic_mass = atomic_masses[atomic_numbers[element_type]] else: atomic_mass = 0.0 properties = {'name': atom.name, 'class': atom.name, 'element': element_type, 'mass': str(atomic_mass)} etree.SubElement(xml_section, 'Type', **properties) xml_section = etree.SubElement(root, 'Residues') xml_residue = etree.SubElement(xml_section, 'Residue', name='MOL') for atom in atoms: etree.SubElement(xml_residue, 'Atom', name=atom.name, type=atom.name) for bond in bonds: etree.SubElement(xml_residue, 'Bond', atomName1=bond[0].name, atomName2=bond[1].name) tree = etree.ElementTree(root) xml = etree.tostring(tree, pretty_print=True).decode('utf-8') with closing(open(save_as, 'w')) as f: f.write(xml) def check_atom_types(cluster, index): nl = NeighborList(natural_cutoffs(cluster), bothways=True, self_interaction=False) nl.update(cluster) class_Al = [atom.index for atom in cluster if atom.symbol == 'Al'] class_Cu = [atom.index for atom in cluster if atom.symbol == 'Cu'] class_H = [atom.index for atom in cluster if atom.symbol == 'H'] class_O_EF = [get_EF_atom_indices(cluster)[-1]] class_O_Cu = [atom.index for atom in cluster if atom.symbol == 'O' and atom.index not in class_O_EF and all(val not in class_H for val in nl.get_neighbors(atom.index)[0])] class_O_H = [atom.index for atom in cluster if atom.symbol == 'O' and atom.index not in class_O_EF + class_O_Cu] if index in class_Al: return 'Al' if index in class_Cu: return 'Cu' if index in class_H: return 'H' if index in class_O_EF: return 'O-EF' if index in class_O_Cu: return 'O-Cu' if index in class_O_H: return 'O-H' else: return 'None' def get_property_types(cluster, property_list): type_dict, repeated_list, whole_type_list, count = {}, [], [], 0 for items in property_list: my_list = [] for val in items: my_list.append(check_atom_types(cluster, val)) whole_type_list.append(my_list) if all(list(pair) not in repeated_list for pair in list(permutations(my_list))): repeated_list.append(my_list) type_dict[count] = my_list count += 1 return type_dict, whole_type_list def _get_index_dict(type_dict, whole_type_list, index_list): index_dict = {} for key, value in type_dict.items(): temp_list = [] for count, items in enumerate(whole_type_list): if any(list(pair) == value for pair in list(permutations(items))): temp_list.append(index_list[count]) index_dict[key] = temp_list return index_dict def get_type_index_pair(type_dict, whole_type_list, index_list): bond_index_dict = _get_index_dict(type_dict, whole_type_list, index_list) type_index_dict = {} for key, value in type_dict.items(): type_index_dict[tuple(value)] = bond_index_dict[key] return type_index_dict def pretty_print(my_dict): for key, value in my_dict.items(): print(key, '-->', value) def shorten_index_list_by_types(type_index_dict, exclude_atom_type=None, exclude_property_type=None, include_property_type=None, case=0): if exclude_atom_type is not None and exclude_property_type is None: case = 1 if exclude_property_type is not None and exclude_atom_type is None: case = 2 if exclude_property_type is not None and exclude_atom_type is not None: case = 3 if include_property_type is not None: case = 4 shortened_list = [] for type_list, index_list in type_index_dict.items(): if case == 1 and all(single_type not in type_list for single_type in exclude_atom_type): shortened_list.extend(index_list) elif case == 2 and all(list(value) not in exclude_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) elif case == 3 and all(single_type not in type_list for single_type in exclude_atom_type) and \ all(list(value) not in exclude_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) elif case == 4 and any(list(value) in include_property_type for value in list(permutations(type_list))): shortened_list.extend(index_list) return shortened_list def set_up_openMM_system(folder_path, cluster_tag_number, shortened_bond_list): pdb = PDBFile(folder_path + '/cluster_%s_labeled.pdb' % cluster_tag_number) atoms = list(pdb.topology.atoms()) for index in shortened_bond_list: pdb.topology.addBond(atoms[index[0]], atoms[index[1]]) bonds = list(pdb.topology.bonds()) write_xml(atoms, bonds, folder_path + '/forcefield.xml') FF = ForceField(folder_path + '/forcefield.xml') system = FF.createSystem(pdb.topology) return pdb, system def custom_openMM_force_object(system, bond_list, bond_type_index_dict, bond_param_dict, angle_list=None, angle_type_index_dict=None, angle_param_dict=None): force = CustomBondForce("D*(1-exp(-alpha*(r-r0)))^2") # Morse bond force.addPerBondParameter("D") force.addPerBondParameter("alpha") force.addPerBondParameter("r0") force.setUsesPeriodicBoundaryConditions(periodic=True) for bond in bond_list: for my_type, my_index in bond_type_index_dict.items(): if any(list(val) in my_index for val in list(permutations(bond))): try: force.addBond(int(bond[0]), int(bond[1]), bond_param_dict.get(my_type)) except: my_type = tuple(reversed(my_type)) force.addBond(int(bond[0]), int(bond[1]), bond_param_dict.get(my_type)) # note: consider updating the info_dict to make it order insensitive system.addForce(force) force = HarmonicAngleForce() # Harmonic angle force.setUsesPeriodicBoundaryConditions(periodic=True) # adding periodic conditions for angle in angle_list: for my_type, my_index in angle_type_index_dict.items(): if any(list(val) in my_index for val in list(permutations(angle))): type_tag = [tuple(val) for val in list(angle_param_dict.keys()) if val in list(permutations(my_type))] force.addAngle(int(angle[0]), int(angle[1]), int(angle[2]), *angle_param_dict.get(type_tag[0])) system.addForce(force) # assert(system.usesPeriodicBoundaryConditions() == True) return system def get_openMM_forces(pdb, system, bond_list, bond_type_index_dict, bond_param_dict, angle_list=None, angle_type_index_dict=None, angle_param_dict=None): system = custom_openMM_force_object(system, bond_list, bond_type_index_dict, bond_param_dict, angle_list, angle_type_index_dict, angle_param_dict) integrator = LangevinMiddleIntegrator(3 * kelvin, 1 / picosecond, 0.4 * picoseconds) # randomly picked simulation = Simulation(pdb.topology, system, integrator) simulation.context.setPositions(pdb.positions) state = simulation.context.getState(getForces=True) forces = np.array(state.getForces(asNumpy=True)) * 1.0364e-2 * 0.1 # convert forces from kJ/nm mol to eV/A return forces # NOTE: section below deals with multiple input structures for force field training def get_EF_O_index(traj): EF_O_index_list = [] for atoms in traj: try: EFAnalyzer = ExtraFrameworkAnalyzer(atoms) EF_O_index_list.append(EFAnalyzer.get_extraframework_cluster()[-1]) except: ... return mode(tuple(EF_O_index_list)) def prep_topologies(folder_path, sample_zeolite, traj_name=None, save_traj=False, del_unlabeled_pdb=False, show_all=False): if traj_name is not None: traj = read(folder_path + '/%s.traj' % traj_name, ':') output_dir = os.path.join(folder_path, traj_name) else: traj = read(folder_path + '/%s.traj' % sample_zeolite, ':') output_dir = os.path.join(folder_path, sample_zeolite) Path(output_dir).mkdir(parents=True, exist_ok=True) cluster_traj, EF_O_index, EF_atoms_index, cluster_EF_index = [], get_EF_O_index(traj[0:100]), [], [] for count, atoms in enumerate(traj): try: cluster, EF_atoms_index, cluster_EF_index = get_capped_cluster(atoms, output_dir, 'cluster_' + str(count), save_traj, [EF_O_index]) label_pdb(output_dir, 'cluster_%s' % str(count), del_unlabeled_pdb) cluster_traj.append(cluster) print(sample_zeolite, count) except: print(sample_zeolite, count, 'failed!') if show_all is True: view(cluster_traj) return EF_atoms_index, cluster_EF_index def reformat_inputs(bond_param_dict, angle_param_dict): bond_type, angle_type, param_list = [], [], [] for types, indices in bond_param_dict.items(): bond_type.append(list(types)) param_list.extend([val for val in np.array(indices)]) for types, indices in angle_param_dict.items(): angle_type.append(list(types)) param_list.extend([val for val in np.array(indices)]) return bond_type, angle_type, param_list def get_required_objects_for_ff(folder_path, cluster_tag_number, included_bond_type, included_angle_type, bond_type_index_dict, angle_type_index_dict): shortened_bond_list = shorten_index_list_by_types(bond_type_index_dict, include_property_type=included_bond_type) shortened_angle_list = shorten_index_list_by_types(angle_type_index_dict, include_property_type=included_angle_type) pdb, system = set_up_openMM_system(folder_path, cluster_tag_number, shortened_bond_list) return pdb, system, shortened_bond_list, shortened_angle_list def get_FF_forces(param, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): bond_param_dict, angle_param_dict, number_of_bond_param = {}, {}, 0 for count, (types, indices) in enumerate(ini_bond_param_dict.items()): bond_param_dict[types] = list(param[count * len(indices):(count + 1) * len(indices)]) number_of_bond_param += len(indices) for count, (types, indices) in enumerate(ini_angle_param_dict.items()): angle_param_dict[types] = list( param[count * len(indices) + number_of_bond_param:(count + 1) * len(indices) + number_of_bond_param]) predicted_f = [] my_dict = copy.deepcopy(info_dict) for config_tag, info_list in my_dict.items(): ff_forces = get_openMM_forces(info_list[0], info_list[1], info_list[2], bond_type_index_dict, bond_param_dict, info_list[3], angle_type_index_dict, angle_param_dict)[EF_index] predicted_f.append([force_list for force_list in ff_forces]) return predicted_f def get_DFT_forces_single(atoms, atom_index): f_vec = atoms.calc.results['forces'][atom_index] # self.atoms.get_forces()[atom_index] f_mag = np.linalg.norm(f_vec) return f_vec def get_residue(param, info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): predicted_f = get_FF_forces(param, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index) residue = np.reshape(np.array(np.reshape(predicted_f, [-1, 3])) - np.array(np.reshape(DFT_f, [-1, 3])), -1) weighted_residue = residue * weights # 39 number of atoms print(np.mean(weighted_residue ** 2)) return np.mean(weighted_residue ** 2) def get_fitting_parameters(initial_param, info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index): # todo: more flexible bond reformating and feeding bounds = ((-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (-np.Inf, np.Inf), (-np.Inf, np.Inf), (0, np.Inf), (0, np.pi), (-np.Inf, np.Inf), (0, np.pi), (-np.Inf, np.Inf), (0, np.pi), (-np.Inf, np.Inf)) res = minimize(get_residue, initial_param, method='Powell', bounds=bounds, options={'ftol': 0.01, 'maxiter': 1000}, args=(info_dict, DFT_f, weights, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, EF_index)) print(res.success) return res def make_parity_plot(ff_forces, dft_forces, atom_name): plt.figure() fig, ax = plt.subplots() plt.plot(dft_forces, ff_forces, 'o') plt.xlabel('DFT_force', fontsize=18) plt.ylabel('FF_force', fontsize=18) lims = [np.min([ax.get_xlim(), ax.get_ylim()]), np.max([ax.get_xlim(), ax.get_ylim()])] ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0) ax.set_aspect('equal') ax.set_xlim(lims) ax.set_ylim(lims) plt.title('Force fitting on %s' % atom_name, fontsize=18) plt.show() def func(): tic = time.perf_counter() zeolite = 'SOD' folder_path, sample_zeolite, traj_name = '/Users/jiaweiguo/Box/openMM_FF', zeolite, zeolite + '_md' # prep_topologies(folder_path, sample_zeolite, traj_name, del_unlabeled_pdb=True) ini_bond_param_dict = {('O-Cu', 'Cu'): [60.097, 2.267, 0.228], ('O-EF', 'Cu'): [4405.247, 4.163, 0.177], ('Al', 'Cu'): [-2.656, 4.608, 0.413]} ini_angle_param_dict = {('Cu', 'O-EF', 'Cu'): [2.458, 16.552], ('O-Cu', 'Cu', 'O-EF'): [3.266, 4.136], ('Al', 'Cu', 'O-EF'): [1.925, 1.673]} included_bond_type, included_angle_type, ini_param = reformat_inputs(ini_bond_param_dict, ini_angle_param_dict) # set up type_index_dict using a single set of data #fixme: randomly pick several initial clusters to built dict cluster = read(os.path.join(folder_path, traj_name) + '/cluster_0_labeled.pdb', '0') bond_index_list, shortened_bond_index_list = get_bonds(cluster, mult=2) bond_type_dict, whole_bond_type_list = get_property_types(cluster, bond_index_list) angle_index_list, shortened_angle_index_list = get_angles(cluster, mult=2) angle_type_dict, whole_angle_type_list = get_property_types(cluster, angle_index_list) bond_type_index_dict = get_type_index_pair(bond_type_dict, whole_bond_type_list, bond_index_list) angle_type_index_dict = get_type_index_pair(angle_type_dict, whole_angle_type_list, angle_index_list) numb_skip = 2000 info_dict, output_path = {}, os.path.join(folder_path, traj_name) files = [files for files in os.listdir(os.path.join(folder_path, traj_name)) if '.pdb' in files] for cluster_tag_number in np.arange(0, len(files), numb_skip): cluster_tag_number = int(cluster_tag_number) pdb, system, shortened_bond_list, shortened_angle_list = \ get_required_objects_for_ff(output_path, cluster_tag_number, included_bond_type, included_angle_type, bond_type_index_dict, angle_type_index_dict) info_dict[cluster_tag_number] = [pdb, system, shortened_bond_list, shortened_angle_list] print(cluster_tag_number) with open(output_path + '/info_dict_%s.pickle' % numb_skip, 'wb') as f: pickle.dump(info_dict, f) with open(folder_path + '/EF_index_dict.pickle', 'rb') as f: EF_index_dict = pickle.load(f) traj = read(folder_path + '/%s.traj' % traj_name, '0::%s' % numb_skip) DFT_f = [] for atoms in traj: DFT_f.append([get_DFT_forces_single(atoms, atom_index=val) for val in EF_index_dict.get(zeolite)[-3:]]) print(np.array(DFT_f).shape) ref_E = read(folder_path + '/%s.traj' % traj_name, '-1').calc.results['energy'] DFT_E = [] for atoms in traj: DFT_E.append(atoms.calc.results['energy']) with open(os.path.join(folder_path, traj_name) + '/info_dict_%s.pickle' % numb_skip, 'rb') as f: info_dict = pickle.load(f) with open(folder_path + '/cluster_EF_index_dict.pickle', 'rb') as f: cluster_EF_index_dict = pickle.load(f) my_dict = copy.deepcopy(info_dict) # important, need to keep openMM "systems" fixed weights = [] for value in np.exp(-(np.array(DFT_E) - ref_E) / len(traj[0]) / (8.617e-5 * 298)): weights.extend([value, value, value, value, value, value, value, value, value]) res = get_fitting_parameters(ini_param, my_dict, DFT_f, np.array(weights), ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, cluster_EF_index_dict.get(zeolite)) print([np.around(float(val), decimals=3) for val in res.x]) FF_f = get_FF_forces(res.x, info_dict, ini_bond_param_dict, ini_angle_param_dict, bond_type_index_dict, angle_type_index_dict, cluster_EF_index_dict.get(zeolite)) make_parity_plot(np.array(np.reshape(FF_f, [-1, 3])), np.array(np.reshape(DFT_f, [-1, 3])), 'Cu-O-Cu') force_dict = {'FF': np.array(np.reshape(FF_f, [-1, 3])), 'DFT': np.array(np.reshape(DFT_f, [-1, 3]))} with open(output_path + '/forces_%s.pickle' % numb_skip, 'wb') as f: pickle.dump(force_dict, f) toc = time.perf_counter() print(f"Program terminated in {toc - tic:0.4f} seconds") if __name__ == '__main__': # func()

true

f71989a26c51d5d0de8be179c705597a99ff7aea

17,373

py

Python

python/ccxt/async_support/bitbay.py

Richard-L-Johnson/ccxt1

903aa1288694f9192b15d22b945508661bdc8807

[ "MIT" ]

13

2019-01-26T14:41:37.000Z

2022-03-26T03:33:12.000Z

python/ccxt/async_support/bitbay.py

Richard-L-Johnson/ccxt1

903aa1288694f9192b15d22b945508661bdc8807

[ "MIT" ]

17

2018-10-02T04:43:13.000Z

2018-11-01T17:07:37.000Z

python/ccxt/async_support/bitbay.py

Richard-L-Johnson/ccxt1

903aa1288694f9192b15d22b945508661bdc8807

[ "MIT" ]

12

2018-12-24T02:19:02.000Z

2022-03-26T05:04:25.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange # ----------------------------------------------------------------------------- try: basestring # Python 3 except NameError: basestring = str # Python 2 import hashlib import json from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import InvalidNonce class bitbay (Exchange): def describe(self): return self.deep_extend(super(bitbay, self).describe(), { 'id': 'bitbay', 'name': 'BitBay', 'countries': ['MT', 'EU'], # Malta 'rateLimit': 1000, 'has': { 'CORS': True, 'withdraw': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/27766132-978a7bd8-5ece-11e7-9540-bc96d1e9bbb8.jpg', 'www': 'https://bitbay.net', 'api': { 'public': 'https://bitbay.net/API/Public', 'private': 'https://bitbay.net/API/Trading/tradingApi.php', }, 'doc': [ 'https://bitbay.net/public-api', 'https://bitbay.net/account/tab-api', 'https://github.com/BitBayNet/API', ], 'fees': 'https://bitbay.net/en/fees', }, 'api': { 'public': { 'get': [ '{id}/all', '{id}/market', '{id}/orderbook', '{id}/ticker', '{id}/trades', ], }, 'private': { 'post': [ 'info', 'trade', 'cancel', 'orderbook', 'orders', 'transfer', 'withdraw', 'history', 'transactions', ], }, }, 'markets': { 'BTC/USD': {'id': 'BTCUSD', 'symbol': 'BTC/USD', 'base': 'BTC', 'quote': 'USD', 'baseId': 'BTC', 'quoteId': 'USD'}, 'BTC/EUR': {'id': 'BTCEUR', 'symbol': 'BTC/EUR', 'base': 'BTC', 'quote': 'EUR', 'baseId': 'BTC', 'quoteId': 'EUR'}, 'BTC/PLN': {'id': 'BTCPLN', 'symbol': 'BTC/PLN', 'base': 'BTC', 'quote': 'PLN', 'baseId': 'BTC', 'quoteId': 'PLN'}, 'LTC/USD': {'id': 'LTCUSD', 'symbol': 'LTC/USD', 'base': 'LTC', 'quote': 'USD', 'baseId': 'LTC', 'quoteId': 'USD'}, 'LTC/EUR': {'id': 'LTCEUR', 'symbol': 'LTC/EUR', 'base': 'LTC', 'quote': 'EUR', 'baseId': 'LTC', 'quoteId': 'EUR'}, 'LTC/PLN': {'id': 'LTCPLN', 'symbol': 'LTC/PLN', 'base': 'LTC', 'quote': 'PLN', 'baseId': 'LTC', 'quoteId': 'PLN'}, 'LTC/BTC': {'id': 'LTCBTC', 'symbol': 'LTC/BTC', 'base': 'LTC', 'quote': 'BTC', 'baseId': 'LTC', 'quoteId': 'BTC'}, 'ETH/USD': {'id': 'ETHUSD', 'symbol': 'ETH/USD', 'base': 'ETH', 'quote': 'USD', 'baseId': 'ETH', 'quoteId': 'USD'}, 'ETH/EUR': {'id': 'ETHEUR', 'symbol': 'ETH/EUR', 'base': 'ETH', 'quote': 'EUR', 'baseId': 'ETH', 'quoteId': 'EUR'}, 'ETH/PLN': {'id': 'ETHPLN', 'symbol': 'ETH/PLN', 'base': 'ETH', 'quote': 'PLN', 'baseId': 'ETH', 'quoteId': 'PLN'}, 'ETH/BTC': {'id': 'ETHBTC', 'symbol': 'ETH/BTC', 'base': 'ETH', 'quote': 'BTC', 'baseId': 'ETH', 'quoteId': 'BTC'}, 'LSK/USD': {'id': 'LSKUSD', 'symbol': 'LSK/USD', 'base': 'LSK', 'quote': 'USD', 'baseId': 'LSK', 'quoteId': 'USD'}, 'LSK/EUR': {'id': 'LSKEUR', 'symbol': 'LSK/EUR', 'base': 'LSK', 'quote': 'EUR', 'baseId': 'LSK', 'quoteId': 'EUR'}, 'LSK/PLN': {'id': 'LSKPLN', 'symbol': 'LSK/PLN', 'base': 'LSK', 'quote': 'PLN', 'baseId': 'LSK', 'quoteId': 'PLN'}, 'LSK/BTC': {'id': 'LSKBTC', 'symbol': 'LSK/BTC', 'base': 'LSK', 'quote': 'BTC', 'baseId': 'LSK', 'quoteId': 'BTC'}, 'BCH/USD': {'id': 'BCCUSD', 'symbol': 'BCH/USD', 'base': 'BCH', 'quote': 'USD', 'baseId': 'BCC', 'quoteId': 'USD'}, 'BCH/EUR': {'id': 'BCCEUR', 'symbol': 'BCH/EUR', 'base': 'BCH', 'quote': 'EUR', 'baseId': 'BCC', 'quoteId': 'EUR'}, 'BCH/PLN': {'id': 'BCCPLN', 'symbol': 'BCH/PLN', 'base': 'BCH', 'quote': 'PLN', 'baseId': 'BCC', 'quoteId': 'PLN'}, 'BCH/BTC': {'id': 'BCCBTC', 'symbol': 'BCH/BTC', 'base': 'BCH', 'quote': 'BTC', 'baseId': 'BCC', 'quoteId': 'BTC'}, 'BTG/USD': {'id': 'BTGUSD', 'symbol': 'BTG/USD', 'base': 'BTG', 'quote': 'USD', 'baseId': 'BTG', 'quoteId': 'USD'}, 'BTG/EUR': {'id': 'BTGEUR', 'symbol': 'BTG/EUR', 'base': 'BTG', 'quote': 'EUR', 'baseId': 'BTG', 'quoteId': 'EUR'}, 'BTG/PLN': {'id': 'BTGPLN', 'symbol': 'BTG/PLN', 'base': 'BTG', 'quote': 'PLN', 'baseId': 'BTG', 'quoteId': 'PLN'}, 'BTG/BTC': {'id': 'BTGBTC', 'symbol': 'BTG/BTC', 'base': 'BTG', 'quote': 'BTC', 'baseId': 'BTG', 'quoteId': 'BTC'}, 'DASH/USD': {'id': 'DASHUSD', 'symbol': 'DASH/USD', 'base': 'DASH', 'quote': 'USD', 'baseId': 'DASH', 'quoteId': 'USD'}, 'DASH/EUR': {'id': 'DASHEUR', 'symbol': 'DASH/EUR', 'base': 'DASH', 'quote': 'EUR', 'baseId': 'DASH', 'quoteId': 'EUR'}, 'DASH/PLN': {'id': 'DASHPLN', 'symbol': 'DASH/PLN', 'base': 'DASH', 'quote': 'PLN', 'baseId': 'DASH', 'quoteId': 'PLN'}, 'DASH/BTC': {'id': 'DASHBTC', 'symbol': 'DASH/BTC', 'base': 'DASH', 'quote': 'BTC', 'baseId': 'DASH', 'quoteId': 'BTC'}, 'GAME/USD': {'id': 'GAMEUSD', 'symbol': 'GAME/USD', 'base': 'GAME', 'quote': 'USD', 'baseId': 'GAME', 'quoteId': 'USD'}, 'GAME/EUR': {'id': 'GAMEEUR', 'symbol': 'GAME/EUR', 'base': 'GAME', 'quote': 'EUR', 'baseId': 'GAME', 'quoteId': 'EUR'}, 'GAME/PLN': {'id': 'GAMEPLN', 'symbol': 'GAME/PLN', 'base': 'GAME', 'quote': 'PLN', 'baseId': 'GAME', 'quoteId': 'PLN'}, 'GAME/BTC': {'id': 'GAMEBTC', 'symbol': 'GAME/BTC', 'base': 'GAME', 'quote': 'BTC', 'baseId': 'GAME', 'quoteId': 'BTC'}, 'XRP/USD': {'id': 'XRPUSD', 'symbol': 'XRP/USD', 'base': 'XRP', 'quote': 'USD', 'baseId': 'XRP', 'quoteId': 'USD'}, 'XRP/EUR': {'id': 'XRPEUR', 'symbol': 'XRP/EUR', 'base': 'XRP', 'quote': 'EUR', 'baseId': 'XRP', 'quoteId': 'EUR'}, 'XRP/PLN': {'id': 'XRPPLN', 'symbol': 'XRP/PLN', 'base': 'XRP', 'quote': 'PLN', 'baseId': 'XRP', 'quoteId': 'PLN'}, 'XRP/BTC': {'id': 'XRPBTC', 'symbol': 'XRP/BTC', 'base': 'XRP', 'quote': 'BTC', 'baseId': 'XRP', 'quoteId': 'BTC'}, # 'XIN/USD': {'id': 'XINUSD', 'symbol': 'XIN/USD', 'base': 'XIN', 'quote': 'USD', 'baseId': 'XIN', 'quoteId': 'USD'}, # 'XIN/EUR': {'id': 'XINEUR', 'symbol': 'XIN/EUR', 'base': 'XIN', 'quote': 'EUR', 'baseId': 'XIN', 'quoteId': 'EUR'}, # 'XIN/PLN': {'id': 'XINPLN', 'symbol': 'XIN/PLN', 'base': 'XIN', 'quote': 'PLN', 'baseId': 'XIN', 'quoteId': 'PLN'}, 'XIN/BTC': {'id': 'XINBTC', 'symbol': 'XIN/BTC', 'base': 'XIN', 'quote': 'BTC', 'baseId': 'XIN', 'quoteId': 'BTC'}, }, 'fees': { 'trading': { 'maker': 0.3 / 100, 'taker': 0.0043, }, 'funding': { 'withdraw': { 'BTC': 0.0009, 'LTC': 0.005, 'ETH': 0.00126, 'LSK': 0.2, 'BCH': 0.0006, 'GAME': 0.005, 'DASH': 0.001, 'BTG': 0.0008, 'PLN': 4, 'EUR': 1.5, }, }, }, 'exceptions': { '400': ExchangeError, # At least one parameter wasn't set '401': InvalidOrder, # Invalid order type '402': InvalidOrder, # No orders with specified currencies '403': InvalidOrder, # Invalid payment currency name '404': InvalidOrder, # Error. Wrong transaction type '405': InvalidOrder, # Order with self id doesn't exist '406': InsufficientFunds, # No enough money or crypto # code 407 not specified are not specified in their docs '408': InvalidOrder, # Invalid currency name '501': AuthenticationError, # Invalid public key '502': AuthenticationError, # Invalid sign '503': InvalidNonce, # Invalid moment parameter. Request time doesn't match current server time '504': ExchangeError, # Invalid method '505': AuthenticationError, # Key has no permission for self action '506': AuthenticationError, # Account locked. Please contact with customer service # codes 507 and 508 are not specified in their docs '509': ExchangeError, # The BIC/SWIFT is required for self currency '510': ExchangeError, # Invalid market name }, }) async def fetch_balance(self, params={}): response = await self.privatePostInfo() if 'balances' in response: balance = response['balances'] result = {'info': balance} codes = list(self.currencies.keys()) for i in range(0, len(codes)): code = codes[i] currency = self.currencies[code] id = currency['id'] account = self.account() if id in balance: account['free'] = float(balance[id]['available']) account['used'] = float(balance[id]['locked']) account['total'] = self.sum(account['free'], account['used']) result[code] = account return self.parse_balance(result) raise ExchangeError(self.id + ' empty balance response ' + self.json(response)) async def fetch_order_book(self, symbol, limit=None, params={}): orderbook = await self.publicGetIdOrderbook(self.extend({ 'id': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook) async def fetch_ticker(self, symbol, params={}): ticker = await self.publicGetIdTicker(self.extend({ 'id': self.market_id(symbol), }, params)) timestamp = self.milliseconds() baseVolume = self.safe_float(ticker, 'volume') vwap = self.safe_float(ticker, 'vwap') quoteVolume = baseVolume * vwap last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'max'), 'low': self.safe_float(ticker, 'min'), 'bid': self.safe_float(ticker, 'bid'), 'bidVolume': None, 'ask': self.safe_float(ticker, 'ask'), 'askVolume': None, 'vwap': vwap, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': self.safe_float(ticker, 'average'), 'baseVolume': baseVolume, 'quoteVolume': quoteVolume, 'info': ticker, } def parse_trade(self, trade, market): timestamp = trade['date'] * 1000 return { 'id': trade['tid'], 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': market['symbol'], 'type': None, 'side': trade['type'], 'price': trade['price'], 'amount': trade['amount'], } async def fetch_trades(self, symbol, since=None, limit=None, params={}): market = self.market(symbol) response = await self.publicGetIdTrades(self.extend({ 'id': market['id'], }, params)) return self.parse_trades(response, market, since, limit) async def create_order(self, symbol, type, side, amount, price=None, params={}): if type != 'limit': raise ExchangeError(self.id + ' allows limit orders only') market = self.market(symbol) return self.privatePostTrade(self.extend({ 'type': side, 'currency': market['baseId'], 'amount': amount, 'payment_currency': market['quoteId'], 'rate': price, }, params)) async def cancel_order(self, id, symbol=None, params={}): return await self.privatePostCancel({'id': id}) def is_fiat(self, currency): fiatCurrencies = { 'USD': True, 'EUR': True, 'PLN': True, } if currency in fiatCurrencies: return True return False async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() method = None currency = self.currency(code) request = { 'currency': currency['id'], 'quantity': amount, } if self.is_fiat(code): method = 'privatePostWithdraw' # request['account'] = params['account'] # they demand an account number # request['express'] = params['express'] # whatever it means, they don't explain # request['bic'] = '' else: method = 'privatePostTransfer' if tag is not None: address += '?dt=' + str(tag) request['address'] = address response = await getattr(self, method)(self.extend(request, params)) return { 'info': response, 'id': None, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'][api] if api == 'public': query = self.omit(params, self.extract_params(path)) url += '/' + self.implode_params(path, params) + '.json' url += '?' + self.urlencode(query) else: self.check_required_credentials() body = self.urlencode(self.extend({ 'method': path, 'moment': self.nonce(), }, params)) headers = { 'Content-Type': 'application/x-www-form-urlencoded', 'API-Key': self.apiKey, 'API-Hash': self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512), } return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, httpCode, reason, url, method, headers, body): if not isinstance(body, basestring): return # fallback to default error handler if len(body) < 2: return if (body[0] == '{') or (body[0] == '['): response = json.loads(body) if 'code' in response: # # bitbay returns the integer 'success': 1 key from their private API # or an integer 'code' value from 0 to 510 and an error message # # {'success': 1, ...} # {'code': 502, 'message': 'Invalid sign'} # {'code': 0, 'message': 'offer funds not exceeding minimums'} # # 400 At least one parameter wasn't set # 401 Invalid order type # 402 No orders with specified currencies # 403 Invalid payment currency name # 404 Error. Wrong transaction type # 405 Order with self id doesn't exist # 406 No enough money or crypto # 408 Invalid currency name # 501 Invalid public key # 502 Invalid sign # 503 Invalid moment parameter. Request time doesn't match current server time # 504 Invalid method # 505 Key has no permission for self action # 506 Account locked. Please contact with customer service # 509 The BIC/SWIFT is required for self currency # 510 Invalid market name # code = response['code'] # always an integer feedback = self.id + ' ' + self.json(response) exceptions = self.exceptions if code in self.exceptions: raise exceptions[code](feedback) else: raise ExchangeError(feedback)

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rt.base.exchange import Exchange try: basestring except NameError: basestring = str import hashlib import json from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import InvalidNonce class bitbay (Exchange): def describe(self): return self.deep_extend(super(bitbay, self).describe(), { 'id': 'bitbay', 'name': 'BitBay', 'countries': ['MT', 'EU'], 'rateLimit': 1000, 'has': { 'CORS': True, 'withdraw': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/27766132-978a7bd8-5ece-11e7-9540-bc96d1e9bbb8.jpg', 'www': 'https://bitbay.net', 'api': { 'public': 'https://bitbay.net/API/Public', 'private': 'https://bitbay.net/API/Trading/tradingApi.php', }, 'doc': [ 'https://bitbay.net/public-api', 'https://bitbay.net/account/tab-api', 'https://github.com/BitBayNet/API', ], 'fees': 'https://bitbay.net/en/fees', }, 'api': { 'public': { 'get': [ '{id}/all', '{id}/market', '{id}/orderbook', '{id}/ticker', '{id}/trades', ], }, 'private': { 'post': [ 'info', 'trade', 'cancel', 'orderbook', 'orders', 'transfer', 'withdraw', 'history', 'transactions', ], }, }, 'markets': { 'BTC/USD': {'id': 'BTCUSD', 'symbol': 'BTC/USD', 'base': 'BTC', 'quote': 'USD', 'baseId': 'BTC', 'quoteId': 'USD'}, 'BTC/EUR': {'id': 'BTCEUR', 'symbol': 'BTC/EUR', 'base': 'BTC', 'quote': 'EUR', 'baseId': 'BTC', 'quoteId': 'EUR'}, 'BTC/PLN': {'id': 'BTCPLN', 'symbol': 'BTC/PLN', 'base': 'BTC', 'quote': 'PLN', 'baseId': 'BTC', 'quoteId': 'PLN'}, 'LTC/USD': {'id': 'LTCUSD', 'symbol': 'LTC/USD', 'base': 'LTC', 'quote': 'USD', 'baseId': 'LTC', 'quoteId': 'USD'}, 'LTC/EUR': {'id': 'LTCEUR', 'symbol': 'LTC/EUR', 'base': 'LTC', 'quote': 'EUR', 'baseId': 'LTC', 'quoteId': 'EUR'}, 'LTC/PLN': {'id': 'LTCPLN', 'symbol': 'LTC/PLN', 'base': 'LTC', 'quote': 'PLN', 'baseId': 'LTC', 'quoteId': 'PLN'}, 'LTC/BTC': {'id': 'LTCBTC', 'symbol': 'LTC/BTC', 'base': 'LTC', 'quote': 'BTC', 'baseId': 'LTC', 'quoteId': 'BTC'}, 'ETH/USD': {'id': 'ETHUSD', 'symbol': 'ETH/USD', 'base': 'ETH', 'quote': 'USD', 'baseId': 'ETH', 'quoteId': 'USD'}, 'ETH/EUR': {'id': 'ETHEUR', 'symbol': 'ETH/EUR', 'base': 'ETH', 'quote': 'EUR', 'baseId': 'ETH', 'quoteId': 'EUR'}, 'ETH/PLN': {'id': 'ETHPLN', 'symbol': 'ETH/PLN', 'base': 'ETH', 'quote': 'PLN', 'baseId': 'ETH', 'quoteId': 'PLN'}, 'ETH/BTC': {'id': 'ETHBTC', 'symbol': 'ETH/BTC', 'base': 'ETH', 'quote': 'BTC', 'baseId': 'ETH', 'quoteId': 'BTC'}, 'LSK/USD': {'id': 'LSKUSD', 'symbol': 'LSK/USD', 'base': 'LSK', 'quote': 'USD', 'baseId': 'LSK', 'quoteId': 'USD'}, 'LSK/EUR': {'id': 'LSKEUR', 'symbol': 'LSK/EUR', 'base': 'LSK', 'quote': 'EUR', 'baseId': 'LSK', 'quoteId': 'EUR'}, 'LSK/PLN': {'id': 'LSKPLN', 'symbol': 'LSK/PLN', 'base': 'LSK', 'quote': 'PLN', 'baseId': 'LSK', 'quoteId': 'PLN'}, 'LSK/BTC': {'id': 'LSKBTC', 'symbol': 'LSK/BTC', 'base': 'LSK', 'quote': 'BTC', 'baseId': 'LSK', 'quoteId': 'BTC'}, 'BCH/USD': {'id': 'BCCUSD', 'symbol': 'BCH/USD', 'base': 'BCH', 'quote': 'USD', 'baseId': 'BCC', 'quoteId': 'USD'}, 'BCH/EUR': {'id': 'BCCEUR', 'symbol': 'BCH/EUR', 'base': 'BCH', 'quote': 'EUR', 'baseId': 'BCC', 'quoteId': 'EUR'}, 'BCH/PLN': {'id': 'BCCPLN', 'symbol': 'BCH/PLN', 'base': 'BCH', 'quote': 'PLN', 'baseId': 'BCC', 'quoteId': 'PLN'}, 'BCH/BTC': {'id': 'BCCBTC', 'symbol': 'BCH/BTC', 'base': 'BCH', 'quote': 'BTC', 'baseId': 'BCC', 'quoteId': 'BTC'}, 'BTG/USD': {'id': 'BTGUSD', 'symbol': 'BTG/USD', 'base': 'BTG', 'quote': 'USD', 'baseId': 'BTG', 'quoteId': 'USD'}, 'BTG/EUR': {'id': 'BTGEUR', 'symbol': 'BTG/EUR', 'base': 'BTG', 'quote': 'EUR', 'baseId': 'BTG', 'quoteId': 'EUR'}, 'BTG/PLN': {'id': 'BTGPLN', 'symbol': 'BTG/PLN', 'base': 'BTG', 'quote': 'PLN', 'baseId': 'BTG', 'quoteId': 'PLN'}, 'BTG/BTC': {'id': 'BTGBTC', 'symbol': 'BTG/BTC', 'base': 'BTG', 'quote': 'BTC', 'baseId': 'BTG', 'quoteId': 'BTC'}, 'DASH/USD': {'id': 'DASHUSD', 'symbol': 'DASH/USD', 'base': 'DASH', 'quote': 'USD', 'baseId': 'DASH', 'quoteId': 'USD'}, 'DASH/EUR': {'id': 'DASHEUR', 'symbol': 'DASH/EUR', 'base': 'DASH', 'quote': 'EUR', 'baseId': 'DASH', 'quoteId': 'EUR'}, 'DASH/PLN': {'id': 'DASHPLN', 'symbol': 'DASH/PLN', 'base': 'DASH', 'quote': 'PLN', 'baseId': 'DASH', 'quoteId': 'PLN'}, 'DASH/BTC': {'id': 'DASHBTC', 'symbol': 'DASH/BTC', 'base': 'DASH', 'quote': 'BTC', 'baseId': 'DASH', 'quoteId': 'BTC'}, 'GAME/USD': {'id': 'GAMEUSD', 'symbol': 'GAME/USD', 'base': 'GAME', 'quote': 'USD', 'baseId': 'GAME', 'quoteId': 'USD'}, 'GAME/EUR': {'id': 'GAMEEUR', 'symbol': 'GAME/EUR', 'base': 'GAME', 'quote': 'EUR', 'baseId': 'GAME', 'quoteId': 'EUR'}, 'GAME/PLN': {'id': 'GAMEPLN', 'symbol': 'GAME/PLN', 'base': 'GAME', 'quote': 'PLN', 'baseId': 'GAME', 'quoteId': 'PLN'}, 'GAME/BTC': {'id': 'GAMEBTC', 'symbol': 'GAME/BTC', 'base': 'GAME', 'quote': 'BTC', 'baseId': 'GAME', 'quoteId': 'BTC'}, 'XRP/USD': {'id': 'XRPUSD', 'symbol': 'XRP/USD', 'base': 'XRP', 'quote': 'USD', 'baseId': 'XRP', 'quoteId': 'USD'}, 'XRP/EUR': {'id': 'XRPEUR', 'symbol': 'XRP/EUR', 'base': 'XRP', 'quote': 'EUR', 'baseId': 'XRP', 'quoteId': 'EUR'}, 'XRP/PLN': {'id': 'XRPPLN', 'symbol': 'XRP/PLN', 'base': 'XRP', 'quote': 'PLN', 'baseId': 'XRP', 'quoteId': 'PLN'}, 'XRP/BTC': {'id': 'XRPBTC', 'symbol': 'XRP/BTC', 'base': 'XRP', 'quote': 'BTC', 'baseId': 'XRP', 'quoteId': 'BTC'}, 'XIN/BTC': {'id': 'XINBTC', 'symbol': 'XIN/BTC', 'base': 'XIN', 'quote': 'BTC', 'baseId': 'XIN', 'quoteId': 'BTC'}, }, 'fees': { 'trading': { 'maker': 0.3 / 100, 'taker': 0.0043, }, 'funding': { 'withdraw': { 'BTC': 0.0009, 'LTC': 0.005, 'ETH': 0.00126, 'LSK': 0.2, 'BCH': 0.0006, 'GAME': 0.005, 'DASH': 0.001, 'BTG': 0.0008, 'PLN': 4, 'EUR': 1.5, }, }, }, 'exceptions': { '400': ExchangeError, '401': InvalidOrder, # Invalid order type '402': InvalidOrder, # No orders with specified currencies '403': InvalidOrder, # Invalid payment currency name '404': InvalidOrder, # Error. Wrong transaction type '405': InvalidOrder, # Order with self id doesn't exist '406': InsufficientFunds, '408': InvalidOrder, '501': AuthenticationError, '502': AuthenticationError, '503': InvalidNonce, '504': ExchangeError, # Invalid method '505': AuthenticationError, # Key has no permission for self action '506': AuthenticationError, # Account locked. Please contact with customer service # codes 507 and 508 are not specified in their docs '509': ExchangeError, # The BIC/SWIFT is required for self currency '510': ExchangeError, # Invalid market name }, }) async def fetch_balance(self, params={}): response = await self.privatePostInfo() if 'balances' in response: balance = response['balances'] result = {'info': balance} codes = list(self.currencies.keys()) for i in range(0, len(codes)): code = codes[i] currency = self.currencies[code] id = currency['id'] account = self.account() if id in balance: account['free'] = float(balance[id]['available']) account['used'] = float(balance[id]['locked']) account['total'] = self.sum(account['free'], account['used']) result[code] = account return self.parse_balance(result) raise ExchangeError(self.id + ' empty balance response ' + self.json(response)) async def fetch_order_book(self, symbol, limit=None, params={}): orderbook = await self.publicGetIdOrderbook(self.extend({ 'id': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook) async def fetch_ticker(self, symbol, params={}): ticker = await self.publicGetIdTicker(self.extend({ 'id': self.market_id(symbol), }, params)) timestamp = self.milliseconds() baseVolume = self.safe_float(ticker, 'volume') vwap = self.safe_float(ticker, 'vwap') quoteVolume = baseVolume * vwap last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'max'), 'low': self.safe_float(ticker, 'min'), 'bid': self.safe_float(ticker, 'bid'), 'bidVolume': None, 'ask': self.safe_float(ticker, 'ask'), 'askVolume': None, 'vwap': vwap, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': self.safe_float(ticker, 'average'), 'baseVolume': baseVolume, 'quoteVolume': quoteVolume, 'info': ticker, } def parse_trade(self, trade, market): timestamp = trade['date'] * 1000 return { 'id': trade['tid'], 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': market['symbol'], 'type': None, 'side': trade['type'], 'price': trade['price'], 'amount': trade['amount'], } async def fetch_trades(self, symbol, since=None, limit=None, params={}): market = self.market(symbol) response = await self.publicGetIdTrades(self.extend({ 'id': market['id'], }, params)) return self.parse_trades(response, market, since, limit) async def create_order(self, symbol, type, side, amount, price=None, params={}): if type != 'limit': raise ExchangeError(self.id + ' allows limit orders only') market = self.market(symbol) return self.privatePostTrade(self.extend({ 'type': side, 'currency': market['baseId'], 'amount': amount, 'payment_currency': market['quoteId'], 'rate': price, }, params)) async def cancel_order(self, id, symbol=None, params={}): return await self.privatePostCancel({'id': id}) def is_fiat(self, currency): fiatCurrencies = { 'USD': True, 'EUR': True, 'PLN': True, } if currency in fiatCurrencies: return True return False async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() method = None currency = self.currency(code) request = { 'currency': currency['id'], 'quantity': amount, } if self.is_fiat(code): method = 'privatePostWithdraw' # request['account'] = params['account'] # they demand an account number # request['express'] = params['express'] # whatever it means, they don't explain else: method = 'privatePostTransfer' if tag is not None: address += '?dt=' + str(tag) request['address'] = address response = await getattr(self, method)(self.extend(request, params)) return { 'info': response, 'id': None, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'][api] if api == 'public': query = self.omit(params, self.extract_params(path)) url += '/' + self.implode_params(path, params) + '.json' url += '?' + self.urlencode(query) else: self.check_required_credentials() body = self.urlencode(self.extend({ 'method': path, 'moment': self.nonce(), }, params)) headers = { 'Content-Type': 'application/x-www-form-urlencoded', 'API-Key': self.apiKey, 'API-Hash': self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512), } return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, httpCode, reason, url, method, headers, body): if not isinstance(body, basestring): return if len(body) < 2: return if (body[0] == '{') or (body[0] == '['): response = json.loads(body) if 'code' in response: # 401 Invalid order type # 402 No orders with specified currencies # 403 Invalid payment currency name # 404 Error. Wrong transaction type # 405 Order with self id doesn't exist # 504 Invalid method # 505 Key has no permission for self action # 506 Account locked. Please contact with customer service # 509 The BIC/SWIFT is required for self currency # 510 Invalid market name # code = response['code'] # always an integer feedback = self.id + ' ' + self.json(response) exceptions = self.exceptions if code in self.exceptions: raise exceptions[code](feedback) else: raise ExchangeError(feedback)

true

f7198ae184bcaa5b0b938cc560dc8df6ff0d66d1

93,728

py

Python

keras/layers/recurrent.py

Duncanswilson/keras

32aa192548b6b59bf407e583fbd246ba9f5f5676

[ "MIT" ]

1

2017-11-01T19:10:35.000Z

keras/layers/recurrent.py

dmaniry/keras

32aa192548b6b59bf407e583fbd246ba9f5f5676

[ "MIT" ]

null

keras/layers/recurrent.py

dmaniry/keras

32aa192548b6b59bf407e583fbd246ba9f5f5676

[ "MIT" ]

1

2019-02-22T03:06:41.000Z

# -*- coding: utf-8 -*- """Recurrent layers and their base classes. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import warnings from .. import backend as K from .. import activations from .. import initializers from .. import regularizers from .. import constraints from ..engine import Layer from ..engine import InputSpec from ..utils.generic_utils import has_arg # Legacy support. from ..legacy.layers import Recurrent from ..legacy import interfaces class StackedRNNCells(Layer): """Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs. # Arguments cells: List of RNN cell instances. # Examples ```python cells = [ keras.layers.LSTMCell(output_dim), keras.layers.LSTMCell(output_dim), keras.layers.LSTMCell(output_dim), ] inputs = keras.Input((timesteps, input_dim)) x = keras.layers.RNN(cells)(inputs) ``` """ def __init__(self, cells, **kwargs): for cell in cells: if not hasattr(cell, 'call'): raise ValueError('All cells must have a `call` method. ' 'received cells:', cells) if not hasattr(cell, 'state_size'): raise ValueError('All cells must have a ' '`state_size` attribute. ' 'received cells:', cells) self.cells = cells super(StackedRNNCells, self).__init__(**kwargs) @property def state_size(self): # States are a flat list # in reverse order of the cell stack. # This allows to preserve the requirement # `stack.state_size[0] == output_dim`. # e.g. states of a 2-layer LSTM would be # `[h2, c2, h1, c1]` # (assuming one LSTM has states [h, c]) state_size = [] for cell in self.cells[::-1]: if hasattr(cell.state_size, '__len__'): state_size += list(cell.state_size) else: state_size.append(cell.state_size) return tuple(state_size) def call(self, inputs, states, **kwargs): # Recover per-cell states. nested_states = [] for cell in self.cells[::-1]: if hasattr(cell.state_size, '__len__'): nested_states.append(states[:len(cell.state_size)]) states = states[len(cell.state_size):] else: nested_states.append([states[0]]) states = states[1:] nested_states = nested_states[::-1] # Call the cells in order and store the returned states. new_nested_states = [] for cell, states in zip(self.cells, nested_states): inputs, states = cell.call(inputs, states, **kwargs) new_nested_states.append(states) # Format the new states as a flat list # in reverse cell order. states = [] for cell_states in new_nested_states[::-1]: states += cell_states return inputs, states def build(self, input_shape): for cell in self.cells: if isinstance(cell, Layer): cell.build(input_shape) if hasattr(cell.state_size, '__len__'): output_dim = cell.state_size[0] else: output_dim = cell.state_size input_shape = (input_shape[0], input_shape[1], output_dim) self.built = True def get_config(self): cells = [] for cell in self.cells: cells.append({'class_name': cell.__class__.__name__, 'config': cell.get_config()}) config = {'cells': cells} base_config = super(StackedRNNCells, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from . import deserialize as deserialize_layer cells = [] for cell_config in config.pop('cells'): cells.append(deserialize_layer(cell_config, custom_objects=custom_objects)) return cls(cells, **config) @property def trainable_weights(self): if not self.trainable: return [] weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.trainable_weights return weights @property def non_trainable_weights(self): weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.non_trainable_weights if not self.trainable: trainable_weights = [] for cell in self.cells: if isinstance(cell, Layer): trainable_weights += cell.trainable_weights return trainable_weights + weights return weights def get_weights(self): """Retrieves the weights of the model. # Returns A flat list of Numpy arrays. """ weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.weights return K.batch_get_value(weights) def set_weights(self, weights): """Sets the weights of the model. # Arguments weights: A list of Numpy arrays with shapes and types matching the output of `model.get_weights()`. """ tuples = [] for cell in self.cells: if isinstance(cell, Layer): num_param = len(cell.weights) weights = weights[:num_param] for sw, w in zip(cell.weights, weights): tuples.append((sw, w)) weights = weights[num_param:] K.batch_set_value(tuples) @property def losses(self): losses = [] for cell in self.cells: if isinstance(cell, Layer): cell_losses = cell.losses losses += cell_losses return losses def get_losses_for(self, inputs=None): losses = [] for cell in self.cells: if isinstance(cell, Layer): cell_losses = cell.get_losses_for(inputs) losses += cell_losses return losses class RNN(Layer): """Base class for recurrent layers. # Arguments cell: A RNN cell instance. A RNN cell is a class that has: - a `call(input_at_t, states_at_t)` method, returning `(output_at_t, states_at_t_plus_1)`. The call method of the cell can also take the optional argument `constants`, see section "Note on passing external constants" below. - a `state_size` attribute. This can be a single integer (single state) in which case it is the size of the recurrent state (which should be the same as the size of the cell output). This can also be a list/tuple of integers (one size per state). In this case, the first entry (`state_size[0]`) should be the same as the size of the cell output. It is also possible for `cell` to be a list of RNN cell instances, in which cases the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. input_dim: dimensionality of the input (integer). This argument (or alternatively, the keyword argument `input_shape`) is required when using this layer as the first layer in a model. input_length: Length of input sequences, to be specified when it is constant. This argument is required if you are going to connect `Flatten` then `Dense` layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g. via the `input_shape` argument) # Input shape 3D tensor with shape `(batch_size, timesteps, input_dim)`. # Output shape - if `return_state`: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape `(batch_size, units)`. - if `return_sequences`: 3D tensor with shape `(batch_size, timesteps, units)`. - else, 2D tensor with shape `(batch_size, units)`. # Masking This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an [Embedding](embeddings.md) layer with the `mask_zero` parameter set to `True`. # Note on using statefulness in RNNs You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches. To enable statefulness: - specify `stateful=True` in the layer constructor. - specify a fixed batch size for your model, by passing if sequential model: `batch_input_shape=(...)` to the first layer in your model. else for functional model with 1 or more Input layers: `batch_shape=(...)` to all the first layers in your model. This is the expected shape of your inputs *including the batch size*. It should be a tuple of integers, e.g. `(32, 10, 100)`. - specify `shuffle=False` when calling fit(). To reset the states of your model, call `.reset_states()` on either a specific layer, or on your entire model. # Note on specifying the initial state of RNNs You can specify the initial state of RNN layers symbolically by calling them with the keyword argument `initial_state`. The value of `initial_state` should be a tensor or list of tensors representing the initial state of the RNN layer. You can specify the initial state of RNN layers numerically by calling `reset_states` with the keyword argument `states`. The value of `states` should be a numpy array or list of numpy arrays representing the initial state of the RNN layer. # Note on passing external constants to RNNs You can pass "external" constants to the cell using the `constants` keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This requires that the `cell.call` method accepts the same keyword argument `constants`. Such constants can be used to condition the cell transformation on additional static inputs (not changing over time), a.k.a. an attention mechanism. # Examples ```python # First, let's define a RNN Cell, as a layer subclass. class MinimalRNNCell(keras.layers.Layer): def __init__(self, units, **kwargs): self.units = units self.state_size = units super(MinimalRNNCell, self).__init__(**kwargs) def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), initializer='uniform', name='kernel') self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), initializer='uniform', name='recurrent_kernel') self.built = True def call(self, inputs, states): prev_output = states[0] h = K.dot(inputs, self.kernel) output = h + K.dot(prev_output, self.recurrent_kernel) return output, [output] # Let's use this cell in a RNN layer: cell = MinimalRNNCell(32) x = keras.Input((None, 5)) layer = RNN(cell) y = layer(x) # Here's how to use the cell to build a stacked RNN: cells = [MinimalRNNCell(32), MinimalRNNCell(64)] x = keras.Input((None, 5)) layer = RNN(cells) y = layer(x) ``` """ def __init__(self, cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if isinstance(cell, (list, tuple)): cell = StackedRNNCells(cell) if not hasattr(cell, 'call'): raise ValueError('`cell` should have a `call` method. ' 'The RNN was passed:', cell) if not hasattr(cell, 'state_size'): raise ValueError('The RNN cell should have ' 'an attribute `state_size` ' '(tuple of integers, ' 'one integer per RNN state).') super(RNN, self).__init__(**kwargs) self.cell = cell self.return_sequences = return_sequences self.return_state = return_state self.go_backwards = go_backwards self.stateful = stateful self.unroll = unroll self.supports_masking = True self.input_spec = [InputSpec(ndim=3)] self.state_spec = None self._states = None self.constants_spec = None self._num_constants = None @property def states(self): if self._states is None: if isinstance(self.cell.state_size, int): num_states = 1 else: num_states = len(self.cell.state_size) return [None for _ in range(num_states)] return self._states @states.setter def states(self, states): self._states = states def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] if hasattr(self.cell.state_size, '__len__'): state_size = self.cell.state_size else: state_size = [self.cell.state_size] output_dim = state_size[0] if self.return_sequences: output_shape = (input_shape[0], input_shape[1], output_dim) else: output_shape = (input_shape[0], output_dim) if self.return_state: state_shape = [(input_shape[0], dim) for dim in state_size] return [output_shape] + state_shape else: return output_shape def compute_mask(self, inputs, mask): if isinstance(mask, list): mask = mask[0] output_mask = mask if self.return_sequences else None if self.return_state: state_mask = [None for _ in self.states] return [output_mask] + state_mask else: return output_mask def build(self, input_shape): # Note input_shape will be list of shapes of initial states and # constants if these are passed in __call__. if self._num_constants is not None: constants_shape = input_shape[-self._num_constants:] else: constants_shape = None if isinstance(input_shape, list): input_shape = input_shape[0] batch_size = input_shape[0] if self.stateful else None input_dim = input_shape[-1] self.input_spec[0] = InputSpec(shape=(batch_size, None, input_dim)) # allow cell (if layer) to build before we set or validate state_spec if isinstance(self.cell, Layer): step_input_shape = (input_shape[0],) + input_shape[2:] if constants_shape is not None: self.cell.build([step_input_shape] + constants_shape) else: self.cell.build(step_input_shape) # set or validate state_spec if hasattr(self.cell.state_size, '__len__'): state_size = list(self.cell.state_size) else: state_size = [self.cell.state_size] if self.state_spec is not None: # initial_state was passed in call, check compatibility if [spec.shape[-1] for spec in self.state_spec] != state_size: raise ValueError( 'An `initial_state` was passed that is not compatible with ' '`cell.state_size`. Received `state_spec`={}; ' 'however `cell.state_size` is ' '{}'.format(self.state_spec, self.cell.state_size)) else: self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] if self.stateful: self.reset_states() def get_initial_state(self, inputs): # build an all-zero tensor of shape (samples, output_dim) initial_state = K.zeros_like(inputs) # (samples, timesteps, input_dim) initial_state = K.sum(initial_state, axis=(1, 2)) # (samples,) initial_state = K.expand_dims(initial_state) # (samples, 1) if hasattr(self.cell.state_size, '__len__'): return [K.tile(initial_state, [1, dim]) for dim in self.cell.state_size] else: return [K.tile(initial_state, [1, self.cell.state_size])] def __call__(self, inputs, initial_state=None, constants=None, **kwargs): inputs, initial_state, constants = self._standardize_args( inputs, initial_state, constants) if initial_state is None and constants is None: return super(RNN, self).__call__(inputs, **kwargs) # If any of `initial_state` or `constants` are specified and are Keras # tensors, then add them to the inputs and temporarily modify the # input_spec to include them. additional_inputs = [] additional_specs = [] if initial_state is not None: kwargs['initial_state'] = initial_state additional_inputs += initial_state self.state_spec = [InputSpec(shape=K.int_shape(state)) for state in initial_state] additional_specs += self.state_spec if constants is not None: kwargs['constants'] = constants additional_inputs += constants self.constants_spec = [InputSpec(shape=K.int_shape(constant)) for constant in constants] self._num_constants = len(constants) additional_specs += self.constants_spec # at this point additional_inputs cannot be empty is_keras_tensor = hasattr(additional_inputs[0], '_keras_history') for tensor in additional_inputs: if hasattr(tensor, '_keras_history') != is_keras_tensor: raise ValueError('The initial state or constants of an RNN' ' layer cannot be specified with a mix of' ' Keras tensors and non-Keras tensors') if is_keras_tensor: # Compute the full input spec, including state and constants full_input = [inputs] + additional_inputs full_input_spec = self.input_spec + additional_specs # Perform the call with temporarily replaced input_spec original_input_spec = self.input_spec self.input_spec = full_input_spec output = super(RNN, self).__call__(full_input, **kwargs) self.input_spec = original_input_spec return output else: return super(RNN, self).__call__(inputs, **kwargs) def call(self, inputs, mask=None, training=None, initial_state=None, constants=None): # input shape: `(samples, time (padded with zeros), input_dim)` # note that the .build() method of subclasses MUST define # self.input_spec and self.state_spec with complete input shapes. if isinstance(inputs, list): inputs = inputs[0] if initial_state is not None: pass elif self.stateful: initial_state = self.states else: initial_state = self.get_initial_state(inputs) if isinstance(mask, list): mask = mask[0] if len(initial_state) != len(self.states): raise ValueError('Layer has ' + str(len(self.states)) + ' states but was passed ' + str(len(initial_state)) + ' initial states.') input_shape = K.int_shape(inputs) timesteps = input_shape[1] if self.unroll and timesteps in [None, 1]: raise ValueError('Cannot unroll a RNN if the ' 'time dimension is undefined or equal to 1. \n' '- If using a Sequential model, ' 'specify the time dimension by passing ' 'an `input_shape` or `batch_input_shape` ' 'argument to your first layer. If your ' 'first layer is an Embedding, you can ' 'also use the `input_length` argument.\n' '- If using the functional API, specify ' 'the time dimension by passing a `shape` ' 'or `batch_shape` argument to your Input layer.') kwargs = {} if has_arg(self.cell.call, 'training'): kwargs['training'] = training if constants: if not has_arg(self.cell.call, 'constants'): raise ValueError('RNN cell does not support constants') def step(inputs, states): constants = states[-self._num_constants:] states = states[:-self._num_constants] return self.cell.call(inputs, states, constants=constants, **kwargs) else: def step(inputs, states): return self.cell.call(inputs, states, **kwargs) last_output, outputs, states = K.rnn(step, inputs, initial_state, constants=constants, go_backwards=self.go_backwards, mask=mask, unroll=self.unroll, input_length=timesteps) if self.stateful: updates = [] for i in range(len(states)): updates.append((self.states[i], states[i])) self.add_update(updates, inputs) if self.return_sequences: output = outputs else: output = last_output # Properly set learning phase if getattr(last_output, '_uses_learning_phase', False): output._uses_learning_phase = True for state in states: state._uses_learning_phase = True if self.return_state: if not isinstance(states, (list, tuple)): states = [states] else: states = list(states) return [output] + states else: return output def _standardize_args(self, inputs, initial_state, constants): """Standardize `__call__` to a single list of tensor inputs. When running a model loaded from file, the input tensors `initial_state` and `constants` can be passed to `RNN.__call__` as part of `inputs` instead of by the dedicated keyword arguments. This method makes sure the arguments are separated and that `initial_state` and `constants` are lists of tensors (or None). # Arguments inputs: tensor or list/tuple of tensors initial_state: tensor or list of tensors or None constants: tensor or list of tensors or None # Returns inputs: tensor initial_state: list of tensors or None constants: list of tensors or None """ if isinstance(inputs, list): assert initial_state is None and constants is None if self._num_constants is not None: constants = inputs[-self._num_constants:] inputs = inputs[:-self._num_constants] if len(inputs) > 1: initial_state = inputs[1:] inputs = inputs[0] def to_list_or_none(x): if x is None or isinstance(x, list): return x if isinstance(x, tuple): return list(x) return [x] initial_state = to_list_or_none(initial_state) constants = to_list_or_none(constants) return inputs, initial_state, constants def reset_states(self, states=None): if not self.stateful: raise AttributeError('Layer must be stateful.') batch_size = self.input_spec[0].shape[0] if not batch_size: raise ValueError('If a RNN is stateful, it needs to know ' 'its batch size. Specify the batch size ' 'of your input tensors: \n' '- If using a Sequential model, ' 'specify the batch size by passing ' 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' 'the batch size by passing a ' '`batch_shape` argument to your Input layer.') # initialize state if None if self.states[0] is None: if hasattr(self.cell.state_size, '__len__'): self.states = [K.zeros((batch_size, dim)) for dim in self.cell.state_size] else: self.states = [K.zeros((batch_size, self.cell.state_size))] elif states is None: if hasattr(self.cell.state_size, '__len__'): for state, dim in zip(self.states, self.cell.state_size): K.set_value(state, np.zeros((batch_size, dim))) else: K.set_value(self.states[0], np.zeros((batch_size, self.cell.state_size))) else: if not isinstance(states, (list, tuple)): states = [states] if len(states) != len(self.states): raise ValueError('Layer ' + self.name + ' expects ' + str(len(self.states)) + ' states, ' 'but it received ' + str(len(states)) + ' state values. Input received: ' + str(states)) for index, (value, state) in enumerate(zip(states, self.states)): if hasattr(self.cell.state_size, '__len__'): dim = self.cell.state_size[index] else: dim = self.cell.state_size if value.shape != (batch_size, dim): raise ValueError('State ' + str(index) + ' is incompatible with layer ' + self.name + ': expected shape=' + str((batch_size, dim)) + ', found shape=' + str(value.shape)) # TODO: consider batch calls to `set_value`. K.set_value(state, value) def get_config(self): config = {'return_sequences': self.return_sequences, 'return_state': self.return_state, 'go_backwards': self.go_backwards, 'stateful': self.stateful, 'unroll': self.unroll} if self._num_constants is not None: config['num_constants'] = self._num_constants cell_config = self.cell.get_config() config['cell'] = {'class_name': self.cell.__class__.__name__, 'config': cell_config} base_config = super(RNN, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from . import deserialize as deserialize_layer cell = deserialize_layer(config.pop('cell'), custom_objects=custom_objects) num_constants = config.pop('num_constants', None) layer = cls(cell, **config) layer._num_constants = num_constants return layer @property def trainable_weights(self): if not self.trainable: return [] if isinstance(self.cell, Layer): return self.cell.trainable_weights return [] @property def non_trainable_weights(self): if isinstance(self.cell, Layer): if not self.trainable: return self.cell.weights return self.cell.non_trainable_weights return [] @property def losses(self): if isinstance(self.cell, Layer): return self.cell.losses return [] def get_losses_for(self, inputs=None): if isinstance(self.cell, Layer): cell_losses = self.cell.get_losses_for(inputs) return cell_losses + super(RNN, self).get_losses_for(inputs) return super(RNN, self).get_losses_for(inputs) class SimpleRNNCell(Layer): """Cell class for SimpleRNN. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. """ def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(SimpleRNNCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.built = True def call(self, inputs, states, training=None): prev_output = states[0] if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training) dp_mask = self._dropout_mask rec_dp_mask = self._recurrent_dropout_mask if dp_mask is not None: h = K.dot(inputs * dp_mask, self.kernel) else: h = K.dot(inputs, self.kernel) if self.bias is not None: h = K.bias_add(h, self.bias) if rec_dp_mask is not None: prev_output *= rec_dp_mask output = h + K.dot(prev_output, self.recurrent_kernel) if self.activation is not None: output = self.activation(output) # Properly set learning phase on output tensor. if 0 < self.dropout + self.recurrent_dropout: if training is None: output._uses_learning_phase = True return output, [output] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(SimpleRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class SimpleRNN(RNN): """Fully-connected RNN where the output is to be fed back to input. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. """ @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if 'implementation' in kwargs: kwargs.pop('implementation') warnings.warn('The `implementation` argument ' 'in `SimpleRNN` has been deprecated. ' 'Please remove it from your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = SimpleRNNCell(units, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout) super(SimpleRNN, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(SimpleRNN, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(SimpleRNN, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config: config.pop('implementation') return cls(**config) class GRUCell(Layer): """Cell class for the GRU layer. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step (see [activations](../activations.md)). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, **kwargs): super(GRUCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.implementation = implementation self.state_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight(shape=(input_dim, self.units * 3), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 3), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units * 3,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_z = self.kernel[:, :self.units] self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units] self.kernel_r = self.kernel[:, self.units: self.units * 2] self.recurrent_kernel_r = self.recurrent_kernel[:, self.units: self.units * 2] self.kernel_h = self.kernel[:, self.units * 2:] self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:] if self.use_bias: self.bias_z = self.bias[:self.units] self.bias_r = self.bias[self.units: self.units * 2] self.bias_h = self.bias[self.units * 2:] else: self.bias_z = None self.bias_r = None self.bias_h = None self.built = True def call(self, inputs, states, training=None): h_tm1 = states[0] # previous memory if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training, count=3) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training, count=3) # dropout matrices for input units dp_mask = self._dropout_mask # dropout matrices for recurrent units rec_dp_mask = self._recurrent_dropout_mask if self.implementation == 1: if 0. < self.dropout < 1.: inputs_z = inputs * dp_mask[0] inputs_r = inputs * dp_mask[1] inputs_h = inputs * dp_mask[2] else: inputs_z = inputs inputs_r = inputs inputs_h = inputs x_z = K.dot(inputs_z, self.kernel_z) x_r = K.dot(inputs_r, self.kernel_r) x_h = K.dot(inputs_h, self.kernel_h) if self.use_bias: x_z = K.bias_add(x_z, self.bias_z) x_r = K.bias_add(x_r, self.bias_r) x_h = K.bias_add(x_h, self.bias_h) if 0. < self.recurrent_dropout < 1.: h_tm1_z = h_tm1 * rec_dp_mask[0] h_tm1_r = h_tm1 * rec_dp_mask[1] h_tm1_h = h_tm1 * rec_dp_mask[2] else: h_tm1_z = h_tm1 h_tm1_r = h_tm1 h_tm1_h = h_tm1 z = self.recurrent_activation(x_z + K.dot(h_tm1_z, self.recurrent_kernel_z)) r = self.recurrent_activation(x_r + K.dot(h_tm1_r, self.recurrent_kernel_r)) hh = self.activation(x_h + K.dot(r * h_tm1_h, self.recurrent_kernel_h)) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] matrix_x = K.dot(inputs, self.kernel) if self.use_bias: matrix_x = K.bias_add(matrix_x, self.bias) if 0. < self.recurrent_dropout < 1.: h_tm1 *= rec_dp_mask[0] matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) x_z = matrix_x[:, :self.units] x_r = matrix_x[:, self.units: 2 * self.units] recurrent_z = matrix_inner[:, :self.units] recurrent_r = matrix_inner[:, self.units: 2 * self.units] z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) x_h = matrix_x[:, 2 * self.units:] recurrent_h = K.dot(r * h_tm1, self.recurrent_kernel[:, 2 * self.units:]) hh = self.activation(x_h + recurrent_h) h = z * h_tm1 + (1 - z) * hh if 0 < self.dropout + self.recurrent_dropout: if training is None: h._uses_learning_phase = True return h, [h] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class GRU(RNN): """Gated Recurrent Unit - Cho et al. 2014. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step (see [activations](../activations.md)). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. # References - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1) - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if implementation == 0: warnings.warn('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = GRUCell(units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation) super(GRU, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(GRU, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(GRU, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) class LSTMCell(Layer): """Cell class for the LSTM layer. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step (see [activations](../activations.md)). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, **kwargs): super(LSTMCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.unit_forget_bias = unit_forget_bias self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.implementation = implementation self.state_size = (self.units, self.units) self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight(shape=(input_dim, self.units * 4), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 4), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: if self.unit_forget_bias: def bias_initializer(_, *args, **kwargs): return K.concatenate([ self.bias_initializer((self.units,), *args, **kwargs), initializers.Ones()((self.units,), *args, **kwargs), self.bias_initializer((self.units * 2,), *args, **kwargs), ]) else: bias_initializer = self.bias_initializer self.bias = self.add_weight(shape=(self.units * 4,), name='bias', initializer=bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_i = self.kernel[:, :self.units] self.kernel_f = self.kernel[:, self.units: self.units * 2] self.kernel_c = self.kernel[:, self.units * 2: self.units * 3] self.kernel_o = self.kernel[:, self.units * 3:] self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units] self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2] self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3] self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:] if self.use_bias: self.bias_i = self.bias[:self.units] self.bias_f = self.bias[self.units: self.units * 2] self.bias_c = self.bias[self.units * 2: self.units * 3] self.bias_o = self.bias[self.units * 3:] else: self.bias_i = None self.bias_f = None self.bias_c = None self.bias_o = None self.built = True def call(self, inputs, states, training=None): if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training, count=4) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training, count=4) # dropout matrices for input units dp_mask = self._dropout_mask # dropout matrices for recurrent units rec_dp_mask = self._recurrent_dropout_mask h_tm1 = states[0] # previous memory state c_tm1 = states[1] # previous carry state if self.implementation == 1: if 0 < self.dropout < 1.: inputs_i = inputs * dp_mask[0] inputs_f = inputs * dp_mask[1] inputs_c = inputs * dp_mask[2] inputs_o = inputs * dp_mask[3] else: inputs_i = inputs inputs_f = inputs inputs_c = inputs inputs_o = inputs x_i = K.dot(inputs_i, self.kernel_i) x_f = K.dot(inputs_f, self.kernel_f) x_c = K.dot(inputs_c, self.kernel_c) x_o = K.dot(inputs_o, self.kernel_o) if self.use_bias: x_i = K.bias_add(x_i, self.bias_i) x_f = K.bias_add(x_f, self.bias_f) x_c = K.bias_add(x_c, self.bias_c) x_o = K.bias_add(x_o, self.bias_o) if 0 < self.recurrent_dropout < 1.: h_tm1_i = h_tm1 * rec_dp_mask[0] h_tm1_f = h_tm1 * rec_dp_mask[1] h_tm1_c = h_tm1 * rec_dp_mask[2] h_tm1_o = h_tm1 * rec_dp_mask[3] else: h_tm1_i = h_tm1 h_tm1_f = h_tm1 h_tm1_c = h_tm1 h_tm1_o = h_tm1 i = self.recurrent_activation(x_i + K.dot(h_tm1_i, self.recurrent_kernel_i)) f = self.recurrent_activation(x_f + K.dot(h_tm1_f, self.recurrent_kernel_f)) c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1_c, self.recurrent_kernel_c)) o = self.recurrent_activation(x_o + K.dot(h_tm1_o, self.recurrent_kernel_o)) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] z = K.dot(inputs, self.kernel) if 0. < self.recurrent_dropout < 1.: h_tm1 *= rec_dp_mask[0] z += K.dot(h_tm1, self.recurrent_kernel) if self.use_bias: z = K.bias_add(z, self.bias) z0 = z[:, :self.units] z1 = z[:, self.units: 2 * self.units] z2 = z[:, 2 * self.units: 3 * self.units] z3 = z[:, 3 * self.units:] i = self.recurrent_activation(z0) f = self.recurrent_activation(z1) c = f * c_tm1 + i * self.activation(z2) o = self.recurrent_activation(z3) h = o * self.activation(c) if 0 < self.dropout + self.recurrent_dropout: if training is None: h._uses_learning_phase = True return h, [h, c] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(LSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class LSTM(RNN): """Long-Short Term Memory layer - Hochreiter 1997. # Arguments units: Positive integer, dimensionality of the output space. activation: Activation function to use (see [activations](../activations.md)). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step (see [activations](../activations.md)). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. (see [initializers](../initializers.md)). bias_initializer: Initializer for the bias vector (see [initializers](../initializers.md)). unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see [regularizer](../regularizers.md)). recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix (see [regularizer](../regularizers.md)). bias_regularizer: Regularizer function applied to the bias vector (see [regularizer](../regularizers.md)). activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see [regularizer](../regularizers.md)). kernel_constraint: Constraint function applied to the `kernel` weights matrix (see [constraints](../constraints.md)). recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix (see [constraints](../constraints.md)). bias_constraint: Constraint function applied to the bias vector (see [constraints](../constraints.md)). dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. # References - [Long short-term memory](http://www.bioinf.jku.at/publications/older/2604.pdf) (original 1997 paper) - [Learning to forget: Continual prediction with LSTM](http://www.mitpressjournals.org/doi/pdf/10.1162/089976600300015015) - [Supervised sequence labeling with recurrent neural networks](http://www.cs.toronto.edu/~graves/preprint.pdf) - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if implementation == 0: warnings.warn('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = LSTMCell(units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, unit_forget_bias=unit_forget_bias, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation) super(LSTM, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(LSTM, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def unit_forget_bias(self): return self.cell.unit_forget_bias @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(LSTM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) def _generate_dropout_ones(inputs, dims): # Currently, CTNK can't instantiate `ones` with symbolic shapes. # Will update workaround once CTNK supports it. if K.backend() == 'cntk': ones = K.ones_like(K.reshape(inputs[:, 0], (-1, 1))) return K.tile(ones, (1, dims)) else: return K.ones((K.shape(inputs)[0], dims)) def _generate_dropout_mask(ones, rate, training=None, count=1): def dropped_inputs(): return K.dropout(ones, rate) if count > 1: return [K.in_train_phase( dropped_inputs, ones, training=training) for _ in range(count)] return K.in_train_phase( dropped_inputs, ones, training=training)

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import warnings from .. import backend as K from .. import activations from .. import initializers from .. import regularizers from .. import constraints from ..engine import Layer from ..engine import InputSpec from ..utils.generic_utils import has_arg from ..legacy.layers import Recurrent from ..legacy import interfaces class StackedRNNCells(Layer): def __init__(self, cells, **kwargs): for cell in cells: if not hasattr(cell, 'call'): raise ValueError('All cells must have a `call` method. ' 'received cells:', cells) if not hasattr(cell, 'state_size'): raise ValueError('All cells must have a ' '`state_size` attribute. ' 'received cells:', cells) self.cells = cells super(StackedRNNCells, self).__init__(**kwargs) @property def state_size(self): state_size = [] for cell in self.cells[::-1]: if hasattr(cell.state_size, '__len__'): state_size += list(cell.state_size) else: state_size.append(cell.state_size) return tuple(state_size) def call(self, inputs, states, **kwargs): nested_states = [] for cell in self.cells[::-1]: if hasattr(cell.state_size, '__len__'): nested_states.append(states[:len(cell.state_size)]) states = states[len(cell.state_size):] else: nested_states.append([states[0]]) states = states[1:] nested_states = nested_states[::-1] new_nested_states = [] for cell, states in zip(self.cells, nested_states): inputs, states = cell.call(inputs, states, **kwargs) new_nested_states.append(states) states = [] for cell_states in new_nested_states[::-1]: states += cell_states return inputs, states def build(self, input_shape): for cell in self.cells: if isinstance(cell, Layer): cell.build(input_shape) if hasattr(cell.state_size, '__len__'): output_dim = cell.state_size[0] else: output_dim = cell.state_size input_shape = (input_shape[0], input_shape[1], output_dim) self.built = True def get_config(self): cells = [] for cell in self.cells: cells.append({'class_name': cell.__class__.__name__, 'config': cell.get_config()}) config = {'cells': cells} base_config = super(StackedRNNCells, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from . import deserialize as deserialize_layer cells = [] for cell_config in config.pop('cells'): cells.append(deserialize_layer(cell_config, custom_objects=custom_objects)) return cls(cells, **config) @property def trainable_weights(self): if not self.trainable: return [] weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.trainable_weights return weights @property def non_trainable_weights(self): weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.non_trainable_weights if not self.trainable: trainable_weights = [] for cell in self.cells: if isinstance(cell, Layer): trainable_weights += cell.trainable_weights return trainable_weights + weights return weights def get_weights(self): weights = [] for cell in self.cells: if isinstance(cell, Layer): weights += cell.weights return K.batch_get_value(weights) def set_weights(self, weights): tuples = [] for cell in self.cells: if isinstance(cell, Layer): num_param = len(cell.weights) weights = weights[:num_param] for sw, w in zip(cell.weights, weights): tuples.append((sw, w)) weights = weights[num_param:] K.batch_set_value(tuples) @property def losses(self): losses = [] for cell in self.cells: if isinstance(cell, Layer): cell_losses = cell.losses losses += cell_losses return losses def get_losses_for(self, inputs=None): losses = [] for cell in self.cells: if isinstance(cell, Layer): cell_losses = cell.get_losses_for(inputs) losses += cell_losses return losses class RNN(Layer): def __init__(self, cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if isinstance(cell, (list, tuple)): cell = StackedRNNCells(cell) if not hasattr(cell, 'call'): raise ValueError('`cell` should have a `call` method. ' 'The RNN was passed:', cell) if not hasattr(cell, 'state_size'): raise ValueError('The RNN cell should have ' 'an attribute `state_size` ' '(tuple of integers, ' 'one integer per RNN state).') super(RNN, self).__init__(**kwargs) self.cell = cell self.return_sequences = return_sequences self.return_state = return_state self.go_backwards = go_backwards self.stateful = stateful self.unroll = unroll self.supports_masking = True self.input_spec = [InputSpec(ndim=3)] self.state_spec = None self._states = None self.constants_spec = None self._num_constants = None @property def states(self): if self._states is None: if isinstance(self.cell.state_size, int): num_states = 1 else: num_states = len(self.cell.state_size) return [None for _ in range(num_states)] return self._states @states.setter def states(self, states): self._states = states def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] if hasattr(self.cell.state_size, '__len__'): state_size = self.cell.state_size else: state_size = [self.cell.state_size] output_dim = state_size[0] if self.return_sequences: output_shape = (input_shape[0], input_shape[1], output_dim) else: output_shape = (input_shape[0], output_dim) if self.return_state: state_shape = [(input_shape[0], dim) for dim in state_size] return [output_shape] + state_shape else: return output_shape def compute_mask(self, inputs, mask): if isinstance(mask, list): mask = mask[0] output_mask = mask if self.return_sequences else None if self.return_state: state_mask = [None for _ in self.states] return [output_mask] + state_mask else: return output_mask def build(self, input_shape): if self._num_constants is not None: constants_shape = input_shape[-self._num_constants:] else: constants_shape = None if isinstance(input_shape, list): input_shape = input_shape[0] batch_size = input_shape[0] if self.stateful else None input_dim = input_shape[-1] self.input_spec[0] = InputSpec(shape=(batch_size, None, input_dim)) if isinstance(self.cell, Layer): step_input_shape = (input_shape[0],) + input_shape[2:] if constants_shape is not None: self.cell.build([step_input_shape] + constants_shape) else: self.cell.build(step_input_shape) if hasattr(self.cell.state_size, '__len__'): state_size = list(self.cell.state_size) else: state_size = [self.cell.state_size] if self.state_spec is not None: if [spec.shape[-1] for spec in self.state_spec] != state_size: raise ValueError( 'An `initial_state` was passed that is not compatible with ' '`cell.state_size`. Received `state_spec`={}; ' 'however `cell.state_size` is ' '{}'.format(self.state_spec, self.cell.state_size)) else: self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] if self.stateful: self.reset_states() def get_initial_state(self, inputs): initial_state = K.zeros_like(inputs) initial_state = K.sum(initial_state, axis=(1, 2)) initial_state = K.expand_dims(initial_state) if hasattr(self.cell.state_size, '__len__'): return [K.tile(initial_state, [1, dim]) for dim in self.cell.state_size] else: return [K.tile(initial_state, [1, self.cell.state_size])] def __call__(self, inputs, initial_state=None, constants=None, **kwargs): inputs, initial_state, constants = self._standardize_args( inputs, initial_state, constants) if initial_state is None and constants is None: return super(RNN, self).__call__(inputs, **kwargs) additional_inputs = [] additional_specs = [] if initial_state is not None: kwargs['initial_state'] = initial_state additional_inputs += initial_state self.state_spec = [InputSpec(shape=K.int_shape(state)) for state in initial_state] additional_specs += self.state_spec if constants is not None: kwargs['constants'] = constants additional_inputs += constants self.constants_spec = [InputSpec(shape=K.int_shape(constant)) for constant in constants] self._num_constants = len(constants) additional_specs += self.constants_spec is_keras_tensor = hasattr(additional_inputs[0], '_keras_history') for tensor in additional_inputs: if hasattr(tensor, '_keras_history') != is_keras_tensor: raise ValueError('The initial state or constants of an RNN' ' layer cannot be specified with a mix of' ' Keras tensors and non-Keras tensors') if is_keras_tensor: full_input = [inputs] + additional_inputs full_input_spec = self.input_spec + additional_specs original_input_spec = self.input_spec self.input_spec = full_input_spec output = super(RNN, self).__call__(full_input, **kwargs) self.input_spec = original_input_spec return output else: return super(RNN, self).__call__(inputs, **kwargs) def call(self, inputs, mask=None, training=None, initial_state=None, constants=None): if isinstance(inputs, list): inputs = inputs[0] if initial_state is not None: pass elif self.stateful: initial_state = self.states else: initial_state = self.get_initial_state(inputs) if isinstance(mask, list): mask = mask[0] if len(initial_state) != len(self.states): raise ValueError('Layer has ' + str(len(self.states)) + ' states but was passed ' + str(len(initial_state)) + ' initial states.') input_shape = K.int_shape(inputs) timesteps = input_shape[1] if self.unroll and timesteps in [None, 1]: raise ValueError('Cannot unroll a RNN if the ' 'time dimension is undefined or equal to 1. \n' '- If using a Sequential model, ' 'specify the time dimension by passing ' 'an `input_shape` or `batch_input_shape` ' 'argument to your first layer. If your ' 'first layer is an Embedding, you can ' 'also use the `input_length` argument.\n' '- If using the functional API, specify ' 'the time dimension by passing a `shape` ' 'or `batch_shape` argument to your Input layer.') kwargs = {} if has_arg(self.cell.call, 'training'): kwargs['training'] = training if constants: if not has_arg(self.cell.call, 'constants'): raise ValueError('RNN cell does not support constants') def step(inputs, states): constants = states[-self._num_constants:] states = states[:-self._num_constants] return self.cell.call(inputs, states, constants=constants, **kwargs) else: def step(inputs, states): return self.cell.call(inputs, states, **kwargs) last_output, outputs, states = K.rnn(step, inputs, initial_state, constants=constants, go_backwards=self.go_backwards, mask=mask, unroll=self.unroll, input_length=timesteps) if self.stateful: updates = [] for i in range(len(states)): updates.append((self.states[i], states[i])) self.add_update(updates, inputs) if self.return_sequences: output = outputs else: output = last_output if getattr(last_output, '_uses_learning_phase', False): output._uses_learning_phase = True for state in states: state._uses_learning_phase = True if self.return_state: if not isinstance(states, (list, tuple)): states = [states] else: states = list(states) return [output] + states else: return output def _standardize_args(self, inputs, initial_state, constants): if isinstance(inputs, list): assert initial_state is None and constants is None if self._num_constants is not None: constants = inputs[-self._num_constants:] inputs = inputs[:-self._num_constants] if len(inputs) > 1: initial_state = inputs[1:] inputs = inputs[0] def to_list_or_none(x): if x is None or isinstance(x, list): return x if isinstance(x, tuple): return list(x) return [x] initial_state = to_list_or_none(initial_state) constants = to_list_or_none(constants) return inputs, initial_state, constants def reset_states(self, states=None): if not self.stateful: raise AttributeError('Layer must be stateful.') batch_size = self.input_spec[0].shape[0] if not batch_size: raise ValueError('If a RNN is stateful, it needs to know ' 'its batch size. Specify the batch size ' 'of your input tensors: \n' '- If using a Sequential model, ' 'specify the batch size by passing ' 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' 'the batch size by passing a ' '`batch_shape` argument to your Input layer.') if self.states[0] is None: if hasattr(self.cell.state_size, '__len__'): self.states = [K.zeros((batch_size, dim)) for dim in self.cell.state_size] else: self.states = [K.zeros((batch_size, self.cell.state_size))] elif states is None: if hasattr(self.cell.state_size, '__len__'): for state, dim in zip(self.states, self.cell.state_size): K.set_value(state, np.zeros((batch_size, dim))) else: K.set_value(self.states[0], np.zeros((batch_size, self.cell.state_size))) else: if not isinstance(states, (list, tuple)): states = [states] if len(states) != len(self.states): raise ValueError('Layer ' + self.name + ' expects ' + str(len(self.states)) + ' states, ' 'but it received ' + str(len(states)) + ' state values. Input received: ' + str(states)) for index, (value, state) in enumerate(zip(states, self.states)): if hasattr(self.cell.state_size, '__len__'): dim = self.cell.state_size[index] else: dim = self.cell.state_size if value.shape != (batch_size, dim): raise ValueError('State ' + str(index) + ' is incompatible with layer ' + self.name + ': expected shape=' + str((batch_size, dim)) + ', found shape=' + str(value.shape)) K.set_value(state, value) def get_config(self): config = {'return_sequences': self.return_sequences, 'return_state': self.return_state, 'go_backwards': self.go_backwards, 'stateful': self.stateful, 'unroll': self.unroll} if self._num_constants is not None: config['num_constants'] = self._num_constants cell_config = self.cell.get_config() config['cell'] = {'class_name': self.cell.__class__.__name__, 'config': cell_config} base_config = super(RNN, self).get_config() return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config, custom_objects=None): from . import deserialize as deserialize_layer cell = deserialize_layer(config.pop('cell'), custom_objects=custom_objects) num_constants = config.pop('num_constants', None) layer = cls(cell, **config) layer._num_constants = num_constants return layer @property def trainable_weights(self): if not self.trainable: return [] if isinstance(self.cell, Layer): return self.cell.trainable_weights return [] @property def non_trainable_weights(self): if isinstance(self.cell, Layer): if not self.trainable: return self.cell.weights return self.cell.non_trainable_weights return [] @property def losses(self): if isinstance(self.cell, Layer): return self.cell.losses return [] def get_losses_for(self, inputs=None): if isinstance(self.cell, Layer): cell_losses = self.cell.get_losses_for(inputs) return cell_losses + super(RNN, self).get_losses_for(inputs) return super(RNN, self).get_losses_for(inputs) class SimpleRNNCell(Layer): def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(SimpleRNNCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.built = True def call(self, inputs, states, training=None): prev_output = states[0] if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training) dp_mask = self._dropout_mask rec_dp_mask = self._recurrent_dropout_mask if dp_mask is not None: h = K.dot(inputs * dp_mask, self.kernel) else: h = K.dot(inputs, self.kernel) if self.bias is not None: h = K.bias_add(h, self.bias) if rec_dp_mask is not None: prev_output *= rec_dp_mask output = h + K.dot(prev_output, self.recurrent_kernel) if self.activation is not None: output = self.activation(output) if 0 < self.dropout + self.recurrent_dropout: if training is None: output._uses_learning_phase = True return output, [output] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(SimpleRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class SimpleRNN(RNN): @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if 'implementation' in kwargs: kwargs.pop('implementation') warnings.warn('The `implementation` argument ' 'in `SimpleRNN` has been deprecated. ' 'Please remove it from your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = SimpleRNNCell(units, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout) super(SimpleRNN, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(SimpleRNN, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout} base_config = super(SimpleRNN, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config: config.pop('implementation') return cls(**config) class GRUCell(Layer): def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, **kwargs): super(GRUCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.implementation = implementation self.state_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight(shape=(input_dim, self.units * 3), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 3), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units * 3,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_z = self.kernel[:, :self.units] self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units] self.kernel_r = self.kernel[:, self.units: self.units * 2] self.recurrent_kernel_r = self.recurrent_kernel[:, self.units: self.units * 2] self.kernel_h = self.kernel[:, self.units * 2:] self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:] if self.use_bias: self.bias_z = self.bias[:self.units] self.bias_r = self.bias[self.units: self.units * 2] self.bias_h = self.bias[self.units * 2:] else: self.bias_z = None self.bias_r = None self.bias_h = None self.built = True def call(self, inputs, states, training=None): h_tm1 = states[0] if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training, count=3) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training, count=3) dp_mask = self._dropout_mask rec_dp_mask = self._recurrent_dropout_mask if self.implementation == 1: if 0. < self.dropout < 1.: inputs_z = inputs * dp_mask[0] inputs_r = inputs * dp_mask[1] inputs_h = inputs * dp_mask[2] else: inputs_z = inputs inputs_r = inputs inputs_h = inputs x_z = K.dot(inputs_z, self.kernel_z) x_r = K.dot(inputs_r, self.kernel_r) x_h = K.dot(inputs_h, self.kernel_h) if self.use_bias: x_z = K.bias_add(x_z, self.bias_z) x_r = K.bias_add(x_r, self.bias_r) x_h = K.bias_add(x_h, self.bias_h) if 0. < self.recurrent_dropout < 1.: h_tm1_z = h_tm1 * rec_dp_mask[0] h_tm1_r = h_tm1 * rec_dp_mask[1] h_tm1_h = h_tm1 * rec_dp_mask[2] else: h_tm1_z = h_tm1 h_tm1_r = h_tm1 h_tm1_h = h_tm1 z = self.recurrent_activation(x_z + K.dot(h_tm1_z, self.recurrent_kernel_z)) r = self.recurrent_activation(x_r + K.dot(h_tm1_r, self.recurrent_kernel_r)) hh = self.activation(x_h + K.dot(r * h_tm1_h, self.recurrent_kernel_h)) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] matrix_x = K.dot(inputs, self.kernel) if self.use_bias: matrix_x = K.bias_add(matrix_x, self.bias) if 0. < self.recurrent_dropout < 1.: h_tm1 *= rec_dp_mask[0] matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) x_z = matrix_x[:, :self.units] x_r = matrix_x[:, self.units: 2 * self.units] recurrent_z = matrix_inner[:, :self.units] recurrent_r = matrix_inner[:, self.units: 2 * self.units] z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) x_h = matrix_x[:, 2 * self.units:] recurrent_h = K.dot(r * h_tm1, self.recurrent_kernel[:, 2 * self.units:]) hh = self.activation(x_h + recurrent_h) h = z * h_tm1 + (1 - z) * hh if 0 < self.dropout + self.recurrent_dropout: if training is None: h._uses_learning_phase = True return h, [h] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class GRU(RNN): @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if implementation == 0: warnings.warn('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = GRUCell(units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation) super(GRU, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(GRU, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(GRU, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) class LSTMCell(Layer): def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, **kwargs): super(LSTMCell, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.unit_forget_bias = unit_forget_bias self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.implementation = implementation self.state_size = (self.units, self.units) self._dropout_mask = None self._recurrent_dropout_mask = None def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight(shape=(input_dim, self.units * 4), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 4), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: if self.unit_forget_bias: def bias_initializer(_, *args, **kwargs): return K.concatenate([ self.bias_initializer((self.units,), *args, **kwargs), initializers.Ones()((self.units,), *args, **kwargs), self.bias_initializer((self.units * 2,), *args, **kwargs), ]) else: bias_initializer = self.bias_initializer self.bias = self.add_weight(shape=(self.units * 4,), name='bias', initializer=bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_i = self.kernel[:, :self.units] self.kernel_f = self.kernel[:, self.units: self.units * 2] self.kernel_c = self.kernel[:, self.units * 2: self.units * 3] self.kernel_o = self.kernel[:, self.units * 3:] self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units] self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2] self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3] self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:] if self.use_bias: self.bias_i = self.bias[:self.units] self.bias_f = self.bias[self.units: self.units * 2] self.bias_c = self.bias[self.units * 2: self.units * 3] self.bias_o = self.bias[self.units * 3:] else: self.bias_i = None self.bias_f = None self.bias_c = None self.bias_o = None self.built = True def call(self, inputs, states, training=None): if 0 < self.dropout < 1 and self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, K.shape(inputs)[-1]), self.dropout, training=training, count=4) if (0 < self.recurrent_dropout < 1 and self._recurrent_dropout_mask is None): self._recurrent_dropout_mask = _generate_dropout_mask( _generate_dropout_ones(inputs, self.units), self.recurrent_dropout, training=training, count=4) dp_mask = self._dropout_mask rec_dp_mask = self._recurrent_dropout_mask h_tm1 = states[0] c_tm1 = states[1] if self.implementation == 1: if 0 < self.dropout < 1.: inputs_i = inputs * dp_mask[0] inputs_f = inputs * dp_mask[1] inputs_c = inputs * dp_mask[2] inputs_o = inputs * dp_mask[3] else: inputs_i = inputs inputs_f = inputs inputs_c = inputs inputs_o = inputs x_i = K.dot(inputs_i, self.kernel_i) x_f = K.dot(inputs_f, self.kernel_f) x_c = K.dot(inputs_c, self.kernel_c) x_o = K.dot(inputs_o, self.kernel_o) if self.use_bias: x_i = K.bias_add(x_i, self.bias_i) x_f = K.bias_add(x_f, self.bias_f) x_c = K.bias_add(x_c, self.bias_c) x_o = K.bias_add(x_o, self.bias_o) if 0 < self.recurrent_dropout < 1.: h_tm1_i = h_tm1 * rec_dp_mask[0] h_tm1_f = h_tm1 * rec_dp_mask[1] h_tm1_c = h_tm1 * rec_dp_mask[2] h_tm1_o = h_tm1 * rec_dp_mask[3] else: h_tm1_i = h_tm1 h_tm1_f = h_tm1 h_tm1_c = h_tm1 h_tm1_o = h_tm1 i = self.recurrent_activation(x_i + K.dot(h_tm1_i, self.recurrent_kernel_i)) f = self.recurrent_activation(x_f + K.dot(h_tm1_f, self.recurrent_kernel_f)) c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1_c, self.recurrent_kernel_c)) o = self.recurrent_activation(x_o + K.dot(h_tm1_o, self.recurrent_kernel_o)) else: if 0. < self.dropout < 1.: inputs *= dp_mask[0] z = K.dot(inputs, self.kernel) if 0. < self.recurrent_dropout < 1.: h_tm1 *= rec_dp_mask[0] z += K.dot(h_tm1, self.recurrent_kernel) if self.use_bias: z = K.bias_add(z, self.bias) z0 = z[:, :self.units] z1 = z[:, self.units: 2 * self.units] z2 = z[:, 2 * self.units: 3 * self.units] z3 = z[:, 3 * self.units:] i = self.recurrent_activation(z0) f = self.recurrent_activation(z1) c = f * c_tm1 + i * self.activation(z2) o = self.recurrent_activation(z3) h = o * self.activation(c) if 0 < self.dropout + self.recurrent_dropout: if training is None: h._uses_learning_phase = True return h, [h, c] def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(LSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items())) class LSTM(RNN): @interfaces.legacy_recurrent_support def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs): if implementation == 0: warnings.warn('`implementation=0` has been deprecated, ' 'and now defaults to `implementation=1`.' 'Please update your layer call.') if K.backend() == 'theano': warnings.warn( 'RNN dropout is no longer supported with the Theano backend ' 'due to technical limitations. ' 'You can either set `dropout` and `recurrent_dropout` to 0, ' 'or use the TensorFlow backend.') dropout = 0. recurrent_dropout = 0. cell = LSTMCell(units, activation=activation, recurrent_activation=recurrent_activation, use_bias=use_bias, kernel_initializer=kernel_initializer, recurrent_initializer=recurrent_initializer, unit_forget_bias=unit_forget_bias, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, recurrent_regularizer=recurrent_regularizer, bias_regularizer=bias_regularizer, kernel_constraint=kernel_constraint, recurrent_constraint=recurrent_constraint, bias_constraint=bias_constraint, dropout=dropout, recurrent_dropout=recurrent_dropout, implementation=implementation) super(LSTM, self).__init__(cell, return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, stateful=stateful, unroll=unroll, **kwargs) self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): return super(LSTM, self).call(inputs, mask=mask, training=training, initial_state=initial_state) @property def units(self): return self.cell.units @property def activation(self): return self.cell.activation @property def recurrent_activation(self): return self.cell.recurrent_activation @property def use_bias(self): return self.cell.use_bias @property def kernel_initializer(self): return self.cell.kernel_initializer @property def recurrent_initializer(self): return self.cell.recurrent_initializer @property def bias_initializer(self): return self.cell.bias_initializer @property def unit_forget_bias(self): return self.cell.unit_forget_bias @property def kernel_regularizer(self): return self.cell.kernel_regularizer @property def recurrent_regularizer(self): return self.cell.recurrent_regularizer @property def bias_regularizer(self): return self.cell.bias_regularizer @property def kernel_constraint(self): return self.cell.kernel_constraint @property def recurrent_constraint(self): return self.cell.recurrent_constraint @property def bias_constraint(self): return self.cell.bias_constraint @property def dropout(self): return self.cell.dropout @property def recurrent_dropout(self): return self.cell.recurrent_dropout @property def implementation(self): return self.cell.implementation def get_config(self): config = {'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout, 'implementation': self.implementation} base_config = super(LSTM, self).get_config() del base_config['cell'] return dict(list(base_config.items()) + list(config.items())) @classmethod def from_config(cls, config): if 'implementation' in config and config['implementation'] == 0: config['implementation'] = 1 return cls(**config) def _generate_dropout_ones(inputs, dims): # Will update workaround once CTNK supports it. if K.backend() == 'cntk': ones = K.ones_like(K.reshape(inputs[:, 0], (-1, 1))) return K.tile(ones, (1, dims)) else: return K.ones((K.shape(inputs)[0], dims)) def _generate_dropout_mask(ones, rate, training=None, count=1): def dropped_inputs(): return K.dropout(ones, rate) if count > 1: return [K.in_train_phase( dropped_inputs, ones, training=training) for _ in range(count)] return K.in_train_phase( dropped_inputs, ones, training=training)

true

f7198b1249cfc281e7acad93f4e91961e055e201

13,206

py

Python

mirage/libs/ble_utils/scapy_btlejack_layers.py

HomeSen/mirage

6beb4df508758bd152f5d929ba3e6353f161ef27

[ "MIT" ]

null

mirage/libs/ble_utils/scapy_btlejack_layers.py

HomeSen/mirage

6beb4df508758bd152f5d929ba3e6353f161ef27

[ "MIT" ]

null

mirage/libs/ble_utils/scapy_btlejack_layers.py

HomeSen/mirage

6beb4df508758bd152f5d929ba3e6353f161ef27

[ "MIT" ]

null

from scapy.all import * ''' This module contains some scapy definitions for communicating with a BTLEJack device. ''' BTLEJACK_PACKETS_TYPES = { 0x1 : "command", 0x2 : "response", 0x4 : "notification" } BTLEJACK_PACKETS_OPCODES = { 0x1 : "version", 0x2 : "reset", 0x3 : "scan_access_address", 0x4 : "recover", 0x5 : "recover_channel_map", 0x6 : "recover_hop_interval", 0x7 : "sniff_connection_requests", 0x8 : "enable_jamming", 0x9 : "enable_hijacking", 0xa : "send_packet", 0xb : "collaborative_channel_map", 0xe : "debug", 0xf : "verbose" } BTLEJACK_NOTIFICATION_TYPES = { 0x0 : "access_address", 0x1 : "crc", 0x2 : "channel_map", 0x3 : "hop_interval", 0x4 : "hop_increment", 0x5 : "packet", 0x6 : "connection_request", 0x7 : "packet_nordic", 0x8 : "hijack_status", 0x9 : "connection_lost", 0xa : "advertisement" } class BTLEJack_Hdr(Packet): name = "BTLEJack Packet" fields_desc = [ XByteField("magic",0xBC), BitEnumField("packet_type",None, 4, BTLEJACK_PACKETS_TYPES), ConditionalField(BitEnumField("opcode",None, 4, BTLEJACK_PACKETS_OPCODES), lambda pkt:pkt.packet_type <= 0x3), ConditionalField(BitEnumField("notification_type",None, 4, BTLEJACK_NOTIFICATION_TYPES), lambda pkt:pkt.packet_type == 0x4), LEShortField("length",None), XByteField("crc",None) ] def pre_dissect(self,data): return data[0:4] + data[-1:] + data[4:-1] def post_build(self,p,pay): if self.crc is None: self.crc = 0xFF for byte in p+pay: self.crc ^= byte if self.length is None: self.length = len(pay) self.crc ^= self.length return p[0:2]+struct.pack('<H',self.length)+pay+struct.pack('B',self.crc) # BTLEJack Commands class BTLEJack_Version_Command(Packet): name = "BTLEJack Version Command" class BTLEJack_Reset_Command(Packet): name = "BTLEJack Reset Command" class BTLEJack_Reset_Command(Packet): name = "BTLEJack Reset Command" class BTLEJack_Scan_Connections_Command(Packet): name = "BTLEJack Scan Connections Command" class BTLEJack_Collaborative_Channel_Map_Command(Packet): name = "BTLEJack Collaborative Channel Map Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("start_channel",0), ByteField("end_channel",37) ] class BTLEJack_Recover_Command(Packet): name = "BTLEJack Recover Command" fields_desc = [ ByteEnumField("operation_type",None, { 0x00 : "recover_crc_init", 0x01 : "recover_channel_map", 0x02 : "recover_hop" }) ] class BTLEJack_Recover_Crcinit_Command(Packet): name = "BTLEJack Recover CRCInit Command" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Channel_Map_Command(Packet): name = "BTLEJack Recover Channel Map Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("start_channel",0), ByteField("end_channel",37), LEIntField("timeout",None) ] class BTLEJack_Recover_Hopping_Parameters_Command(Packet): name = "BTLEJack Recover Hopping Parameters Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Recover_Connection_AA_Command(Packet): name = "BTLEJack Recover Connection AA Command" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Connection_AA_Chm_Command(Packet): name = "BTLEJack Recover Connection AA Chm Command" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Recover_Connection_AA_Chm_HopInterval_Command(Packet): name = "BTLEJack Recover Connection AA Chm Command" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None), XLEShortField("hop_interval",None) ] class BTLEJack_Sniff_Connection_Request_Command(Packet): name = "BTLEJack Sniff Connection Request Command" fields_desc = [ BDAddrField("address",None), ByteField("channel",37) ] class BTLEJack_Sniff_Advertisements_Command(Packet): name = "BTLEJack Sniff Advertisements Command" fields_desc = [ BDAddrField("address",None), ByteField("channel",37) ] class BTLEJack_Jam_Advertisements_Command(Packet): name = "BTLEJack Jam Advertisements Command" fields_desc = [ ByteField("channel",37), ByteField("offset",None), FieldLenField("pattern_length", None,fmt="B", length_of="pattern"), StrField("pattern",None) ] class BTLEJack_Enable_Jamming_Command(Packet): name = "BTLEJack Enable Jamming Command" fields_desc = [ ByteEnumField("enabled",None,{0x00 : "no",0x01 : "yes"}) ] class BTLEJack_Enable_Hijacking_Command(Packet): name = "BTLEJack Enable Hijacking Command" fields_desc = [ ByteEnumField("enabled",None,{0x00 : "no",0x01 : "yes"}) ] class BTLEJack_Send_Packet_Command(Packet): name = "BTLEJack Send Packet Command" fields_desc = [ PacketField("ble_payload",None,BTLE_DATA) ] # BTLEJack Responses class BTLEJack_Send_Packet_Response(Packet): name = "BTLEJack Send Packet Response" class BTLEJack_Enable_Jamming_Response(Packet): name = "BTLEJack Enable Jamming Response" class BTLEJack_Enable_Hijacking_Response(Packet): name = "BTLEJack Enable Hijacking Response" class BTLEJack_Recover_Response(Packet): name = "BTLEJack Recover Response" class BTLEJack_Scan_Connections_Response(Packet): name = "BTLEJack Scan Connections Response" class BTLEJack_Collaborative_Channel_Map_Response(Packet): name = "BTLEJack Collaborative Channel Map Response" class BTLEJack_Version_Response(Packet): name = "BTLEJack Version Response" fields_desc = [ ByteField("major",None), ByteField("minor",None) ] class BTLEJack_Reset_Response(Packet): name = "BTLEJack Reset Response" class BTLEJack_Sniff_Connection_Request_Response(Packet): name = "BTLEJack Sniff Connection Request Response" class BTLEJack_Sniff_Advertisements_Response(Packet): name = "BTLEJack Sniff Advertisements Response" class BTLEJack_Jam_Advertisements_Response(Packet): name = "BTLEJack Jam Advertisements Response" class BTLEJack_Verbose_Response(Packet): name = "BTLEJack Verbose Response" fields_desc = [StrField("message",None)] class BTLEJack_Debug_Response(Packet): name = "BTLEJack Debug Response" fields_desc = [StrField("message",None)] class BTLEJack_Recover_Connection_AA_Response(Packet): name = "BTLEJack Recover Connection AA Response" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Connection_AA_Chm_Response(Packet): name = "BTLEJack Recover Connection AA Chm Response" fields_desc = [ XLEIntField("access_address",None) ] # BTLEJack Notifications class BTLEJack_Access_Address_Notification(Packet): name = "BTLEJack Access Address Notification" fields_desc = [ ByteField("channel",None), ByteField("rssi", None), XLEIntField("access_address",None) ] class BTLEJack_CRCInit_Notification(Packet): name = "BTLEJack CRCInit Notification" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("unused",0) ] class BTLEJack_Channel_Map_Notification(Packet): name = "BTLEJack Channel Map Notification" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Hop_Interval_Notification(Packet): name = "BTLEJack Hop Interval Notification" fields_desc = [ XLEIntField("access_address",None), XLEShortField("hop_interval",None) ] class BTLEJack_Hop_Increment_Notification(Packet): name = "BTLEJack Hop Increment Notification" fields_desc = [ XLEIntField("access_address",None), ByteField("hop_increment",None) ] class BTLEJack_Nordic_Tap_Packet_Notification(Packet): name = "BTLEJack Nordic Tap Packet Notification" fields_desc = [ ByteField("header_length",None), ByteField("flags",None), ByteField("channel",None), ByteField("rssi",None), LEShortField("event_counter",None), LEIntField("delta", None), PacketField("ble_payload",None, BTLE_DATA) ] class BTLEJack_Hijack_Status_Notification(Packet): name = "BTLEJack Hijack Status Notification" fields_desc = [ ByteEnumField("status",None, {0 : "success", 1 : "failure"}) ] class BTLEJack_Connection_Lost_Notification(Packet): name = "BTLEJack Connection Lost Notification" class BTLEJack_Advertisement_Notification(Packet): name = "BTLEJack Advertisement Notification" fields_desc = [ PacketField("ble_payload",None,BTLE_ADV) ] class BTLEJack_Connection_Request_Notification(Packet): name = "BTLEJack Connection Request Notification" fields_desc = [ BitEnumField("RxAdd", 0, 1, {0: "public", 1: "random"}), BitEnumField("TxAdd", 0, 1, {0: "public", 1: "random"}), BitField("RFU", 0, 2), # Unused BitEnumField("PDU_type", 0, 4, {0: "ADV_IND", 1: "ADV_DIRECT_IND", 2: "ADV_NONCONN_IND", 3: "SCAN_REQ", 4: "SCAN_RSP", 5: "CONNECT_REQ", 6: "ADV_SCAN_IND"}), ByteField("payload_length", 0x22), PacketField("ble_payload",None,BTLE_CONNECT_REQ) ] # Binding BTLEJack Commands bind_layers(BTLEJack_Hdr, BTLEJack_Version_Command,packet_type=0x1, opcode=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Reset_Command,packet_type=0x1, opcode=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Scan_Connections_Command, packet_type=0x1,opcode=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Collaborative_Channel_Map_Command,packet_type=0x1,opcode=0xb) bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Command,packet_type=0x1, opcode=0x4) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Crcinit_Command,operation_type=0x00) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Channel_Map_Command,operation_type=0x01) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Hopping_Parameters_Command,operation_type=0x02) #bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Connection_AA_Command,packet_type=0x1,opcode=0x4) #bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Connection_AA_Chm_Command,packet_type=0x1,opcode=0x5) #bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Connection_AA_Chm_HopInterval_Command,packet_type=0x1,opcode=0x6) bind_layers(BTLEJack_Hdr, BTLEJack_Jam_Advertisements_Command,packet_type=0x1, opcode=0x5) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Connection_Request_Command,packet_type=0x1,opcode=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Advertisements_Command,packet_type=0x1,opcode=0xc) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Jamming_Command,packet_type=0x1,opcode=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Hijacking_Command,packet_type=0x1,opcode=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Send_Packet_Command,packet_type=0x1,opcode=0xa) # Binding BTLEJack Responses bind_layers(BTLEJack_Hdr, BTLEJack_Send_Packet_Response,packet_type=0x2,opcode=0xa) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Jamming_Response,packet_type=0x2,opcode=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Hijacking_Response,packet_type=0x2,opcode=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Connection_Request_Response,packet_type=0x2, opcode=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Advertisements_Response,packet_type=0x1,opcode=0xc) ''' bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Connection_AA_Response,packet_type=0x2, opcode=0x4) bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Connection_AA_Chm_Response,packet_type=0x2, opcode=0x5) ''' bind_layers(BTLEJack_Hdr, BTLEJack_Jam_Advertisements_Command,packet_type=0x1,opcode=0x5) bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Response,packet_type=0x2, opcode=0x4) bind_layers(BTLEJack_Hdr, BTLEJack_Version_Response,packet_type=0x2, opcode=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Reset_Response,packet_type=0x2, opcode=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Scan_Connections_Response,packet_type=0x2, opcode=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Collaborative_Channel_Map_Response,packet_type=0x2, opcode=0xb) bind_layers(BTLEJack_Hdr, BTLEJack_Debug_Response,packet_type=0x2, opcode=0xe) bind_layers(BTLEJack_Hdr, BTLEJack_Verbose_Response,packet_type=0x2, opcode=0xf) # Binding BTLEJack Notifications bind_layers(BTLEJack_Hdr, BTLEJack_Access_Address_Notification, packet_type=0x4, notification_type=0x0) bind_layers(BTLEJack_Hdr, BTLEJack_CRCInit_Notification, packet_type=0x4, notification_type=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Channel_Map_Notification, packet_type=0x4, notification_type=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Hop_Interval_Notification, packet_type=0x4, notification_type=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Hop_Increment_Notification, packet_type=0x4, notification_type=0x4) bind_layers(BTLEJack_Hdr, BTLEJack_Nordic_Tap_Packet_Notification, packet_type=0x4, notification_type=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Hijack_Status_Notification, packet_type=0x4, notification_type=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Connection_Lost_Notification, packet_type=0x4, notification_type=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Connection_Request_Notification, packet_type=0x4, notification_type=0x6) bind_layers(BTLEJack_Hdr, BTLEJack_Advertisement_Notification, packet_type=0x4, notification_type=0xa)

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from scapy.all import * BTLEJACK_PACKETS_TYPES = { 0x1 : "command", 0x2 : "response", 0x4 : "notification" } BTLEJACK_PACKETS_OPCODES = { 0x1 : "version", 0x2 : "reset", 0x3 : "scan_access_address", 0x4 : "recover", 0x5 : "recover_channel_map", 0x6 : "recover_hop_interval", 0x7 : "sniff_connection_requests", 0x8 : "enable_jamming", 0x9 : "enable_hijacking", 0xa : "send_packet", 0xb : "collaborative_channel_map", 0xe : "debug", 0xf : "verbose" } BTLEJACK_NOTIFICATION_TYPES = { 0x0 : "access_address", 0x1 : "crc", 0x2 : "channel_map", 0x3 : "hop_interval", 0x4 : "hop_increment", 0x5 : "packet", 0x6 : "connection_request", 0x7 : "packet_nordic", 0x8 : "hijack_status", 0x9 : "connection_lost", 0xa : "advertisement" } class BTLEJack_Hdr(Packet): name = "BTLEJack Packet" fields_desc = [ XByteField("magic",0xBC), BitEnumField("packet_type",None, 4, BTLEJACK_PACKETS_TYPES), ConditionalField(BitEnumField("opcode",None, 4, BTLEJACK_PACKETS_OPCODES), lambda pkt:pkt.packet_type <= 0x3), ConditionalField(BitEnumField("notification_type",None, 4, BTLEJACK_NOTIFICATION_TYPES), lambda pkt:pkt.packet_type == 0x4), LEShortField("length",None), XByteField("crc",None) ] def pre_dissect(self,data): return data[0:4] + data[-1:] + data[4:-1] def post_build(self,p,pay): if self.crc is None: self.crc = 0xFF for byte in p+pay: self.crc ^= byte if self.length is None: self.length = len(pay) self.crc ^= self.length return p[0:2]+struct.pack('<H',self.length)+pay+struct.pack('B',self.crc) class BTLEJack_Version_Command(Packet): name = "BTLEJack Version Command" class BTLEJack_Reset_Command(Packet): name = "BTLEJack Reset Command" class BTLEJack_Reset_Command(Packet): name = "BTLEJack Reset Command" class BTLEJack_Scan_Connections_Command(Packet): name = "BTLEJack Scan Connections Command" class BTLEJack_Collaborative_Channel_Map_Command(Packet): name = "BTLEJack Collaborative Channel Map Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("start_channel",0), ByteField("end_channel",37) ] class BTLEJack_Recover_Command(Packet): name = "BTLEJack Recover Command" fields_desc = [ ByteEnumField("operation_type",None, { 0x00 : "recover_crc_init", 0x01 : "recover_channel_map", 0x02 : "recover_hop" }) ] class BTLEJack_Recover_Crcinit_Command(Packet): name = "BTLEJack Recover CRCInit Command" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Channel_Map_Command(Packet): name = "BTLEJack Recover Channel Map Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("start_channel",0), ByteField("end_channel",37), LEIntField("timeout",None) ] class BTLEJack_Recover_Hopping_Parameters_Command(Packet): name = "BTLEJack Recover Hopping Parameters Command" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Recover_Connection_AA_Command(Packet): name = "BTLEJack Recover Connection AA Command" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Connection_AA_Chm_Command(Packet): name = "BTLEJack Recover Connection AA Chm Command" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Recover_Connection_AA_Chm_HopInterval_Command(Packet): name = "BTLEJack Recover Connection AA Chm Command" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None), XLEShortField("hop_interval",None) ] class BTLEJack_Sniff_Connection_Request_Command(Packet): name = "BTLEJack Sniff Connection Request Command" fields_desc = [ BDAddrField("address",None), ByteField("channel",37) ] class BTLEJack_Sniff_Advertisements_Command(Packet): name = "BTLEJack Sniff Advertisements Command" fields_desc = [ BDAddrField("address",None), ByteField("channel",37) ] class BTLEJack_Jam_Advertisements_Command(Packet): name = "BTLEJack Jam Advertisements Command" fields_desc = [ ByteField("channel",37), ByteField("offset",None), FieldLenField("pattern_length", None,fmt="B", length_of="pattern"), StrField("pattern",None) ] class BTLEJack_Enable_Jamming_Command(Packet): name = "BTLEJack Enable Jamming Command" fields_desc = [ ByteEnumField("enabled",None,{0x00 : "no",0x01 : "yes"}) ] class BTLEJack_Enable_Hijacking_Command(Packet): name = "BTLEJack Enable Hijacking Command" fields_desc = [ ByteEnumField("enabled",None,{0x00 : "no",0x01 : "yes"}) ] class BTLEJack_Send_Packet_Command(Packet): name = "BTLEJack Send Packet Command" fields_desc = [ PacketField("ble_payload",None,BTLE_DATA) ] class BTLEJack_Send_Packet_Response(Packet): name = "BTLEJack Send Packet Response" class BTLEJack_Enable_Jamming_Response(Packet): name = "BTLEJack Enable Jamming Response" class BTLEJack_Enable_Hijacking_Response(Packet): name = "BTLEJack Enable Hijacking Response" class BTLEJack_Recover_Response(Packet): name = "BTLEJack Recover Response" class BTLEJack_Scan_Connections_Response(Packet): name = "BTLEJack Scan Connections Response" class BTLEJack_Collaborative_Channel_Map_Response(Packet): name = "BTLEJack Collaborative Channel Map Response" class BTLEJack_Version_Response(Packet): name = "BTLEJack Version Response" fields_desc = [ ByteField("major",None), ByteField("minor",None) ] class BTLEJack_Reset_Response(Packet): name = "BTLEJack Reset Response" class BTLEJack_Sniff_Connection_Request_Response(Packet): name = "BTLEJack Sniff Connection Request Response" class BTLEJack_Sniff_Advertisements_Response(Packet): name = "BTLEJack Sniff Advertisements Response" class BTLEJack_Jam_Advertisements_Response(Packet): name = "BTLEJack Jam Advertisements Response" class BTLEJack_Verbose_Response(Packet): name = "BTLEJack Verbose Response" fields_desc = [StrField("message",None)] class BTLEJack_Debug_Response(Packet): name = "BTLEJack Debug Response" fields_desc = [StrField("message",None)] class BTLEJack_Recover_Connection_AA_Response(Packet): name = "BTLEJack Recover Connection AA Response" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Recover_Connection_AA_Chm_Response(Packet): name = "BTLEJack Recover Connection AA Chm Response" fields_desc = [ XLEIntField("access_address",None) ] class BTLEJack_Access_Address_Notification(Packet): name = "BTLEJack Access Address Notification" fields_desc = [ ByteField("channel",None), ByteField("rssi", None), XLEIntField("access_address",None) ] class BTLEJack_CRCInit_Notification(Packet): name = "BTLEJack CRCInit Notification" fields_desc = [ XLEIntField("access_address",None), LEX3BytesField("crc_init",None), ByteField("unused",0) ] class BTLEJack_Channel_Map_Notification(Packet): name = "BTLEJack Channel Map Notification" fields_desc = [ XLEIntField("access_address",None), BTLEChanMapField("channel_map",None) ] class BTLEJack_Hop_Interval_Notification(Packet): name = "BTLEJack Hop Interval Notification" fields_desc = [ XLEIntField("access_address",None), XLEShortField("hop_interval",None) ] class BTLEJack_Hop_Increment_Notification(Packet): name = "BTLEJack Hop Increment Notification" fields_desc = [ XLEIntField("access_address",None), ByteField("hop_increment",None) ] class BTLEJack_Nordic_Tap_Packet_Notification(Packet): name = "BTLEJack Nordic Tap Packet Notification" fields_desc = [ ByteField("header_length",None), ByteField("flags",None), ByteField("channel",None), ByteField("rssi",None), LEShortField("event_counter",None), LEIntField("delta", None), PacketField("ble_payload",None, BTLE_DATA) ] class BTLEJack_Hijack_Status_Notification(Packet): name = "BTLEJack Hijack Status Notification" fields_desc = [ ByteEnumField("status",None, {0 : "success", 1 : "failure"}) ] class BTLEJack_Connection_Lost_Notification(Packet): name = "BTLEJack Connection Lost Notification" class BTLEJack_Advertisement_Notification(Packet): name = "BTLEJack Advertisement Notification" fields_desc = [ PacketField("ble_payload",None,BTLE_ADV) ] class BTLEJack_Connection_Request_Notification(Packet): name = "BTLEJack Connection Request Notification" fields_desc = [ BitEnumField("RxAdd", 0, 1, {0: "public", 1: "random"}), BitEnumField("TxAdd", 0, 1, {0: "public", 1: "random"}), BitField("RFU", 0, 2), BitEnumField("PDU_type", 0, 4, {0: "ADV_IND", 1: "ADV_DIRECT_IND", 2: "ADV_NONCONN_IND", 3: "SCAN_REQ", 4: "SCAN_RSP", 5: "CONNECT_REQ", 6: "ADV_SCAN_IND"}), ByteField("payload_length", 0x22), PacketField("ble_payload",None,BTLE_CONNECT_REQ) ] bind_layers(BTLEJack_Hdr, BTLEJack_Version_Command,packet_type=0x1, opcode=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Reset_Command,packet_type=0x1, opcode=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Scan_Connections_Command, packet_type=0x1,opcode=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Collaborative_Channel_Map_Command,packet_type=0x1,opcode=0xb) bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Command,packet_type=0x1, opcode=0x4) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Crcinit_Command,operation_type=0x00) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Channel_Map_Command,operation_type=0x01) bind_layers(BTLEJack_Recover_Command,BTLEJack_Recover_Hopping_Parameters_Command,operation_type=0x02) bind_layers(BTLEJack_Hdr, BTLEJack_Jam_Advertisements_Command,packet_type=0x1, opcode=0x5) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Connection_Request_Command,packet_type=0x1,opcode=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Advertisements_Command,packet_type=0x1,opcode=0xc) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Jamming_Command,packet_type=0x1,opcode=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Hijacking_Command,packet_type=0x1,opcode=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Send_Packet_Command,packet_type=0x1,opcode=0xa) bind_layers(BTLEJack_Hdr, BTLEJack_Send_Packet_Response,packet_type=0x2,opcode=0xa) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Jamming_Response,packet_type=0x2,opcode=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Enable_Hijacking_Response,packet_type=0x2,opcode=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Connection_Request_Response,packet_type=0x2, opcode=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Sniff_Advertisements_Response,packet_type=0x1,opcode=0xc) bind_layers(BTLEJack_Hdr, BTLEJack_Jam_Advertisements_Command,packet_type=0x1,opcode=0x5) bind_layers(BTLEJack_Hdr, BTLEJack_Recover_Response,packet_type=0x2, opcode=0x4) bind_layers(BTLEJack_Hdr, BTLEJack_Version_Response,packet_type=0x2, opcode=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Reset_Response,packet_type=0x2, opcode=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Scan_Connections_Response,packet_type=0x2, opcode=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Collaborative_Channel_Map_Response,packet_type=0x2, opcode=0xb) bind_layers(BTLEJack_Hdr, BTLEJack_Debug_Response,packet_type=0x2, opcode=0xe) bind_layers(BTLEJack_Hdr, BTLEJack_Verbose_Response,packet_type=0x2, opcode=0xf) bind_layers(BTLEJack_Hdr, BTLEJack_Access_Address_Notification, packet_type=0x4, notification_type=0x0) bind_layers(BTLEJack_Hdr, BTLEJack_CRCInit_Notification, packet_type=0x4, notification_type=0x1) bind_layers(BTLEJack_Hdr, BTLEJack_Channel_Map_Notification, packet_type=0x4, notification_type=0x2) bind_layers(BTLEJack_Hdr, BTLEJack_Hop_Interval_Notification, packet_type=0x4, notification_type=0x3) bind_layers(BTLEJack_Hdr, BTLEJack_Hop_Increment_Notification, packet_type=0x4, notification_type=0x4) bind_layers(BTLEJack_Hdr, BTLEJack_Nordic_Tap_Packet_Notification, packet_type=0x4, notification_type=0x7) bind_layers(BTLEJack_Hdr, BTLEJack_Hijack_Status_Notification, packet_type=0x4, notification_type=0x8) bind_layers(BTLEJack_Hdr, BTLEJack_Connection_Lost_Notification, packet_type=0x4, notification_type=0x9) bind_layers(BTLEJack_Hdr, BTLEJack_Connection_Request_Notification, packet_type=0x4, notification_type=0x6) bind_layers(BTLEJack_Hdr, BTLEJack_Advertisement_Notification, packet_type=0x4, notification_type=0xa)

true

f7198b76ba36f1f12ec60d6aea9e6f66c8d175da

7,421

py

Python

backend/server/models.py

thunderlink/thunderfish

a600021187a50bb078d9c36306564470cc6e9fd8

[ "MIT" ]

3

2019-04-18T04:45:27.000Z

2019-11-06T18:17:29.000Z

backend/server/models.py

thunderlink/thunderfish

a600021187a50bb078d9c36306564470cc6e9fd8

[ "MIT" ]

59

2019-04-22T07:05:52.000Z

2022-03-11T23:48:33.000Z

backend/server/models.py

thunderlink/thunderfish

a600021187a50bb078d9c36306564470cc6e9fd8

[ "MIT" ]

4

2019-04-24T05:49:21.000Z

2019-11-21T00:26:00.000Z

from django.db import models from django.contrib.auth.models import User import re from math import sqrt, pi # Path to default image DEFAULT_IMAGE = '../media/app_logo.png' DEFAULT_PROFILE_IMG = 1 DEFAULT_MEETING_IMG = 2 MEDIA_URL = '/media/' # Unique email for each user User._meta.local_fields[7].__dict__['_unique'] = True class Image(models.Model): profile = models.ImageField(blank=True, null=False, default=DEFAULT_IMAGE) title = models.CharField(max_length=100, blank=True) url = models.CharField(max_length=1000, blank=True, null=True) def __str__(self): return str(self.id) class Profile(models.Model): GENDER_MALE = 0 GENDER_FEMALE = 1 GENDER_PRIVATE = 2 GENDER_CHOICES = [(GENDER_MALE, 'Male'), (GENDER_FEMALE, 'Female'), (GENDER_PRIVATE, 'Private')] user = models.OneToOneField(User, on_delete=models.DO_NOTHING) nickname = models.CharField(max_length=20) photo = models.ForeignKey(Image, related_name="profile_photo", on_delete=models.CASCADE, default=DEFAULT_PROFILE_IMG) # email = models.EmailField(max_length=30) name = models.CharField(max_length=50) gender = models.IntegerField(choices=GENDER_CHOICES, default=GENDER_PRIVATE) region = models.CharField(max_length=100, blank = True) # may not be necessary, use API ?? introduce = models.CharField(max_length=200, blank = True) def __str__(self): return self.nickname class Meta: ordering = ('name', ) class Meeting(models.Model): STATUS_RECRUITING = 0 STATUS_COMPLETE = 1 STATUS_CANCELED = 2 STATUS_CHOICES = [(STATUS_RECRUITING, 'Recruiting'), (STATUS_COMPLETE, 'Complete'), (STATUS_CANCELED, 'Canceled')] name = models.CharField(max_length=50) host = models.ForeignKey(Profile, related_name="meeting_hosted", on_delete=models.DO_NOTHING) date = models.DateTimeField('meeting date') posted_date = models.DateTimeField('posted date', auto_now_add=True) participant = models.ManyToManyField(Profile, through = 'Membership') # contributer - people who opened the meeting with the host max_participant = models.IntegerField() deadline = models.DateTimeField('meeting deadline') region = models.CharField(max_length=100, blank=True) photo = models.ForeignKey(Image, related_name="meeting_photo", on_delete=models.CASCADE, default=DEFAULT_MEETING_IMG) content = models.CharField(max_length=500) tag_set = models.ManyToManyField('Tag', blank=True) status = models.IntegerField(choices=STATUS_CHOICES) # 1 as pending, 0 as complete ? open_chat = models.URLField(max_length=100, blank=True) # remove default latitude = models.DecimalField(max_digits=30, decimal_places=15, default=0, blank=True) longitude = models.DecimalField(max_digits=30, decimal_places=15, default=0, blank=True) # content에서 tags를 추출하여, Tag 객체 가져오기, 신규 태그는 Tag instance 생성, 본인의 tag_set에 등록, # Question : Does \w support korean? # We should add exceptional control code for unvalid tag. def tag_save(self, tag_string): tags = re.findall(r'\b(\w+)\b', self.content) if not tags: return for t in tags: tag, tag_created = Tag.objects.get_or_create(name=t) self.tag_set.add(tag) def __str__(self): return self.name @staticmethod def distance_search(result, dist, lat, long): ## Returns list of meetings that is ## less than dist kilometers far from (latitude, longitude) ## Ordered by increasing distance ret = [] for meet in result: delta_phi = abs(float(meet.latitude) - lat) ** 2 delta_theta = abs(float(meet.longitude) - long) ** 2 calculated_distance = float(6371 * sqrt(delta_phi + delta_theta) * 2 * pi / 360) if calculated_distance <= dist: ret.append((result.get(pk=meet.id), calculated_distance)) ret.sort(key = lambda item : item[1]) print(ret) return ret class Meta: ordering = ['-id'] class Tag(models.Model): name = models.CharField(max_length=100, unique=True) def __str__(self): return self.name class Comment(models.Model): date = models.DateTimeField('commented date', auto_now_add=True) comment_text = models.CharField(max_length=1000, default="Test Text") # parent_comment = models.ForeignKey(Comment, on_delete=models.CASCADE) parent_meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE) writer = models.ForeignKey(Profile, on_delete=models.CASCADE) def __str__(self): return self.comment_text # For notification 1 : New comment for host def save(self, *args, **kwargs): notification = Notification(meeting=self.parent_meeting, profile=self.parent_meeting.host, notification = Notification.NOTIFICATION_NEW_COMMENT_FOR_HOST) notification.save() super().save(*args, **kwargs) # we should add url field. class Notification(models.Model): NOTIFICATION_NEW_APPLY = 0 NOTIFICATION_NEW_COMMENT_FOR_HOST = 1 NOTIFICATION_APPLY_REJECTED = 2 NOTIFICATION_APPLY_APPROVED = 3 NOTIFICATION_CHOICES = [(NOTIFICATION_NEW_APPLY, 'new apply'), (NOTIFICATION_NEW_COMMENT_FOR_HOST, 'new comment for host'),(NOTIFICATION_APPLY_REJECTED, 'apply is rejected'),(NOTIFICATION_APPLY_APPROVED, 'apply is approved')] profile = models.ForeignKey(Profile,on_delete=models.CASCADE) checked = models.BooleanField(default=False) meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE, null=True) notification = models.IntegerField(choices=NOTIFICATION_CHOICES) def __str__(self): return str(self.profile) class Meta: ordering = ['checked', '-id'] class Membership(models.Model): STATUS_WAITING = 0 STATUS_APPROVED = 1 STATUS_REJECTED = 2 STATUS_CHOICES = [(STATUS_WAITING, 'waiting'), (STATUS_APPROVED, 'approved'), (STATUS_REJECTED, 'rejected')] profile = models.ForeignKey(Profile, on_delete=models.CASCADE) meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) status = models.IntegerField(choices=STATUS_CHOICES) message = models.CharField(max_length = 500, null=True, blank=True) def __str__(self): return str(self.meeting.id) + '@' + str(self.profile.id) class Meta: unique_together = ( ('profile', 'meeting') ) # For notification 0 : New apply # For notification 2 : Apply rejected # For notification 3 : Apply approved def save(self, *args, **kwargs): if(self.pk==None): notification = Notification(meeting=self.meeting, profile=self.meeting.host, notification = Notification.NOTIFICATION_NEW_APPLY) notification.save() else: if(self.status == self.STATUS_CHOICES[1][0]): notification = Notification(meeting=self.meeting, profile=self.profile, notification = Notification.NOTIFICATION_APPLY_APPROVED) notification.save() print("Notify") elif(self.status == self.STATUS_CHOICES[2][0]): notification = Notification(meeting = self.meeting, profile = self.profile, notification = Notification.NOTIFICATION_APPLY_REJECTED) notification.save() super().save(*args, **kwargs)

41

229

0.694381

from django.db import models from django.contrib.auth.models import User import re from math import sqrt, pi DEFAULT_IMAGE = '../media/app_logo.png' DEFAULT_PROFILE_IMG = 1 DEFAULT_MEETING_IMG = 2 MEDIA_URL = '/media/' User._meta.local_fields[7].__dict__['_unique'] = True class Image(models.Model): profile = models.ImageField(blank=True, null=False, default=DEFAULT_IMAGE) title = models.CharField(max_length=100, blank=True) url = models.CharField(max_length=1000, blank=True, null=True) def __str__(self): return str(self.id) class Profile(models.Model): GENDER_MALE = 0 GENDER_FEMALE = 1 GENDER_PRIVATE = 2 GENDER_CHOICES = [(GENDER_MALE, 'Male'), (GENDER_FEMALE, 'Female'), (GENDER_PRIVATE, 'Private')] user = models.OneToOneField(User, on_delete=models.DO_NOTHING) nickname = models.CharField(max_length=20) photo = models.ForeignKey(Image, related_name="profile_photo", on_delete=models.CASCADE, default=DEFAULT_PROFILE_IMG) name = models.CharField(max_length=50) gender = models.IntegerField(choices=GENDER_CHOICES, default=GENDER_PRIVATE) region = models.CharField(max_length=100, blank = True) introduce = models.CharField(max_length=200, blank = True) def __str__(self): return self.nickname class Meta: ordering = ('name', ) class Meeting(models.Model): STATUS_RECRUITING = 0 STATUS_COMPLETE = 1 STATUS_CANCELED = 2 STATUS_CHOICES = [(STATUS_RECRUITING, 'Recruiting'), (STATUS_COMPLETE, 'Complete'), (STATUS_CANCELED, 'Canceled')] name = models.CharField(max_length=50) host = models.ForeignKey(Profile, related_name="meeting_hosted", on_delete=models.DO_NOTHING) date = models.DateTimeField('meeting date') posted_date = models.DateTimeField('posted date', auto_now_add=True) participant = models.ManyToManyField(Profile, through = 'Membership') max_participant = models.IntegerField() deadline = models.DateTimeField('meeting deadline') region = models.CharField(max_length=100, blank=True) photo = models.ForeignKey(Image, related_name="meeting_photo", on_delete=models.CASCADE, default=DEFAULT_MEETING_IMG) content = models.CharField(max_length=500) tag_set = models.ManyToManyField('Tag', blank=True) status = models.IntegerField(choices=STATUS_CHOICES) open_chat = models.URLField(max_length=100, blank=True) latitude = models.DecimalField(max_digits=30, decimal_places=15, default=0, blank=True) longitude = models.DecimalField(max_digits=30, decimal_places=15, default=0, blank=True) def tag_save(self, tag_string): tags = re.findall(r'\b(\w+)\b', self.content) if not tags: return for t in tags: tag, tag_created = Tag.objects.get_or_create(name=t) self.tag_set.add(tag) def __str__(self): return self.name @staticmethod def distance_search(result, dist, lat, long): delta_theta = abs(float(meet.longitude) - long) ** 2 calculated_distance = float(6371 * sqrt(delta_phi + delta_theta) * 2 * pi / 360) if calculated_distance <= dist: ret.append((result.get(pk=meet.id), calculated_distance)) ret.sort(key = lambda item : item[1]) print(ret) return ret class Meta: ordering = ['-id'] class Tag(models.Model): name = models.CharField(max_length=100, unique=True) def __str__(self): return self.name class Comment(models.Model): date = models.DateTimeField('commented date', auto_now_add=True) comment_text = models.CharField(max_length=1000, default="Test Text") parent_meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE) writer = models.ForeignKey(Profile, on_delete=models.CASCADE) def __str__(self): return self.comment_text def save(self, *args, **kwargs): notification = Notification(meeting=self.parent_meeting, profile=self.parent_meeting.host, notification = Notification.NOTIFICATION_NEW_COMMENT_FOR_HOST) notification.save() super().save(*args, **kwargs) class Notification(models.Model): NOTIFICATION_NEW_APPLY = 0 NOTIFICATION_NEW_COMMENT_FOR_HOST = 1 NOTIFICATION_APPLY_REJECTED = 2 NOTIFICATION_APPLY_APPROVED = 3 NOTIFICATION_CHOICES = [(NOTIFICATION_NEW_APPLY, 'new apply'), (NOTIFICATION_NEW_COMMENT_FOR_HOST, 'new comment for host'),(NOTIFICATION_APPLY_REJECTED, 'apply is rejected'),(NOTIFICATION_APPLY_APPROVED, 'apply is approved')] profile = models.ForeignKey(Profile,on_delete=models.CASCADE) checked = models.BooleanField(default=False) meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE, null=True) notification = models.IntegerField(choices=NOTIFICATION_CHOICES) def __str__(self): return str(self.profile) class Meta: ordering = ['checked', '-id'] class Membership(models.Model): STATUS_WAITING = 0 STATUS_APPROVED = 1 STATUS_REJECTED = 2 STATUS_CHOICES = [(STATUS_WAITING, 'waiting'), (STATUS_APPROVED, 'approved'), (STATUS_REJECTED, 'rejected')] profile = models.ForeignKey(Profile, on_delete=models.CASCADE) meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) status = models.IntegerField(choices=STATUS_CHOICES) message = models.CharField(max_length = 500, null=True, blank=True) def __str__(self): return str(self.meeting.id) + '@' + str(self.profile.id) class Meta: unique_together = ( ('profile', 'meeting') ) def save(self, *args, **kwargs): if(self.pk==None): notification = Notification(meeting=self.meeting, profile=self.meeting.host, notification = Notification.NOTIFICATION_NEW_APPLY) notification.save() else: if(self.status == self.STATUS_CHOICES[1][0]): notification = Notification(meeting=self.meeting, profile=self.profile, notification = Notification.NOTIFICATION_APPLY_APPROVED) notification.save() print("Notify") elif(self.status == self.STATUS_CHOICES[2][0]): notification = Notification(meeting = self.meeting, profile = self.profile, notification = Notification.NOTIFICATION_APPLY_REJECTED) notification.save() super().save(*args, **kwargs)

true

f7198bd1b623cee47276165d5348854e67b0535b

45,311

py

Python

pyNastran/dev/bdf_vectorized/cards/dynamic.py

Msegade/pyNastran

ae36548579c6bb2ee3a4fff207f7211c1986a5ab

[ "BSD-3-Clause" ]

null

pyNastran/dev/bdf_vectorized/cards/dynamic.py

Msegade/pyNastran

ae36548579c6bb2ee3a4fff207f7211c1986a5ab

[ "BSD-3-Clause" ]

null

pyNastran/dev/bdf_vectorized/cards/dynamic.py

Msegade/pyNastran

ae36548579c6bb2ee3a4fff207f7211c1986a5ab

[ "BSD-3-Clause" ]

1

2020-10-04T19:28:07.000Z

# pylint: disable=C0103,R0902,R0904,R0914 """ All dynamic control cards are defined in this file. This includes: * FREQ * FREQ1 * FREQ2 (not implemented) * FREQ3 * FREQ4 * FREQ5 (not implemented) * NLPCI * NLPARM * TSTEP * TSTEPNL All cards are BaseCard objects. """ from math import log, exp, ceil import numpy as np from numpy import unique, hstack from pyNastran.utils.numpy_utils import integer_types from pyNastran.bdf.field_writer_8 import set_blank_if_default from pyNastran.bdf.cards.base_card import BaseCard from pyNastran.bdf.bdf_interface.assign_type import ( integer, integer_or_blank, double, double_or_blank, string_or_blank, blank, fields, components_or_blank ) from pyNastran.bdf.field_writer_8 import print_card_8 from pyNastran.bdf.field_writer_16 import print_card_16 if TYPE_CHECKING: # pragma: no cover from pyNastran.bdf.bdf import BDF class DELAY(BaseCard): type = 'DELAY' def __init__(self, sid, nodes, components, delays, comment=''): """ +-------+-----+-----------+-----+--------+------+-----+--------+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+=====+===========+=====+========+======+=====+========+=====+ | DELAY | SID | POINT ID1 | C1 | T1 | P2 | C2 | T2 | | +-------+-----+-----------+-----+--------+------+-----+--------+-----+ """ if comment: self.comment = comment #: Identification number of DELAY entry. (Integer > 0) self.sid = sid #: Grid, extra, or scalar point identification number. (Integer > 0) self.nodes = nodes #: Component number. (Integers 1 through 6 for grid points; zero or blank for extra #: or scalar points) self.components = components #: Time delay (tau) for designated point Pi and component Ci. (Real) self.delays = delays @classmethod def add_card(cls, card, comment=''): """ Adds a DELAY card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') nodes = [integer(card, 2, 'node')] components = [integer(card, 3, 'components')] delays = [double_or_blank(card, 4, 'delay')] assert components[0] in [0, 1, 2, 3, 4, 5, 6], components if card.field(5): nodes.append(integer(card, 5, 'node')) components.append(integer(card, 6, 'components')) delays.append(double_or_blank(card, 7, 'delay')) assert components[1] in [0, 1, 2, 3, 4, 5, 6], components return DELAY(sid, nodes, components, delays, comment=comment) def add(self, delay): assert self.sid == delay.sid, 'sid=%s delay.sid=%s' % (self.sid, delay.sid) if delay.comment: if hasattr('_comment'): self._comment += delay.comment else: self._comment = delay.comment self.nodes += delay.nodes self.components += delay.components self.delays += delay.delays def get_delay_at_freq(self, freq): return self.nodes, self.components, self.delays #def cross_reference(self, model: BDF) -> None: #""" #Cross links the card so referenced cards can be extracted directly #Parameters #---------- #model : BDF() #the BDF object #""" #msg = ', which is required by DELAY sid=%s' % self.sid #self.nodes_ref = model.Node(self.node_ids, msg=msg) #@property #def node_id1(self): #if isinstance(self.nodes[0], integer_types): #return self.nodes[0] #return self.nodes_ref[0].nid #@property #def node_id2(self): #if isinstance(self.nodes[1], integer_types): #return self.nodes[1] #return self.nodes_ref[1].nid @property def node_ids(self): node_ids = [self.node_id1] if len(self.components) == 2: node_ids.append(self.node_id2) return node_ids def raw_fields(self): list_fields = ['DELAY', self.sid] for nid, comp, delay in zip(self.node_ids, self.components, self.delays): if isinstance(nid, integer_types): nidi = nid else: nidi = nid.nid list_fields += [nidi, comp, delay] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: msg = self.comment node_ids = self.node_ids if size == 8: for nid, comp, delay in zip(node_ids, self.components, self.delays): msg += print_card_8(['DELAY', self.sid, nid, comp, delay]) else: for nid, comp, delay in zip(node_ids, self.components, self.delays): msg += print_card_16(['DELAY', self.sid, nid, comp, delay]) return msg class DPHASE(BaseCard): type = 'DPHASE' def __init__(self, sid, nodes, components, phase_leads, comment=''): """ +--------+-----+-----------+-----+------+------+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +========+=====+===========+=====+======+======+=====+=====+=====+ | DPHASE | SID | POINT ID1 | C1 | TH1 | P2 | C2 | TH2 | | +--------+-----+-----------+-----+------+------+-----+-----+-----+ """ if comment: self.comment = comment self.sid = sid self.nodes = nodes self.components = components self.phase_leads = phase_leads @classmethod def add_card(cls, card, comment=''): """ Adds a DPHASE card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') nodes = [integer(card, 2, 'node')] components = [integer(card, 3, 'components')] phase_leads = [double_or_blank(card, 4, 'phase_lead')] assert components[0] in [0, 1, 2, 3, 4, 5, 6], components if card.field(5): nodes.append(integer(card, 5, 'node')) components.append(integer(card, 6, 'components')) phase_leads.append(double_or_blank(card, 7, 'phase_lead')) assert components[1] in [0, 1, 2, 3, 4, 5, 6], components return DPHASE(sid, nodes, components, phase_leads, comment=comment) def add(self, dphase): assert self.sid == dphase.sid, 'sid=%s dphase.sid=%s' % (self.sid, dphase.sid) if dphase.comment: if hasattr('_comment'): self._comment += dphase.comment else: self._comment = dphase.comment self.nodes += dphase.nodes self.components += dphase.components self.phase_leads += dphase.phase_leads #def cross_reference(self, model: BDF) -> None: #""" #Cross links the card so referenced cards can be extracted directly #Parameters #---------- #model : BDF() #the BDF object #""" #msg = ', which is required by DPHASE sid=%s' % self.sid #self.nodes_ref = model.Nodes(self.node_ids, msg=msg) #@property #def node_id1(self): #if isinstance(self.nodes[0], integer_types): #return self.nodes[0] #return self.nodes_ref[0].nid #@property #def node_id2(self): #if isinstance(self.nodes[1], integer_types): #return self.nodes[1] #return self.nodes_ref[1].nid @property def node_ids(self): node_ids = [self.node_id1] if len(self.components) == 2: node_ids.append(self.node_id2) return node_ids def raw_fields(self): list_fields = ['DPHASE', self.sid] for nid, comp, delay in zip(self.nodes, self.components, self.phase_leads): if isinstance(nid, integer_types): nidi = nid else: nidi = nid.nid list_fields += [nidi, comp, delay] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: msg = self.comment node_ids = self.node_ids if size == 8: for nid, comp, delay in zip(node_ids, self.components, self.phase_leads): msg += print_card_8(['DPHASE', self.sid, nid, comp, delay]) else: for nid, comp, delay in zip(node_ids, self.components, self.phase_leads): msg += print_card_16(['DPHASE', self.sid, nid, comp, delay]) return msg class FREQ(BaseCard): """ Defines a set of frequencies to be used in the solution of frequency response problems. +------+-----+-----+-----+------+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +======+=====+=====+=====+======+=====+=====+=====+=====+ | FREQ | SID | F1 | F2 | etc. | | | | | +------+-----+-----+-----+------+-----+-----+-----+-----+ """ type = 'FREQ' def __init__(self, sid, freqs, comment=''): if comment: self.comment = comment self.sid = sid self.freqs = np.unique(freqs) @classmethod def add_card(cls, card, comment=''): """ Adds a FREQ card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') freqs = fields(double, card, 'freq', i=2, j=len(card)) return FREQ(sid, freqs, comment=comment) def get_freqs(self): return self.freqs def add_frequencies(self, freqs): """ Combines the frequencies from 1 FREQx object with another. All FREQi entries with the same frequency set identification numbers will be used. Duplicate frequencies will be ignored. Parameters ---------- freqs : ??? the frequencies for a FREQx object """ #print("self.freqs = ",self.freqs) #print("freqs = ",freqs) self.freqs = unique(hstack([self.freqs, freqs])) def add_frequency_object(self, freq): """ :param freq: a FREQx object .. seealso:: :func:`addFrequencies` """ self.add_frequencies(freq.freqs) def raw_fields(self): list_fields = ['FREQ', self.sid] + list(self.freqs) return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ1(FREQ): """ Defines a set of frequencies to be used in the solution of frequency response problems by specification of a starting frequency, frequency increment, and the number of increments desired. +-------+-----+-----+-----+-----+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+=====+=====+=====+=====+=====+=====+=====+=====+ | FREQ1 | SID | F1 | DF | NDF | | | | | +-------+-----+-----+-----+-----+-----+-----+-----+-----+ .. note:: this card rewrites as a FREQ card """ type = 'FREQ1' def __init__(self, sid, f1, df, ndf, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.df = df self.ndf = ndf freqs = [] for i in range(ndf): freqs.append(f1 + i * df) self.freqs = unique(freqs) @classmethod def add_card(cls, card, comment=''): """ Adds a FREQ1 card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') f1 = double_or_blank(card, 2, 'f1', 0.0) df = double(card, 3, 'df') ndf = integer_or_blank(card, 4, 'ndf', 1) assert len(card) <= 5, 'len(FREQ card) = %i\ncard=%s' % (len(card), card) return FREQ1(sid, f1, df, ndf, comment=comment) def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ2(FREQ): """ Defines a set of frequencies to be used in the solution of frequency response problems by specification of a starting frequency, final frequency, and the number of logarithmic increments desired. +-------+-----+-----+-----+-----+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+=====+=====+=====+=====+=====+=====+=====+=====+ | FREQ2 | SID | F1 | F2 | NDF | | | | | +-------+-----+-----+-----+-----+-----+-----+-----+-----+ .. note:: this card rewrites as a FREQ card """ type = 'FREQ2' def __init__(self, sid, f1, f2, ndf=1, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.f2 = f2 self.ndf = ndf d = 1. / ndf * log(f2 / f1) freqs = [] for i in range(ndf): freqs.append(f1 * exp(i * d)) # 0 based index self.freqs = np.unique(freqs) @classmethod def add_card(cls, card, comment=''): """ Adds a FREQ2 card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') f1 = double(card, 2, 'f1') # default=0.0 ? f2 = double(card, 3, 'f2') ndf = integer_or_blank(card, 4, 'nf', 1) assert len(card) <= 5, 'len(FREQ2 card) = %i\ncard=%s' % (len(card), card) return FREQ2(sid, f1, f2, ndf, comment=comment) #return FREQ(sid, freqs, comment=comment) class FREQ3(FREQ): """ +-------+-----+------+-------+--------+-----+---------+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | +=======+=====+======+=======+========+=====+=========+ | FREQ3 | SID | F1 | F2 | TYPE | NEF | CLUSTER | +-------+-----+------+-------+--------+-----+---------+ | FREQ3 | 6 | 20.0 | 200.0 | LINEAR | 10 | 2.0 | +-------+-----+------+-------+--------+-----+---------+ """ type = 'FREQ3' def __init__(self, f1, f2=None, Type='LINEAR', nef=10, cluster=1.0, comment=''): if comment: self.comment = comment if f2 is None: f2 = f1 self.sid = sid self.f1 = f1 self.f2 = f2 self.Type = Type self.nef = nef self.cluster = cluster @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') f1 = double(card, 1, 'f1') f2 = integer_or_blank(card, 1, 'f2', f1) Type = string_or_blank(card, 1, 'Type', 'LINEAR') nef = integer_or_blank(card, 1, 'nef', 10) cluster = double_or_blank(card, 1, 'cluster', 1.0) return FREQ3(sid, f1, f2, Type, nef, cluster, comment='') def raw_fields(self): return ['FREQ3', self.sid, self.f1, self.f2, self.Type, self.nef, self.cluster] def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ4(FREQ): """ Defines a set of frequencies used in the solution of modal frequency response problems by specifying the amount of 'spread' around each natural frequency and the number of equally spaced excitation frequencies within the spread. +-------+-----+-----+-----+------+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+=====+=====+=====+======+=====+=====+=====+=====+ | FREQ4 | SID | F1 | F2 | FSPD | NFM | | | | +-------+-----+-----+-----+------+-----+-----+-----+-----+ .. note:: this card rewrites as a FREQ card .. todo:: not done... """ type = 'FREQ4' def __init__(self, sid, f1, f2, fspread, nfm, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.f2 = f2 self.fspread = fspread self.nfm = nfm @classmethod def add_card(cls, card, comment=''): """ Adds a FREQ4 card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') f1 = double_or_blank(card, 2, 'f1', 0.0) f2 = double_or_blank(card, 3, 'f2', 1.e20) fspread = double_or_blank(card, 4, 'fspd', 0.1) nfm = integer_or_blank(card, 5, 'nfm', 3) assert len(card) <= 6, 'len(FREQ card) = %i\ncard=%s' % (len(card), card) return FREQ4(sid, f1, f2, fspread, nfm, comment=comment) def raw_fields(self): list_fields = ['FREQ4', self.sid, self.f1, self.f2, self.fspread, self.nfm] return list_fields def repr_fields(self): return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) #class FREQ5(FREQ): #type = 'FREQ5' #def __init__(self, card=None, data=None, comment=''): #if comment: # self.comment = comment #raise NotImplementedError() #def write_card(self, size: int=8, is_double: bool=False) -> str: #card = self.repr_fields() #if size == 8: #return self.comment + print_card_8(card) #return self.comment + print_card_16(card) class NLPARM(BaseCard): """ Defines a set of parameters for nonlinear static analysis iteration strategy. +--------+--------+------+------+---------+-------+---------+---------+--------+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +========+========+======+======+=========+=======+=========+=========+========+ | NLPARM | ID | NINC | DT | KMETHOD | KSTEP | MAXITER | CONV | INTOUT | +--------+--------+------+------+---------+-------+---------+---------+--------+ | | ESPU | EPSP | EPSW | MAXDIV | MAXQN | MAXLS | FSTRESS | LSTOL | +--------+--------+------+------+---------+-------+---------+---------+--------+ | | MAXBIS | | | | MAXR | | RTOLB | CONV | +--------+--------+------+------+---------+-------+---------+---------+--------+ """ type = 'NLPARM' def __init__(self, nlparm_id, ninc=10, dt=0.0, kmethod='AUTO', kstep=5, max_iter=25, conv='PW', int_out='NO', eps_u=0.01, eps_p=0.01, eps_w=0.01, max_div=3, max_qn=None, max_ls=4, fstress=0.2, ls_tol=0.5, max_bisect=5, max_r=20., rtol_b=20., comment=''): if comment: self.comment = comment self.nlparm_id = nlparm_id self.ninc = ninc self.dt = dt self.kmethod = kmethod self.kstep = kstep self.max_iter = max_iter self.conv = conv self.int_out = int_out # line 2 self.eps_p = eps_p self.eps_u = eps_u self.eps_w = eps_w self.max_div = max_div self.max_qn = max_qn self.max_ls = max_ls self.fstress = fstress self.ls_tol = ls_tol # line 3 self.max_bisect = max_bisect self.max_r = max_r self.rtol_b = rtol_b if self.max_qn is None: if kmethod == 'PFNT': self.max_qn = 0 else: self.max_qn = max_iter @classmethod def add_card(cls, card, comment=''): """ Adds a NLPARM card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ nlparm_id = integer(card, 1, 'nlparm_id') ninc = integer_or_blank(card, 2, 'ninc', 10) dt = double_or_blank(card, 3, 'dt', 0.0) kmethod = string_or_blank(card, 4, 'kmethod', 'AUTO') kstep = integer_or_blank(card, 5, 'kstep', 5) max_iter = integer_or_blank(card, 6, 'max_iter', 25) conv = string_or_blank(card, 7, 'conv', 'PW') int_out = string_or_blank(card, 8, 'intOut', 'NO') # line 2 eps_u = double_or_blank(card, 9, 'eps_u', 0.01) eps_p = double_or_blank(card, 10, 'eps_p', 0.01) eps_w = double_or_blank(card, 11, 'eps_w', 0.01) max_div = integer_or_blank(card, 12, 'max_div', 3) if kmethod == 'PFNT': max_qn = integer_or_blank(card, 13, 'max_qn', 0) else: max_qn = integer_or_blank(card, 13, 'max_qn', max_iter) max_ls = integer_or_blank(card, 14, 'max_ls', 4) fstress = double_or_blank(card, 15, 'fstress', 0.2) ls_tol = double_or_blank(card, 16, 'ls_tol', 0.5) # line 3 max_bisect = integer_or_blank(card, 17, 'max_bisect', 5) max_r = double_or_blank(card, 21, 'max_r', 20.) rtol_b = double_or_blank(card, 23, 'rtol_b', 20.) assert len(card) <= 24, 'len(NLPARM card) = %i\ncard=%s' % (len(card), card) return NLPARM(nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b, comment=comment) @classmethod def add_op2_data(cls, data, comment=''): """ Adds a NLPARM card from the OP2 Parameters ---------- data : List[varies] a list of fields defined in OP2 format comment : str; default='' a comment for the card """ (nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b) = data if kmethod == 1: kmethod = 'AUTO' elif kmethod == 2: kmethod = 'ITER' elif kmethod == 4: kmethod = 'SEMI' elif kmethod == 3: kmethod = 'ADAPT' else: msg = 'nlparm_id=%s kmethod=%r data=%s' % (nlparm_id, kmethod, data) raise NotImplementedError(msg) if conv == 1: conv = 'W' elif conv == 2: conv = 'P' elif conv == 3: conv = 'PW' elif conv == 4: conv = 'U' elif conv == 5: conv = 'UW' elif conv == 6: conv = 'UP' elif conv == 7: conv = 'UPW' else: msg = 'nlparm_id=%s conv=%r data=%s' % (nlparm_id, conv, data) raise NotImplementedError(msg) if int_out == 0: int_out = 'NO' elif int_out == 1: int_out = 'YES' elif int_out == 2: int_out = 'ALL' else: msg = 'nlparm_id=%s int_out=%r data=%s' % (nlparm_id, int_out, data) raise NotImplementedError(msg) return NLPARM(nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b, comment=comment) def raw_fields(self): list_fields = ['NLPARM', self.nlparm_id, self.ninc, self.dt, self.kmethod, self.kstep, self.max_iter, self.conv, self.int_out, self.eps_u, self.eps_p, self.eps_w, self.max_div, self.max_qn, self.max_ls, self.fstress, self.ls_tol, self.max_bisect, None, None, None, self.max_r, None, self.rtol_b] return list_fields def repr_fields(self): ninc = set_blank_if_default(self.ninc, 10) dt = set_blank_if_default(self.dt, 0.0) kmethod = set_blank_if_default(self.kmethod, 'AUTO') kstep = set_blank_if_default(self.kstep, 5) max_iter = set_blank_if_default(self.max_iter, 25) conv = set_blank_if_default(self.conv, 'PW') int_out = set_blank_if_default(self.int_out, 'NO') eps_u = set_blank_if_default(self.eps_u, 0.01) eps_p = set_blank_if_default(self.eps_p, 0.01) eps_w = set_blank_if_default(self.eps_w, 0.01) max_div = set_blank_if_default(self.max_div, 3) max_qn = set_blank_if_default(self.max_qn, self.max_iter) max_ls = set_blank_if_default(self.max_ls, 4) fstress = set_blank_if_default(self.fstress, 0.2) ls_tol = set_blank_if_default(self.ls_tol, 0.5) max_bisect = set_blank_if_default(self.max_bisect, 5) max_r = set_blank_if_default(self.max_r, 20.) rtol_b = set_blank_if_default(self.rtol_b, 20.) list_fields = ['NLPARM', self.nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, None, None, None, max_r, None, rtol_b] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) # having trouble with double precision... return self.comment + print_card_16(card) class NLPCI(BaseCard): type = 'NLPCI' def __init__(self, nlpci_id, Type='CRIS', minalr=0.25, maxalr=4., scale=0., desiter=12, mxinc=20, comment=''): if comment: self.comment = comment self.nlpci_id = nlpci_id self.Type = Type self.minalr = minalr self.maxalr = maxalr self.scale = scale self.desiter = desiter self.mxinc = mxinc @classmethod def add_card(cls, card, comment=''): """ Adds a NLPCI card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ nlpci_id = integer(card, 1, 'nlpci_id') Type = string_or_blank(card, 2, 'Type', 'CRIS') minalr = double_or_blank(card, 3, 'minalr', 0.25) maxalr = double_or_blank(card, 4, 'maxalr', 4.0) scale = double_or_blank(card, 5, 'scale', 0.0) blank(card, 6, 'blank') desiter = integer_or_blank(card, 7, 'desiter', 12) mxinc = integer_or_blank(card, 8, 'mxinc', 20) return NLPCI(nlpci_id, Type=Type, minalr=minalr, maxalr=maxalr, scale=scale, desiter=desiter, mxinc=mxinc, comment=comment) def raw_fields(self): list_fields = ['NLPCI', self.nlpci_id, self.Type, self.minalr, self.maxalr, self.scale, None, self.desiter, self.mxinc] return list_fields def repr_fields(self): #minalr = set_blank_if_default(self.minalr, 0.25) return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TF(BaseCard): """ Defines a dynamic transfer function of the form: (B0 + B1 p + B2 *p2)*ud sum(A0_i + A1_i*p + A2_i*p2)*ui = 0 +----+-----+-----+------+------+------+--------+----+----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +====+=====+=====+======+======+======+========+====+====+ | TF | SID | GD | CD | B0 | B1 | B2 | | | +----+-----+-----+------+------+------+--------+----+----+ | | G_1 | C_1 | A0_1 | A1_1 | A2_1 | etc. | | | +----+-----+-----+------+------+------+--------+----+----+ """ type = 'TF' def __init__(self, sid, nid0, c, b0, b1, b2, nids, components, a, comment=''): if comment: self.comment = comment self.sid = sid self.nid0 = nid0 self.c = c self.b0 = b0 self.b1 = b1 self.b2 = b2 self.nids = nids self.components = components self.a = a def validate(self): pass #assert len(self.grids1) > 0, 'ngrids1=%s\n%s' % (len(self.grids1), str(self)) #def cross_reference(self, model: BDF) -> None: #pass @classmethod def add_card(cls, card, comment=''): """ Adds a TF card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') nid0 = integer(card, 2, 'nid0') # component 0 means an SPOINT/EPOINT c = components_or_blank(card, 3, 'components_0', 0) b0 = double_or_blank(card, 4, 'b0', 0.) b1 = double_or_blank(card, 5, 'b1', 0.) b2 = double_or_blank(card, 6, 'b2', 0.) nfields = len(card) - 9 nrows = nfields // 8 if nfields % 8 > 0: nrows += 1 nids = [] components = [] a = [] for irow in range(nrows): j = irow * 8 + 9 #ifield = irow + 1 nid = integer(card, j, 'grid_%i' % (irow + 1)) component = components_or_blank(card, j + 1, 'components_%i' % (irow + 1), 0) a0 = double_or_blank(card, j + 2, 'a0_%i' % (irow + 1), 0.) a1 = double_or_blank(card, j + 3, 'a1_%i' % (irow + 1), 0.) a2 = double_or_blank(card, j + 4, 'a2_%i' % (irow + 1), 0.) nids.append(nid) components.append(component) a.append([a0, a1, a2]) return TF(sid, nid0, c, b0, b1, b2, nids, components, a, comment=comment) def raw_fields(self): list_fields = ['TF', self.sid, self.nid0, self.c, self.b0, self.b1, self.b2, None, None] for grid, c, (a0, a1, a2) in zip(self.nids, self.components, self.a): list_fields += [grid, c, a0, a1, a2, None, None, None] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: # double precision? card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TSTEP(BaseCard): """ Transient Time Step Defines time step intervals at which a solution will be generated and output in transient analysis. +-------+------+------+------+------+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+======+======+======+======+=====+=====+=====+=====+ | TSTEP | SID | N1 | DT1 | NO1 | | | | | +-------+------+------+------+------+-----+-----+-----+-----+ | | | N2 | DT2 | NO2 | | | | | +-------+------+------+------+------+-----+-----+-----+-----+ | | | etc. | | | | | | | +-------+------+------+------+------+-----+-----+-----+-----+ +-------+------+------+------+------+-----+-----+-----+-----+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=======+======+======+======+======+=====+=====+=====+=====+ | TSTEP | 101 | 9000 | .001 | 9000 | | | | | +-------+------+------+------+------+-----+-----+-----+-----+ | | | 1000 | .001 | 1 | | | | | +-------+------+------+------+------+-----+-----+-----+-----+ """ type = 'TSTEP' def __init__(self, sid, N, DT, NO, comment=''): """ Creates a TSTEP card Parameters ---------- sid : int the time step id N : List[int/None] ??? DT : List[float/None] ??? NO : List[int/None] ??? comment : str; default='' a comment for the card """ if comment: self.comment = comment self.sid = sid #: Number of time steps of value DTi. (Integer > 1) self.N = N #: Time increment (float) self.DT = DT #: Skip factor for output. Every NOi-th step will be saved for output (default=1) self.NO = NO def validate(self): assert len(self.N) == len(self.DT), 'N=%s DT=%s' % (self.N, self.DT) assert len(self.N) == len(self.NO), 'N=%s NO=%s' % (self.N, self.NO) @classmethod def add_card(cls, card, comment=''): """ Adds a TSTEP card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') N = [] DT = [] NO = [] nrows = int(ceil((len(card) - 1.) / 8.)) for i in range(nrows): n = 8 * i + 1 ni = integer_or_blank(card, n + 1, 'N' + str(i), 1) dt = double_or_blank(card, n + 2, 'dt' + str(i), 0.) no = integer_or_blank(card, n + 3, 'NO' + str(i), 1) N.append(ni) DT.append(dt) NO.append(no) return TSTEP(sid, N, DT, NO, comment=comment) def raw_fields(self): list_fields = ['TSTEP', self.sid] for (N, dt, no) in zip(self.N, self.DT, self.NO): list_fields += [N, dt, no, None, None, None, None, None] return list_fields def repr_fields(self): return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TSTEPNL(BaseCard): """ Defines parametric controls and data for nonlinear transient structural or heat transfer analysis. TSTEPNL is intended for SOLs 129, 159, and 600. Parameters for Nonlinear Transient Analysis. +---------+--------+--------+-------+--------+--------+-------+---------+------+ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +=========+========+========+=======+========+========+=======+=========+======+ | TSTEPNL | ID | NDT | DT | NO | METHOD | KSTEP | MAXITER | CONV | +---------+--------+--------+-------+--------+--------+-------+---------+------+ | | ESPU | EPSP | EPSW | MAXDIV | MAXQN | MAXLS | FSTRESS | | +---------+--------+--------+-------+--------+--------+-------+---------+------+ | | MAXBIS | ADJUST | MSTEP | RB | MAXR | UTOL | RTOLB | | +---------+--------+--------+-------+--------+--------+-------+---------+------+ method = None for NX, but apparently TSTEP as well, which is not in the QRG """ type = 'TSTEPNL' allowed_methods = ['AUTO', 'ITER', 'ADAPT', 'SEMI', 'FNT', 'PFNT', # MSC 'TSTEP'] # NX def __init__(self, sid, ndt, dt, no, method='ADAPT', kstep=None, max_iter=10, conv='PW', eps_u=1.e-2, eps_p=1.e-3, eps_w=1.e-6, max_div=2, max_qn=10, max_ls=2, fstress=0.2, max_bisect=5, adjust=5, mstep=None, rb=0.6, max_r=32., utol=0.1, rtol_b=20., min_iter=None, comment=''): """ Creates a TSTEPNL card Parameters ---------- sid : int the time step id ndt : ??? ??? dt : ??? ??? no : ??? ??? eps_u : float; default=1.e-2 ??? eps_p : float; default=1.e-3 ??? eps_w : float; default=1.e-6 ??? max_div : int; default=2 ??? max_qn : int; default=10 ??? max_ls : int; default=2 ??? fstress : float; default=0.2 ??? max_bisect : int; default=5 ??? adjust : int; default=5 ??? mstep : int; default=None ??? rb : float; default=0.6 ??? max_r = float; default=32. ??? utol = float; default=0.1 ??? rtol_b = float; default=20. ??? min_iter : int; default=None not listed in all QRGs comment : str; default='' a comment for the card """ if comment: self.comment = comment # line 1 self.sid = sid self.ndt = ndt self.dt = dt self.no = no self.method = method self.kstep = kstep self.max_iter = max_iter self.conv = conv self.eps_u = eps_u self.eps_p = eps_p self.eps_w = eps_w self.max_div = max_div self.max_qn = max_qn self.max_ls = max_ls self.fstress = fstress # line 3 self.max_bisect = max_bisect self.adjust = adjust self.mstep = mstep self.rb = rb self.max_r = max_r self.utol = utol self.rtol_b = rtol_b self.min_iter = min_iter assert self.ndt >= 3 assert self.dt > 0. def validate(self): if self.method not in self.allowed_methods: msg = 'method=%r allowed_methods=[%s]' % ( self.method, ', '.join(self.allowed_methods)) raise ValueError(msg) @classmethod def add_card(cls, card, comment=''): """ Adds a TSTEPNL card from ``BDF.add_card(...)`` Parameters ---------- card : BDFCard() a BDFCard object comment : str; default='' a comment for the card """ sid = integer(card, 1, 'sid') ndt = integer(card, 2, 'ndt') dt = double(card, 3, 'dt') no = integer_or_blank(card, 4, 'no', 1) #: .. note:: not listed in all QRGs method = string_or_blank(card, 5, 'method', 'ADAPT') if method == 'ADAPT': kstep = integer_or_blank(card, 6, 'kStep', 2) elif method == 'ITER': kstep = integer_or_blank(card, 6, 'kStep', 10) elif method in ['AUTO', 'TSTEP', 'SEMI']: kstep = None #kstep = blank(card, 6, 'kStep') #: .. todo:: not blank else: msg = 'invalid TSTEPNL Method. method=%r; allowed_methods=[%s]' % ( method, ', '.join(cls.allowed_methods)) raise RuntimeError(msg) max_iter = integer_or_blank(card, 7, 'maxIter', 10) conv = string_or_blank(card, 8, 'conv', 'PW') # line 2 eps_u = double_or_blank(card, 9, 'epsU', 1.E-2) eps_p = double_or_blank(card, 10, 'epsP', 1.E-3) eps_w = double_or_blank(card, 11, 'epsW', 1.E-6) max_div = integer_or_blank(card, 12, 'maxDiv', 2) max_qn = integer_or_blank(card, 13, 'maxQn', 10) max_ls = integer_or_blank(card, 14, 'MaxLs', 2) fstress = double_or_blank(card, 15, 'fStress', 0.2) # line 3 max_bisect = integer_or_blank(card, 17, 'maxBisect', 5) adjust = integer_or_blank(card, 18, 'adjust', 5) mstep = integer_or_blank(card, 19, 'mStep') rb = double_or_blank(card, 20, 'rb', 0.6) max_r = double_or_blank(card, 21, 'maxR', 32.) utol = double_or_blank(card, 22, 'uTol', 0.1) rtol_b = double_or_blank(card, 23, 'rTolB', 20.) # not listed in all QRGs min_iter = integer_or_blank(card, 24, 'minIter') assert len(card) <= 25, 'len(TSTEPNL card) = %i\ncard=%s' % (len(card), card) return TSTEPNL( sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b, min_iter, comment=comment) @classmethod def add_op2_data(cls, data, comment=''): """ Adds a TSTEPNL card from the OP2 Parameters ---------- data : List[varies] a list of fields defined in OP2 format comment : str; default='' a comment for the card """ (sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b) = data if method == 1: method = 'AUTO' elif method == 3: method = 'ADAPT' else: raise NotImplementedError('tstepnl=%s method=%r data=%s' % (sid, method, data)) if conv == 3: conv = 'PW' elif conv == 4: conv = 'U' #elif conv == 3: #conv = 'ADAPT' else: raise NotImplementedError('tstepnl=%s conv=%r data=%s' % (sid, conv, data)) min_iter = None # not listed in DMAP 2005 return TSTEPNL( sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b, min_iter, comment=comment) #self.sid = sid #self.ndt = ndt #self.dt = dt #self.no = no #self.method = method #self.kStep = kStep #self.maxIter = maxIter #self.conv = conv ## line 2 #self.epsU = epsU #self.epsP = epsP #self.epsW = epsW #self.maxDiv = maxDiv #self.maxQn = maxQn #self.MaxLs = maxLs #self.fStress = fStress ## line 3 #self.maxBisect = maxBisect #self.adjust = adjust #self.mStep = mStep #self.rb = rb #self.maxR = maxR #self.uTol = uTol #self.rTolB = rTolB def raw_fields(self): list_fields = ['TSTEPNL', self.sid, self.ndt, self.dt, self.no, self.method, self.kstep, self.max_iter, self.conv, self.eps_u, self.eps_p, self.eps_w, self.max_div, self.max_qn, self.max_ls, self.fstress, None, self.max_bisect, self.adjust, self.mstep, self.rb, self.max_r, self.utol, self.rtol_b, self.min_iter] return list_fields def repr_fields(self): #no = set_blank_if_default(self.no,1) no = self.no method = set_blank_if_default(self.method, 'ADAPT') kstep = self.kstep #if self.method == 'ADAPT': #kStep = set_blank_if_default(self.kStep, 2) #elif self.method == 'ITER': #kStep = set_blank_if_default(self.kStep, 10) #else: #msg = 'invalid TSTEPNL Method. method=|%s|' %(self.method) #raise RuntimeError(msg) #maxIter = set_blank_if_default(self.maxIter, 10) conv = set_blank_if_default(self.conv, 'PW') eps_u = set_blank_if_default(self.eps_u, 1e-2) eps_p = set_blank_if_default(self.eps_p, 1e-3) eps_w = set_blank_if_default(self.eps_w, 1e-6) max_div = set_blank_if_default(self.max_div, 2) max_qn = set_blank_if_default(self.max_qn, 10) max_ls = set_blank_if_default(self.max_ls, 2) fstress = set_blank_if_default(self.fstress, 0.2) max_bisect = set_blank_if_default(self.max_bisect, 5) adjust = set_blank_if_default(self.adjust, 5) rb = set_blank_if_default(self.rb, 0.6) max_r = set_blank_if_default(self.max_r, 32.) utol = set_blank_if_default(self.utol, 0.1) rtol_b = set_blank_if_default(self.rtol_b, 20.) list_fields = ['TSTEPNL', self.sid, self.ndt, self.dt, no, method, kstep, self.max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, None, max_bisect, adjust, self.mstep, rb, max_r, utol, rtol_b, self.min_iter] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card)

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from math import log, exp, ceil import numpy as np from numpy import unique, hstack from pyNastran.utils.numpy_utils import integer_types from pyNastran.bdf.field_writer_8 import set_blank_if_default from pyNastran.bdf.cards.base_card import BaseCard from pyNastran.bdf.bdf_interface.assign_type import ( integer, integer_or_blank, double, double_or_blank, string_or_blank, blank, fields, components_or_blank ) from pyNastran.bdf.field_writer_8 import print_card_8 from pyNastran.bdf.field_writer_16 import print_card_16 if TYPE_CHECKING: from pyNastran.bdf.bdf import BDF class DELAY(BaseCard): type = 'DELAY' def __init__(self, sid, nodes, components, delays, comment=''): if comment: self.comment = comment self.sid = sid self.nodes = nodes self.components = components self.delays = delays @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') nodes = [integer(card, 2, 'node')] components = [integer(card, 3, 'components')] delays = [double_or_blank(card, 4, 'delay')] assert components[0] in [0, 1, 2, 3, 4, 5, 6], components if card.field(5): nodes.append(integer(card, 5, 'node')) components.append(integer(card, 6, 'components')) delays.append(double_or_blank(card, 7, 'delay')) assert components[1] in [0, 1, 2, 3, 4, 5, 6], components return DELAY(sid, nodes, components, delays, comment=comment) def add(self, delay): assert self.sid == delay.sid, 'sid=%s delay.sid=%s' % (self.sid, delay.sid) if delay.comment: if hasattr('_comment'): self._comment += delay.comment else: self._comment = delay.comment self.nodes += delay.nodes self.components += delay.components self.delays += delay.delays def get_delay_at_freq(self, freq): return self.nodes, self.components, self.delays #Cross links the card so referenced cards can be extracted directly #Parameters #---------- #model : BDF() #the BDF object #""" @property def node_ids(self): node_ids = [self.node_id1] if len(self.components) == 2: node_ids.append(self.node_id2) return node_ids def raw_fields(self): list_fields = ['DELAY', self.sid] for nid, comp, delay in zip(self.node_ids, self.components, self.delays): if isinstance(nid, integer_types): nidi = nid else: nidi = nid.nid list_fields += [nidi, comp, delay] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: msg = self.comment node_ids = self.node_ids if size == 8: for nid, comp, delay in zip(node_ids, self.components, self.delays): msg += print_card_8(['DELAY', self.sid, nid, comp, delay]) else: for nid, comp, delay in zip(node_ids, self.components, self.delays): msg += print_card_16(['DELAY', self.sid, nid, comp, delay]) return msg class DPHASE(BaseCard): type = 'DPHASE' def __init__(self, sid, nodes, components, phase_leads, comment=''): if comment: self.comment = comment self.sid = sid self.nodes = nodes self.components = components self.phase_leads = phase_leads @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') nodes = [integer(card, 2, 'node')] components = [integer(card, 3, 'components')] phase_leads = [double_or_blank(card, 4, 'phase_lead')] assert components[0] in [0, 1, 2, 3, 4, 5, 6], components if card.field(5): nodes.append(integer(card, 5, 'node')) components.append(integer(card, 6, 'components')) phase_leads.append(double_or_blank(card, 7, 'phase_lead')) assert components[1] in [0, 1, 2, 3, 4, 5, 6], components return DPHASE(sid, nodes, components, phase_leads, comment=comment) def add(self, dphase): assert self.sid == dphase.sid, 'sid=%s dphase.sid=%s' % (self.sid, dphase.sid) if dphase.comment: if hasattr('_comment'): self._comment += dphase.comment else: self._comment = dphase.comment self.nodes += dphase.nodes self.components += dphase.components self.phase_leads += dphase.phase_leads #Cross links the card so referenced cards can be extracted directly #Parameters #---------- #model : BDF() #the BDF object #""" @property def node_ids(self): node_ids = [self.node_id1] if len(self.components) == 2: node_ids.append(self.node_id2) return node_ids def raw_fields(self): list_fields = ['DPHASE', self.sid] for nid, comp, delay in zip(self.nodes, self.components, self.phase_leads): if isinstance(nid, integer_types): nidi = nid else: nidi = nid.nid list_fields += [nidi, comp, delay] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: msg = self.comment node_ids = self.node_ids if size == 8: for nid, comp, delay in zip(node_ids, self.components, self.phase_leads): msg += print_card_8(['DPHASE', self.sid, nid, comp, delay]) else: for nid, comp, delay in zip(node_ids, self.components, self.phase_leads): msg += print_card_16(['DPHASE', self.sid, nid, comp, delay]) return msg class FREQ(BaseCard): type = 'FREQ' def __init__(self, sid, freqs, comment=''): if comment: self.comment = comment self.sid = sid self.freqs = np.unique(freqs) @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') freqs = fields(double, card, 'freq', i=2, j=len(card)) return FREQ(sid, freqs, comment=comment) def get_freqs(self): return self.freqs def add_frequencies(self, freqs): self.freqs = unique(hstack([self.freqs, freqs])) def add_frequency_object(self, freq): self.add_frequencies(freq.freqs) def raw_fields(self): list_fields = ['FREQ', self.sid] + list(self.freqs) return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ1(FREQ): type = 'FREQ1' def __init__(self, sid, f1, df, ndf, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.df = df self.ndf = ndf freqs = [] for i in range(ndf): freqs.append(f1 + i * df) self.freqs = unique(freqs) @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') f1 = double_or_blank(card, 2, 'f1', 0.0) df = double(card, 3, 'df') ndf = integer_or_blank(card, 4, 'ndf', 1) assert len(card) <= 5, 'len(FREQ card) = %i\ncard=%s' % (len(card), card) return FREQ1(sid, f1, df, ndf, comment=comment) def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ2(FREQ): type = 'FREQ2' def __init__(self, sid, f1, f2, ndf=1, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.f2 = f2 self.ndf = ndf d = 1. / ndf * log(f2 / f1) freqs = [] for i in range(ndf): freqs.append(f1 * exp(i * d)) self.freqs = np.unique(freqs) @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') f1 = double(card, 2, 'f1') f2 = double(card, 3, 'f2') ndf = integer_or_blank(card, 4, 'nf', 1) assert len(card) <= 5, 'len(FREQ2 card) = %i\ncard=%s' % (len(card), card) return FREQ2(sid, f1, f2, ndf, comment=comment) class FREQ3(FREQ): type = 'FREQ3' def __init__(self, f1, f2=None, Type='LINEAR', nef=10, cluster=1.0, comment=''): if comment: self.comment = comment if f2 is None: f2 = f1 self.sid = sid self.f1 = f1 self.f2 = f2 self.Type = Type self.nef = nef self.cluster = cluster @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') f1 = double(card, 1, 'f1') f2 = integer_or_blank(card, 1, 'f2', f1) Type = string_or_blank(card, 1, 'Type', 'LINEAR') nef = integer_or_blank(card, 1, 'nef', 10) cluster = double_or_blank(card, 1, 'cluster', 1.0) return FREQ3(sid, f1, f2, Type, nef, cluster, comment='') def raw_fields(self): return ['FREQ3', self.sid, self.f1, self.f2, self.Type, self.nef, self.cluster] def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class FREQ4(FREQ): type = 'FREQ4' def __init__(self, sid, f1, f2, fspread, nfm, comment=''): if comment: self.comment = comment self.sid = sid self.f1 = f1 self.f2 = f2 self.fspread = fspread self.nfm = nfm @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') f1 = double_or_blank(card, 2, 'f1', 0.0) f2 = double_or_blank(card, 3, 'f2', 1.e20) fspread = double_or_blank(card, 4, 'fspd', 0.1) nfm = integer_or_blank(card, 5, 'nfm', 3) assert len(card) <= 6, 'len(FREQ card) = %i\ncard=%s' % (len(card), card) return FREQ4(sid, f1, f2, fspread, nfm, comment=comment) def raw_fields(self): list_fields = ['FREQ4', self.sid, self.f1, self.f2, self.fspread, self.nfm] return list_fields def repr_fields(self): return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class NLPARM(BaseCard): type = 'NLPARM' def __init__(self, nlparm_id, ninc=10, dt=0.0, kmethod='AUTO', kstep=5, max_iter=25, conv='PW', int_out='NO', eps_u=0.01, eps_p=0.01, eps_w=0.01, max_div=3, max_qn=None, max_ls=4, fstress=0.2, ls_tol=0.5, max_bisect=5, max_r=20., rtol_b=20., comment=''): if comment: self.comment = comment self.nlparm_id = nlparm_id self.ninc = ninc self.dt = dt self.kmethod = kmethod self.kstep = kstep self.max_iter = max_iter self.conv = conv self.int_out = int_out self.eps_p = eps_p self.eps_u = eps_u self.eps_w = eps_w self.max_div = max_div self.max_qn = max_qn self.max_ls = max_ls self.fstress = fstress self.ls_tol = ls_tol self.max_bisect = max_bisect self.max_r = max_r self.rtol_b = rtol_b if self.max_qn is None: if kmethod == 'PFNT': self.max_qn = 0 else: self.max_qn = max_iter @classmethod def add_card(cls, card, comment=''): nlparm_id = integer(card, 1, 'nlparm_id') ninc = integer_or_blank(card, 2, 'ninc', 10) dt = double_or_blank(card, 3, 'dt', 0.0) kmethod = string_or_blank(card, 4, 'kmethod', 'AUTO') kstep = integer_or_blank(card, 5, 'kstep', 5) max_iter = integer_or_blank(card, 6, 'max_iter', 25) conv = string_or_blank(card, 7, 'conv', 'PW') int_out = string_or_blank(card, 8, 'intOut', 'NO') eps_u = double_or_blank(card, 9, 'eps_u', 0.01) eps_p = double_or_blank(card, 10, 'eps_p', 0.01) eps_w = double_or_blank(card, 11, 'eps_w', 0.01) max_div = integer_or_blank(card, 12, 'max_div', 3) if kmethod == 'PFNT': max_qn = integer_or_blank(card, 13, 'max_qn', 0) else: max_qn = integer_or_blank(card, 13, 'max_qn', max_iter) max_ls = integer_or_blank(card, 14, 'max_ls', 4) fstress = double_or_blank(card, 15, 'fstress', 0.2) ls_tol = double_or_blank(card, 16, 'ls_tol', 0.5) max_bisect = integer_or_blank(card, 17, 'max_bisect', 5) max_r = double_or_blank(card, 21, 'max_r', 20.) rtol_b = double_or_blank(card, 23, 'rtol_b', 20.) assert len(card) <= 24, 'len(NLPARM card) = %i\ncard=%s' % (len(card), card) return NLPARM(nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b, comment=comment) @classmethod def add_op2_data(cls, data, comment=''): (nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b) = data if kmethod == 1: kmethod = 'AUTO' elif kmethod == 2: kmethod = 'ITER' elif kmethod == 4: kmethod = 'SEMI' elif kmethod == 3: kmethod = 'ADAPT' else: msg = 'nlparm_id=%s kmethod=%r data=%s' % (nlparm_id, kmethod, data) raise NotImplementedError(msg) if conv == 1: conv = 'W' elif conv == 2: conv = 'P' elif conv == 3: conv = 'PW' elif conv == 4: conv = 'U' elif conv == 5: conv = 'UW' elif conv == 6: conv = 'UP' elif conv == 7: conv = 'UPW' else: msg = 'nlparm_id=%s conv=%r data=%s' % (nlparm_id, conv, data) raise NotImplementedError(msg) if int_out == 0: int_out = 'NO' elif int_out == 1: int_out = 'YES' elif int_out == 2: int_out = 'ALL' else: msg = 'nlparm_id=%s int_out=%r data=%s' % (nlparm_id, int_out, data) raise NotImplementedError(msg) return NLPARM(nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, max_r, rtol_b, comment=comment) def raw_fields(self): list_fields = ['NLPARM', self.nlparm_id, self.ninc, self.dt, self.kmethod, self.kstep, self.max_iter, self.conv, self.int_out, self.eps_u, self.eps_p, self.eps_w, self.max_div, self.max_qn, self.max_ls, self.fstress, self.ls_tol, self.max_bisect, None, None, None, self.max_r, None, self.rtol_b] return list_fields def repr_fields(self): ninc = set_blank_if_default(self.ninc, 10) dt = set_blank_if_default(self.dt, 0.0) kmethod = set_blank_if_default(self.kmethod, 'AUTO') kstep = set_blank_if_default(self.kstep, 5) max_iter = set_blank_if_default(self.max_iter, 25) conv = set_blank_if_default(self.conv, 'PW') int_out = set_blank_if_default(self.int_out, 'NO') eps_u = set_blank_if_default(self.eps_u, 0.01) eps_p = set_blank_if_default(self.eps_p, 0.01) eps_w = set_blank_if_default(self.eps_w, 0.01) max_div = set_blank_if_default(self.max_div, 3) max_qn = set_blank_if_default(self.max_qn, self.max_iter) max_ls = set_blank_if_default(self.max_ls, 4) fstress = set_blank_if_default(self.fstress, 0.2) ls_tol = set_blank_if_default(self.ls_tol, 0.5) max_bisect = set_blank_if_default(self.max_bisect, 5) max_r = set_blank_if_default(self.max_r, 20.) rtol_b = set_blank_if_default(self.rtol_b, 20.) list_fields = ['NLPARM', self.nlparm_id, ninc, dt, kmethod, kstep, max_iter, conv, int_out, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, ls_tol, max_bisect, None, None, None, max_r, None, rtol_b] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class NLPCI(BaseCard): type = 'NLPCI' def __init__(self, nlpci_id, Type='CRIS', minalr=0.25, maxalr=4., scale=0., desiter=12, mxinc=20, comment=''): if comment: self.comment = comment self.nlpci_id = nlpci_id self.Type = Type self.minalr = minalr self.maxalr = maxalr self.scale = scale self.desiter = desiter self.mxinc = mxinc @classmethod def add_card(cls, card, comment=''): nlpci_id = integer(card, 1, 'nlpci_id') Type = string_or_blank(card, 2, 'Type', 'CRIS') minalr = double_or_blank(card, 3, 'minalr', 0.25) maxalr = double_or_blank(card, 4, 'maxalr', 4.0) scale = double_or_blank(card, 5, 'scale', 0.0) blank(card, 6, 'blank') desiter = integer_or_blank(card, 7, 'desiter', 12) mxinc = integer_or_blank(card, 8, 'mxinc', 20) return NLPCI(nlpci_id, Type=Type, minalr=minalr, maxalr=maxalr, scale=scale, desiter=desiter, mxinc=mxinc, comment=comment) def raw_fields(self): list_fields = ['NLPCI', self.nlpci_id, self.Type, self.minalr, self.maxalr, self.scale, None, self.desiter, self.mxinc] return list_fields def repr_fields(self): return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TF(BaseCard): type = 'TF' def __init__(self, sid, nid0, c, b0, b1, b2, nids, components, a, comment=''): if comment: self.comment = comment self.sid = sid self.nid0 = nid0 self.c = c self.b0 = b0 self.b1 = b1 self.b2 = b2 self.nids = nids self.components = components self.a = a def validate(self): pass @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') nid0 = integer(card, 2, 'nid0') c = components_or_blank(card, 3, 'components_0', 0) b0 = double_or_blank(card, 4, 'b0', 0.) b1 = double_or_blank(card, 5, 'b1', 0.) b2 = double_or_blank(card, 6, 'b2', 0.) nfields = len(card) - 9 nrows = nfields // 8 if nfields % 8 > 0: nrows += 1 nids = [] components = [] a = [] for irow in range(nrows): j = irow * 8 + 9 nid = integer(card, j, 'grid_%i' % (irow + 1)) component = components_or_blank(card, j + 1, 'components_%i' % (irow + 1), 0) a0 = double_or_blank(card, j + 2, 'a0_%i' % (irow + 1), 0.) a1 = double_or_blank(card, j + 3, 'a1_%i' % (irow + 1), 0.) a2 = double_or_blank(card, j + 4, 'a2_%i' % (irow + 1), 0.) nids.append(nid) components.append(component) a.append([a0, a1, a2]) return TF(sid, nid0, c, b0, b1, b2, nids, components, a, comment=comment) def raw_fields(self): list_fields = ['TF', self.sid, self.nid0, self.c, self.b0, self.b1, self.b2, None, None] for grid, c, (a0, a1, a2) in zip(self.nids, self.components, self.a): list_fields += [grid, c, a0, a1, a2, None, None, None] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TSTEP(BaseCard): type = 'TSTEP' def __init__(self, sid, N, DT, NO, comment=''): if comment: self.comment = comment self.sid = sid self.N = N self.DT = DT self.NO = NO def validate(self): assert len(self.N) == len(self.DT), 'N=%s DT=%s' % (self.N, self.DT) assert len(self.N) == len(self.NO), 'N=%s NO=%s' % (self.N, self.NO) @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') N = [] DT = [] NO = [] nrows = int(ceil((len(card) - 1.) / 8.)) for i in range(nrows): n = 8 * i + 1 ni = integer_or_blank(card, n + 1, 'N' + str(i), 1) dt = double_or_blank(card, n + 2, 'dt' + str(i), 0.) no = integer_or_blank(card, n + 3, 'NO' + str(i), 1) N.append(ni) DT.append(dt) NO.append(no) return TSTEP(sid, N, DT, NO, comment=comment) def raw_fields(self): list_fields = ['TSTEP', self.sid] for (N, dt, no) in zip(self.N, self.DT, self.NO): list_fields += [N, dt, no, None, None, None, None, None] return list_fields def repr_fields(self): return self.raw_fields() def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card) class TSTEPNL(BaseCard): type = 'TSTEPNL' allowed_methods = ['AUTO', 'ITER', 'ADAPT', 'SEMI', 'FNT', 'PFNT', 'TSTEP'] def __init__(self, sid, ndt, dt, no, method='ADAPT', kstep=None, max_iter=10, conv='PW', eps_u=1.e-2, eps_p=1.e-3, eps_w=1.e-6, max_div=2, max_qn=10, max_ls=2, fstress=0.2, max_bisect=5, adjust=5, mstep=None, rb=0.6, max_r=32., utol=0.1, rtol_b=20., min_iter=None, comment=''): if comment: self.comment = comment self.sid = sid self.ndt = ndt self.dt = dt self.no = no self.method = method self.kstep = kstep self.max_iter = max_iter self.conv = conv self.eps_u = eps_u self.eps_p = eps_p self.eps_w = eps_w self.max_div = max_div self.max_qn = max_qn self.max_ls = max_ls self.fstress = fstress self.max_bisect = max_bisect self.adjust = adjust self.mstep = mstep self.rb = rb self.max_r = max_r self.utol = utol self.rtol_b = rtol_b self.min_iter = min_iter assert self.ndt >= 3 assert self.dt > 0. def validate(self): if self.method not in self.allowed_methods: msg = 'method=%r allowed_methods=[%s]' % ( self.method, ', '.join(self.allowed_methods)) raise ValueError(msg) @classmethod def add_card(cls, card, comment=''): sid = integer(card, 1, 'sid') ndt = integer(card, 2, 'ndt') dt = double(card, 3, 'dt') no = integer_or_blank(card, 4, 'no', 1) method = string_or_blank(card, 5, 'method', 'ADAPT') if method == 'ADAPT': kstep = integer_or_blank(card, 6, 'kStep', 2) elif method == 'ITER': kstep = integer_or_blank(card, 6, 'kStep', 10) elif method in ['AUTO', 'TSTEP', 'SEMI']: kstep = None msg = 'invalid TSTEPNL Method. method=%r; allowed_methods=[%s]' % ( method, ', '.join(cls.allowed_methods)) raise RuntimeError(msg) max_iter = integer_or_blank(card, 7, 'maxIter', 10) conv = string_or_blank(card, 8, 'conv', 'PW') eps_u = double_or_blank(card, 9, 'epsU', 1.E-2) eps_p = double_or_blank(card, 10, 'epsP', 1.E-3) eps_w = double_or_blank(card, 11, 'epsW', 1.E-6) max_div = integer_or_blank(card, 12, 'maxDiv', 2) max_qn = integer_or_blank(card, 13, 'maxQn', 10) max_ls = integer_or_blank(card, 14, 'MaxLs', 2) fstress = double_or_blank(card, 15, 'fStress', 0.2) max_bisect = integer_or_blank(card, 17, 'maxBisect', 5) adjust = integer_or_blank(card, 18, 'adjust', 5) mstep = integer_or_blank(card, 19, 'mStep') rb = double_or_blank(card, 20, 'rb', 0.6) max_r = double_or_blank(card, 21, 'maxR', 32.) utol = double_or_blank(card, 22, 'uTol', 0.1) rtol_b = double_or_blank(card, 23, 'rTolB', 20.) min_iter = integer_or_blank(card, 24, 'minIter') assert len(card) <= 25, 'len(TSTEPNL card) = %i\ncard=%s' % (len(card), card) return TSTEPNL( sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b, min_iter, comment=comment) @classmethod def add_op2_data(cls, data, comment=''): (sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b) = data if method == 1: method = 'AUTO' elif method == 3: method = 'ADAPT' else: raise NotImplementedError('tstepnl=%s method=%r data=%s' % (sid, method, data)) if conv == 3: conv = 'PW' elif conv == 4: conv = 'U' else: raise NotImplementedError('tstepnl=%s conv=%r data=%s' % (sid, conv, data)) min_iter = None return TSTEPNL( sid, ndt, dt, no, method, kstep, max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, max_bisect, adjust, mstep, rb, max_r, utol, rtol_b, min_iter, comment=comment) def raw_fields(self): list_fields = ['TSTEPNL', self.sid, self.ndt, self.dt, self.no, self.method, self.kstep, self.max_iter, self.conv, self.eps_u, self.eps_p, self.eps_w, self.max_div, self.max_qn, self.max_ls, self.fstress, None, self.max_bisect, self.adjust, self.mstep, self.rb, self.max_r, self.utol, self.rtol_b, self.min_iter] return list_fields def repr_fields(self): no = self.no method = set_blank_if_default(self.method, 'ADAPT') kstep = self.kstep conv = set_blank_if_default(self.conv, 'PW') eps_u = set_blank_if_default(self.eps_u, 1e-2) eps_p = set_blank_if_default(self.eps_p, 1e-3) eps_w = set_blank_if_default(self.eps_w, 1e-6) max_div = set_blank_if_default(self.max_div, 2) max_qn = set_blank_if_default(self.max_qn, 10) max_ls = set_blank_if_default(self.max_ls, 2) fstress = set_blank_if_default(self.fstress, 0.2) max_bisect = set_blank_if_default(self.max_bisect, 5) adjust = set_blank_if_default(self.adjust, 5) rb = set_blank_if_default(self.rb, 0.6) max_r = set_blank_if_default(self.max_r, 32.) utol = set_blank_if_default(self.utol, 0.1) rtol_b = set_blank_if_default(self.rtol_b, 20.) list_fields = ['TSTEPNL', self.sid, self.ndt, self.dt, no, method, kstep, self.max_iter, conv, eps_u, eps_p, eps_w, max_div, max_qn, max_ls, fstress, None, max_bisect, adjust, self.mstep, rb, max_r, utol, rtol_b, self.min_iter] return list_fields def write_card(self, size: int=8, is_double: bool=False) -> str: card = self.repr_fields() if size == 8: return self.comment + print_card_8(card) return self.comment + print_card_16(card)

true

f7198c8a3b00d357347baf407e57a7dd4b984119

620

py

Python

polls/admin.py

Obsinqsob01/polls

52f42029bd76e7a4f1dbdc947c5217ca9e2c0f1d

[ "MIT" ]

null

polls/admin.py

Obsinqsob01/polls

52f42029bd76e7a4f1dbdc947c5217ca9e2c0f1d

[ "MIT" ]

null

polls/admin.py

Obsinqsob01/polls

52f42029bd76e7a4f1dbdc947c5217ca9e2c0f1d

[ "MIT" ]

null

from django.contrib import admin from .models import Choice, Question class ChoiceInline(admin.TabularInline): model = Choice extra = 3 list_display = ('question_text', 'pub_date') class QuestionAdmin(admin.ModelAdmin): list_display = ('question_text', 'pub_date', 'was_published_recently') fieldsets = [ (None, {'fields': ['question_text']}), ('Date information', {'fields': ['pub_date'], 'classes': ['collapse']}), ] inlines = [ChoiceInline] list_filter = ['pub_date'] search_fields = ['question_text'] admin.site.register(Question, QuestionAdmin)

29.52381

80

0.659677

from django.contrib import admin from .models import Choice, Question class ChoiceInline(admin.TabularInline): model = Choice extra = 3 list_display = ('question_text', 'pub_date') class QuestionAdmin(admin.ModelAdmin): list_display = ('question_text', 'pub_date', 'was_published_recently') fieldsets = [ (None, {'fields': ['question_text']}), ('Date information', {'fields': ['pub_date'], 'classes': ['collapse']}), ] inlines = [ChoiceInline] list_filter = ['pub_date'] search_fields = ['question_text'] admin.site.register(Question, QuestionAdmin)

true

f7198cbf53eb86b681a5ce28880882ab6561e873

706

py

Python

2-add-two-numbers/2-add-two-numbers.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

1

2021-10-10T20:21:18.000Z

2-add-two-numbers/2-add-two-numbers.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

null

2-add-two-numbers/2-add-two-numbers.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

null

# Definition for singly-linked list. # class ListNode: # def __init__(self, val=0, next=None): # self.val = val # self.next = next class Solution: def addTwoNumbers(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]: n = cur = ListNode(-1) carry = 0 while l1 or l2 or carry: if l1: carry += l1.val l1 = l1.next if l2: carry += l2.val l2 = l2.next cur.next = ListNode(carry % 10) cur = cur.next carry = carry // 10 return n.next

27.153846

98

0.441926

class Solution: def addTwoNumbers(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]: n = cur = ListNode(-1) carry = 0 while l1 or l2 or carry: if l1: carry += l1.val l1 = l1.next if l2: carry += l2.val l2 = l2.next cur.next = ListNode(carry % 10) cur = cur.next carry = carry // 10 return n.next

true

f7198d790f74aa6993a89e96a1b3903ca05a53bc

15,654

py

Python

manim/scene/three_d_scene.py

behackl/manim

3759b73d555792d077e1d77c854d5dbe88043b98

[ "MIT" ]

2

2020-11-17T19:00:44.000Z

2021-10-17T16:14:55.000Z

manim/scene/three_d_scene.py

behackl/manim

3759b73d555792d077e1d77c854d5dbe88043b98

[ "MIT" ]

null

manim/scene/three_d_scene.py

behackl/manim

3759b73d555792d077e1d77c854d5dbe88043b98

[ "MIT" ]

null

"""A scene suitable for rendering three-dimensional objects and animations.""" __all__ = ["ThreeDScene", "SpecialThreeDScene"] from typing import Iterable, Optional, Sequence, Union import numpy as np from .. import config from ..animation.animation import Animation from ..animation.transform import ApplyMethod from ..camera.three_d_camera import ThreeDCamera from ..constants import DEGREES from ..mobject.coordinate_systems import ThreeDAxes from ..mobject.geometry import Line from ..mobject.mobject import Mobject from ..mobject.three_dimensions import Sphere from ..mobject.types.vectorized_mobject import VectorizedPoint, VGroup from ..mobject.value_tracker import ValueTracker from ..scene.scene import Scene from ..utils.config_ops import merge_dicts_recursively class ThreeDScene(Scene): """ This is a Scene, with special configurations and properties that make it suitable for Three Dimensional Scenes. """ def __init__( self, camera_class=ThreeDCamera, ambient_camera_rotation=None, default_angled_camera_orientation_kwargs=None, **kwargs, ): self.ambient_camera_rotation = ambient_camera_rotation if default_angled_camera_orientation_kwargs is None: default_angled_camera_orientation_kwargs = { "phi": 70 * DEGREES, "theta": -135 * DEGREES, } self.default_angled_camera_orientation_kwargs = ( default_angled_camera_orientation_kwargs ) super().__init__(camera_class=camera_class, **kwargs) def set_camera_orientation( self, phi: Optional[float] = None, theta: Optional[float] = None, gamma: Optional[float] = None, distance: Optional[float] = None, frame_center: Optional[Union["Mobject", Sequence[float]]] = None, ): """ This method sets the orientation of the camera in the scene. Parameters ---------- phi : int or float, optional The polar angle i.e the angle between Z_AXIS and Camera through ORIGIN in radians. theta : int or float, optional The azimuthal angle i.e the angle that spins the camera around the Z_AXIS. distance : int or float, optional The radial distance between ORIGIN and Camera. gamma : int or float, optional The rotation of the camera about the vector from the ORIGIN to the Camera. frame_center : list, tuple or np.array, optional The new center of the camera frame in cartesian coordinates. """ if phi is not None: self.renderer.camera.set_phi(phi) if theta is not None: self.renderer.camera.set_theta(theta) if distance is not None: self.renderer.camera.set_distance(distance) if gamma is not None: self.renderer.camera.set_gamma(gamma) if frame_center is not None: self.renderer.camera._frame_center.move_to(frame_center) def begin_ambient_camera_rotation(self, rate=0.02, about="theta"): """ This method begins an ambient rotation of the camera about the Z_AXIS, in the anticlockwise direction Parameters ---------- rate : int or float, optional The rate at which the camera should rotate about the Z_AXIS. Negative rate means clockwise rotation. about: (str) one of 3 options: ["theta", "phi", "gamma"]. defaults to theta. """ # TODO, use a ValueTracker for rate, so that it # can begin and end smoothly if about.lower() == "phi": x = self.renderer.camera.phi_tracker elif about.lower() == "gamma": x = self.renderer.camera.gamma_tracker elif about.lower() == "theta": x = self.renderer.camera.theta_tracker else: raise ValueError("Invalid ambient rotation angle.") x.add_updater(lambda m, dt: m.increment_value(rate * dt)) self.add(x) def stop_ambient_camera_rotation(self, about="theta"): """ This method stops all ambient camera rotation. """ if about.lower() == "phi": x = self.renderer.camera.phi_tracker elif about.lower() == "gamma": x = self.renderer.camera.gamma_tracker elif about.lower() == "theta": x = self.renderer.camera.theta_tracker else: raise ValueError("Invalid ambient rotation angle.") x.clear_updaters() self.remove(x) def begin_3dillusion_camera_rotation( self, rate=1, origin_theta=-60 * DEGREES, origin_phi=75 * DEGREES ): val_tracker_theta = ValueTracker(0) def update_theta(m, dt): val_tracker_theta.increment_value(dt * rate) val_for_left_right = 0.2 * np.sin(val_tracker_theta.get_value()) return m.set_value(origin_theta + val_for_left_right) self.renderer.camera.theta_tracker.add_updater(update_theta) self.add(self.renderer.camera.theta_tracker) val_tracker_phi = ValueTracker(0) def update_phi(m, dt): val_tracker_phi.increment_value(dt * rate) val_for_up_down = 0.1 * np.cos(val_tracker_phi.get_value()) return m.set_value(origin_phi + val_for_up_down) self.renderer.camera.phi_tracker.add_updater(update_phi) self.add(self.renderer.camera.phi_tracker) def stop_3dillusion_camera_rotation(self): """ This method stops all illusion camera rotations. """ self.renderer.camera.theta_tracker.clear_updaters() self.remove(self.renderer.camera.theta_tracker) self.renderer.camera.phi_tracker.clear_updaters() self.remove(self.renderer.camera.phi_tracker) def move_camera( self, phi: Optional[float] = None, theta: Optional[float] = None, gamma: Optional[float] = None, distance: Optional[float] = None, frame_center: Optional[Union["Mobject", Sequence[float]]] = None, added_anims: Iterable["Animation"] = [], **kwargs, ): """ This method animates the movement of the camera to the given spherical coordinates. Parameters ---------- phi : int or float, optional The polar angle i.e the angle between Z_AXIS and Camera through ORIGIN in radians. theta : int or float, optional The azimuthal angle i.e the angle that spins the camera around the Z_AXIS. distance : int or float, optional The radial distance between ORIGIN and Camera. gamma : int or float, optional The rotation of the camera about the vector from the ORIGIN to the Camera. frame_center : list, tuple or np.array, optional The new center of the camera frame in cartesian coordinates. added_anims : list, optional Any other animations to be played at the same time. """ anims = [] value_tracker_pairs = [ (phi, self.renderer.camera.phi_tracker), (theta, self.renderer.camera.theta_tracker), (distance, self.renderer.camera.distance_tracker), (gamma, self.renderer.camera.gamma_tracker), ] for value, tracker in value_tracker_pairs: if value is not None: anims.append(ApplyMethod(tracker.set_value, value, **kwargs)) if frame_center is not None: anims.append( ApplyMethod( self.renderer.camera._frame_center.move_to, frame_center, **kwargs ) ) self.play(*anims + added_anims) # These lines are added to improve performance. If manim thinks that frame_center is moving, # it is required to redraw every object. These lines remove frame_center from the Scene once # its animation is done, ensuring that manim does not think that it is moving. Since the # frame_center is never actually drawn, this shouldn't break anything. if frame_center is not None: self.remove(self.renderer.camera._frame_center) def get_moving_mobjects(self, *animations): """ This method returns a list of all of the Mobjects in the Scene that are moving, that are also in the animations passed. Parameters ---------- *animations : Animation The animations whose mobjects will be checked. """ moving_mobjects = Scene.get_moving_mobjects(self, *animations) camera_mobjects = self.renderer.camera.get_value_trackers() + [ self.renderer.camera._frame_center ] if any([cm in moving_mobjects for cm in camera_mobjects]): return self.mobjects return moving_mobjects def add_fixed_orientation_mobjects(self, *mobjects, **kwargs): """ This method is used to prevent the rotation and tilting of mobjects as the camera moves around. The mobject can still move in the x,y,z directions, but will always be at the angle (relative to the camera) that it was at when it was passed through this method.) Parameters ---------- *mobjects : Mobject The Mobject(s) whose orientation must be fixed. **kwargs Some valid kwargs are use_static_center_func : bool center_func : function """ self.add(*mobjects) self.renderer.camera.add_fixed_orientation_mobjects(*mobjects, **kwargs) def add_fixed_in_frame_mobjects(self, *mobjects): """ This method is used to prevent the rotation and movement of mobjects as the camera moves around. The mobject is essentially overlaid, and is not impacted by the camera's movement in any way. Parameters ---------- *mobjects : Mobjects The Mobjects whose orientation must be fixed. """ self.add(*mobjects) self.renderer.camera.add_fixed_in_frame_mobjects(*mobjects) def remove_fixed_orientation_mobjects(self, *mobjects): """ This method "unfixes" the orientation of the mobjects passed, meaning they will no longer be at the same angle relative to the camera. This only makes sense if the mobject was passed through add_fixed_orientation_mobjects first. Parameters ---------- *mobjects : Mobjects The Mobjects whose orientation must be unfixed. """ self.renderer.camera.remove_fixed_orientation_mobjects(*mobjects) def remove_fixed_in_frame_mobjects(self, *mobjects): """ This method undoes what add_fixed_in_frame_mobjects does. It allows the mobject to be affected by the movement of the camera. Parameters ---------- *mobjects : Mobjects The Mobjects whose position and orientation must be unfixed. """ self.renderer.camera.remove_fixed_in_frame_mobjects(*mobjects) ## def set_to_default_angled_camera_orientation(self, **kwargs): """ This method sets the default_angled_camera_orientation to the keyword arguments passed, and sets the camera to that orientation. Parameters ---------- **kwargs Some recognised kwargs are phi, theta, distance, gamma, which have the same meaning as the parameters in set_camera_orientation. """ config = dict( self.default_camera_orientation_kwargs ) # Where doe this come from? config.update(kwargs) self.set_camera_orientation(**config) class SpecialThreeDScene(ThreeDScene): """An extension of :class:`ThreeDScene` with more settings. It has some extra configuration for axes, spheres, and an override for low quality rendering. Further key differences are: * The camera shades applicable 3DMobjects by default, except if rendering in low quality. * Some default params for Spheres and Axes have been added. """ def __init__( self, cut_axes_at_radius=True, camera_config={"should_apply_shading": True, "exponential_projection": True}, three_d_axes_config={ "num_axis_pieces": 1, "axis_config": { "unit_size": 2, "tick_frequency": 1, "numbers_with_elongated_ticks": [0, 1, 2], "stroke_width": 2, }, }, sphere_config={"radius": 2, "resolution": (24, 48)}, default_angled_camera_position={ "phi": 70 * DEGREES, "theta": -110 * DEGREES, }, # When scene is extracted with -l flag, this # configuration will override the above configuration. low_quality_config={ "camera_config": {"should_apply_shading": False}, "three_d_axes_config": {"num_axis_pieces": 1}, "sphere_config": {"resolution": (12, 24)}, }, **kwargs, ): self.cut_axes_at_radius = cut_axes_at_radius self.camera_config = camera_config self.three_d_axes_config = three_d_axes_config self.sphere_config = sphere_config self.default_angled_camera_position = default_angled_camera_position self.low_quality_config = low_quality_config if self.renderer.camera_config["pixel_width"] == config["pixel_width"]: _config = {} else: _config = self.low_quality_config _config = merge_dicts_recursively(_config, kwargs) ThreeDScene.__init__(self, **_config) def get_axes(self): """Return a set of 3D axes. Returns ------- :class:`.ThreeDAxes` A set of 3D axes. """ axes = ThreeDAxes(**self.three_d_axes_config) for axis in axes: if self.cut_axes_at_radius: p0 = axis.get_start() p1 = axis.number_to_point(-1) p2 = axis.number_to_point(1) p3 = axis.get_end() new_pieces = VGroup(Line(p0, p1), Line(p1, p2), Line(p2, p3)) for piece in new_pieces: piece.shade_in_3d = True new_pieces.match_style(axis.pieces) axis.pieces.submobjects = new_pieces.submobjects for tick in axis.tick_marks: tick.add(VectorizedPoint(1.5 * tick.get_center())) return axes def get_sphere(self, **kwargs): """ Returns a sphere with the passed keyword arguments as properties. Parameters ---------- **kwargs Any valid parameter of :class:`~.Sphere` or :class:`~.Surface`. Returns ------- :class:`~.Sphere` The sphere object. """ config = merge_dicts_recursively(self.sphere_config, kwargs) return Sphere(**config) def get_default_camera_position(self): """ Returns the default_angled_camera position. Returns ------- dict Dictionary of phi, theta, distance, and gamma. """ return self.default_angled_camera_position def set_camera_to_default_position(self): """ Sets the camera to its default position. """ self.set_camera_orientation(**self.default_angled_camera_position)

35.986207

100

0.622972

__all__ = ["ThreeDScene", "SpecialThreeDScene"] from typing import Iterable, Optional, Sequence, Union import numpy as np from .. import config from ..animation.animation import Animation from ..animation.transform import ApplyMethod from ..camera.three_d_camera import ThreeDCamera from ..constants import DEGREES from ..mobject.coordinate_systems import ThreeDAxes from ..mobject.geometry import Line from ..mobject.mobject import Mobject from ..mobject.three_dimensions import Sphere from ..mobject.types.vectorized_mobject import VectorizedPoint, VGroup from ..mobject.value_tracker import ValueTracker from ..scene.scene import Scene from ..utils.config_ops import merge_dicts_recursively class ThreeDScene(Scene): def __init__( self, camera_class=ThreeDCamera, ambient_camera_rotation=None, default_angled_camera_orientation_kwargs=None, **kwargs, ): self.ambient_camera_rotation = ambient_camera_rotation if default_angled_camera_orientation_kwargs is None: default_angled_camera_orientation_kwargs = { "phi": 70 * DEGREES, "theta": -135 * DEGREES, } self.default_angled_camera_orientation_kwargs = ( default_angled_camera_orientation_kwargs ) super().__init__(camera_class=camera_class, **kwargs) def set_camera_orientation( self, phi: Optional[float] = None, theta: Optional[float] = None, gamma: Optional[float] = None, distance: Optional[float] = None, frame_center: Optional[Union["Mobject", Sequence[float]]] = None, ): if phi is not None: self.renderer.camera.set_phi(phi) if theta is not None: self.renderer.camera.set_theta(theta) if distance is not None: self.renderer.camera.set_distance(distance) if gamma is not None: self.renderer.camera.set_gamma(gamma) if frame_center is not None: self.renderer.camera._frame_center.move_to(frame_center) def begin_ambient_camera_rotation(self, rate=0.02, about="theta"): if about.lower() == "phi": x = self.renderer.camera.phi_tracker elif about.lower() == "gamma": x = self.renderer.camera.gamma_tracker elif about.lower() == "theta": x = self.renderer.camera.theta_tracker else: raise ValueError("Invalid ambient rotation angle.") x.add_updater(lambda m, dt: m.increment_value(rate * dt)) self.add(x) def stop_ambient_camera_rotation(self, about="theta"): if about.lower() == "phi": x = self.renderer.camera.phi_tracker elif about.lower() == "gamma": x = self.renderer.camera.gamma_tracker elif about.lower() == "theta": x = self.renderer.camera.theta_tracker else: raise ValueError("Invalid ambient rotation angle.") x.clear_updaters() self.remove(x) def begin_3dillusion_camera_rotation( self, rate=1, origin_theta=-60 * DEGREES, origin_phi=75 * DEGREES ): val_tracker_theta = ValueTracker(0) def update_theta(m, dt): val_tracker_theta.increment_value(dt * rate) val_for_left_right = 0.2 * np.sin(val_tracker_theta.get_value()) return m.set_value(origin_theta + val_for_left_right) self.renderer.camera.theta_tracker.add_updater(update_theta) self.add(self.renderer.camera.theta_tracker) val_tracker_phi = ValueTracker(0) def update_phi(m, dt): val_tracker_phi.increment_value(dt * rate) val_for_up_down = 0.1 * np.cos(val_tracker_phi.get_value()) return m.set_value(origin_phi + val_for_up_down) self.renderer.camera.phi_tracker.add_updater(update_phi) self.add(self.renderer.camera.phi_tracker) def stop_3dillusion_camera_rotation(self): self.renderer.camera.theta_tracker.clear_updaters() self.remove(self.renderer.camera.theta_tracker) self.renderer.camera.phi_tracker.clear_updaters() self.remove(self.renderer.camera.phi_tracker) def move_camera( self, phi: Optional[float] = None, theta: Optional[float] = None, gamma: Optional[float] = None, distance: Optional[float] = None, frame_center: Optional[Union["Mobject", Sequence[float]]] = None, added_anims: Iterable["Animation"] = [], **kwargs, ): anims = [] value_tracker_pairs = [ (phi, self.renderer.camera.phi_tracker), (theta, self.renderer.camera.theta_tracker), (distance, self.renderer.camera.distance_tracker), (gamma, self.renderer.camera.gamma_tracker), ] for value, tracker in value_tracker_pairs: if value is not None: anims.append(ApplyMethod(tracker.set_value, value, **kwargs)) if frame_center is not None: anims.append( ApplyMethod( self.renderer.camera._frame_center.move_to, frame_center, **kwargs ) ) self.play(*anims + added_anims) if frame_center is not None: self.remove(self.renderer.camera._frame_center) def get_moving_mobjects(self, *animations): moving_mobjects = Scene.get_moving_mobjects(self, *animations) camera_mobjects = self.renderer.camera.get_value_trackers() + [ self.renderer.camera._frame_center ] if any([cm in moving_mobjects for cm in camera_mobjects]): return self.mobjects return moving_mobjects def add_fixed_orientation_mobjects(self, *mobjects, **kwargs): self.add(*mobjects) self.renderer.camera.add_fixed_orientation_mobjects(*mobjects, **kwargs) def add_fixed_in_frame_mobjects(self, *mobjects): self.add(*mobjects) self.renderer.camera.add_fixed_in_frame_mobjects(*mobjects) def remove_fixed_orientation_mobjects(self, *mobjects): self.renderer.camera.remove_fixed_orientation_mobjects(*mobjects) def remove_fixed_in_frame_mobjects(self, *mobjects): self.renderer.camera.remove_fixed_in_frame_mobjects(*mobjects) ## def set_to_default_angled_camera_orientation(self, **kwargs): config = dict( self.default_camera_orientation_kwargs ) # Where doe this come from? config.update(kwargs) self.set_camera_orientation(**config) class SpecialThreeDScene(ThreeDScene): def __init__( self, cut_axes_at_radius=True, camera_config={"should_apply_shading": True, "exponential_projection": True}, three_d_axes_config={ "num_axis_pieces": 1, "axis_config": { "unit_size": 2, "tick_frequency": 1, "numbers_with_elongated_ticks": [0, 1, 2], "stroke_width": 2, }, }, sphere_config={"radius": 2, "resolution": (24, 48)}, default_angled_camera_position={ "phi": 70 * DEGREES, "theta": -110 * DEGREES, }, # When scene is extracted with -l flag, this # configuration will override the above configuration. low_quality_config={ "camera_config": {"should_apply_shading": False}, "three_d_axes_config": {"num_axis_pieces": 1}, "sphere_config": {"resolution": (12, 24)}, }, **kwargs, ): self.cut_axes_at_radius = cut_axes_at_radius self.camera_config = camera_config self.three_d_axes_config = three_d_axes_config self.sphere_config = sphere_config self.default_angled_camera_position = default_angled_camera_position self.low_quality_config = low_quality_config if self.renderer.camera_config["pixel_width"] == config["pixel_width"]: _config = {} else: _config = self.low_quality_config _config = merge_dicts_recursively(_config, kwargs) ThreeDScene.__init__(self, **_config) def get_axes(self): axes = ThreeDAxes(**self.three_d_axes_config) for axis in axes: if self.cut_axes_at_radius: p0 = axis.get_start() p1 = axis.number_to_point(-1) p2 = axis.number_to_point(1) p3 = axis.get_end() new_pieces = VGroup(Line(p0, p1), Line(p1, p2), Line(p2, p3)) for piece in new_pieces: piece.shade_in_3d = True new_pieces.match_style(axis.pieces) axis.pieces.submobjects = new_pieces.submobjects for tick in axis.tick_marks: tick.add(VectorizedPoint(1.5 * tick.get_center())) return axes def get_sphere(self, **kwargs): config = merge_dicts_recursively(self.sphere_config, kwargs) return Sphere(**config) def get_default_camera_position(self): return self.default_angled_camera_position def set_camera_to_default_position(self): self.set_camera_orientation(**self.default_angled_camera_position)

true

f7198e330d6123f84319f87eb566ae8978c38f58

7,124

py

Python

corehq/apps/reports/urls.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

1

2020-07-14T13:00:23.000Z

corehq/apps/reports/urls.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

94

2020-12-11T06:57:31.000Z

2022-03-15T10:24:06.000Z

corehq/apps/reports/urls.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

null

import logging from django.conf.urls import include, url from django.core.exceptions import ImproperlyConfigured from corehq.apps.reports.standard.forms.reports import ReprocessXFormErrorView from corehq.apps.userreports.reports.view import ( ConfigurableReportView, CustomConfigurableReportDispatcher, ) from corehq.apps.userreports.views import ( ConfigureReport, EditReportInBuilder, ReportBuilderDataSourceSelect, ReportBuilderPaywallActivatingSubscription, ReportBuilderPaywallPricing, ReportPreview, ) from .dispatcher import ( CustomProjectReportDispatcher, ProjectReportDispatcher, ) from .filters import urls as filter_urls from .util import get_installed_custom_modules from .views import ( AddSavedReportConfigView, CaseAttachmentsView, CaseDataView, EditFormInstance, FormDataView, MySavedReportsView, ScheduledReportsView, archive_form, case_form_data, case_forms, case_property_changes, case_property_names, case_xml, close_case_view, delete_config, delete_scheduled_report, download_case_history, download_form, edit_case_view, edit_form, email_report, export_case_transactions, export_report, project_health_user_details, rebuild_case_view, resave_case_view, resave_form_view, restore_edit, send_test_scheduled_report, unarchive_form, undo_close_case_view, view_scheduled_report, ) custom_report_urls = [ CustomProjectReportDispatcher.url_pattern(), ] urlpatterns = [ ConfigurableReportView.url_pattern(), CustomConfigurableReportDispatcher.url_pattern(), # Report Builder url(r'^builder/select_source/$', ReportBuilderDataSourceSelect.as_view(), name=ReportBuilderDataSourceSelect.urlname), url(r'^builder/configure/$', ConfigureReport.as_view(), name=ConfigureReport.urlname), url(r'^builder/preview/(?P<data_source>[\w\-]+)/$', ReportPreview.as_view(), name=ReportPreview.urlname), url(r'^builder/edit/(?P<report_id>[\w\-]+)/$', EditReportInBuilder.as_view(), name='edit_report_in_builder'), url(r'builder/subscribe/pricing/$', ReportBuilderPaywallPricing.as_view(), name=ReportBuilderPaywallPricing.urlname), url(r'builder/subscribe/activating_subscription/$', ReportBuilderPaywallActivatingSubscription.as_view(), name=ReportBuilderPaywallActivatingSubscription.urlname), url(r'^$', MySavedReportsView.as_view(), name="reports_home"), url(r'^saved/', MySavedReportsView.as_view(), name=MySavedReportsView.urlname), url(r'^saved_reports', MySavedReportsView.as_view(), name="old_saved_reports"), url(r'^case_data/(?P<case_id>[\w\-]+)/$', CaseDataView.as_view(), name=CaseDataView.urlname), url(r'^case_data/(?P<case_id>[\w\-]+)/forms/$', case_forms, name="single_case_forms"), url(r'^case_data/(?P<case_id>[\w\-]+)/attachments/$', CaseAttachmentsView.as_view(), name=CaseAttachmentsView.urlname), url(r'^case_data/(?P<case_id>[\w\-]+)/view/xml/$', case_xml, name="single_case_xml"), url(r'^case_data/(?P<case_id>[\w\-]+)/properties/$', case_property_names, name="case_property_names"), url(r'^case_data/(?P<case_id>[\w\-]+)/history/$', download_case_history, name="download_case_history"), url(r'^case_data/(?P<case_id>[\w\-]+)/edit/$', edit_case_view, name="edit_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/rebuild/$', rebuild_case_view, name="rebuild_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/resave/$', resave_case_view, name="resave_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/close/$', close_case_view, name="close_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/undo-close/(?P<xform_id>[\w\-:]+)/$', undo_close_case_view, name="undo_close_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/export_transactions/$', export_case_transactions, name="export_case_transactions"), url(r'^case_data/(?P<case_id>[\w\-]+)/(?P<xform_id>[\w\-:]+)/$', case_form_data, name="case_form_data"), url(r'^case_data/(?P<case_id>[\w\-]+)/case_property/(?P<case_property_name>[\w_\-.]+)/$', case_property_changes, name="case_property_changes"), # Download and view form data url(r'^form_data/(?P<instance_id>[\w\-:]+)/$', FormDataView.as_view(), name=FormDataView.urlname), url(r'^form_data/(?P<instance_id>[\w\-:]+)/download/$', download_form, name='download_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/edit/$', EditFormInstance.as_view(), name='edit_form_instance'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/restore_version/$', restore_edit, name='restore_edit'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/correct_data/$', edit_form, name='edit_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/archive/$', archive_form, name='archive_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/unarchive/$', unarchive_form, name='unarchive_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/rebuild/$', resave_form_view, name='resave_form'), # project health ajax url(r'^project_health/ajax/(?P<user_id>[\w\-]+)/$', project_health_user_details, name='project_health_user_details'), # Full Excel export url(r'^full_excel_export/(?P<export_hash>[\w\-]+)/(?P<format>[\w\-]+)$', export_report, name="export_report"), # once off email url(r"^email_onceoff/(?P<report_slug>[\w_]+)/$", email_report, kwargs=dict(once=True), name='email_report'), url(r"^custom/email_onceoff/(?P<report_slug>[\w_]+)/$", email_report, kwargs=dict(report_type=CustomProjectReportDispatcher.prefix, once=True), name='email_onceoff'), # Saved reports url(r"^configs$", AddSavedReportConfigView.as_view(), name=AddSavedReportConfigView.name), url(r"^configs/(?P<config_id>[\w-]+)$", delete_config, name='delete_report_config'), # Scheduled reports url(r'^scheduled_reports/(?P<scheduled_report_id>[\w-]+)?$', ScheduledReportsView.as_view(), name=ScheduledReportsView.urlname), url(r'^scheduled_report/(?P<scheduled_report_id>[\w-]+)/delete$', delete_scheduled_report, name='delete_scheduled_report'), url(r'^send_test_scheduled_report/(?P<scheduled_report_id>[\w-]+)/$', send_test_scheduled_report, name='send_test_scheduled_report'), url(r'^view_scheduled_report/(?P<scheduled_report_id>[\w_]+)/$', view_scheduled_report, name='view_scheduled_report'), # V2 Reports url(r'^v2/', include('corehq.apps.reports.v2.urls')), # Internal Use url(r'^reprocess_error_form/$', ReprocessXFormErrorView.as_view(), name=ReprocessXFormErrorView.urlname), url(r'^custom/', include(custom_report_urls)), url(r'^filters/', include(filter_urls)), ProjectReportDispatcher.url_pattern(), ] for module in get_installed_custom_modules(): module_name = module.__name__.split('.')[-1] try: custom_report_urls += [ url(r"^%s/" % module_name, include('{0}.urls'.format(module.__name__))), ] except ImproperlyConfigured: logging.info("Module %s does not provide urls" % module_name)

44.525

114

0.701291

import logging from django.conf.urls import include, url from django.core.exceptions import ImproperlyConfigured from corehq.apps.reports.standard.forms.reports import ReprocessXFormErrorView from corehq.apps.userreports.reports.view import ( ConfigurableReportView, CustomConfigurableReportDispatcher, ) from corehq.apps.userreports.views import ( ConfigureReport, EditReportInBuilder, ReportBuilderDataSourceSelect, ReportBuilderPaywallActivatingSubscription, ReportBuilderPaywallPricing, ReportPreview, ) from .dispatcher import ( CustomProjectReportDispatcher, ProjectReportDispatcher, ) from .filters import urls as filter_urls from .util import get_installed_custom_modules from .views import ( AddSavedReportConfigView, CaseAttachmentsView, CaseDataView, EditFormInstance, FormDataView, MySavedReportsView, ScheduledReportsView, archive_form, case_form_data, case_forms, case_property_changes, case_property_names, case_xml, close_case_view, delete_config, delete_scheduled_report, download_case_history, download_form, edit_case_view, edit_form, email_report, export_case_transactions, export_report, project_health_user_details, rebuild_case_view, resave_case_view, resave_form_view, restore_edit, send_test_scheduled_report, unarchive_form, undo_close_case_view, view_scheduled_report, ) custom_report_urls = [ CustomProjectReportDispatcher.url_pattern(), ] urlpatterns = [ ConfigurableReportView.url_pattern(), CustomConfigurableReportDispatcher.url_pattern(), url(r'^builder/select_source/$', ReportBuilderDataSourceSelect.as_view(), name=ReportBuilderDataSourceSelect.urlname), url(r'^builder/configure/$', ConfigureReport.as_view(), name=ConfigureReport.urlname), url(r'^builder/preview/(?P<data_source>[\w\-]+)/$', ReportPreview.as_view(), name=ReportPreview.urlname), url(r'^builder/edit/(?P<report_id>[\w\-]+)/$', EditReportInBuilder.as_view(), name='edit_report_in_builder'), url(r'builder/subscribe/pricing/$', ReportBuilderPaywallPricing.as_view(), name=ReportBuilderPaywallPricing.urlname), url(r'builder/subscribe/activating_subscription/$', ReportBuilderPaywallActivatingSubscription.as_view(), name=ReportBuilderPaywallActivatingSubscription.urlname), url(r'^$', MySavedReportsView.as_view(), name="reports_home"), url(r'^saved/', MySavedReportsView.as_view(), name=MySavedReportsView.urlname), url(r'^saved_reports', MySavedReportsView.as_view(), name="old_saved_reports"), url(r'^case_data/(?P<case_id>[\w\-]+)/$', CaseDataView.as_view(), name=CaseDataView.urlname), url(r'^case_data/(?P<case_id>[\w\-]+)/forms/$', case_forms, name="single_case_forms"), url(r'^case_data/(?P<case_id>[\w\-]+)/attachments/$', CaseAttachmentsView.as_view(), name=CaseAttachmentsView.urlname), url(r'^case_data/(?P<case_id>[\w\-]+)/view/xml/$', case_xml, name="single_case_xml"), url(r'^case_data/(?P<case_id>[\w\-]+)/properties/$', case_property_names, name="case_property_names"), url(r'^case_data/(?P<case_id>[\w\-]+)/history/$', download_case_history, name="download_case_history"), url(r'^case_data/(?P<case_id>[\w\-]+)/edit/$', edit_case_view, name="edit_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/rebuild/$', rebuild_case_view, name="rebuild_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/resave/$', resave_case_view, name="resave_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/close/$', close_case_view, name="close_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/undo-close/(?P<xform_id>[\w\-:]+)/$', undo_close_case_view, name="undo_close_case"), url(r'^case_data/(?P<case_id>[\w\-]+)/export_transactions/$', export_case_transactions, name="export_case_transactions"), url(r'^case_data/(?P<case_id>[\w\-]+)/(?P<xform_id>[\w\-:]+)/$', case_form_data, name="case_form_data"), url(r'^case_data/(?P<case_id>[\w\-]+)/case_property/(?P<case_property_name>[\w_\-.]+)/$', case_property_changes, name="case_property_changes"), url(r'^form_data/(?P<instance_id>[\w\-:]+)/$', FormDataView.as_view(), name=FormDataView.urlname), url(r'^form_data/(?P<instance_id>[\w\-:]+)/download/$', download_form, name='download_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/edit/$', EditFormInstance.as_view(), name='edit_form_instance'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/restore_version/$', restore_edit, name='restore_edit'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/correct_data/$', edit_form, name='edit_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/archive/$', archive_form, name='archive_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/unarchive/$', unarchive_form, name='unarchive_form'), url(r'^form_data/(?P<instance_id>[\w\-:]+)/rebuild/$', resave_form_view, name='resave_form'), url(r'^project_health/ajax/(?P<user_id>[\w\-]+)/$', project_health_user_details, name='project_health_user_details'), url(r'^full_excel_export/(?P<export_hash>[\w\-]+)/(?P<format>[\w\-]+)$', export_report, name="export_report"), url(r"^email_onceoff/(?P<report_slug>[\w_]+)/$", email_report, kwargs=dict(once=True), name='email_report'), url(r"^custom/email_onceoff/(?P<report_slug>[\w_]+)/$", email_report, kwargs=dict(report_type=CustomProjectReportDispatcher.prefix, once=True), name='email_onceoff'), url(r"^configs$", AddSavedReportConfigView.as_view(), name=AddSavedReportConfigView.name), url(r"^configs/(?P<config_id>[\w-]+)$", delete_config, name='delete_report_config'), url(r'^scheduled_reports/(?P<scheduled_report_id>[\w-]+)?$', ScheduledReportsView.as_view(), name=ScheduledReportsView.urlname), url(r'^scheduled_report/(?P<scheduled_report_id>[\w-]+)/delete$', delete_scheduled_report, name='delete_scheduled_report'), url(r'^send_test_scheduled_report/(?P<scheduled_report_id>[\w-]+)/$', send_test_scheduled_report, name='send_test_scheduled_report'), url(r'^view_scheduled_report/(?P<scheduled_report_id>[\w_]+)/$', view_scheduled_report, name='view_scheduled_report'), url(r'^v2/', include('corehq.apps.reports.v2.urls')), url(r'^reprocess_error_form/$', ReprocessXFormErrorView.as_view(), name=ReprocessXFormErrorView.urlname), url(r'^custom/', include(custom_report_urls)), url(r'^filters/', include(filter_urls)), ProjectReportDispatcher.url_pattern(), ] for module in get_installed_custom_modules(): module_name = module.__name__.split('.')[-1] try: custom_report_urls += [ url(r"^%s/" % module_name, include('{0}.urls'.format(module.__name__))), ] except ImproperlyConfigured: logging.info("Module %s does not provide urls" % module_name)

true

f7198e35f24a43baae21005438b0076176ee416a

561

py

Python

oving_8_c.py

W3OP/Oving_9_round2

090cbc3b135840914659d50c6fa48ab756e5449e

[ "MIT" ]

null

oving_8_c.py

W3OP/Oving_9_round2

090cbc3b135840914659d50c6fa48ab756e5449e

[ "MIT" ]

null

oving_8_c.py

W3OP/Oving_9_round2

090cbc3b135840914659d50c6fa48ab756e5449e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Oct 22 10:13:37 2021 @author: palme """ import oving_8_b as o8b test = o8b.Quiz("Hvor mange bein har en hest", [1, 2, 3, 4],4) print(test) dude = int(input("Svar: ")) svar1 = test.svaret(dude) if svar1: print("Svaret er rett") else: print("Svaret er feil") print("\n \n") test2 = o8b.Quiz("Hvilket land er i i nå?", ["norge", "sverie", "danmark"],1) print(test2) dude2 = int(input("Svar: ")) svar2 = test2.svaret(dude2) if svar2: print("Svaret er rett!") else: print("Svaret er feil")

15.162162

77

0.611408

import oving_8_b as o8b test = o8b.Quiz("Hvor mange bein har en hest", [1, 2, 3, 4],4) print(test) dude = int(input("Svar: ")) svar1 = test.svaret(dude) if svar1: print("Svaret er rett") else: print("Svaret er feil") print("\n \n") test2 = o8b.Quiz("Hvilket land er i i nå?", ["norge", "sverie", "danmark"],1) print(test2) dude2 = int(input("Svar: ")) svar2 = test2.svaret(dude2) if svar2: print("Svaret er rett!") else: print("Svaret er feil")

true

f7198ec98548e880b167ef7ccfc9be00d9b58137

5,121

py

Python

zipkin/binding/pyramid/pyramidhook.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

null

zipkin/binding/pyramid/pyramidhook.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

null

zipkin/binding/pyramid/pyramidhook.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

null

from __future__ import absolute_import import time import logging from pyramid.tweens import INGRESS from pyramid.settings import aslist from zipkin import local from zipkin.api import stack_trace from zipkin.models import Trace, Annotation from zipkin.util import int_or_none from zipkin.client import log as zipkin_log from zipkin.config import configure as configure_zk log = logging.getLogger(__name__) class AllTraceTweenView(object): endpoint = None @classmethod def configure(cls, settings): default_name = "Registry" # Keep compat with `registry.__name__` ? name = settings.get("zipkin.service_name", default_name) bindings = aslist(settings.get("zipkin.bindings", "requests celery xmlrpclib")) cls.endpoint = configure_zk( name, settings, use_requests="requests" in bindings, use_celery="celery" in bindings, use_xmlrpclib="xmlrpclib" in bindings, ) def __init__(self, handler, registry): self.handler = handler self.trace = None def track_start_request(self, request): headers = request.headers trace_name = request.path_qs if request.matched_route: # we only get a matched route if we've gone through the router. trace_name = request.matched_route.pattern trace = Trace( request.method + " " + trace_name, int_or_none(headers.get("X-B3-TraceId", None)), int_or_none(headers.get("X-B3-SpanId", None)), int_or_none(headers.get("X-B3-ParentSpanId", None)), endpoint=self.endpoint, ) if "X-B3-TraceId" not in headers: log.info("no trace info from request: %s", request.path_qs) if request.matchdict: # matchdict maybe none if no route is registered for k, v in request.matchdict.items(): trace.record(Annotation.string("route.param.%s" % k, v)) trace.record(Annotation.string("http.path", request.path_qs)) log.info("new trace %r", trace.trace_id) stack_trace(trace) trace.record(Annotation.server_recv()) self.trace = trace def track_end_request(self, request, response): if self.trace: self.trace.record(Annotation.server_send()) log.info("reporting trace %s", self.trace.name) response.headers["Trace-Id"] = str(self.trace.trace_id) zipkin_log(self.trace) def __call__(self, request): self.track_start_request(request) response = None try: response = self.handler(request) finally: # request.response in case an exception is raised ? self.track_end_request(request, response or request.response) local().reset() self.trace = None return response or request.response class SlowQueryTweenView(AllTraceTweenView): max_duration = None @classmethod def configure(cls, settings): super(SlowQueryTweenView, cls).configure(settings) setting = settings.get("zipkin.slow_log_duration_exceed") if setting is None: log.error( "Missing setting 'zipkin.slow_log_duration_exceed' %r", list(settings.keys()), ) return try: cls.max_duration = float(setting) except ValueError: log.error("Invalid setting 'zipkin.slow_log_duration_exceed'") def __init__(self, handler, registry): super(SlowQueryTweenView, self).__init__(handler, registry) self.start = None def track_start_request(self, request): self.start = time.time() super(SlowQueryTweenView, self).track_start_request(request) def track_end_request(self, request, response): if self.max_duration is None: # unconfigure, we don't care return if self.start: duration = time.time() - self.start if duration > self.max_duration: super(SlowQueryTweenView, self).track_end_request(request, response) def includeme(config): """Include the zipkin definitions""" # Attach the subscriber a couple of times, this allow to start logging as # early as possible. Later calls on the same request will enhance the more # we proceed through the stack (after authentication, after router, ...) settings = config.registry.settings tween_factory = settings.get("zipkin.tween_factory", "all") assert tween_factory in ["all", "slow_query"] if tween_factory == "all": tween_factory = AllTraceTweenView elif tween_factory == "slow_query": tween_factory = SlowQueryTweenView else: log.error( "Invalid value for settings 'zipkin.tween_factory', should be all or slow_query, not %s", tween_factory, ) return tween_factory.configure(settings) config.add_tween( "{}.{}".format(tween_factory.__module__, tween_factory.__name__), under=INGRESS, )

32.617834

101

0.641672

from __future__ import absolute_import import time import logging from pyramid.tweens import INGRESS from pyramid.settings import aslist from zipkin import local from zipkin.api import stack_trace from zipkin.models import Trace, Annotation from zipkin.util import int_or_none from zipkin.client import log as zipkin_log from zipkin.config import configure as configure_zk log = logging.getLogger(__name__) class AllTraceTweenView(object): endpoint = None @classmethod def configure(cls, settings): default_name = "Registry" name = settings.get("zipkin.service_name", default_name) bindings = aslist(settings.get("zipkin.bindings", "requests celery xmlrpclib")) cls.endpoint = configure_zk( name, settings, use_requests="requests" in bindings, use_celery="celery" in bindings, use_xmlrpclib="xmlrpclib" in bindings, ) def __init__(self, handler, registry): self.handler = handler self.trace = None def track_start_request(self, request): headers = request.headers trace_name = request.path_qs if request.matched_route: trace_name = request.matched_route.pattern trace = Trace( request.method + " " + trace_name, int_or_none(headers.get("X-B3-TraceId", None)), int_or_none(headers.get("X-B3-SpanId", None)), int_or_none(headers.get("X-B3-ParentSpanId", None)), endpoint=self.endpoint, ) if "X-B3-TraceId" not in headers: log.info("no trace info from request: %s", request.path_qs) if request.matchdict: # matchdict maybe none if no route is registered for k, v in request.matchdict.items(): trace.record(Annotation.string("route.param.%s" % k, v)) trace.record(Annotation.string("http.path", request.path_qs)) log.info("new trace %r", trace.trace_id) stack_trace(trace) trace.record(Annotation.server_recv()) self.trace = trace def track_end_request(self, request, response): if self.trace: self.trace.record(Annotation.server_send()) log.info("reporting trace %s", self.trace.name) response.headers["Trace-Id"] = str(self.trace.trace_id) zipkin_log(self.trace) def __call__(self, request): self.track_start_request(request) response = None try: response = self.handler(request) finally: # request.response in case an exception is raised ? self.track_end_request(request, response or request.response) local().reset() self.trace = None return response or request.response class SlowQueryTweenView(AllTraceTweenView): max_duration = None @classmethod def configure(cls, settings): super(SlowQueryTweenView, cls).configure(settings) setting = settings.get("zipkin.slow_log_duration_exceed") if setting is None: log.error( "Missing setting 'zipkin.slow_log_duration_exceed' %r", list(settings.keys()), ) return try: cls.max_duration = float(setting) except ValueError: log.error("Invalid setting 'zipkin.slow_log_duration_exceed'") def __init__(self, handler, registry): super(SlowQueryTweenView, self).__init__(handler, registry) self.start = None def track_start_request(self, request): self.start = time.time() super(SlowQueryTweenView, self).track_start_request(request) def track_end_request(self, request, response): if self.max_duration is None: # unconfigure, we don't care return if self.start: duration = time.time() - self.start if duration > self.max_duration: super(SlowQueryTweenView, self).track_end_request(request, response) def includeme(config): settings = config.registry.settings tween_factory = settings.get("zipkin.tween_factory", "all") assert tween_factory in ["all", "slow_query"] if tween_factory == "all": tween_factory = AllTraceTweenView elif tween_factory == "slow_query": tween_factory = SlowQueryTweenView else: log.error( "Invalid value for settings 'zipkin.tween_factory', should be all or slow_query, not %s", tween_factory, ) return tween_factory.configure(settings) config.add_tween( "{}.{}".format(tween_factory.__module__, tween_factory.__name__), under=INGRESS, )

true

f7198ece6a41b7a5f0f2edead87cf05f2c1c0cd4

10,093

py

Python

sdks/python/http_client/v1/polyaxon_sdk/models/v1_bayes.py

onilton/polyaxon

3b0d7cbeead74e62eb0eedbb2962f605ebb9fa81

[ "Apache-2.0" ]

null

sdks/python/http_client/v1/polyaxon_sdk/models/v1_bayes.py

onilton/polyaxon

3b0d7cbeead74e62eb0eedbb2962f605ebb9fa81

[ "Apache-2.0" ]

null

sdks/python/http_client/v1/polyaxon_sdk/models/v1_bayes.py

onilton/polyaxon

3b0d7cbeead74e62eb0eedbb2962f605ebb9fa81

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2018-2021 Polyaxon, Inc. # # 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. # coding: utf-8 """ Polyaxon SDKs and REST API specification. Polyaxon SDKs and REST API specification. # noqa: E501 The version of the OpenAPI document: 1.9.4 Contact: contact@polyaxon.com Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from polyaxon_sdk.configuration import Configuration class V1Bayes(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'kind': 'str', 'params': 'dict(str, object)', 'num_initial_runs': 'int', 'max_iterations': 'int', 'utility_function': 'object', 'metric': 'V1OptimizationMetric', 'seed': 'int', 'concurrency': 'int', 'tuner': 'V1Tuner', 'early_stopping': 'list[object]' } attribute_map = { 'kind': 'kind', 'params': 'params', 'num_initial_runs': 'numInitialRuns', 'max_iterations': 'maxIterations', 'utility_function': 'utilityFunction', 'metric': 'metric', 'seed': 'seed', 'concurrency': 'concurrency', 'tuner': 'tuner', 'early_stopping': 'earlyStopping' } def __init__(self, kind='bayes', params=None, num_initial_runs=None, max_iterations=None, utility_function=None, metric=None, seed=None, concurrency=None, tuner=None, early_stopping=None, local_vars_configuration=None): # noqa: E501 """V1Bayes - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._kind = None self._params = None self._num_initial_runs = None self._max_iterations = None self._utility_function = None self._metric = None self._seed = None self._concurrency = None self._tuner = None self._early_stopping = None self.discriminator = None if kind is not None: self.kind = kind if params is not None: self.params = params if num_initial_runs is not None: self.num_initial_runs = num_initial_runs if max_iterations is not None: self.max_iterations = max_iterations if utility_function is not None: self.utility_function = utility_function if metric is not None: self.metric = metric if seed is not None: self.seed = seed if concurrency is not None: self.concurrency = concurrency if tuner is not None: self.tuner = tuner if early_stopping is not None: self.early_stopping = early_stopping @property def kind(self): """Gets the kind of this V1Bayes. # noqa: E501 :return: The kind of this V1Bayes. # noqa: E501 :rtype: str """ return self._kind @kind.setter def kind(self, kind): """Sets the kind of this V1Bayes. :param kind: The kind of this V1Bayes. # noqa: E501 :type: str """ self._kind = kind @property def params(self): """Gets the params of this V1Bayes. # noqa: E501 :return: The params of this V1Bayes. # noqa: E501 :rtype: dict(str, object) """ return self._params @params.setter def params(self, params): """Sets the params of this V1Bayes. :param params: The params of this V1Bayes. # noqa: E501 :type: dict(str, object) """ self._params = params @property def num_initial_runs(self): """Gets the num_initial_runs of this V1Bayes. # noqa: E501 :return: The num_initial_runs of this V1Bayes. # noqa: E501 :rtype: int """ return self._num_initial_runs @num_initial_runs.setter def num_initial_runs(self, num_initial_runs): """Sets the num_initial_runs of this V1Bayes. :param num_initial_runs: The num_initial_runs of this V1Bayes. # noqa: E501 :type: int """ self._num_initial_runs = num_initial_runs @property def max_iterations(self): """Gets the max_iterations of this V1Bayes. # noqa: E501 :return: The max_iterations of this V1Bayes. # noqa: E501 :rtype: int """ return self._max_iterations @max_iterations.setter def max_iterations(self, max_iterations): """Sets the max_iterations of this V1Bayes. :param max_iterations: The max_iterations of this V1Bayes. # noqa: E501 :type: int """ self._max_iterations = max_iterations @property def utility_function(self): """Gets the utility_function of this V1Bayes. # noqa: E501 :return: The utility_function of this V1Bayes. # noqa: E501 :rtype: object """ return self._utility_function @utility_function.setter def utility_function(self, utility_function): """Sets the utility_function of this V1Bayes. :param utility_function: The utility_function of this V1Bayes. # noqa: E501 :type: object """ self._utility_function = utility_function @property def metric(self): """Gets the metric of this V1Bayes. # noqa: E501 :return: The metric of this V1Bayes. # noqa: E501 :rtype: V1OptimizationMetric """ return self._metric @metric.setter def metric(self, metric): """Sets the metric of this V1Bayes. :param metric: The metric of this V1Bayes. # noqa: E501 :type: V1OptimizationMetric """ self._metric = metric @property def seed(self): """Gets the seed of this V1Bayes. # noqa: E501 :return: The seed of this V1Bayes. # noqa: E501 :rtype: int """ return self._seed @seed.setter def seed(self, seed): """Sets the seed of this V1Bayes. :param seed: The seed of this V1Bayes. # noqa: E501 :type: int """ self._seed = seed @property def concurrency(self): """Gets the concurrency of this V1Bayes. # noqa: E501 :return: The concurrency of this V1Bayes. # noqa: E501 :rtype: int """ return self._concurrency @concurrency.setter def concurrency(self, concurrency): """Sets the concurrency of this V1Bayes. :param concurrency: The concurrency of this V1Bayes. # noqa: E501 :type: int """ self._concurrency = concurrency @property def tuner(self): """Gets the tuner of this V1Bayes. # noqa: E501 :return: The tuner of this V1Bayes. # noqa: E501 :rtype: V1Tuner """ return self._tuner @tuner.setter def tuner(self, tuner): """Sets the tuner of this V1Bayes. :param tuner: The tuner of this V1Bayes. # noqa: E501 :type: V1Tuner """ self._tuner = tuner @property def early_stopping(self): """Gets the early_stopping of this V1Bayes. # noqa: E501 :return: The early_stopping of this V1Bayes. # noqa: E501 :rtype: list[object] """ return self._early_stopping @early_stopping.setter def early_stopping(self, early_stopping): """Sets the early_stopping of this V1Bayes. :param early_stopping: The early_stopping of this V1Bayes. # noqa: E501 :type: list[object] """ self._early_stopping = early_stopping def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, V1Bayes): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, V1Bayes): return True return self.to_dict() != other.to_dict()

27.13172

237

0.593382

import pprint import re import six from polyaxon_sdk.configuration import Configuration class V1Bayes(object): openapi_types = { 'kind': 'str', 'params': 'dict(str, object)', 'num_initial_runs': 'int', 'max_iterations': 'int', 'utility_function': 'object', 'metric': 'V1OptimizationMetric', 'seed': 'int', 'concurrency': 'int', 'tuner': 'V1Tuner', 'early_stopping': 'list[object]' } attribute_map = { 'kind': 'kind', 'params': 'params', 'num_initial_runs': 'numInitialRuns', 'max_iterations': 'maxIterations', 'utility_function': 'utilityFunction', 'metric': 'metric', 'seed': 'seed', 'concurrency': 'concurrency', 'tuner': 'tuner', 'early_stopping': 'earlyStopping' } def __init__(self, kind='bayes', params=None, num_initial_runs=None, max_iterations=None, utility_function=None, metric=None, seed=None, concurrency=None, tuner=None, early_stopping=None, local_vars_configuration=None): if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._kind = None self._params = None self._num_initial_runs = None self._max_iterations = None self._utility_function = None self._metric = None self._seed = None self._concurrency = None self._tuner = None self._early_stopping = None self.discriminator = None if kind is not None: self.kind = kind if params is not None: self.params = params if num_initial_runs is not None: self.num_initial_runs = num_initial_runs if max_iterations is not None: self.max_iterations = max_iterations if utility_function is not None: self.utility_function = utility_function if metric is not None: self.metric = metric if seed is not None: self.seed = seed if concurrency is not None: self.concurrency = concurrency if tuner is not None: self.tuner = tuner if early_stopping is not None: self.early_stopping = early_stopping @property def kind(self): return self._kind @kind.setter def kind(self, kind): self._kind = kind @property def params(self): return self._params @params.setter def params(self, params): self._params = params @property def num_initial_runs(self): return self._num_initial_runs @num_initial_runs.setter def num_initial_runs(self, num_initial_runs): self._num_initial_runs = num_initial_runs @property def max_iterations(self): return self._max_iterations @max_iterations.setter def max_iterations(self, max_iterations): self._max_iterations = max_iterations @property def utility_function(self): return self._utility_function @utility_function.setter def utility_function(self, utility_function): self._utility_function = utility_function @property def metric(self): return self._metric @metric.setter def metric(self, metric): self._metric = metric @property def seed(self): return self._seed @seed.setter def seed(self, seed): self._seed = seed @property def concurrency(self): return self._concurrency @concurrency.setter def concurrency(self, concurrency): self._concurrency = concurrency @property def tuner(self): return self._tuner @tuner.setter def tuner(self, tuner): self._tuner = tuner @property def early_stopping(self): return self._early_stopping @early_stopping.setter def early_stopping(self, early_stopping): self._early_stopping = early_stopping def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, V1Bayes): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, V1Bayes): return True return self.to_dict() != other.to_dict()

true

f7198f349b0048d3b6330725d65dfdf36b553ff4

1,458

py

Python

soltrannet/__init__.py

hengwei-chan/molecular_attention_transformer

29193d4155df528e3a6a0c1e0da39111d0b8db93

[ "Apache-2.0" ]

16

2021-03-10T17:10:06.000Z

2022-03-16T13:07:58.000Z

soltrannet/__init__.py

hengwei-chan/molecular_attention_transformer

29193d4155df528e3a6a0c1e0da39111d0b8db93

[ "Apache-2.0" ]

null

soltrannet/__init__.py

hengwei-chan/molecular_attention_transformer

29193d4155df528e3a6a0c1e0da39111d0b8db93

[ "Apache-2.0" ]

10

2021-06-01T03:36:08.000Z

2022-03-18T16:58:25.000Z

from .predict import predict import argparse import sys, multiprocessing import torch def _parse_args(): parser=argparse.ArgumentParser(description="Run SolTranNet aqueous solubility predictor") parser.add_argument('input',nargs='?',type=argparse.FileType('r'),default=sys.stdin,help='PATH to the file containing the SMILES you wish to use. Assumes the content is 1 SMILE per line.') parser.add_argument('output',nargs='?',type=argparse.FileType('w'),default=sys.stdout,help='Name of the output file. Defaults to stdout.') parser.add_argument('--batchsize',default=32,type=int,help='Batch size for the data loader. Defaults to 32.') parser.add_argument('--cpus',default=multiprocessing.cpu_count(),type=int,help='Number of CPU cores to use for the data loader. Defaults to use all available cores. Pass 0 to only run on 1 CPU.') parser.add_argument('--cpu_predict',action='store_true',help='Flag to force the predictions to be made on only the CPU. Default behavior is to use GPU if available.') args=parser.parse_args() return args def _run(args): smiles=[x.rstrip() for x in args.input] if args.cpu_predict: predictions=predict(smiles,batch_size=args.batchsize,num_workers=args.cpus,device=torch.device('cpu')) else: predictions=predict(smiles,batch_size=args.batchsize,num_workers=args.cpus) for pred, smi, warn in predictions: args.output.write(f'{smi},{pred:.3f},{warn}\n')

52.071429

199

0.739369

from .predict import predict import argparse import sys, multiprocessing import torch def _parse_args(): parser=argparse.ArgumentParser(description="Run SolTranNet aqueous solubility predictor") parser.add_argument('input',nargs='?',type=argparse.FileType('r'),default=sys.stdin,help='PATH to the file containing the SMILES you wish to use. Assumes the content is 1 SMILE per line.') parser.add_argument('output',nargs='?',type=argparse.FileType('w'),default=sys.stdout,help='Name of the output file. Defaults to stdout.') parser.add_argument('--batchsize',default=32,type=int,help='Batch size for the data loader. Defaults to 32.') parser.add_argument('--cpus',default=multiprocessing.cpu_count(),type=int,help='Number of CPU cores to use for the data loader. Defaults to use all available cores. Pass 0 to only run on 1 CPU.') parser.add_argument('--cpu_predict',action='store_true',help='Flag to force the predictions to be made on only the CPU. Default behavior is to use GPU if available.') args=parser.parse_args() return args def _run(args): smiles=[x.rstrip() for x in args.input] if args.cpu_predict: predictions=predict(smiles,batch_size=args.batchsize,num_workers=args.cpus,device=torch.device('cpu')) else: predictions=predict(smiles,batch_size=args.batchsize,num_workers=args.cpus) for pred, smi, warn in predictions: args.output.write(f'{smi},{pred:.3f},{warn}\n')

true

f7198f927dcfc0aeb6186a86d48263d8c4b1d8eb

5,831

py

Python

src/garage/torch/algos/_utils.py

adibellathur/garage

8394f0cf2b77c0a5b3a7b1ea977fa6cb3f9df0ca

[ "MIT" ]

1

2020-02-19T00:01:29.000Z

src/garage/torch/algos/_utils.py

Ashutosh-Adhikari/garage

482a26a07d46091f878c41b582f1478588e397ff

[ "MIT" ]

null

src/garage/torch/algos/_utils.py

Ashutosh-Adhikari/garage

482a26a07d46091f878c41b582f1478588e397ff

[ "MIT" ]

1

2020-02-13T12:05:35.000Z

"""Utility functions used by PyTorch algorithms.""" import torch import torch.nn.functional as F class _Default: # pylint: disable=too-few-public-methods """A wrapper class to represent default arguments. Args: val (object): Argument value. """ def __init__(self, val): self.val = val def make_optimizer(optimizer_type, module, **kwargs): """Create an optimizer for PyTorch algos. Args: optimizer_type (Union[type, tuple[type, dict]]): Type of optimizer. This can be an optimizer type such as 'torch.optim.Adam' or a tuple of type and dictionary, where dictionary contains arguments to initialize the optimizer e.g. (torch.optim.Adam, {'lr' = 1e-3}) module (torch.nn.Module): The module whose parameters needs to be optimized. kwargs (dict): Other keyword arguments to initialize optimizer. This is not used when `optimizer_type` is tuple. Returns: torch.optim.Optimizer: Constructed optimizer. Raises: ValueError: Raises value error when `optimizer_type` is tuple, and non-default argument is passed in `kwargs`. """ if isinstance(optimizer_type, tuple): opt_type, opt_args = optimizer_type for name, arg in kwargs.items(): if not isinstance(arg, _Default): raise ValueError('Should not specify {} and explicit \ optimizer args at the same time'.format(name)) return opt_type(module.parameters(), **opt_args) opt_args = {} for name, arg in kwargs.items(): if isinstance(arg, _Default): opt_args[name] = arg.val else: opt_args[name] = arg return optimizer_type(module.parameters(), **opt_args) def compute_advantages(discount, gae_lambda, max_path_length, baselines, rewards): """Calculate advantages. Advantages are a discounted cumulative sum. Calculate advantages using a baseline (value function) according to Generalized Advantage Estimation (GAE) The discounted cumulative sum can be computed using conv2d with filter. filter: [1, (discount * gae_lambda), (discount * gae_lambda) ^ 2, ...] where the length is same with max_path_length. baselines and rewards are also has same shape. baselines: [ [b_11, b_12, b_13, ... b_1n], [b_21, b_22, b_23, ... b_2n], ... [b_m1, b_m2, b_m3, ... b_mn] ] rewards: [ [r_11, r_12, r_13, ... r_1n], [r_21, r_22, r_23, ... r_2n], ... [r_m1, r_m2, r_m3, ... r_mn] ] Args: discount (float): RL discount factor (i.e. gamma). gae_lambda (float): Lambda, as used for Generalized Advantage Estimation (GAE). max_path_length (int): Maximum length of a single rollout. baselines (torch.Tensor): A 2D vector of value function estimates with shape (N, T), where N is the batch dimension (number of episodes) and T is the maximum path length experienced by the agent. If an episode terminates in fewer than T time steps, the remaining elements in that episode should be set to 0. rewards (torch.Tensor): A 2D vector of per-step rewards with shape (N, T), where N is the batch dimension (number of episodes) and T is the maximum path length experienced by the agent. If an episode terminates in fewer than T time steps, the remaining elements in that episode should be set to 0. Returns: torch.Tensor: A 2D vector of calculated advantage values with shape (N, T), where N is the batch dimension (number of episodes) and T is the maximum path length experienced by the agent. If an episode terminates in fewer than T time steps, the remaining values in that episode should be set to 0. """ adv_filter = torch.full((1, 1, 1, max_path_length - 1), discount * gae_lambda) adv_filter = torch.cumprod(F.pad(adv_filter, (1, 0), value=1), dim=-1) deltas = (rewards + discount * F.pad(baselines, (0, 1))[:, 1:] - baselines) deltas = F.pad(deltas, (0, max_path_length - 1)).unsqueeze(0).unsqueeze(0) advantages = F.conv2d(deltas, adv_filter, stride=1).squeeze() return advantages def pad_to_last(nums, total_length, axis=-1, val=0): """Pad val to last in nums in given axis. length of the result in given axis should be total_length. Raises: IndexError: If the input axis value is out of range of the nums array Args: nums (numpy.ndarray): The array to pad. total_length (int): The final width of the Array. axis (int): Axis along which a sum is performed. val (int): The value to set the padded value. Returns: torch.Tensor: Padded array """ tensor = torch.Tensor(nums) axis = (axis + len(tensor.shape)) if axis < 0 else axis if len(tensor.shape) <= axis: raise IndexError('axis {} is out of range {}'.format( axis, tensor.shape)) padding_config = [0, 0] * len(tensor.shape) padding_idx = abs(axis - len(tensor.shape)) * 2 - 1 padding_config[padding_idx] = max(total_length - tensor.shape[axis], val) return F.pad(tensor, padding_config) def filter_valids(tensor, valids): """Filter out tensor using valids (last index of valid tensors). valids contains last indices of each rows. Args: tensor (torch.Tensor): The tensor to filter valids (list[int]): Array of length of the valid values Returns: torch.Tensor: Filtered Tensor """ return [tensor[i][:valids[i]] for i in range(len(valids))]

35.993827

79

0.630081

import torch import torch.nn.functional as F class _Default: def __init__(self, val): self.val = val def make_optimizer(optimizer_type, module, **kwargs): if isinstance(optimizer_type, tuple): opt_type, opt_args = optimizer_type for name, arg in kwargs.items(): if not isinstance(arg, _Default): raise ValueError('Should not specify {} and explicit \ optimizer args at the same time'.format(name)) return opt_type(module.parameters(), **opt_args) opt_args = {} for name, arg in kwargs.items(): if isinstance(arg, _Default): opt_args[name] = arg.val else: opt_args[name] = arg return optimizer_type(module.parameters(), **opt_args) def compute_advantages(discount, gae_lambda, max_path_length, baselines, rewards): adv_filter = torch.full((1, 1, 1, max_path_length - 1), discount * gae_lambda) adv_filter = torch.cumprod(F.pad(adv_filter, (1, 0), value=1), dim=-1) deltas = (rewards + discount * F.pad(baselines, (0, 1))[:, 1:] - baselines) deltas = F.pad(deltas, (0, max_path_length - 1)).unsqueeze(0).unsqueeze(0) advantages = F.conv2d(deltas, adv_filter, stride=1).squeeze() return advantages def pad_to_last(nums, total_length, axis=-1, val=0): tensor = torch.Tensor(nums) axis = (axis + len(tensor.shape)) if axis < 0 else axis if len(tensor.shape) <= axis: raise IndexError('axis {} is out of range {}'.format( axis, tensor.shape)) padding_config = [0, 0] * len(tensor.shape) padding_idx = abs(axis - len(tensor.shape)) * 2 - 1 padding_config[padding_idx] = max(total_length - tensor.shape[axis], val) return F.pad(tensor, padding_config) def filter_valids(tensor, valids): return [tensor[i][:valids[i]] for i in range(len(valids))]

true

f7198f9535491c7521d5ae47ee77aaa8910d0441

801

py

Python

tests/test_export_id.py

David-Le-Nir/sphinxcontrib-needs

fe809445505fa1e9bf5963eab1d6283dad405e92

[ "MIT" ]

null

tests/test_export_id.py

David-Le-Nir/sphinxcontrib-needs

fe809445505fa1e9bf5963eab1d6283dad405e92

[ "MIT" ]

2

2022-02-13T19:49:18.000Z

tests/test_export_id.py

David-Le-Nir/sphinxcontrib-needs

fe809445505fa1e9bf5963eab1d6283dad405e92

[ "MIT" ]

null

import json import os from pathlib import Path from sphinx_testing import with_app @with_app(buildername="needs", srcdir="doc_test/doc_export_id") def test_export_id(app, status, warning): app.build() content = Path(app.outdir, "needs.json").read_text() assert "filters" in content content_obj = json.loads(content) assert content_obj is not None assert "created" in content_obj assert "FLOW_1" in content_obj["versions"]["1.0"]["filters"] assert "TABLE_1" in content_obj["versions"]["1.0"]["filters"] assert "LIST_1" in content_obj["versions"]["1.0"]["filters"] @with_app(buildername="html", srcdir="doc_test/doc_export_id") def test_export_id_html(app, status, warning): app.build() assert not os.path.exists(os.path.join(app.outdir, "needs.json"))

30.807692

69

0.716604

import json import os from pathlib import Path from sphinx_testing import with_app @with_app(buildername="needs", srcdir="doc_test/doc_export_id") def test_export_id(app, status, warning): app.build() content = Path(app.outdir, "needs.json").read_text() assert "filters" in content content_obj = json.loads(content) assert content_obj is not None assert "created" in content_obj assert "FLOW_1" in content_obj["versions"]["1.0"]["filters"] assert "TABLE_1" in content_obj["versions"]["1.0"]["filters"] assert "LIST_1" in content_obj["versions"]["1.0"]["filters"] @with_app(buildername="html", srcdir="doc_test/doc_export_id") def test_export_id_html(app, status, warning): app.build() assert not os.path.exists(os.path.join(app.outdir, "needs.json"))

true

f719907ff48a40bf779cf6020839f0d298c921ad

7,308

py

Python

wavedata/tools/core/voxel_grid_2d.py

amuamushu/wavedata

1745c646ff3a76b38a81c439a0edd900c986c9f7

[ "MIT" ]

null

wavedata/tools/core/voxel_grid_2d.py

amuamushu/wavedata

1745c646ff3a76b38a81c439a0edd900c986c9f7

[ "MIT" ]

null

wavedata/tools/core/voxel_grid_2d.py

amuamushu/wavedata

1745c646ff3a76b38a81c439a0edd900c986c9f7

[ "MIT" ]

null

import numpy as np from wavedata.wavedata.tools.core import geometry_utils class VoxelGrid2D(object): """ Voxel grids represent occupancy info. The voxelize_2d method projects a point cloud onto a plane, while saving height and point density information for each voxel. """ # Class Constants VOXEL_EMPTY = -1 VOXEL_FILLED = 0 def __init__(self): # Quantization size of the voxel grid self.voxel_size = 0.0 # Voxels at the most negative/positive xyz self.min_voxel_coord = np.array([]) self.max_voxel_coord = np.array([]) # Size of the voxel grid along each axis self.num_divisions = np.array([0, 0, 0]) # Points in sorted order, to match the order of the voxels self.points = [] # Indices of filled voxels self.voxel_indices = [] # Max point height in projected voxel self.heights = [] # Number of points corresponding to projected voxel self.num_pts_in_voxel = [] # Full occupancy grid, VOXEL_EMPTY or VOXEL_FILLED self.leaf_layout_2d = [] def voxelize_2d(self, pts, voxel_size, extents=None, ground_plane=None, create_leaf_layout=True): """Voxelizes the point cloud into a 2D voxel grid by projecting it down into a flat plane, and stores the maximum point height, and number of points corresponding to the voxel :param pts: Point cloud as N x [x, y, z] :param voxel_size: Quantization size for the grid :param extents: Optional, specifies the full extents of the point cloud. Used for creating same sized voxel grids. :param ground_plane: Plane coefficients (a, b, c, d), xz plane used if not specified :param create_leaf_layout: Set this to False to create an empty leaf_layout, which will save computation time. """ # Check if points are 3D, otherwise early exit if pts.shape[1] != 3: raise ValueError("Points have the wrong shape: {}".format( pts.shape)) self.voxel_size = voxel_size # Discretize voxel coordinates to given quantization size discrete_pts = np.floor(pts / voxel_size).astype(np.int32) # Use Lex Sort, sort by x, then z, then y ( x_col = discrete_pts[:, 0] y_col = discrete_pts[:, 1] z_col = discrete_pts[:, 2] sorted_order = np.lexsort((y_col, z_col, x_col)) # Save original points in sorted order self.points = pts[sorted_order] # Save discrete points in sorted order discrete_pts = discrete_pts[sorted_order] # Project all points to a 2D plane discrete_pts_2d = discrete_pts.copy() discrete_pts_2d[:, 1] = 0 # Format the array to c-contiguous array for unique function contiguous_array = np.ascontiguousarray(discrete_pts_2d).view( np.dtype((np.void, discrete_pts_2d.dtype.itemsize * discrete_pts_2d.shape[1]))) # The new coordinates are the discretized array with its unique indexes _, unique_indices = np.unique(contiguous_array, return_index=True) # Sort unique indices to preserve order unique_indices.sort() voxel_coords = discrete_pts_2d[unique_indices] # Number of points per voxel, last voxel calculated separately num_points_in_voxel = np.diff(unique_indices) num_points_in_voxel = np.append(num_points_in_voxel, discrete_pts_2d.shape[0] - unique_indices[-1]) if ground_plane is None: # Use first point in voxel as highest point height_in_voxel = self.points[unique_indices, 1] else: # Ground plane provided height_in_voxel = geometry_utils.dist_to_plane( ground_plane, self.points[unique_indices]) # Set the height and number of points for each voxel self.heights = height_in_voxel self.num_pts_in_voxel = num_points_in_voxel # Find the minimum and maximum voxel coordinates if extents is not None: # Check provided extents extents_transpose = np.array(extents).transpose() if extents_transpose.shape != (2, 3): raise ValueError("Extents are the wrong shape {}".format( extents.shape)) # Set voxel grid extents self.min_voxel_coord = np.floor(extents_transpose[0] / voxel_size) self.max_voxel_coord = \ np.ceil((extents_transpose[1] / voxel_size) - 1) self.min_voxel_coord[1] = 0 self.max_voxel_coord[1] = 0 # Check that points are bounded by new extents if not (self.min_voxel_coord <= np.amin(voxel_coords, axis=0)).all(): raise ValueError("Extents are smaller than min_voxel_coord") if not (self.max_voxel_coord >= np.amax(voxel_coords, axis=0)).all(): raise ValueError("Extents are smaller than max_voxel_coord") else: # Automatically calculate extents self.min_voxel_coord = np.amin(voxel_coords, axis=0) self.max_voxel_coord = np.amax(voxel_coords, axis=0) # Get the voxel grid dimensions self.num_divisions = ((self.max_voxel_coord - self.min_voxel_coord) + 1).astype(np.int32) # Bring the min voxel to the origin self.voxel_indices = (voxel_coords - self.min_voxel_coord).astype(int) if create_leaf_layout: # Create Voxel Object with -1 as empty/occluded, 0 as occupied self.leaf_layout_2d = self.VOXEL_EMPTY * \ np.ones(self.num_divisions.astype(int)) # Fill out the leaf layout self.leaf_layout_2d[self.voxel_indices[:, 0], 0, self.voxel_indices[:, 2]] = \ self.VOXEL_FILLED def map_to_index(self, map_index): """Converts map coordinate values to 1-based discretized grid index coordinate. Note: Any values outside the extent of the grid will be forced to be the maximum grid coordinate. :param map_index: N x 2 points :return: N x length(dim) (grid coordinate) [] if min_voxel_coord or voxel_size or grid_index or dim is not set """ if self.voxel_size == 0 \ or len(self.min_voxel_coord) == 0 \ or len(map_index) == 0: return [] num_divisions_2d = self.num_divisions[[0, 2]] min_voxel_coord_2d = self.min_voxel_coord[[0, 2]] # Truncate index (same as np.floor for positive values) and clip # to valid voxel index range indices = np.int32(map_index / self.voxel_size) - min_voxel_coord_2d indices[:, 0] = np.clip(indices[:, 0], 0, num_divisions_2d[0]) indices[:, 1] = np.clip(indices[:, 1], 0, num_divisions_2d[1]) return indices

39.080214

87

0.601122

import numpy as np from wavedata.wavedata.tools.core import geometry_utils class VoxelGrid2D(object): VOXEL_EMPTY = -1 VOXEL_FILLED = 0 def __init__(self): self.voxel_size = 0.0 self.min_voxel_coord = np.array([]) self.max_voxel_coord = np.array([]) self.num_divisions = np.array([0, 0, 0]) self.points = [] self.voxel_indices = [] self.heights = [] self.num_pts_in_voxel = [] self.leaf_layout_2d = [] def voxelize_2d(self, pts, voxel_size, extents=None, ground_plane=None, create_leaf_layout=True): if pts.shape[1] != 3: raise ValueError("Points have the wrong shape: {}".format( pts.shape)) self.voxel_size = voxel_size discrete_pts = np.floor(pts / voxel_size).astype(np.int32) x_col = discrete_pts[:, 0] y_col = discrete_pts[:, 1] z_col = discrete_pts[:, 2] sorted_order = np.lexsort((y_col, z_col, x_col)) self.points = pts[sorted_order] discrete_pts = discrete_pts[sorted_order] discrete_pts_2d = discrete_pts.copy() discrete_pts_2d[:, 1] = 0 contiguous_array = np.ascontiguousarray(discrete_pts_2d).view( np.dtype((np.void, discrete_pts_2d.dtype.itemsize * discrete_pts_2d.shape[1]))) _, unique_indices = np.unique(contiguous_array, return_index=True) unique_indices.sort() voxel_coords = discrete_pts_2d[unique_indices] num_points_in_voxel = np.diff(unique_indices) num_points_in_voxel = np.append(num_points_in_voxel, discrete_pts_2d.shape[0] - unique_indices[-1]) if ground_plane is None: height_in_voxel = self.points[unique_indices, 1] else: height_in_voxel = geometry_utils.dist_to_plane( ground_plane, self.points[unique_indices]) self.heights = height_in_voxel self.num_pts_in_voxel = num_points_in_voxel if extents is not None: extents_transpose = np.array(extents).transpose() if extents_transpose.shape != (2, 3): raise ValueError("Extents are the wrong shape {}".format( extents.shape)) self.min_voxel_coord = np.floor(extents_transpose[0] / voxel_size) self.max_voxel_coord = \ np.ceil((extents_transpose[1] / voxel_size) - 1) self.min_voxel_coord[1] = 0 self.max_voxel_coord[1] = 0 if not (self.min_voxel_coord <= np.amin(voxel_coords, axis=0)).all(): raise ValueError("Extents are smaller than min_voxel_coord") if not (self.max_voxel_coord >= np.amax(voxel_coords, axis=0)).all(): raise ValueError("Extents are smaller than max_voxel_coord") else: self.min_voxel_coord = np.amin(voxel_coords, axis=0) self.max_voxel_coord = np.amax(voxel_coords, axis=0) self.num_divisions = ((self.max_voxel_coord - self.min_voxel_coord) + 1).astype(np.int32) self.voxel_indices = (voxel_coords - self.min_voxel_coord).astype(int) if create_leaf_layout: self.leaf_layout_2d = self.VOXEL_EMPTY * \ np.ones(self.num_divisions.astype(int)) self.leaf_layout_2d[self.voxel_indices[:, 0], 0, self.voxel_indices[:, 2]] = \ self.VOXEL_FILLED def map_to_index(self, map_index): if self.voxel_size == 0 \ or len(self.min_voxel_coord) == 0 \ or len(map_index) == 0: return [] num_divisions_2d = self.num_divisions[[0, 2]] min_voxel_coord_2d = self.min_voxel_coord[[0, 2]] indices = np.int32(map_index / self.voxel_size) - min_voxel_coord_2d indices[:, 0] = np.clip(indices[:, 0], 0, num_divisions_2d[0]) indices[:, 1] = np.clip(indices[:, 1], 0, num_divisions_2d[1]) return indices

true

f719919bea61d2bf5cccc3f7d4e1bee9157cfd2e

1,230

py

Python

service/scripts/resetadmin.py

OA-DeepGreen/jper

042719a790a34f877050a32f896b947ce4407b4e

[ "Apache-2.0" ]

null

service/scripts/resetadmin.py

OA-DeepGreen/jper

042719a790a34f877050a32f896b947ce4407b4e

[ "Apache-2.0" ]

1

2022-02-03T12:35:18.000Z

service/scripts/resetadmin.py

OA-DeepGreen/jper

042719a790a34f877050a32f896b947ce4407b4e

[ "Apache-2.0" ]

3

2016-07-15T07:29:33.000Z

2020-02-03T11:20:34.000Z

""" This is a script to reset the admin account in a live system. On production this should be run once, and never again, as it removes the old account and builds a new one in its place. This means no historical data will be kept from the before time. """ from octopus.core import add_configuration, app from service.models import Account if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() # some general script running features parser.add_argument("-c", "--config", help="additional configuration to load (e.g. for testing)") args = parser.parse_args() if args.config: add_configuration(app, args.config) a = Account.pull('admin') if not a: a = Account() username = 'admin' password = 'D33pGr33n' params = { "id": username, "role": ["admin"], "email": "green@deepgreen.org", "api_key": "admin", "password": password } a.add_account(params) a.save() print("superuser account reseted for user " + username + " with password " + password) print("THIS SUPERUSER ACCOUNT IS INSECURE! GENERATE A NEW PASSWORD FOR IT IMMEDIATELY! OR CREATE A NEW ACCOUNT AND DELETE THIS ONE...")

31.538462

139

0.664228

from octopus.core import add_configuration, app from service.models import Account if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument("-c", "--config", help="additional configuration to load (e.g. for testing)") args = parser.parse_args() if args.config: add_configuration(app, args.config) a = Account.pull('admin') if not a: a = Account() username = 'admin' password = 'D33pGr33n' params = { "id": username, "role": ["admin"], "email": "green@deepgreen.org", "api_key": "admin", "password": password } a.add_account(params) a.save() print("superuser account reseted for user " + username + " with password " + password) print("THIS SUPERUSER ACCOUNT IS INSECURE! GENERATE A NEW PASSWORD FOR IT IMMEDIATELY! OR CREATE A NEW ACCOUNT AND DELETE THIS ONE...")

true

f719923795059f5abc5f26d2960058e68c7ca4e6

539

py

Python

game_data/migrations/0003_auto_20210103_1621.py

cmerwin3/Adventure_Project

1816978e952f1250049e8d1e7fcf172620903596

[ "Apache-2.0" ]

null

game_data/migrations/0003_auto_20210103_1621.py

cmerwin3/Adventure_Project

1816978e952f1250049e8d1e7fcf172620903596

[ "Apache-2.0" ]

null

game_data/migrations/0003_auto_20210103_1621.py

cmerwin3/Adventure_Project

1816978e952f1250049e8d1e7fcf172620903596

[ "Apache-2.0" ]

null

# Generated by Django 3.1.1 on 2021-01-03 22:21 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('game_data', '0002_auto_20201220_2025'), ] operations = [ migrations.RemoveField( model_name='gamedata', name='pin', ), migrations.AddField( model_name='gamedata', name='password', field=models.CharField(default=1, max_length=30), preserve_default=False, ), ]

22.458333

61

0.575139

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('game_data', '0002_auto_20201220_2025'), ] operations = [ migrations.RemoveField( model_name='gamedata', name='pin', ), migrations.AddField( model_name='gamedata', name='password', field=models.CharField(default=1, max_length=30), preserve_default=False, ), ]

true

f719927ab980abbbc3d3ffdce109f65dd7ddd35e

118

py

Python

framework/conf.py

shew91/Retropy

9feb34855b997c48d93a5343a9842788d19582e6

[ "MIT" ]

13

2018-06-02T09:11:15.000Z

2020-08-29T01:01:19.000Z

framework/conf.py

shew91/Retropy

9feb34855b997c48d93a5343a9842788d19582e6

[ "MIT" ]

1

2021-01-17T14:03:13.000Z

framework/conf.py

shew91/Retropy

9feb34855b997c48d93a5343a9842788d19582e6

[ "MIT" ]

6

2018-06-02T16:20:47.000Z

2021-12-30T22:26:54.000Z

# (hack) Global configs conf_cache_disk = True conf_cache_memory = True conf_cache_fails = False ignoredAssets = []

14.75

24

0.771186

conf_cache_disk = True conf_cache_memory = True conf_cache_fails = False ignoredAssets = []

true

f71992c33b60881673856eebed695c0f089619b3

8,381

py

Python

adwords_python3_examples_10.1.0/v201802/shopping/add_product_partition_tree.py

xyla-io/hazel

260ce906761d8b808c21ca61b44cc71ca3329e8c

[ "MIT" ]

null

adwords_python3_examples_10.1.0/v201802/shopping/add_product_partition_tree.py

xyla-io/hazel

260ce906761d8b808c21ca61b44cc71ca3329e8c

[ "MIT" ]

null

adwords_python3_examples_10.1.0/v201802/shopping/add_product_partition_tree.py

xyla-io/hazel

260ce906761d8b808c21ca61b44cc71ca3329e8c

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright 2016 Google Inc. 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. """This example creates a ProductPartition tree. The LoadFromStorage method is pulling credentials and properties from a "googleads.yaml" file. By default, it looks for this file in your home directory. For more information, see the "Caching authentication information" section of our README. """ # Import appropriate modules from the client library. from googleads import adwords ADGROUP_ID = 'INSERT_AD_GROUP_ID_HERE' class ProductPartitionHelper(object): """A helper for creating ProductPartition trees.""" def __init__(self, adgroup_id): """Initializer. Args: adgroup_id: The ID of the AdGroup that we wish to attach the partition tree to. """ # The next temporary criterion ID to be used. # When creating our tree we need to specify the parent-child relationships # between nodes. However, until a criterion has been created on the server # we do not have a criterion ID with which to refer to it. # Instead we can specify temporary IDs that are specific to a single mutate # request. Once the criteria have been created they are assigned an ID as # normal and the temporary ID will no longer refer to it. # A valid temporary ID is any negative integer. self.next_id = -1 # The set of mutate operations needed to create the current tree. self.operations = [] self.adgroup_id = adgroup_id def CreateSubdivision(self, parent=None, value=None): """Creates a subdivision node. Args: parent: The node that should be this node's parent. value: The value being partitioned on. Returns: A new subdivision node. """ division = { 'xsi_type': 'ProductPartition', 'partitionType': 'SUBDIVISION', 'id': str(self.next_id) } # The root has neither a parent nor a value. if parent is not None: division['parentCriterionId'] = parent['id'] division['caseValue'] = value adgroup_criterion = { 'xsi_type': 'BiddableAdGroupCriterion', 'adGroupId': self.adgroup_id, 'criterion': division } self.CreateAddOperation(adgroup_criterion) self.next_id -= 1 return division def CreateUnit(self, parent=None, value=None, bid_amount=None): """Creates a unit node. Args: parent: The node that should be this node's parent. value: The value being partitioned on. bid_amount: The amount to bid for matching products, in micros. Returns: A new unit node. """ unit = { 'xsi_type': 'ProductPartition', 'partitionType': 'UNIT' } # The root node has neither a parent nor a value. if parent is not None: unit['parentCriterionId'] = parent['id'] unit['caseValue'] = value if bid_amount is not None and bid_amount > 0: bidding_strategy_configuration = { 'bids': [{ 'xsi_type': 'CpcBid', 'bid': { 'xsi_type': 'Money', 'microAmount': str(bid_amount) } }] } adgroup_criterion = { 'xsi_type': 'BiddableAdGroupCriterion', 'biddingStrategyConfiguration': bidding_strategy_configuration } else: adgroup_criterion = { 'xsi_type': 'NegativeAdGroupCriterion' } adgroup_criterion['adGroupId'] = self.adgroup_id adgroup_criterion['criterion'] = unit self.CreateAddOperation(adgroup_criterion) return unit def GetOperations(self): """Returns the set of mutate operations needed to create the current tree. Returns: The set of operations """ return self.operations def CreateAddOperation(self, criterion): """Creates an AdGroupCriterionOperation for the given criterion. Args: criterion: The criterion we want to add. """ operation = { 'operator': 'ADD', 'operand': criterion } self.operations.append(operation) def main(client, adgroup_id): """Runs the example.""" adgroup_criterion_service = client.GetService( 'AdGroupCriterionService', version='v201802') helper = ProductPartitionHelper(adgroup_id) # The most trivial partition tree has only a unit node as the root, e.g.: # helper.CreateUnit(bid_amount=100000) root = helper.CreateSubdivision() new_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', 'condition': 'NEW' } used_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', 'condition': 'USED' } other_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', } helper.CreateUnit(root, new_product_canonical_condition, 200000) helper.CreateUnit(root, used_product_canonical_condition, 100000) other_condition = helper.CreateSubdivision( root, other_product_canonical_condition) cool_product_brand = { 'xsi_type': 'ProductBrand', 'value': 'CoolBrand' } cheap_product_brand = { 'xsi_type': 'ProductBrand', 'value': 'CheapBrand' } other_product_brand = { 'xsi_type': 'ProductBrand', } helper.CreateUnit(other_condition, cool_product_brand, 900000) helper.CreateUnit(other_condition, cheap_product_brand, 10000) other_brand = helper.CreateSubdivision(other_condition, other_product_brand) # The value for the bidding category is a fixed ID for the 'Luggage & Bags' # category. You can retrieve IDs for categories from the ConstantDataService. # See the 'GetProductTaxonomy' example for more details. luggage_category = { 'xsi_type': 'ProductBiddingCategory', 'type': 'BIDDING_CATEGORY_L1', 'value': '-5914235892932915235' } generic_category = { 'xsi_type': 'ProductBiddingCategory', 'type': 'BIDDING_CATEGORY_L1', } helper.CreateUnit(other_brand, luggage_category, 750000) helper.CreateUnit(other_brand, generic_category, 110000) # Make the mutate request result = adgroup_criterion_service.mutate(helper.GetOperations()) children = {} root_node = None # For each criterion, make an array containing each of its children. # We always create the parent before the child, so we can rely on that here. for adgroup_criterion in result['value']: children[adgroup_criterion['criterion']['id']] = [] if 'parentCriterionId' in adgroup_criterion['criterion']: children[adgroup_criterion['criterion']['parentCriterionId']].append( adgroup_criterion['criterion']) else: root_node = adgroup_criterion['criterion'] # Show the tree DisplayTree(root_node, children) def DisplayTree(node, children, level=0): """Recursively display a node and each of its children. Args: node: The node we're displaying the children of. children: Children of the parent node. level: How deep in the tree we are. """ value = '' node_type = '' if 'caseValue' in node: case_value = node['caseValue'] node_type = case_value['ProductDimension.Type'] if node_type == 'ProductCanonicalCondition': value = (case_value['condition'] if 'condition' in case_value else 'OTHER') elif node_type == 'ProductBiddingCategory': value = '%s(%s)' % (case_value['type'], case_value['value'] if 'value' in case_value else 'OTHER') else: value = (case_value['value'] if 'value' in case_value else 'OTHER') print(('%sid: %s, node_type: %s, value: %s\n' % (' ' * level, node['id'], node_type, value))) for child_node in children[node['id']]: DisplayTree(child_node, children, level + 1) if __name__ == '__main__': # Initialize client object. adwords_client = adwords.AdWordsClient.LoadFromStorage() main(adwords_client, ADGROUP_ID)

29.932143

79

0.681064

from googleads import adwords ADGROUP_ID = 'INSERT_AD_GROUP_ID_HERE' class ProductPartitionHelper(object): def __init__(self, adgroup_id): self.next_id = -1 self.operations = [] self.adgroup_id = adgroup_id def CreateSubdivision(self, parent=None, value=None): division = { 'xsi_type': 'ProductPartition', 'partitionType': 'SUBDIVISION', 'id': str(self.next_id) } if parent is not None: division['parentCriterionId'] = parent['id'] division['caseValue'] = value adgroup_criterion = { 'xsi_type': 'BiddableAdGroupCriterion', 'adGroupId': self.adgroup_id, 'criterion': division } self.CreateAddOperation(adgroup_criterion) self.next_id -= 1 return division def CreateUnit(self, parent=None, value=None, bid_amount=None): unit = { 'xsi_type': 'ProductPartition', 'partitionType': 'UNIT' } if parent is not None: unit['parentCriterionId'] = parent['id'] unit['caseValue'] = value if bid_amount is not None and bid_amount > 0: bidding_strategy_configuration = { 'bids': [{ 'xsi_type': 'CpcBid', 'bid': { 'xsi_type': 'Money', 'microAmount': str(bid_amount) } }] } adgroup_criterion = { 'xsi_type': 'BiddableAdGroupCriterion', 'biddingStrategyConfiguration': bidding_strategy_configuration } else: adgroup_criterion = { 'xsi_type': 'NegativeAdGroupCriterion' } adgroup_criterion['adGroupId'] = self.adgroup_id adgroup_criterion['criterion'] = unit self.CreateAddOperation(adgroup_criterion) return unit def GetOperations(self): return self.operations def CreateAddOperation(self, criterion): operation = { 'operator': 'ADD', 'operand': criterion } self.operations.append(operation) def main(client, adgroup_id): adgroup_criterion_service = client.GetService( 'AdGroupCriterionService', version='v201802') helper = ProductPartitionHelper(adgroup_id) root = helper.CreateSubdivision() new_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', 'condition': 'NEW' } used_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', 'condition': 'USED' } other_product_canonical_condition = { 'xsi_type': 'ProductCanonicalCondition', } helper.CreateUnit(root, new_product_canonical_condition, 200000) helper.CreateUnit(root, used_product_canonical_condition, 100000) other_condition = helper.CreateSubdivision( root, other_product_canonical_condition) cool_product_brand = { 'xsi_type': 'ProductBrand', 'value': 'CoolBrand' } cheap_product_brand = { 'xsi_type': 'ProductBrand', 'value': 'CheapBrand' } other_product_brand = { 'xsi_type': 'ProductBrand', } helper.CreateUnit(other_condition, cool_product_brand, 900000) helper.CreateUnit(other_condition, cheap_product_brand, 10000) other_brand = helper.CreateSubdivision(other_condition, other_product_brand) luggage_category = { 'xsi_type': 'ProductBiddingCategory', 'type': 'BIDDING_CATEGORY_L1', 'value': '-5914235892932915235' } generic_category = { 'xsi_type': 'ProductBiddingCategory', 'type': 'BIDDING_CATEGORY_L1', } helper.CreateUnit(other_brand, luggage_category, 750000) helper.CreateUnit(other_brand, generic_category, 110000) result = adgroup_criterion_service.mutate(helper.GetOperations()) children = {} root_node = None for adgroup_criterion in result['value']: children[adgroup_criterion['criterion']['id']] = [] if 'parentCriterionId' in adgroup_criterion['criterion']: children[adgroup_criterion['criterion']['parentCriterionId']].append( adgroup_criterion['criterion']) else: root_node = adgroup_criterion['criterion'] DisplayTree(root_node, children) def DisplayTree(node, children, level=0): value = '' node_type = '' if 'caseValue' in node: case_value = node['caseValue'] node_type = case_value['ProductDimension.Type'] if node_type == 'ProductCanonicalCondition': value = (case_value['condition'] if 'condition' in case_value else 'OTHER') elif node_type == 'ProductBiddingCategory': value = '%s(%s)' % (case_value['type'], case_value['value'] if 'value' in case_value else 'OTHER') else: value = (case_value['value'] if 'value' in case_value else 'OTHER') print(('%sid: %s, node_type: %s, value: %s\n' % (' ' * level, node['id'], node_type, value))) for child_node in children[node['id']]: DisplayTree(child_node, children, level + 1) if __name__ == '__main__': adwords_client = adwords.AdWordsClient.LoadFromStorage() main(adwords_client, ADGROUP_ID)

true

f71992ce83b2d3db02c5c551a3d398f75815bd4c

1,118

py

Python

tests/test_integration.py

vadim2404/pybox

3c4686245dca3d58afa5b923bcfede2172436bfd

[ "MIT" ]

null

tests/test_integration.py

vadim2404/pybox

3c4686245dca3d58afa5b923bcfede2172436bfd

[ "MIT" ]

null

tests/test_integration.py

vadim2404/pybox

3c4686245dca3d58afa5b923bcfede2172436bfd

[ "MIT" ]

null

from pybox.inject import Inject, InjectLazy from pybox.service import IService, ServiceMode class SingletonService(IService): def who_am_i(self): print(f'Singleton {id(self)}') class FactoryService(IService): singleton = Inject(SingletonService) @classmethod def service_mode(self): return ServiceMode.FACTORY def who_am_i(self): print(f'Factory {id(self)}') class A: singleton1 = Inject(SingletonService) singleton2 = InjectLazy(SingletonService) factory1 = Inject(FactoryService) factory2 = InjectLazy(FactoryService) def who_am_i(self): print(f'A {id(self)}') if __name__ == '__main__': a = A() assert a.singleton1 is a.singleton2 assert isinstance(a.singleton1, SingletonService) assert isinstance(a.factory1, FactoryService) assert isinstance(a.factory2, FactoryService) assert a.factory1 is not a.factory2 a.factory1.who_am_i() a.factory2.who_am_i() a.singleton1.who_am_i() a.singleton2.who_am_i() a.factory1.singleton.who_am_i() a.factory2.singleton.who_am_i() a.who_am_i()

23.787234

53

0.701252

from pybox.inject import Inject, InjectLazy from pybox.service import IService, ServiceMode class SingletonService(IService): def who_am_i(self): print(f'Singleton {id(self)}') class FactoryService(IService): singleton = Inject(SingletonService) @classmethod def service_mode(self): return ServiceMode.FACTORY def who_am_i(self): print(f'Factory {id(self)}') class A: singleton1 = Inject(SingletonService) singleton2 = InjectLazy(SingletonService) factory1 = Inject(FactoryService) factory2 = InjectLazy(FactoryService) def who_am_i(self): print(f'A {id(self)}') if __name__ == '__main__': a = A() assert a.singleton1 is a.singleton2 assert isinstance(a.singleton1, SingletonService) assert isinstance(a.factory1, FactoryService) assert isinstance(a.factory2, FactoryService) assert a.factory1 is not a.factory2 a.factory1.who_am_i() a.factory2.who_am_i() a.singleton1.who_am_i() a.singleton2.who_am_i() a.factory1.singleton.who_am_i() a.factory2.singleton.who_am_i() a.who_am_i()

true

f719931b5d6abfb3ad9bbf8bcd7dabd34ac4e957

1,023

py

Python

stacked_queue/stack_queue.py

steveflys/data-structures-and-algorithms

9c89cb24449ca7bc09578408cba3c877fe74e000

[ "MIT" ]

null

stacked_queue/stack_queue.py

steveflys/data-structures-and-algorithms

9c89cb24449ca7bc09578408cba3c877fe74e000

[ "MIT" ]

3

2018-05-01T18:07:50.000Z

2018-05-11T16:52:16.000Z

stacked_queue/stack_queue.py

steveflys/data-structures-and-algorithms

9c89cb24449ca7bc09578408cba3c877fe74e000

[ "MIT" ]

null

from .node import Node from .stack import Stack class Stack_Queue: def __init__(self): self.stack_front = Stack() self.stack_back = Stack() self._size = 0 def enqueue(self, val): """This will add a node the back of the queue and increment the ._size""" try: node = Node(val) except TypeError: raise TypeError('Cannot enqueue a value of none') node._next = self.stack_back.top self.stack_back.top = node self._size += 1 return self.stack_back.top def dequeue(self): """remove the node at the front of the queue, decrement the ._size and return the value""" while self.stack_back.top._next: self.stack_front.push(self.stack_back.pop()) val = self.stack_back.pop() while self.stack_front.top._next: self.stack_back.push(self.stack_front.pop()) self.stack_back.push(self.stack_front.pop()) self._size -= 1 return val

25.575

98

0.605083

from .node import Node from .stack import Stack class Stack_Queue: def __init__(self): self.stack_front = Stack() self.stack_back = Stack() self._size = 0 def enqueue(self, val): try: node = Node(val) except TypeError: raise TypeError('Cannot enqueue a value of none') node._next = self.stack_back.top self.stack_back.top = node self._size += 1 return self.stack_back.top def dequeue(self): while self.stack_back.top._next: self.stack_front.push(self.stack_back.pop()) val = self.stack_back.pop() while self.stack_front.top._next: self.stack_back.push(self.stack_front.pop()) self.stack_back.push(self.stack_front.pop()) self._size -= 1 return val

true

f7199346c4d451ef333dfac98139b138cfe947b2

1,924

py

Python

_discord.py

blairg23/discord-scheduler-bot

bd6bcc25b51b50c9eeca195adefe5cfc2eab4923

[ "MIT" ]

null

_discord.py

blairg23/discord-scheduler-bot

bd6bcc25b51b50c9eeca195adefe5cfc2eab4923

[ "MIT" ]

null

_discord.py

blairg23/discord-scheduler-bot

bd6bcc25b51b50c9eeca195adefe5cfc2eab4923

[ "MIT" ]

null

import discord import asyncio from datetime import datetime class Discord: _instance = None client = None def __new__(class_, *args, **kwargs): if not isinstance(class_._instance, class_): class_._instance = object.__new__(class_, *args, **kwargs) return class_._instance def __init__(self): if self.client is None: self.client = discord.Client() if self.client is not None: print("Discord bot pooling created successfully") def get_client(self): return self.client async def send_message(self, channel, content="", embed=None): ''' Just a wrapper for sending messages, so I don't have to deal with exceptions inside code ''' try: return await self.client.send_message(channel, content=content, embed=embed) except Exception as e: pass #print("ERROR: cmonBruh (send_message) - "+ str(e) + " " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")) async def get_message(self, channel, id): ''' Wrapper for getting a message to handle exceptions ''' msg = None try: msg = await self.client.get_message(channel, id) except Exception as e: pass #print("ERROR: SwiftStrike (get_message) - "+ str(e) + " " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")) return msg async def edit_message(self, message, new_content=None, embed=None): ''' Wrapper for editing a message to handle exceptions ''' msg = None try: msg = await self.client.edit_message(message, new_content=new_content, embed=embed) except Exception as e: pass #print("ERROR: :rage: (edit_message) - "+ str(e) + " " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")) return msg

34.357143

118

0.576403

import discord import asyncio from datetime import datetime class Discord: _instance = None client = None def __new__(class_, *args, **kwargs): if not isinstance(class_._instance, class_): class_._instance = object.__new__(class_, *args, **kwargs) return class_._instance def __init__(self): if self.client is None: self.client = discord.Client() if self.client is not None: print("Discord bot pooling created successfully") def get_client(self): return self.client async def send_message(self, channel, content="", embed=None): try: return await self.client.send_message(channel, content=content, embed=embed) except Exception as e: pass async def get_message(self, channel, id): msg = None try: msg = await self.client.get_message(channel, id) except Exception as e: pass return msg async def edit_message(self, message, new_content=None, embed=None): msg = None try: msg = await self.client.edit_message(message, new_content=new_content, embed=embed) except Exception as e: pass return msg

true

f719938d7b8a9a714e3e8d344249a6a2588ede43

3,085

py

Python

src/app/routes.py

taishengG/jama-slack-integration

746b7186ceaf955ca81e9e0ad4862141ce35eb8d

[ "MIT" ]

null

src/app/routes.py

taishengG/jama-slack-integration

746b7186ceaf955ca81e9e0ad4862141ce35eb8d

[ "MIT" ]

null

src/app/routes.py

taishengG/jama-slack-integration

746b7186ceaf955ca81e9e0ad4862141ce35eb8d

[ "MIT" ]

null

import os import requests import json from flask import request, make_response from app import app from app import route_handler as rt_handle """ This module handles the "intake" of requests to the server. The requests are then passed off the route_handler.py where arguments are then parsed and passed off to other packages for the different functionalities: comment, create, search. All verification for reqets is made at this level. Attributes: base_url (String): Module level variable pulls in environment variable (JAMA_URL). which is the url of the specified Jama instance. url_rule (String): Variable uses environment variable which stands for the main/base url slug. Example: URL_RULE="/jama" """ base_url = os.environ['JAMA_URL'] url_rule = os.environ['URL_RULE'] @app.route(url_rule + "/dialog", methods=['GET', 'PUT', 'POST']) def jama_dialog(): """API intake for dialog submissions from Slack. Passes json payload off to route_handler, otherwise an error is thrown. Args: None Returns: Response Class object """ if not rt_handle.verify_req(request): return make_response("", 401) print("DIALOG") try: submit_payload = json.loads(request.form['payload']) return rt_handle.resolve_dialog_submit(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule + '/menu', methods=['GET', 'PUT', 'POST']) def jama_menu(): """API intake to pass off dynamic dialog data to Slack. Passes json payload off to route_handler, otherwise an error is thrown. Args: None Returns: Response Class object """ if not rt_handle.verify_req(request): return make_response("", 401) print("MENU") try: submit_payload = json.loads(request.form["payload"]) return rt_handle.resolve_menu_req(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule + '/bot', methods=['GET', 'PUT', 'POST']) def jama_bot(): """API intake to pass off slackbot data to Slack. Passes json payload off to route_handler, otherwise an error is thrown. Args: None Returns: Response Class object """ if not rt_handle.verify_req(request): return make_response("", 401) print("BOT") try: submit_payload = request.get_json() return rt_handle.resolve_bot_req(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule, methods=['GET', 'PUT', 'POST']) def jama(): """API intake to pass off dynamic dialog data to Slack. Passes json payload off to route_handler, otherwise an error is thrown. Args: None Returns: Response Class object """ if not rt_handle.verify_req(request): return make_response("", 401) return rt_handle.resolve_jama_req(base_url, request)

24.879032

72

0.666775

import os import requests import json from flask import request, make_response from app import app from app import route_handler as rt_handle base_url = os.environ['JAMA_URL'] url_rule = os.environ['URL_RULE'] @app.route(url_rule + "/dialog", methods=['GET', 'PUT', 'POST']) def jama_dialog(): if not rt_handle.verify_req(request): return make_response("", 401) print("DIALOG") try: submit_payload = json.loads(request.form['payload']) return rt_handle.resolve_dialog_submit(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule + '/menu', methods=['GET', 'PUT', 'POST']) def jama_menu(): if not rt_handle.verify_req(request): return make_response("", 401) print("MENU") try: submit_payload = json.loads(request.form["payload"]) return rt_handle.resolve_menu_req(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule + '/bot', methods=['GET', 'PUT', 'POST']) def jama_bot(): if not rt_handle.verify_req(request): return make_response("", 401) print("BOT") try: submit_payload = request.get_json() return rt_handle.resolve_bot_req(base_url, submit_payload) except Exception as err: print(err) return make_response("", 500) @app.route(url_rule, methods=['GET', 'PUT', 'POST']) def jama(): if not rt_handle.verify_req(request): return make_response("", 401) return rt_handle.resolve_jama_req(base_url, request)

true

f719944e384288656e4d709f07457f69d21c6a92

1,473

pyw

Python

Tkinter/tk5.pyw

Jav10/Python

b419a86825313b8ee537757079c95f3097f4dbad

[ "MIT" ]

null

Tkinter/tk5.pyw

Jav10/Python

b419a86825313b8ee537757079c95f3097f4dbad

[ "MIT" ]

null

Tkinter/tk5.pyw

Jav10/Python

b419a86825313b8ee537757079c95f3097f4dbad

[ "MIT" ]

null

#GUI con TKinter #Autor: Javier Arturo Hernández Sosa #Fecha: 20/Sep/2017 #Descripcion: Curso Python FES Acatlán from tkinter import * #Definición de funciones def suma(): r.set(x.get() + y.get()) def multi(): r.set(x.get() * y.get()) def resta(): r.set(x.get() - y.get()) def dividir(): r.set(x.get() / y.get()) #Ventana raíz root = Tk() #Configuración raíz root.geometry("300x300") root.title("Botones y funciones") root.config(bd=15) #variables para widgets x = DoubleVar() y = DoubleVar() r = StringVar() #Entradas y resultado numero1 = Entry(root,textvariable=x, justify="center") numero2 =Entry(root,textvariable=y, justify="center") resultado = Entry(root, textvariable=r, justify="center", state="disabled") #stated para bloquear el widget #Empaquetado numero1.grid(row=0,column=0,padx=5,pady=5) numero2.grid(row=0,column=1,padx=5,pady=5) resultado.grid(row=3,column=0, columnspan=2,padx=5,pady=5) #Expandir columnas #Botones sumar = Button(root, text="Sumar", command=suma) #botones, command para pasar funcion sumar.grid(row=1,column=0,padx=5,pady=5) multiplicar = Button(root, text="Multiplicar", command=multi) multiplicar.grid(row=1,column=1,padx=5,pady=5) restar = Button(root, text="Restar", command=resta) restar.grid(row=2,column=0,padx=5,pady=5) dividir = Button(root, text="Dividir", command=dividir) dividir.grid(row=2,column=1,padx=5,pady=5) #loop principal root.mainloop()

25.396552

108

0.696538

from tkinter import * def suma(): r.set(x.get() + y.get()) def multi(): r.set(x.get() * y.get()) def resta(): r.set(x.get() - y.get()) def dividir(): r.set(x.get() / y.get()) root = Tk() root.geometry("300x300") root.title("Botones y funciones") root.config(bd=15) x = DoubleVar() y = DoubleVar() r = StringVar() numero1 = Entry(root,textvariable=x, justify="center") numero2 =Entry(root,textvariable=y, justify="center") resultado = Entry(root, textvariable=r, justify="center", state="disabled") numero1.grid(row=0,column=0,padx=5,pady=5) numero2.grid(row=0,column=1,padx=5,pady=5) resultado.grid(row=3,column=0, columnspan=2,padx=5,pady=5) sumar = Button(root, text="Sumar", command=suma) sumar.grid(row=1,column=0,padx=5,pady=5) multiplicar = Button(root, text="Multiplicar", command=multi) multiplicar.grid(row=1,column=1,padx=5,pady=5) restar = Button(root, text="Restar", command=resta) restar.grid(row=2,column=0,padx=5,pady=5) dividir = Button(root, text="Dividir", command=dividir) dividir.grid(row=2,column=1,padx=5,pady=5) root.mainloop()

true

f719946d0d254ecdc9ccfe5fb6f0233c8c62eb2a

1,485

py

Python

src/data/dataset.py

zmcx16/ReclassifyAnimeCG

f5f95b229447564502564d9ffc7edf6215fec83d

[ "MIT" ]

3

2021-10-30T10:13:40.000Z

2021-12-12T10:26:14.000Z

src/data/dataset.py

zmcx16/ReclassifyAnimeCG

f5f95b229447564502564d9ffc7edf6215fec83d

[ "MIT" ]

null

src/data/dataset.py

zmcx16/ReclassifyAnimeCG

f5f95b229447564502564d9ffc7edf6215fec83d

[ "MIT" ]

null

import torch from torch.utils.data import Dataset, DataLoader import numpy as np from PIL import Image Image.MAX_IMAGE_PIXELS = None from data import get_train_transform, get_test_transform class CustomDataset(Dataset): img_aug = True imgs = [] transform = None def __init__(self, label_file, image_set, input_size): with open(label_file, 'r', encoding="utf-8") as f: self.imgs = list(map(lambda line: line.strip().split('|'), f)) if image_set == 'train': self.transform = get_train_transform(size=input_size) else: self.transform = get_test_transform(size=input_size) self.input_size = input_size def __getitem__(self, index): # print(self.imgs) # print(index) # print(len(self.imgs[index])) img_path, label = self.imgs[index] # print(img_path) img = Image.open(img_path).convert('RGB') if self.img_aug: img = self.transform(img) else: img = np.array(img) img = torch.from_numpy(img) return img, torch.from_numpy(np.array(int(label))) def __len__(self): return len(self.imgs) def get_datasets_and_dataloader(label_path, image_set, batch_size, input_size): _dataset = CustomDataset(label_path, image_set=image_set, input_size=input_size) _dataloader = DataLoader(_dataset, batch_size=batch_size, shuffle=True, num_workers=2) return _dataset, _dataloader

30.9375

90

0.658586

import torch from torch.utils.data import Dataset, DataLoader import numpy as np from PIL import Image Image.MAX_IMAGE_PIXELS = None from data import get_train_transform, get_test_transform class CustomDataset(Dataset): img_aug = True imgs = [] transform = None def __init__(self, label_file, image_set, input_size): with open(label_file, 'r', encoding="utf-8") as f: self.imgs = list(map(lambda line: line.strip().split('|'), f)) if image_set == 'train': self.transform = get_train_transform(size=input_size) else: self.transform = get_test_transform(size=input_size) self.input_size = input_size def __getitem__(self, index): img_path, label = self.imgs[index] img = Image.open(img_path).convert('RGB') if self.img_aug: img = self.transform(img) else: img = np.array(img) img = torch.from_numpy(img) return img, torch.from_numpy(np.array(int(label))) def __len__(self): return len(self.imgs) def get_datasets_and_dataloader(label_path, image_set, batch_size, input_size): _dataset = CustomDataset(label_path, image_set=image_set, input_size=input_size) _dataloader = DataLoader(_dataset, batch_size=batch_size, shuffle=True, num_workers=2) return _dataset, _dataloader

true

f71994d1600fc241664b82c32779973864dfe5a1

337

py

Python

AHtask2.py

Irinakene/AHtask

6f776477c6867b8f7650394aac1c3292bced8ca9

[ "MIT" ]

null

AHtask2.py

Irinakene/AHtask

6f776477c6867b8f7650394aac1c3292bced8ca9

[ "MIT" ]

null

AHtask2.py

Irinakene/AHtask

6f776477c6867b8f7650394aac1c3292bced8ca9

[ "MIT" ]

null

import csv name = input('Enter your name: ') email = input('Enter your email: ') phone = input('Enter your phone: ') githublink = input('Enter your githublink: ') save = input('Save to CSV? ') if save == 'yes': file = open('results.csv', 'a') csv_writer = csv.writer(file) csv_writer.writerow([name, githublink, email, phone])

22.466667

54

0.664688

import csv name = input('Enter your name: ') email = input('Enter your email: ') phone = input('Enter your phone: ') githublink = input('Enter your githublink: ') save = input('Save to CSV? ') if save == 'yes': file = open('results.csv', 'a') csv_writer = csv.writer(file) csv_writer.writerow([name, githublink, email, phone])

true

f719957c1c4356a3f8209af000c59c417741c746

8,827

py

Python

parameter_sweep.py

yairchn/SCAMPy

a204b4220d722cf3dbf4e81997f8d2ed7a7324a9

[ "Apache-2.0" ]

1

2018-08-23T21:53:01.000Z

parameter_sweep.py

yairchn/SCAMPy

a204b4220d722cf3dbf4e81997f8d2ed7a7324a9

[ "Apache-2.0" ]

1

2019-09-08T03:32:04.000Z

parameter_sweep.py

yairchn/SCAMPy

a204b4220d722cf3dbf4e81997f8d2ed7a7324a9

[ "Apache-2.0" ]

1

2018-08-23T21:53:14.000Z

import subprocess import argparse import json import pprint from sys import exit import uuid import ast import numpy as np import netCDF4 as nc import os # python parameter_sweep.py case_name def main(): parser = argparse.ArgumentParser(prog='Paramlist Generator') parser.add_argument('case_name') args = parser.parse_args() case_name = args.case_name file_case = open(case_name + '_sweep.in').read() namelist = json.loads(file_case) uuid = namelist['meta']['uuid'] print(uuid) path = namelist['output']['output_root'] + 'Output.' + case_name + '.' + uuid[-5:] + '/stats/Stats.' + case_name + '.nc' path1 = namelist['output']['output_root'] + 'Output.' + case_name + '.' + uuid[-5:] + '/paramlist_sweep.in' tmax = namelist['time_stepping']['t_max'] #dt = namelist['time_stepping']['dt'] freq = namelist['stats_io']['frequency'] nz = namelist['grid']['nz'] nt = int(tmax/freq)+1 print nt II=1 nvar = 11 sweep_var = np.linspace(0.7, 2.2, num=nvar) #sweep_var = [0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18] _z = np.zeros((nz)) _t = np.zeros((nt)) _lwp = np.zeros((nt,nvar)) _cloud_cover = np.zeros((nt,nvar)) _cloud_top = np.zeros((nt,nvar)) _cloud_base = np.zeros((nt,nvar)) _updraft_area = np.zeros((nt,nz,nvar)) _ql_mean = np.zeros((nt,nz,nvar)) _updraft_w = np.zeros((nt,nz,nvar)) _thetal_mean = np.zeros((nt,nz,nvar)) _massflux = np.zeros((nt, nz, nvar)) _buoyancy_mean = np.zeros((nt,nz,nvar)) _env_tke = np.zeros((nt,nz,nvar)) _updraft_thetal_precip = np.zeros((nt,nz,nvar)) _sweep_var = np.zeros(nvar) for i in range(0,nvar): sweep_var_i = sweep_var[i] paramlist = sweep(sweep_var_i) write_file(paramlist) file_case = open('paramlist_sweep.in').read() current = json.loads(file_case) print('========================') print('running '+case_name+' var = '+ str(sweep_var_i)) print('========================') subprocess.call("python main.py " + case_name + "_sweep.in paramlist_sweep.in", shell=True) data = nc.Dataset(path, 'r') zz = data.groups['profiles'].variables['z'] tt = data.groups['profiles'].variables['t'] lwp_ = np.multiply(data.groups['timeseries'].variables['lwp'], 1.0) cloud_cover_ = np.multiply(data.groups['timeseries'].variables['cloud_cover'],1.0) cloud_top_ = np.multiply(data.groups['timeseries'].variables['cloud_top'],1.0) cloud_base_ = np.multiply(data.groups['timeseries'].variables['cloud_base'],1.0) updraft_area_ = np.multiply(data.groups['profiles'].variables['updraft_area'],1.0) ql_mean_ = np.multiply(data.groups['profiles'].variables['ql_mean'],1.0) updraft_w_ = np.multiply(data.groups['profiles'].variables['updraft_w'],1.0) thetal_mean_ = np.multiply(data.groups['profiles'].variables['thetal_mean'],1.0) massflux_ = np.multiply(data.groups['profiles'].variables['massflux'], 1.0) buoyancy_mean_ = np.multiply(data.groups['profiles'].variables['buoyancy_mean'],1.0) env_tke_ = np.multiply(data.groups['profiles'].variables['env_tke'],1.0) updraft_thetal_precip_ = np.multiply(data.groups['profiles'].variables['updraft_thetal_precip'], 1.0) print np.shape(lwp_) try: _lwp[:, II] = lwp_[0:nt] _cloud_cover[:,II] = cloud_cover_[0:nt] _cloud_top[:,II] = cloud_top_[0:nt] _cloud_base[:,II] = cloud_base_[0:nt] _t = tt[0:nt] _z = zz _updraft_area[:,:,II] = updraft_area_[0:nt,0:nz] _ql_mean[:,:,II] = ql_mean_[0:nt,0:nz] _updraft_w[:,:,II] = updraft_w_[0:nt,0:nz] _thetal_mean[:,:,II] = thetal_mean_[0:nt,0:nz] _massflux[:, :, II] = massflux_[0:nt, 0:nz] _buoyancy_mean[:,:,II] = buoyancy_mean_[0:nt,0:nz] _env_tke[:,:,II] = env_tke_[0:nt,0:nz] _updraft_thetal_precip[:,:,II] = updraft_thetal_precip_[0:nt,0:nz] _sweep_var[II] = sweep_var_i II += 1 except: pass os.remove(path) os.remove(path1) destination = '/Users/yaircohen/Documents/SCAMPy_out/parameter_sweep/' out_stats = nc.Dataset(destination + '/Stats.sweep_' + case_name + '.nc', 'w', format='NETCDF4') grp_stats = out_stats.createGroup('profiles') grp_stats.createDimension('z', nz) grp_stats.createDimension('t', nt) grp_stats.createDimension('var', II) t = grp_stats.createVariable('t', 'f4', 't') z = grp_stats.createVariable('z', 'f4', 'z') var = grp_stats.createVariable('var', 'f4', 'var') lwp = grp_stats.createVariable('lwp', 'f4', ('t', 'var')) cloud_cover = grp_stats.createVariable('cloud_cover', 'f4', ('t', 'var')) cloud_top = grp_stats.createVariable('cloud_top', 'f4', ('t', 'var')) cloud_base = grp_stats.createVariable('cloud_base', 'f4', ('t', 'var')) updraft_area = grp_stats.createVariable('updraft_area', 'f4', ('t', 'z','var')) ql_mean = grp_stats.createVariable('ql_mean', 'f4', ('t', 'z', 'var')) updraft_w = grp_stats.createVariable('updraft_w', 'f4', ('t', 'z', 'var')) thetal_mean = grp_stats.createVariable('thetal_mean', 'f4', ('t', 'z', 'var')) massflux = grp_stats.createVariable('massflux', 'f4', ('t', 'z', 'var')) buoyancy_mean = grp_stats.createVariable('buoyancy_mean', 'f4', ('t', 'z', 'var')) env_tke = grp_stats.createVariable('env_tke', 'f4', ('t', 'z', 'var')) updraft_thetal_precip = grp_stats.createVariable('updraft_thetal_precip', 'f4', ('t', 'z', 'var')) print '---------------------------------' print np.shape(var) print np.shape(_sweep_var) print II print '---------------------------------' var[:] = _sweep_var[0:II] print np.shape(_t) print np.shape(t) #t[:] = _t #z[:] = _z print '---------------------------------' print np.shape(lwp) print np.shape(_lwp) print II print '---------------------------------' lwp[:,:] = _lwp[:,0:II] cloud_cover[:,:] = _cloud_cover[:,0:II] cloud_top[:,:] = _cloud_top[:,0:II] cloud_base[:,:] = _cloud_base[:,0:II] updraft_area[:,:,:] = _updraft_area[:,:,0:II] ql_mean[:,:,:] = _ql_mean[:,:,0:II] updraft_w[:,:,:] = _updraft_w[:,:,0:II] massflux[:,:,:] = _massflux[:,:,0:II] buoyancy_mean[:,:,:] = _buoyancy_mean[:,:,0:II] env_tke[:,:,:] = _env_tke[:,:,0:II] updraft_thetal_precip[:, :, :] = _updraft_thetal_precip[:,:,0:II] out_stats.close() print('========================') print('======= SWEEP END ======') print('========================') def sweep(sweep_var_i): # vel_pressure_coeff_i paramlist = {} paramlist['meta'] = {} paramlist['meta']['casename'] = 'sweep' paramlist['turbulence'] = {} paramlist['turbulence']['prandtl_number'] = 1.0 paramlist['turbulence']['Ri_bulk_crit'] = 0.0 paramlist['turbulence']['EDMF_PrognosticTKE'] = {} paramlist['turbulence']['EDMF_PrognosticTKE']['surface_area'] = sweep_var_i #paramlist['turbulence']['EDMF_PrognosticTKE']['surface_scalar_coeff'] = 0.1 paramlist['turbulence']['EDMF_PrognosticTKE']['tke_ed_coeff'] = 0.1 #paramlist['turbulence']['EDMF_PrognosticTKE']['w_entr_coeff'] = 0.5 # "b1" #paramlist['turbulence']['EDMF_PrognosticTKE']['w_buoy_coeff'] = 0.5 # "b2" paramlist['turbulence']['EDMF_PrognosticTKE']['tke_diss_coeff'] = 0.3 paramlist['turbulence']['EDMF_PrognosticTKE']['max_area_factor'] = 10.0 paramlist['turbulence']['EDMF_PrognosticTKE']['entrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_PrognosticTKE']['detrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_PrognosticTKE']['vel_pressure_coeff'] = 5e-5 paramlist['turbulence']['EDMF_PrognosticTKE']['vel_buoy_coeff'] = 0.6666666666666666 paramlist['turbulence']['EDMF_BulkSteady'] = {} paramlist['turbulence']['EDMF_BulkSteady']['surface_area'] = 0.1 paramlist['turbulence']['EDMF_BulkSteady']['w_entr_coeff'] = 2.0 paramlist['turbulence']['EDMF_BulkSteady']['w_buoy_coeff'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['max_area_factor'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['entrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['detrainment_factor'] = 1.0 paramlist['turbulence']['updraft_microphysics'] = {} paramlist['turbulence']['updraft_microphysics']['max_supersaturation'] = 0.1 return paramlist def write_file(paramlist): fh = open('paramlist_'+paramlist['meta']['casename']+ '.in', 'w') json.dump(paramlist, fh, sort_keys=True, indent=4) fh.close() return if __name__ == '__main__': main()

40.865741

124

0.60972

import subprocess import argparse import json import pprint from sys import exit import uuid import ast import numpy as np import netCDF4 as nc import os def main(): parser = argparse.ArgumentParser(prog='Paramlist Generator') parser.add_argument('case_name') args = parser.parse_args() case_name = args.case_name file_case = open(case_name + '_sweep.in').read() namelist = json.loads(file_case) uuid = namelist['meta']['uuid'] print(uuid) path = namelist['output']['output_root'] + 'Output.' + case_name + '.' + uuid[-5:] + '/stats/Stats.' + case_name + '.nc' path1 = namelist['output']['output_root'] + 'Output.' + case_name + '.' + uuid[-5:] + '/paramlist_sweep.in' tmax = namelist['time_stepping']['t_max'] freq = namelist['stats_io']['frequency'] nz = namelist['grid']['nz'] nt = int(tmax/freq)+1 print nt II=1 nvar = 11 sweep_var = np.linspace(0.7, 2.2, num=nvar) _z = np.zeros((nz)) _t = np.zeros((nt)) _lwp = np.zeros((nt,nvar)) _cloud_cover = np.zeros((nt,nvar)) _cloud_top = np.zeros((nt,nvar)) _cloud_base = np.zeros((nt,nvar)) _updraft_area = np.zeros((nt,nz,nvar)) _ql_mean = np.zeros((nt,nz,nvar)) _updraft_w = np.zeros((nt,nz,nvar)) _thetal_mean = np.zeros((nt,nz,nvar)) _massflux = np.zeros((nt, nz, nvar)) _buoyancy_mean = np.zeros((nt,nz,nvar)) _env_tke = np.zeros((nt,nz,nvar)) _updraft_thetal_precip = np.zeros((nt,nz,nvar)) _sweep_var = np.zeros(nvar) for i in range(0,nvar): sweep_var_i = sweep_var[i] paramlist = sweep(sweep_var_i) write_file(paramlist) file_case = open('paramlist_sweep.in').read() current = json.loads(file_case) print('========================') print('running '+case_name+' var = '+ str(sweep_var_i)) print('========================') subprocess.call("python main.py " + case_name + "_sweep.in paramlist_sweep.in", shell=True) data = nc.Dataset(path, 'r') zz = data.groups['profiles'].variables['z'] tt = data.groups['profiles'].variables['t'] lwp_ = np.multiply(data.groups['timeseries'].variables['lwp'], 1.0) cloud_cover_ = np.multiply(data.groups['timeseries'].variables['cloud_cover'],1.0) cloud_top_ = np.multiply(data.groups['timeseries'].variables['cloud_top'],1.0) cloud_base_ = np.multiply(data.groups['timeseries'].variables['cloud_base'],1.0) updraft_area_ = np.multiply(data.groups['profiles'].variables['updraft_area'],1.0) ql_mean_ = np.multiply(data.groups['profiles'].variables['ql_mean'],1.0) updraft_w_ = np.multiply(data.groups['profiles'].variables['updraft_w'],1.0) thetal_mean_ = np.multiply(data.groups['profiles'].variables['thetal_mean'],1.0) massflux_ = np.multiply(data.groups['profiles'].variables['massflux'], 1.0) buoyancy_mean_ = np.multiply(data.groups['profiles'].variables['buoyancy_mean'],1.0) env_tke_ = np.multiply(data.groups['profiles'].variables['env_tke'],1.0) updraft_thetal_precip_ = np.multiply(data.groups['profiles'].variables['updraft_thetal_precip'], 1.0) print np.shape(lwp_) try: _lwp[:, II] = lwp_[0:nt] _cloud_cover[:,II] = cloud_cover_[0:nt] _cloud_top[:,II] = cloud_top_[0:nt] _cloud_base[:,II] = cloud_base_[0:nt] _t = tt[0:nt] _z = zz _updraft_area[:,:,II] = updraft_area_[0:nt,0:nz] _ql_mean[:,:,II] = ql_mean_[0:nt,0:nz] _updraft_w[:,:,II] = updraft_w_[0:nt,0:nz] _thetal_mean[:,:,II] = thetal_mean_[0:nt,0:nz] _massflux[:, :, II] = massflux_[0:nt, 0:nz] _buoyancy_mean[:,:,II] = buoyancy_mean_[0:nt,0:nz] _env_tke[:,:,II] = env_tke_[0:nt,0:nz] _updraft_thetal_precip[:,:,II] = updraft_thetal_precip_[0:nt,0:nz] _sweep_var[II] = sweep_var_i II += 1 except: pass os.remove(path) os.remove(path1) destination = '/Users/yaircohen/Documents/SCAMPy_out/parameter_sweep/' out_stats = nc.Dataset(destination + '/Stats.sweep_' + case_name + '.nc', 'w', format='NETCDF4') grp_stats = out_stats.createGroup('profiles') grp_stats.createDimension('z', nz) grp_stats.createDimension('t', nt) grp_stats.createDimension('var', II) t = grp_stats.createVariable('t', 'f4', 't') z = grp_stats.createVariable('z', 'f4', 'z') var = grp_stats.createVariable('var', 'f4', 'var') lwp = grp_stats.createVariable('lwp', 'f4', ('t', 'var')) cloud_cover = grp_stats.createVariable('cloud_cover', 'f4', ('t', 'var')) cloud_top = grp_stats.createVariable('cloud_top', 'f4', ('t', 'var')) cloud_base = grp_stats.createVariable('cloud_base', 'f4', ('t', 'var')) updraft_area = grp_stats.createVariable('updraft_area', 'f4', ('t', 'z','var')) ql_mean = grp_stats.createVariable('ql_mean', 'f4', ('t', 'z', 'var')) updraft_w = grp_stats.createVariable('updraft_w', 'f4', ('t', 'z', 'var')) thetal_mean = grp_stats.createVariable('thetal_mean', 'f4', ('t', 'z', 'var')) massflux = grp_stats.createVariable('massflux', 'f4', ('t', 'z', 'var')) buoyancy_mean = grp_stats.createVariable('buoyancy_mean', 'f4', ('t', 'z', 'var')) env_tke = grp_stats.createVariable('env_tke', 'f4', ('t', 'z', 'var')) updraft_thetal_precip = grp_stats.createVariable('updraft_thetal_precip', 'f4', ('t', 'z', 'var')) print '---------------------------------' print np.shape(var) print np.shape(_sweep_var) print II print '---------------------------------' var[:] = _sweep_var[0:II] print np.shape(_t) print np.shape(t) print '---------------------------------' print np.shape(lwp) print np.shape(_lwp) print II print '---------------------------------' lwp[:,:] = _lwp[:,0:II] cloud_cover[:,:] = _cloud_cover[:,0:II] cloud_top[:,:] = _cloud_top[:,0:II] cloud_base[:,:] = _cloud_base[:,0:II] updraft_area[:,:,:] = _updraft_area[:,:,0:II] ql_mean[:,:,:] = _ql_mean[:,:,0:II] updraft_w[:,:,:] = _updraft_w[:,:,0:II] massflux[:,:,:] = _massflux[:,:,0:II] buoyancy_mean[:,:,:] = _buoyancy_mean[:,:,0:II] env_tke[:,:,:] = _env_tke[:,:,0:II] updraft_thetal_precip[:, :, :] = _updraft_thetal_precip[:,:,0:II] out_stats.close() print('========================') print('======= SWEEP END ======') print('========================') def sweep(sweep_var_i): paramlist = {} paramlist['meta'] = {} paramlist['meta']['casename'] = 'sweep' paramlist['turbulence'] = {} paramlist['turbulence']['prandtl_number'] = 1.0 paramlist['turbulence']['Ri_bulk_crit'] = 0.0 paramlist['turbulence']['EDMF_PrognosticTKE'] = {} paramlist['turbulence']['EDMF_PrognosticTKE']['surface_area'] = sweep_var_i paramlist['turbulence']['EDMF_PrognosticTKE']['tke_ed_coeff'] = 0.1 ramlist['turbulence']['EDMF_PrognosticTKE']['tke_diss_coeff'] = 0.3 paramlist['turbulence']['EDMF_PrognosticTKE']['max_area_factor'] = 10.0 paramlist['turbulence']['EDMF_PrognosticTKE']['entrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_PrognosticTKE']['detrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_PrognosticTKE']['vel_pressure_coeff'] = 5e-5 paramlist['turbulence']['EDMF_PrognosticTKE']['vel_buoy_coeff'] = 0.6666666666666666 paramlist['turbulence']['EDMF_BulkSteady'] = {} paramlist['turbulence']['EDMF_BulkSteady']['surface_area'] = 0.1 paramlist['turbulence']['EDMF_BulkSteady']['w_entr_coeff'] = 2.0 paramlist['turbulence']['EDMF_BulkSteady']['w_buoy_coeff'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['max_area_factor'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['entrainment_factor'] = 1.0 paramlist['turbulence']['EDMF_BulkSteady']['detrainment_factor'] = 1.0 paramlist['turbulence']['updraft_microphysics'] = {} paramlist['turbulence']['updraft_microphysics']['max_supersaturation'] = 0.1 return paramlist def write_file(paramlist): fh = open('paramlist_'+paramlist['meta']['casename']+ '.in', 'w') json.dump(paramlist, fh, sort_keys=True, indent=4) fh.close() return if __name__ == '__main__': main()

false

true

f71997563231cf56306173544d65e6f6f5c14345

27,571

py

Python

sdk/keyvault/azure-keyvault-keys/azure/keyvault/keys/_client.py

ankitarorabit/azure-sdk-for-python

dd90281cbad9400f8080754a5ef2f56791a5a88f

[ "MIT" ]

null

sdk/keyvault/azure-keyvault-keys/azure/keyvault/keys/_client.py

ankitarorabit/azure-sdk-for-python

dd90281cbad9400f8080754a5ef2f56791a5a88f

[ "MIT" ]

1

2021-05-31T08:56:01.000Z

sdk/keyvault/azure-keyvault-keys/azure/keyvault/keys/_client.py

ankitarorabit/azure-sdk-for-python

dd90281cbad9400f8080754a5ef2f56791a5a88f

[ "MIT" ]

null

# ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # ------------------------------------ from functools import partial from azure.core.tracing.decorator import distributed_trace from ._shared import KeyVaultClientBase from ._shared.exceptions import error_map as _error_map from ._shared._polling import DeleteRecoverPollingMethod, KeyVaultOperationPoller from ._models import KeyVaultKey, KeyProperties, DeletedKey try: from typing import TYPE_CHECKING except ImportError: TYPE_CHECKING = False if TYPE_CHECKING: # pylint:disable=unused-import from typing import Any, Optional, Union from azure.core.paging import ItemPaged from ._models import JsonWebKey class KeyClient(KeyVaultClientBase): """A high-level interface for managing a vault's keys. :param str vault_url: URL of the vault the client will access. This is also called the vault's "DNS Name". :param credential: An object which can provide an access token for the vault, such as a credential from :mod:`azure.identity` :keyword api_version: version of the Key Vault API to use. Defaults to the most recent. :paramtype api_version: ~azure.keyvault.keys.ApiVersion :keyword transport: transport to use. Defaults to :class:`~azure.core.pipeline.transport.RequestsTransport`. :paramtype transport: ~azure.core.pipeline.transport.HttpTransport Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START create_key_client] :end-before: [END create_key_client] :language: python :caption: Create a new ``KeyClient`` :dedent: 4 """ # pylint:disable=protected-access @distributed_trace def create_key(self, name, key_type, **kwargs): # type: (str, Union[str, azure.keyvault.keys.KeyType], **Any) -> KeyVaultKey """Create a key or, if `name` is already in use, create a new version of the key. Requires keys/create permission. :param str name: The name of the new key. :param key_type: The type of key to create :type key_type: ~azure.keyvault.keys.KeyType or str :keyword int size: Key size in bits. Applies only to RSA and symmetric keys. Consider using :func:`create_rsa_key` or :func:`create_oct_key` instead. :keyword curve: Elliptic curve name. Applies only to elliptic curve keys. Defaults to the NIST P-256 elliptic curve. To create an elliptic curve key, consider using :func:`create_ec_key` instead. :paramtype curve: ~azure.keyvault.keys.KeyCurveName or str :keyword int public_exponent: The RSA public exponent to use. Applies only to RSA keys created in a Managed HSM. :keyword key_operations: Allowed key operations :paramtype key_operations: list[~azure.keyvault.keys.KeyOperation or str] :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The created key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START create_key] :end-before: [END create_key] :language: python :caption: Create a key :dedent: 8 """ enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyCreateParameters( kty=key_type, key_size=kwargs.pop("size", None), key_attributes=attributes, key_ops=kwargs.pop("key_operations", None), tags=kwargs.pop("tags", None), curve=kwargs.pop("curve", None), public_exponent=kwargs.pop("public_exponent", None) ) bundle = self._client.create_key( vault_base_url=self.vault_url, key_name=name, parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def create_rsa_key(self, name, **kwargs): # type: (str, **Any) -> KeyVaultKey """Create a new RSA key or, if `name` is already in use, create a new version of the key Requires the keys/create permission. :param str name: The name for the new key. :keyword int size: Key size in bits, for example 2048, 3072, or 4096. :keyword int public_exponent: The RSA public exponent to use. Applies only to RSA keys created in a Managed HSM. :keyword bool hardware_protected: Whether the key should be created in a hardware security module. Defaults to ``False``. :keyword key_operations: Allowed key operations :paramtype key_operations: list[~azure.keyvault.keys.KeyOperation or str] :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The created key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START create_rsa_key] :end-before: [END create_rsa_key] :language: python :caption: Create RSA key :dedent: 8 """ hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="RSA-HSM" if hsm else "RSA", **kwargs) @distributed_trace def create_ec_key(self, name, **kwargs): # type: (str, **Any) -> KeyVaultKey """Create a new elliptic curve key or, if `name` is already in use, create a new version of the key. Requires the keys/create permission. :param str name: The name for the new key. :keyword curve: Elliptic curve name. Defaults to the NIST P-256 elliptic curve. :paramtype curve: ~azure.keyvault.keys.KeyCurveName or str :keyword key_operations: Allowed key operations :paramtype key_operations: list[~azure.keyvault.keys.KeyOperation or str] :keyword bool hardware_protected: Whether the key should be created in a hardware security module. Defaults to ``False``. :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The created key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START create_ec_key] :end-before: [END create_ec_key] :language: python :caption: Create an elliptic curve key :dedent: 8 """ hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="EC-HSM" if hsm else "EC", **kwargs) @distributed_trace def create_oct_key(self, name, **kwargs): # type: (str, **Any) -> KeyVaultKey """Create a new octet sequence (symmetric) key or, if `name` is already in use, create a new version of the key. Requires the keys/create permission. :param str name: The name for the new key. :keyword int size: Key size in bits, for example 128, 192, or 256. :keyword key_operations: Allowed key operations. :paramtype key_operations: list[~azure.keyvault.keys.KeyOperation or str] :keyword bool hardware_protected: Whether the key should be created in a hardware security module. Defaults to ``False``. :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The created key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START create_oct_key] :end-before: [END create_oct_key] :language: python :caption: Create an octet sequence (symmetric) key :dedent: 8 """ hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="oct-HSM" if hsm else "oct", **kwargs) @distributed_trace def begin_delete_key(self, name, **kwargs): # type: (str, **Any) -> DeletedKey """Delete all versions of a key and its cryptographic material. Requires keys/delete permission. When this method returns Key Vault has begun deleting the key. Deletion may take several seconds in a vault with soft-delete enabled. This method therefore returns a poller enabling you to wait for deletion to complete. :param str name: The name of the key to delete. :returns: A poller for the delete key operation. The poller's `result` method returns the :class:`~azure.keyvault.keys.DeletedKey` without waiting for deletion to complete. If the vault has soft-delete enabled and you want to permanently delete the key with :func:`purge_deleted_key`, call the poller's `wait` method first. It will block until the deletion is complete. The `wait` method requires keys/get permission. :rtype: ~azure.core.polling.LROPoller[~azure.keyvault.keys.DeletedKey] :raises: :class:`~azure.core.exceptions.ResourceNotFoundError` if the key doesn't exist, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START delete_key] :end-before: [END delete_key] :language: python :caption: Delete a key :dedent: 8 """ polling_interval = kwargs.pop("_polling_interval", None) if polling_interval is None: polling_interval = 2 deleted_key = DeletedKey._from_deleted_key_bundle( self._client.delete_key(self.vault_url, name, error_map=_error_map, **kwargs) ) command = partial(self.get_deleted_key, name=name, **kwargs) polling_method = DeleteRecoverPollingMethod( # no recovery ID means soft-delete is disabled, in which case we initialize the poller as finished finished=deleted_key.recovery_id is None, command=command, final_resource=deleted_key, interval=polling_interval, ) return KeyVaultOperationPoller(polling_method) @distributed_trace def get_key(self, name, version=None, **kwargs): # type: (str, Optional[str], **Any) -> KeyVaultKey """Get a key's attributes and, if it's an asymmetric key, its public material. Requires keys/get permission. :param str name: The name of the key to get. :param str version: (optional) A specific version of the key to get. If not specified, gets the latest version of the key. :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.ResourceNotFoundError` if the key doesn't exist, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START get_key] :end-before: [END get_key] :language: python :caption: Get a key :dedent: 8 """ bundle = self._client.get_key(self.vault_url, name, key_version=version or "", error_map=_error_map, **kwargs) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def get_deleted_key(self, name, **kwargs): # type: (str, **Any) -> DeletedKey """Get a deleted key. Possible only in a vault with soft-delete enabled. Requires keys/get permission. :param str name: The name of the key :returns: The deleted key :rtype: ~azure.keyvault.keys.DeletedKey :raises: :class:`~azure.core.exceptions.ResourceNotFoundError` if the key doesn't exist, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START get_deleted_key] :end-before: [END get_deleted_key] :language: python :caption: Get a deleted key :dedent: 8 """ bundle = self._client.get_deleted_key(self.vault_url, name, error_map=_error_map, **kwargs) return DeletedKey._from_deleted_key_bundle(bundle) @distributed_trace def list_deleted_keys(self, **kwargs): # type: (**Any) -> ItemPaged[DeletedKey] """List all deleted keys, including the public part of each. Possible only in a vault with soft-delete enabled. Requires keys/list permission. :returns: An iterator of deleted keys :rtype: ~azure.core.paging.ItemPaged[~azure.keyvault.keys.DeletedKey] Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START list_deleted_keys] :end-before: [END list_deleted_keys] :language: python :caption: List all the deleted keys :dedent: 8 """ return self._client.get_deleted_keys( self._vault_url, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [DeletedKey._from_deleted_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def list_properties_of_keys(self, **kwargs): # type: (**Any) -> ItemPaged[KeyProperties] """List identifiers and properties of all keys in the vault. Requires keys/list permission. :returns: An iterator of keys without their cryptographic material or version information :rtype: ~azure.core.paging.ItemPaged[~azure.keyvault.keys.KeyProperties] Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START list_keys] :end-before: [END list_keys] :language: python :caption: List all keys :dedent: 8 """ return self._client.get_keys( self._vault_url, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [KeyProperties._from_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def list_properties_of_key_versions(self, name, **kwargs): # type: (str, **Any) -> ItemPaged[KeyProperties] """List the identifiers and properties of a key's versions. Requires keys/list permission. :param str name: The name of the key :returns: An iterator of keys without their cryptographic material :rtype: ~azure.core.paging.ItemPaged[~azure.keyvault.keys.KeyProperties] Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START list_properties_of_key_versions] :end-before: [END list_properties_of_key_versions] :language: python :caption: List all versions of a key :dedent: 8 """ return self._client.get_key_versions( self._vault_url, name, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [KeyProperties._from_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def purge_deleted_key(self, name, **kwargs): # type: (str, **Any) -> None """Permanently deletes a deleted key. Only possible in a vault with soft-delete enabled. Performs an irreversible deletion of the specified key, without possibility for recovery. The operation is not available if the :py:attr:`~azure.keyvault.keys.KeyProperties.recovery_level` does not specify 'Purgeable'. This method is only necessary for purging a key before its :py:attr:`~azure.keyvault.keys.DeletedKey.scheduled_purge_date`. Requires keys/purge permission. :param str name: The name of the deleted key to purge :returns: None :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. code-block:: python # if the vault has soft-delete enabled, purge permanently deletes a deleted key # (with soft-delete disabled, begin_delete_key is permanent) key_client.purge_deleted_key("key-name") """ self._client.purge_deleted_key(vault_base_url=self.vault_url, key_name=name, error_map=_error_map, **kwargs) @distributed_trace def begin_recover_deleted_key(self, name, **kwargs): # type: (str, **Any) -> KeyVaultKey """Recover a deleted key to its latest version. Possible only in a vault with soft-delete enabled. Requires keys/recover permission. When this method returns Key Vault has begun recovering the key. Recovery may take several seconds. This method therefore returns a poller enabling you to wait for recovery to complete. Waiting is only necessary when you want to use the recovered key in another operation immediately. :param str name: The name of the deleted key to recover :returns: A poller for the recovery operation. The poller's `result` method returns the recovered :class:`~azure.keyvault.keys.KeyVaultKey` without waiting for recovery to complete. If you want to use the recovered key immediately, call the poller's `wait` method, which blocks until the key is ready to use. The `wait` method requires keys/get permission. :rtype: ~azure.core.polling.LROPoller[~azure.keyvault.keys.KeyVaultKey] :raises: :class:`~azure.core.exceptions.HttpResponseError` Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START recover_deleted_key] :end-before: [END recover_deleted_key] :language: python :caption: Recover a deleted key :dedent: 8 """ polling_interval = kwargs.pop("_polling_interval", None) if polling_interval is None: polling_interval = 2 recovered_key = KeyVaultKey._from_key_bundle( self._client.recover_deleted_key( vault_base_url=self.vault_url, key_name=name, error_map=_error_map, **kwargs ) ) command = partial(self.get_key, name=name, **kwargs) polling_method = DeleteRecoverPollingMethod( finished=False, command=command, final_resource=recovered_key, interval=polling_interval, ) return KeyVaultOperationPoller(polling_method) @distributed_trace def update_key_properties(self, name, version=None, **kwargs): # type: (str, Optional[str], **Any) -> KeyVaultKey """Change a key's properties (not its cryptographic material). Requires keys/update permission. :param str name: The name of key to update :param str version: (optional) The version of the key to update. If unspecified, the latest version is updated. :keyword key_operations: Allowed key operations :paramtype key_operations: list[~azure.keyvault.keys.KeyOperation or str] :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The updated key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.ResourceNotFoundError` if the key doesn't exist, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START update_key] :end-before: [END update_key] :language: python :caption: Update a key's attributes :dedent: 8 """ enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyUpdateParameters( key_ops=kwargs.pop("key_operations", None), key_attributes=attributes, tags=kwargs.pop("tags", None) ) bundle = self._client.update_key( self.vault_url, name, key_version=version or "", parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def backup_key(self, name, **kwargs): # type: (str, **Any) -> bytes """Back up a key in a protected form useable only by Azure Key Vault. Requires keys/backup permission. This is intended to allow copying a key from one vault to another. Both vaults must be owned by the same Azure subscription. Also, backup / restore cannot be performed across geopolitical boundaries. For example, a backup from a vault in a USA region cannot be restored to a vault in an EU region. :param str name: The name of the key to back up :rtype: bytes :raises: :class:`~azure.core.exceptions.ResourceNotFoundError` if the key doesn't exist, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START backup_key] :end-before: [END backup_key] :language: python :caption: Get a key backup :dedent: 8 """ backup_result = self._client.backup_key(self.vault_url, name, error_map=_error_map, **kwargs) return backup_result.value @distributed_trace def restore_key_backup(self, backup, **kwargs): # type: (bytes, **Any) -> KeyVaultKey """Restore a key backup to the vault. Requires keys/restore permission. This imports all versions of the key, with its name, attributes, and access control policies. If the key's name is already in use, restoring it will fail. Also, the target vault must be owned by the same Microsoft Azure subscription as the source vault. :param bytes backup: A key backup as returned by :func:`backup_key` :returns: The restored key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.ResourceExistsError` if the backed up key's name is already in use, :class:`~azure.core.exceptions.HttpResponseError` for other errors Example: .. literalinclude:: ../tests/test_samples_keys.py :start-after: [START restore_key_backup] :end-before: [END restore_key_backup] :language: python :caption: Restore a key backup :dedent: 8 """ bundle = self._client.restore_key( self.vault_url, parameters=self._models.KeyRestoreParameters(key_bundle_backup=backup), error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def import_key(self, name, key, **kwargs): # type: (str, JsonWebKey, **Any) -> KeyVaultKey """Import a key created externally. Requires keys/import permission. If `name` is already in use, the key will be imported as a new version. :param str name: Name for the imported key :param key: The JSON web key to import :type key: ~azure.keyvault.keys.JsonWebKey :keyword bool hardware_protected: Whether the key should be backed by a hardware security module :keyword bool enabled: Whether the key is enabled for use. :keyword tags: Application specific metadata in the form of key-value pairs. :paramtype tags: dict[str, str] :keyword ~datetime.datetime not_before: Not before date of the key in UTC :keyword ~datetime.datetime expires_on: Expiry date of the key in UTC :returns: The imported key :rtype: ~azure.keyvault.keys.KeyVaultKey :raises: :class:`~azure.core.exceptions.HttpResponseError` """ enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyImportParameters( key=key._to_generated_model(), key_attributes=attributes, hsm=kwargs.pop("hardware_protected", None), tags=kwargs.pop("tags", None) ) bundle = self._client.import_key( self.vault_url, name, parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle)

45.875208

120

0.641507

from functools import partial from azure.core.tracing.decorator import distributed_trace from ._shared import KeyVaultClientBase from ._shared.exceptions import error_map as _error_map from ._shared._polling import DeleteRecoverPollingMethod, KeyVaultOperationPoller from ._models import KeyVaultKey, KeyProperties, DeletedKey try: from typing import TYPE_CHECKING except ImportError: TYPE_CHECKING = False if TYPE_CHECKING: from typing import Any, Optional, Union from azure.core.paging import ItemPaged from ._models import JsonWebKey class KeyClient(KeyVaultClientBase): @distributed_trace def create_key(self, name, key_type, **kwargs): enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyCreateParameters( kty=key_type, key_size=kwargs.pop("size", None), key_attributes=attributes, key_ops=kwargs.pop("key_operations", None), tags=kwargs.pop("tags", None), curve=kwargs.pop("curve", None), public_exponent=kwargs.pop("public_exponent", None) ) bundle = self._client.create_key( vault_base_url=self.vault_url, key_name=name, parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def create_rsa_key(self, name, **kwargs): hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="RSA-HSM" if hsm else "RSA", **kwargs) @distributed_trace def create_ec_key(self, name, **kwargs): hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="EC-HSM" if hsm else "EC", **kwargs) @distributed_trace def create_oct_key(self, name, **kwargs): hsm = kwargs.pop("hardware_protected", False) return self.create_key(name, key_type="oct-HSM" if hsm else "oct", **kwargs) @distributed_trace def begin_delete_key(self, name, **kwargs): polling_interval = kwargs.pop("_polling_interval", None) if polling_interval is None: polling_interval = 2 deleted_key = DeletedKey._from_deleted_key_bundle( self._client.delete_key(self.vault_url, name, error_map=_error_map, **kwargs) ) command = partial(self.get_deleted_key, name=name, **kwargs) polling_method = DeleteRecoverPollingMethod( finished=deleted_key.recovery_id is None, command=command, final_resource=deleted_key, interval=polling_interval, ) return KeyVaultOperationPoller(polling_method) @distributed_trace def get_key(self, name, version=None, **kwargs): bundle = self._client.get_key(self.vault_url, name, key_version=version or "", error_map=_error_map, **kwargs) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def get_deleted_key(self, name, **kwargs): bundle = self._client.get_deleted_key(self.vault_url, name, error_map=_error_map, **kwargs) return DeletedKey._from_deleted_key_bundle(bundle) @distributed_trace def list_deleted_keys(self, **kwargs): return self._client.get_deleted_keys( self._vault_url, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [DeletedKey._from_deleted_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def list_properties_of_keys(self, **kwargs): return self._client.get_keys( self._vault_url, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [KeyProperties._from_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def list_properties_of_key_versions(self, name, **kwargs): return self._client.get_key_versions( self._vault_url, name, maxresults=kwargs.pop("max_page_size", None), cls=lambda objs: [KeyProperties._from_key_item(x) for x in objs], error_map=_error_map, **kwargs ) @distributed_trace def purge_deleted_key(self, name, **kwargs): self._client.purge_deleted_key(vault_base_url=self.vault_url, key_name=name, error_map=_error_map, **kwargs) @distributed_trace def begin_recover_deleted_key(self, name, **kwargs): polling_interval = kwargs.pop("_polling_interval", None) if polling_interval is None: polling_interval = 2 recovered_key = KeyVaultKey._from_key_bundle( self._client.recover_deleted_key( vault_base_url=self.vault_url, key_name=name, error_map=_error_map, **kwargs ) ) command = partial(self.get_key, name=name, **kwargs) polling_method = DeleteRecoverPollingMethod( finished=False, command=command, final_resource=recovered_key, interval=polling_interval, ) return KeyVaultOperationPoller(polling_method) @distributed_trace def update_key_properties(self, name, version=None, **kwargs): enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyUpdateParameters( key_ops=kwargs.pop("key_operations", None), key_attributes=attributes, tags=kwargs.pop("tags", None) ) bundle = self._client.update_key( self.vault_url, name, key_version=version or "", parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def backup_key(self, name, **kwargs): backup_result = self._client.backup_key(self.vault_url, name, error_map=_error_map, **kwargs) return backup_result.value @distributed_trace def restore_key_backup(self, backup, **kwargs): bundle = self._client.restore_key( self.vault_url, parameters=self._models.KeyRestoreParameters(key_bundle_backup=backup), error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle) @distributed_trace def import_key(self, name, key, **kwargs): enabled = kwargs.pop("enabled", None) not_before = kwargs.pop("not_before", None) expires_on = kwargs.pop("expires_on", None) if enabled is not None or not_before is not None or expires_on is not None: attributes = self._models.KeyAttributes(enabled=enabled, not_before=not_before, expires=expires_on) else: attributes = None parameters = self._models.KeyImportParameters( key=key._to_generated_model(), key_attributes=attributes, hsm=kwargs.pop("hardware_protected", None), tags=kwargs.pop("tags", None) ) bundle = self._client.import_key( self.vault_url, name, parameters=parameters, error_map=_error_map, **kwargs ) return KeyVaultKey._from_key_bundle(bundle)

true

f7199764cac1f3e56cc1b5f43ff6f14fb40c8601

3,487

py

Python

tests/test_cookies.py

tripsolutions/pyramid_jwt

320ed080216971467ae5e12b1f9888b50a9a29b7

[ "BSD-2-Clause" ]

null

tests/test_cookies.py

tripsolutions/pyramid_jwt

320ed080216971467ae5e12b1f9888b50a9a29b7

[ "BSD-2-Clause" ]

null

tests/test_cookies.py

tripsolutions/pyramid_jwt

320ed080216971467ae5e12b1f9888b50a9a29b7

[ "BSD-2-Clause" ]

null

import uuid import pytest from pyramid.interfaces import IAuthenticationPolicy from webob import Request from zope.interface.verify import verifyObject from pyramid_jwt.policy import JWTCookieAuthenticationPolicy @pytest.fixture(scope="module") def principal(): return str(uuid.uuid4()) def test_interface(): verifyObject(IAuthenticationPolicy, JWTCookieAuthenticationPolicy("secret")) def test_cookie(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret") token = policy.create_token(principal) cookie = policy.remember(dummy_request, token).pop() assert len(cookie) == 2 header, cookie = cookie assert header == "Set-Cookie" assert len(cookie) > 0 def test_cookie_name(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth") token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) assert name == "auth" def test_secure_cookie(): policy = JWTCookieAuthenticationPolicy("secret", https_only=True) dummy_request = Request.blank("/") token = policy.create_token(str(uuid.uuid4())) _, cookie = policy.remember(dummy_request, token).pop() assert "; secure;" in cookie assert "; HttpOnly" in cookie def test_insecure_cookie(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False) token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() assert "; secure;" not in cookie assert "; HttpOnly" in cookie def test_cookie_decode(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False) token = policy.create_token(principal) header, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert claims["sub"] == principal def test_invalid_cookie_reissue(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False, reissue_time=10) token = "invalid value" header, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert not claims def test_cookie_max_age(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth", expiration=100) _, cookie = policy.remember(dummy_request, principal).pop() _, value = cookie.split("=", 1) _, meta = value.split(";", 1) assert "Max-Age=100" in meta assert "expires" in meta @pytest.mark.freeze_time def test_expired_token(principal, freezer): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth", expiration=1) token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) freezer.tick(delta=2) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert claims == {}

29.058333

88

0.706051

import uuid import pytest from pyramid.interfaces import IAuthenticationPolicy from webob import Request from zope.interface.verify import verifyObject from pyramid_jwt.policy import JWTCookieAuthenticationPolicy @pytest.fixture(scope="module") def principal(): return str(uuid.uuid4()) def test_interface(): verifyObject(IAuthenticationPolicy, JWTCookieAuthenticationPolicy("secret")) def test_cookie(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret") token = policy.create_token(principal) cookie = policy.remember(dummy_request, token).pop() assert len(cookie) == 2 header, cookie = cookie assert header == "Set-Cookie" assert len(cookie) > 0 def test_cookie_name(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth") token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) assert name == "auth" def test_secure_cookie(): policy = JWTCookieAuthenticationPolicy("secret", https_only=True) dummy_request = Request.blank("/") token = policy.create_token(str(uuid.uuid4())) _, cookie = policy.remember(dummy_request, token).pop() assert "; secure;" in cookie assert "; HttpOnly" in cookie def test_insecure_cookie(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False) token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() assert "; secure;" not in cookie assert "; HttpOnly" in cookie def test_cookie_decode(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False) token = policy.create_token(principal) header, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert claims["sub"] == principal def test_invalid_cookie_reissue(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", https_only=False, reissue_time=10) token = "invalid value" header, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert not claims def test_cookie_max_age(principal): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth", expiration=100) _, cookie = policy.remember(dummy_request, principal).pop() _, value = cookie.split("=", 1) _, meta = value.split(";", 1) assert "Max-Age=100" in meta assert "expires" in meta @pytest.mark.freeze_time def test_expired_token(principal, freezer): dummy_request = Request.blank("/") policy = JWTCookieAuthenticationPolicy("secret", cookie_name="auth", expiration=1) token = policy.create_token(principal) _, cookie = policy.remember(dummy_request, token).pop() name, value = cookie.split("=", 1) freezer.tick(delta=2) value, _ = value.split(";", 1) dummy_request.cookies = {name: value} claims = policy.get_claims(dummy_request) assert claims == {}

true

f719982c32746d402b0277ba15a13000bcc77119

94

py

Python

my_classes/.history/ModulesPackages_PackageNamespaces/example3b/main_20210726185941.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

my_classes/.history/ModulesPackages_PackageNamespaces/example3b/main_20210726185941.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

my_classes/.history/ModulesPackages_PackageNamespaces/example3b/main_20210726185941.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

import sys import importer module1 = importer.import_('module1', 'module1_source.py', '.')

13.428571

63

0.723404

import sys import importer module1 = importer.import_('module1', 'module1_source.py', '.')

true

f719994b12c769c14062f52ec104eb9f369ef914

757

py

Python

Exercicios Loop/exercicio 35 - secao 06.py

cristinamais/exercicios_python

8a09b0b68ffaa62d13afb952998e890a79667c7e

[ "MIT" ]

null

Exercicios Loop/exercicio 35 - secao 06.py

cristinamais/exercicios_python

8a09b0b68ffaa62d13afb952998e890a79667c7e

[ "MIT" ]

null

Exercicios Loop/exercicio 35 - secao 06.py

cristinamais/exercicios_python

8a09b0b68ffaa62d13afb952998e890a79667c7e

[ "MIT" ]

null

""" 35 - Faça um programa que some os números impares contidos em um intervalo definido pelo usuário. O usuário define o valor inicial do intervalo e o valor final deste intervalo e o programa deve somar todos os números ímpares contidos neste intervalo (começando por um valor maior que o valor final) deve ser escrito uma mensagem de erro na tela, "Intervalo de valores inválido" e o programa termina. Exemplo de tela de saída: Digite o valor inicial e valor final: 5 10 Soma dos ímpares neste intervalo: 21 """ impar = 0 inicial, final = [int(x) for x in input("Digite o valor inicial e valor final: ").split()] for i in list(range(inicial, final)): if i % 2 != 0: impar = impar + i print(f'A soma dos ímpares neste intervalo é {impar}')

42.055556

104

0.73712

impar = 0 inicial, final = [int(x) for x in input("Digite o valor inicial e valor final: ").split()] for i in list(range(inicial, final)): if i % 2 != 0: impar = impar + i print(f'A soma dos ímpares neste intervalo é {impar}')

true

f71999d547a46a0a1493f4a1de55c28d65419f04

421

py

Python

strava/cli/activity/commands.py

dparret/strava-cli

2426ea7f3fe4580aea352476b261cec31d3f0b11

[ "MIT" ]

null

strava/cli/activity/commands.py

dparret/strava-cli

2426ea7f3fe4580aea352476b261cec31d3f0b11

[ "MIT" ]

null

strava/cli/activity/commands.py

dparret/strava-cli

2426ea7f3fe4580aea352476b261cec31d3f0b11

[ "MIT" ]

null

import click from strava.commands import get_activity, get_constrain_activity, get_weekly_activity, get_lap_activity @click.group(name='activity', help='[GROUP] Get the summary of one or multiple activities.') def cli_activity(): pass cli_activity.add_command(get_activity) cli_activity.add_command(get_constrain_activity) cli_activity.add_command(get_weekly_activity) cli_activity.add_command(get_lap_activity)

28.066667

103

0.83848

import click from strava.commands import get_activity, get_constrain_activity, get_weekly_activity, get_lap_activity @click.group(name='activity', help='[GROUP] Get the summary of one or multiple activities.') def cli_activity(): pass cli_activity.add_command(get_activity) cli_activity.add_command(get_constrain_activity) cli_activity.add_command(get_weekly_activity) cli_activity.add_command(get_lap_activity)

true

f7199aebd95eaaf673576198d3754ac18ebe3786

4,928

py

Python

3.Netdata_package/zipcontents/bin/netdata/usr/libexec/netdata/python.d/cpuidle.chart.py

NordicID/ar8x_samples

2ac78750d6f4ff924628d1e225990f4bfcecfda0

[ "MIT" ]

4

2017-10-17T13:28:28.000Z

2020-12-23T09:46:10.000Z

3.Netdata_package/zipcontents/bin/netdata/usr/libexec/netdata/python.d/cpuidle.chart.py

NordicID/ar8x_samples

2ac78750d6f4ff924628d1e225990f4bfcecfda0

[ "MIT" ]

8

2019-02-09T15:29:12.000Z

2021-03-15T17:45:49.000Z

3.Netdata_package/zipcontents/bin/netdata/usr/libexec/netdata/python.d/cpuidle.chart.py

NordicID/ar8x_samples

2ac78750d6f4ff924628d1e225990f4bfcecfda0

[ "MIT" ]

3

2018-05-24T16:27:43.000Z

2019-08-04T23:39:22.000Z

# -*- coding: utf-8 -*- # Description: cpuidle netdata python.d module # Author: Steven Noonan (tycho) import glob import os import platform import time from base import SimpleService import ctypes syscall = ctypes.CDLL('libc.so.6').syscall # default module values (can be overridden per job in `config`) # update_every = 2 class Service(SimpleService): def __init__(self, configuration=None, name=None): prefix = os.getenv('NETDATA_HOST_PREFIX', "") if prefix.endswith('/'): prefix = prefix[:-1] self.sys_dir = prefix + "/sys/devices/system/cpu" self.schedstat_path = prefix + "/proc/schedstat" SimpleService.__init__(self, configuration=configuration, name=name) self.order = [] self.definitions = {} self._orig_name = "" self.assignment = {} def __gettid(self): # This is horrendous. We need the *thread id* (not the *process id*), # but there's no Python standard library way of doing that. If you need # to enable this module on a non-x86 machine type, you'll have to find # the Linux syscall number for gettid() and add it to the dictionary # below. syscalls = { 'i386': 224, 'x86_64': 186, } if platform.machine() not in syscalls: return None tid = syscall(syscalls[platform.machine()]) return tid def __wake_cpus(self): # Requires Python 3.3+. This will "tickle" each CPU to force it to # update its idle counters. if hasattr(os, 'sched_setaffinity'): pid = self.__gettid() save_affinity = os.sched_getaffinity(pid) for idx in range(0, len(self.assignment)): os.sched_setaffinity(pid, [idx]) os.sched_getaffinity(pid) os.sched_setaffinity(pid, save_affinity) def __read_schedstat(self): cpus = {} for line in open(self.schedstat_path, 'r'): if not line.startswith('cpu'): continue line = line.rstrip().split() cpu = line[0] active_time = line[7] cpus[cpu] = int(active_time) // 1000 return cpus def _get_data(self): results = {} # This line is critical for the stats to update. If we don't "tickle" # all the CPUs, then all the counters stop counting. self.__wake_cpus() # Use the kernel scheduler stats to determine how much time was spent # in C0 (active). schedstat = self.__read_schedstat() for cpu, metrics in self.assignment.items(): update_time = schedstat[cpu] results[cpu + '_active_time'] = update_time for metric, path in metrics.items(): residency = int(open(path, 'r').read()) results[metric] = residency return results def check(self): if self.__gettid() is None: self.error("Cannot get thread ID. Stats would be completely broken.") return False self._orig_name = self.chart_name for path in sorted(glob.glob(self.sys_dir + '/cpu*/cpuidle/state*/name')): # ['', 'sys', 'devices', 'system', 'cpu', 'cpu0', 'cpuidle', 'state3', 'name'] path_elem = path.split('/') cpu = path_elem[-4] state = path_elem[-2] statename = open(path, 'rt').read().rstrip() orderid = '%s_cpuidle' % (cpu,) if orderid not in self.definitions: self.order.append(orderid) active_name = '%s_active_time' % (cpu,) self.definitions[orderid] = { 'options': [None, 'C-state residency', 'time%', 'cpuidle', None, 'stacked'], 'lines': [ [active_name, 'C0 (active)', 'percentage-of-incremental-row', 1, 1], ], } self.assignment[cpu] = {} defid = '%s_%s_time' % (orderid, state) self.definitions[orderid]['lines'].append( [defid, statename, 'percentage-of-incremental-row', 1, 1] ) self.assignment[cpu][defid] = '/'.join(path_elem[:-1] + ['time']) # Sort order by kernel-specified CPU index self.order.sort(key=lambda x: int(x.split('_')[0][3:])) if len(self.definitions) == 0: self.error("couldn't find cstate stats") return False return True def create(self): self.chart_name = "cpu" status = SimpleService.create(self) self.chart_name = self._orig_name return status def update(self, interval): self.chart_name = "cpu" status = SimpleService.update(self, interval=interval) self.chart_name = self._orig_name return status # vim: set ts=4 sts=4 sw=4 et:

34.222222

96

0.565544

import glob import os import platform import time from base import SimpleService import ctypes syscall = ctypes.CDLL('libc.so.6').syscall class Service(SimpleService): def __init__(self, configuration=None, name=None): prefix = os.getenv('NETDATA_HOST_PREFIX', "") if prefix.endswith('/'): prefix = prefix[:-1] self.sys_dir = prefix + "/sys/devices/system/cpu" self.schedstat_path = prefix + "/proc/schedstat" SimpleService.__init__(self, configuration=configuration, name=name) self.order = [] self.definitions = {} self._orig_name = "" self.assignment = {} def __gettid(self): # to enable this module on a non-x86 machine type, you'll have to find syscalls = { 'i386': 224, 'x86_64': 186, } if platform.machine() not in syscalls: return None tid = syscall(syscalls[platform.machine()]) return tid def __wake_cpus(self): if hasattr(os, 'sched_setaffinity'): pid = self.__gettid() save_affinity = os.sched_getaffinity(pid) for idx in range(0, len(self.assignment)): os.sched_setaffinity(pid, [idx]) os.sched_getaffinity(pid) os.sched_setaffinity(pid, save_affinity) def __read_schedstat(self): cpus = {} for line in open(self.schedstat_path, 'r'): if not line.startswith('cpu'): continue line = line.rstrip().split() cpu = line[0] active_time = line[7] cpus[cpu] = int(active_time) // 1000 return cpus def _get_data(self): results = {} # all the CPUs, then all the counters stop counting. self.__wake_cpus() # Use the kernel scheduler stats to determine how much time was spent # in C0 (active). schedstat = self.__read_schedstat() for cpu, metrics in self.assignment.items(): update_time = schedstat[cpu] results[cpu + '_active_time'] = update_time for metric, path in metrics.items(): residency = int(open(path, 'r').read()) results[metric] = residency return results def check(self): if self.__gettid() is None: self.error("Cannot get thread ID. Stats would be completely broken.") return False self._orig_name = self.chart_name for path in sorted(glob.glob(self.sys_dir + '/cpu*/cpuidle/state*/name')): # ['', 'sys', 'devices', 'system', 'cpu', 'cpu0', 'cpuidle', 'state3', 'name'] path_elem = path.split('/') cpu = path_elem[-4] state = path_elem[-2] statename = open(path, 'rt').read().rstrip() orderid = '%s_cpuidle' % (cpu,) if orderid not in self.definitions: self.order.append(orderid) active_name = '%s_active_time' % (cpu,) self.definitions[orderid] = { 'options': [None, 'C-state residency', 'time%', 'cpuidle', None, 'stacked'], 'lines': [ [active_name, 'C0 (active)', 'percentage-of-incremental-row', 1, 1], ], } self.assignment[cpu] = {} defid = '%s_%s_time' % (orderid, state) self.definitions[orderid]['lines'].append( [defid, statename, 'percentage-of-incremental-row', 1, 1] ) self.assignment[cpu][defid] = '/'.join(path_elem[:-1] + ['time']) # Sort order by kernel-specified CPU index self.order.sort(key=lambda x: int(x.split('_')[0][3:])) if len(self.definitions) == 0: self.error("couldn't find cstate stats") return False return True def create(self): self.chart_name = "cpu" status = SimpleService.create(self) self.chart_name = self._orig_name return status def update(self, interval): self.chart_name = "cpu" status = SimpleService.update(self, interval=interval) self.chart_name = self._orig_name return status

true

f7199b4c4ff664a5de4259b1a156f514807f75ec

358

py

Python

Ch6/picnic_table.py

dmdinh22/ATBS

3ddd331757cc434faa5f27997b178f8a39e3b5d2

[ "MIT" ]

null

Ch6/picnic_table.py

dmdinh22/ATBS

3ddd331757cc434faa5f27997b178f8a39e3b5d2

[ "MIT" ]

null

Ch6/picnic_table.py

dmdinh22/ATBS

3ddd331757cc434faa5f27997b178f8a39e3b5d2

[ "MIT" ]

null

def print_picnic(itemsDict, leftWidth, rightWidth): print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-')) for k, v in itemsDict.items(): print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth)) picnic_items = {'sandwiches': 4, 'apples': 12, 'cups': 4, 'cookies': 8000} print_picnic(picnic_items, 12, 5) print_picnic(picnic_items, 20, 6)

44.75

74

0.684358

def print_picnic(itemsDict, leftWidth, rightWidth): print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-')) for k, v in itemsDict.items(): print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth)) picnic_items = {'sandwiches': 4, 'apples': 12, 'cups': 4, 'cookies': 8000} print_picnic(picnic_items, 12, 5) print_picnic(picnic_items, 20, 6)

true

f7199b5ab43ce56280af5d2f042fc3bf18ea33f9

241

py

Python

examples/externalpyproc/test.py

scala-steward/prox

fdcab42cbdbe6a1cf4d9ffde796657d75dac6235

[ "Apache-2.0" ]

95

2018-01-19T00:09:22.000Z

2022-02-05T15:22:59.000Z

examples/externalpyproc/test.py

scala-steward/prox

fdcab42cbdbe6a1cf4d9ffde796657d75dac6235

[ "Apache-2.0" ]

312

2017-11-22T19:41:41.000Z

2022-03-30T13:31:06.000Z

examples/externalpyproc/test.py

scala-steward/prox

fdcab42cbdbe6a1cf4d9ffde796657d75dac6235

[ "Apache-2.0" ]

6

2018-05-02T10:30:44.000Z

2020-10-17T17:06:11.000Z

import sys def run(): stop = False while not stop: line = sys.stdin.readline().strip() if len(line) == 0: stop = True else: print line + "!?!?" sys.stdout.flush() run()

15.0625

43

0.452282

import sys def run(): stop = False while not stop: line = sys.stdin.readline().strip() if len(line) == 0: stop = True else: print line + "!?!?" sys.stdout.flush() run()

false

true

f7199b6017b06f096a888ac161723abab17bf6d1

80

py

Python

notebooks/_solutions/13-raster-processing42.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

58

2020-10-09T10:10:59.000Z

2022-03-07T14:58:07.000Z

notebooks/_solutions/13-raster-processing42.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

24

2020-09-30T19:57:14.000Z

2021-10-05T07:21:09.000Z

notebooks/_solutions/13-raster-processing42.py

jorisvandenbossche/DS-python-geospatial

893a12edc5c203a75815f6dcb5f1e18c577c8cd5

[ "BSD-3-Clause" ]

19

2020-10-05T09:32:18.000Z

2022-03-20T00:09:14.000Z

green = geopandas.read_file("data/gent/vector/parken-gent.geojson") green.head()

40

67

0.7875

green = geopandas.read_file("data/gent/vector/parken-gent.geojson") green.head()

true

f7199bd2f937de5095eb9d5c4cafe386b70039eb

1,325

py

Python

kale/util/ints.py

inan0812/kale-blockchain

1b502fe21a4be10b4db0171c3a7030079dcefa1b

[ "Apache-2.0" ]

null

kale/util/ints.py

inan0812/kale-blockchain

1b502fe21a4be10b4db0171c3a7030079dcefa1b

[ "Apache-2.0" ]

null

kale/util/ints.py

inan0812/kale-blockchain

1b502fe21a4be10b4db0171c3a7030079dcefa1b

[ "Apache-2.0" ]

null

from typing import Any, BinaryIO from kale.util.struct_stream import StructStream class int8(StructStream): PACK = "!b" class uint8(StructStream): PACK = "!B" class int16(StructStream): PACK = "!h" class uint16(StructStream): PACK = "!H" class int32(StructStream): PACK = "!l" class uint32(StructStream): PACK = "!L" class int64(StructStream): PACK = "!q" class uint64(StructStream): PACK = "!Q" class uint128(int): @classmethod def parse(cls, f: BinaryIO) -> Any: read_bytes = f.read(16) assert len(read_bytes) == 16 n = int.from_bytes(read_bytes, "big", signed=False) assert n <= (2 ** 128) - 1 and n >= 0 return cls(n) def stream(self, f): assert self <= (2 ** 128) - 1 and self >= 0 f.write(self.to_bytes(16, "big", signed=False)) class int512(int): # Uses 65 bytes to fit in the sign bit @classmethod def parse(cls, f: BinaryIO) -> Any: read_bytes = f.read(65) assert len(read_bytes) == 65 n = int.from_bytes(read_bytes, "big", signed=True) assert n <= (2 ** 512) - 1 and n >= -(2 ** 512) return cls(n) def stream(self, f): assert self <= (2 ** 512) - 1 and self >= -(2 ** 512) f.write(self.to_bytes(65, "big", signed=True))

20.384615

61

0.577358

from typing import Any, BinaryIO from kale.util.struct_stream import StructStream class int8(StructStream): PACK = "!b" class uint8(StructStream): PACK = "!B" class int16(StructStream): PACK = "!h" class uint16(StructStream): PACK = "!H" class int32(StructStream): PACK = "!l" class uint32(StructStream): PACK = "!L" class int64(StructStream): PACK = "!q" class uint64(StructStream): PACK = "!Q" class uint128(int): @classmethod def parse(cls, f: BinaryIO) -> Any: read_bytes = f.read(16) assert len(read_bytes) == 16 n = int.from_bytes(read_bytes, "big", signed=False) assert n <= (2 ** 128) - 1 and n >= 0 return cls(n) def stream(self, f): assert self <= (2 ** 128) - 1 and self >= 0 f.write(self.to_bytes(16, "big", signed=False)) class int512(int): @classmethod def parse(cls, f: BinaryIO) -> Any: read_bytes = f.read(65) assert len(read_bytes) == 65 n = int.from_bytes(read_bytes, "big", signed=True) assert n <= (2 ** 512) - 1 and n >= -(2 ** 512) return cls(n) def stream(self, f): assert self <= (2 ** 512) - 1 and self >= -(2 ** 512) f.write(self.to_bytes(65, "big", signed=True))

true

f7199cc541ada1d15fae75b62fc319d80df9c669

428

py

Python

src/upload/admin.py

bpilkerton/vendor-upload

ba43b620340c9fffd26cf6a8ee5bc9f97ffabda1

[ "Unlicense" ]

null

src/upload/admin.py

bpilkerton/vendor-upload

ba43b620340c9fffd26cf6a8ee5bc9f97ffabda1

[ "Unlicense" ]

null

src/upload/admin.py

bpilkerton/vendor-upload

ba43b620340c9fffd26cf6a8ee5bc9f97ffabda1

[ "Unlicense" ]

null

from django.contrib import admin from .models import Upload,VendorData class UploadAdmin(admin.ModelAdmin): list_display = ('id','uploaded_file','uploaded_date') class VendordataAdmin(admin.ModelAdmin): list_display = ('id','sub_id','first_name','last_name','status') admin.site.site_header = "Subscription Fulfillment Upload" admin.site.register(Upload, UploadAdmin) admin.site.register(VendorData, VendordataAdmin)

32.923077

68

0.785047

from django.contrib import admin from .models import Upload,VendorData class UploadAdmin(admin.ModelAdmin): list_display = ('id','uploaded_file','uploaded_date') class VendordataAdmin(admin.ModelAdmin): list_display = ('id','sub_id','first_name','last_name','status') admin.site.site_header = "Subscription Fulfillment Upload" admin.site.register(Upload, UploadAdmin) admin.site.register(VendorData, VendordataAdmin)

true

f7199d3d3a6e51cfe86975c9d26b03a1bb377073

228

py

Python

Django Rest Class Based API view/Person/admin.py

abhisheksahu92/Django-Rest-Framework

45ddafb93ed1f2e232d2f537f144bf79cb30bf3d

[ "MIT" ]

null

Django Rest Class Based API view/Person/admin.py

abhisheksahu92/Django-Rest-Framework

45ddafb93ed1f2e232d2f537f144bf79cb30bf3d

[ "MIT" ]

null

Django Rest Class Based API view/Person/admin.py

abhisheksahu92/Django-Rest-Framework

45ddafb93ed1f2e232d2f537f144bf79cb30bf3d

[ "MIT" ]

null

from django.contrib import admin from .models import Person # Register your models here. @admin.register(Person) class PersonModel(admin.ModelAdmin): list_display = ['first_name','last_name','email','phone','date_of_birth']

32.571429

77

0.77193

from django.contrib import admin from .models import Person @admin.register(Person) class PersonModel(admin.ModelAdmin): list_display = ['first_name','last_name','email','phone','date_of_birth']

true

f7199de7d432eb5ce623737f74e8d53b751b22d7

9,267

py

Python

ckan/views/admin.py

robin-NEC/ckan

71a82c4b0bb499fd3a6d1ccfd038b2231f50f92a

[ "BSD-3-Clause" ]

1

2021-10-01T12:47:19.000Z

ckan/views/admin.py

robin-NEC/ckan

71a82c4b0bb499fd3a6d1ccfd038b2231f50f92a

[ "BSD-3-Clause" ]

null

ckan/views/admin.py

robin-NEC/ckan

71a82c4b0bb499fd3a6d1ccfd038b2231f50f92a

[ "BSD-3-Clause" ]

2

2018-01-21T17:03:08.000Z

2019-07-23T08:49:52.000Z

# encoding: utf-8 from __future__ import annotations import logging from typing import Any, Union, cast, List from flask import Blueprint from flask.views import MethodView from flask.wrappers import Response import ckan.lib.app_globals as app_globals import ckan.lib.base as base import ckan.lib.helpers as h import ckan.lib.navl.dictization_functions as dict_fns import ckan.logic as logic import ckan.model as model import ckan.logic.schema from ckan.common import g, _, config, request from ckan.views.home import CACHE_PARAMETERS from ckan.types import Context, Query log = logging.getLogger(__name__) admin = Blueprint(u'admin', __name__, url_prefix=u'/ckan-admin') def _get_sysadmins() -> "Query[model.User]": q = model.Session.query(model.User).filter( # type_ignore_reason: incomplete SQLAlchemy types model.User.sysadmin.is_(True), # type: ignore model.User.state == u'active') return q def _get_config_options() -> dict[str, list[dict[str, str]]]: homepages = [{ u'value': u'1', u'text': (u'Introductory area, search, featured' u' group and featured organization') }, { u'value': u'2', u'text': (u'Search, stats, introductory area, ' u'featured organization and featured group') }, { u'value': u'3', u'text': u'Search, introductory area and stats' }] return dict(homepages=homepages) def _get_config_items() -> list[str]: return [ u'ckan.site_title', u'ckan.main_css', u'ckan.site_description', u'ckan.site_logo', u'ckan.site_about', u'ckan.site_intro_text', u'ckan.site_custom_css', u'ckan.homepage_style' ] @admin.before_request def before_request() -> None: try: context = cast( Context, {"model": model, "user": g.user, "auth_user_obj": g.userobj} ) logic.check_access(u'sysadmin', context) except logic.NotAuthorized: base.abort(403, _(u'Need to be system administrator to administer')) def index() -> str: data = dict(sysadmins=[a.name for a in _get_sysadmins()]) return base.render(u'admin/index.html', extra_vars=data) class ResetConfigView(MethodView): def get(self) -> Union[str, Response]: if u'cancel' in request.args: return h.redirect_to(u'admin.config') return base.render(u'admin/confirm_reset.html', extra_vars={}) def post(self) -> Response: # remove sys info items for item in _get_config_items(): model.delete_system_info(item) # reset to values in config app_globals.reset() return h.redirect_to(u'admin.config') class ConfigView(MethodView): def get(self) -> str: items = _get_config_options() schema = ckan.logic.schema.update_configuration_schema() data = {} for key in schema: data[key] = config.get(key) vars: dict[str, Any] = dict(data=data, errors={}, **items) return base.render(u'admin/config.html', extra_vars=vars) def post(self) -> Union[str, Response]: try: req: dict[str, Any] = request.form.copy() req.update(request.files.to_dict()) data_dict = logic.clean_dict( dict_fns.unflatten( logic.tuplize_dict( logic.parse_params(req, ignore_keys=CACHE_PARAMETERS)))) del data_dict['save'] data = logic.get_action(u'config_option_update')({ u'user': g.user }, data_dict) except logic.ValidationError as e: items = _get_config_options() data = request.form errors = e.error_dict error_summary = e.error_summary vars = dict(data=data, errors=errors, error_summary=error_summary, form_items=items, **items) return base.render(u'admin/config.html', extra_vars=vars) return h.redirect_to(u'admin.config') class TrashView(MethodView): def __init__(self): self.deleted_packages = self._get_deleted_datasets() self.deleted_orgs = model.Session.query(model.Group).filter_by( state=model.State.DELETED, is_organization=True) self.deleted_groups = model.Session.query(model.Group).filter_by( state=model.State.DELETED, is_organization=False) self.deleted_entities = { u'package': self.deleted_packages, u'organization': self.deleted_orgs, u'group': self.deleted_groups } self.messages = { u'confirm': { u'all': _(u'Are you sure you want to purge everything?'), u'package': _(u'Are you sure you want to purge datasets?'), u'organization': _(u'Are you sure you want to purge organizations?'), u'group': _(u'Are you sure you want to purge groups?') }, u'success': { u'package': _(u'{number} datasets have been purged'), u'organization': _(u'{number} organizations have been purged'), u'group': _(u'{number} groups have been purged') }, u'empty': { u'package': _(u'There are no datasets to purge'), u'organization': _(u'There are no organizations to purge'), u'group': _(u'There are no groups to purge') } } def _get_deleted_datasets( self ) -> Union["Query[model.Package]", List[Any]]: if config.get_value('ckan.search.remove_deleted_packages'): return self._get_deleted_datasets_from_db() else: return self._get_deleted_datasets_from_search_index() def _get_deleted_datasets_from_db(self) -> "Query[model.Package]": return model.Session.query( model.Package ).filter_by( state=model.State.DELETED ) def _get_deleted_datasets_from_search_index(self) -> List[Any]: package_search = logic.get_action('package_search') search_params = { 'fq': '+state:deleted', 'include_private': True, } base_results = package_search( {'ignore_auth': True}, search_params ) return base_results['results'] def get(self) -> str: ent_type = request.args.get(u'name') if ent_type: return base.render(u'admin/snippets/confirm_delete.html', extra_vars={ u'ent_type': ent_type, u'messages': self.messages}) data = dict(data=self.deleted_entities, messages=self.messages) return base.render(u'admin/trash.html', extra_vars=data) def post(self) -> Response: if u'cancel' in request.form: return h.redirect_to(u'admin.trash') req_action = request.form.get(u'action', '') if req_action == u'all': self.purge_all() elif req_action in (u'package', u'organization', u'group'): self.purge_entity(req_action) else: h.flash_error(_(u'Action not implemented.')) return h.redirect_to(u'admin.trash') def purge_all(self): actions = (u'dataset_purge', u'group_purge', u'organization_purge') entities = ( self.deleted_packages, self.deleted_groups, self.deleted_orgs ) for action, deleted_entities in zip(actions, entities): for entity in deleted_entities: ent_id = entity.id if hasattr(entity, 'id') \ else entity['id'] # type: ignore logic.get_action(action)( {u'user': g.user}, {u'id': ent_id} ) model.Session.remove() h.flash_success(_(u'Massive purge complete')) def purge_entity(self, ent_type: str): entities = self.deleted_entities[ent_type] number = len(entities) if type(entities) == list else entities.count() for ent in entities: entity_id = ent.id if hasattr(ent, 'id') else ent['id'] logic.get_action(self._get_purge_action(ent_type))( {u'user': g.user}, {u'id': entity_id} ) model.Session.remove() h.flash_success(self.messages[u'success'][ent_type].format( number=number )) @staticmethod def _get_purge_action(ent_type: str) -> str: actions = { "package": "dataset_purge", "organization": "organization_purge", "group": "group_purge", } return actions[ent_type] admin.add_url_rule( u'/', view_func=index, methods=['GET'], strict_slashes=False ) admin.add_url_rule(u'/reset_config', view_func=ResetConfigView.as_view(str(u'reset_config'))) admin.add_url_rule(u'/config', view_func=ConfigView.as_view(str(u'config'))) admin.add_url_rule(u'/trash', view_func=TrashView.as_view(str(u'trash')))

33.698182

79

0.589403

from __future__ import annotations import logging from typing import Any, Union, cast, List from flask import Blueprint from flask.views import MethodView from flask.wrappers import Response import ckan.lib.app_globals as app_globals import ckan.lib.base as base import ckan.lib.helpers as h import ckan.lib.navl.dictization_functions as dict_fns import ckan.logic as logic import ckan.model as model import ckan.logic.schema from ckan.common import g, _, config, request from ckan.views.home import CACHE_PARAMETERS from ckan.types import Context, Query log = logging.getLogger(__name__) admin = Blueprint(u'admin', __name__, url_prefix=u'/ckan-admin') def _get_sysadmins() -> "Query[model.User]": q = model.Session.query(model.User).filter( model.User.sysadmin.is_(True), model.User.state == u'active') return q def _get_config_options() -> dict[str, list[dict[str, str]]]: homepages = [{ u'value': u'1', u'text': (u'Introductory area, search, featured' u' group and featured organization') }, { u'value': u'2', u'text': (u'Search, stats, introductory area, ' u'featured organization and featured group') }, { u'value': u'3', u'text': u'Search, introductory area and stats' }] return dict(homepages=homepages) def _get_config_items() -> list[str]: return [ u'ckan.site_title', u'ckan.main_css', u'ckan.site_description', u'ckan.site_logo', u'ckan.site_about', u'ckan.site_intro_text', u'ckan.site_custom_css', u'ckan.homepage_style' ] @admin.before_request def before_request() -> None: try: context = cast( Context, {"model": model, "user": g.user, "auth_user_obj": g.userobj} ) logic.check_access(u'sysadmin', context) except logic.NotAuthorized: base.abort(403, _(u'Need to be system administrator to administer')) def index() -> str: data = dict(sysadmins=[a.name for a in _get_sysadmins()]) return base.render(u'admin/index.html', extra_vars=data) class ResetConfigView(MethodView): def get(self) -> Union[str, Response]: if u'cancel' in request.args: return h.redirect_to(u'admin.config') return base.render(u'admin/confirm_reset.html', extra_vars={}) def post(self) -> Response: for item in _get_config_items(): model.delete_system_info(item) app_globals.reset() return h.redirect_to(u'admin.config') class ConfigView(MethodView): def get(self) -> str: items = _get_config_options() schema = ckan.logic.schema.update_configuration_schema() data = {} for key in schema: data[key] = config.get(key) vars: dict[str, Any] = dict(data=data, errors={}, **items) return base.render(u'admin/config.html', extra_vars=vars) def post(self) -> Union[str, Response]: try: req: dict[str, Any] = request.form.copy() req.update(request.files.to_dict()) data_dict = logic.clean_dict( dict_fns.unflatten( logic.tuplize_dict( logic.parse_params(req, ignore_keys=CACHE_PARAMETERS)))) del data_dict['save'] data = logic.get_action(u'config_option_update')({ u'user': g.user }, data_dict) except logic.ValidationError as e: items = _get_config_options() data = request.form errors = e.error_dict error_summary = e.error_summary vars = dict(data=data, errors=errors, error_summary=error_summary, form_items=items, **items) return base.render(u'admin/config.html', extra_vars=vars) return h.redirect_to(u'admin.config') class TrashView(MethodView): def __init__(self): self.deleted_packages = self._get_deleted_datasets() self.deleted_orgs = model.Session.query(model.Group).filter_by( state=model.State.DELETED, is_organization=True) self.deleted_groups = model.Session.query(model.Group).filter_by( state=model.State.DELETED, is_organization=False) self.deleted_entities = { u'package': self.deleted_packages, u'organization': self.deleted_orgs, u'group': self.deleted_groups } self.messages = { u'confirm': { u'all': _(u'Are you sure you want to purge everything?'), u'package': _(u'Are you sure you want to purge datasets?'), u'organization': _(u'Are you sure you want to purge organizations?'), u'group': _(u'Are you sure you want to purge groups?') }, u'success': { u'package': _(u'{number} datasets have been purged'), u'organization': _(u'{number} organizations have been purged'), u'group': _(u'{number} groups have been purged') }, u'empty': { u'package': _(u'There are no datasets to purge'), u'organization': _(u'There are no organizations to purge'), u'group': _(u'There are no groups to purge') } } def _get_deleted_datasets( self ) -> Union["Query[model.Package]", List[Any]]: if config.get_value('ckan.search.remove_deleted_packages'): return self._get_deleted_datasets_from_db() else: return self._get_deleted_datasets_from_search_index() def _get_deleted_datasets_from_db(self) -> "Query[model.Package]": return model.Session.query( model.Package ).filter_by( state=model.State.DELETED ) def _get_deleted_datasets_from_search_index(self) -> List[Any]: package_search = logic.get_action('package_search') search_params = { 'fq': '+state:deleted', 'include_private': True, } base_results = package_search( {'ignore_auth': True}, search_params ) return base_results['results'] def get(self) -> str: ent_type = request.args.get(u'name') if ent_type: return base.render(u'admin/snippets/confirm_delete.html', extra_vars={ u'ent_type': ent_type, u'messages': self.messages}) data = dict(data=self.deleted_entities, messages=self.messages) return base.render(u'admin/trash.html', extra_vars=data) def post(self) -> Response: if u'cancel' in request.form: return h.redirect_to(u'admin.trash') req_action = request.form.get(u'action', '') if req_action == u'all': self.purge_all() elif req_action in (u'package', u'organization', u'group'): self.purge_entity(req_action) else: h.flash_error(_(u'Action not implemented.')) return h.redirect_to(u'admin.trash') def purge_all(self): actions = (u'dataset_purge', u'group_purge', u'organization_purge') entities = ( self.deleted_packages, self.deleted_groups, self.deleted_orgs ) for action, deleted_entities in zip(actions, entities): for entity in deleted_entities: ent_id = entity.id if hasattr(entity, 'id') \ else entity['id'] logic.get_action(action)( {u'user': g.user}, {u'id': ent_id} ) model.Session.remove() h.flash_success(_(u'Massive purge complete')) def purge_entity(self, ent_type: str): entities = self.deleted_entities[ent_type] number = len(entities) if type(entities) == list else entities.count() for ent in entities: entity_id = ent.id if hasattr(ent, 'id') else ent['id'] logic.get_action(self._get_purge_action(ent_type))( {u'user': g.user}, {u'id': entity_id} ) model.Session.remove() h.flash_success(self.messages[u'success'][ent_type].format( number=number )) @staticmethod def _get_purge_action(ent_type: str) -> str: actions = { "package": "dataset_purge", "organization": "organization_purge", "group": "group_purge", } return actions[ent_type] admin.add_url_rule( u'/', view_func=index, methods=['GET'], strict_slashes=False ) admin.add_url_rule(u'/reset_config', view_func=ResetConfigView.as_view(str(u'reset_config'))) admin.add_url_rule(u'/config', view_func=ConfigView.as_view(str(u'config'))) admin.add_url_rule(u'/trash', view_func=TrashView.as_view(str(u'trash')))

true

f7199e3af009f350705cd13527301b007761a105

2,956

py

Python

examples/interface/CP.py

jeffhammond/Elemental

a9e6236ce9d92dd56c7d3cd5ffd52f796a35cd0c

[ "Apache-2.0" ]

null

examples/interface/CP.py

jeffhammond/Elemental

a9e6236ce9d92dd56c7d3cd5ffd52f796a35cd0c

[ "Apache-2.0" ]

null

examples/interface/CP.py

jeffhammond/Elemental

a9e6236ce9d92dd56c7d3cd5ffd52f796a35cd0c

[ "Apache-2.0" ]

null

# # Copyright (c) 2009-2016, Jack Poulson # All rights reserved. # # This file is part of Elemental and is under the BSD 2-Clause License, # which can be found in the LICENSE file in the root directory, or at # http://opensource.org/licenses/BSD-2-Clause # import El n0 = 50 n1 = 50 display = False worldRank = El.mpi.WorldRank() worldSize = El.mpi.WorldSize() # Stack two 2D finite-difference matrices on top of each other # and make the last column dense def StackedFD2D(N0,N1): A = El.DistSparseMatrix() height = 2*N0*N1 width = N0*N1 A.Resize(height,width) localHeight = A.LocalHeight() A.Reserve(6*localHeight) for sLoc in xrange(localHeight): s = A.GlobalRow(sLoc) if s < N0*N1: x0 = s % N0 x1 = s / N0 A.QueueLocalUpdate( sLoc, s, 11 ) if x0 > 0: A.QueueLocalUpdate( sLoc, s-1, -1 ) if x0+1 < N0: A.QueueLocalUpdate( sLoc, s+1, 2 ) if x1 > 0: A.QueueLocalUpdate( sLoc, s-N0, -3 ) if x1+1 < N1: A.QueueLocalUpdate( sLoc, s+N0, 4 ) else: sRel = s-N0*N1 x0 = sRel % N0 x1 = sRel / N0 A.QueueLocalUpdate( sLoc, sRel, -2 ) if x0 > 0: A.QueueLocalUpdate( sLoc, sRel-1, -1 ) if x0+1 < N0: A.QueueLocalUpdate( sLoc, sRel+1, -2 ) if x1 > 0: A.QueueLocalUpdate( sLoc, sRel-N0, -3 ) if x1+1 < N1: A.QueueLocalUpdate( sLoc, sRel+N0, 3 ) # The dense last column A.QueueLocalUpdate( sLoc, width-1, -10/height ); A.ProcessQueues() return A A = StackedFD2D(n0,n1) b = El.DistMultiVec() El.Gaussian( b, 2*n0*n1, 1 ) if display: El.Display( A, "A" ) El.Display( b, "b" ) ctrl = El.LPAffineCtrl_d() ctrl.mehrotraCtrl.progress = True ctrl.mehrotraCtrl.solveCtrl.progress = True startCP = El.mpi.Time() x = El.CP( A, b, ctrl ) endCP = El.mpi.Time() if worldRank == 0: print "CP time:", endCP-startCP, "seconds" if display: El.Display( x, "x" ) bTwoNorm = El.Nrm2( b ) bInfNorm = El.MaxNorm( b ) r = El.DistMultiVec() El.Copy( b, r ) El.Multiply( El.NORMAL, -1., A, x, 1., r ) if display: El.Display( r, "r" ) rTwoNorm = El.Nrm2( r ) rInfNorm = El.MaxNorm( r ) if worldRank == 0: print "|| b ||_2 =", bTwoNorm print "|| b ||_oo =", bInfNorm print "|| A x - b ||_2 =", rTwoNorm print "|| A x - b ||_oo =", rInfNorm startLS = El.mpi.Time() xLS = El.LeastSquares(A,b) endLS = El.mpi.Time() if worldRank == 0: print "LS time:", endLS-startLS, "seconds" if display: El.Display( xLS, "x_{LS}" ) rLS = El.DistMultiVec() El.Copy( b, rLS ) El.Multiply( El.NORMAL, -1., A, xLS, 1., rLS ) if display: El.Display( rLS, "A x_{LS} - b" ) rLSTwoNorm = El.Nrm2(rLS) rLSInfNorm = El.MaxNorm(rLS) if worldRank == 0: print "|| A x_{LS} - b ||_2 =", rLSTwoNorm print "|| A x_{LS} - b ||_oo =", rLSInfNorm # Require the user to press a button before the figures are closed El.Finalize() if worldSize == 1: raw_input('Press Enter to exit')

25.704348

73

0.609269

import El n0 = 50 n1 = 50 display = False worldRank = El.mpi.WorldRank() worldSize = El.mpi.WorldSize() def StackedFD2D(N0,N1): A = El.DistSparseMatrix() height = 2*N0*N1 width = N0*N1 A.Resize(height,width) localHeight = A.LocalHeight() A.Reserve(6*localHeight) for sLoc in xrange(localHeight): s = A.GlobalRow(sLoc) if s < N0*N1: x0 = s % N0 x1 = s / N0 A.QueueLocalUpdate( sLoc, s, 11 ) if x0 > 0: A.QueueLocalUpdate( sLoc, s-1, -1 ) if x0+1 < N0: A.QueueLocalUpdate( sLoc, s+1, 2 ) if x1 > 0: A.QueueLocalUpdate( sLoc, s-N0, -3 ) if x1+1 < N1: A.QueueLocalUpdate( sLoc, s+N0, 4 ) else: sRel = s-N0*N1 x0 = sRel % N0 x1 = sRel / N0 A.QueueLocalUpdate( sLoc, sRel, -2 ) if x0 > 0: A.QueueLocalUpdate( sLoc, sRel-1, -1 ) if x0+1 < N0: A.QueueLocalUpdate( sLoc, sRel+1, -2 ) if x1 > 0: A.QueueLocalUpdate( sLoc, sRel-N0, -3 ) if x1+1 < N1: A.QueueLocalUpdate( sLoc, sRel+N0, 3 ) A.QueueLocalUpdate( sLoc, width-1, -10/height ); A.ProcessQueues() return A A = StackedFD2D(n0,n1) b = El.DistMultiVec() El.Gaussian( b, 2*n0*n1, 1 ) if display: El.Display( A, "A" ) El.Display( b, "b" ) ctrl = El.LPAffineCtrl_d() ctrl.mehrotraCtrl.progress = True ctrl.mehrotraCtrl.solveCtrl.progress = True startCP = El.mpi.Time() x = El.CP( A, b, ctrl ) endCP = El.mpi.Time() if worldRank == 0: print "CP time:", endCP-startCP, "seconds" if display: El.Display( x, "x" ) bTwoNorm = El.Nrm2( b ) bInfNorm = El.MaxNorm( b ) r = El.DistMultiVec() El.Copy( b, r ) El.Multiply( El.NORMAL, -1., A, x, 1., r ) if display: El.Display( r, "r" ) rTwoNorm = El.Nrm2( r ) rInfNorm = El.MaxNorm( r ) if worldRank == 0: print "|| b ||_2 =", bTwoNorm print "|| b ||_oo =", bInfNorm print "|| A x - b ||_2 =", rTwoNorm print "|| A x - b ||_oo =", rInfNorm startLS = El.mpi.Time() xLS = El.LeastSquares(A,b) endLS = El.mpi.Time() if worldRank == 0: print "LS time:", endLS-startLS, "seconds" if display: El.Display( xLS, "x_{LS}" ) rLS = El.DistMultiVec() El.Copy( b, rLS ) El.Multiply( El.NORMAL, -1., A, xLS, 1., rLS ) if display: El.Display( rLS, "A x_{LS} - b" ) rLSTwoNorm = El.Nrm2(rLS) rLSInfNorm = El.MaxNorm(rLS) if worldRank == 0: print "|| A x_{LS} - b ||_2 =", rLSTwoNorm print "|| A x_{LS} - b ||_oo =", rLSInfNorm El.Finalize() if worldSize == 1: raw_input('Press Enter to exit')

false

true

f7199e876ff568e200ceb2dbf17c8e228d670c71

1,919

py

Python

test/Entry.py

EmanueleCannizzaro/scons

6baa4e65cdf4df6951473545b69435711864e509

[ "MIT" ]

1

2019-09-18T06:37:02.000Z

test/Entry.py

EmanueleCannizzaro/scons

6baa4e65cdf4df6951473545b69435711864e509

[ "MIT" ]

null

test/Entry.py

EmanueleCannizzaro/scons

6baa4e65cdf4df6951473545b69435711864e509

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright (c) 2001 - 2016 The SCons Foundation # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # __revision__ = "test/Entry.py rel_2.5.1:3735:9dc6cee5c168 2016/11/03 14:02:02 bdbaddog" """ Verify that the Entry() global function and environment method work correctly, and that the former does not try to expand construction variables. """ import TestSCons test = TestSCons.TestSCons() test.write('SConstruct', """ env = Environment(FOO = 'fff', BAR = 'bbb') print Entry('ddd') print Entry('$FOO') print Entry('${BAR}_$BAR') print env.Entry('eee') print env.Entry('$FOO') print env.Entry('${BAR}_$BAR') """) test.run(stdout = test.wrap_stdout(read_str = """\ ddd $FOO ${BAR}_$BAR eee fff bbb_bbb """, build_str = """\ scons: `.' is up to date. """)) test.pass_test() # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

29.523077

87

0.738927

__revision__ = "test/Entry.py rel_2.5.1:3735:9dc6cee5c168 2016/11/03 14:02:02 bdbaddog" import TestSCons test = TestSCons.TestSCons() test.write('SConstruct', """ env = Environment(FOO = 'fff', BAR = 'bbb') print Entry('ddd') print Entry('$FOO') print Entry('${BAR}_$BAR') print env.Entry('eee') print env.Entry('$FOO') print env.Entry('${BAR}_$BAR') """) test.run(stdout = test.wrap_stdout(read_str = """\ ddd $FOO ${BAR}_$BAR eee fff bbb_bbb """, build_str = """\ scons: `.' is up to date. """)) test.pass_test() # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

true

f719a19921e0717fd82f09f6ab40bd54a1718ceb

18,564

py

Python

sciencebeam_parser/models/model.py

elifesciences/sciencebeam-parser

66964f283612b8d6fa8a23ad8790292c1ec07651

[ "MIT" ]

13

2021-08-04T12:11:17.000Z

2022-03-28T20:41:20.000Z

sciencebeam_parser/models/model.py

elifesciences/sciencebeam-parser

66964f283612b8d6fa8a23ad8790292c1ec07651

[ "MIT" ]

33

2021-08-05T08:37:59.000Z

2022-03-29T18:42:09.000Z

sciencebeam_parser/models/model.py

elifesciences/sciencebeam-parser

66964f283612b8d6fa8a23ad8790292c1ec07651

[ "MIT" ]

1

2022-01-05T14:53:06.000Z

import logging from abc import ABC, abstractmethod from collections import defaultdict from dataclasses import dataclass, field from typing import ( Callable, Dict, Iterable, List, NamedTuple, Optional, Sequence, Set, Tuple, TypeVar, Union ) from sciencebeam_trainer_delft.sequence_labelling.reader import load_data_crf_lines from sciencebeam_parser.utils.labels import get_split_prefix_label, strip_tag_prefix from sciencebeam_parser.document.layout_document import ( LayoutToken, LayoutLine, LayoutBlock, LayoutPage, LayoutDocument ) from sciencebeam_parser.models.data import ( AppFeaturesContext, DocumentFeaturesContext, LabeledLayoutModelData, LayoutModelData, ModelDataGenerator ) from sciencebeam_parser.models.extract import ModelSemanticExtractor from sciencebeam_parser.models.training_data import TeiTrainingDataGenerator from sciencebeam_parser.document.semantic_document import SemanticContentWrapper from sciencebeam_parser.models.model_impl import ModelImpl, T_ModelImplFactory from sciencebeam_parser.utils.lazy import LazyLoaded, Preloadable LOGGER = logging.getLogger(__name__) T = TypeVar('T') U = TypeVar('U') @dataclass class LayoutModelLabel: label: str label_token_text: str layout_line: Optional[LayoutLine] = field(repr=False, default=None) layout_token: Optional[LayoutToken] = field(repr=False, default=None) class LabeledLayoutToken(NamedTuple): label: str layout_token: LayoutToken class NewDocumentMarker: pass NEW_DOCUMENT_MARKER = NewDocumentMarker() def iter_entities_including_other(seq: List[str]) -> Iterable[Tuple[str, int, int]]: """ Similar to get_entities, but also other (`O`) tag """ prev_tag = 'O' prev_start = 0 for index, prefixed_tag in enumerate(seq): prefix, tag = get_split_prefix_label(prefixed_tag) if prefix == 'B' or tag != prev_tag: if prev_start < index: yield prev_tag, prev_start, index - 1 prev_tag = tag prev_start = index if prev_start < len(seq): yield prev_tag, prev_start, len(seq) - 1 def get_entities_including_other(seq: List[str]) -> List[Tuple[str, int, int]]: return list(iter_entities_including_other(seq)) class LayoutDocumentLabelResult: def __init__( self, layout_document: LayoutDocument, layout_model_label_iterable: Iterable[LayoutModelLabel] ): self.layout_document = layout_document self.layout_model_label_list = list(layout_model_label_iterable) self.layout_document_labels_by_label: Dict[str, List[LayoutModelLabel]] = ( defaultdict(list) ) for layout_model_label in self.layout_model_label_list: tag_without_prefix = strip_tag_prefix(layout_model_label.label) self.layout_document_labels_by_label[tag_without_prefix].append( layout_model_label ) def get_available_labels(self) -> Set[str]: return set(self.layout_document_labels_by_label.keys()) def get_layout_document_labels_by_labels(self, labels: List[str]) -> List[LayoutModelLabel]: if not labels: return [] if len(labels) == 1: return self.layout_document_labels_by_label.get(labels[0], []) result: List[LayoutModelLabel] = [] for label in labels: result.extend(self.layout_document_labels_by_label.get(label, [])) return result def get_filtered_document_by_label(self, label: str) -> LayoutDocument: return self.get_filtered_document_by_labels([label]) def get_filtered_document_by_labels( self, labels: List[str] ): # pylint: disable=too-many-branches layout_document = LayoutDocument(pages=[]) layout_document_labels = self.get_layout_document_labels_by_labels(labels) if not layout_document_labels: LOGGER.warning( 'no layout_lines_to_include found for: %r, available keys=%r', labels, self.layout_document_labels_by_label.keys() ) return layout_document layout_token_ids_to_include = { id(layout_document_label.layout_token) for layout_document_label in layout_document_labels if layout_document_label.layout_token } LOGGER.debug('layout_tokens_to_include: %s', layout_token_ids_to_include) layout_line_ids_to_include: Set[int] = set() if not layout_token_ids_to_include: layout_line_ids_to_include = { id(layout_document_label.layout_line) for layout_document_label in layout_document_labels if layout_document_label.layout_line } LOGGER.debug('layout_line_ids_to_include: %s', layout_line_ids_to_include) result_page: Optional[LayoutPage] = None for page in self.layout_document.pages: # pylint: disable=too-many-nested-blocks result_page = None result_block: Optional[LayoutBlock] = None for block in page.blocks: result_block = None for line in block.lines: accepted_line: Optional[LayoutLine] = None if layout_token_ids_to_include: accepted_tokens: List[LayoutToken] = [] for token in line.tokens: if id(token) in layout_token_ids_to_include: accepted_tokens.append(token) if not accepted_tokens: continue if len(line.tokens) == accepted_tokens: accepted_line = line else: accepted_line = LayoutLine(tokens=accepted_tokens) else: if id(line) not in layout_line_ids_to_include: continue accepted_line = line if result_page is None: result_page = LayoutPage(blocks=[]) layout_document.pages.append(result_page) if result_block is None: result_block = LayoutBlock(lines=[]) result_page.blocks.append(result_block) result_block.lines.append(accepted_line) return layout_document def iter_entity_layout_blocks_for_labeled_layout_tokens( labeled_layout_tokens: Iterable[LabeledLayoutToken] ) -> Iterable[Tuple[str, LayoutBlock]]: layout_tokens = [result.layout_token for result in labeled_layout_tokens] labels = [result.label for result in labeled_layout_tokens] LOGGER.debug('layout_tokens: %s', layout_tokens) LOGGER.debug('labels: %s', labels) for tag, start, end in get_entities_including_other(list(labels)): yield tag, LayoutBlock.for_tokens(layout_tokens[start:end + 1]) def iter_entity_values_predicted_labels( tag_result: List[Tuple[str, str]] ) -> Iterable[Tuple[str, str]]: tokens, labels = zip(*tag_result) LOGGER.debug('tokens: %s', tokens) LOGGER.debug('labels: %s', labels) for tag, start, end in get_entities_including_other(list(labels)): yield tag, ' '.join(tokens[start:end + 1]) def iter_labeled_layout_token_for_layout_model_label( layout_model_label_iterable: Iterable[LayoutModelLabel] ) -> Iterable[LabeledLayoutToken]: for layout_model_label in layout_model_label_iterable: layout_token = layout_model_label.layout_token assert layout_token is not None yield LabeledLayoutToken( layout_model_label.label, layout_token ) def iter_data_lines_for_model_data_iterables( model_data_iterables: Iterable[Iterable[LayoutModelData]] ) -> Iterable[str]: for index, model_data_list in enumerate(model_data_iterables): if index > 0: yield '' for model_data in model_data_list: yield model_data.data_line class Model(ABC, Preloadable): def __init__( self, model_impl_factory: Optional[T_ModelImplFactory], model_config: Optional[dict] = None ) -> None: self._model_impl_factory = model_impl_factory self._lazy_model_impl = LazyLoaded[ModelImpl](self._load_model_impl) self.model_config = model_config or {} def __repr__(self) -> str: return '%s(model_config=%r, loaded=%r)' % ( type(self).__name__, self.model_config, self._lazy_model_impl.is_loaded ) @abstractmethod def get_data_generator( self, document_features_context: DocumentFeaturesContext ) -> ModelDataGenerator: pass # @abstractmethod def get_semantic_extractor(self) -> ModelSemanticExtractor: raise NotImplementedError() # @abstractmethod def get_tei_training_data_generator(self) -> TeiTrainingDataGenerator: raise NotImplementedError() def _load_model_impl(self) -> ModelImpl: assert self._model_impl_factory, 'model impl factory required' LOGGER.info('creating model impl: %r', self._model_impl_factory) model_impl = self._model_impl_factory() if not isinstance(model_impl, ModelImpl): raise TypeError('invalid model impl type: %r' % model_impl) return model_impl @property def model_impl(self) -> ModelImpl: was_loaded = self._lazy_model_impl.is_loaded model_impl = self._lazy_model_impl.get() if was_loaded: LOGGER.info('model impl already loaded: %r', model_impl) return model_impl def preload(self): model_impl = self.model_impl model_impl.preload() def iter_semantic_content_for_entity_blocks( self, entity_tokens: Iterable[Tuple[str, LayoutBlock]], **kwargs ) -> Iterable[SemanticContentWrapper]: return self.get_semantic_extractor().iter_semantic_content_for_entity_blocks( entity_tokens, **kwargs ) def predict_labels( self, texts: List[List[str]], features: List[List[List[str]]], output_format: Optional[str] = None ) -> List[List[Tuple[str, str]]]: return self.model_impl.predict_labels(texts, features, output_format) def _iter_flat_label_model_data_lists_to( # pylint: disable=too-many-locals self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]], item_factory: Callable[[str, LayoutModelData], T] ) -> Iterable[Union[T, NewDocumentMarker]]: # Note: currently we do need a list model_data_lists = list(model_data_list_iterable) if not model_data_lists: return data_lines = list(iter_data_lines_for_model_data_iterables( model_data_lists )) texts, features = load_data_crf_lines(data_lines) texts = texts.tolist() tag_result = self.predict_labels( texts=texts, features=features, output_format=None ) if not tag_result: return if len(tag_result) != len(model_data_lists): raise AssertionError('tag result does not match number of docs: %d != %d' % ( len(tag_result), len(model_data_lists) )) for index, (doc_tag_result, model_data_list) in enumerate( zip(tag_result, model_data_lists) ): if index > 0: yield NEW_DOCUMENT_MARKER if len(doc_tag_result) != len(model_data_list): raise AssertionError('doc tag result does not match data: %d != %d' % ( len(doc_tag_result), len(model_data_list) )) for token_tag_result, token_model_data in zip(doc_tag_result, model_data_list): label_token_text, token_label = token_tag_result if label_token_text != token_model_data.label_token_text: raise AssertionError( f'actual: {repr(label_token_text)}' f', expected: {repr(token_model_data.label_token_text)}' ) yield item_factory( token_label, token_model_data ) def _iter_stacked_label_model_data_lists_to( self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]], item_factory: Callable[[str, LayoutModelData], T] ) -> Iterable[Sequence[T]]: # Note: currently we do need a list model_data_lists = list(model_data_list_iterable) if not model_data_lists: return doc_items: List[T] = [] result_doc_count = 0 for item in self._iter_flat_label_model_data_lists_to( model_data_lists, item_factory=item_factory ): if isinstance(item, NewDocumentMarker): yield doc_items doc_items = [] result_doc_count += 1 continue doc_items.append(item) if result_doc_count < len(model_data_lists): yield doc_items def iter_label_layout_documents( self, layout_documents: List[LayoutDocument], app_features_context: AppFeaturesContext ) -> Iterable[List[LayoutModelLabel]]: doc_layout_model_labels: List[LayoutModelLabel] = [] result_doc_count = 0 for layout_model_label in self._iter_label_layout_documents( layout_documents, app_features_context=app_features_context ): if isinstance(layout_model_label, NewDocumentMarker): yield doc_layout_model_labels doc_layout_model_labels = [] result_doc_count += 1 continue doc_layout_model_labels.append(layout_model_label) if result_doc_count < len(layout_documents): yield doc_layout_model_labels def iter_label_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> Iterable[LayoutModelLabel]: for layout_model_label in self._iter_label_layout_documents( [layout_document], app_features_context=app_features_context ): assert isinstance(layout_model_label, LayoutModelLabel) yield layout_model_label def _iter_label_layout_documents( # pylint: disable=too-many-locals self, layout_documents: Iterable[LayoutDocument], app_features_context: AppFeaturesContext ) -> Iterable[Union[LayoutModelLabel, NewDocumentMarker]]: data_generator = self.get_data_generator( document_features_context=DocumentFeaturesContext( app_features_context=app_features_context ) ) model_data_lists = [ list(data_generator.iter_model_data_for_layout_document( layout_document )) for layout_document in layout_documents ] return self._iter_flat_label_model_data_lists_to( model_data_lists, lambda label, model_data: LayoutModelLabel( label=label, label_token_text=model_data.label_token_text, layout_line=model_data.layout_line, layout_token=model_data.layout_token ) ) def iter_labeled_model_data_list_for_model_data_list_iterable( self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]] ) -> Iterable[Sequence[LabeledLayoutModelData]]: return self._iter_stacked_label_model_data_lists_to( model_data_list_iterable, lambda label, model_data: LabeledLayoutModelData.from_model_data( model_data, label=label ) ) def get_label_layout_document_result( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> LayoutDocumentLabelResult: return LayoutDocumentLabelResult( layout_document=layout_document, layout_model_label_iterable=self.iter_label_layout_document( layout_document, app_features_context=app_features_context ) ) def iter_predict_labels_for_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> Iterable[LabeledLayoutToken]: # Note: this should get merged with Model.iter_label_layout_document yield from iter_labeled_layout_token_for_layout_model_label( self.iter_label_layout_document( layout_document, app_features_context=app_features_context ) ) def predict_labels_for_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> List[LabeledLayoutToken]: return list(self.iter_predict_labels_for_layout_document( layout_document, app_features_context=app_features_context )) def predict_labels_for_layout_documents( self, layout_documents: List[LayoutDocument], app_features_context: AppFeaturesContext ) -> List[List[LabeledLayoutToken]]: return [ list(iter_labeled_layout_token_for_layout_model_label( layout_model_labels )) for layout_model_labels in self.iter_label_layout_documents( layout_documents, app_features_context=app_features_context ) ] def iter_entity_layout_blocks_for_labeled_layout_tokens( self, labeled_layout_tokens: Iterable[LabeledLayoutToken] ) -> Iterable[Tuple[str, LayoutBlock]]: return iter_entity_layout_blocks_for_labeled_layout_tokens(labeled_layout_tokens) def iter_semantic_content_for_labeled_layout_tokens( self, labeled_layout_tokens: Iterable[LabeledLayoutToken], **kwargs ) -> Iterable[SemanticContentWrapper]: return self.iter_semantic_content_for_entity_blocks( self.iter_entity_layout_blocks_for_labeled_layout_tokens( labeled_layout_tokens ), **kwargs )

37.053892

96

0.652823

import logging from abc import ABC, abstractmethod from collections import defaultdict from dataclasses import dataclass, field from typing import ( Callable, Dict, Iterable, List, NamedTuple, Optional, Sequence, Set, Tuple, TypeVar, Union ) from sciencebeam_trainer_delft.sequence_labelling.reader import load_data_crf_lines from sciencebeam_parser.utils.labels import get_split_prefix_label, strip_tag_prefix from sciencebeam_parser.document.layout_document import ( LayoutToken, LayoutLine, LayoutBlock, LayoutPage, LayoutDocument ) from sciencebeam_parser.models.data import ( AppFeaturesContext, DocumentFeaturesContext, LabeledLayoutModelData, LayoutModelData, ModelDataGenerator ) from sciencebeam_parser.models.extract import ModelSemanticExtractor from sciencebeam_parser.models.training_data import TeiTrainingDataGenerator from sciencebeam_parser.document.semantic_document import SemanticContentWrapper from sciencebeam_parser.models.model_impl import ModelImpl, T_ModelImplFactory from sciencebeam_parser.utils.lazy import LazyLoaded, Preloadable LOGGER = logging.getLogger(__name__) T = TypeVar('T') U = TypeVar('U') @dataclass class LayoutModelLabel: label: str label_token_text: str layout_line: Optional[LayoutLine] = field(repr=False, default=None) layout_token: Optional[LayoutToken] = field(repr=False, default=None) class LabeledLayoutToken(NamedTuple): label: str layout_token: LayoutToken class NewDocumentMarker: pass NEW_DOCUMENT_MARKER = NewDocumentMarker() def iter_entities_including_other(seq: List[str]) -> Iterable[Tuple[str, int, int]]: prev_tag = 'O' prev_start = 0 for index, prefixed_tag in enumerate(seq): prefix, tag = get_split_prefix_label(prefixed_tag) if prefix == 'B' or tag != prev_tag: if prev_start < index: yield prev_tag, prev_start, index - 1 prev_tag = tag prev_start = index if prev_start < len(seq): yield prev_tag, prev_start, len(seq) - 1 def get_entities_including_other(seq: List[str]) -> List[Tuple[str, int, int]]: return list(iter_entities_including_other(seq)) class LayoutDocumentLabelResult: def __init__( self, layout_document: LayoutDocument, layout_model_label_iterable: Iterable[LayoutModelLabel] ): self.layout_document = layout_document self.layout_model_label_list = list(layout_model_label_iterable) self.layout_document_labels_by_label: Dict[str, List[LayoutModelLabel]] = ( defaultdict(list) ) for layout_model_label in self.layout_model_label_list: tag_without_prefix = strip_tag_prefix(layout_model_label.label) self.layout_document_labels_by_label[tag_without_prefix].append( layout_model_label ) def get_available_labels(self) -> Set[str]: return set(self.layout_document_labels_by_label.keys()) def get_layout_document_labels_by_labels(self, labels: List[str]) -> List[LayoutModelLabel]: if not labels: return [] if len(labels) == 1: return self.layout_document_labels_by_label.get(labels[0], []) result: List[LayoutModelLabel] = [] for label in labels: result.extend(self.layout_document_labels_by_label.get(label, [])) return result def get_filtered_document_by_label(self, label: str) -> LayoutDocument: return self.get_filtered_document_by_labels([label]) def get_filtered_document_by_labels( self, labels: List[str] ): layout_document = LayoutDocument(pages=[]) layout_document_labels = self.get_layout_document_labels_by_labels(labels) if not layout_document_labels: LOGGER.warning( 'no layout_lines_to_include found for: %r, available keys=%r', labels, self.layout_document_labels_by_label.keys() ) return layout_document layout_token_ids_to_include = { id(layout_document_label.layout_token) for layout_document_label in layout_document_labels if layout_document_label.layout_token } LOGGER.debug('layout_tokens_to_include: %s', layout_token_ids_to_include) layout_line_ids_to_include: Set[int] = set() if not layout_token_ids_to_include: layout_line_ids_to_include = { id(layout_document_label.layout_line) for layout_document_label in layout_document_labels if layout_document_label.layout_line } LOGGER.debug('layout_line_ids_to_include: %s', layout_line_ids_to_include) result_page: Optional[LayoutPage] = None for page in self.layout_document.pages: result_page = None result_block: Optional[LayoutBlock] = None for block in page.blocks: result_block = None for line in block.lines: accepted_line: Optional[LayoutLine] = None if layout_token_ids_to_include: accepted_tokens: List[LayoutToken] = [] for token in line.tokens: if id(token) in layout_token_ids_to_include: accepted_tokens.append(token) if not accepted_tokens: continue if len(line.tokens) == accepted_tokens: accepted_line = line else: accepted_line = LayoutLine(tokens=accepted_tokens) else: if id(line) not in layout_line_ids_to_include: continue accepted_line = line if result_page is None: result_page = LayoutPage(blocks=[]) layout_document.pages.append(result_page) if result_block is None: result_block = LayoutBlock(lines=[]) result_page.blocks.append(result_block) result_block.lines.append(accepted_line) return layout_document def iter_entity_layout_blocks_for_labeled_layout_tokens( labeled_layout_tokens: Iterable[LabeledLayoutToken] ) -> Iterable[Tuple[str, LayoutBlock]]: layout_tokens = [result.layout_token for result in labeled_layout_tokens] labels = [result.label for result in labeled_layout_tokens] LOGGER.debug('layout_tokens: %s', layout_tokens) LOGGER.debug('labels: %s', labels) for tag, start, end in get_entities_including_other(list(labels)): yield tag, LayoutBlock.for_tokens(layout_tokens[start:end + 1]) def iter_entity_values_predicted_labels( tag_result: List[Tuple[str, str]] ) -> Iterable[Tuple[str, str]]: tokens, labels = zip(*tag_result) LOGGER.debug('tokens: %s', tokens) LOGGER.debug('labels: %s', labels) for tag, start, end in get_entities_including_other(list(labels)): yield tag, ' '.join(tokens[start:end + 1]) def iter_labeled_layout_token_for_layout_model_label( layout_model_label_iterable: Iterable[LayoutModelLabel] ) -> Iterable[LabeledLayoutToken]: for layout_model_label in layout_model_label_iterable: layout_token = layout_model_label.layout_token assert layout_token is not None yield LabeledLayoutToken( layout_model_label.label, layout_token ) def iter_data_lines_for_model_data_iterables( model_data_iterables: Iterable[Iterable[LayoutModelData]] ) -> Iterable[str]: for index, model_data_list in enumerate(model_data_iterables): if index > 0: yield '' for model_data in model_data_list: yield model_data.data_line class Model(ABC, Preloadable): def __init__( self, model_impl_factory: Optional[T_ModelImplFactory], model_config: Optional[dict] = None ) -> None: self._model_impl_factory = model_impl_factory self._lazy_model_impl = LazyLoaded[ModelImpl](self._load_model_impl) self.model_config = model_config or {} def __repr__(self) -> str: return '%s(model_config=%r, loaded=%r)' % ( type(self).__name__, self.model_config, self._lazy_model_impl.is_loaded ) @abstractmethod def get_data_generator( self, document_features_context: DocumentFeaturesContext ) -> ModelDataGenerator: pass def get_semantic_extractor(self) -> ModelSemanticExtractor: raise NotImplementedError() def get_tei_training_data_generator(self) -> TeiTrainingDataGenerator: raise NotImplementedError() def _load_model_impl(self) -> ModelImpl: assert self._model_impl_factory, 'model impl factory required' LOGGER.info('creating model impl: %r', self._model_impl_factory) model_impl = self._model_impl_factory() if not isinstance(model_impl, ModelImpl): raise TypeError('invalid model impl type: %r' % model_impl) return model_impl @property def model_impl(self) -> ModelImpl: was_loaded = self._lazy_model_impl.is_loaded model_impl = self._lazy_model_impl.get() if was_loaded: LOGGER.info('model impl already loaded: %r', model_impl) return model_impl def preload(self): model_impl = self.model_impl model_impl.preload() def iter_semantic_content_for_entity_blocks( self, entity_tokens: Iterable[Tuple[str, LayoutBlock]], **kwargs ) -> Iterable[SemanticContentWrapper]: return self.get_semantic_extractor().iter_semantic_content_for_entity_blocks( entity_tokens, **kwargs ) def predict_labels( self, texts: List[List[str]], features: List[List[List[str]]], output_format: Optional[str] = None ) -> List[List[Tuple[str, str]]]: return self.model_impl.predict_labels(texts, features, output_format) def _iter_flat_label_model_data_lists_to( self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]], item_factory: Callable[[str, LayoutModelData], T] ) -> Iterable[Union[T, NewDocumentMarker]]: model_data_lists = list(model_data_list_iterable) if not model_data_lists: return data_lines = list(iter_data_lines_for_model_data_iterables( model_data_lists )) texts, features = load_data_crf_lines(data_lines) texts = texts.tolist() tag_result = self.predict_labels( texts=texts, features=features, output_format=None ) if not tag_result: return if len(tag_result) != len(model_data_lists): raise AssertionError('tag result does not match number of docs: %d != %d' % ( len(tag_result), len(model_data_lists) )) for index, (doc_tag_result, model_data_list) in enumerate( zip(tag_result, model_data_lists) ): if index > 0: yield NEW_DOCUMENT_MARKER if len(doc_tag_result) != len(model_data_list): raise AssertionError('doc tag result does not match data: %d != %d' % ( len(doc_tag_result), len(model_data_list) )) for token_tag_result, token_model_data in zip(doc_tag_result, model_data_list): label_token_text, token_label = token_tag_result if label_token_text != token_model_data.label_token_text: raise AssertionError( f'actual: {repr(label_token_text)}' f', expected: {repr(token_model_data.label_token_text)}' ) yield item_factory( token_label, token_model_data ) def _iter_stacked_label_model_data_lists_to( self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]], item_factory: Callable[[str, LayoutModelData], T] ) -> Iterable[Sequence[T]]: model_data_lists = list(model_data_list_iterable) if not model_data_lists: return doc_items: List[T] = [] result_doc_count = 0 for item in self._iter_flat_label_model_data_lists_to( model_data_lists, item_factory=item_factory ): if isinstance(item, NewDocumentMarker): yield doc_items doc_items = [] result_doc_count += 1 continue doc_items.append(item) if result_doc_count < len(model_data_lists): yield doc_items def iter_label_layout_documents( self, layout_documents: List[LayoutDocument], app_features_context: AppFeaturesContext ) -> Iterable[List[LayoutModelLabel]]: doc_layout_model_labels: List[LayoutModelLabel] = [] result_doc_count = 0 for layout_model_label in self._iter_label_layout_documents( layout_documents, app_features_context=app_features_context ): if isinstance(layout_model_label, NewDocumentMarker): yield doc_layout_model_labels doc_layout_model_labels = [] result_doc_count += 1 continue doc_layout_model_labels.append(layout_model_label) if result_doc_count < len(layout_documents): yield doc_layout_model_labels def iter_label_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> Iterable[LayoutModelLabel]: for layout_model_label in self._iter_label_layout_documents( [layout_document], app_features_context=app_features_context ): assert isinstance(layout_model_label, LayoutModelLabel) yield layout_model_label def _iter_label_layout_documents( self, layout_documents: Iterable[LayoutDocument], app_features_context: AppFeaturesContext ) -> Iterable[Union[LayoutModelLabel, NewDocumentMarker]]: data_generator = self.get_data_generator( document_features_context=DocumentFeaturesContext( app_features_context=app_features_context ) ) model_data_lists = [ list(data_generator.iter_model_data_for_layout_document( layout_document )) for layout_document in layout_documents ] return self._iter_flat_label_model_data_lists_to( model_data_lists, lambda label, model_data: LayoutModelLabel( label=label, label_token_text=model_data.label_token_text, layout_line=model_data.layout_line, layout_token=model_data.layout_token ) ) def iter_labeled_model_data_list_for_model_data_list_iterable( self, model_data_list_iterable: Iterable[Sequence[LayoutModelData]] ) -> Iterable[Sequence[LabeledLayoutModelData]]: return self._iter_stacked_label_model_data_lists_to( model_data_list_iterable, lambda label, model_data: LabeledLayoutModelData.from_model_data( model_data, label=label ) ) def get_label_layout_document_result( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> LayoutDocumentLabelResult: return LayoutDocumentLabelResult( layout_document=layout_document, layout_model_label_iterable=self.iter_label_layout_document( layout_document, app_features_context=app_features_context ) ) def iter_predict_labels_for_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> Iterable[LabeledLayoutToken]: yield from iter_labeled_layout_token_for_layout_model_label( self.iter_label_layout_document( layout_document, app_features_context=app_features_context ) ) def predict_labels_for_layout_document( self, layout_document: LayoutDocument, app_features_context: AppFeaturesContext ) -> List[LabeledLayoutToken]: return list(self.iter_predict_labels_for_layout_document( layout_document, app_features_context=app_features_context )) def predict_labels_for_layout_documents( self, layout_documents: List[LayoutDocument], app_features_context: AppFeaturesContext ) -> List[List[LabeledLayoutToken]]: return [ list(iter_labeled_layout_token_for_layout_model_label( layout_model_labels )) for layout_model_labels in self.iter_label_layout_documents( layout_documents, app_features_context=app_features_context ) ] def iter_entity_layout_blocks_for_labeled_layout_tokens( self, labeled_layout_tokens: Iterable[LabeledLayoutToken] ) -> Iterable[Tuple[str, LayoutBlock]]: return iter_entity_layout_blocks_for_labeled_layout_tokens(labeled_layout_tokens) def iter_semantic_content_for_labeled_layout_tokens( self, labeled_layout_tokens: Iterable[LabeledLayoutToken], **kwargs ) -> Iterable[SemanticContentWrapper]: return self.iter_semantic_content_for_entity_blocks( self.iter_entity_layout_blocks_for_labeled_layout_tokens( labeled_layout_tokens ), **kwargs )

true

f719a28a0f454eca48dc84c19a7a003b8073c988

225

py

Python

tests/test_case_files/class_test_1.py

calkerns/dyc

ddc35e6c183137dc30b2a3a2f481098280167bd1

[ "MIT" ]

100

2019-04-04T23:38:20.000Z

2022-03-30T18:14:16.000Z

tests/test_case_files/class_test_1.py

calkerns/dyc

ddc35e6c183137dc30b2a3a2f481098280167bd1

[ "MIT" ]

51

2019-04-04T20:18:47.000Z

2021-10-05T17:17:20.000Z

tests/test_case_files/class_test_1.py

calkerns/dyc

ddc35e6c183137dc30b2a3a2f481098280167bd1

[ "MIT" ]

63

2019-04-04T20:38:57.000Z

2021-05-25T02:23:16.000Z

class MyClass: x = 1 class MyClass1(Parent1): y = 1 class MyClass2(Parent1, Parent2): z = 1 class MyClass3(Parent1): a = 1 class MyClass4(Parent1, Parent2): b = 1

10.714286

41

0.515556

class MyClass: x = 1 class MyClass1(Parent1): y = 1 class MyClass2(Parent1, Parent2): z = 1 class MyClass3(Parent1): a = 1 class MyClass4(Parent1, Parent2): b = 1

true

f719a460ed4a51e9b13467d22b0a48aecf11f8ca

346

py

Python

students/k3343/laboratory_works/Rolinskiy_Sergey/Laba_1/project_first_app/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

10

2020-03-20T09:06:12.000Z

2021-07-27T13:06:02.000Z

students/k3343/laboratory_works/Rolinskiy_Sergey/Laba_1/project_first_app/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

134

2020-03-23T09:47:48.000Z

2022-03-12T01:05:19.000Z

students/k3343/laboratory_works/Rolinskiy_Sergey/Laba_1/project_first_app/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

71

2020-03-20T12:45:56.000Z

2021-10-31T19:22:25.000Z

from django.urls import path from django.conf.urls import url from project_first_app.views import * urlpatterns = [ path('',main,name='main'), path('createowner/',createowner,name='createowner'), path('login/',log_in,name='login'), path(r'<int:ho_id>',review,name='detail') ] #path(r'getowners/<int:ow_id>',detail,name='detail'),

31.454545

56

0.699422

from django.urls import path from django.conf.urls import url from project_first_app.views import * urlpatterns = [ path('',main,name='main'), path('createowner/',createowner,name='createowner'), path('login/',log_in,name='login'), path(r'<int:ho_id>',review,name='detail') ]

true

f719a465158b15ac1c1bfd62374aefc6ed61f38a

36,465

py

Python

owslib/iso.py

peterataylor/OWSLib

8c15832da0c27dadfb567929ddd52a7570b7c231

[ "BSD-3-Clause" ]

1

2015-03-16T05:22:04.000Z

owslib/iso.py

peterataylor/OWSLib

8c15832da0c27dadfb567929ddd52a7570b7c231

[ "BSD-3-Clause" ]

null

owslib/iso.py

peterataylor/OWSLib

8c15832da0c27dadfb567929ddd52a7570b7c231

[ "BSD-3-Clause" ]

null

# -*- coding: ISO-8859-15 -*- # ============================================================================= # Copyright (c) 2009 Tom Kralidis # # Authors : Tom Kralidis <tomkralidis@gmail.com> # Angelos Tzotsos <tzotsos@gmail.com> # # Contact email: tomkralidis@gmail.com # ============================================================================= """ ISO metadata parser """ from owslib.etree import etree from owslib import util from owslib.namespaces import Namespaces # default variables def get_namespaces(): n = Namespaces() ns = n.get_namespaces(["gco","gmd","gml","gml32","gmx","gts","srv","xlink"]) ns[None] = n.get_namespace("gmd") return ns namespaces = get_namespaces() class MD_Metadata(object): """ Process gmd:MD_Metadata """ def __init__(self, md=None): if md is None: self.xml = None self.identifier = None self.parentidentifier = None self.language = None self.dataseturi = None self.languagecode = None self.datestamp = None self.charset = None self.hierarchy = None self.contact = [] self.datetimestamp = None self.stdname = None self.stdver = None self.referencesystem = None self.identification = None self.serviceidentification = None self.identificationinfo = [] self.distribution = None self.dataquality = None else: if hasattr(md, 'getroot'): # standalone document self.xml = etree.tostring(md.getroot()) else: # part of a larger document self.xml = etree.tostring(md) val = md.find(util.nspath_eval('gmd:fileIdentifier/gco:CharacterString', namespaces)) self.identifier = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:parentIdentifier/gco:CharacterString', namespaces)) self.parentidentifier = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:language/gco:CharacterString', namespaces)) self.language = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:dataSetURI/gco:CharacterString', namespaces)) self.dataseturi = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:language/gmd:LanguageCode', namespaces)) self.languagecode = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:dateStamp/gco:Date', namespaces)) self.datestamp = util.testXMLValue(val) if not self.datestamp: val = md.find(util.nspath_eval('gmd:dateStamp/gco:DateTime', namespaces)) self.datestamp = util.testXMLValue(val) self.charset = _testCodeListValue(md.find(util.nspath_eval('gmd:characterSet/gmd:MD_CharacterSetCode', namespaces))) self.hierarchy = _testCodeListValue(md.find(util.nspath_eval('gmd:hierarchyLevel/gmd:MD_ScopeCode', namespaces))) self.contact = [] for i in md.findall(util.nspath_eval('gmd:contact/gmd:CI_ResponsibleParty', namespaces)): o = CI_ResponsibleParty(i) self.contact.append(o) val = md.find(util.nspath_eval('gmd:dateStamp/gco:DateTime', namespaces)) self.datetimestamp = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:metadataStandardName/gco:CharacterString', namespaces)) self.stdname = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:metadataStandardVersion/gco:CharacterString', namespaces)) self.stdver = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:referenceSystemInfo/gmd:MD_ReferenceSystem', namespaces)) if val is not None: self.referencesystem = MD_ReferenceSystem(val) else: self.referencesystem = None # TODO: merge .identificationinfo into .identification #warnings.warn( # 'the .identification and .serviceidentification properties will merge into ' # '.identification being a list of properties. This is currently implemented ' # 'in .identificationinfo. ' # 'Please see https://github.com/geopython/OWSLib/issues/38 for more information', # FutureWarning) val = md.find(util.nspath_eval('gmd:identificationInfo/gmd:MD_DataIdentification', namespaces)) val2 = md.find(util.nspath_eval('gmd:identificationInfo/srv:SV_ServiceIdentification', namespaces)) if val is not None: self.identification = MD_DataIdentification(val, 'dataset') self.serviceidentification = None elif val2 is not None: self.identification = MD_DataIdentification(val2, 'service') self.serviceidentification = SV_ServiceIdentification(val2) else: self.identification = None self.serviceidentification = None self.identificationinfo = [] for idinfo in md.findall(util.nspath_eval('gmd:identificationInfo', namespaces)): val = list(idinfo)[0] tagval = util.xmltag_split(val.tag) if tagval == 'MD_DataIdentification': self.identificationinfo.append(MD_DataIdentification(val, 'dataset')) elif tagval == 'MD_ServiceIdentification': self.identificationinfo.append(MD_DataIdentification(val, 'service')) elif tagval == 'SV_ServiceIdentification': self.identificationinfo.append(SV_ServiceIdentification(val)) val = md.find(util.nspath_eval('gmd:distributionInfo/gmd:MD_Distribution', namespaces)) if val is not None: self.distribution = MD_Distribution(val) else: self.distribution = None val = md.find(util.nspath_eval('gmd:dataQualityInfo/gmd:DQ_DataQuality', namespaces)) if val is not None: self.dataquality = DQ_DataQuality(val) else: self.dataquality = None class CI_Date(object): """ process CI_Date """ def __init__(self, md=None): if md is None: self.date = None self.type = None else: val = md.find(util.nspath_eval('gmd:date/gco:Date', namespaces)) if val is not None: self.date = util.testXMLValue(val) else: val = md.find(util.nspath_eval('gmd:date/gco:DateTime', namespaces)) if val is not None: self.date = util.testXMLValue(val) else: self.date = None val = md.find(util.nspath_eval('gmd:dateType/gmd:CI_DateTypeCode', namespaces)) self.type = _testCodeListValue(val) class CI_ResponsibleParty(object): """ process CI_ResponsibleParty """ def __init__(self, md=None): if md is None: self.name = None self.organization = None self.position = None self.phone = None self.fax = None self.address = None self.city = None self.region = None self.postcode = None self.country = None self.email = None self.onlineresource = None self.role = None else: val = md.find(util.nspath_eval('gmd:individualName/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:organisationName/gco:CharacterString', namespaces)) self.organization = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:positionName/gco:CharacterString', namespaces)) self.position = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:phone/gmd:CI_Telephone/gmd:voice/gco:CharacterString', namespaces)) self.phone = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:phone/gmd:CI_Telephone/gmd:facsimile/gco:CharacterString', namespaces)) self.fax = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:deliveryPoint/gco:CharacterString', namespaces)) self.address = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:city/gco:CharacterString', namespaces)) self.city = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:administrativeArea/gco:CharacterString', namespaces)) self.region = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:postalCode/gco:CharacterString', namespaces)) self.postcode = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:country/gco:CharacterString', namespaces)) self.country = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:electronicMailAddress/gco:CharacterString', namespaces)) self.email = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:onlineResource/gmd:CI_OnlineResource', namespaces)) if val is not None: self.onlineresource = CI_OnlineResource(val) else: self.onlineresource = None self.role = _testCodeListValue(md.find(util.nspath_eval('gmd:role/gmd:CI_RoleCode', namespaces))) class MD_DataIdentification(object): """ process MD_DataIdentification """ def __init__(self, md=None, identtype=None): if md is None: self.identtype = None self.title = None self.alternatetitle = None self.aggregationinfo = None self.uricode = [] self.uricodespace = [] self.date = [] self.datetype = [] self.uselimitation = [] self.accessconstraints = [] self.classification = [] self.otherconstraints = [] self.securityconstraints = [] self.useconstraints = [] self.denominators = [] self.distance = [] self.uom = [] self.resourcelanguage = [] self.creator = None self.publisher = None self.originator = None self.edition = None self.abstract = None self.purpose = None self.status = None self.contact = [] self.keywords = [] self.topiccategory = [] self.supplementalinformation = None self.extent = None self.bbox = None self.temporalextent_start = None self.temporalextent_end = None else: self.identtype = identtype val = md.find(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) self.title = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:alternateTitle/gco:CharacterString', namespaces)) self.alternatetitle = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:aggregationInfo', namespaces)) self.aggregationinfo = util.testXMLValue(val) self.uricode = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:identifier/gmd:RS_Identifier/gmd:code/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uricode.append(val) self.uricodespace = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:identifier/gmd:RS_Identifier/gmd:codeSpace/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uricodespace.append(val) self.date = [] self.datetype = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:date/gmd:CI_Date', namespaces)): self.date.append(CI_Date(i)) self.uselimitation = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_Constraints/gmd:useLimitation/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uselimitation.append(val) self.accessconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:accessConstraints/gmd:MD_RestrictionCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.accessconstraints.append(val) self.classification = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:accessConstraints/gmd:MD_ClassificationCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.classification.append(val) self.otherconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:otherConstraints/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.otherconstraints.append(val) self.securityconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_SecurityConstraints/gmd:useLimitation', namespaces)): val = util.testXMLValue(i) if val is not None: self.securityconstraints.append(val) self.useconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:useConstraints/gmd:MD_RestrictionCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.useconstraints.append(val) self.denominators = [] for i in md.findall(util.nspath_eval('gmd:spatialResolution/gmd:MD_Resolution/gmd:equivalentScale/gmd:MD_RepresentativeFraction/gmd:denominator/gco:Integer', namespaces)): val = util.testXMLValue(i) if val is not None: self.denominators.append(val) self.distance = [] self.uom = [] for i in md.findall(util.nspath_eval('gmd:spatialResolution/gmd:MD_Resolution/gmd:distance/gco:Distance', namespaces)): val = util.testXMLValue(i) if val is not None: self.distance.append(val) self.uom.append(i.get("uom")) self.resourcelanguage = [] for i in md.findall(util.nspath_eval('gmd:language/gmd:LanguageCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.resourcelanguage.append(val) val = md.find(util.nspath_eval('gmd:pointOfContact/gmd:CI_ResponsibleParty/gmd:organisationName', namespaces)) if val is not None: val2 = val.find(util.nspath_eval('gmd:role/gmd:CI_RoleCode', namespaces)) if val2 is not None: clv = _testCodeListValue(val) if clv == 'originator': self.creator = util.testXMLValue(val) elif clv == 'publisher': self.publisher = util.testXMLValue(val) elif clv == 'contributor': self.originator = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:edition/gco:CharacterString', namespaces)) self.edition = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:abstract/gco:CharacterString', namespaces)) self.abstract = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:purpose/gco:CharacterString', namespaces)) self.purpose = util.testXMLValue(val) self.status = _testCodeListValue(md.find(util.nspath_eval('gmd:status/gmd:MD_ProgressCode', namespaces))) self.contact = [] for i in md.findall(util.nspath_eval('gmd:pointOfContact/gmd:CI_ResponsibleParty', namespaces)): o = CI_ResponsibleParty(i) self.contact.append(o) self.keywords = [] for i in md.findall(util.nspath_eval('gmd:descriptiveKeywords', namespaces)): mdkw = {} mdkw['type'] = _testCodeListValue(i.find(util.nspath_eval('gmd:MD_Keywords/gmd:type/gmd:MD_KeywordTypeCode', namespaces))) mdkw['thesaurus'] = {} val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) mdkw['thesaurus']['title'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:date/gco:Date', namespaces)) mdkw['thesaurus']['date'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:dateType/gmd:CI_DateTypeCode', namespaces)) mdkw['thesaurus']['datetype'] = util.testXMLValue(val) mdkw['keywords'] = [] for k in i.findall(util.nspath_eval('gmd:MD_Keywords/gmd:keyword', namespaces)): val = k.find(util.nspath_eval('gco:CharacterString', namespaces)) if val is not None: val2 = util.testXMLValue(val) if val2 is not None: mdkw['keywords'].append(val2) self.keywords.append(mdkw) self.topiccategory = [] for i in md.findall(util.nspath_eval('gmd:topicCategory/gmd:MD_TopicCategoryCode', namespaces)): val = util.testXMLValue(i) if val is not None: self.topiccategory.append(val) val = md.find(util.nspath_eval('gmd:supplementalInformation/gco:CharacterString', namespaces)) self.supplementalinformation = util.testXMLValue(val) # There may be multiple geographicElement, create an extent # from the one containing either an EX_GeographicBoundingBox or EX_BoundingPolygon. # The schema also specifies an EX_GeographicDescription. This is not implemented yet. val = None val2 = None val3 = None extents = md.findall(util.nspath_eval('gmd:extent', namespaces)) extents.extend(md.findall(util.nspath_eval('srv:extent', namespaces))) for extent in extents: if val is None: for e in extent.findall(util.nspath_eval('gmd:EX_Extent/gmd:geographicElement', namespaces)): if e.find(util.nspath_eval('gmd:EX_GeographicBoundingBox', namespaces)) is not None or e.find(util.nspath_eval('gmd:EX_BoundingPolygon', namespaces)) is not None: val = e break self.extent = EX_Extent(val) self.bbox = self.extent.boundingBox # for backwards compatibility if val2 is None: val2 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml:TimePeriod/gml:beginPosition', namespaces)) if val2 is None: val2 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml32:TimePeriod/gml32:beginPosition', namespaces)) self.temporalextent_start = util.testXMLValue(val2) if val3 is None: val3 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml:TimePeriod/gml:endPosition', namespaces)) if val3 is None: val3 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml32:TimePeriod/gml32:endPosition', namespaces)) self.temporalextent_end = util.testXMLValue(val3) class MD_Distributor(object): """ process MD_Distributor """ def __init__(self, md=None): if md is None: self.contact = None self.online = [] else: self.contact = None val = md.find(util.nspath_eval('gmd:MD_Distributor/gmd:distributorContact/gmd:CI_ResponsibleParty', namespaces)) if val is not None: self.contact = CI_ResponsibleParty(val) self.online = [] for ol in md.findall(util.nspath_eval('gmd:MD_Distributor/gmd:distributorTransferOptions/gmd:MD_DigitalTransferOptions/gmd:onLine/gmd:CI_OnlineResource', namespaces)): self.online.append(CI_OnlineResource(ol)) class MD_Distribution(object): """ process MD_Distribution """ def __init__(self, md=None): if md is None: self.format = None self.version = None self.distributor = [] self.online = [] pass else: val = md.find(util.nspath_eval('gmd:distributionFormat/gmd:MD_Format/gmd:name/gco:CharacterString', namespaces)) self.format = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:distributionFormat/gmd:MD_Format/gmd:version/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) self.distributor = [] for dist in md.findall(util.nspath_eval('gmd:distributor', namespaces)): self.distributor.append(MD_Distributor(dist)) self.online = [] for ol in md.findall(util.nspath_eval('gmd:transferOptions/gmd:MD_DigitalTransferOptions/gmd:onLine/gmd:CI_OnlineResource', namespaces)): self.online.append(CI_OnlineResource(ol)) class DQ_DataQuality(object): ''' process DQ_DataQuality''' def __init__(self, md=None): if md is None: self.conformancetitle = [] self.conformancedate = [] self.conformancedatetype = [] self.conformancedegree = [] self.lineage = None self.specificationtitle = None self.specificationdate = [] else: self.conformancetitle = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancetitle.append(val) self.conformancedate = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:date/gco:Date', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancedate.append(val) self.conformancedatetype = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:dateType/gmd:CI_DateTypeCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.conformancedatetype.append(val) self.conformancedegree = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:pass/gco:Boolean', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancedegree.append(val) val = md.find(util.nspath_eval('gmd:lineage/gmd:LI_Lineage/gmd:statement/gco:CharacterString', namespaces)) self.lineage = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) self.specificationtitle = util.testXMLValue(val) self.specificationdate = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date', namespaces)): val = util.testXMLValue(i) if val is not None: self.specificationdate.append(val) class SV_ServiceIdentification(object): """ process SV_ServiceIdentification """ def __init__(self, md=None): if md is None: self.identtype = 'service' self.type = None self.version = None self.fees = None self.bbox = None self.couplingtype = None self.operations = [] self.operateson = [] else: self.identtype = 'service' val = md.find(util.nspath_eval('srv:serviceType/gco:LocalName', namespaces)) self.type = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:serviceTypeVersion/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:accessProperties/gmd:MD_StandardOrderProcess/gmd:fees/gco:CharacterString', namespaces)) self.fees = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:extent/gmd:EX_Extent', namespaces)) if val is not None: self.bbox = EX_Extent(val) else: self.bbox = None self.couplingtype = _testCodeListValue(md.find(util.nspath_eval('gmd:couplingType/gmd:SV_CouplingType', namespaces))) self.operations = [] for i in md.findall(util.nspath_eval('srv:containsOperations', namespaces)): tmp = {} val = i.find(util.nspath_eval('srv:SV_OperationMetadata/srv:operationName/gco:CharacterString', namespaces)) tmp['name'] = util.testXMLValue(val) tmp['dcplist'] = [] for d in i.findall(util.nspath_eval('srv:SV_OperationMetadata/srv:DCP', namespaces)): tmp2 = _testCodeListValue(d.find(util.nspath_eval('srv:DCPList', namespaces))) tmp['dcplist'].append(tmp2) tmp['connectpoint'] = [] for d in i.findall(util.nspath_eval('srv:SV_OperationMetadata/srv:connectPoint', namespaces)): tmp3 = d.find(util.nspath_eval('gmd:CI_OnlineResource', namespaces)) tmp['connectpoint'].append(CI_OnlineResource(tmp3)) self.operations.append(tmp) self.operateson = [] for i in md.findall(util.nspath_eval('srv:operatesOn', namespaces)): tmp = {} tmp['uuidref'] = i.attrib.get('uuidref') tmp['href'] = i.attrib.get(util.nspath_eval('xlink:href', namespaces)) tmp['title'] = i.attrib.get(util.nspath_eval('xlink:title', namespaces)) self.operateson.append(tmp) class CI_OnlineResource(object): """ process CI_OnlineResource """ def __init__(self,md=None): if md is None: self.url = None self.protocol = None self.name = None self.description = None self.function = None else: val = md.find(util.nspath_eval('gmd:linkage/gmd:URL', namespaces)) self.url = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:protocol/gco:CharacterString', namespaces)) self.protocol = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:name/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:description/gco:CharacterString', namespaces)) self.description = util.testXMLValue(val) self.function = _testCodeListValue(md.find(util.nspath_eval('gmd:function/gmd:CI_OnLineFunctionCode', namespaces))) class EX_GeographicBoundingBox(object): def __init__(self, md=None): if md is None: self.minx = None self.maxx = None self.miny = None self.maxy = None else: val = md.find(util.nspath_eval('gmd:westBoundLongitude/gco:Decimal', namespaces)) self.minx = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:eastBoundLongitude/gco:Decimal', namespaces)) self.maxx = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:southBoundLatitude/gco:Decimal', namespaces)) self.miny = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:northBoundLatitude/gco:Decimal', namespaces)) self.maxy = util.testXMLValue(val) class EX_Polygon(object): def __init__(self, md=None): if md is None: self.exterior_ring = None self.interior_rings = [] else: linear_ring = md.find(util.nspath_eval('gml32:Polygon/gml32:exterior/gml32:LinearRing', namespaces)) if linear_ring is not None: self.exterior_ring = self._coordinates_for_ring(linear_ring) interior_ring_elements = md.findall(util.nspath_eval('gml32:Polygon/gml32:interior', namespaces)) self.interior_rings = [] for iring_element in interior_ring_elements: linear_ring = iring_element.find(util.nspath_eval('gml32:LinearRing', namespaces)) self.interior_rings.append(self._coordinates_for_ring(linear_ring)) def _coordinates_for_ring(self, linear_ring): coordinates = [] positions = linear_ring.findall(util.nspath_eval('gml32:pos', namespaces)) for pos in positions: tokens = pos.text.split() coords = tuple([float(t) for t in tokens]) coordinates.append(coords) return coordinates class EX_GeographicBoundingPolygon(object): def __init__(self, md=None): if md is None: self.is_extent = None self.polygons = [] else: val = md.find(util.nspath_eval('gmd:extentTypeCode', namespaces)) self.is_extent = util.testXMLValue(val) md_polygons = md.findall(util.nspath_eval('gmd:polygon', namespaces)) self.polygons = [] for val in md_polygons: self.polygons.append(EX_Polygon(val)) class EX_Extent(object): """ process EX_Extent """ def __init__(self, md=None): if md is None: self.boundingBox = None self.boundingPolygon = None self.description_code = None else: self.boundingBox = None self.boundingPolygon = None if md is not None: bboxElement = md.find(util.nspath_eval('gmd:EX_GeographicBoundingBox', namespaces)) if bboxElement is not None: self.boundingBox = EX_GeographicBoundingBox(bboxElement) polygonElement = md.find(util.nspath_eval('gmd:EX_BoundingPolygon', namespaces)) if polygonElement is not None: self.boundingPolygon = EX_GeographicBoundingPolygon(polygonElement) val = md.find(util.nspath_eval('gmd:EX_GeographicDescription/gmd:geographicIdentifier/gmd:MD_Identifier/gmd:code/gco:CharacterString', namespaces)) self.description_code = util.testXMLValue(val) class MD_ReferenceSystem(object): """ process MD_ReferenceSystem """ def __init__(self, md): if md is None: pass else: val = md.find(util.nspath_eval('gmd:referenceSystemIdentifier/gmd:RS_Identifier/gmd:code/gco:CharacterString', namespaces)) self.code = util.testXMLValue(val) def _testCodeListValue(elpath): """ get gco:CodeListValue_Type attribute, else get text content """ if elpath is not None: # try to get @codeListValue val = util.testXMLValue(elpath.attrib.get('codeListValue'), True) if val is not None: return val else: # see if there is element text return util.testXMLValue(elpath) else: return None class CodelistCatalogue(object): """ process CT_CodelistCatalogue """ def __init__(self, ct): val = ct.find(util.nspath_eval('gmx:name/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:scope/gco:CharacterString', namespaces)) self.scope = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:fieldOfApplication/gco:CharacterString', namespaces)) self.fieldapp = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:versionNumber/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:versionDate/gco:Date', namespaces)) self.date = util.testXMLValue(val) self.dictionaries = {} for i in ct.findall(util.nspath_eval('gmx:codelistItem/gmx:CodeListDictionary', namespaces)): id = i.attrib.get(util.nspath_eval('gml32:id', namespaces)) self.dictionaries[id] = {} val = i.find(util.nspath_eval('gml32:description', namespaces)) self.dictionaries[id]['description'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gml32:identifier', namespaces)) self.dictionaries[id]['identifier'] = util.testXMLValue(val) self.dictionaries[id]['entries'] = {} for j in i.findall(util.nspath_eval('gmx:codeEntry', namespaces)): id2 = j.find(util.nspath_eval('gmx:CodeDefinition', namespaces)).attrib.get(util.nspath_eval('gml32:id', namespaces)) self.dictionaries[id]['entries'][id2] = {} val = j.find(util.nspath_eval('gmx:CodeDefinition/gml32:description', namespaces)) self.dictionaries[id]['entries'][id2]['description'] = util.testXMLValue(val) val = j.find(util.nspath_eval('gmx:CodeDefinition/gml32:identifier', namespaces)) self.dictionaries[id]['entries'][id2]['identifier'] = util.testXMLValue(val) val = j.find(util.nspath_eval('gmx:CodeDefinition', namespaces)).attrib.get('codeSpace') self.dictionaries[id]['entries'][id2]['codespace'] = util.testXMLValue(val, True) def getcodelistdictionaries(self): return self.dictionaries.keys() def getcodedefinitionidentifiers(self, cdl): if self.dictionaries.has_key(cdl): ids = [] for i in self.dictionaries[cdl]['entries']: ids.append(self.dictionaries[cdl]['entries'][i]['identifier']) return ids else: return None

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from owslib.etree import etree from owslib import util from owslib.namespaces import Namespaces def get_namespaces(): n = Namespaces() ns = n.get_namespaces(["gco","gmd","gml","gml32","gmx","gts","srv","xlink"]) ns[None] = n.get_namespace("gmd") return ns namespaces = get_namespaces() class MD_Metadata(object): def __init__(self, md=None): if md is None: self.xml = None self.identifier = None self.parentidentifier = None self.language = None self.dataseturi = None self.languagecode = None self.datestamp = None self.charset = None self.hierarchy = None self.contact = [] self.datetimestamp = None self.stdname = None self.stdver = None self.referencesystem = None self.identification = None self.serviceidentification = None self.identificationinfo = [] self.distribution = None self.dataquality = None else: if hasattr(md, 'getroot'): self.xml = etree.tostring(md.getroot()) else: self.xml = etree.tostring(md) val = md.find(util.nspath_eval('gmd:fileIdentifier/gco:CharacterString', namespaces)) self.identifier = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:parentIdentifier/gco:CharacterString', namespaces)) self.parentidentifier = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:language/gco:CharacterString', namespaces)) self.language = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:dataSetURI/gco:CharacterString', namespaces)) self.dataseturi = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:language/gmd:LanguageCode', namespaces)) self.languagecode = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:dateStamp/gco:Date', namespaces)) self.datestamp = util.testXMLValue(val) if not self.datestamp: val = md.find(util.nspath_eval('gmd:dateStamp/gco:DateTime', namespaces)) self.datestamp = util.testXMLValue(val) self.charset = _testCodeListValue(md.find(util.nspath_eval('gmd:characterSet/gmd:MD_CharacterSetCode', namespaces))) self.hierarchy = _testCodeListValue(md.find(util.nspath_eval('gmd:hierarchyLevel/gmd:MD_ScopeCode', namespaces))) self.contact = [] for i in md.findall(util.nspath_eval('gmd:contact/gmd:CI_ResponsibleParty', namespaces)): o = CI_ResponsibleParty(i) self.contact.append(o) val = md.find(util.nspath_eval('gmd:dateStamp/gco:DateTime', namespaces)) self.datetimestamp = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:metadataStandardName/gco:CharacterString', namespaces)) self.stdname = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:metadataStandardVersion/gco:CharacterString', namespaces)) self.stdver = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:referenceSystemInfo/gmd:MD_ReferenceSystem', namespaces)) if val is not None: self.referencesystem = MD_ReferenceSystem(val) else: self.referencesystem = None val = md.find(util.nspath_eval('gmd:identificationInfo/gmd:MD_DataIdentification', namespaces)) val2 = md.find(util.nspath_eval('gmd:identificationInfo/srv:SV_ServiceIdentification', namespaces)) if val is not None: self.identification = MD_DataIdentification(val, 'dataset') self.serviceidentification = None elif val2 is not None: self.identification = MD_DataIdentification(val2, 'service') self.serviceidentification = SV_ServiceIdentification(val2) else: self.identification = None self.serviceidentification = None self.identificationinfo = [] for idinfo in md.findall(util.nspath_eval('gmd:identificationInfo', namespaces)): val = list(idinfo)[0] tagval = util.xmltag_split(val.tag) if tagval == 'MD_DataIdentification': self.identificationinfo.append(MD_DataIdentification(val, 'dataset')) elif tagval == 'MD_ServiceIdentification': self.identificationinfo.append(MD_DataIdentification(val, 'service')) elif tagval == 'SV_ServiceIdentification': self.identificationinfo.append(SV_ServiceIdentification(val)) val = md.find(util.nspath_eval('gmd:distributionInfo/gmd:MD_Distribution', namespaces)) if val is not None: self.distribution = MD_Distribution(val) else: self.distribution = None val = md.find(util.nspath_eval('gmd:dataQualityInfo/gmd:DQ_DataQuality', namespaces)) if val is not None: self.dataquality = DQ_DataQuality(val) else: self.dataquality = None class CI_Date(object): def __init__(self, md=None): if md is None: self.date = None self.type = None else: val = md.find(util.nspath_eval('gmd:date/gco:Date', namespaces)) if val is not None: self.date = util.testXMLValue(val) else: val = md.find(util.nspath_eval('gmd:date/gco:DateTime', namespaces)) if val is not None: self.date = util.testXMLValue(val) else: self.date = None val = md.find(util.nspath_eval('gmd:dateType/gmd:CI_DateTypeCode', namespaces)) self.type = _testCodeListValue(val) class CI_ResponsibleParty(object): def __init__(self, md=None): if md is None: self.name = None self.organization = None self.position = None self.phone = None self.fax = None self.address = None self.city = None self.region = None self.postcode = None self.country = None self.email = None self.onlineresource = None self.role = None else: val = md.find(util.nspath_eval('gmd:individualName/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:organisationName/gco:CharacterString', namespaces)) self.organization = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:positionName/gco:CharacterString', namespaces)) self.position = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:phone/gmd:CI_Telephone/gmd:voice/gco:CharacterString', namespaces)) self.phone = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:phone/gmd:CI_Telephone/gmd:facsimile/gco:CharacterString', namespaces)) self.fax = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:deliveryPoint/gco:CharacterString', namespaces)) self.address = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:city/gco:CharacterString', namespaces)) self.city = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:administrativeArea/gco:CharacterString', namespaces)) self.region = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:postalCode/gco:CharacterString', namespaces)) self.postcode = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:country/gco:CharacterString', namespaces)) self.country = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:address/gmd:CI_Address/gmd:electronicMailAddress/gco:CharacterString', namespaces)) self.email = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:contactInfo/gmd:CI_Contact/gmd:onlineResource/gmd:CI_OnlineResource', namespaces)) if val is not None: self.onlineresource = CI_OnlineResource(val) else: self.onlineresource = None self.role = _testCodeListValue(md.find(util.nspath_eval('gmd:role/gmd:CI_RoleCode', namespaces))) class MD_DataIdentification(object): def __init__(self, md=None, identtype=None): if md is None: self.identtype = None self.title = None self.alternatetitle = None self.aggregationinfo = None self.uricode = [] self.uricodespace = [] self.date = [] self.datetype = [] self.uselimitation = [] self.accessconstraints = [] self.classification = [] self.otherconstraints = [] self.securityconstraints = [] self.useconstraints = [] self.denominators = [] self.distance = [] self.uom = [] self.resourcelanguage = [] self.creator = None self.publisher = None self.originator = None self.edition = None self.abstract = None self.purpose = None self.status = None self.contact = [] self.keywords = [] self.topiccategory = [] self.supplementalinformation = None self.extent = None self.bbox = None self.temporalextent_start = None self.temporalextent_end = None else: self.identtype = identtype val = md.find(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) self.title = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:alternateTitle/gco:CharacterString', namespaces)) self.alternatetitle = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:aggregationInfo', namespaces)) self.aggregationinfo = util.testXMLValue(val) self.uricode = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:identifier/gmd:RS_Identifier/gmd:code/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uricode.append(val) self.uricodespace = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:identifier/gmd:RS_Identifier/gmd:codeSpace/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uricodespace.append(val) self.date = [] self.datetype = [] for i in md.findall(util.nspath_eval('gmd:citation/gmd:CI_Citation/gmd:date/gmd:CI_Date', namespaces)): self.date.append(CI_Date(i)) self.uselimitation = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_Constraints/gmd:useLimitation/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.uselimitation.append(val) self.accessconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:accessConstraints/gmd:MD_RestrictionCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.accessconstraints.append(val) self.classification = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:accessConstraints/gmd:MD_ClassificationCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.classification.append(val) self.otherconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:otherConstraints/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.otherconstraints.append(val) self.securityconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_SecurityConstraints/gmd:useLimitation', namespaces)): val = util.testXMLValue(i) if val is not None: self.securityconstraints.append(val) self.useconstraints = [] for i in md.findall(util.nspath_eval('gmd:resourceConstraints/gmd:MD_LegalConstraints/gmd:useConstraints/gmd:MD_RestrictionCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.useconstraints.append(val) self.denominators = [] for i in md.findall(util.nspath_eval('gmd:spatialResolution/gmd:MD_Resolution/gmd:equivalentScale/gmd:MD_RepresentativeFraction/gmd:denominator/gco:Integer', namespaces)): val = util.testXMLValue(i) if val is not None: self.denominators.append(val) self.distance = [] self.uom = [] for i in md.findall(util.nspath_eval('gmd:spatialResolution/gmd:MD_Resolution/gmd:distance/gco:Distance', namespaces)): val = util.testXMLValue(i) if val is not None: self.distance.append(val) self.uom.append(i.get("uom")) self.resourcelanguage = [] for i in md.findall(util.nspath_eval('gmd:language/gmd:LanguageCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.resourcelanguage.append(val) val = md.find(util.nspath_eval('gmd:pointOfContact/gmd:CI_ResponsibleParty/gmd:organisationName', namespaces)) if val is not None: val2 = val.find(util.nspath_eval('gmd:role/gmd:CI_RoleCode', namespaces)) if val2 is not None: clv = _testCodeListValue(val) if clv == 'originator': self.creator = util.testXMLValue(val) elif clv == 'publisher': self.publisher = util.testXMLValue(val) elif clv == 'contributor': self.originator = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:edition/gco:CharacterString', namespaces)) self.edition = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:abstract/gco:CharacterString', namespaces)) self.abstract = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:purpose/gco:CharacterString', namespaces)) self.purpose = util.testXMLValue(val) self.status = _testCodeListValue(md.find(util.nspath_eval('gmd:status/gmd:MD_ProgressCode', namespaces))) self.contact = [] for i in md.findall(util.nspath_eval('gmd:pointOfContact/gmd:CI_ResponsibleParty', namespaces)): o = CI_ResponsibleParty(i) self.contact.append(o) self.keywords = [] for i in md.findall(util.nspath_eval('gmd:descriptiveKeywords', namespaces)): mdkw = {} mdkw['type'] = _testCodeListValue(i.find(util.nspath_eval('gmd:MD_Keywords/gmd:type/gmd:MD_KeywordTypeCode', namespaces))) mdkw['thesaurus'] = {} val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) mdkw['thesaurus']['title'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:date/gco:Date', namespaces)) mdkw['thesaurus']['date'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gmd:MD_Keywords/gmd:thesaurusName/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:dateType/gmd:CI_DateTypeCode', namespaces)) mdkw['thesaurus']['datetype'] = util.testXMLValue(val) mdkw['keywords'] = [] for k in i.findall(util.nspath_eval('gmd:MD_Keywords/gmd:keyword', namespaces)): val = k.find(util.nspath_eval('gco:CharacterString', namespaces)) if val is not None: val2 = util.testXMLValue(val) if val2 is not None: mdkw['keywords'].append(val2) self.keywords.append(mdkw) self.topiccategory = [] for i in md.findall(util.nspath_eval('gmd:topicCategory/gmd:MD_TopicCategoryCode', namespaces)): val = util.testXMLValue(i) if val is not None: self.topiccategory.append(val) val = md.find(util.nspath_eval('gmd:supplementalInformation/gco:CharacterString', namespaces)) self.supplementalinformation = util.testXMLValue(val) val = None val2 = None val3 = None extents = md.findall(util.nspath_eval('gmd:extent', namespaces)) extents.extend(md.findall(util.nspath_eval('srv:extent', namespaces))) for extent in extents: if val is None: for e in extent.findall(util.nspath_eval('gmd:EX_Extent/gmd:geographicElement', namespaces)): if e.find(util.nspath_eval('gmd:EX_GeographicBoundingBox', namespaces)) is not None or e.find(util.nspath_eval('gmd:EX_BoundingPolygon', namespaces)) is not None: val = e break self.extent = EX_Extent(val) self.bbox = self.extent.boundingBox if val2 is None: val2 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml:TimePeriod/gml:beginPosition', namespaces)) if val2 is None: val2 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml32:TimePeriod/gml32:beginPosition', namespaces)) self.temporalextent_start = util.testXMLValue(val2) if val3 is None: val3 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml:TimePeriod/gml:endPosition', namespaces)) if val3 is None: val3 = extent.find(util.nspath_eval('gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent/gmd:extent/gml32:TimePeriod/gml32:endPosition', namespaces)) self.temporalextent_end = util.testXMLValue(val3) class MD_Distributor(object): def __init__(self, md=None): if md is None: self.contact = None self.online = [] else: self.contact = None val = md.find(util.nspath_eval('gmd:MD_Distributor/gmd:distributorContact/gmd:CI_ResponsibleParty', namespaces)) if val is not None: self.contact = CI_ResponsibleParty(val) self.online = [] for ol in md.findall(util.nspath_eval('gmd:MD_Distributor/gmd:distributorTransferOptions/gmd:MD_DigitalTransferOptions/gmd:onLine/gmd:CI_OnlineResource', namespaces)): self.online.append(CI_OnlineResource(ol)) class MD_Distribution(object): def __init__(self, md=None): if md is None: self.format = None self.version = None self.distributor = [] self.online = [] pass else: val = md.find(util.nspath_eval('gmd:distributionFormat/gmd:MD_Format/gmd:name/gco:CharacterString', namespaces)) self.format = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:distributionFormat/gmd:MD_Format/gmd:version/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) self.distributor = [] for dist in md.findall(util.nspath_eval('gmd:distributor', namespaces)): self.distributor.append(MD_Distributor(dist)) self.online = [] for ol in md.findall(util.nspath_eval('gmd:transferOptions/gmd:MD_DigitalTransferOptions/gmd:onLine/gmd:CI_OnlineResource', namespaces)): self.online.append(CI_OnlineResource(ol)) class DQ_DataQuality(object): def __init__(self, md=None): if md is None: self.conformancetitle = [] self.conformancedate = [] self.conformancedatetype = [] self.conformancedegree = [] self.lineage = None self.specificationtitle = None self.specificationdate = [] else: self.conformancetitle = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancetitle.append(val) self.conformancedate = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:date/gco:Date', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancedate.append(val) self.conformancedatetype = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date/gmd:dateType/gmd:CI_DateTypeCode', namespaces)): val = _testCodeListValue(i) if val is not None: self.conformancedatetype.append(val) self.conformancedegree = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:pass/gco:Boolean', namespaces)): val = util.testXMLValue(i) if val is not None: self.conformancedegree.append(val) val = md.find(util.nspath_eval('gmd:lineage/gmd:LI_Lineage/gmd:statement/gco:CharacterString', namespaces)) self.lineage = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:title/gco:CharacterString', namespaces)) self.specificationtitle = util.testXMLValue(val) self.specificationdate = [] for i in md.findall(util.nspath_eval('gmd:report/gmd:DQ_DomainConsistency/gmd:result/gmd:DQ_ConformanceResult/gmd:specification/gmd:CI_Citation/gmd:date/gmd:CI_Date', namespaces)): val = util.testXMLValue(i) if val is not None: self.specificationdate.append(val) class SV_ServiceIdentification(object): def __init__(self, md=None): if md is None: self.identtype = 'service' self.type = None self.version = None self.fees = None self.bbox = None self.couplingtype = None self.operations = [] self.operateson = [] else: self.identtype = 'service' val = md.find(util.nspath_eval('srv:serviceType/gco:LocalName', namespaces)) self.type = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:serviceTypeVersion/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:accessProperties/gmd:MD_StandardOrderProcess/gmd:fees/gco:CharacterString', namespaces)) self.fees = util.testXMLValue(val) val = md.find(util.nspath_eval('srv:extent/gmd:EX_Extent', namespaces)) if val is not None: self.bbox = EX_Extent(val) else: self.bbox = None self.couplingtype = _testCodeListValue(md.find(util.nspath_eval('gmd:couplingType/gmd:SV_CouplingType', namespaces))) self.operations = [] for i in md.findall(util.nspath_eval('srv:containsOperations', namespaces)): tmp = {} val = i.find(util.nspath_eval('srv:SV_OperationMetadata/srv:operationName/gco:CharacterString', namespaces)) tmp['name'] = util.testXMLValue(val) tmp['dcplist'] = [] for d in i.findall(util.nspath_eval('srv:SV_OperationMetadata/srv:DCP', namespaces)): tmp2 = _testCodeListValue(d.find(util.nspath_eval('srv:DCPList', namespaces))) tmp['dcplist'].append(tmp2) tmp['connectpoint'] = [] for d in i.findall(util.nspath_eval('srv:SV_OperationMetadata/srv:connectPoint', namespaces)): tmp3 = d.find(util.nspath_eval('gmd:CI_OnlineResource', namespaces)) tmp['connectpoint'].append(CI_OnlineResource(tmp3)) self.operations.append(tmp) self.operateson = [] for i in md.findall(util.nspath_eval('srv:operatesOn', namespaces)): tmp = {} tmp['uuidref'] = i.attrib.get('uuidref') tmp['href'] = i.attrib.get(util.nspath_eval('xlink:href', namespaces)) tmp['title'] = i.attrib.get(util.nspath_eval('xlink:title', namespaces)) self.operateson.append(tmp) class CI_OnlineResource(object): def __init__(self,md=None): if md is None: self.url = None self.protocol = None self.name = None self.description = None self.function = None else: val = md.find(util.nspath_eval('gmd:linkage/gmd:URL', namespaces)) self.url = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:protocol/gco:CharacterString', namespaces)) self.protocol = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:name/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:description/gco:CharacterString', namespaces)) self.description = util.testXMLValue(val) self.function = _testCodeListValue(md.find(util.nspath_eval('gmd:function/gmd:CI_OnLineFunctionCode', namespaces))) class EX_GeographicBoundingBox(object): def __init__(self, md=None): if md is None: self.minx = None self.maxx = None self.miny = None self.maxy = None else: val = md.find(util.nspath_eval('gmd:westBoundLongitude/gco:Decimal', namespaces)) self.minx = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:eastBoundLongitude/gco:Decimal', namespaces)) self.maxx = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:southBoundLatitude/gco:Decimal', namespaces)) self.miny = util.testXMLValue(val) val = md.find(util.nspath_eval('gmd:northBoundLatitude/gco:Decimal', namespaces)) self.maxy = util.testXMLValue(val) class EX_Polygon(object): def __init__(self, md=None): if md is None: self.exterior_ring = None self.interior_rings = [] else: linear_ring = md.find(util.nspath_eval('gml32:Polygon/gml32:exterior/gml32:LinearRing', namespaces)) if linear_ring is not None: self.exterior_ring = self._coordinates_for_ring(linear_ring) interior_ring_elements = md.findall(util.nspath_eval('gml32:Polygon/gml32:interior', namespaces)) self.interior_rings = [] for iring_element in interior_ring_elements: linear_ring = iring_element.find(util.nspath_eval('gml32:LinearRing', namespaces)) self.interior_rings.append(self._coordinates_for_ring(linear_ring)) def _coordinates_for_ring(self, linear_ring): coordinates = [] positions = linear_ring.findall(util.nspath_eval('gml32:pos', namespaces)) for pos in positions: tokens = pos.text.split() coords = tuple([float(t) for t in tokens]) coordinates.append(coords) return coordinates class EX_GeographicBoundingPolygon(object): def __init__(self, md=None): if md is None: self.is_extent = None self.polygons = [] else: val = md.find(util.nspath_eval('gmd:extentTypeCode', namespaces)) self.is_extent = util.testXMLValue(val) md_polygons = md.findall(util.nspath_eval('gmd:polygon', namespaces)) self.polygons = [] for val in md_polygons: self.polygons.append(EX_Polygon(val)) class EX_Extent(object): def __init__(self, md=None): if md is None: self.boundingBox = None self.boundingPolygon = None self.description_code = None else: self.boundingBox = None self.boundingPolygon = None if md is not None: bboxElement = md.find(util.nspath_eval('gmd:EX_GeographicBoundingBox', namespaces)) if bboxElement is not None: self.boundingBox = EX_GeographicBoundingBox(bboxElement) polygonElement = md.find(util.nspath_eval('gmd:EX_BoundingPolygon', namespaces)) if polygonElement is not None: self.boundingPolygon = EX_GeographicBoundingPolygon(polygonElement) val = md.find(util.nspath_eval('gmd:EX_GeographicDescription/gmd:geographicIdentifier/gmd:MD_Identifier/gmd:code/gco:CharacterString', namespaces)) self.description_code = util.testXMLValue(val) class MD_ReferenceSystem(object): def __init__(self, md): if md is None: pass else: val = md.find(util.nspath_eval('gmd:referenceSystemIdentifier/gmd:RS_Identifier/gmd:code/gco:CharacterString', namespaces)) self.code = util.testXMLValue(val) def _testCodeListValue(elpath): if elpath is not None: val = util.testXMLValue(elpath.attrib.get('codeListValue'), True) if val is not None: return val else: return util.testXMLValue(elpath) else: return None class CodelistCatalogue(object): def __init__(self, ct): val = ct.find(util.nspath_eval('gmx:name/gco:CharacterString', namespaces)) self.name = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:scope/gco:CharacterString', namespaces)) self.scope = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:fieldOfApplication/gco:CharacterString', namespaces)) self.fieldapp = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:versionNumber/gco:CharacterString', namespaces)) self.version = util.testXMLValue(val) val = ct.find(util.nspath_eval('gmx:versionDate/gco:Date', namespaces)) self.date = util.testXMLValue(val) self.dictionaries = {} for i in ct.findall(util.nspath_eval('gmx:codelistItem/gmx:CodeListDictionary', namespaces)): id = i.attrib.get(util.nspath_eval('gml32:id', namespaces)) self.dictionaries[id] = {} val = i.find(util.nspath_eval('gml32:description', namespaces)) self.dictionaries[id]['description'] = util.testXMLValue(val) val = i.find(util.nspath_eval('gml32:identifier', namespaces)) self.dictionaries[id]['identifier'] = util.testXMLValue(val) self.dictionaries[id]['entries'] = {} for j in i.findall(util.nspath_eval('gmx:codeEntry', namespaces)): id2 = j.find(util.nspath_eval('gmx:CodeDefinition', namespaces)).attrib.get(util.nspath_eval('gml32:id', namespaces)) self.dictionaries[id]['entries'][id2] = {} val = j.find(util.nspath_eval('gmx:CodeDefinition/gml32:description', namespaces)) self.dictionaries[id]['entries'][id2]['description'] = util.testXMLValue(val) val = j.find(util.nspath_eval('gmx:CodeDefinition/gml32:identifier', namespaces)) self.dictionaries[id]['entries'][id2]['identifier'] = util.testXMLValue(val) val = j.find(util.nspath_eval('gmx:CodeDefinition', namespaces)).attrib.get('codeSpace') self.dictionaries[id]['entries'][id2]['codespace'] = util.testXMLValue(val, True) def getcodelistdictionaries(self): return self.dictionaries.keys() def getcodedefinitionidentifiers(self, cdl): if self.dictionaries.has_key(cdl): ids = [] for i in self.dictionaries[cdl]['entries']: ids.append(self.dictionaries[cdl]['entries'][i]['identifier']) return ids else: return None

true

f719a47cc5a7d23e73cc98dbe3e60cc827cae0aa

3,057

py

Python

fun.py

Grymlock/Guardian_Bot

0fac4cd37038a46d1d8b6eed3fbb79832bd7abf9

[ "MIT" ]

1

2018-06-22T03:52:49.000Z

fun.py

Grymlock/Guardian_Bot

0fac4cd37038a46d1d8b6eed3fbb79832bd7abf9

[ "MIT" ]

null

fun.py

Grymlock/Guardian_Bot

0fac4cd37038a46d1d8b6eed3fbb79832bd7abf9

[ "MIT" ]

null

import discord import constants as c from discord.ext import commands import random as r urls=['https://cdn.discordapp.com/attachments/433007901800398858/433047585121501194/maxresdefault.jpg','https://cdn.discordapp.com/attachments/442868510776098818/442879211296915466/9bt3n9w40bp01.jpg','https://cdn.discordapp.com/attachments/442323518860951589/443142715761360915/Dap.PNG',"https://cdn.discordapp.com/attachments/442323518860951589/443250907501821964/IMG_20180222_192827.jpg"] badWords=["gamer","frick","fudge","heck","bubby"] class Fun: def __init__(self,bot): self.bot=bot @commands.command() async def dab(self,ctx, *, member: discord.Member): "everybody pause at 1:18" try: if member.id==426560497781833748 or member.id==c.owner_id: await ctx.send("haha no") else: em=discord.Embed(title="",description='') rand=r.randint(0,3) em.set_image(url=str(urls[rand])) await ctx.send(embed=em) await ctx.send(str(member.mention)) except: await ctx.send("Invalid user") @commands.command() async def bruhcat(self,ctx): "bruh" catembed=discord.Embed() catembed.set_image(url="https://cdn.discordapp.com/attachments/444325494264037377/445300639631671296/bruh.gif") await ctx.send(embed=catembed) @commands.command() async def blicky(self,ctx): em=discord.Embed() em.set_image(url="https://cdn.discordapp.com/attachments/444325494264037377/445407209359409163/27c3yf.png") await ctx.send(embed=em) async def on_message(self,message): if message.author.bot:#prevents the bot from reacting to itself pass else: for word in badWords: if message.content==(word): await message.channel.send(f"Please do not use the word '{word}' or I will report you and block you") ran=r.randint(1,2000) if ran==1: await message.channel.send("^Are you listening to this retard lmao") if message.content==("gm") or message.content==("good morning"): await message.channel.send("Another day closer to death" + str(message.author.mention)) if message.content==("gn") or message.content==("good night"): await message.channel.send("sleep tight boyo") if message.content==("good bye"): await message.channel.send("bye loser") if message.content==("what do we want"): await message.channel.send("Equality for women") if message.content==("when do we want it"): await message.channel.send("Now") async def on_member_ban(self,guild,member): if member==c.owner_id: await guild.owner.send("Can y'all stop banning my master") def setup(bot): bot.add_cog(Fun(bot))

44.955882

391

0.615309

import discord import constants as c from discord.ext import commands import random as r urls=['https://cdn.discordapp.com/attachments/433007901800398858/433047585121501194/maxresdefault.jpg','https://cdn.discordapp.com/attachments/442868510776098818/442879211296915466/9bt3n9w40bp01.jpg','https://cdn.discordapp.com/attachments/442323518860951589/443142715761360915/Dap.PNG',"https://cdn.discordapp.com/attachments/442323518860951589/443250907501821964/IMG_20180222_192827.jpg"] badWords=["gamer","frick","fudge","heck","bubby"] class Fun: def __init__(self,bot): self.bot=bot @commands.command() async def dab(self,ctx, *, member: discord.Member): try: if member.id==426560497781833748 or member.id==c.owner_id: await ctx.send("haha no") else: em=discord.Embed(title="",description='') rand=r.randint(0,3) em.set_image(url=str(urls[rand])) await ctx.send(embed=em) await ctx.send(str(member.mention)) except: await ctx.send("Invalid user") @commands.command() async def bruhcat(self,ctx): catembed=discord.Embed() catembed.set_image(url="https://cdn.discordapp.com/attachments/444325494264037377/445300639631671296/bruh.gif") await ctx.send(embed=catembed) @commands.command() async def blicky(self,ctx): em=discord.Embed() em.set_image(url="https://cdn.discordapp.com/attachments/444325494264037377/445407209359409163/27c3yf.png") await ctx.send(embed=em) async def on_message(self,message): if message.author.bot: pass else: for word in badWords: if message.content==(word): await message.channel.send(f"Please do not use the word '{word}' or I will report you and block you") ran=r.randint(1,2000) if ran==1: await message.channel.send("^Are you listening to this retard lmao") if message.content==("gm") or message.content==("good morning"): await message.channel.send("Another day closer to death" + str(message.author.mention)) if message.content==("gn") or message.content==("good night"): await message.channel.send("sleep tight boyo") if message.content==("good bye"): await message.channel.send("bye loser") if message.content==("what do we want"): await message.channel.send("Equality for women") if message.content==("when do we want it"): await message.channel.send("Now") async def on_member_ban(self,guild,member): if member==c.owner_id: await guild.owner.send("Can y'all stop banning my master") def setup(bot): bot.add_cog(Fun(bot))

true

f719a5d3a4154a174de4fc3bb0bdc9ef6f49b521

1,012

py

Python

test/schemes/test_qz.py

stormymcstorm/condensa

ee3bf993b0032e5d84aeb3cc7f0ddcdb8d846bd9

[ "Apache-2.0" ]

153

2019-05-29T15:10:38.000Z

2022-03-05T05:20:55.000Z

test/schemes/test_qz.py

rogerxujiang/condensa

c7321e0a362f73eca9349769b341a7dd688ee1b9

[ "Apache-2.0" ]

5

2019-07-11T20:56:38.000Z

2022-03-14T10:12:15.000Z

test/schemes/test_qz.py

rogerxujiang/condensa

c7321e0a362f73eca9349769b341a7dd688ee1b9

[ "Apache-2.0" ]

21

2019-05-30T22:21:54.000Z

2022-03-14T07:06:52.000Z

# Copyright 2019 NVIDIA Corporation # # 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 torch import condensa from condensa import schemes def test_float16(device): scheme = schemes.Quantize(condensa.float16) fc = torch.nn.Linear(100, 10).float().to(device) scheme.pi(fc) assert fc.weight.dtype == torch.float16 scheme.delta(fc) assert fc.weight.dtype == torch.float32 if __name__ == '__main__': test_float16('cpu') if torch.cuda.is_available(): test_float16('cpu')

30.666667

74

0.733202

import torch import condensa from condensa import schemes def test_float16(device): scheme = schemes.Quantize(condensa.float16) fc = torch.nn.Linear(100, 10).float().to(device) scheme.pi(fc) assert fc.weight.dtype == torch.float16 scheme.delta(fc) assert fc.weight.dtype == torch.float32 if __name__ == '__main__': test_float16('cpu') if torch.cuda.is_available(): test_float16('cpu')

true

f719a5ec02915c2d40aa2c28ddf93147dd695082

6,791

py

Python

objects/CSCG/_3d/forms/standard/base/export/field.py

mathischeap/mifem

3242e253fb01ca205a76568eaac7bbdb99e3f059

[ "MIT" ]

1

2020-10-14T12:48:35.000Z

objects/CSCG/_3d/forms/standard/base/export/field.py

mathischeap/mifem

3242e253fb01ca205a76568eaac7bbdb99e3f059

[ "MIT" ]

null

objects/CSCG/_3d/forms/standard/base/export/field.py

mathischeap/mifem

3242e253fb01ca205a76568eaac7bbdb99e3f059

[ "MIT" ]

null

"""We want to export the field to some data files. """ from root.config.main import * from screws.freeze.main import FrozenOnly from screws.miscellaneous.timer import check_filename, check_no_splcharacter from scipy.io import savemat class _3dCSC_SF_Export_Field(FrozenOnly): """""" def __init__(self, sf): """""" assert '3dCSCG_standard_form' in sf.standard_properties.tags self._sf_ = sf self._freeze_self_() def to_file(self, filename, numOfSamples=1e6, regions=None): """""" filename, extension = check_filename(filename) if extension is None: extension = 'txt' supported_formats = ('txt', 'mat') assert extension in supported_formats, \ f"format={extension} is not among the supported formats {supported_formats}." if isinstance(numOfSamples, (int, float)): assert numOfSamples > 0, f"numOfSamples={numOfSamples} is wrong." numOfSamples = [numOfSamples, numOfSamples, numOfSamples] else: assert isinstance(numOfSamples, (tuple, list)) and len(numOfSamples) == 3, \ f"numOfSamples={numOfSamples} wrong." for nos in numOfSamples: assert isinstance(nos, (int, float)) and nos > 0, f"numOfSamples={numOfSamples} wrong." mesh = self._sf_.mesh if regions is None: regions = mesh.domain.regions.names elif isinstance(regions, str): regions = [regions,] else: pass assert isinstance(regions, (list, tuple)), f"regions={regions} is wrong." assert len(set(regions)) == len(regions), f"regions={regions} has repeated regions." for i, r in enumerate(regions): assert r in mesh.domain.regions, f"regions[{i}]={r} is wrong." rst = list() for i in range(3): density = int((numOfSamples[i] / mesh.elements.GLOBAL_num) ** (1/3)) + 1 interval = 2 / density rst.append(np.linspace(-1 + interval/2, 1-interval/2, density)) xyz, v = self._sf_.reconstruct(*rst, regions=regions) # Now, we gather xyz & v from all cores into Master Core, store in XYZ & V --- BELOW --- if rAnk == mAster_rank: X = [None for _ in range(mesh.elements.GLOBAL_num)] Y = [None for _ in range(mesh.elements.GLOBAL_num)] Z = [None for _ in range(mesh.elements.GLOBAL_num)] Vx = [None for _ in range(mesh.elements.GLOBAL_num)] if self._sf_.k in (1, 2): Vy = [None for _ in range(mesh.elements.GLOBAL_num)] Vz = [None for _ in range(mesh.elements.GLOBAL_num)] for j in mesh.elements.indices: X[j] = xyz[j][0] Y[j] = xyz[j][1] Z[j] = xyz[j][2] Vx[j] = v[j][0] if self._sf_.k in (1, 2): # noinspection PyUnboundLocalVariable Vy[j] = v[j][1] # noinspection PyUnboundLocalVariable Vz[j] = v[j][2] for i in sLave_ranks: xyz, v = cOmm.recv(source=i, tag=0) for j in xyz: X[j] = xyz[j][0] Y[j] = xyz[j][1] Z[j] = xyz[j][2] Vx[j] = v[j][0] if self._sf_.k in (1, 2): Vy[j] = v[j][1] Vz[j] = v[j][2] del xyz, v else: cOmm.send([xyz, v], dest=mAster_rank, tag=0) del xyz, v # Now, we reshape the XYZ and V for export in the master core. -------- BELOW ---------- if rAnk == mAster_rank: if self._sf_.k in (1, 2): # noinspection PyUnboundLocalVariable X, Y, Z, Vx, Vy, Vz = mesh.do.regionwsie_stack(X, Y, Z, Vx, Vy, Vz) else: # noinspection PyUnboundLocalVariable X, Y, Z, V = mesh.do.regionwsie_stack(X, Y, Z, Vx) for rn in regions: assert rn in X and rn in Y and rn in Z, "Data not full!" x, y, z = X[rn], Y[rn], Z[rn] if self._sf_.k in (1, 2): vx, vy, vz = Vx[rn], Vy[rn], Vz[rn] else: # noinspection PyUnboundLocalVariable vx = V[rn] # we take care of the file names ------------------ BELOW ----------------------- RN = rn[2:] # if regions name is R:center, we select assert check_no_splcharacter(RN), f"region name={RN} wrong." FILE_NAME = filename + '__InRegion_' + RN if self._sf_.k in (1, 2): FILE_NAME += '__x_y_z_vx_vy_vz' else: FILE_NAME += '__x_y_z_v' FILE_NAME = FILE_NAME + '.' + extension # It's time to do the save or writing ------------------- BELOW ----------------- if extension == 'txt': # for .txt, we have to flat the data ===================== x = x.ravel(order='F')[:,np.newaxis] y = y.ravel(order='F')[:,np.newaxis] z = z.ravel(order='F')[:,np.newaxis] if self._sf_.k in (1, 2): vx = vx.ravel(order='F')[:,np.newaxis] # noinspection PyUnboundLocalVariable vy = vy.ravel(order='F')[:,np.newaxis] # noinspection PyUnboundLocalVariable vz = vz.ravel(order='F')[:,np.newaxis] else: vx = vx.ravel(order='F')[:,np.newaxis] if self._sf_.k in (1, 2): # noinspection PyUnboundLocalVariable TO_BE_WRITTEN = np.hstack((x, y, z, vx, vy, vz)) else: TO_BE_WRITTEN = np.hstack((x, y, z, vx)) # noinspection PyTypeChecker np.savetxt(FILE_NAME, TO_BE_WRITTEN) elif extension == 'mat': # for .mat, we save 3-d arrays. ========================== m_dic = dict() m_dic['x'] = x m_dic['y'] = y m_dic['z'] = z if self._sf_.k in (1, 2): m_dic['vx'] = vx m_dic['vy'] = vy m_dic['vz'] = vz else: m_dic['v'] = vx savemat(FILE_NAME, m_dic) else: raise Exception(f"Format=.{extension} is not supported.")

41.408537

103

0.472684

from root.config.main import * from screws.freeze.main import FrozenOnly from screws.miscellaneous.timer import check_filename, check_no_splcharacter from scipy.io import savemat class _3dCSC_SF_Export_Field(FrozenOnly): def __init__(self, sf): assert '3dCSCG_standard_form' in sf.standard_properties.tags self._sf_ = sf self._freeze_self_() def to_file(self, filename, numOfSamples=1e6, regions=None): filename, extension = check_filename(filename) if extension is None: extension = 'txt' supported_formats = ('txt', 'mat') assert extension in supported_formats, \ f"format={extension} is not among the supported formats {supported_formats}." if isinstance(numOfSamples, (int, float)): assert numOfSamples > 0, f"numOfSamples={numOfSamples} is wrong." numOfSamples = [numOfSamples, numOfSamples, numOfSamples] else: assert isinstance(numOfSamples, (tuple, list)) and len(numOfSamples) == 3, \ f"numOfSamples={numOfSamples} wrong." for nos in numOfSamples: assert isinstance(nos, (int, float)) and nos > 0, f"numOfSamples={numOfSamples} wrong." mesh = self._sf_.mesh if regions is None: regions = mesh.domain.regions.names elif isinstance(regions, str): regions = [regions,] else: pass assert isinstance(regions, (list, tuple)), f"regions={regions} is wrong." assert len(set(regions)) == len(regions), f"regions={regions} has repeated regions." for i, r in enumerate(regions): assert r in mesh.domain.regions, f"regions[{i}]={r} is wrong." rst = list() for i in range(3): density = int((numOfSamples[i] / mesh.elements.GLOBAL_num) ** (1/3)) + 1 interval = 2 / density rst.append(np.linspace(-1 + interval/2, 1-interval/2, density)) xyz, v = self._sf_.reconstruct(*rst, regions=regions) if rAnk == mAster_rank: X = [None for _ in range(mesh.elements.GLOBAL_num)] Y = [None for _ in range(mesh.elements.GLOBAL_num)] Z = [None for _ in range(mesh.elements.GLOBAL_num)] Vx = [None for _ in range(mesh.elements.GLOBAL_num)] if self._sf_.k in (1, 2): Vy = [None for _ in range(mesh.elements.GLOBAL_num)] Vz = [None for _ in range(mesh.elements.GLOBAL_num)] for j in mesh.elements.indices: X[j] = xyz[j][0] Y[j] = xyz[j][1] Z[j] = xyz[j][2] Vx[j] = v[j][0] if self._sf_.k in (1, 2): Vy[j] = v[j][1] Vz[j] = v[j][2] for i in sLave_ranks: xyz, v = cOmm.recv(source=i, tag=0) for j in xyz: X[j] = xyz[j][0] Y[j] = xyz[j][1] Z[j] = xyz[j][2] Vx[j] = v[j][0] if self._sf_.k in (1, 2): Vy[j] = v[j][1] Vz[j] = v[j][2] del xyz, v else: cOmm.send([xyz, v], dest=mAster_rank, tag=0) del xyz, v if rAnk == mAster_rank: if self._sf_.k in (1, 2): X, Y, Z, Vx, Vy, Vz = mesh.do.regionwsie_stack(X, Y, Z, Vx, Vy, Vz) else: X, Y, Z, V = mesh.do.regionwsie_stack(X, Y, Z, Vx) for rn in regions: assert rn in X and rn in Y and rn in Z, "Data not full!" x, y, z = X[rn], Y[rn], Z[rn] if self._sf_.k in (1, 2): vx, vy, vz = Vx[rn], Vy[rn], Vz[rn] else: vx = V[rn] RN = rn[2:] assert check_no_splcharacter(RN), f"region name={RN} wrong." FILE_NAME = filename + '__InRegion_' + RN if self._sf_.k in (1, 2): FILE_NAME += '__x_y_z_vx_vy_vz' else: FILE_NAME += '__x_y_z_v' FILE_NAME = FILE_NAME + '.' + extension if extension == 'txt': # for .txt, we have to flat the data ===================== x = x.ravel(order='F')[:,np.newaxis] y = y.ravel(order='F')[:,np.newaxis] z = z.ravel(order='F')[:,np.newaxis] if self._sf_.k in (1, 2): vx = vx.ravel(order='F')[:,np.newaxis] # noinspection PyUnboundLocalVariable vy = vy.ravel(order='F')[:,np.newaxis] # noinspection PyUnboundLocalVariable vz = vz.ravel(order='F')[:,np.newaxis] else: vx = vx.ravel(order='F')[:,np.newaxis] if self._sf_.k in (1, 2): # noinspection PyUnboundLocalVariable TO_BE_WRITTEN = np.hstack((x, y, z, vx, vy, vz)) else: TO_BE_WRITTEN = np.hstack((x, y, z, vx)) # noinspection PyTypeChecker np.savetxt(FILE_NAME, TO_BE_WRITTEN) elif extension == 'mat': # for .mat, we save 3-d arrays. ========================== m_dic = dict() m_dic['x'] = x m_dic['y'] = y m_dic['z'] = z if self._sf_.k in (1, 2): m_dic['vx'] = vx m_dic['vy'] = vy m_dic['vz'] = vz else: m_dic['v'] = vx savemat(FILE_NAME, m_dic) else: raise Exception(f"Format=.{extension} is not supported.")

true

f719a60077cb4b23bbe3c54efafc1d30bc3f8163

3,252

py

Python

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

[ "MIT" ]

1

2019-03-13T16:05:11.000Z

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

[ "MIT" ]

null

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

[ "MIT" ]

1

2019-03-15T09:09:08.000Z

from easydict import EasyDict as edict # from pathlib import Path import torch import os from torchvision import transforms as trans from utils.constants import * list_model = ['wget https://www.dropbox.com/s/akktsgxp0n8cwn2/model_mobilefacenet.pth?dl=0 -O model_mobilefacenet.pth', 'wget https://www.dropbox.com/s/kzo52d9neybjxsb/model_ir_se50.pth?dl=0 -O model_ir_se50.pth', 'wget https://www.dropbox.com/s/rxavczg9dlxy3a8/model_ir50.pth?dl=0 -O model_ir50.pth'] def get_config(mode = 'app', net_size = 'large', net_mode = 'ir_se', use_mtcnn = 1, threshold = 1.25): conf = edict() conf.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") conf.input_size = [112, 112] conf.face_limit = 5 conf.min_face_size = 30 conf.mode = mode conf.net_size = net_size if mode =='app': assert net_size in ['mobi', 'large', None], 'net_size should be mobi or large, please change in cogfig.py' conf.use_tensor = True conf.work_path = WORK_PATH conf.model_path = '%s/models'%WORK_PATH conf.log_path = '%s/log'%WORK_PATH conf.save_path = '%s/save'%WORK_PATH conf.facebank_path = '%s/Face_bank'%WORK_PATH conf.threshold = threshold if use_mtcnn: conf.use_mtcnn = True else: conf.use_mtcnn = False #when inference, at maximum detect 10 faces in one image, my laptop is slow conf.test_transform = trans.Compose([ trans.ToTensor(), trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ]) if net_size == 'large': conf.use_mobilfacenet = False if net_mode == 'ir_se': conf.net_mode = 'ir_se' # or 'ir' conf.weight_path = '%s/weights/model_ir_se50.pth'%WORK_PATH conf.url = list_model[1] else: conf.net_mode = 'ir' # or 'ir' conf.weight_path = '%s/weights/model_ir50.pth'%WORK_PATH conf.url = list_model[2] if net_size =='mobi': conf.use_mobilfacenet = True conf.weight_path = '%s/weights/model_mobilefacenet.pth'%WORK_PATH conf.url = list_model[0] conf.video_source = 0 if mode =='training_eval': conf.lr = 1e-3 conf.milestones = [18,30,42] conf.momentum = 0.9 conf.pin_memory = True # conf.num_workers = 4 # when batchsize is 200 conf.num_workers = 3 conf.train_root = "/mnt/01D4A1D481139570/Dataset/Face/casia" conf.file_list = '/mnt/01D4A1D481139570/Dataset/Face/casia_train.txt' conf.batch_size = 4 conf.lfw_root = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/lfw_align_112' conf.lfw_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/pairs.txt' conf.agedb_root = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb30_align_112' conf.agedb_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb_30_pair.txt' conf.cfp_root = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/CFP_FP_aligned_112' conf.cfp_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/cfp_fp_pair.txt' return conf

47.823529

119

0.634071

from easydict import EasyDict as edict import torch import os from torchvision import transforms as trans from utils.constants import * list_model = ['wget https://www.dropbox.com/s/akktsgxp0n8cwn2/model_mobilefacenet.pth?dl=0 -O model_mobilefacenet.pth', 'wget https://www.dropbox.com/s/kzo52d9neybjxsb/model_ir_se50.pth?dl=0 -O model_ir_se50.pth', 'wget https://www.dropbox.com/s/rxavczg9dlxy3a8/model_ir50.pth?dl=0 -O model_ir50.pth'] def get_config(mode = 'app', net_size = 'large', net_mode = 'ir_se', use_mtcnn = 1, threshold = 1.25): conf = edict() conf.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") conf.input_size = [112, 112] conf.face_limit = 5 conf.min_face_size = 30 conf.mode = mode conf.net_size = net_size if mode =='app': assert net_size in ['mobi', 'large', None], 'net_size should be mobi or large, please change in cogfig.py' conf.use_tensor = True conf.work_path = WORK_PATH conf.model_path = '%s/models'%WORK_PATH conf.log_path = '%s/log'%WORK_PATH conf.save_path = '%s/save'%WORK_PATH conf.facebank_path = '%s/Face_bank'%WORK_PATH conf.threshold = threshold if use_mtcnn: conf.use_mtcnn = True else: conf.use_mtcnn = False conf.test_transform = trans.Compose([ trans.ToTensor(), trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ]) if net_size == 'large': conf.use_mobilfacenet = False if net_mode == 'ir_se': conf.net_mode = 'ir_se' conf.weight_path = '%s/weights/model_ir_se50.pth'%WORK_PATH conf.url = list_model[1] else: conf.net_mode = 'ir' conf.weight_path = '%s/weights/model_ir50.pth'%WORK_PATH conf.url = list_model[2] if net_size =='mobi': conf.use_mobilfacenet = True conf.weight_path = '%s/weights/model_mobilefacenet.pth'%WORK_PATH conf.url = list_model[0] conf.video_source = 0 if mode =='training_eval': conf.lr = 1e-3 conf.milestones = [18,30,42] conf.momentum = 0.9 conf.pin_memory = True rs = 3 conf.train_root = "/mnt/01D4A1D481139570/Dataset/Face/casia" conf.file_list = '/mnt/01D4A1D481139570/Dataset/Face/casia_train.txt' conf.batch_size = 4 conf.lfw_root = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/lfw_align_112' conf.lfw_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/pairs.txt' conf.agedb_root = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb30_align_112' conf.agedb_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb_30_pair.txt' conf.cfp_root = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/CFP_FP_aligned_112' conf.cfp_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/cfp_fp_pair.txt' return conf

true

f719a616152547d0300a25992cdb6dbefb41b0a6

16,599

py

Python

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

[ "MIT" ]

null

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

[ "MIT" ]

null

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

[ "MIT" ]

null

# Copyright 2019 Graphcore Ltd. from statistics import mean import numpy as np import os import re import subprocess import sys import time """Library of utility functions common between frameworks""" def parse_results_for_speed(output, iter_tolerance, speed_tolerance): """Look for <iter number> sec/itr. <speed number> {other stuff}""" found_a_result = False for line in output.split("\n"): matches = re.match(r"([\d.]+) +sec/itr. +([\d.]+)", line) if matches: found_a_result = True iterations, speed = matches.groups() iterations = float(iterations) speed = float(speed) _verify_model_numbers( iter_tolerance, iterations, speed_tolerance, speed, line ) if not found_a_result: raise AssertionError("No results detected in this run") def parse_results_for_accuracy(output, expected_accuracies, acc_tolerance): """Look for Accuracy=<accuracy>%""" accuracies = [] for line in output.split("\n"): if re.match(r" + Accuracy=+([\d.]+)%", line): accuracy = float(re.match(r" + Accuracy=+([\d.]+)%", line).groups()[0]) accuracies.append(accuracy) elif re.search(r"Validation accuracy", line): accuracy_str = re.search(r"accuracy:\s(.*)", line).group(1) accuracy = float(accuracy_str[:accuracy_str.rfind("%")]) accuracies.append(accuracy) if len(accuracies) == 0: raise AssertionError("No results detected in this run") elif len(accuracies) != len(expected_accuracies): raise AssertionError("Expected accuracies and parsed accuracies have" " different lengths") _verify_model_accuracies(accuracies, expected_accuracies, acc_tolerance) def _verify_model_numbers(iter_tolerance, iterations, speed_tolerance, speed, line): iter_error = "" speed_error = "" # Verify iteration speed if iterations > iter_tolerance[1]: iter_error = ("The time per iteration has regressed above" " the tolerance maximum: " + str(iter_tolerance[1])) elif iterations < iter_tolerance[0]: iter_error = ("Time taken to compete an iteration was " "suspiciously fast. Please verify the model" " is operating correctly and tune tolerances" " accordingly.") # Verify item processing speed if speed < speed_tolerance[0]: speed_error = ("The number of items processed per second" " has regressed below the tolerance: " + str(speed_tolerance[0])) elif speed > speed_tolerance[1]: speed_error = ("The number of items processed per second" " was suspiciously high. Please verify the" " model is behaving correctly and tune" " tolerances accordingly.") if iter_error and speed_error: sys.stderr.write("\n".join([line, iter_error, speed_error])) raise AssertionError("Timings out of tolerance range") elif iter_error or speed_error: sys.stderr.write(line) raise AssertionError(iter_error + speed_error) def _verify_model_accuracies(accuracies, expected_accuracy, acc_tolerance): """Asserts a list of accuracies is within a list of expected accuracies with a tolerance applied. Args: accuracies: A list of floats representing the accuracies (%) produced by the model at each step. expected_accuracy: A list of floats representing the expected accuracies (%) produced by the model at each step. acc_tolerance: A float representing a percentage tolerance applied on top of the expected accuracies that the accuracies produced by the model should sit within. Raises: Assertion Error: Accuracy produced by the model are not within the expected limits. """ for iter_num in range(len(accuracies)): exp_acc = expected_accuracy[iter_num] exp_acc_str = ( "{0} = {1} +- {2} = [{3:.{5}f}, {4:.{5}f}]".format( "Expected accuracy (%)".ljust(22), exp_acc, acc_tolerance, exp_acc - acc_tolerance, exp_acc + acc_tolerance, 2 ) ) acc = accuracies[iter_num] acc_str = "{} = {:.{}f}".format( "Accuracy (%)".ljust(22), acc, 2 ) full_acc_str = "{}\n{}".format(acc_str, exp_acc_str) if acc < exp_acc - acc_tolerance: raise AssertionError( "After iteration {}, the model is less accurate" " than expected.\n" "{}".format(iter_num + 1, full_acc_str) ) elif acc > exp_acc + acc_tolerance: raise AssertionError( "After iteration {}, the model is producing an accuracy" " that is suspiciously high and should be reviewed.\n" "{}".format(iter_num + 1, full_acc_str) ) def assert_result_equals_tensor_value(output, tensor): """Searches for a single tensor result in the first line of the output Searches the first line of the string output for a line with format '[array([3., 8.], dtype=float32)]' and asserts its equal to the numpy tensor argument Args: output: String containing the string representation of a numpy tensor tensor: numpy tensor representing the expected result Returns: None Raises: Assertion Error: Output is not in correct format Assertion Error: Output does not contain a string representation of a numpy array Assertion Error: Output numpy array does not equal the expected numpy array """ # TODO - np representation over multiple lines # TODO - large np array output # TODO - multiple dimension np output list_regex = r"^\[.*?\]$" np_array_str_regex = r"array$.*?, dtype=.*?$$" first_line = output.split("\n")[0] if not re.match(list_regex, first_line): raise AssertionError( "Result not in expected string format." " Expecting stringified list " " eg. [array([3., 8.], dtype=float32)]" ) contents = first_line[1:-1] if not re.match(np_array_str_regex, contents): raise AssertionError( "Expecting numpy representation " "array with dtype " "eg. array([3., 8.], dtype=float32)" ) assert contents == np.array_repr(tensor), ( "Output value {} does not " "equal expected value {}".format(np.array_repr(contents), tensor) ) def parse_results_for_ipus_used(output): """Finds the number of IPUs used in the model by looking for string with format ' On 2 IPUs.' in output""" shards_regex = r" On ([\d.]+) IPUs." for line in output.split("\n"): matches = re.match(shards_regex, line) if matches: shards = matches.group(1) return int(shards) raise AssertionError("Expecting line detailing IPU usage " "eg. ' On 2 IPUs.'") def assert_shards(output, expected_shards): """Verify the expected number of shards used were actually used""" actual_shards = parse_results_for_ipus_used(output) assert actual_shards == expected_shards def get_final_accuracy(output): """Find and return the accuracy reported in a test's output.""" result_regex = r"Accuracy=([\d.]+)\%" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_final_loss(output): """Find and return the loss reported in a test's output.""" result_regex = r"Loss=([\d.]+)" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_average_speeds(output): """Finds the average seconds/iteration and tokens/second Args: output: String representing the output of a test. Returns: A tuple where the first element is a float representing the average iterations per second and the second the average tokens processed per second """ result_regex = r"([\d.]+) +sec/itr. +([\d.]+)" results = parse_results_with_regex(output, result_regex) itr_sec_list = results[0] tokens_sec_list = results[1] return mean(itr_sec_list), mean(tokens_sec_list) def parse_results_with_regex(output, regex): """Find and returns the regex matching results in output Looks through the output line by line looking for a matching regex. The function assembles a list of lists where each parent list is the results for that position in the regex string and each item in the child lists represents an order of the results found in the output Args: output: String representing the output of a test. regex: Regex of result to find. Returns: A list of lists of floats. Parent list represents the result at each position in the regex. Child list contains results received in the order they were output. Raises: AssertionError: a line matching the regex could not be found in the output """ results = [] for line in output.split("\n"): matches = re.search(regex, line) if matches: number_of_results = matches.lastindex if results == []: results = [None] * number_of_results for match_index in range(0, number_of_results): result = float(matches.group(match_index + 1)) if results[match_index]: results[match_index].append(result) continue results[match_index] = [result] if results == []: raise AssertionError("Regex {} not found in result".format(regex)) return results def get_total_epochs(output): """Finds the number of epochs model has run through by looking for string with format 'Epoch #3' in the models raw output""" epochs = None for line in output.split("\n"): epoch_match = re.search(r"Epoch #([\d.]+)", line) if epoch_match: epochs = int(epoch_match.group(1)) if not epochs: raise AssertionError("Epochs not found in output, eg. " "Epoch #3") return epochs def assert_total_run_time(total_time, time_range): """Checks total run time is within the required range Args: total_time: float representing number of seconds the test took to run time_range: a tuple of floats where the first element is the minimum time the test should run in in seconds and the second the maximum Raises: AssertionError: if the total_time is not between the minimum time and maximum time """ minimum_time = time_range[0] maximum_time = time_range[1] assert total_time >= minimum_time assert total_time <= maximum_time def assert_final_accuracy(output, minimum, maximum): """Gets the final accuracy given a raw model output and checks its value is between the minimum and maximum Args: output: String representing the raw output of a model minimum: a float representing a percentage (between 0.0% and 100%) that is the minimum accuracy for the model after running maximum: a float representing a percentage (between 0.0% and 100%) that is the maximum accuracy for the model after running Raises: AssertionError: if the final accuracy is not between the maximum and minimum percentages """ accuracy = get_final_accuracy(output) assert accuracy >= minimum assert accuracy <= maximum def run_python_script_helper(cwd, script, **kwargs): """A function that given a path and python script name, runs the script with kwargs as the command line arguments Args: cwd: string representing the directory of the python script script: string representing the full name of the python script kwargs: dictionary of string key and values that form the command line arguments when the script is run. Returns: A string representing the raw output of the python script run Raises: AssertionError: if the final accuracy is not between the maximum and minimum percentages """ py_version = "python{}".format(sys.version_info[0]) cmd = [py_version, script] if kwargs: args = [ str(item) for sublist in kwargs.items() for item in sublist if item != "" ] cmd.extend(args) out = subprocess.check_output(cmd, cwd=cwd, universal_newlines=True) print(out) return out def run_test_helper(subprocess_function, total_run_time=None, total_run_time_tolerance=0.1, **kwargs): """Helper function for running tests Takes in testable parameters, runs the test and checks the relevant parameters against test results Args: subprocess_function: the function that runs a subprocess of the model in question total_run_time_range: tuple float representing the expected upper and lower bounds for the total time taken to run the test Returns: A String representing the raw output of the models subprocess Raises: AssertionError: If the accuracy, time taken etc. are not within the expected bounds """ start_time = time.time() out = subprocess_function(**kwargs) total_time = time.time() - start_time if total_run_time: total_run_time_range = range_from_tolerances( total_run_time, total_run_time_tolerance ) assert_total_run_time(total_time, total_run_time_range) return out def range_from_tolerances(value, tolerance): """Helper function that takes a value and applies the tolerance Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied symmetrically across the value argument Returns: A tuple of floats, the first element representing the tolerance applied below the value (minimum) and the second above (maximum) """ return ( get_minimum_with_tolerance(value, tolerance), get_maximum_with_tolerance(value, tolerance), ) def get_minimum_with_tolerance(value, tolerance): """Helper function that takes a value and applies the tolerance below the value Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied to the value argument Returns: A float representing the tolerance applied below the value (maximum) """ return value * (1 - tolerance) def get_maximum_with_tolerance(value, tolerance): """Helper function that takes a value and applies the tolerance above the value Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied to the value argument Returns: A float representing the tolerance applied above the value (minimum) """ return value * (1 + tolerance) def check_data_exists(data_path, expected_files_list): """Helper function that checks the expected data exists in a directory Args: data_path: A string representing the directory of where the data is expected to be expected_files_list: a list of strings representing the expected file names in the data_path directory Returns: A boolean which represents whether the expected files are found in the data_path directory """ if os.path.exists(data_path): for filename in expected_files_list: if not os.path.isfile(os.path.join(data_path, filename)): return False return True return False

34.36646

85

0.636123

from statistics import mean import numpy as np import os import re import subprocess import sys import time def parse_results_for_speed(output, iter_tolerance, speed_tolerance): found_a_result = False for line in output.split("\n"): matches = re.match(r"([\d.]+) +sec/itr. +([\d.]+)", line) if matches: found_a_result = True iterations, speed = matches.groups() iterations = float(iterations) speed = float(speed) _verify_model_numbers( iter_tolerance, iterations, speed_tolerance, speed, line ) if not found_a_result: raise AssertionError("No results detected in this run") def parse_results_for_accuracy(output, expected_accuracies, acc_tolerance): accuracies = [] for line in output.split("\n"): if re.match(r" + Accuracy=+([\d.]+)%", line): accuracy = float(re.match(r" + Accuracy=+([\d.]+)%", line).groups()[0]) accuracies.append(accuracy) elif re.search(r"Validation accuracy", line): accuracy_str = re.search(r"accuracy:\s(.*)", line).group(1) accuracy = float(accuracy_str[:accuracy_str.rfind("%")]) accuracies.append(accuracy) if len(accuracies) == 0: raise AssertionError("No results detected in this run") elif len(accuracies) != len(expected_accuracies): raise AssertionError("Expected accuracies and parsed accuracies have" " different lengths") _verify_model_accuracies(accuracies, expected_accuracies, acc_tolerance) def _verify_model_numbers(iter_tolerance, iterations, speed_tolerance, speed, line): iter_error = "" speed_error = "" if iterations > iter_tolerance[1]: iter_error = ("The time per iteration has regressed above" " the tolerance maximum: " + str(iter_tolerance[1])) elif iterations < iter_tolerance[0]: iter_error = ("Time taken to compete an iteration was " "suspiciously fast. Please verify the model" " is operating correctly and tune tolerances" " accordingly.") if speed < speed_tolerance[0]: speed_error = ("The number of items processed per second" " has regressed below the tolerance: " + str(speed_tolerance[0])) elif speed > speed_tolerance[1]: speed_error = ("The number of items processed per second" " was suspiciously high. Please verify the" " model is behaving correctly and tune" " tolerances accordingly.") if iter_error and speed_error: sys.stderr.write("\n".join([line, iter_error, speed_error])) raise AssertionError("Timings out of tolerance range") elif iter_error or speed_error: sys.stderr.write(line) raise AssertionError(iter_error + speed_error) def _verify_model_accuracies(accuracies, expected_accuracy, acc_tolerance): for iter_num in range(len(accuracies)): exp_acc = expected_accuracy[iter_num] exp_acc_str = ( "{0} = {1} +- {2} = [{3:.{5}f}, {4:.{5}f}]".format( "Expected accuracy (%)".ljust(22), exp_acc, acc_tolerance, exp_acc - acc_tolerance, exp_acc + acc_tolerance, 2 ) ) acc = accuracies[iter_num] acc_str = "{} = {:.{}f}".format( "Accuracy (%)".ljust(22), acc, 2 ) full_acc_str = "{}\n{}".format(acc_str, exp_acc_str) if acc < exp_acc - acc_tolerance: raise AssertionError( "After iteration {}, the model is less accurate" " than expected.\n" "{}".format(iter_num + 1, full_acc_str) ) elif acc > exp_acc + acc_tolerance: raise AssertionError( "After iteration {}, the model is producing an accuracy" " that is suspiciously high and should be reviewed.\n" "{}".format(iter_num + 1, full_acc_str) ) def assert_result_equals_tensor_value(output, tensor): list_regex = r"^\[.*?\]$" np_array_str_regex = r"array$.*?, dtype=.*?$$" first_line = output.split("\n")[0] if not re.match(list_regex, first_line): raise AssertionError( "Result not in expected string format." " Expecting stringified list " " eg. [array([3., 8.], dtype=float32)]" ) contents = first_line[1:-1] if not re.match(np_array_str_regex, contents): raise AssertionError( "Expecting numpy representation " "array with dtype " "eg. array([3., 8.], dtype=float32)" ) assert contents == np.array_repr(tensor), ( "Output value {} does not " "equal expected value {}".format(np.array_repr(contents), tensor) ) def parse_results_for_ipus_used(output): shards_regex = r" On ([\d.]+) IPUs." for line in output.split("\n"): matches = re.match(shards_regex, line) if matches: shards = matches.group(1) return int(shards) raise AssertionError("Expecting line detailing IPU usage " "eg. ' On 2 IPUs.'") def assert_shards(output, expected_shards): actual_shards = parse_results_for_ipus_used(output) assert actual_shards == expected_shards def get_final_accuracy(output): result_regex = r"Accuracy=([\d.]+)\%" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_final_loss(output): result_regex = r"Loss=([\d.]+)" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_average_speeds(output): result_regex = r"([\d.]+) +sec/itr. +([\d.]+)" results = parse_results_with_regex(output, result_regex) itr_sec_list = results[0] tokens_sec_list = results[1] return mean(itr_sec_list), mean(tokens_sec_list) def parse_results_with_regex(output, regex): results = [] for line in output.split("\n"): matches = re.search(regex, line) if matches: number_of_results = matches.lastindex if results == []: results = [None] * number_of_results for match_index in range(0, number_of_results): result = float(matches.group(match_index + 1)) if results[match_index]: results[match_index].append(result) continue results[match_index] = [result] if results == []: raise AssertionError("Regex {} not found in result".format(regex)) return results def get_total_epochs(output): epochs = None for line in output.split("\n"): epoch_match = re.search(r"Epoch #([\d.]+)", line) if epoch_match: epochs = int(epoch_match.group(1)) if not epochs: raise AssertionError("Epochs not found in output, eg. " "Epoch #3") return epochs def assert_total_run_time(total_time, time_range): minimum_time = time_range[0] maximum_time = time_range[1] assert total_time >= minimum_time assert total_time <= maximum_time def assert_final_accuracy(output, minimum, maximum): accuracy = get_final_accuracy(output) assert accuracy >= minimum assert accuracy <= maximum def run_python_script_helper(cwd, script, **kwargs): py_version = "python{}".format(sys.version_info[0]) cmd = [py_version, script] if kwargs: args = [ str(item) for sublist in kwargs.items() for item in sublist if item != "" ] cmd.extend(args) out = subprocess.check_output(cmd, cwd=cwd, universal_newlines=True) print(out) return out def run_test_helper(subprocess_function, total_run_time=None, total_run_time_tolerance=0.1, **kwargs): start_time = time.time() out = subprocess_function(**kwargs) total_time = time.time() - start_time if total_run_time: total_run_time_range = range_from_tolerances( total_run_time, total_run_time_tolerance ) assert_total_run_time(total_time, total_run_time_range) return out def range_from_tolerances(value, tolerance): return ( get_minimum_with_tolerance(value, tolerance), get_maximum_with_tolerance(value, tolerance), ) def get_minimum_with_tolerance(value, tolerance): return value * (1 - tolerance) def get_maximum_with_tolerance(value, tolerance): return value * (1 + tolerance) def check_data_exists(data_path, expected_files_list): if os.path.exists(data_path): for filename in expected_files_list: if not os.path.isfile(os.path.join(data_path, filename)): return False return True return False

true

f719a788aa6769dc9f43b9f60b9a57cc0504643a

1,535

py

Python

code/clients/requests.py

lpmatos/gitlab-analytics

47a220bb54efa473f01bf033291f65b38accdbca

[ "MIT" ]

2

2020-09-16T11:03:01.000Z

2021-07-30T07:05:58.000Z

code/clients/requests.py

lpmatos/gitlab-analytics

47a220bb54efa473f01bf033291f65b38accdbca

[ "MIT" ]

null

code/clients/requests.py

lpmatos/gitlab-analytics

47a220bb54efa473f01bf033291f65b38accdbca

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import annotations import requests from validators.url import URL from abc import ABC, abstractmethod from requests.adapters import HTTPAdapter from typing import Text, NoReturn, Callable, Dict from requests.packages.urllib3.util.retry import Retry class RequestResponse: def __init__(self, response: Text) -> NoReturn: self.status = response.status_code self.reason = response.reason self.json = response.json() def get_json(self) -> Dict: return self.json class RequestsImplementation(ABC): def __init__(self, url: Text, *args, **kwargs) -> NoReturn: if URL.url_validator(url): if not kwargs["is_secure"]: url = url.replace("https", "http") self.url = url self._logger = kwargs["logger"] if kwargs["retry"]: self.session = self.requests_retry_session(kwargs["session"]) else: self.session = requests.Session() @staticmethod def requests_retry_session(retries=3, backoff_factor=0.3, status_forcelist=(500, 502, 504), session=None) -> requests.Session(): session = session or requests.Session() retry = Retry(total=retries, read=retries, connect=retries, backoff_factor=backoff_factor, status_forcelist=status_forcelist,) adapter = HTTPAdapter(max_retries=retry) session.mount("http://", adapter) session.mount("https://", adapter) return session @abstractmethod def get(self) -> NoReturn: pass @property def logger(self) -> Callable: return self._logger

28.962264

130

0.704235

from __future__ import annotations import requests from validators.url import URL from abc import ABC, abstractmethod from requests.adapters import HTTPAdapter from typing import Text, NoReturn, Callable, Dict from requests.packages.urllib3.util.retry import Retry class RequestResponse: def __init__(self, response: Text) -> NoReturn: self.status = response.status_code self.reason = response.reason self.json = response.json() def get_json(self) -> Dict: return self.json class RequestsImplementation(ABC): def __init__(self, url: Text, *args, **kwargs) -> NoReturn: if URL.url_validator(url): if not kwargs["is_secure"]: url = url.replace("https", "http") self.url = url self._logger = kwargs["logger"] if kwargs["retry"]: self.session = self.requests_retry_session(kwargs["session"]) else: self.session = requests.Session() @staticmethod def requests_retry_session(retries=3, backoff_factor=0.3, status_forcelist=(500, 502, 504), session=None) -> requests.Session(): session = session or requests.Session() retry = Retry(total=retries, read=retries, connect=retries, backoff_factor=backoff_factor, status_forcelist=status_forcelist,) adapter = HTTPAdapter(max_retries=retry) session.mount("http://", adapter) session.mount("https://", adapter) return session @abstractmethod def get(self) -> NoReturn: pass @property def logger(self) -> Callable: return self._logger

true

f719a9168a4d3106600fffcc47c14cc90f3cadc7

6,299

py

Python

official/vision/detection/dataloader/tf_example_decoder.py

gujralsanyam22/models

d96f8f043dbe2b5ca8ea1785f57df8faf68d8875

[ "Apache-2.0" ]

153

2020-10-25T13:58:04.000Z

2022-03-07T06:01:54.000Z

official/vision/detection/dataloader/tf_example_decoder.py

yangxl-2014-fe/models

11ea5237818e791a5717716d5413977f4c4db1e3

[ "Apache-2.0" ]

11

2020-07-13T08:29:00.000Z

2022-03-24T07:21:09.000Z

official/vision/detection/dataloader/tf_example_decoder.py

yangxl-2014-fe/models

11ea5237818e791a5717716d5413977f4c4db1e3

[ "Apache-2.0" ]

23

2020-10-25T14:44:47.000Z

2021-03-31T02:12:13.000Z

# Copyright 2019 The TensorFlow 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. # ============================================================================== """Tensorflow Example proto decoder for object detection. A decoder to decode string tensors containing serialized tensorflow.Example protos for object detection. """ import tensorflow as tf class TfExampleDecoder(object): """Tensorflow Example proto decoder.""" def __init__(self, include_mask=False): self._include_mask = include_mask self._keys_to_features = { 'image/encoded': tf.io.FixedLenFeature((), tf.string), 'image/source_id': tf.io.FixedLenFeature((), tf.string), 'image/height': tf.io.FixedLenFeature((), tf.int64), 'image/width': tf.io.FixedLenFeature((), tf.int64), 'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32), 'image/object/class/label': tf.io.VarLenFeature(tf.int64), 'image/object/area': tf.io.VarLenFeature(tf.float32), 'image/object/is_crowd': tf.io.VarLenFeature(tf.int64), } if include_mask: self._keys_to_features.update({ 'image/object/mask': tf.io.VarLenFeature(tf.string), }) def _decode_image(self, parsed_tensors): """Decodes the image and set its static shape.""" image = tf.io.decode_image(parsed_tensors['image/encoded'], channels=3) image.set_shape([None, None, 3]) return image def _decode_boxes(self, parsed_tensors): """Concat box coordinates in the format of [ymin, xmin, ymax, xmax].""" xmin = parsed_tensors['image/object/bbox/xmin'] xmax = parsed_tensors['image/object/bbox/xmax'] ymin = parsed_tensors['image/object/bbox/ymin'] ymax = parsed_tensors['image/object/bbox/ymax'] return tf.stack([ymin, xmin, ymax, xmax], axis=-1) def _decode_masks(self, parsed_tensors): """Decode a set of PNG masks to the tf.float32 tensors.""" def _decode_png_mask(png_bytes): mask = tf.squeeze( tf.io.decode_png(png_bytes, channels=1, dtype=tf.uint8), axis=-1) mask = tf.cast(mask, dtype=tf.float32) mask.set_shape([None, None]) return mask height = parsed_tensors['image/height'] width = parsed_tensors['image/width'] masks = parsed_tensors['image/object/mask'] return tf.cond( pred=tf.greater(tf.size(input=masks), 0), true_fn=lambda: tf.map_fn(_decode_png_mask, masks, dtype=tf.float32), false_fn=lambda: tf.zeros([0, height, width], dtype=tf.float32)) def _decode_areas(self, parsed_tensors): xmin = parsed_tensors['image/object/bbox/xmin'] xmax = parsed_tensors['image/object/bbox/xmax'] ymin = parsed_tensors['image/object/bbox/ymin'] ymax = parsed_tensors['image/object/bbox/ymax'] return tf.cond( tf.greater(tf.shape(parsed_tensors['image/object/area'])[0], 0), lambda: parsed_tensors['image/object/area'], lambda: (xmax - xmin) * (ymax - ymin)) def decode(self, serialized_example): """Decode the serialized example. Args: serialized_example: a single serialized tf.Example string. Returns: decoded_tensors: a dictionary of tensors with the following fields: - image: a uint8 tensor of shape [None, None, 3]. - source_id: a string scalar tensor. - height: an integer scalar tensor. - width: an integer scalar tensor. - groundtruth_classes: a int64 tensor of shape [None]. - groundtruth_is_crowd: a bool tensor of shape [None]. - groundtruth_area: a float32 tensor of shape [None]. - groundtruth_boxes: a float32 tensor of shape [None, 4]. - groundtruth_instance_masks: a float32 tensor of shape [None, None, None]. - groundtruth_instance_masks_png: a string tensor of shape [None]. """ parsed_tensors = tf.io.parse_single_example( serialized=serialized_example, features=self._keys_to_features) for k in parsed_tensors: if isinstance(parsed_tensors[k], tf.SparseTensor): if parsed_tensors[k].dtype == tf.string: parsed_tensors[k] = tf.sparse.to_dense( parsed_tensors[k], default_value='') else: parsed_tensors[k] = tf.sparse.to_dense( parsed_tensors[k], default_value=0) image = self._decode_image(parsed_tensors) boxes = self._decode_boxes(parsed_tensors) areas = self._decode_areas(parsed_tensors) is_crowds = tf.cond( tf.greater(tf.shape(parsed_tensors['image/object/is_crowd'])[0], 0), lambda: tf.cast(parsed_tensors['image/object/is_crowd'], dtype=tf.bool), lambda: tf.zeros_like(parsed_tensors['image/object/class/label'], dtype=tf.bool)) # pylint: disable=line-too-long if self._include_mask: masks = self._decode_masks(parsed_tensors) decoded_tensors = { 'image': image, 'source_id': parsed_tensors['image/source_id'], 'height': parsed_tensors['image/height'], 'width': parsed_tensors['image/width'], 'groundtruth_classes': parsed_tensors['image/object/class/label'], 'groundtruth_is_crowd': is_crowds, 'groundtruth_area': areas, 'groundtruth_boxes': boxes, } if self._include_mask: decoded_tensors.update({ 'groundtruth_instance_masks': masks, 'groundtruth_instance_masks_png': parsed_tensors['image/object/mask'], }) return decoded_tensors

40.121019

122

0.657247

import tensorflow as tf class TfExampleDecoder(object): def __init__(self, include_mask=False): self._include_mask = include_mask self._keys_to_features = { 'image/encoded': tf.io.FixedLenFeature((), tf.string), 'image/source_id': tf.io.FixedLenFeature((), tf.string), 'image/height': tf.io.FixedLenFeature((), tf.int64), 'image/width': tf.io.FixedLenFeature((), tf.int64), 'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32), 'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32), 'image/object/class/label': tf.io.VarLenFeature(tf.int64), 'image/object/area': tf.io.VarLenFeature(tf.float32), 'image/object/is_crowd': tf.io.VarLenFeature(tf.int64), } if include_mask: self._keys_to_features.update({ 'image/object/mask': tf.io.VarLenFeature(tf.string), }) def _decode_image(self, parsed_tensors): image = tf.io.decode_image(parsed_tensors['image/encoded'], channels=3) image.set_shape([None, None, 3]) return image def _decode_boxes(self, parsed_tensors): xmin = parsed_tensors['image/object/bbox/xmin'] xmax = parsed_tensors['image/object/bbox/xmax'] ymin = parsed_tensors['image/object/bbox/ymin'] ymax = parsed_tensors['image/object/bbox/ymax'] return tf.stack([ymin, xmin, ymax, xmax], axis=-1) def _decode_masks(self, parsed_tensors): def _decode_png_mask(png_bytes): mask = tf.squeeze( tf.io.decode_png(png_bytes, channels=1, dtype=tf.uint8), axis=-1) mask = tf.cast(mask, dtype=tf.float32) mask.set_shape([None, None]) return mask height = parsed_tensors['image/height'] width = parsed_tensors['image/width'] masks = parsed_tensors['image/object/mask'] return tf.cond( pred=tf.greater(tf.size(input=masks), 0), true_fn=lambda: tf.map_fn(_decode_png_mask, masks, dtype=tf.float32), false_fn=lambda: tf.zeros([0, height, width], dtype=tf.float32)) def _decode_areas(self, parsed_tensors): xmin = parsed_tensors['image/object/bbox/xmin'] xmax = parsed_tensors['image/object/bbox/xmax'] ymin = parsed_tensors['image/object/bbox/ymin'] ymax = parsed_tensors['image/object/bbox/ymax'] return tf.cond( tf.greater(tf.shape(parsed_tensors['image/object/area'])[0], 0), lambda: parsed_tensors['image/object/area'], lambda: (xmax - xmin) * (ymax - ymin)) def decode(self, serialized_example): parsed_tensors = tf.io.parse_single_example( serialized=serialized_example, features=self._keys_to_features) for k in parsed_tensors: if isinstance(parsed_tensors[k], tf.SparseTensor): if parsed_tensors[k].dtype == tf.string: parsed_tensors[k] = tf.sparse.to_dense( parsed_tensors[k], default_value='') else: parsed_tensors[k] = tf.sparse.to_dense( parsed_tensors[k], default_value=0) image = self._decode_image(parsed_tensors) boxes = self._decode_boxes(parsed_tensors) areas = self._decode_areas(parsed_tensors) is_crowds = tf.cond( tf.greater(tf.shape(parsed_tensors['image/object/is_crowd'])[0], 0), lambda: tf.cast(parsed_tensors['image/object/is_crowd'], dtype=tf.bool), lambda: tf.zeros_like(parsed_tensors['image/object/class/label'], dtype=tf.bool)) if self._include_mask: masks = self._decode_masks(parsed_tensors) decoded_tensors = { 'image': image, 'source_id': parsed_tensors['image/source_id'], 'height': parsed_tensors['image/height'], 'width': parsed_tensors['image/width'], 'groundtruth_classes': parsed_tensors['image/object/class/label'], 'groundtruth_is_crowd': is_crowds, 'groundtruth_area': areas, 'groundtruth_boxes': boxes, } if self._include_mask: decoded_tensors.update({ 'groundtruth_instance_masks': masks, 'groundtruth_instance_masks_png': parsed_tensors['image/object/mask'], }) return decoded_tensors

true

f719a9bfc05dbb1ca8c4fffbbf92b7f387621266

859

py

Python

taskobra/orm/components/cpu.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

null

taskobra/orm/components/cpu.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

43

2020-02-06T22:23:42.000Z

2020-04-29T23:56:43.000Z

taskobra/orm/components/cpu.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

2

2020-02-06T21:01:42.000Z

2020-02-06T23:43:11.000Z

# Libraries from sqlalchemy import Column, Float, ForeignKey, Integer, String # Taskobra from taskobra.orm.components import Component class CPU(Component): __tablename__ = "CPU" unique_id = Column(Integer, ForeignKey("Component.unique_id"), primary_key=True) manufacturer = Column(String) model = Column(String) isa = Column(String) tdp = Column(Integer) core_count = Column(Integer) threads_per_core = Column(Integer) nominal_frequency = Column(Float) maximum_frequency = Column(Float) __mapper_args__ = { "polymorphic_identity": __tablename__, } @property def threads(self): return self.core_count * self.threads_per_core def __repr__(self): return f"<CPU({self.manufacturer} {self.model} ({self.core_count}/{self.threads}x{self.nominal_frequency} GHz {self.isa}))>"

29.62069

132

0.705471

from sqlalchemy import Column, Float, ForeignKey, Integer, String from taskobra.orm.components import Component class CPU(Component): __tablename__ = "CPU" unique_id = Column(Integer, ForeignKey("Component.unique_id"), primary_key=True) manufacturer = Column(String) model = Column(String) isa = Column(String) tdp = Column(Integer) core_count = Column(Integer) threads_per_core = Column(Integer) nominal_frequency = Column(Float) maximum_frequency = Column(Float) __mapper_args__ = { "polymorphic_identity": __tablename__, } @property def threads(self): return self.core_count * self.threads_per_core def __repr__(self): return f"<CPU({self.manufacturer} {self.model} ({self.core_count}/{self.threads}x{self.nominal_frequency} GHz {self.isa}))>"

true

f719a9d668b8a403e901541f650b87db1bf30dbc

1,112

py

Python

music/migrations/0010_auto_20150427_2304.py

Amoki/Amoki-Music

77b0e426fe9cc6c9cd12346a5e5e81a62362bb83

[ "MIT" ]

3

2015-06-16T11:12:29.000Z

2019-05-03T09:09:21.000Z

music/migrations/0010_auto_20150427_2304.py

Amoki/Amoki-Music

77b0e426fe9cc6c9cd12346a5e5e81a62362bb83

[ "MIT" ]

16

2015-08-18T14:35:55.000Z

2021-06-10T17:31:04.000Z

music/migrations/0010_auto_20150427_2304.py

Amoki/Amoki-Music

77b0e426fe9cc6c9cd12346a5e5e81a62362bb83

[ "MIT" ]

1

2016-10-19T14:48:52.000Z

from __future__ import unicode_literals from django.db import models, migrations def set_sources(apps, schema_editor): # We can't import the Person model directly as it may be a newer # version than this migration expects. We use the historical version. Source = apps.get_model("music", "Source") TemporaryMusic = apps.get_model("music", "TemporaryMusic") youtube = Source.objects.get(name="Youtube") for tempMusic in TemporaryMusic.objects.all(): tempMusic.source = youtube tempMusic.save() class Migration(migrations.Migration): dependencies = [ ('music', '0009_auto_20150427_2038'), ] operations = [ migrations.AddField( model_name='temporarymusic', name='source', field=models.ForeignKey(to='music.Source', null=True, on_delete=models.CASCADE), ), migrations.RunPython(set_sources), migrations.AlterField( model_name='temporarymusic', name='source', field=models.ForeignKey(to='music.Source', on_delete=models.CASCADE), ), ]

30.888889

92

0.654676

from __future__ import unicode_literals from django.db import models, migrations def set_sources(apps, schema_editor): # version than this migration expects. We use the historical version. Source = apps.get_model("music", "Source") TemporaryMusic = apps.get_model("music", "TemporaryMusic") youtube = Source.objects.get(name="Youtube") for tempMusic in TemporaryMusic.objects.all(): tempMusic.source = youtube tempMusic.save() class Migration(migrations.Migration): dependencies = [ ('music', '0009_auto_20150427_2038'), ] operations = [ migrations.AddField( model_name='temporarymusic', name='source', field=models.ForeignKey(to='music.Source', null=True, on_delete=models.CASCADE), ), migrations.RunPython(set_sources), migrations.AlterField( model_name='temporarymusic', name='source', field=models.ForeignKey(to='music.Source', on_delete=models.CASCADE), ), ]

true

f719aae1c7a532a452c6a6c2a3522f59f033bbfa

1,533

py

Python

tests/test_fieldtype_model.py

MasterScott/Formasaurus

d7d916237a6d2ca4c80c4c8ae5d66999c8beebed

[ "MIT" ]

132

2015-04-18T01:53:52.000Z

2022-03-31T08:33:26.000Z

tests/test_fieldtype_model.py

Eglet27/Formasaurus

d7d916237a6d2ca4c80c4c8ae5d66999c8beebed

[ "MIT" ]

26

2015-07-08T20:09:26.000Z

2022-03-03T16:50:08.000Z

tests/test_fieldtype_model.py

Eglet27/Formasaurus

d7d916237a6d2ca4c80c4c8ae5d66999c8beebed

[ "MIT" ]

63

2015-02-17T08:41:00.000Z

2022-03-31T08:58:18.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, division import itertools import numpy as np from sklearn_crfsuite.metrics import flat_accuracy_score from formasaurus.fieldtype_model import ( train, _PRECISE_C1_C2, _REALISTIC_C1_C2, get_Xy, ) def test_training(storage, capsys): annotations = (a for a in storage.iter_annotations( simplify_form_types=True, simplify_field_types=True, ) if a.fields_annotated) annotations = list(itertools.islice(annotations, 0, 300)) crf = train( annotations=annotations, use_precise_form_types=False, optimize_hyperparameters_iters=2, optimize_hyperparameters_folds=2, optimize_hyperparameters_jobs=-1, full_form_type_names=False, full_field_type_names=False ) out, err = capsys.readouterr() assert 'Training on 300 forms' in out assert 'realistic form types' in out assert 'Best hyperparameters' in out assert 0.0 < crf.c1 < 2.5 assert 0.0 < crf.c2 < 0.9 assert crf.c1, crf.c2 != _REALISTIC_C1_C2 assert crf.c1, crf.c2 != _PRECISE_C1_C2 form_types = np.asarray([a.type for a in annotations]) X, y = get_Xy(annotations, form_types, full_type_names=False) y_pred = crf.predict(X) score = flat_accuracy_score(y, y_pred) assert 0.9 < score < 1.0 # overfitting FTW! field_schema = storage.get_field_schema() short_names = set(field_schema.types_inv.keys()) assert set(crf.classes_).issubset(short_names)

28.924528

65

0.701239

from __future__ import absolute_import, division import itertools import numpy as np from sklearn_crfsuite.metrics import flat_accuracy_score from formasaurus.fieldtype_model import ( train, _PRECISE_C1_C2, _REALISTIC_C1_C2, get_Xy, ) def test_training(storage, capsys): annotations = (a for a in storage.iter_annotations( simplify_form_types=True, simplify_field_types=True, ) if a.fields_annotated) annotations = list(itertools.islice(annotations, 0, 300)) crf = train( annotations=annotations, use_precise_form_types=False, optimize_hyperparameters_iters=2, optimize_hyperparameters_folds=2, optimize_hyperparameters_jobs=-1, full_form_type_names=False, full_field_type_names=False ) out, err = capsys.readouterr() assert 'Training on 300 forms' in out assert 'realistic form types' in out assert 'Best hyperparameters' in out assert 0.0 < crf.c1 < 2.5 assert 0.0 < crf.c2 < 0.9 assert crf.c1, crf.c2 != _REALISTIC_C1_C2 assert crf.c1, crf.c2 != _PRECISE_C1_C2 form_types = np.asarray([a.type for a in annotations]) X, y = get_Xy(annotations, form_types, full_type_names=False) y_pred = crf.predict(X) score = flat_accuracy_score(y, y_pred) assert 0.9 < score < 1.0 field_schema = storage.get_field_schema() short_names = set(field_schema.types_inv.keys()) assert set(crf.classes_).issubset(short_names)

true

f719ac12ab39a81ed2df4d9c929c5f6b2e9f5724

2,399

py

Python

Lib/glyphsLib/__main__.py

silnrsi/glyphsLib

fc9ac286874e30130679430b028a173062c311a0

[ "Apache-2.0" ]

1

2019-01-19T05:50:30.000Z

Lib/glyphsLib/__main__.py

DalavanCloud/glyphsLib

fc9ac286874e30130679430b028a173062c311a0

[ "Apache-2.0" ]

null

Lib/glyphsLib/__main__.py

DalavanCloud/glyphsLib

fc9ac286874e30130679430b028a173062c311a0

[ "Apache-2.0" ]

1

2019-01-19T05:50:14.000Z

# Copyright 2015 Google Inc. 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. from __future__ import print_function, division, absolute_import, unicode_literals import sys import argparse import glyphsLib description = """\n Converts a Glyphs.app source file into UFO masters or UFO instances and MutatorMath designspace. """ def parse_options(args): parser = argparse.ArgumentParser(description=description) parser.add_argument("--version", action="version", version='glyphsLib %s' % (glyphsLib.__version__)) parser.add_argument("-g", "--glyphs", metavar="GLYPHS", required=True, help="Glyphs file to convert.") parser.add_argument("-m", "--masters", metavar="MASTERS", default="master_ufo", help="Ouput masters UFO to folder MASTERS. " "(default: %(default)s)") parser.add_argument("-n", "--instances", metavar="INSTANCES", nargs="?", const="instance_ufo", default=None, help="Output and generate interpolated instances UFO " "to folder INSTANCES. " "(default: %(const)s)") parser.add_argument("-r", "--round-instances", action="store_true", help="Apply integer rounding to all geometry when " "interpolating") options = parser.parse_args(args) return options def main(args=None): opt = parse_options(args) if opt.glyphs is not None: if opt.instances is None: glyphsLib.build_masters(opt.glyphs, opt.masters) else: glyphsLib.build_instances(opt.glyphs, opt.masters, opt.instances, round_geometry=opt.round_instances) if __name__ == '__main__': main(sys.argv[1:])

38.693548

82

0.631513

from __future__ import print_function, division, absolute_import, unicode_literals import sys import argparse import glyphsLib description = """\n Converts a Glyphs.app source file into UFO masters or UFO instances and MutatorMath designspace. """ def parse_options(args): parser = argparse.ArgumentParser(description=description) parser.add_argument("--version", action="version", version='glyphsLib %s' % (glyphsLib.__version__)) parser.add_argument("-g", "--glyphs", metavar="GLYPHS", required=True, help="Glyphs file to convert.") parser.add_argument("-m", "--masters", metavar="MASTERS", default="master_ufo", help="Ouput masters UFO to folder MASTERS. " "(default: %(default)s)") parser.add_argument("-n", "--instances", metavar="INSTANCES", nargs="?", const="instance_ufo", default=None, help="Output and generate interpolated instances UFO " "to folder INSTANCES. " "(default: %(const)s)") parser.add_argument("-r", "--round-instances", action="store_true", help="Apply integer rounding to all geometry when " "interpolating") options = parser.parse_args(args) return options def main(args=None): opt = parse_options(args) if opt.glyphs is not None: if opt.instances is None: glyphsLib.build_masters(opt.glyphs, opt.masters) else: glyphsLib.build_instances(opt.glyphs, opt.masters, opt.instances, round_geometry=opt.round_instances) if __name__ == '__main__': main(sys.argv[1:])

true

f719ac201c882a4f33c304211ff792834b6fe5b0

640

py

Python

fm2o2.py

dumpydog212/fm2o2

b5e173735bb08466d6c20f7868725e627260dd88

[ "MIT" ]

null

fm2o2.py

dumpydog212/fm2o2

b5e173735bb08466d6c20f7868725e627260dd88

[ "MIT" ]

null

fm2o2.py

dumpydog212/fm2o2

b5e173735bb08466d6c20f7868725e627260dd88

[ "MIT" ]

null

import glob import os from xml.dom import minidom import xml.etree.ElementTree as ET path = r"C:\Users\shamb\Desktop\dita_demo" valid_path = r"C:\Users\shamb\Desktop\dita_demo_scrubbed" wildcard = "*.xml" full_path = os.path.join(path, wildcard) os.makedirs(valid_path, exist_ok=True) file_list = glob.glob(full_path) print("The file set includes:") for this_file in file_list: print(this_file) # mydoc = minidom.parse(this_file) # print(type(mydoc)) tree = ET.parse(this_file) root = tree.getroot() print('\nAll item data:') for elem in root: for subelem in elem: print(subelem.text)

22.068966

57

0.696875

import glob import os from xml.dom import minidom import xml.etree.ElementTree as ET path = r"C:\Users\shamb\Desktop\dita_demo" valid_path = r"C:\Users\shamb\Desktop\dita_demo_scrubbed" wildcard = "*.xml" full_path = os.path.join(path, wildcard) os.makedirs(valid_path, exist_ok=True) file_list = glob.glob(full_path) print("The file set includes:") for this_file in file_list: print(this_file) tree = ET.parse(this_file) root = tree.getroot() print('\nAll item data:') for elem in root: for subelem in elem: print(subelem.text)

true

f719acd0bf5519f70da4e2324dadedc8b1906093

12,049

py

Python

gooddata-afm-client/gooddata_afm_client/model/included_dimension_props.py

gooddata/gooddata-python-sdk

df4d4a4d730ab376960ae2ed01e7d86498e85c6a

[ "MIT" ]

7

2022-01-24T16:27:06.000Z

2022-02-25T10:18:49.000Z

gooddata-afm-client/gooddata_afm_client/model/included_dimension_props.py

gooddata/gooddata-python-sdk

df4d4a4d730ab376960ae2ed01e7d86498e85c6a

[ "MIT" ]

29

2022-01-20T15:45:38.000Z

2022-03-31T09:39:25.000Z

gooddata-afm-client/gooddata_afm_client/model/included_dimension_props.py

gooddata/gooddata-python-sdk

df4d4a4d730ab376960ae2ed01e7d86498e85c6a

[ "MIT" ]

7

2022-01-20T07:11:15.000Z

2022-03-09T14:50:17.000Z

""" OpenAPI definition No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: v0 Contact: support@gooddata.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from gooddata_afm_client.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from gooddata_afm_client.exceptions import ApiAttributeError class IncludedDimensionProps(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = True @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'dimension_attributes_values': ({str: ([str],)},), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'dimension_attributes_values': 'dimensionAttributesValues', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, dimension_attributes_values, *args, **kwargs): # noqa: E501 """IncludedDimensionProps - a model defined in OpenAPI Args: dimension_attributes_values ({str: ([str],)}): Allows to customize for which attribute values the grand total will be computed. If the values for particular attribute are not specified then the totals for all values are computed. Note that this also covers the case of individual metrics (treated as values of the \"measureGroup\" pseudo attribute). Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.dimension_attributes_values = dimension_attributes_values for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, dimension_attributes_values, *args, **kwargs): # noqa: E501 """IncludedDimensionProps - a model defined in OpenAPI Args: dimension_attributes_values ({str: ([str],)}): Allows to customize for which attribute values the grand total will be computed. If the values for particular attribute are not specified then the totals for all values are computed. Note that this also covers the case of individual metrics (treated as values of the \"measureGroup\" pseudo attribute). Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.dimension_attributes_values = dimension_attributes_values for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

45.813688

361

0.590256

import re import sys from gooddata_afm_client.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from gooddata_afm_client.exceptions import ApiAttributeError class IncludedDimensionProps(ModelNormal): allowed_values = { } validations = { } @cached_property def additional_properties_type(): return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = True @cached_property def openapi_types(): return { 'dimension_attributes_values': ({str: ([str],)},), } @cached_property def discriminator(): return None attribute_map = { 'dimension_attributes_values': 'dimensionAttributesValues', } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, dimension_attributes_values, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.dimension_attributes_values = dimension_attributes_values for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, dimension_attributes_values, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.dimension_attributes_values = dimension_attributes_values for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

true

f719ad57e58a44fc929ef55ed10a1ee635466eb2

326

py

Python

setup.py

droberin/cyberdyne-dyndns

7d495390413cff2829f6b00a482f7b9dff3dcb5a

[ "MIT" ]

null

setup.py

droberin/cyberdyne-dyndns

7d495390413cff2829f6b00a482f7b9dff3dcb5a

[ "MIT" ]

null

setup.py

droberin/cyberdyne-dyndns

7d495390413cff2829f6b00a482f7b9dff3dcb5a

[ "MIT" ]

null

from distutils.core import setup setup( name='cyberdynedyndnscli', version='0.1.0', packages=['cyberdynedyndnscli'], url='https://github.com/droberin/cyberdynedyndnscli', license='MIT', author='DRoBeR', author_email='drober+software@gmail.com', description='Cyberdyne.es Dynamic DNS client' )

25.076923

57

0.699387

from distutils.core import setup setup( name='cyberdynedyndnscli', version='0.1.0', packages=['cyberdynedyndnscli'], url='https://github.com/droberin/cyberdynedyndnscli', license='MIT', author='DRoBeR', author_email='drober+software@gmail.com', description='Cyberdyne.es Dynamic DNS client' )

true

f719ae112f660d822e36dfe8386ebed7cf3c5760

13,464

py

Python

Doc/tools/extensions/pyspecific.py

deadsnakes/python3.4

e8ac58ab083b57aa04b46c79f764c68bdab607a0

[ "CNRI-Python-GPL-Compatible" ]

null

Doc/tools/extensions/pyspecific.py

deadsnakes/python3.4

e8ac58ab083b57aa04b46c79f764c68bdab607a0

[ "CNRI-Python-GPL-Compatible" ]

null

Doc/tools/extensions/pyspecific.py

deadsnakes/python3.4

e8ac58ab083b57aa04b46c79f764c68bdab607a0

[ "CNRI-Python-GPL-Compatible" ]

null

# -*- coding: utf-8 -*- """ pyspecific.py ~~~~~~~~~~~~~ Sphinx extension with Python doc-specific markup. :copyright: 2008-2014 by Georg Brandl. :license: Python license. """ import re import codecs from os import path from time import asctime from pprint import pformat from docutils.io import StringOutput from docutils.parsers.rst import Directive from docutils.utils import new_document from docutils import nodes, utils from sphinx import addnodes from sphinx.builders import Builder from sphinx.util.nodes import split_explicit_title from sphinx.writers.html import HTMLTranslator from sphinx.writers.text import TextWriter from sphinx.writers.latex import LaTeXTranslator from sphinx.domains.python import PyModulelevel, PyClassmember # Support for checking for suspicious markup import suspicious ISSUE_URI = 'https://bugs.python.org/issue%s' SOURCE_URI = 'https://github.com/python/cpython/tree/3.4/%s' # monkey-patch reST parser to disable alphabetic and roman enumerated lists from docutils.parsers.rst.states import Body Body.enum.converters['loweralpha'] = \ Body.enum.converters['upperalpha'] = \ Body.enum.converters['lowerroman'] = \ Body.enum.converters['upperroman'] = lambda x: None # monkey-patch HTML and LaTeX translators to keep doctest blocks in the # doctest docs themselves orig_visit_literal_block = HTMLTranslator.visit_literal_block orig_depart_literal_block = LaTeXTranslator.depart_literal_block def new_visit_literal_block(self, node): meta = self.builder.env.metadata[self.builder.current_docname] old_trim_doctest_flags = self.highlighter.trim_doctest_flags if 'keepdoctest' in meta: self.highlighter.trim_doctest_flags = False try: orig_visit_literal_block(self, node) finally: self.highlighter.trim_doctest_flags = old_trim_doctest_flags def new_depart_literal_block(self, node): meta = self.builder.env.metadata[self.curfilestack[-1]] old_trim_doctest_flags = self.highlighter.trim_doctest_flags if 'keepdoctest' in meta: self.highlighter.trim_doctest_flags = False try: orig_depart_literal_block(self, node) finally: self.highlighter.trim_doctest_flags = old_trim_doctest_flags HTMLTranslator.visit_literal_block = new_visit_literal_block LaTeXTranslator.depart_literal_block = new_depart_literal_block # Support for marking up and linking to bugs.python.org issues def issue_role(typ, rawtext, text, lineno, inliner, options={}, content=[]): issue = utils.unescape(text) text = 'issue ' + issue refnode = nodes.reference(text, text, refuri=ISSUE_URI % issue) return [refnode], [] # Support for linking to Python source files easily def source_role(typ, rawtext, text, lineno, inliner, options={}, content=[]): has_t, title, target = split_explicit_title(text) title = utils.unescape(title) target = utils.unescape(target) refnode = nodes.reference(title, title, refuri=SOURCE_URI % target) return [refnode], [] # Support for marking up implementation details class ImplementationDetail(Directive): has_content = True required_arguments = 0 optional_arguments = 1 final_argument_whitespace = True def run(self): pnode = nodes.compound(classes=['impl-detail']) content = self.content add_text = nodes.strong('CPython implementation detail:', 'CPython implementation detail:') if self.arguments: n, m = self.state.inline_text(self.arguments[0], self.lineno) pnode.append(nodes.paragraph('', '', *(n + m))) self.state.nested_parse(content, self.content_offset, pnode) if pnode.children and isinstance(pnode[0], nodes.paragraph): pnode[0].insert(0, add_text) pnode[0].insert(1, nodes.Text(' ')) else: pnode.insert(0, nodes.paragraph('', '', add_text)) return [pnode] # Support for documenting decorators class PyDecoratorMixin(object): def handle_signature(self, sig, signode): ret = super(PyDecoratorMixin, self).handle_signature(sig, signode) signode.insert(0, addnodes.desc_addname('@', '@')) return ret def needs_arglist(self): return False class PyDecoratorFunction(PyDecoratorMixin, PyModulelevel): def run(self): # a decorator function is a function after all self.name = 'py:function' return PyModulelevel.run(self) class PyDecoratorMethod(PyDecoratorMixin, PyClassmember): def run(self): self.name = 'py:method' return PyClassmember.run(self) class PyCoroutineMixin(object): def handle_signature(self, sig, signode): ret = super(PyCoroutineMixin, self).handle_signature(sig, signode) signode.insert(0, addnodes.desc_annotation('coroutine ', 'coroutine ')) return ret class PyCoroutineFunction(PyCoroutineMixin, PyModulelevel): def run(self): self.name = 'py:function' return PyModulelevel.run(self) class PyCoroutineMethod(PyCoroutineMixin, PyClassmember): def run(self): self.name = 'py:method' return PyClassmember.run(self) # Support for documenting version of removal in deprecations class DeprecatedRemoved(Directive): has_content = True required_arguments = 2 optional_arguments = 1 final_argument_whitespace = True option_spec = {} _label = 'Deprecated since version %s, will be removed in version %s' def run(self): node = addnodes.versionmodified() node.document = self.state.document node['type'] = 'deprecated-removed' version = (self.arguments[0], self.arguments[1]) node['version'] = version text = self._label % version if len(self.arguments) == 3: inodes, messages = self.state.inline_text(self.arguments[2], self.lineno+1) para = nodes.paragraph(self.arguments[2], '', *inodes) node.append(para) else: messages = [] if self.content: self.state.nested_parse(self.content, self.content_offset, node) if len(node): if isinstance(node[0], nodes.paragraph) and node[0].rawsource: content = nodes.inline(node[0].rawsource, translatable=True) content.source = node[0].source content.line = node[0].line content += node[0].children node[0].replace_self(nodes.paragraph('', '', content)) node[0].insert(0, nodes.inline('', '%s: ' % text, classes=['versionmodified'])) else: para = nodes.paragraph('', '', nodes.inline('', '%s.' % text, classes=['versionmodified'])) node.append(para) env = self.state.document.settings.env env.note_versionchange('deprecated', version[0], node, self.lineno) return [node] + messages # Support for including Misc/NEWS issue_re = re.compile('([Ii])ssue #([0-9]+)') whatsnew_re = re.compile(r"(?im)^what's new in (.*?)\??$") class MiscNews(Directive): has_content = False required_arguments = 1 optional_arguments = 0 final_argument_whitespace = False option_spec = {} def run(self): fname = self.arguments[0] source = self.state_machine.input_lines.source( self.lineno - self.state_machine.input_offset - 1) source_dir = path.dirname(path.abspath(source)) fpath = path.join(source_dir, fname) self.state.document.settings.record_dependencies.add(fpath) try: fp = codecs.open(fpath, encoding='utf-8') try: content = fp.read() finally: fp.close() except Exception: text = 'The NEWS file is not available.' node = nodes.strong(text, text) return [node] content = issue_re.sub(r'`\1ssue #\2 <https://bugs.python.org/\2>`__', content) content = whatsnew_re.sub(r'\1', content) # remove first 3 lines as they are the main heading lines = ['.. default-role:: obj', ''] + content.splitlines()[3:] self.state_machine.insert_input(lines, fname) return [] # Support for building "topic help" for pydoc pydoc_topic_labels = [ 'assert', 'assignment', 'atom-identifiers', 'atom-literals', 'attribute-access', 'attribute-references', 'augassign', 'binary', 'bitwise', 'bltin-code-objects', 'bltin-ellipsis-object', 'bltin-null-object', 'bltin-type-objects', 'booleans', 'break', 'callable-types', 'calls', 'class', 'comparisons', 'compound', 'context-managers', 'continue', 'conversions', 'customization', 'debugger', 'del', 'dict', 'dynamic-features', 'else', 'exceptions', 'execmodel', 'exprlists', 'floating', 'for', 'formatstrings', 'function', 'global', 'id-classes', 'identifiers', 'if', 'imaginary', 'import', 'in', 'integers', 'lambda', 'lists', 'naming', 'nonlocal', 'numbers', 'numeric-types', 'objects', 'operator-summary', 'pass', 'power', 'raise', 'return', 'sequence-types', 'shifting', 'slicings', 'specialattrs', 'specialnames', 'string-methods', 'strings', 'subscriptions', 'truth', 'try', 'types', 'typesfunctions', 'typesmapping', 'typesmethods', 'typesmodules', 'typesseq', 'typesseq-mutable', 'unary', 'while', 'with', 'yield' ] class PydocTopicsBuilder(Builder): name = 'pydoc-topics' def init(self): self.topics = {} def get_outdated_docs(self): return 'all pydoc topics' def get_target_uri(self, docname, typ=None): return '' # no URIs def write(self, *ignored): writer = TextWriter(self) for label in self.status_iterator(pydoc_topic_labels, 'building topics... ', length=len(pydoc_topic_labels)): if label not in self.env.domaindata['std']['labels']: self.warn('label %r not in documentation' % label) continue docname, labelid, sectname = self.env.domaindata['std']['labels'][label] doctree = self.env.get_and_resolve_doctree(docname, self) document = new_document('<section node>') document.append(doctree.ids[labelid]) destination = StringOutput(encoding='utf-8') writer.write(document, destination) self.topics[label] = writer.output def finish(self): f = open(path.join(self.outdir, 'topics.py'), 'wb') try: f.write('# -*- coding: utf-8 -*-\n'.encode('utf-8')) f.write(('# Autogenerated by Sphinx on %s\n' % asctime()).encode('utf-8')) f.write(('topics = ' + pformat(self.topics) + '\n').encode('utf-8')) finally: f.close() # Support for documenting Opcodes opcode_sig_re = re.compile(r'(\w+(?:\+\d)?)(?:\s*$(.*)$)?') def parse_opcode_signature(env, sig, signode): """Transform an opcode signature into RST nodes.""" m = opcode_sig_re.match(sig) if m is None: raise ValueError opname, arglist = m.groups() signode += addnodes.desc_name(opname, opname) if arglist is not None: paramlist = addnodes.desc_parameterlist() signode += paramlist paramlist += addnodes.desc_parameter(arglist, arglist) return opname.strip() # Support for documenting pdb commands pdbcmd_sig_re = re.compile(r'([a-z()!]+)\s*(.*)') # later... # pdbargs_tokens_re = re.compile(r'''[a-zA-Z]+ | # identifiers # [.,:]+ | # punctuation # [\[\]()] | # parens # \s+ # whitespace # ''', re.X) def parse_pdb_command(env, sig, signode): """Transform a pdb command signature into RST nodes.""" m = pdbcmd_sig_re.match(sig) if m is None: raise ValueError name, args = m.groups() fullname = name.replace('(', '').replace(')', '') signode += addnodes.desc_name(name, name) if args: signode += addnodes.desc_addname(' '+args, ' '+args) return fullname def setup(app): app.add_role('issue', issue_role) app.add_role('source', source_role) app.add_directive('impl-detail', ImplementationDetail) app.add_directive('deprecated-removed', DeprecatedRemoved) app.add_builder(PydocTopicsBuilder) app.add_builder(suspicious.CheckSuspiciousMarkupBuilder) app.add_description_unit('opcode', 'opcode', '%s (opcode)', parse_opcode_signature) app.add_description_unit('pdbcommand', 'pdbcmd', '%s (pdb command)', parse_pdb_command) app.add_description_unit('2to3fixer', '2to3fixer', '%s (2to3 fixer)') app.add_directive_to_domain('py', 'decorator', PyDecoratorFunction) app.add_directive_to_domain('py', 'decoratormethod', PyDecoratorMethod) app.add_directive_to_domain('py', 'coroutinefunction', PyCoroutineFunction) app.add_directive_to_domain('py', 'coroutinemethod', PyCoroutineMethod) app.add_directive('miscnews', MiscNews) return {'version': '1.0', 'parallel_read_safe': True}

36.096515

86

0.635844

import re import codecs from os import path from time import asctime from pprint import pformat from docutils.io import StringOutput from docutils.parsers.rst import Directive from docutils.utils import new_document from docutils import nodes, utils from sphinx import addnodes from sphinx.builders import Builder from sphinx.util.nodes import split_explicit_title from sphinx.writers.html import HTMLTranslator from sphinx.writers.text import TextWriter from sphinx.writers.latex import LaTeXTranslator from sphinx.domains.python import PyModulelevel, PyClassmember import suspicious ISSUE_URI = 'https://bugs.python.org/issue%s' SOURCE_URI = 'https://github.com/python/cpython/tree/3.4/%s' from docutils.parsers.rst.states import Body Body.enum.converters['loweralpha'] = \ Body.enum.converters['upperalpha'] = \ Body.enum.converters['lowerroman'] = \ Body.enum.converters['upperroman'] = lambda x: None orig_visit_literal_block = HTMLTranslator.visit_literal_block orig_depart_literal_block = LaTeXTranslator.depart_literal_block def new_visit_literal_block(self, node): meta = self.builder.env.metadata[self.builder.current_docname] old_trim_doctest_flags = self.highlighter.trim_doctest_flags if 'keepdoctest' in meta: self.highlighter.trim_doctest_flags = False try: orig_visit_literal_block(self, node) finally: self.highlighter.trim_doctest_flags = old_trim_doctest_flags def new_depart_literal_block(self, node): meta = self.builder.env.metadata[self.curfilestack[-1]] old_trim_doctest_flags = self.highlighter.trim_doctest_flags if 'keepdoctest' in meta: self.highlighter.trim_doctest_flags = False try: orig_depart_literal_block(self, node) finally: self.highlighter.trim_doctest_flags = old_trim_doctest_flags HTMLTranslator.visit_literal_block = new_visit_literal_block LaTeXTranslator.depart_literal_block = new_depart_literal_block def issue_role(typ, rawtext, text, lineno, inliner, options={}, content=[]): issue = utils.unescape(text) text = 'issue ' + issue refnode = nodes.reference(text, text, refuri=ISSUE_URI % issue) return [refnode], [] def source_role(typ, rawtext, text, lineno, inliner, options={}, content=[]): has_t, title, target = split_explicit_title(text) title = utils.unescape(title) target = utils.unescape(target) refnode = nodes.reference(title, title, refuri=SOURCE_URI % target) return [refnode], [] class ImplementationDetail(Directive): has_content = True required_arguments = 0 optional_arguments = 1 final_argument_whitespace = True def run(self): pnode = nodes.compound(classes=['impl-detail']) content = self.content add_text = nodes.strong('CPython implementation detail:', 'CPython implementation detail:') if self.arguments: n, m = self.state.inline_text(self.arguments[0], self.lineno) pnode.append(nodes.paragraph('', '', *(n + m))) self.state.nested_parse(content, self.content_offset, pnode) if pnode.children and isinstance(pnode[0], nodes.paragraph): pnode[0].insert(0, add_text) pnode[0].insert(1, nodes.Text(' ')) else: pnode.insert(0, nodes.paragraph('', '', add_text)) return [pnode] class PyDecoratorMixin(object): def handle_signature(self, sig, signode): ret = super(PyDecoratorMixin, self).handle_signature(sig, signode) signode.insert(0, addnodes.desc_addname('@', '@')) return ret def needs_arglist(self): return False class PyDecoratorFunction(PyDecoratorMixin, PyModulelevel): def run(self): self.name = 'py:function' return PyModulelevel.run(self) class PyDecoratorMethod(PyDecoratorMixin, PyClassmember): def run(self): self.name = 'py:method' return PyClassmember.run(self) class PyCoroutineMixin(object): def handle_signature(self, sig, signode): ret = super(PyCoroutineMixin, self).handle_signature(sig, signode) signode.insert(0, addnodes.desc_annotation('coroutine ', 'coroutine ')) return ret class PyCoroutineFunction(PyCoroutineMixin, PyModulelevel): def run(self): self.name = 'py:function' return PyModulelevel.run(self) class PyCoroutineMethod(PyCoroutineMixin, PyClassmember): def run(self): self.name = 'py:method' return PyClassmember.run(self) class DeprecatedRemoved(Directive): has_content = True required_arguments = 2 optional_arguments = 1 final_argument_whitespace = True option_spec = {} _label = 'Deprecated since version %s, will be removed in version %s' def run(self): node = addnodes.versionmodified() node.document = self.state.document node['type'] = 'deprecated-removed' version = (self.arguments[0], self.arguments[1]) node['version'] = version text = self._label % version if len(self.arguments) == 3: inodes, messages = self.state.inline_text(self.arguments[2], self.lineno+1) para = nodes.paragraph(self.arguments[2], '', *inodes) node.append(para) else: messages = [] if self.content: self.state.nested_parse(self.content, self.content_offset, node) if len(node): if isinstance(node[0], nodes.paragraph) and node[0].rawsource: content = nodes.inline(node[0].rawsource, translatable=True) content.source = node[0].source content.line = node[0].line content += node[0].children node[0].replace_self(nodes.paragraph('', '', content)) node[0].insert(0, nodes.inline('', '%s: ' % text, classes=['versionmodified'])) else: para = nodes.paragraph('', '', nodes.inline('', '%s.' % text, classes=['versionmodified'])) node.append(para) env = self.state.document.settings.env env.note_versionchange('deprecated', version[0], node, self.lineno) return [node] + messages issue_re = re.compile('([Ii])ssue #([0-9]+)') whatsnew_re = re.compile(r"(?im)^what's new in (.*?)\??$") class MiscNews(Directive): has_content = False required_arguments = 1 optional_arguments = 0 final_argument_whitespace = False option_spec = {} def run(self): fname = self.arguments[0] source = self.state_machine.input_lines.source( self.lineno - self.state_machine.input_offset - 1) source_dir = path.dirname(path.abspath(source)) fpath = path.join(source_dir, fname) self.state.document.settings.record_dependencies.add(fpath) try: fp = codecs.open(fpath, encoding='utf-8') try: content = fp.read() finally: fp.close() except Exception: text = 'The NEWS file is not available.' node = nodes.strong(text, text) return [node] content = issue_re.sub(r'`\1ssue content) content = whatsnew_re.sub(r'\1', content) # remove first 3 lines as they are the main heading lines = ['.. default-role:: obj', ''] + content.splitlines()[3:] self.state_machine.insert_input(lines, fname) return [] # Support for building "topic help" for pydoc pydoc_topic_labels = [ 'assert', 'assignment', 'atom-identifiers', 'atom-literals', 'attribute-access', 'attribute-references', 'augassign', 'binary', 'bitwise', 'bltin-code-objects', 'bltin-ellipsis-object', 'bltin-null-object', 'bltin-type-objects', 'booleans', 'break', 'callable-types', 'calls', 'class', 'comparisons', 'compound', 'context-managers', 'continue', 'conversions', 'customization', 'debugger', 'del', 'dict', 'dynamic-features', 'else', 'exceptions', 'execmodel', 'exprlists', 'floating', 'for', 'formatstrings', 'function', 'global', 'id-classes', 'identifiers', 'if', 'imaginary', 'import', 'in', 'integers', 'lambda', 'lists', 'naming', 'nonlocal', 'numbers', 'numeric-types', 'objects', 'operator-summary', 'pass', 'power', 'raise', 'return', 'sequence-types', 'shifting', 'slicings', 'specialattrs', 'specialnames', 'string-methods', 'strings', 'subscriptions', 'truth', 'try', 'types', 'typesfunctions', 'typesmapping', 'typesmethods', 'typesmodules', 'typesseq', 'typesseq-mutable', 'unary', 'while', 'with', 'yield' ] class PydocTopicsBuilder(Builder): name = 'pydoc-topics' def init(self): self.topics = {} def get_outdated_docs(self): return 'all pydoc topics' def get_target_uri(self, docname, typ=None): return '' # no URIs def write(self, *ignored): writer = TextWriter(self) for label in self.status_iterator(pydoc_topic_labels, 'building topics... ', length=len(pydoc_topic_labels)): if label not in self.env.domaindata['std']['labels']: self.warn('label %r not in documentation' % label) continue docname, labelid, sectname = self.env.domaindata['std']['labels'][label] doctree = self.env.get_and_resolve_doctree(docname, self) document = new_document('<section node>') document.append(doctree.ids[labelid]) destination = StringOutput(encoding='utf-8') writer.write(document, destination) self.topics[label] = writer.output def finish(self): f = open(path.join(self.outdir, 'topics.py'), 'wb') try: f.write(' f.write((' f.write(('topics = ' + pformat(self.topics) + '\n').encode('utf-8')) finally: f.close() # Support for documenting Opcodes opcode_sig_re = re.compile(r'(\w+(?:\+\d)?)(?:\s*$(.*)$)?') def parse_opcode_signature(env, sig, signode): m = opcode_sig_re.match(sig) if m is None: raise ValueError opname, arglist = m.groups() signode += addnodes.desc_name(opname, opname) if arglist is not None: paramlist = addnodes.desc_parameterlist() signode += paramlist paramlist += addnodes.desc_parameter(arglist, arglist) return opname.strip() # Support for documenting pdb commands pdbcmd_sig_re = re.compile(r'([a-z()!]+)\s*(.*)') # later... # pdbargs_tokens_re = re.compile(r'''[a-zA-Z]+ | # identifiers # [.,:]+ | # punctuation # [\[\]()] | # parens # \s+ # whitespace # ''', re.X) def parse_pdb_command(env, sig, signode): m = pdbcmd_sig_re.match(sig) if m is None: raise ValueError name, args = m.groups() fullname = name.replace('(', '').replace(')', '') signode += addnodes.desc_name(name, name) if args: signode += addnodes.desc_addname(' '+args, ' '+args) return fullname def setup(app): app.add_role('issue', issue_role) app.add_role('source', source_role) app.add_directive('impl-detail', ImplementationDetail) app.add_directive('deprecated-removed', DeprecatedRemoved) app.add_builder(PydocTopicsBuilder) app.add_builder(suspicious.CheckSuspiciousMarkupBuilder) app.add_description_unit('opcode', 'opcode', '%s (opcode)', parse_opcode_signature) app.add_description_unit('pdbcommand', 'pdbcmd', '%s (pdb command)', parse_pdb_command) app.add_description_unit('2to3fixer', '2to3fixer', '%s (2to3 fixer)') app.add_directive_to_domain('py', 'decorator', PyDecoratorFunction) app.add_directive_to_domain('py', 'decoratormethod', PyDecoratorMethod) app.add_directive_to_domain('py', 'coroutinefunction', PyCoroutineFunction) app.add_directive_to_domain('py', 'coroutinemethod', PyCoroutineMethod) app.add_directive('miscnews', MiscNews) return {'version': '1.0', 'parallel_read_safe': True}

true

f719ae360e05e3d0b1462b0875f0af93d02276fd

5,643

py

Python

airflow/executors/debug_executor.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

3

2019-12-11T15:54:13.000Z

2021-05-24T20:21:08.000Z

airflow/executors/debug_executor.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

8

2021-02-08T20:40:47.000Z

2022-03-29T22:27:53.000Z

airflow/executors/debug_executor.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

2

2021-01-11T13:53:03.000Z

2021-10-02T05:06:34.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """ This module contains DebugExecutor that is a single process executor meaning it does not use multiprocessing. """ import threading from typing import Any, Dict, List, Optional from airflow.configuration import conf from airflow.executors.base_executor import BaseExecutor from airflow.models.taskinstance import TaskInstance, TaskInstanceKeyType from airflow.utils.state import State class DebugExecutor(BaseExecutor): """ This executor is meant for debugging purposes. It can be used with SQLite. It executes one task instance at time. Additionally to support working with sensors, all sensors ``mode`` will be automatically set to "reschedule". """ _terminated = threading.Event() def __init__(self): super().__init__() self.tasks_to_run: List[TaskInstance] = [] # Place where we keep information for task instance raw run self.tasks_params: Dict[TaskInstanceKeyType, Dict[str, Any]] = {} self.fail_fast = conf.getboolean("debug", "fail_fast") def execute_async(self, *args, **kwargs) -> None: """ The method is replaced by custom trigger_task implementation. """ def sync(self) -> None: task_succeeded = True while self.tasks_to_run: ti = self.tasks_to_run.pop(0) if self.fail_fast and not task_succeeded: self.log.info("Setting %s to %s", ti.key, State.UPSTREAM_FAILED) ti.set_state(State.UPSTREAM_FAILED) self.change_state(ti.key, State.UPSTREAM_FAILED) continue if self._terminated.is_set(): self.log.info( "Executor is terminated! Stopping %s to %s", ti.key, State.FAILED ) ti.set_state(State.FAILED) self.change_state(ti.key, State.FAILED) continue task_succeeded = self._run_task(ti) def _run_task(self, ti: TaskInstance) -> bool: self.log.debug("Executing task: %s", ti) key = ti.key try: params = self.tasks_params.pop(ti.key, {}) ti._run_raw_task( # pylint: disable=protected-access job_id=ti.job_id, **params ) self.change_state(key, State.SUCCESS) return True except Exception as e: # pylint: disable=broad-except self.change_state(key, State.FAILED) self.log.exception("Failed to execute task: %s.", str(e)) return False def queue_task_instance( self, task_instance: TaskInstance, mark_success: bool = False, pickle_id: Optional[str] = None, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, pool: Optional[str] = None, cfg_path: Optional[str] = None, ) -> None: """ Queues task instance with empty command because we do not need it. """ self.queue_command( task_instance, [str(task_instance)], # Just for better logging, it's not used anywhere priority=task_instance.task.priority_weight_total, queue=task_instance.task.queue, ) # Save params for TaskInstance._run_raw_task self.tasks_params[task_instance.key] = { "mark_success": mark_success, "pool": pool, } def trigger_tasks(self, open_slots: int) -> None: """ Triggers tasks. Instead of calling exec_async we just add task instance to tasks_to_run queue. :param open_slots: Number of open slots """ sorted_queue = sorted( [(k, v) for k, v in self.queued_tasks.items()], # pylint: disable=unnecessary-comprehension key=lambda x: x[1][1], reverse=True, ) for _ in range(min((open_slots, len(self.queued_tasks)))): key, (_, _, _, ti) = sorted_queue.pop(0) self.queued_tasks.pop(key) self.running.add(key) self.tasks_to_run.append(ti) # type: ignore def end(self) -> None: """ When the method is called we just set states of queued tasks to UPSTREAM_FAILED marking them as not executed. """ for ti in self.tasks_to_run: self.log.info("Setting %s to %s", ti.key, State.UPSTREAM_FAILED) ti.set_state(State.UPSTREAM_FAILED) self.change_state(ti.key, State.UPSTREAM_FAILED) def terminate(self) -> None: self._terminated.set() def change_state(self, key: TaskInstanceKeyType, state: str) -> None: self.log.debug("Popping %s from executor task queue.", key) self.running.remove(key) self.event_buffer[key] = state

37.370861

104

0.633174

import threading from typing import Any, Dict, List, Optional from airflow.configuration import conf from airflow.executors.base_executor import BaseExecutor from airflow.models.taskinstance import TaskInstance, TaskInstanceKeyType from airflow.utils.state import State class DebugExecutor(BaseExecutor): _terminated = threading.Event() def __init__(self): super().__init__() self.tasks_to_run: List[TaskInstance] = [] self.tasks_params: Dict[TaskInstanceKeyType, Dict[str, Any]] = {} self.fail_fast = conf.getboolean("debug", "fail_fast") def execute_async(self, *args, **kwargs) -> None: def sync(self) -> None: task_succeeded = True while self.tasks_to_run: ti = self.tasks_to_run.pop(0) if self.fail_fast and not task_succeeded: self.log.info("Setting %s to %s", ti.key, State.UPSTREAM_FAILED) ti.set_state(State.UPSTREAM_FAILED) self.change_state(ti.key, State.UPSTREAM_FAILED) continue if self._terminated.is_set(): self.log.info( "Executor is terminated! Stopping %s to %s", ti.key, State.FAILED ) ti.set_state(State.FAILED) self.change_state(ti.key, State.FAILED) continue task_succeeded = self._run_task(ti) def _run_task(self, ti: TaskInstance) -> bool: self.log.debug("Executing task: %s", ti) key = ti.key try: params = self.tasks_params.pop(ti.key, {}) ti._run_raw_task( job_id=ti.job_id, **params ) self.change_state(key, State.SUCCESS) return True except Exception as e: self.change_state(key, State.FAILED) self.log.exception("Failed to execute task: %s.", str(e)) return False def queue_task_instance( self, task_instance: TaskInstance, mark_success: bool = False, pickle_id: Optional[str] = None, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, pool: Optional[str] = None, cfg_path: Optional[str] = None, ) -> None: self.queue_command( task_instance, [str(task_instance)], priority=task_instance.task.priority_weight_total, queue=task_instance.task.queue, ) # Save params for TaskInstance._run_raw_task self.tasks_params[task_instance.key] = { "mark_success": mark_success, "pool": pool, } def trigger_tasks(self, open_slots: int) -> None: sorted_queue = sorted( [(k, v) for k, v in self.queued_tasks.items()], # pylint: disable=unnecessary-comprehension key=lambda x: x[1][1], reverse=True, ) for _ in range(min((open_slots, len(self.queued_tasks)))): key, (_, _, _, ti) = sorted_queue.pop(0) self.queued_tasks.pop(key) self.running.add(key) self.tasks_to_run.append(ti) # type: ignore def end(self) -> None: for ti in self.tasks_to_run: self.log.info("Setting %s to %s", ti.key, State.UPSTREAM_FAILED) ti.set_state(State.UPSTREAM_FAILED) self.change_state(ti.key, State.UPSTREAM_FAILED) def terminate(self) -> None: self._terminated.set() def change_state(self, key: TaskInstanceKeyType, state: str) -> None: self.log.debug("Popping %s from executor task queue.", key) self.running.remove(key) self.event_buffer[key] = state

true

f719af5392c1befb33e7fc5a3df49b8e3154b0ce

2,063

py

Python

aliyun-python-sdk-eas/aliyunsdkeas/request/v20210701/ListServicesRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

aliyun-python-sdk-eas/aliyunsdkeas/request/v20210701/ListServicesRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

aliyun-python-sdk-eas/aliyunsdkeas/request/v20210701/ListServicesRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. from aliyunsdkcore.request import RoaRequest from aliyunsdkeas.endpoint import endpoint_data class ListServicesRequest(RoaRequest): def __init__(self): RoaRequest.__init__(self, 'eas', '2021-07-01', 'ListServices','eas') self.set_uri_pattern('/api/v2/services') self.set_method('GET') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Filter(self): return self.get_query_params().get('Filter') def set_Filter(self,Filter): self.add_query_param('Filter',Filter) def get_PageSize(self): return self.get_query_params().get('PageSize') def set_PageSize(self,PageSize): self.add_query_param('PageSize',PageSize) def get_Sort(self): return self.get_query_params().get('Sort') def set_Sort(self,Sort): self.add_query_param('Sort',Sort) def get_PageNumber(self): return self.get_query_params().get('PageNumber') def set_PageNumber(self,PageNumber): self.add_query_param('PageNumber',PageNumber) def get_Order(self): return self.get_query_params().get('Order') def set_Order(self,Order): self.add_query_param('Order',Order)

32.746032

74

0.750848

from aliyunsdkcore.request import RoaRequest from aliyunsdkeas.endpoint import endpoint_data class ListServicesRequest(RoaRequest): def __init__(self): RoaRequest.__init__(self, 'eas', '2021-07-01', 'ListServices','eas') self.set_uri_pattern('/api/v2/services') self.set_method('GET') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Filter(self): return self.get_query_params().get('Filter') def set_Filter(self,Filter): self.add_query_param('Filter',Filter) def get_PageSize(self): return self.get_query_params().get('PageSize') def set_PageSize(self,PageSize): self.add_query_param('PageSize',PageSize) def get_Sort(self): return self.get_query_params().get('Sort') def set_Sort(self,Sort): self.add_query_param('Sort',Sort) def get_PageNumber(self): return self.get_query_params().get('PageNumber') def set_PageNumber(self,PageNumber): self.add_query_param('PageNumber',PageNumber) def get_Order(self): return self.get_query_params().get('Order') def set_Order(self,Order): self.add_query_param('Order',Order)

true

f719af5c196d30f0eb97eff99d60406c1d503639

1,912

py

Python

tests/unit/recommenders/models/test_newsrec_utils.py

enowy/Recommenders

60033231b9167438032843c23158c0c776856e0e

[ "MIT" ]

10

2019-05-06T21:57:10.000Z

2019-05-07T06:15:39.000Z

tests/unit/recommenders/models/test_newsrec_utils.py

enowy/Recommenders

60033231b9167438032843c23158c0c776856e0e

[ "MIT" ]

2

2022-01-19T20:24:51.000Z

2022-02-18T20:25:24.000Z

tests/unit/recommenders/models/test_newsrec_utils.py

enowy/Recommenders

60033231b9167438032843c23158c0c776856e0e

[ "MIT" ]

3

2019-05-06T22:24:21.000Z

2019-05-07T02:50:46.000Z

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import os import pytest try: from recommenders.models.deeprec.deeprec_utils import download_deeprec_resources from recommenders.models.newsrec.newsrec_utils import prepare_hparams, load_yaml except ImportError: pass # skip this import if we are in cpu environment @pytest.mark.parametrize( "must_exist_attributes", ["wordEmb_file", "wordDict_file", "userDict_file"] ) @pytest.mark.gpu def test_prepare_hparams(must_exist_attributes, deeprec_resource_path): wordEmb_file = os.path.join(deeprec_resource_path, "mind", "utils", "embedding.npy") userDict_file = os.path.join( deeprec_resource_path, "mind", "utils", "uid2index.pkl" ) wordDict_file = os.path.join( deeprec_resource_path, "mind", "utils", "word_dict.pkl" ) yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( r"https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(deeprec_resource_path, "mind", "utils"), "MINDdemo_utils.zip", ) hparams = prepare_hparams( yaml_file, wordEmb_file=wordEmb_file, wordDict_file=wordDict_file, userDict_file=userDict_file, epochs=1, ) assert hasattr(hparams, must_exist_attributes) @pytest.mark.gpu def test_load_yaml_file(deeprec_resource_path): yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( "https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(deeprec_resource_path, "mind", "utils"), "MINDdemo_utils.zip", ) config = load_yaml(yaml_file) assert config is not None

33.54386

88

0.69613

import os import pytest try: from recommenders.models.deeprec.deeprec_utils import download_deeprec_resources from recommenders.models.newsrec.newsrec_utils import prepare_hparams, load_yaml except ImportError: pass @pytest.mark.parametrize( "must_exist_attributes", ["wordEmb_file", "wordDict_file", "userDict_file"] ) @pytest.mark.gpu def test_prepare_hparams(must_exist_attributes, deeprec_resource_path): wordEmb_file = os.path.join(deeprec_resource_path, "mind", "utils", "embedding.npy") userDict_file = os.path.join( deeprec_resource_path, "mind", "utils", "uid2index.pkl" ) wordDict_file = os.path.join( deeprec_resource_path, "mind", "utils", "word_dict.pkl" ) yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( r"https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(deeprec_resource_path, "mind", "utils"), "MINDdemo_utils.zip", ) hparams = prepare_hparams( yaml_file, wordEmb_file=wordEmb_file, wordDict_file=wordDict_file, userDict_file=userDict_file, epochs=1, ) assert hasattr(hparams, must_exist_attributes) @pytest.mark.gpu def test_load_yaml_file(deeprec_resource_path): yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( "https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(deeprec_resource_path, "mind", "utils"), "MINDdemo_utils.zip", ) config = load_yaml(yaml_file) assert config is not None

true

f719af7723defb10087e667c5753c6f31f956520

12,081

py

Python

Self_Driving_Car/P1/LaneLines-P1/P1.py

Wentaobi/Udacity

00af9c36b42d6bca5f2d42d2744efed2ddb51587

[ "Apache-2.0" ]

null

Self_Driving_Car/P1/LaneLines-P1/P1.py

Wentaobi/Udacity

00af9c36b42d6bca5f2d42d2744efed2ddb51587

[ "Apache-2.0" ]

null

Self_Driving_Car/P1/LaneLines-P1/P1.py

Wentaobi/Udacity

00af9c36b42d6bca5f2d42d2744efed2ddb51587

[ "Apache-2.0" ]

null

#importing some useful packages import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import cv2 #reading in an image image = mpimg.imread('test_images/solidWhiteRight.jpg'); #printing out some stats and plotting print('This image is:', type(image), 'with dimesions:', image.shape) plt.imshow(image); #call as plt.imshow(gray, cmap='gray') to show a grayscaled image import math def grayscale(img): """Applies the Grayscale transform This will return an image with only one color channel but NOTE: to see the returned image as grayscale you should call plt.imshow(gray, cmap='gray')""" return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # Or use BGR2GRAY if you read an image with cv2.imread() # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) def hsv(img): return cv2.cvtColor(img, cv2.COLOR_RGB2HSV) def canny(img, low_threshold, high_threshold): """Applies the Canny transform""" return cv2.Canny(img, low_threshold, high_threshold) def gaussian_blur(img, kernel_size): """Applies a Gaussian Noise kernel""" return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0) def region_of_interest(img, vertices): """ Applies an image mask. Only keeps the region of the image defined by the polygon formed from `vertices`. The rest of the image is set to black. """ #defining a blank mask to start with mask = np.zeros_like(img) #defining a 3 channel or 1 channel color to fill the mask with depending on the input image if len(img.shape) > 2: channel_count = img.shape[2] # i.e. 3 or 4 depending on your image ignore_mask_color = (255,) * channel_count else: ignore_mask_color = 255 #filling pixels inside the polygon defined by "vertices" with the fill color cv2.fillPoly(mask, vertices, ignore_mask_color) #returning the image only where mask pixels are nonzero masked_image = cv2.bitwise_and(img, mask) return masked_image def draw_lines(img, lines, color=[255, 0, 0], thickness=13): """ NOTE: this is the function you might want to use as a starting point once you want to average/extrapolate the line segments you detect to map out the full extent of the lane (going from the result shown in raw-lines-example.mp4 to that shown in P1_example.mp4). Think about things like separating line segments by their slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left line vs. the right line. Then, you can average the position of each of the lines and extrapolate to the top and bottom of the lane. This function draws `lines` with `color` and `thickness`. Lines are drawn on the image inplace (mutates the image). If you want to make the lines semi-transparent, think about combining this function with the weighted_img() function below """ x_size = img.shape[1] y_size = img.shape[0] lines_slope_intercept = np.zeros(shape=(len(lines),2)) for index,line in enumerate(lines): for x1,y1,x2,y2 in line: slope = (y2-y1)/(x2-x1) intercept = y1 - x1 * slope lines_slope_intercept[index]=[slope,intercept] max_slope_line = lines_slope_intercept[lines_slope_intercept.argmax(axis=0)[0]] min_slope_line = lines_slope_intercept[lines_slope_intercept.argmin(axis=0)[0]] left_slopes = [] left_intercepts = [] right_slopes = [] right_intercepts = [] # this gets slopes and intercepts of lines similar to the lines with the max (immediate left) and min # (immediate right) slopes (i.e. slope and intercept within x%) for line in lines_slope_intercept: if abs(line[0] - max_slope_line[0]) < 0.15 and abs(line[1] - max_slope_line[1]) < (0.15 * x_size): left_slopes.append(line[0]) left_intercepts.append(line[1]) elif abs(line[0] - min_slope_line[0]) < 0.15 and abs(line[1] - min_slope_line[1]) < (0.15 * x_size): right_slopes.append(line[0]) right_intercepts.append(line[1]) # left and right lines are averages of these slopes and intercepts, extrapolate lines to edges and center* # *roughly new_lines = np.zeros(shape=(1,2,4), dtype=np.int32) if len(left_slopes) > 0: left_line = [sum(left_slopes)/len(left_slopes),sum(left_intercepts)/len(left_intercepts)] left_bottom_x = (y_size - left_line[1])/left_line[0] left_top_x = (y_size*.575 - left_line[1])/left_line[0] if (left_bottom_x >= 0): new_lines[0][0] =[left_bottom_x,y_size,left_top_x,y_size*.575] if len(right_slopes) > 0: right_line = [sum(right_slopes)/len(right_slopes),sum(right_intercepts)/len(right_intercepts)] right_bottom_x = (y_size - right_line[1])/right_line[0] right_top_x = (y_size*.575 - right_line[1])/right_line[0] if (right_bottom_x <= x_size): new_lines[0][1]=[right_bottom_x,y_size,right_top_x,y_size*.575] for line in new_lines: for x1,y1,x2,y2 in line: cv2.line(img, (x1, y1), (x2, y2), color, thickness) def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): """ `img` should be the output of a Canny transform. Returns an image with hough lines drawn. """ lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap) line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) draw_lines(line_img, lines) return line_img # Python 3 has support for cool math symbols. def weighted_img(img, initial_img, α=0.8, β=1., λ=0.): """ `img` is the output of the hough_lines(), An image with lines drawn on it. Should be a blank image (all black) with lines drawn on it. `initial_img` should be the image before any processing. The result image is computed as follows: initial_img * α + img * β + λ NOTE: initial_img and img must be the same shape! """ return cv2.addWeighted(initial_img, α, img, β, λ) import os os.listdir("test_images/") #reading in an image for index, img in enumerate(os.listdir("test_images/")): image = mpimg.imread('test_images/' + img) gray_img = grayscale(image) hsv_img = hsv(image) # define range of color in HSV lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) # Threshold the HSV image to get only yellow/white yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) # Bitwise-AND mask and original image full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) fig = plt.figure(figsize=(6,10)) plt.imshow(result, cmap="gray") #call as plt.imshow(gray, cmap='gray') to show a grayscaled image #reading in an image for index, img in enumerate(os.listdir("test_images2/")): image = mpimg.imread('test_images2/' + img) gray_img = grayscale(image) hsv_img = hsv(image) # define range of color in HSV lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) # Threshold the HSV image to get only yellow/white yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) # Bitwise-AND mask and original image full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) fig = plt.figure(figsize=(8,10)) plt.imshow(result, cmap="gray") #call as plt.imshow(gray, cmap='gray') to show a grayscaled image # Import everything needed to edit/save/watch video clips from moviepy.editor import VideoFileClip # from IPython.display import HTML def process_image(image): # NOTE: The output you return should be a color image (3 channel) for processing video below # TODO: put your pipeline here, # you should return the final output (image with lines are drawn on lanes) gray_img = grayscale(image) hsv_img = hsv(image) # define range of color in HSV lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) # Threshold the HSV image to get only yellow/white yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) # Bitwise-AND mask and original image full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) #return cv2.cvtColor(masked_img, cv2.COLOR_GRAY2RGB) return result white_output = 'white.mp4' clip1 = VideoFileClip("solidWhiteRight.mp4") white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! white_clip.write_videofile(white_output, audio=False) # HTML(""" # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(white_output)) yellow_output = 'yellow.mp4' clip2 = VideoFileClip('solidYellowLeft.mp4') yellow_clip = clip2.fl_image(process_image) yellow_clip.write_videofile(yellow_output, audio=False) # HTML(""" # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(yellow_output)) challenge_output = 'extra.mp4' clip2 = VideoFileClip('challenge.mp4') challenge_clip = clip2.fl_image(process_image) challenge_clip.write_videofile(challenge_output, audio=False) # # HTML(""" # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(challenge_output))

35.848665

122

0.698121

import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import cv2 image = mpimg.imread('test_images/solidWhiteRight.jpg'); print('This image is:', type(image), 'with dimesions:', image.shape) plt.imshow(image); import math def grayscale(img): return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) def hsv(img): return cv2.cvtColor(img, cv2.COLOR_RGB2HSV) def canny(img, low_threshold, high_threshold): return cv2.Canny(img, low_threshold, high_threshold) def gaussian_blur(img, kernel_size): return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0) def region_of_interest(img, vertices): mask = np.zeros_like(img) if len(img.shape) > 2: channel_count = img.shape[2] ignore_mask_color = (255,) * channel_count else: ignore_mask_color = 255 cv2.fillPoly(mask, vertices, ignore_mask_color) masked_image = cv2.bitwise_and(img, mask) return masked_image def draw_lines(img, lines, color=[255, 0, 0], thickness=13): x_size = img.shape[1] y_size = img.shape[0] lines_slope_intercept = np.zeros(shape=(len(lines),2)) for index,line in enumerate(lines): for x1,y1,x2,y2 in line: slope = (y2-y1)/(x2-x1) intercept = y1 - x1 * slope lines_slope_intercept[index]=[slope,intercept] max_slope_line = lines_slope_intercept[lines_slope_intercept.argmax(axis=0)[0]] min_slope_line = lines_slope_intercept[lines_slope_intercept.argmin(axis=0)[0]] left_slopes = [] left_intercepts = [] right_slopes = [] right_intercepts = [] for line in lines_slope_intercept: if abs(line[0] - max_slope_line[0]) < 0.15 and abs(line[1] - max_slope_line[1]) < (0.15 * x_size): left_slopes.append(line[0]) left_intercepts.append(line[1]) elif abs(line[0] - min_slope_line[0]) < 0.15 and abs(line[1] - min_slope_line[1]) < (0.15 * x_size): right_slopes.append(line[0]) right_intercepts.append(line[1]) new_lines = np.zeros(shape=(1,2,4), dtype=np.int32) if len(left_slopes) > 0: left_line = [sum(left_slopes)/len(left_slopes),sum(left_intercepts)/len(left_intercepts)] left_bottom_x = (y_size - left_line[1])/left_line[0] left_top_x = (y_size*.575 - left_line[1])/left_line[0] if (left_bottom_x >= 0): new_lines[0][0] =[left_bottom_x,y_size,left_top_x,y_size*.575] if len(right_slopes) > 0: right_line = [sum(right_slopes)/len(right_slopes),sum(right_intercepts)/len(right_intercepts)] right_bottom_x = (y_size - right_line[1])/right_line[0] right_top_x = (y_size*.575 - right_line[1])/right_line[0] if (right_bottom_x <= x_size): new_lines[0][1]=[right_bottom_x,y_size,right_top_x,y_size*.575] for line in new_lines: for x1,y1,x2,y2 in line: cv2.line(img, (x1, y1), (x2, y2), color, thickness) def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap) line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) draw_lines(line_img, lines) return line_img def weighted_img(img, initial_img, α=0.8, β=1., λ=0.): return cv2.addWeighted(initial_img, α, img, β, λ) import os os.listdir("test_images/") for index, img in enumerate(os.listdir("test_images/")): image = mpimg.imread('test_images/' + img) gray_img = grayscale(image) hsv_img = hsv(image) lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) fig = plt.figure(figsize=(6,10)) plt.imshow(result, cmap="gray") for index, img in enumerate(os.listdir("test_images2/")): image = mpimg.imread('test_images2/' + img) gray_img = grayscale(image) hsv_img = hsv(image) lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) fig = plt.figure(figsize=(8,10)) plt.imshow(result, cmap="gray") from moviepy.editor import VideoFileClip def process_image(image): gray_img = grayscale(image) hsv_img = hsv(image) lower_yel = np.array([20,100,100]) upper_yel = np.array([30,255,255]) lower_wht = np.array([0,0,235]) upper_wht = np.array([255,255,255]) yellow_mask = cv2.inRange(hsv_img, lower_yel, upper_yel) white_mask = cv2.inRange(hsv_img, lower_wht, upper_wht) full_mask = cv2.bitwise_or(yellow_mask, white_mask) subdued_gray = (gray_img / 2).astype('uint8') boosted_lanes = cv2.bitwise_or(subdued_gray, full_mask) kernel_size = 5 blurred_img = gaussian_blur(boosted_lanes,kernel_size) canny_low_threshold = 60 canny_high_threshold = 150 edges_img = canny(blurred_img,canny_low_threshold,canny_high_threshold) x = edges_img.shape[1] y = edges_img.shape[0] vertices = np.array([[(x*0.,y),(x*.475, y*.575), (x*.525, y*.575), (x,y)]], dtype=np.int32) masked_img = region_of_interest(edges_img, vertices) hough_rho = 3 hough_theta = np.pi/180 hough_threshold = 70 hough_min_line_length = 70 hough_max_line_gap = 250 hough_img = hough_lines(masked_img,hough_rho,hough_theta,hough_threshold,hough_min_line_length,hough_max_line_gap) result = weighted_img(hough_img,image) return result white_output = 'white.mp4' clip1 = VideoFileClip("solidWhiteRight.mp4") white_clip = clip1.fl_image(process_image) white_clip.write_videofile(white_output, audio=False) # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(white_output)) yellow_output = 'yellow.mp4' clip2 = VideoFileClip('solidYellowLeft.mp4') yellow_clip = clip2.fl_image(process_image) yellow_clip.write_videofile(yellow_output, audio=False) # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(yellow_output)) challenge_output = 'extra.mp4' clip2 = VideoFileClip('challenge.mp4') challenge_clip = clip2.fl_image(process_image) challenge_clip.write_videofile(challenge_output, audio=False) # <video width="960" height="540" controls> # <source src="{0}"> # </video> # """.format(challenge_output))

true

f719afb71003662d81876c64edd582861d9f11a6

1,088

py

Python

exercicios-Python/desaf045.py

marcelo-py/Exercicios-Python

d654d54821983897dbc377a2d3db97671dd75b5b

[ "MIT" ]

null

exercicios-Python/desaf045.py

marcelo-py/Exercicios-Python

d654d54821983897dbc377a2d3db97671dd75b5b

[ "MIT" ]

null

exercicios-Python/desaf045.py

marcelo-py/Exercicios-Python

d654d54821983897dbc377a2d3db97671dd75b5b

[ "MIT" ]

null

import random from emoji import emojize from time import sleep itens = ('PEDRA', 'PAPEL', 'TESOURA') print (emojize('''Suas opções: [0] PEDRA :punch: [1] PAPEL :hand: [2] TESOURA :v:''',use_aliases=True)) escolha = int(input('Qual sua escolha? ')) computador = random.randint(0,2) print('JO') sleep(1) print('KEN') sleep(1) print('PO!!!') print('-='*20) print('O computador escolheu {}'.format(itens[computador])) if escolha == 0: print('Você escolheu PEDRA') if computador == 1: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 2: print('Você ganhou!!!') elif escolha == 1: print('Você escolheu PAPEL') if computador == 2: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 0 : print('Você ganhou!!!') elif escolha == 2: print('Você escolheu TESOURA') if computador == 0: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 1 : print('Você ganhou!!!') print('=-'*20)

25.302326

59

0.607537

import random from emoji import emojize from time import sleep itens = ('PEDRA', 'PAPEL', 'TESOURA') print (emojize('''Suas opções: [0] PEDRA :punch: [1] PAPEL :hand: [2] TESOURA :v:''',use_aliases=True)) escolha = int(input('Qual sua escolha? ')) computador = random.randint(0,2) print('JO') sleep(1) print('KEN') sleep(1) print('PO!!!') print('-='*20) print('O computador escolheu {}'.format(itens[computador])) if escolha == 0: print('Você escolheu PEDRA') if computador == 1: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 2: print('Você ganhou!!!') elif escolha == 1: print('Você escolheu PAPEL') if computador == 2: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 0 : print('Você ganhou!!!') elif escolha == 2: print('Você escolheu TESOURA') if computador == 0: print('Você perdeu') elif escolha == computador: print('EMPATE') elif computador == 1 : print('Você ganhou!!!') print('=-'*20)

true

f719afef6ce3f033481568e9522937db2bfbd069

86

py

Python

my_exceptions.py

robert-dzikowski/api-smoke-test

64394049ce82a0cf80fc128587a4a83e491725b7

[ "MIT" ]

1

2021-01-30T23:01:00.000Z

my_exceptions.py

robert-dzikowski/api-smoke-test

64394049ce82a0cf80fc128587a4a83e491725b7

[ "MIT" ]

null

my_exceptions.py

robert-dzikowski/api-smoke-test

64394049ce82a0cf80fc128587a4a83e491725b7

[ "MIT" ]

null

class TestFail(Exception): """ Exception raised when test has failed. """

17.2

42

0.627907

class TestFail(Exception):

true

f719b0534049d456a9239569b20111fc6dcfa5fb

292

py

Python

esphome/components/json/__init__.py

TheEggi/esphomeyaml

98e8cc1edc7b29891e8100eb484922e5c2d4fc33

[ "MIT" ]

null

esphome/components/json/__init__.py

TheEggi/esphomeyaml

98e8cc1edc7b29891e8100eb484922e5c2d4fc33

[ "MIT" ]

null

esphome/components/json/__init__.py

TheEggi/esphomeyaml

98e8cc1edc7b29891e8100eb484922e5c2d4fc33

[ "MIT" ]

null

import esphome.codegen as cg from esphome.core import coroutine_with_priority json_ns = cg.esphome_ns.namespace('json') @coroutine_with_priority(1.0) def to_code(config): cg.add_library('ArduinoJson-esphomelib', '5.13.3') cg.add_define('USE_JSON') cg.add_global(json_ns.using)

24.333333

54

0.763699

import esphome.codegen as cg from esphome.core import coroutine_with_priority json_ns = cg.esphome_ns.namespace('json') @coroutine_with_priority(1.0) def to_code(config): cg.add_library('ArduinoJson-esphomelib', '5.13.3') cg.add_define('USE_JSON') cg.add_global(json_ns.using)

true

f719b0960e13ee24f7ce64d60d298220d2513dc0

53

py

Python

shiftscheduler/gui/constants.py

c-rainbow/nurse-scheduling

8537c875e46772700499a89dec3a30a796434fe0

[ "MIT" ]

2

2020-04-16T17:03:56.000Z

2021-04-08T17:23:21.000Z

shiftscheduler/gui/constants.py

c-rainbow/nurse-scheduling

8537c875e46772700499a89dec3a30a796434fe0

[ "MIT" ]

null

shiftscheduler/gui/constants.py

c-rainbow/nurse-scheduling

8537c875e46772700499a89dec3a30a796434fe0

[ "MIT" ]

1

2020-05-04T18:03:59.000Z

EXCEL_FILE_TYPE = (("Excel 2007 files","*.xlsx"),)

13.25

50

0.622642

EXCEL_FILE_TYPE = (("Excel 2007 files","*.xlsx"),)

true

f719b09aaa3ce37ed804af7fc5327f4ef6a12908

645

py

Python

noxfile.py

HarshNarayanJha/diddi-and-the-bugs

82af417a2ab324de7bde38736bfc42430b6b46fa

[ "MIT" ]

null

noxfile.py

HarshNarayanJha/diddi-and-the-bugs

82af417a2ab324de7bde38736bfc42430b6b46fa

[ "MIT" ]

null

noxfile.py

HarshNarayanJha/diddi-and-the-bugs

82af417a2ab324de7bde38736bfc42430b6b46fa

[ "MIT" ]

null

""" I use Nox here to reformat the code. """ import nox files = ["noxfile.py", "main.py", "setup.py"] @nox.session(name="keep-codebase-clean") def keep_codebase_clean(session): "Run formatters." session.install("-r", "test-requirements.txt") session.run("isort", *files) session.run("black", *files) @nox.session(name="check-quality") def check_quality(session): "Check the style and quality." session.install("-r", "test-requirements.txt") session.run("flake8", *files, "--max-line-length=127") session.run("isort", "--check-only", *files) session.run("black", "--check", *files)

26.875

59

0.632558

import nox files = ["noxfile.py", "main.py", "setup.py"] @nox.session(name="keep-codebase-clean") def keep_codebase_clean(session): session.install("-r", "test-requirements.txt") session.run("isort", *files) session.run("black", *files) @nox.session(name="check-quality") def check_quality(session): session.install("-r", "test-requirements.txt") session.run("flake8", *files, "--max-line-length=127") session.run("isort", "--check-only", *files) session.run("black", "--check", *files)

true