content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
from graviteeio_cli.lint.types.function_result import FunctionResult
def length(value, **kwargs):
"""Count the length of a string an or array, the number of properties in an object, or a numeric value, and define minimum and/or maximum values."""
min = None
max = None
if "min" in kwargs and type(kwargs["min"]) is int:
min = kwargs["min"]
if "max" in kwargs and type(kwargs["max"]) is int:
max = kwargs["max"]
value_length = 0
if value:
if type(value) is (int or float):
value_length = value
else:
value_length = len(value)
results = []
if min and value_length < min:
results.append(
FunctionResult("min length is {}".format(min))
)
if max and value_length > max:
results.append(
FunctionResult("max length is {}".format(max))
)
return results
| [
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39... | 2.433155 | 374 |
# coding: utf-8
"""
Unofficial python library for the SmartRecruiters API
The SmartRecruiters API provides a platform to integrate services or applications, build apps and create fully customizable career sites. It exposes SmartRecruiters functionality and allows to connect and build software enhancing it.
OpenAPI spec version: 1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
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547... | 3.869048 | 168 |
name = input('Hello! What is your name? : ')
print('Nice to meet you,', name + '!')
print()
age = int(input('How old are you ' + name + '? : '))
print()
x = age + 1
print(' ', x, end=' ')
if x >= 11 and x <= 19:
print('', end='')
elif x % 10 == 1:
print('', end='')
elif x % 10 >= 2 and x % 10 <= 4:
print('', end='')
else:
print('', end='')
print('!')
| [
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345,... | 2.308642 | 162 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import yfinance as yf
yf.pdr_override()
import datetime as dt
# input
symbol = 'AAPL'
start = dt.date.today() - dt.timedelta(days = 365*2)
end = dt.date.today()
# Read data
df = yf.download(symbol,start,end)
# R=red, O=orange, Y=yellow, G=green, B=blue, I = indigo, and V=violet
df['Red'] = df['Adj Close'].rolling(2).mean()
df['Orange'] = df['Red'].rolling(2).mean()
df['Yellow'] = df['Orange'].rolling(2).mean()
df['Green'] = df['Yellow'].rolling(2).mean()
df['Blue'] = df['Green'].rolling(2).mean()
df['Indigo'] = df['Blue'].rolling(2).mean()
df['Violet'] = df['Indigo'].rolling(2).mean()
df = df.dropna()
colors = ['k','r', 'orange', 'yellow', 'g', 'b', 'indigo', 'violet']
df[['Adj Close','Red','Orange','Yellow','Green','Blue','Indigo','Violet']].plot(colors=colors, figsize=(18,12))
plt.fill_between(df.index, df['Low'], df['High'], color='grey', alpha=0.4)
plt.plot(df['Low'], c='darkred', linestyle='--', drawstyle="steps")
plt.plot(df['High'], c='forestgreen', linestyle='--', drawstyle="steps")
plt.title('Rainbow Charts')
plt.legend(loc='best')
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
# ## Candlestick with Rainbow
from matplotlib import dates as mdates
dfc = df.copy()
dfc['VolumePositive'] = dfc['Open'] < dfc['Adj Close']
#dfc = dfc.dropna()
dfc = dfc.reset_index()
dfc['Date'] = mdates.date2num(dfc['Date'].tolist())
from mplfinance.original_flavor import candlestick_ohlc
fig, ax1 = plt.subplots(figsize=(20,12))
candlestick_ohlc(ax1,dfc.values, width=0.5, colorup='g', colordown='r', alpha=1.0)
#colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
#labels = ['Red', 'Orange', 'Yellow', 'Green', 'Blue', 'Indigo', 'Violet']
for i in dfc[['Red', 'Orange', 'Yellow', 'Green', 'Blue', 'Indigo', 'Violet']]:
ax1.plot(dfc['Date'], dfc[i], color=i, label=i)
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
ax1.grid(True, which='both')
ax1.minorticks_on()
ax1v = ax1.twinx()
colors = dfc.VolumePositive.map({True: 'g', False: 'r'})
ax1v.bar(dfc.Date, dfc['Volume'], color=colors, alpha=0.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.set_ylim(0, 3*df.Volume.max())
ax1.set_title('Stock '+ symbol +' Closing Price')
ax1.set_ylabel('Price')
ax1.set_xlabel('Date')
ax1.legend(loc='best')
plt.show() | [
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14149,... | 2.394212 | 1,002 |
import requests
from urllib.request import urlopen
from urllib.request import urlretrieve
import cgi
import os.path
pdf_downloader() | [
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1... | 3.136364 | 44 |
import spacy
import json, os
import dill as pickle
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sqlalchemy import create_engine, select, MetaData, Table, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from typing import List, Dict, Any
from flask import current_app
from app.models import Dataset
# from vta.operators import featurize
# from vta.operators import clean
# from vta.operators import select
# from vta import spacy_nlp
from .tex_column import TexColumn
from .tex_metadata import MetadataItem
from .tex_vis import TexVis
from ..types import VTAColumnType, VisType
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69... | 3.406699 | 209 |
from uuid import uuid4
from sqlalchemy import Boolean, Column, DateTime, Integer, String
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.orm import relationship
from .database import Base
# class User(Base):
# __tablename__ = "users"
# id = Column(Integer, primary_key=True, index=True)
# email = Column(String, unique=True, index=True)
# hashed_password = Column(String)
# is_active = Column(Boolean, default=True)
# items = relationship("Item", back_populates="owner")
# class Item(Base):
# __tablename__ = "items"
# id = Column(Integer, primary_key=True, index=True)
# title = Column(String, index=True)
# description = Column(String, index=True)
# owner_id = Column(Integer, ForeignKey("users.id"))
# owner = relationship("User", back_populates="items")
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... | 2.802013 | 298 |
mesh_pic_bandits = 0
mesh_pic_mb_warrior_1 = 1
mesh_pic_messenger = 2
mesh_pic_prisoner_man = 3
mesh_pic_prisoner_fem = 4
mesh_pic_prisoner_wilderness = 5
mesh_pic_siege_sighted = 6
mesh_pic_siege_sighted_fem = 7
mesh_pic_camp = 8
mesh_pic_payment = 9
mesh_pic_escape_1 = 10
mesh_pic_escape_1_fem = 11
mesh_pic_victory = 12
mesh_pic_defeat = 13
mesh_pic_wounded = 14
mesh_pic_wounded_fem = 15
mesh_pic_steppe_bandits = 16
mesh_pic_mountain_bandits = 17
mesh_pic_sea_raiders = 18
mesh_pic_deserters = 19
mesh_pic_forest_bandits = 20
mesh_pic_cattle = 21
mesh_pic_looted_village = 22
mesh_pic_village_p = 23
mesh_pic_village_s = 24
mesh_pic_village_w = 25
mesh_pic_recruits = 26
mesh_pic_arms_swadian = 27
mesh_pic_castle1 = 28
mesh_pic_castledes = 29
mesh_pic_castlesnow = 30
mesh_pic_charge = 31
mesh_pic_khergit = 32
mesh_pic_nord = 33
mesh_pic_rhodock = 34
mesh_pic_sally_out = 35
mesh_pic_siege_attack = 36
mesh_pic_swad = 37
mesh_pic_town1 = 38
mesh_pic_towndes = 39
mesh_pic_townriot = 40
mesh_pic_townsnow = 41
mesh_pic_vaegir = 42
mesh_pic_villageriot = 43
mesh_pic_sarranid_encounter = 44
mesh_pic_mort = 45
mesh_mp_score_a = 46
mesh_mp_score_b = 47
mesh_portrait_blend_out = 48
mesh_load_window = 49
mesh_checkbox_off = 50
mesh_checkbox_on = 51
mesh_white_plane = 52
mesh_white_dot = 53
mesh_player_dot = 54
mesh_flag_infantry = 55
mesh_flag_archers = 56
mesh_flag_cavalry = 57
mesh_inv_slot = 58
mesh_mp_ingame_menu = 59
mesh_mp_inventory_left = 60
mesh_mp_inventory_right = 61
mesh_mp_inventory_choose = 62
mesh_mp_inventory_slot_glove = 63
mesh_mp_inventory_slot_horse = 64
mesh_mp_inventory_slot_armor = 65
mesh_mp_inventory_slot_helmet = 66
mesh_mp_inventory_slot_boot = 67
mesh_mp_inventory_slot_empty = 68
mesh_mp_inventory_slot_equip = 69
mesh_mp_inventory_left_arrow = 70
mesh_mp_inventory_right_arrow = 71
mesh_mp_ui_host_main = 72
mesh_mp_ui_host_maps_1 = 73
mesh_mp_ui_host_maps_2 = 74
mesh_mp_ui_host_maps_3 = 75
mesh_mp_ui_host_maps_4 = 76
mesh_mp_ui_host_maps_5 = 77
mesh_mp_ui_host_maps_6 = 78
mesh_mp_ui_host_maps_7 = 79
mesh_mp_ui_host_maps_8 = 80
mesh_mp_ui_host_maps_9 = 81
mesh_mp_ui_host_maps_10 = 82
mesh_mp_ui_host_maps_11 = 83
mesh_mp_ui_host_maps_12 = 84
mesh_mp_ui_host_maps_13 = 85
mesh_mp_ui_host_maps_randomp = 86
mesh_mp_ui_host_maps_randoms = 87
mesh_mp_ui_command_panel = 88
mesh_mp_ui_command_border_l = 89
mesh_mp_ui_command_border_r = 90
mesh_mp_ui_welcome_panel = 91
mesh_flag_project_sw = 92
mesh_flag_project_vg = 93
mesh_flag_project_kh = 94
mesh_flag_project_nd = 95
mesh_flag_project_rh = 96
mesh_flag_project_sr = 97
mesh_flag_projects_end = 98
mesh_flag_project_sw_miss = 99
mesh_flag_project_vg_miss = 100
mesh_flag_project_kh_miss = 101
mesh_flag_project_nd_miss = 102
mesh_flag_project_rh_miss = 103
mesh_flag_project_sr_miss = 104
mesh_flag_project_misses_end = 105
mesh_color_picker = 106
mesh_custom_map_banner_01 = 107
mesh_custom_map_banner_02 = 108
mesh_custom_map_banner_03 = 109
mesh_custom_banner_01 = 110
mesh_custom_banner_02 = 111
mesh_custom_banner_bg = 112
mesh_custom_banner_fg01 = 113
mesh_custom_banner_fg02 = 114
mesh_custom_banner_fg03 = 115
mesh_custom_banner_fg04 = 116
mesh_custom_banner_fg05 = 117
mesh_custom_banner_fg06 = 118
mesh_custom_banner_fg07 = 119
mesh_custom_banner_fg08 = 120
mesh_custom_banner_fg09 = 121
mesh_custom_banner_fg10 = 122
mesh_custom_banner_fg11 = 123
mesh_custom_banner_fg12 = 124
mesh_custom_banner_fg13 = 125
mesh_custom_banner_fg14 = 126
mesh_custom_banner_fg15 = 127
mesh_custom_banner_fg16 = 128
mesh_custom_banner_fg17 = 129
mesh_custom_banner_fg18 = 130
mesh_custom_banner_fg19 = 131
mesh_custom_banner_fg20 = 132
mesh_custom_banner_fg21 = 133
mesh_custom_banner_fg22 = 134
mesh_custom_banner_fg23 = 135
mesh_custom_banner_charge_01 = 136
mesh_custom_banner_charge_02 = 137
mesh_custom_banner_charge_03 = 138
mesh_custom_banner_charge_04 = 139
mesh_custom_banner_charge_05 = 140
mesh_custom_banner_charge_06 = 141
mesh_custom_banner_charge_07 = 142
mesh_custom_banner_charge_08 = 143
mesh_custom_banner_charge_09 = 144
mesh_custom_banner_charge_10 = 145
mesh_custom_banner_charge_11 = 146
mesh_custom_banner_charge_12 = 147
mesh_custom_banner_charge_13 = 148
mesh_custom_banner_charge_14 = 149
mesh_custom_banner_charge_15 = 150
mesh_custom_banner_charge_16 = 151
mesh_custom_banner_charge_17 = 152
mesh_custom_banner_charge_18 = 153
mesh_custom_banner_charge_19 = 154
mesh_custom_banner_charge_20 = 155
mesh_custom_banner_charge_21 = 156
mesh_custom_banner_charge_22 = 157
mesh_custom_banner_charge_23 = 158
mesh_custom_banner_charge_24 = 159
mesh_custom_banner_charge_25 = 160
mesh_custom_banner_charge_26 = 161
mesh_custom_banner_charge_27 = 162
mesh_custom_banner_charge_28 = 163
mesh_custom_banner_charge_29 = 164
mesh_custom_banner_charge_30 = 165
mesh_custom_banner_charge_31 = 166
mesh_custom_banner_charge_32 = 167
mesh_custom_banner_charge_33 = 168
mesh_custom_banner_charge_34 = 169
mesh_custom_banner_charge_35 = 170
mesh_custom_banner_charge_36 = 171
mesh_custom_banner_charge_37 = 172
mesh_custom_banner_charge_38 = 173
mesh_custom_banner_charge_39 = 174
mesh_custom_banner_charge_40 = 175
mesh_custom_banner_charge_41 = 176
mesh_custom_banner_charge_42 = 177
mesh_custom_banner_charge_43 = 178
mesh_custom_banner_charge_44 = 179
mesh_custom_banner_charge_45 = 180
mesh_custom_banner_charge_46 = 181
mesh_tableau_mesh_custom_banner = 182
mesh_tableau_mesh_custom_banner_square = 183
mesh_tableau_mesh_custom_banner_tall = 184
mesh_tableau_mesh_custom_banner_short = 185
mesh_tableau_mesh_shield_round_1 = 186
mesh_tableau_mesh_shield_round_2 = 187
mesh_tableau_mesh_shield_round_3 = 188
mesh_tableau_mesh_shield_round_4 = 189
mesh_tableau_mesh_shield_round_5 = 190
mesh_tableau_mesh_shield_small_round_1 = 191
mesh_tableau_mesh_shield_small_round_2 = 192
mesh_tableau_mesh_shield_small_round_3 = 193
mesh_tableau_mesh_shield_kite_1 = 194
mesh_tableau_mesh_shield_kite_2 = 195
mesh_tableau_mesh_shield_kite_3 = 196
mesh_tableau_mesh_shield_kite_4 = 197
mesh_tableau_mesh_shield_heater_1 = 198
mesh_tableau_mesh_shield_heater_2 = 199
mesh_tableau_mesh_shield_pavise_1 = 200
mesh_tableau_mesh_shield_pavise_2 = 201
mesh_heraldic_armor_bg = 202
mesh_tableau_mesh_heraldic_armor_a = 203
mesh_tableau_mesh_heraldic_armor_b = 204
mesh_tableau_mesh_heraldic_armor_c = 205
mesh_tableau_mesh_heraldic_armor_d = 206
mesh_outer_terrain_plain_1 = 207
mesh_banner_a01 = 208
mesh_banner_a02 = 209
mesh_banner_a03 = 210
mesh_banner_a04 = 211
mesh_banner_a05 = 212
mesh_banner_a06 = 213
mesh_banner_a07 = 214
mesh_banner_a08 = 215
mesh_banner_a09 = 216
mesh_banner_a10 = 217
mesh_banner_a11 = 218
mesh_banner_a12 = 219
mesh_banner_a13 = 220
mesh_banner_a14 = 221
mesh_banner_a15 = 222
mesh_banner_a16 = 223
mesh_banner_a17 = 224
mesh_banner_a18 = 225
mesh_banner_a19 = 226
mesh_banner_a20 = 227
mesh_banner_a21 = 228
mesh_banner_b01 = 229
mesh_banner_b02 = 230
mesh_banner_b03 = 231
mesh_banner_b04 = 232
mesh_banner_b05 = 233
mesh_banner_b06 = 234
mesh_banner_b07 = 235
mesh_banner_b08 = 236
mesh_banner_b09 = 237
mesh_banner_b10 = 238
mesh_banner_b11 = 239
mesh_banner_b12 = 240
mesh_banner_b13 = 241
mesh_banner_b14 = 242
mesh_banner_b15 = 243
mesh_banner_b16 = 244
mesh_banner_b17 = 245
mesh_banner_b18 = 246
mesh_banner_b19 = 247
mesh_banner_b20 = 248
mesh_banner_b21 = 249
mesh_banner_c01 = 250
mesh_banner_c02 = 251
mesh_banner_c03 = 252
mesh_banner_c04 = 253
mesh_banner_c05 = 254
mesh_banner_c06 = 255
mesh_banner_c07 = 256
mesh_banner_c08 = 257
mesh_banner_c09 = 258
mesh_banner_c10 = 259
mesh_banner_c11 = 260
mesh_banner_c12 = 261
mesh_banner_c13 = 262
mesh_banner_c14 = 263
mesh_banner_c15 = 264
mesh_banner_c16 = 265
mesh_banner_c17 = 266
mesh_banner_c18 = 267
mesh_banner_c19 = 268
mesh_banner_c20 = 269
mesh_banner_c21 = 270
mesh_banner_d01 = 271
mesh_banner_d02 = 272
mesh_banner_d03 = 273
mesh_banner_d04 = 274
mesh_banner_d05 = 275
mesh_banner_d06 = 276
mesh_banner_d07 = 277
mesh_banner_d08 = 278
mesh_banner_d09 = 279
mesh_banner_d10 = 280
mesh_banner_d11 = 281
mesh_banner_d12 = 282
mesh_banner_d13 = 283
mesh_banner_d14 = 284
mesh_banner_d15 = 285
mesh_banner_d16 = 286
mesh_banner_d17 = 287
mesh_banner_d18 = 288
mesh_banner_d19 = 289
mesh_banner_d20 = 290
mesh_banner_d21 = 291
mesh_banner_e01 = 292
mesh_banner_e02 = 293
mesh_banner_e03 = 294
mesh_banner_e04 = 295
mesh_banner_e05 = 296
mesh_banner_e06 = 297
mesh_banner_e07 = 298
mesh_banner_e08 = 299
mesh_banner_e09 = 300
mesh_banner_e10 = 301
mesh_banner_e11 = 302
mesh_banner_e12 = 303
mesh_banner_e13 = 304
mesh_banner_e14 = 305
mesh_banner_e15 = 306
mesh_banner_e16 = 307
mesh_banner_e17 = 308
mesh_banner_e18 = 309
mesh_banner_e19 = 310
mesh_banner_e20 = 311
mesh_banner_e21 = 312
mesh_banner_f01 = 313
mesh_banner_f02 = 314
mesh_banner_f03 = 315
mesh_banner_f04 = 316
mesh_banner_f05 = 317
mesh_banner_f06 = 318
mesh_banner_f07 = 319
mesh_banner_f08 = 320
mesh_banner_f09 = 321
mesh_banner_f10 = 322
mesh_banner_f11 = 323
mesh_banner_f12 = 324
mesh_banner_f13 = 325
mesh_banner_f14 = 326
mesh_banner_f15 = 327
mesh_banner_f16 = 328
mesh_banner_f17 = 329
mesh_banner_f18 = 330
mesh_banner_f19 = 331
mesh_banner_f20 = 332
mesh_banner_h01 = 333
mesh_banner_h02 = 334
mesh_banner_h03 = 335
mesh_banner_h04 = 336
mesh_banner_h05 = 337
mesh_banner_h06 = 338
mesh_banner_h07 = 339
mesh_banner_h08 = 340
mesh_banner_h09 = 341
mesh_banner_h10 = 342
mesh_banner_h11 = 343
mesh_banner_h12 = 344
mesh_banner_h13 = 345
mesh_banner_h14 = 346
mesh_banner_h15 = 347
mesh_banner_h16 = 348
mesh_banner_h17 = 349
mesh_banner_h18 = 350
mesh_banner_h19 = 351
mesh_banner_h20 = 352
mesh_banner_h21 = 353
mesh_banner_i01 = 354
mesh_banner_i02 = 355
mesh_banner_i03 = 356
mesh_banner_i04 = 357
mesh_banner_i05 = 358
mesh_banner_i06 = 359
mesh_banner_i07 = 360
mesh_banner_i08 = 361
mesh_banner_i09 = 362
mesh_banner_i10 = 363
mesh_banner_i11 = 364
mesh_banner_i12 = 365
mesh_banner_i13 = 366
mesh_banner_i14 = 367
mesh_banner_i15 = 368
mesh_banner_i16 = 369
mesh_banner_i17 = 370
mesh_banner_i18 = 371
mesh_banner_i19 = 372
mesh_banner_i20 = 373
mesh_banner_i21 = 374
mesh_banner_k01 = 375
mesh_banner_k02 = 376
mesh_banner_k03 = 377
mesh_banner_k04 = 378
mesh_banner_k05 = 379
mesh_banner_k06 = 380
mesh_banner_k07 = 381
mesh_banner_k08 = 382
mesh_banner_k09 = 383
mesh_banner_k10 = 384
mesh_banner_k11 = 385
mesh_banner_k12 = 386
mesh_banner_k13 = 387
mesh_banner_k14 = 388
mesh_banner_k15 = 389
mesh_banner_k16 = 390
mesh_banner_k17 = 391
mesh_banner_k18 = 392
mesh_banner_k19 = 393
mesh_banner_k20 = 394
mesh_banner_g01 = 395
mesh_banner_g02 = 396
mesh_banner_g03 = 397
mesh_banner_g04 = 398
mesh_banner_g05 = 399
mesh_banner_g06 = 400
mesh_banner_g07 = 401
mesh_banner_g08 = 402
mesh_banner_g09 = 403
mesh_banner_g10 = 404
mesh_banner_kingdom_a = 405
mesh_banner_kingdom_b = 406
mesh_banner_kingdom_c = 407
mesh_banner_kingdom_d = 408
mesh_banner_kingdom_e = 409
mesh_banner_kingdom_f = 410
mesh_banner_kingdom_g = 411
mesh_banner_kingdom_h = 412
mesh_banner_kingdom_i = 413
mesh_banner_kingdom_j = 414
mesh_banner_kingdom_k = 415
mesh_banner_kingdom_l = 416
mesh_banner_kingdom_ll = 417
mesh_banner_kingdom_m = 418
mesh_banner_kingdom_n = 419
mesh_banner_kingdom_o = 420
mesh_banner_kingdom_p = 421
mesh_banner_kingdom_q = 422
mesh_banner_kingdom_r = 423
mesh_banner_kingdom_s = 424
mesh_banner_kingdom_t = 425
mesh_banner_kingdom_u = 426
mesh_banner_kingdom_v = 427
mesh_banner_kingdom_w = 428
mesh_banner_kingdom_x = 429
mesh_banner_kingdom_y = 430
mesh_banner_kingdom_z = 431
mesh_banner_kingdom_2a = 432
mesh_banner_kingdom_2b = 433
mesh_banner_kingdom_2c = 434
mesh_banner_kingdom_2d = 435
mesh_banner_k21 = 436
mesh_arms_a01 = 437
mesh_arms_a02 = 438
mesh_arms_a03 = 439
mesh_arms_a04 = 440
mesh_arms_a05 = 441
mesh_arms_a06 = 442
mesh_arms_a07 = 443
mesh_arms_a08 = 444
mesh_arms_a09 = 445
mesh_arms_a10 = 446
mesh_arms_a11 = 447
mesh_arms_a12 = 448
mesh_arms_a13 = 449
mesh_arms_a14 = 450
mesh_arms_a15 = 451
mesh_arms_a16 = 452
mesh_arms_a17 = 453
mesh_arms_a18 = 454
mesh_arms_a19 = 455
mesh_arms_a20 = 456
mesh_arms_a21 = 457
mesh_arms_b01 = 458
mesh_arms_b02 = 459
mesh_arms_b03 = 460
mesh_arms_b04 = 461
mesh_arms_b05 = 462
mesh_arms_b06 = 463
mesh_arms_b07 = 464
mesh_arms_b08 = 465
mesh_arms_b09 = 466
mesh_arms_b10 = 467
mesh_arms_b11 = 468
mesh_arms_b12 = 469
mesh_arms_b13 = 470
mesh_arms_b14 = 471
mesh_arms_b15 = 472
mesh_arms_b16 = 473
mesh_arms_b17 = 474
mesh_arms_b18 = 475
mesh_arms_b19 = 476
mesh_arms_b20 = 477
mesh_arms_b21 = 478
mesh_arms_c01 = 479
mesh_arms_c02 = 480
mesh_arms_c03 = 481
mesh_arms_c04 = 482
mesh_arms_c05 = 483
mesh_arms_c06 = 484
mesh_arms_c07 = 485
mesh_arms_c08 = 486
mesh_arms_c09 = 487
mesh_arms_c10 = 488
mesh_arms_c11 = 489
mesh_arms_c12 = 490
mesh_arms_c13 = 491
mesh_arms_c14 = 492
mesh_arms_c15 = 493
mesh_arms_c16 = 494
mesh_arms_c17 = 495
mesh_arms_c18 = 496
mesh_arms_c19 = 497
mesh_arms_c20 = 498
mesh_arms_c21 = 499
mesh_arms_d01 = 500
mesh_arms_d02 = 501
mesh_arms_d03 = 502
mesh_arms_d04 = 503
mesh_arms_d05 = 504
mesh_arms_d06 = 505
mesh_arms_d07 = 506
mesh_arms_d08 = 507
mesh_arms_d09 = 508
mesh_arms_d10 = 509
mesh_arms_d11 = 510
mesh_arms_d12 = 511
mesh_arms_d13 = 512
mesh_arms_d14 = 513
mesh_arms_d15 = 514
mesh_arms_d16 = 515
mesh_arms_d17 = 516
mesh_arms_d18 = 517
mesh_arms_d19 = 518
mesh_arms_d20 = 519
mesh_arms_d21 = 520
mesh_arms_e01 = 521
mesh_arms_e02 = 522
mesh_arms_e03 = 523
mesh_arms_e04 = 524
mesh_arms_e05 = 525
mesh_arms_e06 = 526
mesh_arms_e07 = 527
mesh_arms_e08 = 528
mesh_arms_e09 = 529
mesh_arms_e10 = 530
mesh_arms_e11 = 531
mesh_arms_e12 = 532
mesh_arms_e13 = 533
mesh_arms_e14 = 534
mesh_arms_e15 = 535
mesh_arms_e16 = 536
mesh_arms_e17 = 537
mesh_arms_e18 = 538
mesh_arms_e19 = 539
mesh_arms_e20 = 540
mesh_arms_e21 = 541
mesh_arms_f01 = 542
mesh_arms_f02 = 543
mesh_arms_f03 = 544
mesh_arms_f04 = 545
mesh_arms_f05 = 546
mesh_arms_f06 = 547
mesh_arms_f07 = 548
mesh_arms_f08 = 549
mesh_arms_f09 = 550
mesh_arms_f10 = 551
mesh_arms_f11 = 552
mesh_arms_f12 = 553
mesh_arms_f13 = 554
mesh_arms_f14 = 555
mesh_arms_f15 = 556
mesh_arms_f16 = 557
mesh_arms_f17 = 558
mesh_arms_f18 = 559
mesh_arms_f19 = 560
mesh_arms_f20 = 561
mesh_arms_h01 = 562
mesh_arms_h02 = 563
mesh_arms_h03 = 564
mesh_arms_h04 = 565
mesh_arms_h05 = 566
mesh_arms_h06 = 567
mesh_arms_h07 = 568
mesh_arms_h08 = 569
mesh_arms_h09 = 570
mesh_arms_h10 = 571
mesh_arms_h11 = 572
mesh_arms_h12 = 573
mesh_arms_h13 = 574
mesh_arms_h14 = 575
mesh_arms_h15 = 576
mesh_arms_h16 = 577
mesh_arms_h17 = 578
mesh_arms_h18 = 579
mesh_arms_h19 = 580
mesh_arms_h20 = 581
mesh_arms_h21 = 582
mesh_arms_i01 = 583
mesh_arms_i02 = 584
mesh_arms_i03 = 585
mesh_arms_i04 = 586
mesh_arms_i05 = 587
mesh_arms_i06 = 588
mesh_arms_i07 = 589
mesh_arms_i08 = 590
mesh_arms_i09 = 591
mesh_arms_i10 = 592
mesh_arms_i11 = 593
mesh_arms_i12 = 594
mesh_arms_i13 = 595
mesh_arms_i14 = 596
mesh_arms_i15 = 597
mesh_arms_i16 = 598
mesh_arms_i17 = 599
mesh_arms_i18 = 600
mesh_arms_i19 = 601
mesh_arms_i20 = 602
mesh_arms_i21 = 603
mesh_arms_k01 = 604
mesh_arms_k02 = 605
mesh_arms_k03 = 606
mesh_arms_k04 = 607
mesh_arms_k05 = 608
mesh_arms_k06 = 609
mesh_arms_k07 = 610
mesh_arms_k08 = 611
mesh_arms_k09 = 612
mesh_arms_k10 = 613
mesh_arms_k11 = 614
mesh_arms_k12 = 615
mesh_arms_k13 = 616
mesh_arms_k14 = 617
mesh_arms_k15 = 618
mesh_arms_k16 = 619
mesh_arms_k17 = 620
mesh_arms_k18 = 621
mesh_arms_k19 = 622
mesh_arms_k20 = 623
mesh_arms_g01 = 624
mesh_arms_g02 = 625
mesh_arms_g03 = 626
mesh_arms_g04 = 627
mesh_arms_g05 = 628
mesh_arms_g06 = 629
mesh_arms_g07 = 630
mesh_arms_g08 = 631
mesh_arms_g09 = 632
mesh_arms_g10 = 633
mesh_arms_kingdom_a = 634
mesh_arms_kingdom_b = 635
mesh_arms_kingdom_c = 636
mesh_arms_kingdom_d = 637
mesh_arms_kingdom_e = 638
mesh_arms_kingdom_f = 639
mesh_arms_kingdom_g = 640
mesh_arms_kingdom_h = 641
mesh_arms_kingdom_i = 642
mesh_arms_kingdom_j = 643
mesh_arms_kingdom_k = 644
mesh_arms_kingdom_l = 645
mesh_arms_kingdom_ll = 646
mesh_arms_kingdom_m = 647
mesh_arms_kingdom_n = 648
mesh_arms_kingdom_o = 649
mesh_arms_kingdom_p = 650
mesh_arms_kingdom_q = 651
mesh_arms_kingdom_r = 652
mesh_arms_kingdom_s = 653
mesh_arms_kingdom_t = 654
mesh_arms_kingdom_u = 655
mesh_arms_kingdom_v = 656
mesh_arms_kingdom_w = 657
mesh_arms_kingdom_x = 658
mesh_arms_kingdom_y = 659
mesh_arms_kingdom_z = 660
mesh_arms_kingdom_2a = 661
mesh_arms_kingdom_2b = 662
mesh_arms_kingdom_2c = 663
mesh_arms_kingdom_2d = 664
mesh_arms_k21 = 665
mesh_banners_default_a = 666
mesh_banners_default_b = 667
mesh_banners_default_c = 668
mesh_banners_default_d = 669
mesh_banners_default_e = 670
mesh_troop_label_banner = 671
mesh_ui_kingdom_shield_1 = 672
mesh_ui_kingdom_shield_2 = 673
mesh_ui_kingdom_shield_3 = 674
mesh_ui_kingdom_shield_4 = 675
mesh_ui_kingdom_shield_5 = 676
mesh_ui_kingdom_shield_6 = 677
mesh_ui_kingdom_shield_7 = 678
mesh_ui_kingdom_shield_8 = 679
mesh_ui_kingdom_shield_9 = 680
mesh_ui_kingdom_shield_10 = 681
mesh_ui_kingdom_shield_11 = 682
mesh_ui_kingdom_shield_12 = 683
mesh_ui_kingdom_shield_13 = 684
mesh_ui_kingdom_shield_14 = 685
mesh_ui_kingdom_shield_15 = 686
mesh_ui_kingdom_shield_16 = 687
mesh_ui_kingdom_shield_17 = 688
mesh_ui_kingdom_shield_18 = 689
mesh_ui_kingdom_shield_19 = 690
mesh_ui_kingdom_shield_20 = 691
mesh_ui_kingdom_shield_21 = 692
mesh_ui_kingdom_shield_22 = 693
mesh_ui_kingdom_shield_23 = 694
mesh_ui_kingdom_shield_24 = 695
mesh_ui_kingdom_shield_25 = 696
mesh_ui_kingdom_shield_26 = 697
mesh_ui_kingdom_shield_27 = 698
mesh_ui_kingdom_shield_28 = 699
mesh_ui_kingdom_shield_29 = 700
mesh_ui_kingdom_shield_30 = 701
mesh_ui_kingdom_shield_31 = 702
mesh_mouse_arrow_down = 703
mesh_mouse_arrow_right = 704
mesh_mouse_arrow_left = 705
mesh_mouse_arrow_up = 706
mesh_mouse_arrow_plus = 707
mesh_mouse_left_click = 708
mesh_mouse_right_click = 709
mesh_status_ammo_ready = 710
mesh_main_menu_background = 711
mesh_loading_background = 712
mesh_ui_quick_battle_a = 713
mesh_white_bg_plane_a = 714
mesh_cb_ui_icon_infantry = 715
mesh_cb_ui_icon_archer = 716
mesh_cb_ui_icon_horseman = 717
mesh_cb_ui_main = 718
mesh_cb_ui_maps_scene_01 = 719
mesh_cb_ui_maps_scene_02 = 720
mesh_cb_ui_maps_scene_03 = 721
mesh_cb_ui_maps_scene_04 = 722
mesh_cb_ui_maps_scene_05 = 723
mesh_cb_ui_maps_scene_06 = 724
mesh_cb_ui_maps_scene_07 = 725
mesh_cb_ui_maps_scene_08 = 726
mesh_cb_ui_maps_scene_09 = 727
mesh_mp_ui_host_maps_14 = 728
mesh_mp_ui_host_maps_15 = 729
mesh_ui_kingdom_shield_7 = 730
mesh_flag_project_rb = 731
mesh_flag_project_rb_miss = 732
mesh_mp_ui_host_maps_16 = 733
mesh_mp_ui_host_maps_17 = 734
mesh_mp_ui_host_maps_18 = 735
mesh_mp_ui_host_maps_19 = 736
mesh_mp_ui_host_maps_20 = 737
mesh_pic_mb_warrior_2 = 738
mesh_pic_mb_warrior_3 = 739
mesh_pic_mb_warrior_4 = 740
mesh_pic_mercenary = 741
mesh_facegen_board = 742
mesh_status_background = 743
mesh_status_health_bar = 744
mesh_game_log_window = 745
mesh_restore_game_panel = 746
mesh_message_window = 747
mesh_party_window_b = 748
mesh_party_member_button = 749
mesh_party_member_button_pressed = 750
mesh_longer_button = 751
mesh_longer_button_down = 752
mesh_button_1 = 753
mesh_button_1_down = 754
mesh_used_button = 755
mesh_used_button_down = 756
mesh_longer_button = 757
mesh_longer_button_down = 758
mesh_options_window = 759
mesh_message_window = 760
mesh_note_window = 761
mesh_left_button = 762
mesh_left_button_down = 763
mesh_left_button_hl = 764
mesh_right_button = 765
mesh_right_button_down = 766
mesh_right_button_hl = 767
mesh_center_button = 768
mesh_drop_button = 769
mesh_drop_button_down = 770
mesh_drop_button_hl = 771
mesh_drop_button_child = 772
mesh_drop_button_child_down = 773
mesh_drop_button_child_hl = 774
mesh_num_1 = 775
mesh_num_2 = 776
mesh_num_3 = 777
mesh_num_4 = 778
mesh_num_5 = 779
mesh_num_6 = 780
mesh_num_7 = 781
mesh_num_8 = 782
mesh_num_9 = 783
mesh_num_10 = 784
mesh_num_11 = 785
mesh_num_12 = 786
mesh_num_13 = 787
mesh_num_14 = 788
mesh_num_15 = 789
mesh_num_16 = 790
mesh_num_17 = 791
mesh_num_18 = 792
mesh_num_19 = 793
mesh_num_20 = 794
mesh_num_21 = 795
mesh_num_22 = 796
mesh_num_23 = 797
mesh_num_24 = 798
mesh_num_25 = 799
mesh_num_26 = 800
mesh_num_27 = 801
mesh_num_28 = 802
mesh_num_29 = 803
mesh_num_30 = 804
mesh_num_31 = 805
mesh_num_32 = 806
mesh_num_33 = 807
mesh_num_34 = 808
mesh_num_35 = 809
mesh_num_36 = 810
mesh_num_37 = 811
mesh_num_38 = 812
mesh_num_39 = 813
mesh_num_40 = 814
mesh_num_41 = 815
mesh_num_42 = 816
mesh_num_43 = 817
mesh_num_44 = 818
mesh_num_45 = 819
mesh_num_46 = 820
mesh_num_47 = 821
mesh_num_48 = 822
mesh_message_window = 823
mesh_face_gen_window = 824
mesh_order_frame = 825
mesh_tableau_mesh_early_transitional_heraldic_banner = 826
mesh_tableau_mesh_early_transitional_heraldic = 827
mesh_tableau_mesh_samurai_heraldic_flag = 828
mesh_tableau_mesh_banner_spear = 829
mesh_invisi_st_plane_fullsc = 830
mesh_bt_flag_1 = 831
mesh_bt_flag_2 = 832
mesh_bt_flag_3 = 833
mesh_pic_bt_crossbow = 834
mesh_pic_bt_shield = 835
mesh_pic_bt_horse_archer = 836
mesh_pic_bt_twohand = 837
mesh_pic_bt_bow = 838
mesh_pic_bt_horse = 839
mesh_pic_bt_musket = 840
mesh_pic_bt_leader = 841
mesh_bt_cion_tier1 = 842
mesh_bt_cion_tier2 = 843
mesh_bt_cion_tier3 = 844
mesh_bt_cion_tier4 = 845
mesh_bt_cion_tier5 = 846
mesh_bt_cion_tier6 = 847
mesh_pic_bt_charge_auto = 848
mesh_pic_bt_hold = 849
mesh_pic_bt_followme = 850
mesh_pic_bt_unite = 851
mesh_pic_bt_divide = 852
mesh_pic_bt_advan = 853
mesh_pic_bt_fall = 854
mesh_pic_bt_holdfire = 855
mesh_pic_bt_anyw = 856
mesh_pic_bt_clicked = 857
mesh_pic_bt_return = 858
mesh_pic_camp_meet = 859
mesh_pic_meetlady = 860
mesh_pic_meetlady2 = 861
mesh_pic_meetlady3 = 862
mesh_1pic_ruin_0 = 863
mesh_1pic_ruin_1 = 864
mesh_1pic_ruin_2 = 865
mesh_1pic_ruin_3 = 866
mesh_1pic_ruin_4 = 867
mesh_1pic_ruin_5 = 868
mesh_1pic_ruin_6 = 869
mesh_1pic_ruin_7 = 870
mesh_1pic_ruin_8 = 871
mesh_1pic_ruin_9 = 872
mesh_1pic_ruin_10 = 873
mesh_1pic_ruin_11 = 874
mesh_1pic_ruin_12 = 875
mesh_1pic_ruin_13 = 876
mesh_1pic_ruin_14 = 877
mesh_1pic_ruin_15 = 878
mesh_1pic_ruin_16 = 879
mesh_1pic_ruin_17 = 880
mesh_1pic_ruin_18 = 881
mesh_1pic_ruin_19 = 882
mesh_1pic_ruin_20 = 883
mesh_1pic_ruin_21 = 884
mesh_1pic_ruin_22 = 885
mesh_1pic_ruin_23 = 886
mesh_1pic_ruin_24 = 887
mesh_1pic_ruin_25 = 888
mesh_1pic_ruin_26 = 889
mesh_1pic_ruin_27 = 890
mesh_1pic_ruin_28 = 891
mesh_1pic_ruin_29 = 892
mesh_1pic_ruin_30 = 893
mesh_1pic_ruin_31 = 894
mesh_1pic_ruin_32 = 895
mesh_1pic_ruin_33 = 896
mesh_1pic_ruin_34 = 897
mesh_1pic_ruin_35 = 898
mesh_1pic_ruin_36 = 899
mesh_1pic_ruin_37 = 900
mesh_1pic_ruin_38 = 901
mesh_1pic_ruin_39 = 902
mesh_1pic_ruin_40 = 903
mesh_1pic_ruin_41 = 904
mesh_1pic_ruin_42 = 905
mesh_1pic_ruin_43 = 906
mesh_1pic_ruin_44 = 907
mesh_1pic_ruin_45 = 908
mesh_1pic_ruin_46 = 909
mesh_1pic_ruin_47 = 910
mesh_1pic_ruin_48 = 911
mesh_1pic_ruin_49 = 912
mesh_1pic_ruin_50 = 913
mesh_1pic_ruin_51 = 914
mesh_1pic_ruin_52 = 915
mesh_1pic_ruin_53 = 916
mesh_1pic_ruin_54 = 917
mesh_1pic_ruin_55 = 918
mesh_1pic_ruin_56 = 919
mesh_1pic_ruin_57 = 920
mesh_1pic_ruin_58 = 921
mesh_1pic_ruin_59 = 922
mesh_1pic_ruin_60 = 923
mesh_1pic_ruin_61 = 924
mesh_1pic_ruin_62 = 925
mesh_1pic_ruin_63 = 926
mesh_1pic_ruin_64 = 927
mesh_1pic_ruin_65 = 928
mesh_1pic_ruin_66 = 929
mesh_1pic_ruin_67 = 930
mesh_1pic_ruin_68 = 931
mesh_1pic_ruin_69 = 932
mesh_1pic_ruin_70 = 933
mesh_1pic_ruin_71 = 934
mesh_1pic_ruin_72 = 935
mesh_1pic_ruin_73 = 936
mesh_1pic_ruin_74 = 937
mesh_1pic_ruin_75 = 938
mesh_1pic_ruin_76 = 939
mesh_1pic_ruin_77 = 940
mesh_1pic_ruin_78 = 941
mesh_1pic_ruin_79 = 942
mesh_1pic_ruin_80 = 943
mesh_1pic_ruin_81 = 944
mesh_1pic_ruin_82 = 945
mesh_1pic_ruin_83 = 946
mesh_1pic_ruin_84 = 947
mesh_1pic_ruin_85 = 948
mesh_1pic_ruin_86 = 949
mesh_1pic_ruin_87 = 950
mesh_1pic_ruin_88 = 951
mesh_1pic_ruin_89 = 952
mesh_1pic_ruin_90 = 953
mesh_1pic_ruin_91 = 954
mesh_1pic_ruin_92 = 955
mesh_1pic_ruin_93 = 956
mesh_1pic_ruin_94 = 957
mesh_1pic_ruin_95 = 958
mesh_1pic_ruin_96 = 959
mesh_1pic_ruin_97 = 960
mesh_1pic_ruin_98 = 961
mesh_1pic_ruin_99 = 962
mesh_1pic_ruin_100 = 963
mesh_1pic_ruin_101 = 964
mesh_1pic_ruin_102 = 965
mesh_1pic_ruin_103 = 966
mesh_1pic_ruin_104 = 967
mesh_1pic_ruin_105 = 968
mesh_1pic_ruin_106 = 969
mesh_1pic_ruin_107 = 970
mesh_1pic_ruin_108 = 971
mesh_1pic_ruin_109 = 972
mesh_1pic_ruin_110 = 973
mesh_1pic_ruin_111 = 974
mesh_1pic_ruin_112 = 975
mesh_1pic_ruin_113 = 976
mesh_1pic_ruin_114 = 977
mesh_1pic_ruin_115 = 978
mesh_1pic_ruin_116 = 979
mesh_1pic_ruin_117 = 980
mesh_1pic_ruin_118 = 981
mesh_1pic_ruin_119 = 982
mesh_1pic_ruin_120 = 983
mesh_1pic_ruin_121 = 984
mesh_1pic_ruin_122 = 985
mesh_1pic_ruin_123 = 986
mesh_1pic_ruin_124 = 987
mesh_1pic_ruin_125 = 988
mesh_1pic_ruin_126 = 989
mesh_1pic_ruin_127 = 990
mesh_1pic_ruin_128 = 991
mesh_1pic_ruin_129 = 992
mesh_1pic_ruin_130 = 993
mesh_1pic_ruin_131 = 994
mesh_1pic_ruin_132 = 995
mesh_1pic_ruin_133 = 996
mesh_1pic_ruin_134 = 997
mesh_1pic_ruin_135 = 998
mesh_1pic_ruin_136 = 999
mesh_1pic_ruin_137 = 1000
mesh_1pic_ruin_138 = 1001
mesh_1pic_ruin_139 = 1002
mesh_1pic_ruin_140 = 1003
mesh_1pic_ruin_141 = 1004
mesh_1pic_ruin_142 = 1005
mesh_1pic_ruin_143 = 1006
mesh_1pic_ruin_144 = 1007
mesh_1pic_ruin_145 = 1008
mesh_1pic_ruin_146 = 1009
mesh_1pic_ruin_ex1 = 1010
mesh_1pic_ruin_ex2 = 1011
mesh_1pic_ruin_ex3 = 1012
mesh_1pic_ruin_ex4 = 1013
mesh_1pic_ruin_ex5 = 1014
mesh_1pic_ruin_ex6 = 1015
mesh_1pic_ruin_ex7 = 1016
mesh_1pic_ruin_ex8 = 1017
mesh_1pic_ruin_ex9 = 1018
mesh_1pic_ruin_ex10 = 1019
mesh_1pic_ruin_ex11 = 1020
mesh_1pic_ruin_ex12 = 1021
mesh_1pic_ruin_ex13 = 1022
mesh_1pic_ruin_ex14 = 1023
mesh_1pic_ruin_ex15 = 1024
mesh_1pic_ruin_ex16 = 1025
mesh_1pic_ruin_ex17 = 1026
mesh_1pic_ruin_ex18 = 1027
mesh_1pic_ruin_ex19 = 1028
mesh_1pic_ruin_ex20 = 1029
mesh_1pic_ruin_ex21 = 1030
mesh_1pic_ruin_ex22 = 1031
mesh_1pic_ruin_ex23 = 1032
mesh_1pic_ruin_ex24 = 1033
mesh_1pic_ruin_ex25 = 1034
mesh_pic_encounter1 = 1035
mesh_pic_encounter2 = 1036
mesh_pic_encounter3 = 1037
mesh_pic_xex8 = 1038
mesh_pic_xex9 = 1039
mesh_pic_xex10 = 1040
mesh_pic_xex11 = 1041
mesh_pic_xex12 = 1042
mesh_pic_xex13 = 1043
mesh_pic_xex14 = 1044
mesh_st_tercio = 1045
mesh_st_pincer_movement = 1046
mesh_encounter4vik = 1047
mesh_encounter5pirate = 1048
mesh_pic_ship_shipyard = 1049
mesh_st_pic_plain = 1050
mesh_st_pic_desert = 1051
mesh_st_pic_mount = 1052
mesh_st_pic_snow = 1053
mesh_st_pic_sea = 1054
mesh_st_lancecharge = 1055
mesh_st_ccccharge = 1056
mesh_st_viking = 1057
mesh_black_st_plane = 1058
mesh_invisi_st_plane = 1059
mesh_pic_invisi_backgrounds = 1060
mesh_pic_policy_choose_prt = 1061
mesh_pic_policy_choose_prt_bk = 1062
mesh_pic_religion_screenn = 1063
mesh_pic_gbt_punch = 1064
mesh_pic_gbt_lick = 1065
mesh_pic_gbt_finger = 1066
mesh_pic_gbt_love = 1067
mesh_pic_gbt_place = 1068
mesh_pic_gbt_bed_sheet = 1069
mesh_pic_money_bag = 1070
mesh_pic_sea_backg = 1071
mesh_tableau_mesh_flag = 1072
mesh_pic_backg_inv = 1073
mesh_pic_library = 1074
mesh_pic_fuck_back = 1075
mesh_pic_ghost_ship_encount = 1076
mesh_pic_visit_train = 1077
mesh_pic_weknow = 1078
mesh_pic_bank_back = 1079
mesh_pic_wm_blank = 1080
mesh_pic_wm_horse = 1081
mesh_pic_wm_finewood = 1082
mesh_pic_wm_iron = 1083
mesh_pic_wm_elephant = 1084
mesh_pic_wm_whale = 1085
mesh_pic_wm_fish = 1086
mesh_pic_wm_maize = 1087
mesh_pic_wm_copper = 1088
mesh_pic_wm_marble = 1089
mesh_pic_wm_pearl = 1090
mesh_pic_wm_gem = 1091
mesh_pic_wm_ceramic = 1092
mesh_pic_wm_gold = 1093
mesh_pic_wm_silver = 1094
mesh_pic_wm_ivory = 1095
mesh_pic_wm_coffee = 1096
mesh_pic_wm_cacao = 1097
mesh_pic_wm_silk = 1098
mesh_pic_wm_nutmeg = 1099
mesh_pic_wm_allspice = 1100
mesh_pic_wm_cinnamon = 1101
mesh_pic_wm_clove = 1102
mesh_pic_wm_pepper = 1103
mesh_pic_wm_tabaco = 1104
mesh_pic_wm_tea = 1105
mesh_pic_marry = 1106
mesh_pic_religion_symbol_0 = 1107
mesh_pic_religion_symbol_1 = 1108
mesh_pic_religion_symbol_2 = 1109
mesh_pic_religion_symbol_3 = 1110
mesh_pic_religion_symbol_4 = 1111
mesh_pic_religion_symbol_5 = 1112
mesh_pic_religion_symbol_6 = 1113
mesh_pic_religion_symbol_7 = 1114
mesh_pic_religion_symbol_8 = 1115
mesh_pic_religion_symbol_9 = 1116
mesh_pic_religion_symbol_10 = 1117
mesh_pic_religion_symbol_11 = 1118
mesh_pic_religion_symbol_12 = 1119
mesh_pic_religion_symbol_13 = 1120
mesh_pic_religion_symbol_14 = 1121
mesh_pic_religion_symbol_15 = 1122
mesh_pic_religion_symbol_16 = 1123
mesh_pic_disaster_volcano = 1124
mesh_pic_disaster_earthquake = 1125
mesh_pic_disaster_storm = 1126
mesh_pic_disaster_typhoon = 1127
mesh_pic_disaster_fire = 1128
mesh_pic_disaster_sand = 1129
mesh_pic_disaster_tides = 1130
mesh_pic_disaster_ice = 1131
mesh_pic_disaster_flood = 1132
mesh_flag_div_1 = 1133
mesh_flag_div_2 = 1134
mesh_flag_div_3 = 1135
mesh_flag_div_4 = 1136
mesh_flag_div_5 = 1137
mesh_flag_div_6 = 1138
mesh_flag_div_7 = 1139
mesh_flag_div_8 = 1140
mesh_flag_div_9 = 1141
mesh_pic_battle_tile_2 = 1142
mesh_pic_battle_tile_3 = 1143
mesh_pic_battle_tile_4 = 1144
mesh_pic_battle_tile_5 = 1145
mesh_pic_battle_tile_6 = 1146
mesh_pic_battle_tile_7 = 1147
mesh_pic_battle_tile_8 = 1148
mesh_pic_battle_tile_9 = 1149
mesh_pic_battle_tile_10 = 1150
mesh_pic_battle_tile_11 = 1151
mesh_pic_battle_tile_s1 = 1152
mesh_pic_battle_tile_s2 = 1153
mesh_pic_battle_tile_s3 = 1154
mesh_pic_battle_tile_s4 = 1155
mesh_pic_battle_tile_n1 = 1156
mesh_pic_gameover = 1157
mesh_pic_cla_mercernary = 1158
mesh_pic_cla_merchant = 1159
mesh_pic_cla_adventurer = 1160
mesh_pic_cla_lord = 1161
mesh_pic_cla_bandit = 1162
mesh_pic_cla_pirate = 1163
mesh_pic_ptown_euro = 1164
mesh_pic_ptown_snow = 1165
mesh_pic_ptown_roman = 1166
mesh_pic_ptown_arab = 1167
mesh_pic_ptown_wooden = 1168
mesh_pic_ptown_asia = 1169
mesh_pic_ptown_asia_2 = 1170
mesh_pic_ptown_jap = 1171
mesh_pic_ptown_uurt = 1172
mesh_pic_ptown_teepee = 1173
mesh_pic_meetlady4 = 1174
mesh_pic_battle_formation_backriver = 1175
mesh_pic_battle_formation_sideattack = 1176
mesh_pic_battle_formation_backattack = 1177
mesh_pic_battle_formation_8door = 1178
mesh_pic_battle_formation_encampment = 1179
mesh_pic_battle_formation_lionheart = 1180
mesh_pic_battle_formation_mangudai = 1181
mesh_pic_battle_formation_pincer = 1182
mesh_pic_battle_formation_base = 1183
mesh_OrteliusWorldMap1570 = 1184
mesh_pic_portrait_yoritomo = 1185
mesh_pic_portrait_munemori = 1186
mesh_pic_portrait_xiaozong = 1187
mesh_pic_portrait_shizong = 1188
mesh_pic_portrait_genghiskhan = 1189
mesh_pic_portrait_philip_ii = 1190
mesh_pic_portrait_richard_i = 1191
mesh_pic_portrait_barbarossa = 1192
mesh_pic_portrait_alfonso_viii = 1193
mesh_pic_portrait_yaqub = 1194
mesh_pic_portrait_baldwin = 1195
mesh_pic_portrait_saladin = 1196
mesh_pic_portrait_tekish = 1197
mesh_pic_portrait_ghiyath = 1198
mesh_pic_portrait_akbar = 1199
mesh_pic_portrait_ivan = 1200
mesh_pic_portrait_frederick_ii = 1201
mesh_pic_portrait_maxi = 1202
mesh_pic_portrait_john_iii = 1203
mesh_pic_portrait_selimii = 1204
mesh_pic_portrait_stephen = 1205
mesh_pic_portrait_elizabeth = 1206
mesh_pic_portrait_philip = 1207
mesh_pic_portrait_sebastian = 1208
mesh_pic_portrait_william = 1209
mesh_pic_portrait_wanli = 1210
mesh_pic_portrait_oda = 1211
mesh_town_t_plain = 1212
mesh_town_t_water = 1213
mesh_town_t_hill = 1214
mesh_town_t_desert = 1215
mesh_town_t_snow = 1216
mesh_town_t_mountain = 1217
mesh_town_t_mil = 1218
mesh_town_t_ore = 1219
mesh_town_t_horse = 1220
mesh_town_t_holy = 1221
mesh_town_t_pasture = 1222
mesh_town_t_mine = 1223
mesh_town_t_market = 1224
mesh_town_t_barrack = 1225
mesh_town_t_farm = 1226
mesh_town_t_hall = 1227
mesh_town_t_prison = 1228
mesh_town_t_library = 1229
mesh_town_t_temple = 1230
mesh_town_t_smithy = 1231
mesh_white_plane_upper = 1232
mesh_white_plane_center = 1233
mesh_town_e_onehand = 1234
mesh_town_e_twohand = 1235
mesh_town_e_polearm = 1236
mesh_town_e_bow = 1237
mesh_town_e_crossbow = 1238
mesh_town_e_arquebus = 1239
mesh_town_e_ammo = 1240
mesh_town_e_light = 1241
mesh_town_e_heavy = 1242
mesh_town_e_horse = 1243
mesh_town_e_siege = 1244
mesh_town_e_wood = 1245
mesh_town_e_shipammo = 1246
mesh_town_d_onehand = 1247
mesh_town_d_twohand = 1248
mesh_town_d_polearm = 1249
mesh_town_d_bow = 1250
mesh_town_d_crossbow = 1251
mesh_town_d_arquebus = 1252
mesh_town_d_ammo = 1253
mesh_town_d_light = 1254
mesh_town_d_heavy = 1255
mesh_town_d_horse = 1256
mesh_town_d_siege = 1257
mesh_town_d_wood = 1258
mesh_town_d_shipammo = 1259
mesh_status_troop_ratio_bar = 1260
mesh_status_troop_ratio_bar_button = 1261
| [
76,
5069,
62,
16564,
62,
3903,
896,
796,
657,
198,
76,
5069,
62,
16564,
62,
2022,
62,
5767,
7701,
62,
16,
796,
352,
198,
76,
5069,
62,
16564,
62,
37348,
6540,
796,
362,
198,
76,
5069,
62,
16564,
62,
35156,
263,
62,
805,
796,
513... | 2.031134 | 15,899 |
# ----------------------------------------------------------------------------
# Copyright (c) 2017-2018, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import json
import pandas as pd
| [
2,
16529,
10541,
198,
2,
15069,
357,
66,
8,
2177,
12,
7908,
11,
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2,
198,
2,
383,
1336,
5964,
318,
287,... | 5.333333 | 72 |
# -*- coding: utf-8 -*-
import os
import sys
import state_workflow_sdk.api.state_workflow.callback_pb2
import state_workflow_sdk.api.state_workflow.createStateWorkflow_pb2
import state_workflow_sdk.model.state_workflow.stateWorkflow_pb2
import state_workflow_sdk.api.state_workflow.deleteStateWorkflow_pb2
import google.protobuf.empty_pb2
import state_workflow_sdk.api.state_workflow.filterInstanceOfStateWorkflow_pb2
import state_workflow_sdk.api.state_workflow.searchStateWorkflow_pb2
import state_workflow_sdk.api.state_workflow.transitWorkflowStatus_pb2
import state_workflow_sdk.utils.http_util
import google.protobuf.json_format
| [
2,
532,
9,
12,
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25,
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69,
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... | 2.723849 | 239 |
"""Test for motif.classify.mvgaussian
"""
from __future__ import print_function
import unittest
import numpy as np
from motif.contour_classifiers import random_forest
| [
37811,
14402,
329,
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62... | 3.4 | 50 |
"""
Profile-aware session wrapper.
"""
from os import environ
from botocore.exceptions import ProfileNotFound
from botocore.session import Session
from awsenv.cache import CachedSession
def get_default_profile_name():
"""
Get the default profile name from the environment.
"""
return environ.get("AWS_DEFAULT_PROFILE", "default")
class AWSProfile(AWSSession):
"""
AWS profile configuration.
"""
def __init__(self,
profile,
session_duration,
cached_session,
account_id=None):
"""
Configure a session for a profile.
:param profile: the name of the profile to use, if any
:param session_duration: the duration of the session (in seconds)
must be in the range 900-3600
:param cached_session: the cached session to use, if any
:param account_id: the account id for profile auto-generation (if any)
"""
self.session_duration = session_duration
self.cached_session = cached_session
self.account_id = account_id
super(AWSProfile, self).__init__(profile)
def to_envvars(self):
return {
"AWS_ACCESS_KEY_ID": self.access_key_id,
"AWS_DEFAULT_REGION": self.region_name,
"AWS_PROFILE": self.profile,
"AWS_SECRET_ACCESS_KEY": self.secret_access_key,
"AWS_SESSION_NAME": self.session_name,
"AWS_SESSION_TOKEN": self.session_token,
}
def update_credentials(self):
"""
Update the profile's credentials by assuming a role, if necessary.
"""
if not self.role_arn:
return
if self.cached_session is not None:
# use current role
access_key, secret_key = self.current_role()
else:
# assume role to get a new token
access_key, secret_key = self.assume_role()
if access_key and secret_key:
self.session.set_credentials(
access_key=access_key,
secret_key=secret_key,
token=self.cached_session.token if self.cached_session else None,
)
def current_role(self):
"""
Load credentials for the current role.
"""
return (
environ.get("AWS_ACCESS_KEY_ID", self.access_key_id),
environ.get("AWS_SECRET_ACCESS_KEY", self.secret_access_key),
)
def assume_role(self):
"""
Assume a role.
"""
# we need to pass in the regions and keys because botocore does not
# automatically merge configuration from the source_profile
sts_client = self.session.create_client(
service_name="sts",
region_name=self.region_name,
aws_access_key_id=self.access_key_id,
aws_secret_access_key=self.secret_access_key,
)
session_name = CachedSession.make_name()
result = sts_client.assume_role(**{
"RoleArn": self.role_arn,
"RoleSessionName": session_name,
"DurationSeconds": self.session_duration,
})
# update the cached session
self.cached_session = CachedSession(
name=session_name,
token=result["Credentials"]["SessionToken"],
profile=self.profile,
)
return (
result["Credentials"]["AccessKeyId"],
result["Credentials"]["SecretAccessKey"],
)
| [
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198,
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420,
382,
13,
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198,
6738,
10214,
420,
382,
13,
29891,
1330,
23575,
198,
198,
6738,... | 2.171393 | 1,622 |
import torch
import os
import torch.nn.functional as F
import numpy as np
import copy
from torch import nn
from torch.optim import Adam
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from typing import Tuple
from .ppo import PPO, PPOExpert
from .utils import CULoss
from cail.network import AIRLDiscrim, Classifier
from cail.buffer import SerializedBuffer
| [
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4... | 3.173913 | 138 |
import numpy as np
import os, shutil
import imageio
baseDir = "data/train_verbose"
outDir = "data/train"
#baseDir = "data/test_verbose"
#outDir = "data/test"
outDirVidCopy = "data/videos"
combineVidsAll = {"smoke" : ["densMean", "densSlice", "velMean", "velSlice", "presMean", "presSlice"],
"liquid": ["flagsMean", "flagsSlice", "velMean", "velSlice", "phiMean", "phiSlice"] }
convertData = True
processVid = True
copyVidOnly = False
ignoreTop = ["shapes", "waves"]
ignoreSim = []
ignoreFrameDict = {}
excludeIgnoreFrame = False
topDirs = os.listdir(baseDir)
topDirs.sort()
#shutil.rmtree(outDir)
#os.makedirs(outDir)
# top level folders
for topDir in topDirs:
mantaMsg("\n" + topDir)
if ignoreTop and any( item in topDir for item in ignoreTop ) :
mantaMsg("Ignored")
continue
simDir = os.path.join(baseDir, topDir)
sims = os.listdir(simDir)
sims.sort()
# sim_000000 folders
for sim in sims:
if ignoreSim and any( item in sim for item in ignoreSim ) :
mantaMsg(sim + " - Ignored")
continue
currentDir = os.path.join(simDir, sim)
files = os.listdir(currentDir)
files.sort()
destDir = os.path.join(outDir, topDir, sim)
#if os.path.isdir(destDir):
# shutil.rmtree(destDir)
if not os.path.isdir(destDir):
os.makedirs(destDir)
# single files
for file in files:
filePath = os.path.join(currentDir, file)
# copy src folder to destination
if os.path.isdir(filePath) and file == "src":
dest = os.path.join(destDir, "src")
if not os.path.isdir(dest):
shutil.copytree(filePath, dest, symlinks=False)
# combine video files
elif os.path.isdir(filePath) and file == "render":
if not processVid:
continue
dest = os.path.join(destDir, "render")
if copyVidOnly:
shutil.copytree(filePath, dest, symlinks=False)
continue
if not os.path.isdir(dest):
os.makedirs(dest)
#mantaMsg(file)
renderDir = os.path.join(currentDir, "render")
vidFiles = os.listdir(renderDir)
if "smoke" in topDir: combineVids = combineVidsAll["smoke"]
elif "liquid" in topDir: combineVids = combineVidsAll["liquid"]
else: combineVids = [""]
for vidFile in vidFiles:
if combineVids[0] + "00.mp4" not in vidFile:
continue
vidLine = []
for combineVid in combineVids:
# find all video part files corresponding to current one
vidParts = []
i = 0
while os.path.exists(os.path.join(renderDir, vidFile.replace(combineVids[0]+"00.mp4", combineVid+"%02d.mp4" % i))):
vidParts.append(vidFile.replace(combineVids[0]+"00.mp4", combineVid+"%02d.mp4" % i))
i += 1
assert len(vidParts) == 11
# combine each video part file
loadedVids = []
for part in vidParts:
currentFile = os.path.join(renderDir, part)
loaded = imageio.mimread(currentFile)
#mantaMsg(len(loaded))
#mantaMsg(loaded[0].shape)
loadedVids.append(loaded)
#temp1 = np.concatenate(loadedVids[0:4], axis=2)
#temp2 = np.concatenate(loadedVids[4:8], axis=2)
#temp3 = np.concatenate(loadedVids[8:11]+[np.zeros_like(loadedVids[0])], axis=2)
#vidLine.append(np.concatenate([temp1, temp2, temp3], axis=1))
vidLine.append(np.concatenate(loadedVids, axis=2))
combined = np.concatenate(vidLine, axis=1)
# save combined file
if combineVids[0] == "": newName = os.path.join(dest, "%s_%s_%s.mp4" % (topDir, sim, vidFile.replace("00.mp4", ".mp4")))
else: newName = os.path.join(dest, "%s_%s.mp4" % (topDir, sim))
imageio.mimwrite(newName, combined, quality=6, fps=11, ffmpeg_log_level="error")
# save copy
if combineVids[0] == "": newNameCopy = os.path.join(outDirVidCopy, "%s_%s_%s.mp4" % (topDir, sim, vidFile.replace("00.mp4", ".mp4")))
else: newNameCopy = os.path.join(outDirVidCopy, "%s_%s.mp4" % (topDir, sim))
imageio.mimwrite(newNameCopy, combined, quality=6, fps=11, ffmpeg_log_level="error")
# copy description files to destination
elif os.path.splitext(filePath)[1] == ".json" or os.path.splitext(filePath)[1] == ".py" or os.path.splitext(filePath)[1] == ".log":
shutil.copy(filePath, destDir)
# ignore other dirs and non .npz files
elif os.path.isdir(filePath) or os.path.splitext(filePath)[1] != ".npz" or "part00" not in file:
continue
# combine part files
else:
if not convertData:
continue
if ignoreFrameDict:
filterFrames = []
for key, value in ignoreFrameDict.items():
if key in topDir:
filterFrames = value
break
assert (filterFrames != []), "Keys in filterFrameDict don't match dataDir structure!"
# continue for frames when excluding or including according to filter
if excludeIgnoreFrame == any( item in file for item in filterFrames ):
continue
# find all part files corresponding to current one
parts = [file]
i = 1
while os.path.exists(os.path.join(currentDir, file.replace("part00", "part%02d" % i))):
parts.append(file.replace("part00", "part%02d" % i))
i += 1
assert len(parts) == 11
# combine each part file
domain = np.load(os.path.join(currentDir, parts[0]))['arr_0']
res = domain.shape[0]
combined = np.zeros([len(parts), res, res, res, domain.shape[3]])
for f in range(len(parts)):
currentFile = os.path.join(currentDir, parts[f])
loaded = np.load(currentFile)['arr_0']
combined[f] = loaded
# save combined file
newName = file.replace("_part00", "")
np.savez_compressed( os.path.join(destDir, newName), combined )
loaded = np.load( os.path.join(destDir, newName) )['arr_0']
mantaMsg(os.path.join(sim, newName) + "\t" + str(loaded.shape))
| [
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2,
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796,
36... | 1.836801 | 3,989 |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
from abc import ABC, abstractmethod
import warnings
from qiskit import QuantumCircuit
def _to_dia_matrix(self, mode=None):
warnings.warn("_to_dia_matrix method is removed, use the `MatrixOperator` class to get diagonal matrix. And "
"the current deprecated method does NOT modify the original object, it returns the dia_matrix",
DeprecationWarning)
from .op_converter import to_matrix_operator
mat_op = to_matrix_operator(self)
return mat_op.dia_matrix
def enable_summarize_circuits(self):
warnings.warn("enable_summarize_circuits method is removed. Enable the summary at QuantumInstance",
DeprecationWarning)
def disable_summarize_circuits(self):
warnings.warn("disable_summarize_circuits method is removed. Disable the summary at QuantumInstance",
DeprecationWarning)
def find_Z2_symmetries(self):
warnings.warn("The `find_Z2_symmetries` method is deprecated and it will be removed after 0.6, "
"Use the class method in the `Z2Symmetries` class instead",
DeprecationWarning)
from .weighted_pauli_operator import Z2Symmetries
from .op_converter import to_weighted_pauli_operator
wp_op = to_weighted_pauli_operator(self)
self._z2_symmetries = Z2Symmetries.find_Z2_symmetries(wp_op)
return self._z2_symmetries.symmetries, self._z2_symmetries.sq_paulis, \
self._z2_symmetries.cliffords, self._z2_symmetries.sq_list
def to_grouped_paulis(self):
warnings.warn("to_grouped_paulis method is deprecated and it will be removed after 0.6. And the current "
"deprecated method does NOT modify the original object, it returns the grouped weighted pauli "
"operator. Please check the qiskit.aqua.operators.op_convertor for converting to different "
"types of operators. For grouping paulis, you can create your own grouping func to create the "
"class you need.",
DeprecationWarning)
from .op_converter import to_tpb_grouped_weighted_pauli_operator
from .tpb_grouped_weighted_pauli_operator import TPBGroupedWeightedPauliOperator
return to_tpb_grouped_weighted_pauli_operator(self, grouping_func=TPBGroupedWeightedPauliOperator.sorted_grouping)
def to_paulis(self):
warnings.warn("to_paulis method is deprecated and it will be removed after 0.6. And the current deprecated "
"method does NOT modify the original object, it returns the weighted pauli operator."
"Please check the qiskit.aqua.operators.op_convertor for converting to different types of "
"operators",
DeprecationWarning)
from .op_converter import to_weighted_pauli_operator
return to_weighted_pauli_operator(self)
def to_matrix(self):
warnings.warn("to_matrix method is deprecated and it will be removed after 0.6. And the current deprecated "
"method does NOT modify the original object, it returns the matrix operator."
"Please check the qiskit.aqua.operators.op_convertor for converting to different types of "
"operators",
DeprecationWarning)
from .op_converter import to_matrix_operator
return to_matrix_operator(self)
def to_weighted_pauli_operator(self):
warnings.warn("to_weighted_apuli_operator method is temporary helper method and it will be removed after 0.6. "
"Please check the qiskit.aqua.operators.op_convertor for converting to different types of "
"operators",
DeprecationWarning)
from .op_converter import to_weighted_pauli_operator
return to_weighted_pauli_operator(self)
def to_matrix_operator(self):
warnings.warn("to_matrix_operator method is temporary helper method and it will be removed after 0.6. "
"Please check the qiskit.aqua.operators.op_convertor for converting to different types of "
"operators",
DeprecationWarning)
from .op_converter import to_matrix_operator
return to_matrix_operator(self)
def to_tpb_grouped_weighted_pauli_operator(self):
warnings.warn("to_tpb_grouped_weighted_pauli_operator method is temporary helper method and it will be "
"removed after 0.6. Please check the qiskit.aqua.operators.op_convertor for converting to "
"different types of operators",
DeprecationWarning)
from .op_converter import to_tpb_grouped_weighted_pauli_operator
from .tpb_grouped_weighted_pauli_operator import TPBGroupedWeightedPauliOperator
return to_tpb_grouped_weighted_pauli_operator(
self, grouping_func=TPBGroupedWeightedPauliOperator.sorted_grouping)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
2,
770,
2438,
318,
636,
286,
1195,
1984,
270,
13,
198,
2,
198,
2,
357,
34,
8,
15069,
19764,
13130,
13,
198,
2,
198,
2,
770,
2438,
318,
11971,
739,
262,
24843,... | 2.399828 | 2,331 |
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
import re
def convert_data_sparse_matrix(df, row_label = 'stock_code', col_label = 'name_of_ccass_participant', value_label = 'shareholding'):
"""
Pivot table
"""
try:
# Prepare zero matrix
row_dim = len(df[row_label].unique())
col_dim = len(df[col_label].unique())
sparse_matrix = np.zeros((row_dim, col_dim))
# Prepare label to index dictionaries
row_ind_dict = {label: ind for ind, label in enumerate(sorted(df[row_label].unique().tolist()))}
col_ind_dict = {label: ind for ind, label in enumerate(sorted(df[col_label].unique().tolist()))}
# Transform row_label column and col_label column to index
df['row_ind'] = df[row_label].apply(lambda x: row_ind_dict[x])
df['col_ind'] = df[col_label].apply(lambda x: col_ind_dict[x])
for ind, row in df.iterrows():
# Get index and shareholding
row_ind = row['row_ind']
col_ind = row['col_ind']
value = row[value_label]
# Assign to sparse matrix
sparse_matrix[row_ind, col_ind] += value
return sparse_matrix, row_ind_dict, col_ind_dict
except Exception as e:
print(e)
return None
def cluster_predict(label, min_pts = 'auto'):
"""
Input: an array of clsutered label for each instance
return: an array of anomal label for each instance
"""
try:
# Get Unqiue label and its counts
(unique, counts) = np.unique(label, return_counts = True)
# Define minimum points that it should have in a cluster, if auto, it will take the min count
if min_pts == 'auto':
min_pts = min(counts)
print('Minimum points of a cluster among the clusters: ', min_pts)
else:
min_pts = int(min_pts)
# Prepare label_dict for mapping
label_dict = {label: 0 if count > min_pts else 1 for label, count in zip(unique, counts)}
# Map label_dict to label
return np.array([label_dict[i] for i in label])
except Exception as e:
print(e)
return None
| [
11748,
19798,
292,
355,
279,
67,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
1341,
35720,
13,
4164,
10466,
1330,
10802,
62,
6759,
8609,
198,
11748,
302,
198,
198,
4299,
10385,
62,
7890,
62,
82,
29572,
62,
6759,
8609,
7,
7568,
11,
... | 2.307772 | 965 |
'''
Prepare some files to test the upload functionality.
'''
import sys
sys.path.append('../')
from database import *
from pymongo import MongoClient
mongo = MongoClient(MONGOURI)
db = mongo['SCV']
coll = db['dataset']
from gene_expression import *
expr_df, meta_doc = load_read_counts_and_meta(organism='mouse', gse='GSE96870')
# rename the samples
expr_df.columns = ['sample_%d' % i for i in range(len(expr_df.columns))]
meta_df = pd.DataFrame(meta_doc['meta_df'])
meta_df.index = expr_df.columns
meta_df.index.name = 'sample_ID'
# parse the meta_df a bit
meta_df['Sample_characteristics_ch1'] = meta_df['Sample_characteristics_ch1'].map(lambda x:x.split('\t'))
keys_from_char_ch1 = [item.split(': ')[0] for item in meta_df['Sample_characteristics_ch1'][0]]
for i, key in enumerate(keys_from_char_ch1):
meta_df[key] = meta_df['Sample_characteristics_ch1'].map(lambda x:x[i].split(': ')[1])
# drop unnecessary columns in meta_df
meta_df = meta_df.drop(['Sample_characteristics_ch1',
'Sample_relation', 'Sample_geo_accession', 'Sample_supplementary_file_1'],
axis=1)
# fake a column of continuous values
meta_df['random_continuous_attr'] = np.random.randn(meta_df.shape[0])
meta_df.to_csv('../data/sample_metadata.csv')
# raw read counts
expr_df.to_csv('../data/sample_read_counts_%dx%d.csv' % expr_df.shape)
# CPMs
expr_df = compute_CPMs(expr_df)
expr_df.to_csv('../data/sample_CPMs_%dx%d.csv' % expr_df.shape)
| [
7061,
6,
198,
37534,
533,
617,
3696,
284,
1332,
262,
9516,
11244,
13,
198,
7061,
6,
198,
11748,
25064,
198,
17597,
13,
6978,
13,
33295,
10786,
40720,
11537,
198,
6738,
6831,
1330,
1635,
198,
6738,
279,
4948,
25162,
1330,
42591,
11792,
... | 2.56553 | 557 |
from flask import Flask, request
from flask_restful import Resource, reqparse
from flask_jwt_extended import create_access_token, jwt_required
from app.api.V1.models import Product, products
# Get a single specific product
| [
6738,
42903,
1330,
46947,
11,
2581,
198,
6738,
42903,
62,
2118,
913,
1330,
20857,
11,
43089,
29572,
198,
6738,
42903,
62,
73,
46569,
62,
2302,
1631,
1330,
2251,
62,
15526,
62,
30001,
11,
474,
46569,
62,
35827,
198,
198,
6738,
598,
13,... | 3.677419 | 62 |
import time
from multiprocessing.pool import Pool
if __name__ == '__main__':
t = time.time()
p1 = Pool(processes=30)
p2 = Pool(processes=30)
p3 = Pool(processes=30)
num1 = range(2, 7072)
num2 = range(2, 369)
num3 = range(2, 85)
prime_list1 = p1.map(is_prime, num1)
p1.close()
p1.join()
prime_list2 = p2.map(is_prime, num2)
p2.close()
p2.join()
prime_list3 = p3.map(is_prime, num3)
p3.close()
p3.join()
prime_list1_clear = [x for x in prime_list1 if x is not None]
prime_list2_clear = [x for x in prime_list2 if x is not None]
prime_list3_clear = [x for x in prime_list3 if x is not None]
result_list = []
for i in prime_list1_clear:
print(i)
for j in prime_list2_clear:
for k in prime_list3_clear:
test_num = i**2 + j**3 + k**4
if test_num < 50000000:
result_list.append(test_num)
print(str(len(list(set(result_list)))))
print('time:'+str(time.time()-t))
| [
11748,
640,
198,
6738,
18540,
305,
919,
278,
13,
7742,
1330,
19850,
628,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
256,
796,
640,
13,
2435,
3419,
198,
220,
220,
220,
279,
16,
796,
19850,... | 2.017613 | 511 |
# recursion function (Clean Word)
print(CleanWord("wwwooooorrrrllddd"))
| [
2,
664,
24197,
2163,
357,
32657,
9678,
8,
198,
198,
4798,
7,
32657,
26449,
7203,
2503,
13321,
273,
21062,
81,
297,
1860,
67,
48774,
198
] | 2.92 | 25 |
import typing
from commands2 import CommandBase
from subsystems.cameracontroller import CameraSubsystem
| [
11748,
19720,
198,
6738,
9729,
17,
1330,
9455,
14881,
198,
198,
6738,
39335,
82,
13,
66,
2382,
330,
756,
10646,
1330,
20432,
7004,
10057,
628
] | 4.24 | 25 |
from django.shortcuts import render,redirect
from django.http import HttpResponse, Http404,HttpResponseRedirect
import datetime as dt
from .models import Post,Comment,Follow,Profile
from django.contrib.auth.decorators import login_required
from .forms import NewPostForm, NewCommentForm, AddProfileForm
from django.contrib.auth.models import User
def delete_post(request,post_id):
post= Post.objects.get(pk=post_id)
post.delete_post()
return redirect('my_profile')
return render(request, 'my_profile')
| [
6738,
42625,
14208,
13,
19509,
23779,
1330,
8543,
11,
445,
1060,
198,
6738,
42625,
14208,
13,
4023,
1330,
367,
29281,
31077,
11,
367,
29281,
26429,
11,
43481,
31077,
7738,
1060,
198,
11748,
4818,
8079,
355,
288,
83,
198,
6738,
764,
2753... | 3.169697 | 165 |
import sys
import wx
sys.path.insert(0, 'lib.zip')
from lib.TetrisGame import TetrisGame
if __name__ == '__main__':
app = wx.PySimpleApp()
frame = TetrisGame(None)
frame.Show(True)
app.MainLoop()
| [
11748,
25064,
201,
198,
11748,
266,
87,
201,
198,
201,
198,
17597,
13,
6978,
13,
28463,
7,
15,
11,
705,
8019,
13,
13344,
11537,
201,
198,
6738,
9195,
13,
51,
316,
2442,
8777,
1330,
27351,
2442,
8777,
201,
198,
201,
198,
201,
198,
... | 2.106195 | 113 |
import argparse
import torch
import logger
import models
import utils
NUM_NODES = {
'moments': 391,
'multimoments': 391,
'kinetics': 608,
}
CRITERIONS = {
'CE': {'func': torch.nn.CrossEntropyLoss},
'MSE': {'func': torch.nn.MSELoss},
'BCE': {'func': torch.nn.BCEWithLogitsLoss},
}
OPTIMIZERS = {
'SGD': {
'func': torch.optim.SGD,
'lr': 0.001,
'momentum': 0.9,
'weight_decay': 5e-4,
},
'Adam': {'func': torch.optim.Adam, 'weight_decay': 5e-4},
}
SCHEDULER_DEFAULTS = {'CosineAnnealingLR': {'T_max': 100}}
METAFILE_FILE = {
'moments': {
'train': 'metadata/moments_train_abstraction_sets.json',
'val': 'metadata/moments_val_abstraction_sets.json',
},
'kinetics': {
'train': 'metadata/kinetics_train_abstraction_sets.json',
'val': 'metadata/kinetics_val_abstraction_sets.json',
},
}
FEATURES_FILE = {
'moments': {
'train': 'metadata/resnet3d50_moments_train_features.pth',
'val': 'metadata/resnet3d50_moments_val_features.pth',
'test': 'metadata/resnet3d50_moments_test_features.pth',
},
'kinetics': {
'train': 'metadata/resnet3d50_kinetics_train_features.pth',
'val': 'metadata/resnet3d50_kinetics_val_features.pth',
'test': 'metadata/resnet3d50_kinetics_test_features.pth',
},
}
EMBEDDING_FILE = {
'moments': {
'train': 'metadata/moments_train_embeddings.pth',
'val': 'metadata/moments_val_embeddings.pth',
},
'kinetics': {
'train': 'metadata/kinetics_train_embeddings.pth',
'val': 'metadata/kinetics_val_embeddings.pth',
'test': 'metadata/kinetics_test_embeddings.pth',
},
}
EMBEDDING_CATEGORIES_FILE = {
'moments': 'metadata/moments_category_embeddings.pth',
'kinetics': 'metadata/kinetics_category_embeddings.pth',
}
LIST_FILE = {
'moments': {
'train': 'metadata/moments_train_listfile.txt',
'val': 'metadata/moments_val_listfile.txt',
'test': 'metadata/moments_test_listfile.txt',
},
'kinetics': {
'train': 'metadata/kinetics_train_listfile.txt',
'val': 'metadata/kinetics_val_listfile.txt',
'test': 'metadata/kinetics_test_listfile.txt',
},
}
RANKING_FILE = {
'moments': 'metadata/moments_human_abstraction_sets.json',
'kinetics': 'metadata/kinetics_human_abstraction_sets.json',
}
GRAPH_FILE = {
'moments': 'metadata/moments_graph.json',
'kinetics': 'metadata/kinetics_graph.json',
}
| [
11748,
1822,
29572,
198,
11748,
28034,
198,
198,
11748,
49706,
198,
11748,
4981,
198,
11748,
3384,
4487,
198,
198,
41359,
62,
45,
3727,
1546,
796,
1391,
198,
220,
220,
220,
705,
32542,
658,
10354,
5014,
16,
11,
198,
220,
220,
220,
705... | 2.147708 | 1,178 |
import scrapy
from bs4 import BeautifulSoup
from lab3.items import Lab3Item
| [
11748,
15881,
88,
198,
6738,
275,
82,
19,
1330,
23762,
50,
10486,
198,
6738,
2248,
18,
13,
23814,
1330,
3498,
18,
7449,
628
] | 3.347826 | 23 |
import matplotlib.pyplot as plt
from lifelines import (WeibullFitter, ExponentialFitter,
LogNormalFitter, LogLogisticFitter)
import pandas as pd
data = pd.read_csv('Dataset/telco_customer.csv')
data['tenure'] = pd.to_numeric(data['tenure'])
data = data[data['tenure'] > 0]
# Replace yes and No in the Churn column to 1 and 0. 1 for the event and 0 for the censured data.
data['Churn'] = data['Churn'].apply(lambda x: 1 if x == 'Yes' else 0)
fig, axes = plt.subplots(2, 2, figsize=(
16, 12))
T = data['tenure']
E = data['Churn']
wbf = WeibullFitter().fit(T, E, label='WeibullFitter')
ef = ExponentialFitter().fit(T, E, label='ExponentialFitter')
lnf = LogNormalFitter().fit(T, E, label='LogNormalFitter')
llf = LogLogisticFitter().fit(T, E, label='LogLogisticFitter')
wbf.plot_cumulative_hazard(ax=axes[0][0])
ef.plot_cumulative_hazard(ax=axes[0][1])
lnf.plot_cumulative_hazard(ax=axes[1][0])
llf.plot_cumulative_hazard(ax=axes[1][1])
plt.suptitle(
'Parametric Model Implementation of cumulative hazard function on the Telco dataset')
fig.text(0.5, 0.04, 'Timeline', ha='center')
fig.text(0.04, 0.5, 'Probability', va='center', rotation='vertical')
plt.savefig('Images/WeiExpLogx.jpeg')
plt.show()
| [
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
6738,
3868,
20655,
1330,
357,
1135,
571,
724,
37,
1967,
11,
5518,
35470,
37,
1967,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2... | 2.472 | 500 |
import re
import num2words
INT_PATTERN = re.compile(r'^-?[0-9]+$')
FLOAT_PATTERN = re.compile(r'^-?[0-9]+[,\.][0-9]+$')
ORDINAL_PATTERN = re.compile(r'^[0-9]+\.?$')
NUM_PATTERN = re.compile(r'^-?[0-9]+([,\.][0-9]+$)?')
| [
11748,
302,
198,
11748,
997,
17,
10879,
628,
198,
12394,
62,
47,
1404,
31800,
796,
302,
13,
5589,
576,
7,
81,
6,
61,
12,
30,
58,
15,
12,
24,
48688,
3,
11537,
198,
3697,
46,
1404,
62,
47,
1404,
31800,
796,
302,
13,
5589,
576,
7... | 1.757813 | 128 |
from django.test import TransactionTestCase
from django.test import TestCase
from django.urls import reverse
from home_page.models import Search
from ebaysdk.finding import Connection as finding
| [
6738,
42625,
14208,
13,
9288,
1330,
45389,
14402,
20448,
198,
6738,
42625,
14208,
13,
9288,
1330,
6208,
20448,
198,
6738,
42625,
14208,
13,
6371,
82,
1330,
9575,
198,
6738,
1363,
62,
7700,
13,
27530,
1330,
11140,
198,
6738,
36649,
592,
... | 2.345455 | 110 |
from key_generator.key_generator import generate
all_sizes_required = [(100, '100'), (500, '500'), (1000, '1K'), (5000, '5K'), (10000, '10K'), (50000, '50K'), (100000, '100K'), (500000, '500K')]
for file_size in all_sizes_required:
OUTPUT_PATH = "./string_test_" + file_size[1] + ".txt"
STRING_COUNT = file_size[0]
output_file = open(OUTPUT_PATH, "w")
for i in range(STRING_COUNT):
string = ""
recipient = generate(
num_of_atom = 1,
type_of_value = "hex",
capital = "mix",
extras = ["-", "_"],
seed = i
).get_key()
domain = generate(
num_of_atom = 2,
separator = ".",
min_atom_len = 3,
max_atom_len = 5,
type_of_value = "hex",
capital = "mix",
extras = ["-"],
seed = i
).get_key()
string = recipient + "@" + domain
output_file.write(string + "\n")
output_file.close()
print("Done with " + OUTPUT_PATH)
| [
6738,
1994,
62,
8612,
1352,
13,
2539,
62,
8612,
1352,
1330,
7716,
198,
198,
439,
62,
82,
4340,
62,
35827,
796,
47527,
3064,
11,
705,
3064,
33809,
357,
4059,
11,
705,
4059,
33809,
357,
12825,
11,
705,
16,
42,
33809,
357,
27641,
11,
... | 2.263021 | 384 |
###########################################
# Author : Bastien Girardet, Deborah De Wolff
# Date : 13.05.2018
# Course : Applications in Object-oriented Programming and Databases
# Teachers : Binswanger Johannes, Zrcher Ruben
# Project : Bibliotek
# Name : portfolio_book.py Portfolio_book Flask_restful resource
# #########################################
from flask_restful import Resource, reqparse
from models.portfolio_book import PortfolioBookModel
from models.book import BookModel
| [
29113,
7804,
21017,
198,
2,
220,
220,
6434,
1058,
17520,
2013,
23837,
446,
316,
11,
36976,
1024,
27094,
487,
198,
2,
220,
220,
7536,
1058,
1511,
13,
2713,
13,
7908,
198,
2,
220,
220,
20537,
1058,
26622,
287,
9515,
12,
17107,
30297,
... | 3.853846 | 130 |
import logging
from absl import app
from sensor_msgs.msg import Image
from insert_table_op import InsertTableOperator
from insert_block_op import InsertBlockOperator
from init_robot_op import InitRobotOperator
from gel_sight_op import GelSightOperator
from mock_loc_obj_op import MockLocateObjectOperator
from goto_xyz_op import GoToXYZOperator
from move_above_object_op import MoveAboveObjectOperator
from mock_gripper_op import MockGripperOperator
from mock_grasp_object_op import MockGraspObjectOperator
from raise_object_op import RaiseObjectOperator
from mock_predict_grip_op import MockPredictGripOperator
from random_position_op import RandomPositionOperator
from mock_ungrasp_object_op import MockUngraspObjectOperator
import erdos.graph
from erdos.ros.ros_subscriber_op import ROSSubscriberOp
logger = logging.getLogger(__name__)
table_init_arguments = {"_x": 0.75, "_y": 0.0, "_z": 0.0, "ref_frame": "world"}
block_init_arguments = {
"_x": 0.4225,
"_y": 0.1265,
"_z": 0.7725,
"ref_frame": "world"
}
robot_init_arguments = {
"joint_angles": {
'right_j0': -0.041662954890248294,
'right_j1': -1.0258291091425074,
'right_j2': 0.0293680414401436,
'right_j3': 2.17518162913313,
'right_j4': -0.06703022873354225,
'right_j5': 0.3968371433926965,
'right_j6': 1.7659649178699421
},
"limb_name": "right"
}
if __name__ == "__main__":
app.run(main)
| [
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... | 2.463248 | 585 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import jsonfield.fields
from django.conf import settings
from django.db import migrations, models
| [
2,
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9,
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1420... | 3.215686 | 51 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2017 Tomoki Hayashi (Nagoya University)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import print_function
import argparse
import numpy as np
from sklearn.preprocessing import StandardScaler
from utils import read_hdf5
from utils import read_txt
from utils import write_hdf5
if __name__ == "__main__":
main()
| [
2,
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2,
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... | 2.789116 | 147 |
# from decimal import Decimal
import collections as coll
import sys
import math as mt
# import random as rd
# import bisect as bi
import time
sys.setrecursionlimit(1000000)
# import numpy as np
# Starting Time
time1 = time.time()
######## CODE STARTS FROM HERE ########
n = uno()
a, b, c, ans = 0, 1, 0, 0
while c <= n:
c = a + b
if ~c & 1:
ans += c
b, a = c, b
print(ans)
# End Time
time2 = time.time()
print("\nTime Taken:", (time2 - time1) * 1000)
| [
2,
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1330,
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... | 2.560847 | 189 |
from robosuite.models.objects import MujocoXMLObject
from robosuite.utils.mjcf_utils import xml_path_completion, array_to_string, string_to_array
| [
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1... | 2.839286 | 56 |
# Problem description: http://python3.softuni.bg/student/lecture/assignment/56b749af7e4f59b649b7e626/
if __name__ == '__main__':
main()
| [
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367... | 2.440678 | 59 |
# LOGGING
# ---------------------------------------------------------------------------------------------------------------------#
import logging
logger = logging.getLogger('django')
# IMPORTS
# ---------------------------------------------------------------------------------------------------------------------#
# shortcuts
from django.shortcuts import render
# contrib.auth
from django.contrib.auth.decorators import login_required
# views.generic
from django.views.generic import DetailView
#
from .models import *
# GENERIC CLASS BASED VIEWS
# ---------------------------------------------------------------------------------------------------------------------#
# CUSTOM VIEWS
# ---------------------------------------------------------------------------------------------------------------------# | [
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1... | 6.121212 | 132 |
# Adam Beardsley
# starting from from adafruit example
# https://learn.adafruit.com/welcome-to-circuitpython/creating-and-editing-code
#
import board
import digitalio
import time
led = digitalio.DigitalInOut(board.LED)
led.direction = digitalio.Direction.OUTPUT
ramp_time = 3 # Time to ramp up, in seconds
period = 0.01 # Time per cycle, in seconds
step = period / ramp_time # how much to increment the brightness each cycle
while True:
brightness = 0 # Start off
while brightness < 1:
T_on = brightness * period
T_off = period - T_on
led.value = True
time.sleep(T_on)
led.value = False
time.sleep(T_off)
brightness += step
# Convince yourself the expression for step (line 14) is correct
# How can you *test* that step is correct?
# Can you reverse the program (start bright, get dim)
| [
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... | 2.822368 | 304 |
import argparse
from site_checker import SiteChecker
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Check sites text.")
parser.add_argument("config", type=str, nargs=1, help="Path to config json file.")
parser.add_argument(
"-a",
dest="apiKey",
type=str,
nargs=1,
required=True,
help="Pushbullet API key.",
)
parser.add_argument(
"-m", dest="maxFailCount", type=int, nargs=1, help="Max fail count."
)
parser.add_argument(
"-u", dest="updateCycle", type=int, nargs=1, help="Update cycle in second"
)
parser.add_argument(
"-v", dest="isVerbose", action="store_true", help="Verbose mode."
)
parser.add_argument(
"-q",
dest="isQuiet",
action="store_true",
help="Quiet mode. Does not call pushbullet",
)
args = parser.parse_args()
k = SiteChecker(
args.config[0],
args.apiKey[0],
args.isQuiet,
args.isVerbose,
args.maxFailCount,
args.updateCycle,
)
k.check()
| [
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1822,
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13,
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1713,
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7,
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... | 2.184891 | 503 |
import os
from pathlib import Path
from datetime import datetime
from json import dumps
import flask as fsk
from flask import request, jsonify, Response
app = fsk.Flask(__name__)
app.config['DEBUG'] = False
homedir = os.getenv('HOME')
if __name__ == "__main__":
app.run(host='0.0.0.0', port=4960)
| [
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11,
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198,
198,
1324,
... | 2.816514 | 109 |
#!/usr/bin/env python3
####################################################################################################
# Created by EH (NL) https://github.com/strebrah/Solaredge_Domoticz_Modbus #
# Date: August 2020 #
# Version: 0.1 #
# Designed for python 3.7 (based on the requirements of the 'solaredge_modbus' library.) #
# Thanks to Niels for the 'solaredge_modbus' library https://pypi.org/project/solaredge-modbus/ #
# Capabilities: #
# * Creating a hardware device in Domoticz #
# * Creating sensors for the data types in Domoticz #
# * Sending the solaredge modbus data to Domoticz #
# How to use #
# 1. Enter your configuration in the 'dz_se_settings.ini' file #
# 2. configure crontab task for periodic data transfer to Domoticz. #
# example: #
# sudo crontab -e #
# for example, every minute #
# */1 * * * * /usr/bin/python3 /home/pi/domoticz/scripts/python/dz_se_comm.py #
####################################################################################################
import requests
import configparser
import time
import solaredge_modbus
from dz_se_lib import domoticz_create_hardware
from dz_se_lib import domoticz_create_devices
from dz_se_lib import domoticz_retrieve_device_idx
from dz_se_lib import domoticz_transceive_data
from dz_se_lib import get_path_to_init_file
if __name__ == "__main__":
settings = configparser.ConfigParser()
settings._interpolation = configparser.ExtendedInterpolation()
settings.read(get_path_to_init_file())
domoticz_ip = settings.get('GENERAL SETTINGS', 'domoticz_ip')
domoticz_port = settings.get('GENERAL SETTINGS', 'domoticz_port')
inverter = solaredge_modbus.Inverter(host=settings.get('GENERAL SETTINGS', 'solaredge_inverter_ip'),
port=settings.get('GENERAL SETTINGS', 'solaredge_inverter_port'), timeout=1,
unit=1)
# Get values from Solaredge inverter over TCP Modbus
if settings.get('GENERAL SETTINGS', 'domoticz_solaredge_comm_init_done') == '0':
session = requests.Session()
# SET HARDWARE IN DOMOTICZ
DOMOTICZ_HW_IDX = domoticz_create_hardware(domoticz_ip, domoticz_port, settings, session)
# CREATE DEVICES IN DOMOTICZ
domoticz_create_devices(domoticz_ip, domoticz_port, settings, session, DOMOTICZ_HW_IDX)
# GET ALL SENSOR IDX VALUES AND STORE
domoticz_retrieve_device_idx(domoticz_ip, domoticz_port, settings, session)
session.close()
else:
time.sleep(0.5)
session = requests.Session()
domoticz_transceive_data(domoticz_ip, domoticz_port, settings, session, inverter)
session.close()
| [
2,
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14,
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62,
... | 1.877487 | 1,910 |
import senti_lexis
import datetime, string, numpy, spwrap, random time, sys, re
from sklearn import svm
from sklearn import cross_validation
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.cross_validation import KFold
from scipy.sparse import csr_matrix
if __name__ == "__main__":
main()
| [
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62,
12... | 3.106796 | 103 |
print(machine())
| [
198,
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7,
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28955,
628,
198
] | 2.857143 | 7 |
from numpy import isnan
from wonambi import Dataset
from .paths import axon_abf_file
d = Dataset(axon_abf_file)
| [
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7... | 2.458333 | 48 |
from AutoMxL.Preprocessing.Categorical import *
from AutoMxL.Preprocessing.Date import *
from AutoMxL.Preprocessing.Outliers import *
from AutoMxL.Preprocessing.Missing_Values import *
import unittest
import pandas as pd
import math
# test config
df = pd.read_csv('tests/df_test_bis.csv')
"""
------------------------------------------------------------------------------------------------
"""
df_to_date = all_to_date(df, ['Date_nai', 'American_date_nai'], verbose=False)
df_to_anc, new_var_list = date_to_anc(df_to_date, l_var=['American_date_nai', 'Date_nai'], date_ref='27/10/2010')
"""
------------------------------------------------------------------------------------------------
"""
"""
------------------------------------------------------------------------------------------------
"""
| [
6738,
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13,
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75,
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1330,
1635,
198,
6... | 3.834123 | 211 |
"""
Session: 4
Topic: Conditional: IF ELSE statement
"""
x = 20
y = 100
if (x > y):
print ('x > y is true')
print ('new line 1')
else:
print('x > y is false')
print('new line 2')
print ('new line 3')
| [
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25,
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331,
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198,
220,
220,
220,
3601,
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87,
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331,
318,
... | 2.477273 | 88 |
if __name__ == "__main__":
with open("input.txt") as f:
raw = f.read()
commands = [x for x in raw.split("\n")]
horizontal, depth = chain_commands(commands)
print(f"First answer is {horizontal*depth}")
# print(f"Second answer is {count_increasing(measurements, 3)}")
| [
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3419,
628,
220,
220,
22... | 2.525424 | 118 |
import codecs
import json
import rank
import train_ranker
#Files to be present in home dir
TRAINING_FILE_CITIES = 'manual_7_cities.jl'
TRAINING_FILE_NAMES = 'manual_50_names.jl'
TRAINING_FILE_ETHNICITIES = 'manual_50_ethnicities.jl'
ACTUAL_FILE_CITIES = 'manual_50_cities.jl'
ACTUAL_FILE_NAMES = 'manual_50_names.jl'
ACTUAL_FILE_ETHNICITIES = 'manual_50_ethnicities.jl'
EMBEDDINGS_FILE = 'unigram-part-00000-v2.json'
FIELD_NAMES_CITIES = {
"text_field": "readability_text",
"annotated_field":"annotated_cities",
"correct_field":"correct_cities"
}
FIELD_NAMES_NAMES = {
"text_field": "readability_text",
"annotated_field":"annotated_names",
"correct_field":"correct_names"
}
FIELD_NAMES_ETHNICITIES = {
"text_field": "readability_text",
"annotated_field":"annotated_ethnicities",
"correct_field":"correct_ethnicities"
}
def get_texts(json_object):
"""
Parsing logic for getting texts
"""
texts = list()
texts.append(json_object.get(FIELD_NAMES_CITIES['text_field']))
return texts
def get_annotated_list(json_object):
"""
Parsing logic for getting annotated field
"""
return json_object.get(FIELD_NAMES_CITIES['annotated_field'])
embeddings_dict = read_embedding_file(EMBEDDINGS_FILE)
classifier = train_ranker.train_ranker(embeddings_dict, TRAINING_FILE_CITIES, FIELD_NAMES_CITIES)
with codecs.open(ACTUAL_FILE_CITIES, 'r', 'utf-8') as f:
for line in f:
obj = json.loads(line)
list_of_texts = get_texts(obj)
annotated_list = get_annotated_list(obj)
print "Annotated tokens:",
print annotated_list
ranked_list = rank.rank(embeddings_dict, list_of_texts, annotated_list, classifier)
print "Ranked List:",
print ranked_list
| [
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62,
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62,
34,
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796,
705,
805,
723,
62,
22,
62,... | 2.415531 | 734 |
''' Calculat the leap year'''
from datetime import date
year = int(input('What year do you want to analyse? Type 0 for the current year.'))
if year == 0:
year = date.today().year
if year%4 ==0 and year%100 != 0 or year%400 == 0:
print(F"The year {year} it's a LEAP year.".)
else:
print(F"The year {year} isn't a LEAP year.") | [
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6,
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262,
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220,
329,
262,
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614,
2637,
4008,
198,
361,
614,
6624,
... | 2.823529 | 119 |
#!/usr/bin/env python
#coding=utf-8
import numpy
if __name__ == "__main__":
task_list = get_task_list()
for task in task_list:
print "%d\t%d" % (task.cpu, task.mem)
| [
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62,
35... | 2.152941 | 85 |
# This is an empty python file to expose this directory to it's parent
| [
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] | 4.4375 | 16 |
import os
import cv2
from sklearn.cluster import KMeans, DBSCAN, MiniBatchKMeans
from scipy import spatial
from sklearn.preprocessing import StandardScaler
import numpy as np
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(description='Challenge presentation example')
parser.add_argument('--data_path',
'-d',
type=str,
default='dataset',
help='Dataset path')
parser.add_argument('--output_dim',
'-o',
type=int,
default=20,
help='Descriptor length')
parser.add_argument('--save_dir',
'-s',
type=str,
default=None,
help='Save or not gallery/query feats')
parser.add_argument('--random',
'-r',
action='store_true',
help='Random run')
args = parser.parse_args()
def topk_accuracy(gt_label, matched_label, k=1):
matched_label = matched_label[:, :k]
total = matched_label.shape[0]
correct = 0
for q_idx, q_lbl in enumerate(gt_label):
correct+= np.any(q_lbl == matched_label[q_idx, :]).item()
acc_tmp = correct/total
return acc_tmp
def main():
data_path = 'C:/Users/21032/Desktop/dataset'
# we define training dataset
training_path = os.path.join(data_path, 'training')
# we define validation dataset
validation_path = os.path.join(data_path, 'validation')
gallery_path = os.path.join(validation_path, 'gallery')
query_path = os.path.join(validation_path, 'query')
training_dataset = Dataset(data_path=training_path)
gallery_dataset = Dataset(data_path=gallery_path)
query_dataset = Dataset(data_path=query_path)
# get training data and classes
training_paths, training_classes = training_dataset.get_data_paths()
# we get validation gallery and query data
gallery_paths, gallery_classes = gallery_dataset.get_data_paths()
query_paths, query_classes = query_dataset.get_data_paths()
if not args.random:
feature_extractor = cv2.SIFT_create()
# we define model for clustering
model = KMeans(n_clusters=args.output_dim, n_init=10, max_iter=5000, verbose=False)
# model = MiniBatchKMeans(n_clusters=args.output_dim, random_state=0, batch_size=100, max_iter=100, verbose=False)
scale = StandardScaler()
# we define the feature extractor providing the model
extractor = FeatureExtractor(feature_extractor=feature_extractor,
model=model,
scale=scale,
out_dim=args.output_dim)
# we fit the KMeans clustering model
extractor.fit_model(training_paths)
extractor.fit_scaler(training_paths)
# now we can use features
# we get query features
query_features = extractor.extract_features(query_paths)
query_features = extractor.scale_features(query_features)
# we get gallery features
gallery_features = extractor.extract_features(gallery_paths)
gallery_features = extractor.scale_features(gallery_features)
print(gallery_features.shape, query_features.shape)
pairwise_dist = spatial.distance.cdist(query_features, gallery_features, 'minkowski', p=2.)
print('--> Computed distances and got c-dist {}'.format(pairwise_dist.shape))
indices = np.argsort(pairwise_dist, axis=-1)
else:
indices = np.random.randint(len(gallery_paths),
size=(len(query_paths), len(gallery_paths)))
gallery_matches = gallery_classes[indices]
print('########## RESULTS ##########')
for k in [1, 3, 10]:
topk_acc = topk_accuracy(query_classes, gallery_matches, k)
print('--> Top-{:d} Accuracy: {:.3f}'.format(k, topk_acc))
if __name__ == '__main__':
main()
| [
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13,
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369... | 2.237354 | 1,799 |
"""
Prepare training, validation, and testing data after preprocessing of the large dataset. Used in
training and evaluating models.
"""
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
def feature_selection(data, features):
"""
Choose which features to use for training.
:param data: preprocessed dataset
:param features: list of features to use
:return: data with selected features
"""
return data[features]
def prepare_data(data, label="loan_status", valid_split=0.2, test_split=0.3):
"""
Splits and returns the training and validation sets for the data.
:param data: preprocessed dataset
:param label: label of data
:param valid_split: percentage to use as validation data
:param test_split: percentage to use as test data
:returns: training, validation, testing sets
"""
X_train = data.drop(label, axis=1) # define training features set
y_train = data[label] # define training label set
# use part of the data as testing data
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=test_split, random_state=0)
# use part of the training data as validation data
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=valid_split, random_state=0)
return X_train, X_valid, X_test, y_train, y_valid, y_test
| [
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... | 3.061269 | 457 |
#!/usr/bin/env python
###############################################################
# \package CyberRadioDriver.radio
#
# \brief Defines basic functionality for radio handler objects.
#
# \note This module defines basic behavior only. To customize
# a radio handler class for a particular radio, derive a new
# class from the appropriate base class. It is recommended
# that behavior specific to a given radio be placed in the
# module that supports that radio.
#
# \author NH
# \author DA
# \author MN
# \copyright Copyright (c) 2014-2021 CyberRadio Solutions, Inc.
# All rights reserved.
#
###############################################################
# Imports from other modules in this package
from . import command
from . import components
from . import configKeys
from . import log
from . import transport
# Imports from external modules
# Python standard library imports
import ast
import copy
import datetime
import json
import math
import sys
import time
import traceback
import threading
##
# \internal
# \brief Returns the MAC address and IP address for a given Ethernet interface.
#
# \param ifname The name of t# \author DA
# \param ifname The Ethernet system interface ("eth0", for example).
# \returns A 2-tuple: (MAC Address, IP Address).
##
# \internal
# \brief VITA 49 interface specification class.
#
# The _ifSpec class describes how the VITA 49 interface is set up for
# a particular radio. Each radio should have its own interface
# specification, implemented as a subclass of _ifSpec.
#
# Radio handler classes need to set static member "ifSpec" to the interface
# specification class that the radio uses.
#-- Radio Handler Objects ---------------------------------------------#
##
# \brief Base radio handler class.
#
# This class implements the CyberRadioDriver.IRadio interface.
#
# To add a supported radio to this driver, derive a class from
# _radio and change the static members of the new class to describe the
# capabilities of that particular radio. Each supported radio should
# have its own module under the CyberRadioDriver.radios package tree.
#
# A radio handler object maintains a series of component objects, one
# per component of each type (tuner, WBDDC, NBDDC, etc.). Each component
# object is responsible for managing the hardware object that it represents.
# Each component object is also responsible for querying the component's
# current configuration and for maintaining the object's configuration
# as it changes during radio operation.
#
# A radio handler object also maintains its own configuration, for settings
# that occur at the radio level and are not managed by a component object.
#
# \note Several static members of this class have no function within the
# code, but instead help CyberRadioDriver.getRadioObjectDocstring() generate
# appropriate documentation for derived radio handler classes.
#
# \implements CyberRadioDriver::IRadio
##
# \brief Gets the pulse-per-second (PPS) rising edge from the radio.
#
# \copydetails CyberRadioDriver::IRadio::getPps()
def getPps(self):
if self.ppsCmd is not None:
cmd = command.pps(parent=self,query=True,
verbose=self.verbose, logFile=self.logFile)
cmd.send(self.sendCommand, timeout=cmd.timeout)
return cmd.success
else:
return False
##
# \brief Sets the time for the next PPS rising edge on the radio.
#
# \copydetails CyberRadioDriver::IRadio::setTimeNextPps()
##
# \brief Gets the current radio time.
#
# \copydetails CyberRadioDriver::IRadio::getTimeNow()
##
# \brief Gets the time for the next PPS rising edge on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTimeNextPps()
##
# \brief Gets the status from the radio.
#
# \copydetails CyberRadioDriver::IRadio::getStatus()
##
# \brief Gets the RF tuner status from the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTstatus()
##
# \brief Sets the reference mode on the radio.
#
# \copydetails CyberRadioDriver::IRadio::setReferenceMode()
##
# \brief Sets the reference bypass mode on the radio.
#
# \copydetails CyberRadioDriver::IRadio::setBypassMode()
##
# \brief Sets the time adjustment for tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::setTimeAdjustment()
##
# \brief Sets the calibration frequency on the radio.
#
# \copydetails CyberRadioDriver::IRadio::setCalibrationFrequency()
##
# \brief Gets the current GPS position.
#
# \copydetails CyberRadioDriver::IRadio::getGpsPosition()
##
# \brief Gets the current radio temperature.
#
# \copydetails CyberRadioDriver::IRadio::getTemperature()
##
# \brief Gets the current GPIO output bits.
#
# \copydetails CyberRadioDriver::IRadio::getGpioOutput()
##
# \brief Gets the GPIO output settings for a given sequence index.
#
# \copydetails CyberRadioDriver::IRadio::getGpioOutputByIndex()
##
# \brief Sets the current GPIO output bits.
#
# \copydetails CyberRadioDriver::IRadio::setGpioOutput()
##
# \brief Sets the GPIO output settings for a given sequence index.
#
# \copydetails CyberRadioDriver::IRadio::setGpioOutputByIndex()
##
# \brief Gets the current bandwith of the given tuner.
# \copydetails CyberRadioDriver::IRadio::getTunerBandwidth()
##
# \brief Gets the name of the radio.
#
# \copydetails CyberRadioDriver::IRadio::getName()
##
# \brief Gets the number of tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumTuner()
##
# \brief Gets the number of tuner boards on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumTunerBoards()
##
# \brief Gets the index range for the tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerIndexRange()
##
# \brief Gets the frequency range for the tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerFrequencyRange()
##
# \brief Gets the frequency resolution for tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerFrequencyRes()
##
# \brief Gets the frequency unit for tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerFrequencyUnit()
##
# \brief Gets the attenuation range for the tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerAttenuationRange()
##
# \brief Gets the attenuation resolution for tuners on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTunerAttenuationRes()
##
# \brief Gets the ifFilter list for the tuners of the radio
#
# \copydetails CyberRadioDriver::IRadio::getTunerIfFilterList()
##
# \brief Gets whether or not the radio supports setting tuner
# bandwidth
#
# \copydetails CyberRadioDriver::IRadio::isTunerBandwidthSettable()
##
# \brief Gets the number of wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumWbddc()
##
# \brief Gets whether the DDCs on the radio have selectable sources.
#
# \copydetails CyberRadioDriver::IRadio::isDdcSelectableSource()
##
# \brief Gets whether the wideband or narrowband DDCs on the radio are tunable.
#
# \copydetails CyberRadioDriver::IRadio::isNbddcTunable()
##
# \brief Gets the index range for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcIndexRange()
##
# \brief Gets whether the wideband DDCs on the radio are tunable.
#
# \copydetails CyberRadioDriver::IRadio::isWbddcSelectableSource()
##
# \brief Gets whether the wideband DDCs on the radio have selectable
# sources.
#
# \copydetails CyberRadioDriver::IRadio::isWbddcTunable()
##
# \brief Gets the frequency offset range for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcFrequencyRange()
##
# \brief Gets the frequency offset resolution for wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcFrequencyRes()
##
# \brief Gets the allowed rate set for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcRateSet()
##
# \brief Gets the allowed rate list for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcRateList()
##
# \brief Gets the allowed rate set for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcBwSet()
##
# \brief Gets the allowed rate list for the wideband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcBwList()
##
# \brief Gets the number of narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumNbddc()
##
# \brief Gets the index range for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcIndexRange()
##
# \brief Gets whether the narrowband DDCs on the radio are tunable.
#
# \copydetails CyberRadioDriver::IRadio::isNbddcTunable()
##
# \brief Gets whether the narrowband DDCs on the radio have selectable
# sources.
#
# \copydetails CyberRadioDriver::IRadio::isNbddcSelectableSource()
##
# \brief Gets the frequency offset range for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcFrequencyRange()
##
# \brief Gets the frequency offset resolution for narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcFrequencyRes()
##
# \brief Gets the allowed rate set for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcRateSet()
##
# \brief Gets the allowed rate list for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcRateList()
##
# \brief Gets the allowed rate set for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcBwSet()
##
# \brief Gets the allowed rate list for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcBwList()
##
# \brief Gets the number of narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumFftStream()
##
# \brief Gets the index range for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamIndexRange()
##
# \brief Gets the allowed rate set for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamRateSet()
##
# \brief Gets the allowed rate list for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamRateList()
##
# \brief Gets the allowed window set for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamWindowSet()
##
# \brief Gets the allowed window list for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamWindowList()
##
# \brief Gets the allowed size set for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamSizeSet()
##
# \brief Gets the allowed size list for the FFTs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getFftStreamSizeList()
##
# \brief Gets the ADC sample rate for the radio.
#
# \copydetails CyberRadioDriver::IRadio::getAdcRate()
##
# \brief Gets the VITA 49 header size for the radio.
#
# \copydetails CyberRadioDriver::IRadio::getVitaHeaderSize()
##
# \brief Gets the VITA 49 payload size for the radio.
#
# \copydetails CyberRadioDriver::IRadio::getVitaPayloadSize()
##
# \brief Gets the VITA 49 tail size for the radio.
#
# \copydetails CyberRadioDriver::IRadio::getVitaTailSize()
##
# \brief Gets dictionary with information about VITA 49 framing.
#
# \copydetails CyberRadioDriver::IRadio::getVitaFrameInfoDict()
# \brief Gets whether data coming from the radio is byte-swapped with
# respect to the endianness of the host operating system.
#
# \copydetails CyberRadioDriver::IRadio::isByteswapped()
##
# \brief Gets whether data coming from the radio has I and Q data swapped.
#
# \copydetails CyberRadioDriver::IRadio::isIqSwapped()
##
# \brief Gets the byte order for data coming from the radio.
#
# \copydetails CyberRadioDriver::IRadio::getByteOrder()
##
# \brief Gets the number of Gigabit Ethernet interfaces on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumGigE()
##
# \brief Gets the index range for the Gigabit Ethernet interfaces on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getGigEIndexRange()
##
# \brief Gets the number of destination IP address table entries available for
# each Gigabit Ethernet interface on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumGigEDipEntries()
##
# \brief Gets the index range for the destination IP address table entries
# available for the Gigabit Ethernet interfaces on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getGigEDipEntryIndexRange()
##
# \brief Gets the list of connection modes that the radio supports.
#
# \copydetails CyberRadioDriver::IRadio::getConnectionModeList()
##
# \brief Gets whether the radio supports a given connection mode.
#
# \copydetails CyberRadioDriver::IRadio::isConnectionModeSupported()
##
# \brief Gets the radio's default baud rate.
#
# \copydetails CyberRadioDriver::IRadio::getDefaultBaudrate()
##
# \brief Gets the radio's default control port.
#
# \copydetails CyberRadioDriver::IRadio::getDefaultControlPort()
##
# \brief Gets the allowed VITA enable options set for the radio.
#
# \copydetails CyberRadioDriver::IRadio::getVitaEnableOptionSet()
##
# \brief Gets the number of transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumTransmitters()
##
# \brief Gets the index range for the transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterIndexRange()
##
# \brief Gets the frequency range for the transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterFrequencyRange()
##
# \brief Gets the frequency resolution for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterFrequencyRes()
##
# \brief Gets the frequency unit for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterFrequencyUnit()
##
# \brief Gets the attenuation range for the transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterAttenuationRange()
##
# \brief Gets the attenuation resolution for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterAttenuationRes()
##
# \brief Gets whether transmitters on the radio support continuous-wave
# (CW) tone generation.
#
# \copydetails CyberRadioDriver::IRadio::transmitterSupportsCW()
##
# \brief Gets the number of CW tone generators for each transmitter.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWNum()
##
# \brief Gets the CW tone generator index range for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWIndexRange()
##
# \brief Gets the CW tone generator frequency range for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWFrequencyRange()
##
# \brief Gets the CW tone generator frequency resolution for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWFrequencyRes()
##
# \brief Gets the CW tone generator amplitude range for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWAmplitudeRange()
##
# \brief Gets the CW tone generator amplitude resolution for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWAmplitudeRes()
##
# \brief Gets the CW tone generator phase range for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWPhaseRange()
##
# \brief Gets the CW tone generator phase resolution for transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWPhaseRes()
##
# \brief Gets whether transmitters on the radio support sweep functions
# during continuous-wave (CW) tone generation.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWPhaseRes()
##
# \brief Gets the CW tone generator sweep start frequency range for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStartRange()
##
# \brief Gets the CW tone generator sweep start frequency resolution for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStartRes()
##
# \brief Gets the CW tone generator sweep stop frequency range for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStopRange()
##
# \brief Gets the CW tone generator sweep stop frequency resolution for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStopRes()
##
# \brief Gets the CW tone generator sweep step frequency range for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStepRange()
##
# \brief Gets the CW tone generator sweep step frequency resolution for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepStepRes()
##
# \brief Gets the CW tone generator sweep dwell time range for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepDwellRange()
##
# \brief Gets the CW tone generator sweep dwell time resolution for
# transmitters on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTransmitterCWSweepDwellRes()
##
# \brief Gets the number of wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumWbduc()
##
# \brief Gets the index range for the wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducIndexRange()
##
# \brief Gets the frequency offset range for the wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducFrequencyRange()
##
# \brief Gets the frequency resolution for wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducFrequencyRes()
##
# \brief Gets the frequency unit for wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducFrequencyUnit()
##
# \brief Gets the attenuation range for the wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducAttenuationRange()
##
# \brief Gets the attenuation resolution for wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducAttenuationRes()
##
# \brief Gets the allowed rate set for the wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducRateSet()
##
# \brief Gets the allowed rate list for the wideband DUCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducRateList()
##
# \brief Gets whether or not the wideband DUCs on the radio support loading
# sample snapshots.
#
# \copydetails CyberRadioDriver::IRadio::wbducSupportsSnapshotLoad()
##
# \brief Gets whether or not the wideband DUCs on the radio support
# transmitting sample snapshots.
#
# \copydetails CyberRadioDriver::IRadio::wbducSupportsSnapshotTransmit()
##
# \brief Gets the index range for the DDC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcGroupIndexRange()
##
# \brief Gets the number of wideband DDC groups on the radio.
# \copydetails CyberRadioDriver::IRadio::getNumWbddcGroups()
##
# \brief Gets the index range for the wideband DDC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbddcGroupIndexRange()
##
# \brief Gets the number of narrowband DDC groups on the radio.
# \copydetails CyberRadioDriver::IRadio::getNumNbddcGroups()
##
# \brief Gets the index range for the narrowband DDC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcGroupIndexRange()
##
# \brief Gets the number of combined DDC groups on the radio.
# \copydetails CyberRadioDriver::IRadio::getNumCombinedDdcGroups()
##
# \brief Gets the index range for the combined DDC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getCombinedDdcGroupIndexRange()
##
# \brief Gets the number of wideband DUC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumWbducGroups()
##
# \brief Gets the index range for the wideband DUC groups on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getWbducGroupIndexRange()
# ------------- Deprecated/Helper Methods ----------------- #
##
# \internal
# \brief Define this object's string representation.
def __str__(self):
return self.name
##
# \internal
# \brief Helper function that returns an index list.
def _getIndexList(self,objIndex,objDict):
if objIndex is None:
return list(objDict.keys())
elif type(objIndex) is int:
return [objIndex,] if objIndex in list(objDict.keys()) else []
elif type(objIndex) is list:
return [i for i in objIndex if i in list(objDict.keys())]
else:
return []
##
# \internal
# \brief Helper function that "normalizes" an input configuration dictionary
# section by doing the following:
# <ul>
# <li> Ensuring that keys for any enumerated entries are integers
# <li> Expanding sub-dictionaries with the special "all" key
# <li> Performing specialization for individual entries
#
# \param configDict The incoming configuration dictionary.
# \param entryIndexList The list of entry indices (used in expanding "all" keys).
# \return The new configuration dictionary.
##
# \internal
# \brief Helper function that "normalizes" an input configuration dictionary
# by doing the following:
# <ul>
# <li> Ensuring that keys for component enumerations are integers
# <li> Expanding sub-dictionaries with the special "all" key
# <li> Performing specialization for individual components or entries
# \param configDict The incoming configuration dictionary.
# \return The new configuration dictionary.
##
# \brief Gets the radio configuration.
#
# \deprecated Use getConfiguration() instead.
#
# \return The dictionary of radio settings.
##
# \internal
# \brief Helper function for setting the tuner configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the tuner configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the tuner configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the DDC configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the DDC configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the DDC configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the IP configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for querying the IP configuration.
# \param gigEPortIndex 10-Gig data port index, or None to query all data ports.
##
# \internal
# \brief Helper function for querying the IP configuration for radios without
# 10-Gig Ethernet interfaces.
##
# \internal
# \brief Helper function for querying the IP configuration for radios with
# 10-Gig Ethernet interfaces.
# \param gigEPortIndex 10-Gig data port index, or None to query all data ports.
##
# \internal
# \brief Helper function for setting the transmitter configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the transmitter configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the transmitter configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for setting the DUC configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the DUC configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the DUC configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for getting the DDC group configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the DDC group configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the DDC group configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the combined DDC group configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the combined DDC group configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the combined DDC group configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the DUC group configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the DUC group configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the DUC group configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for getting the tuner group configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the tuner group configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for setting the tuner group configuration.
#
# Deprecated in favor of setConfiguration().
##
# \internal
# \brief Helper function for setting the FFT stream configuration.
#
# Deprecated in favor of setConfiguration().
#
##
# \internal
# \brief Helper function for getting the FFT stream configuration.
#
# Deprecated in favor of getConfiguration().
##
# \internal
# \brief Helper function for querying the FFT stream configuration.
#
# Deprecated in favor of queryConfiguration().
##
# \internal
# \brief Helper function for configuring the IP addresses.
##
# \brief Gets the number of DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNumDdc()
##
# \brief Gets the allowed rate set for the DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getDdcRateSet()
##
# \brief Gets the allowed rate list for the DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getDdcRateList()
##
# \brief Gets the allowed bandwidth set for the DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getDdcBwSet()
##
# \brief Gets the allowed bandwidth list for the DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getDdcBwList()
##
# \brief Gets the set of available DDC data formats.
#
# \copydetails CyberRadioDriver::IRadio::getDdcDataFormat()
##
# \brief Gets the frequency offset range for the narrowband DDCs on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getNbddcFrequencyRange()
##
# \brief Gets the list of DDC indexes for a specified type.
#
# \copydetails CyberRadioDriver::IRadio::getDdcIndexRange()
##
# \internal
# \brief Convenience method for configuring the Ethernet addresses on a radio that does not
# have Gigabit Ethernet ports.
#
# \param sip The source IP address. If this is None, the source IP address will not
# be changed.
# \param dip The destination IP address. If this is None, the destination IP address
# will not be changed.
# \param dmac The destination MAC address. If this is None, the destination MAC address
# will not be changed.
# \return True if the configuration succeeded, False otherwise.
def setIpConfiguration(self, sip=None, dip=None, dmac=None):
configDict = {
configKeys.CONFIG_IP: {
}
}
if sip is not None:
configDict[configKeys.CONFIG_IP][configKeys.IP_SOURCE] = copy.deepcopy(sip)
if dip is not None:
configDict[configKeys.CONFIG_IP][configKeys.IP_DEST] = copy.deepcopy(dip)
if dmac is not None:
configDict[configKeys.CONFIG_IP][configKeys.MAC_DEST] = copy.deepcopy(dmac)
return self._setConfiguration(configDict)
##
# \internal
##
# \internal
# \brief Sets tuner configuration (old-style).
#
# \deprecated Use setConfiguration() instead.
#
# \param frequency Tuner frequency.
# \param attenuation Tuner attenuation.
# \param tunerIndex Either None (configure all tuners), an index number (configure
# a specific tuner), or a list of index numbers (configure a set of tuners).
# \return True if successful, False otherwise.
##
# \internal
# \brief Gets tuner configuration (old-style).
#
# \deprecated Use getConfiguration() instead.
#
# \param tunerIndex Either None (get for all tuners), an index number (get for
# a specific tuner), or a list of index numbers (get for a set of tuners).
# \return A dictionary with configuration information.
##
# \internal
# \brief Sets tuner frequency (old-style).
#
# \deprecated Use setConfiguration() instead.
#
# \param frequency Tuner frequency.
# \param tunerIndex Either None (configure all tuners), an index number (configure
# a specific tuner), or a list of index numbers (configure a set of tuners).
# \return True if successful, False otherwise.
##
# \internal
# \brief Gets tuner frequency information (old-style).
#
# \deprecated Use getConfiguration() instead.
#
# \param tunerIndex Either None (get for all tuners), an index number (get for
# a specific tuner), or a list of index numbers (get for a set of tuners).
# \return A dictionary with frequency information.
##
# \internal
# \brief Sets tuner attenuation (old-style).
#
# \deprecated Use setConfiguration() instead.
#
# \param attenuation Tuner attenuation.
# \param tunerIndex Either None (configure all tuners), an index number (configure
# a specific tuner), or a list of index numbers (configure a set of tuners).
# \return True if successful, False otherwise.
##
# \internal
# \brief Gets tuner attenuation information (old-style).
#
# \deprecated Use getConfiguration() instead.
#
# \param tunerIndex Either None (get for all tuners), an index number (get for
# a specific tuner), or a list of index numbers (get for a set of tuners).
# \return A dictionary with attenuation information.
##
# \internal
# \brief Sets DDC configuration (old-style).
#
# \deprecated Use setConfiguration() instead.
#
# \param wideband Whether the DDC is a wideband DDC.
# \param ddcIndex Either None (configure all DDCs), an index number (configure
# a specific DDC), or a list of index numbers (configure a set of DDCs).
# \param rfIndex DDC RF index number.
# \param rateIndex DDC rate index number.
# \param udpDest UDP destination.
# \param frequency Frequency offset.
# \param enable 1 if DDC is enabled, 0 if not.
# \param vitaEnable VITA 49 streaming option, as appropriate for the radio.
# \param streamId VITA 49 stream ID.
# \return True if successful, False otherwise.
##
# \brief Disables ethernet flow control on the radio.
#
# \copydetails CyberRadioDriver::IRadio::disableTenGigFlowControl()
##
# \brief Enables ethernet flow control on the radio.
#
# \copydetails CyberRadioDriver::IRadio::enableTenGigFlowControl()
##
# \brief method to enable or disable ethernet flow control on the radio.
#
# \copydetails CyberRadioDriver::IRadio::getTenGigFlowControlStatus()
##
# \brief Queries status of flow control handling.
#
# \copydetails CyberRadioDriver::IRadio::getTenGigFlowControlStatus()
##
# \brief Performs coherent tuning.
#
# \copydetails CyberRadioDriver::IRadio::coherentTune()
##
# \brief Gets the current FPGA state.
#
# \copydetails CyberRadioDriver::IRadio::getFpgaState()
##
# \brief Sets the current FPGA state.
#
# \copydetails CyberRadioDriver::IRadio::setFpgaState()
# OVERRIDE
##
# \brief Sets whether or not the object is in verbose mode.
#
# \copydetails CyberRadioDriver::IRadio::setVerbose()
##
# \brief Sets the log file.
#
# \copydetails CyberRadioDriver::IRadio::setLogFile()
##
# \brief Gets the list of connected data port interface indices.
#
# \copydetails CyberRadioDriver::IRadio::getConnectedDataPorts()
##
# \internal
# \brief Converts a user-specified time string into a number of seconds
# since 1/1/70.
#
# The time string can be either:
# \li Absolute time, in any supported format
# \li Relative time specified as now{-n}, where n is a number of seconds
# \li Relative time specified as now{-[[H:]MM:]SS}
# \li "begin", which is the beginning of known time (1/1/70)
# \li "end", which is the end of trackable time and far beyond the
# useful life of this utility (01/18/2038)
#
# \throws RuntimeException if the time string format cannot be understood.
# \param timestr The time string.
# \param utc Whether or not the user's time string is in UTC time.
# \return The time, in number of seconds since the Epoch
##
# Converts a time string ([+-][[H:]M:]S) to a time in seconds.
#
# \note Hours and minutes are not bounded in any way. These strings provide the
# same result:
# \li "7200"
# \li "120:00"
# \li "2:00:00"
#
# \throws RuntimeError if the time is formatted improperly.
# \param timeStr The time string.
# \return The number of seconds.
##
# \internal
# \brief Radio handler class that supports nothing more complicated than
# identifying a connected radio.
#
# Used internally to support radio auto-detection.
#
# This class implements the CyberRadioDriver.IRadio interface.
#
##
# \brief Radio function (mode) command used by JSON-based radios.
#
##
# \internal
# \brief Radio handler class that supports nothing more complicated than
# identifying a connected radio.
#
# Used internally to support radio auto-detection.
#
# This class implements the CyberRadioDriver.IRadio interface.
#
#-- End Radio Handler Objects --------------------------------------------------#
#-- NOTE: Radio handler objects for supporting specific radios need to be
# implemented under the CyberRadioDriver.radios package tree.
| [
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... | 2.740954 | 13,874 |
from typing import List, Tuple
import numpy as np
from alpha_zero.Board import Board
| [
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] | 3.52 | 25 |
"""
Project : bugaboo
Filename : forms.py
Author : zhancongc
Description :
"""
from flask_wtf import FlaskForm
from wtforms import StringField, BooleanField, TextAreaField, SelectField, FileField, IntegerField, PasswordField, SubmitField
from wtforms.validators import DataRequired
| [
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42... | 3.212766 | 94 |
import pandas as pd
import numpy as np
from tqdm import tqdm
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] | 2.818182 | 22 |
from django.shortcuts import render
from django.utils import timezone
from todo.models import Todo
from django.http import HttpResponseRedirect
| [
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1060,
198
] | 3.692308 | 39 |
import random
category = ['python', 'java', 'kotlin', 'javascript']
computer = random.choice(category)
hidden = list(len(computer) * "-")
print("H A N G M A N")
counter = 8
while counter > 0:
print()
print("".join(hidden))
letter = input("Input a letter: ")
if (letter in hidden) or (letter in hidden and times == 7):
counter -= 1
print("No improvements")
elif letter in set(computer):
where = 0
for i in range(computer.count(letter)):
where = computer.index(letter, 0 + where)
hidden[where] = letter
where += where + 1
if "-" not in hidden:
print()
print("".join(hidden))
print("You guessed the word!")
print("You survived!")
break
else:
counter -= 1
print("No such letter in the word")
print(counter)
else:
print("You are hanged!") | [
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1... | 2.590476 | 315 |
# -*- coding: utf-8 -*-
"""
task
"""
__author__ = "XuWeitao"
import CommonUtilities
import rawSql
def getTasksList(UserID):
"""
:param UserID:IDALL
:return:{
"SerialNo":, "CreateTime":, "LastModifiedTime":,
"ProductNo":, "ColorNo":, "ArriveTime":, "Name":,
"GongYingShang":{"id":, "name":},
"WuLiao":{"id":ID, "name":, "cata":},
"DaoLiaoZongShu":, "DanWei":{"id":ID, "name":}
"DaoLiaoZongShu2":, "DanWei":{"id":ID, "name":},
"XieZuoRen":
}
"""
raw = rawSql.Raw_sql()
raw.sql = """SELECT SerialNo, CONVERT(VARCHAR(16), CreateTime, 20) CreateTime, CONVERT(VARCHAR(16), LastModifiedTime, 20) LastModifiedTime,
ProductNo, ColorNo, CONVERT(VARCHAR(10), ArriveTime, 20) ArriveTime, dbo.getUserNameByUserID(UserID), SupplierID,
dbo.getSupplierNameByID(SupplierID), MaterialID, dbo.getMaterialNameByID(MaterialID),
dbo.getMaterialTypeNameByID(dbo.getMaterialTypeIDByMaterialID(MaterialID)), DaoLiaoZongShu, UnitID,
dbo.getUnitNameByID(UnitID), DaoLiaoZongShu2, UnitID2, dbo.getUnitNameByID(UnitID2) AS DanWei2, Inspectors, UserID
FROM RMI_TASK WITH(NOLOCK)"""
#
if UserID != 'ALL':
raw.sql += " WHERE CHARINDEX('%s', Inspectors) > 0 AND State = 2" % UserID
else:
raw.sql += " WHERE State = 0"
res = raw.query_all()
jsonReturn = list()
for row in res:
#@
Inspectors = row[18].split('@')
InspectorList = list()
for inspectorNo in Inspectors:
if inspectorNo == row[19]:
continue
raw.sql = "SELECT DBO.getUserNameByUserID('%s')"%inspectorNo
inspectorName = raw.query_one()
if inspectorName:
inspectorName = inspectorName[0]
InspectorList.append({'Name':inspectorName, 'ID':inspectorNo})
jsonReturn.append({
"SerialNo":row[0], "CreateTime":row[1], "LastModifiedTime":row[2],
"ProductNo":row[3], "ColorNo":row[4], "ArriveTime":row[5], "Name":row[6],
"GongYingShang":{"id":row[7], "name":row[8]},
"WuLiao":{"id":row[9], "name":row[10], "cata":row[11]},
"DaoLiaoZongShu":row[12], "DanWei":{"id":row[13], "name":row[14]},
"DaoLiaoZongShu2":row[15], "DanWei2":{"id":row[16], "name":row[17]},
"XieZuoRen":InspectorList
})
return jsonReturn
def editTaskInfo(taskInfo, userID):
"""
isNew
:param taskInfo:
:param userID:ID
:return:
"""
raw = rawSql.Raw_sql()
#
if "isReturn" in taskInfo:
raw.sql = "UPDATE RMI_TASK WITH(ROWLOCK) SET State = 2 WHERE SerialNo = '%s'"%taskInfo['SerialNo']
raw.update()
else:
isNew = True if taskInfo['isNew'] == "True" else False
#NonenullJSONNone
taskInfo['DaoLiaoZongShu2'] = False if 'DaoLiaoZongShu2' not in taskInfo else taskInfo['DaoLiaoZongShu2']
taskInfo['DanWei2'] = {'id':None} if 'DanWei2' not in taskInfo else taskInfo['DanWei2']
#ID
if 'XieZuoRen' in taskInfo:
taskInfo['XieZuoRen'].append({'ID':userID})
taskInfo['Inspectors'] = "@".join([User['ID'] for User in taskInfo['XieZuoRen']])
else:
taskInfo['Inspectors'] = userID
if isNew:
raw.sql = """INSERT INTO RMI_TASK WITH(ROWLOCK) (CreateTime, LastModifiedTime, ProductNo, ColorNo,
ArriveTime, UserID, FlowID, MaterialID, SupplierID, UnitID, DaoLiaoZongShu, DaoLiaoZongShu2, UnitID2, Inspectors)
VALUES ( getdate(), getdate(),'%s','%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', %s, %s, '%s' );""" % (
taskInfo['ProductNo'], taskInfo['ColorNo'], taskInfo['ArriveTime'][:10], userID,
taskInfo['FlowID'], taskInfo['WuLiao']['id'], taskInfo['GongYingShang']['id'],
taskInfo['DanWei']['id'], taskInfo['DaoLiaoZongShu'],
"'"+unicode(taskInfo['DaoLiaoZongShu2'])+"'" if taskInfo['DaoLiaoZongShu2'] else "NULL",
"'"+unicode(taskInfo['DanWei2']['id'])+"'" if taskInfo['DanWei2']['id'] else "NULL", taskInfo['Inspectors'] )
raw.update()
#
raw.sql = "SELECT TOP 1 SerialNo FROM RMI_TASK WHERE UserID = '%s' AND State = 2 ORDER BY CreateTime desc"%userID
return raw.query_one()[0]
else:
raw.sql = """UPDATE RMI_TASK WITH(ROWLOCK) SET MaterialID = '%s',SupplierID = '%s', UnitID = '%s',
DaoLiaoZongShu = '%s', ProductNo = '%s', ColorNo = '%s', ArriveTime = '%s', DaoLiaoZongShu2 = %s,
UnitID2 = %s, Inspectors = '%s'
WHERE SerialNo = '%s'""" % (
taskInfo['WuLiao']['id'], taskInfo['GongYingShang']['id'], taskInfo['DanWei']['id'],
taskInfo['DaoLiaoZongShu'], taskInfo['ProductNo'], taskInfo['ColorNo'],
taskInfo['ArriveTime'][:10].replace('-',''),
"'"+unicode(taskInfo['DaoLiaoZongShu2'])+"'" if taskInfo['DaoLiaoZongShu2'] else "NULL",
"'"+unicode(taskInfo['DanWei2']['id'])+"'" if taskInfo['DanWei2']['id'] else "NULL", taskInfo['Inspectors'],
taskInfo['SerialNo'])
raw.update()
def getFlowList():
"""
:return:{"name":FlowName,"value":FlowID}
"""
raw = rawSql.Raw_sql()
raw.sql = "SELECT FlowID AS value, FlowName AS name FROM RMI_WORK_FLOW WITH(NOLOCK)"
res, columns = raw.query_all(needColumnName=True)
return CommonUtilities.translateQueryResIntoDict(columns, res)
def commitTaskBySerialNo(SerialNo):
"""
:param SerialNo:
:return:
"""
raw = rawSql.Raw_sql()
raw.sql = "UPDATE RMI_TASK SET State = 0 WHERE SerialNo = '%s'"%SerialNo
raw.update()
return
def deleteTaskBySerialNo(SerialNo):
"""
RMI_TASK
:param SerialNo:
:return:
"""
#TODOupdate_other_tables_when_delete_rmi_taskF01
raw = rawSql.Raw_sql()
raw.sql = "DELETE FROM RMI_TASK WHERE SerialNo='%s'"%SerialNo
raw.update()
#call trigger delete all task info in rmi_task_process...
return
def getAllMaterialByName(fuzzyName):
"""
:param fuzzyName:
:return:{'id':ID,'name':,'cata':}
"""
raw = rawSql.Raw_sql()
raw.sql = """SELECT MaterialID AS id, MaterialName AS name, dbo.getMaterialTypeNameByID(MaterialTypeID) AS cata
FROM RMI_MATERIAL_NAME WITH(NOLOCK)"""
if fuzzyName:
raw.sql += """ WHERE MaterialName LIKE '%%%%%s%%%%'"""%fuzzyName
res, cols = raw.query_all(needColumnName=True)
return CommonUtilities.translateQueryResIntoDict(cols, res)
else: #
return [{"name":u'', "id":"", "cata":""}]
| [
2,
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9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
35943,
198,
37811,
198,
834,
9800,
834,
796,
366,
55,
84,
1135,
5350,
78,
1,
198,
11748,
8070,
18274,
2410,
198,
11748,
8246,
50,
13976,
198,
198,
4299,
65... | 2.206609 | 2,754 |
# coding=utf-8
# Copyright 2017 The Tensor2Tensor 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.
"""Creates a RevNet with the bottleneck residual function.
Implements the following equations described in the RevNet paper:
y1 = x1 + f(x2)
y2 = x2 + g(y1)
However, in practice, the authors use the following equations to downsample
tensors inside a RevNet block:
y1 = h(x1) + f(x2)
y2 = h(x2) + g(y1)
In this case, h is the downsampling function used to change number of channels.
These modified equations are evident in the authors' code online:
https://github.com/renmengye/revnet-public
For reference, the original paper can be found here:
https://arxiv.org/pdf/1707.04585.pdf
"""
# Dependency imports
from tensor2tensor.layers import common_hparams
from tensor2tensor.layers import rev_block
from tensor2tensor.utils import registry
from tensor2tensor.utils import t2t_model
import tensorflow as tf
CONFIG = {'2d': {'conv': tf.layers.conv2d,
'max_pool': tf.layers.max_pooling2d,
'avg_pool': tf.layers.average_pooling2d,
'split_axis': 3,
'reduction_dimensions': [1, 2]
},
'3d': {'conv': tf.layers.conv3d,
'max_pool': tf.layers.max_pooling3d,
'avg_pool': tf.layers.average_pooling2d,
'split_axis': 4,
'reduction_dimensions': [1, 2, 3]
}
}
def f(x, depth1, depth2, dim='2d', first_batch_norm=True, layer_stride=1,
training=True, padding='SAME'):
"""Applies bottleneck residual function for 104-layer RevNet.
Args:
x: input tensor
depth1: Number of output channels for the first and second conv layers.
depth2: Number of output channels for the third conv layer.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
first_batch_norm: Whether to keep the first batch norm layer or not.
Typically used in the first RevNet block.
layer_stride: Stride for the first conv filter. Note that this particular
104-layer RevNet architecture only varies the stride for the first conv
filter. The stride for the second conv filter is always set to 1.
training: True for train phase, False for eval phase.
padding: Padding for each conv layer.
Returns:
Output tensor after applying residual function for 104-layer RevNet.
"""
conv = CONFIG[dim]['conv']
with tf.variable_scope('f'):
if first_batch_norm:
net = tf.layers.batch_normalization(x, training=training)
net = tf.nn.relu(net)
else:
net = x
net = conv(net, depth1, 1, strides=layer_stride,
padding=padding, activation=None)
net = tf.layers.batch_normalization(net, training=training)
net = tf.nn.relu(net)
net = conv(net, depth1, 3, strides=1,
padding=padding, activation=None)
net = tf.layers.batch_normalization(net, training=training)
net = tf.nn.relu(net)
net = conv(net, depth2, 1, strides=1,
padding=padding, activation=None)
return net
def h(x, output_channels, dim='2d', layer_stride=1, scope='h'):
"""Downsamples 'x' using a 1x1 convolution filter and a chosen stride.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
layer_stride: What stride to use. Usually 1 or 2.
scope: Optional variable scope for the h function.
This function uses a 1x1 convolution filter and a chosen stride to downsample
the input tensor x.
Returns:
A downsampled tensor of size [N, H/2, W/2, output_channels] if layer_stride
is 2, else returns a tensor of size [N, H, W, output_channels] if
layer_stride is 1.
"""
conv = CONFIG[dim]['conv']
with tf.variable_scope(scope):
x = conv(x, output_channels, 1, strides=layer_stride, padding='SAME',
activation=None)
return x
def init(images, num_channels, dim='2d', training=True, scope='init'):
"""Standard ResNet initial block used as first RevNet block.
Args:
images: [N, H, W, 3] tensor of input images to the model.
num_channels: Output depth of convolutional layer in initial block.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
training: True for train phase, False for eval phase.
scope: Optional scope for the init block.
Returns:
Two [N, H, W, C] output activations from input images.
"""
conv = CONFIG[dim]['conv']
pool = CONFIG[dim]['max_pool']
with tf.variable_scope(scope):
net = conv(images, num_channels, 7, strides=2,
padding='SAME', activation=None)
net = tf.layers.batch_normalization(net, training=training)
net = tf.nn.relu(net)
net = pool(net, pool_size=3, strides=2)
x1, x2 = tf.split(net, 2, axis=CONFIG[dim]['split_axis'])
return x1, x2
def unit(x1, x2, block_num, depth1, depth2, num_layers, dim='2d',
first_batch_norm=True, stride=1, training=True):
"""Implements bottleneck RevNet unit from authors' RevNet-104 architecture.
Args:
x1: [N, H, W, C] tensor of network activations.
x2: [N, H, W, C] tensor of network activations.
block_num: integer ID of block
depth1: First depth in bottleneck residual unit.
depth2: Second depth in bottleneck residual unit.
num_layers: Number of layers in the RevNet block.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
first_batch_norm: Whether to keep the first batch norm layer or not.
Typically used in the first RevNet block.
stride: Stride for the residual function.
training: True for train phase, False for eval phase.
Returns:
Two [N, H, W, C] output activation tensors.
"""
scope_name = 'unit_%d' % block_num
with tf.variable_scope(scope_name):
# Manual implementation of downsampling
with tf.variable_scope('downsampling'):
with tf.variable_scope('x1'):
hx1 = h(x1, depth2, dim=dim, layer_stride=stride)
fx2 = f(x2, depth1, depth2, dim=dim, layer_stride=stride,
first_batch_norm=first_batch_norm, training=training)
x1 = hx1 + fx2
with tf.variable_scope('x2'):
hx2 = h(x2, depth2, dim=dim, layer_stride=stride)
fx1 = f(x1, depth1, depth2, dim=dim, training=training)
x2 = hx2 + fx1
# Full block using memory-efficient rev_block implementation.
with tf.variable_scope('full_block'):
residual_func = lambda x: f(x, depth1, depth2, dim=dim, training=training)
x1, x2 = rev_block.rev_block(x1, x2,
residual_func,
residual_func,
num_layers=num_layers)
return x1, x2
def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
"""Converts activations from last RevNet block to pre-logits.
Args:
x1: [NxHxWxC] tensor of network activations.
x2: [NxHxWxC] tensor of network activations.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
training: True for train phase, False for eval phase.
scope: Optional variable scope for the final block.
Returns:
[N, hidden_dim] pre-logits tensor from activations x1 and x2.
"""
# Final batch norm and relu
with tf.variable_scope(scope):
y = tf.concat([x1, x2], axis=CONFIG[dim]['split_axis'])
y = tf.layers.batch_normalization(y, training=training)
y = tf.nn.relu(y)
# Global average pooling
net = tf.reduce_mean(y, CONFIG[dim]['reduction_dimensions'],
name='final_pool', keep_dims=True)
return net
def revnet104(inputs, hparams, reuse=None):
"""Uses Tensor2Tensor memory optimized RevNet block to build a RevNet.
Args:
inputs: [NxHxWx3] tensor of input images to the model.
hparams: HParams object that contains the following parameters,
in addition to the parameters contained in the basic_params1() object in
the common_hparams module:
num_channels_first - A Python list where each element represents the
depth of the first and third convolutional layers in the bottleneck
residual unit for a given block.
num_channels_second - A Python list where each element represents the
depth of the second convolutional layer in the bottleneck residual
unit for a given block.
num_layers_per_block - A Python list containing the number of RevNet
layers for each block.
first_batch_norm - A Python list containing booleans representing the
presence of a batch norm layer at the beginning of a given block.
strides - A Python list containing integers representing the stride of
the residual function for each block.
num_channels_init_block - An integer representing the number of channels
for the convolutional layer in the initial block.
dimension - A string (either "2d" or "3d") that decides if the RevNet is
2-dimensional or 3-dimensional.
reuse: Whether to reuse the default variable scope.
Returns:
[batch_size, hidden_dim] pre-logits tensor from the bottleneck RevNet.
"""
training = hparams.mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('RevNet104', reuse=reuse):
x1, x2 = init(inputs,
num_channels=hparams.num_channels_init_block,
dim=hparams.dim,
training=training)
for block_num in range(1, len(hparams.num_layers_per_block)):
block = {'depth1': hparams.num_channels_first[block_num],
'depth2': hparams.num_channels_second[block_num],
'num_layers': hparams.num_layers_per_block[block_num],
'first_batch_norm': hparams.first_batch_norm[block_num],
'stride': hparams.strides[block_num]}
x1, x2 = unit(x1, x2, block_num, dim=hparams.dim, training=training,
**block)
pre_logits = final_block(x1, x2, dim=hparams.dim, training=training)
return pre_logits
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
15069,
2177,
383,
309,
22854,
17,
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13,
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2,
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739,
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15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2... | 2.556661 | 4,121 |
import pytest
from datetime import date, timedelta
from adapters import repository
from domain.model import Batch, OrderLine, allocate, OutOfStock
from domain import model
from service_layer import handlers, unit_of_work
today = date.today()
tomorrow = today + timedelta(days=1)
later = tomorrow + timedelta(days=10)
| [
11748,
12972,
9288,
198,
6738,
4818,
8079,
1330,
3128,
11,
28805,
12514,
198,
198,
6738,
46363,
1330,
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198,
6738,
7386,
13,
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1330,
347,
963,
11,
8284,
13949,
11,
31935,
11,
3806,
5189,
26207,
198,
6738,
7386,
1330,
2746,
198... | 3.569892 | 93 |
import FWCore.ParameterSet.Config as cms
electronEfficiencyThresholds = [36, 68, 128, 176]
electronEfficiencyBins = []
electronEfficiencyBins.extend(list(xrange(0, 120, 10)))
electronEfficiencyBins.extend(list(xrange(120, 180, 20)))
electronEfficiencyBins.extend(list(xrange(180, 300, 40)))
electronEfficiencyBins.extend(list(xrange(300, 400, 100)))
# just copy for now
photonEfficiencyThresholds = electronEfficiencyThresholds
photonEfficiencyBins = electronEfficiencyBins
l1tEGammaOfflineDQM = cms.EDAnalyzer(
"L1TEGammaOffline",
electronCollection=cms.InputTag("gedGsfElectrons"),
photonCollection=cms.InputTag("photons"),
caloJetCollection=cms.InputTag("ak4CaloJets"),
caloMETCollection=cms.InputTag("caloMet"),
conversionsCollection=cms.InputTag("allConversions"),
PVCollection=cms.InputTag("offlinePrimaryVerticesWithBS"),
beamSpotCollection=cms.InputTag("offlineBeamSpot"),
TriggerEvent=cms.InputTag('hltTriggerSummaryAOD', '', 'HLT'),
TriggerResults=cms.InputTag('TriggerResults', '', 'HLT'),
# last filter of HLTEle27WP80Sequence
TriggerFilter=cms.InputTag('hltEle27WP80TrackIsoFilter', '', 'HLT'),
TriggerPath=cms.string('HLT_Ele27_WP80_v13'),
stage2CaloLayer2EGammaSource=cms.InputTag("caloStage2Digis", "EGamma"),
histFolder=cms.string('L1T/L1TEGamma'),
electronEfficiencyThresholds=cms.vdouble(electronEfficiencyThresholds),
electronEfficiencyBins=cms.vdouble(electronEfficiencyBins),
photonEfficiencyThresholds=cms.vdouble(photonEfficiencyThresholds),
photonEfficiencyBins=cms.vdouble(photonEfficiencyBins),
)
l1tEGammaOfflineDQMEmu = l1tEGammaOfflineDQM.clone(
stage2CaloLayer2EGammaSource=cms.InputTag("simCaloStage2Digis"),
histFolder=cms.string('L1TEMU/L1TEGamma'),
)
| [
11748,
48849,
14055,
13,
36301,
7248,
13,
16934,
355,
269,
907,
198,
198,
9509,
1313,
36,
35590,
817,
10126,
82,
796,
685,
2623,
11,
8257,
11,
13108,
11,
26937,
60,
198,
198,
9509,
1313,
36,
35590,
33,
1040,
796,
17635,
198,
9509,
1... | 2.649852 | 674 |
#!python
# external
import numpy as np
import numba
| [
2,
0,
29412,
198,
198,
2,
7097,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
997,
7012,
628,
628,
628,
628,
628,
628,
628
] | 2.75 | 24 |
tokens = input().split(' ')
print(result(tokens)) | [
198,
198,
83,
482,
641,
796,
5128,
22446,
35312,
10786,
705,
8,
198,
198,
4798,
7,
20274,
7,
83,
482,
641,
4008
] | 2.363636 | 22 |
# Order must match format junk
# NOTIFY_ALL is kinda special, if you registerNotifier
# with it, you get ALL notifications.
NOTIFY_ALL = 0 # Get all notifications
NOTIFY_SIGNAL = 1 # Callback on signal/exception
NOTIFY_BREAK = 2 # Callback on breakpoint / sigtrap
NOTIFY_STEP = 3 # Callback on singlestep complete
NOTIFY_SYSCALL = 4 # Callback on syscall (linux only for now)
NOTIFY_CONTINUE = 5 # Callback on continue (not done for step)
NOTIFY_EXIT = 6 # Callback on process exit
NOTIFY_ATTACH = 7 # Callback on successful attach
NOTIFY_DETACH = 8 # Callback on impending process detach
# The following notifiers are *only* available on some platforms
# (and may be kinda faked out ala library load events on posix)
NOTIFY_LOAD_LIBRARY = 9
NOTIFY_UNLOAD_LIBRARY = 10
NOTIFY_CREATE_THREAD = 11
NOTIFY_EXIT_THREAD = 12
NOTIFY_DEBUG_PRINT = 13 # Some platforms support this (win32).
NOTIFY_MAX = 20
# File Descriptor / Handle Types
FD_UNKNOWN = 0 # Unknown or we don't have a type for it
FD_FILE = 1
FD_SOCKET = 2
FD_PIPE = 3
FD_LOCK = 4 # Win32 Mutant/Lock/Semaphore
FD_EVENT = 5 # Win32 Event/KeyedEvent
FD_THREAD = 6 # Win32 Thread
FD_REGKEY = 7 # Win32 Registry Key
# Vtrace Symbol Types
SYM_MISC = -1
SYM_GLOBAL = 0 # Global (mostly vars)
SYM_LOCAL = 1 # Locals
SYM_FUNCTION = 2 # Functions
SYM_SECTION = 3 # Binary section
SYM_META = 4 # Info that we enumerate
# Vtrace Symbol Offsets
VSYM_NAME = 0
VSYM_ADDR = 1
VSYM_SIZE = 2
VSYM_TYPE = 3
VSYM_FILE = 4
| [
2,
8284,
1276,
2872,
5794,
18556,
198,
2,
5626,
5064,
56,
62,
7036,
318,
17855,
2041,
11,
611,
345,
7881,
3673,
7483,
198,
2,
351,
340,
11,
345,
651,
11096,
19605,
13,
198,
11929,
5064,
56,
62,
7036,
796,
657,
220,
220,
220,
220,
... | 2.584746 | 590 |
# Purpose: Grouping entities by DXF attributes or a key function.
# Copyright (c) 2017-2021, Manfred Moitzi
# License: MIT License
from typing import Iterable, Hashable, Dict, List, TYPE_CHECKING
from ezdxf.lldxf.const import DXFValueError, DXFAttributeError
if TYPE_CHECKING:
from ezdxf.eztypes import DXFEntity, KeyFunc
def groupby(
entities: Iterable["DXFEntity"], dxfattrib: str = "", key: "KeyFunc" = None
) -> Dict[Hashable, List["DXFEntity"]]:
"""
Groups a sequence of DXF entities by a DXF attribute like ``'layer'``,
returns a dict with `dxfattrib` values as key and a list of entities
matching this `dxfattrib`.
A `key` function can be used to combine some DXF attributes (e.g. layer and
color) and should return a hashable data type like a tuple of strings,
integers or floats, `key` function example::
def group_key(entity: DXFEntity):
return entity.dxf.layer, entity.dxf.color
For not suitable DXF entities return ``None`` to exclude this entity, in
this case it's not required, because :func:`groupby` catches
:class:`DXFAttributeError` exceptions to exclude entities, which do not
provide layer and/or color attributes, automatically.
Result dict for `dxfattrib` = ``'layer'`` may look like this::
{
'0': [ ... list of entities ],
'ExampleLayer1': [ ... ],
'ExampleLayer2': [ ... ],
...
}
Result dict for `key` = `group_key`, which returns a ``(layer, color)``
tuple, may look like this::
{
('0', 1): [ ... list of entities ],
('0', 3): [ ... ],
('0', 7): [ ... ],
('ExampleLayer1', 1): [ ... ],
('ExampleLayer1', 2): [ ... ],
('ExampleLayer1', 5): [ ... ],
('ExampleLayer2', 7): [ ... ],
...
}
All entity containers (modelspace, paperspace layouts and blocks) and the
:class:`~ezdxf.query.EntityQuery` object have a dedicated :meth:`groupby`
method.
Args:
entities: sequence of DXF entities to group by a DXF attribute or a
`key` function
dxfattrib: grouping DXF attribute like ``'layer'``
key: key function, which accepts a :class:`DXFEntity` as argument and
returns a hashable grouping key or ``None`` to ignore this entity
"""
if all((dxfattrib, key)):
raise DXFValueError(
"Specify a dxfattrib or a key function, but not both."
)
if dxfattrib != "":
key = lambda entity: entity.dxf.get_default(dxfattrib)
if key is None:
raise DXFValueError(
"no valid argument found, specify a dxfattrib or a key function, "
"but not both."
)
result: Dict[Hashable, List["DXFEntity"]] = dict()
for dxf_entity in entities:
if not dxf_entity.is_alive:
continue
try:
group_key = key(dxf_entity)
except DXFAttributeError:
# ignore DXF entities, which do not support all query attributes
continue
if group_key is not None:
group = result.setdefault(group_key, [])
group.append(dxf_entity)
return result
| [
2,
32039,
25,
4912,
278,
12066,
416,
19393,
37,
12608,
393,
257,
1994,
2163,
13,
198,
2,
15069,
357,
66,
8,
2177,
12,
1238,
2481,
11,
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4270,
4224,
72,
198,
2,
13789,
25,
17168,
13789,
198,
6738,
19720,
1330,
40806,
540,
... | 2.447626 | 1,327 |
# coding=utf-8
import unittest
from companycase import CompanyCase
if __name__ == '__main__':
unittest.main()
| [
2,
19617,
28,
40477,
12,
23,
198,
11748,
555,
715,
395,
198,
6738,
1664,
7442,
1330,
5834,
20448,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
628
] | 2.853659 | 41 |
#!/usr/bin/env python3
import os
import tqdm
import torch
import random
import numpy as np
import torch.nn as nn
import configargparse
import torch.optim as optim
from tensorboard import program
from torch.utils.tensorboard import SummaryWriter
import yaml
from models import FeatureNet
from datasets import get_dataset
from losses import MemReplayLoss
from utils.evaluation import RecognitionEvaluator
from utils.misc import save_model, load_model, GlobalStepCounter, ProgressBarDescription
if __name__ == "__main__":
run()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
11748,
28686,
198,
11748,
256,
80,
36020,
198,
11748,
28034,
198,
11748,
4738,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
28034,
13,
20471,
355,
299,
77,
198,
11748,
4566,
... | 3.464516 | 155 |
# from .panos import PanosSkillet
# from .docker import DockerSkillet
# from .pan_validation import PanValidationSkillet
# from .python3 import Python3Skillet
# from .rest import RestSkillet
# from .template import TemplateSkillet
# from .workflow import WorkflowSkillet
| [
2,
422,
764,
6839,
418,
1330,
5961,
418,
15739,
32512,
198,
2,
422,
764,
45986,
1330,
25716,
15739,
32512,
198,
2,
422,
764,
6839,
62,
12102,
341,
1330,
5961,
7762,
24765,
15739,
32512,
198,
2,
422,
764,
29412,
18,
1330,
11361,
18,
... | 3.662162 | 74 |
"""
Views for the rss_proxy djangoapp.
"""
import requests
from django.conf import settings
from django.core.cache import cache
from django.http import HttpResponse, HttpResponseNotFound
from lms.djangoapps.rss_proxy.models import WhitelistedRssUrl
CACHE_KEY_RSS = "rss_proxy.{url}"
def proxy(request):
"""
Proxy requests for the given RSS url if it has been whitelisted.
"""
url = request.GET.get('url')
if url and WhitelistedRssUrl.objects.filter(url=url).exists():
# Check cache for RSS if the given url is whitelisted
cache_key = CACHE_KEY_RSS.format(url=url)
status_code = 200
rss = cache.get(cache_key, '')
print(cache_key)
print('Cached rss: %s' % rss)
if not rss:
# Go get the RSS from the URL if it was not cached
resp = requests.get(url)
status_code = resp.status_code
if status_code == 200:
# Cache RSS
rss = resp.content
cache.set(cache_key, rss, settings.RSS_PROXY_CACHE_TIMEOUT)
return HttpResponse(rss, status=status_code, content_type='application/xml')
return HttpResponseNotFound()
| [
37811,
198,
7680,
82,
329,
262,
374,
824,
62,
36436,
42625,
14208,
1324,
13,
198,
37811,
628,
198,
11748,
7007,
198,
6738,
42625,
14208,
13,
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1330,
6460,
198,
6738,
42625,
14208,
13,
7295,
13,
23870,
1330,
12940,
198,
6738,
42625,... | 2.337255 | 510 |
#!/usr/bin/env python
numTemp = raw_input('Enter a number: ')
num = int(numTemp)
if num > 0:
print '>0'
elif num ==0:
print '0'
else:
print '<0'
| [
2,
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14629,
14,
8800,
14,
24330,
21015,
198,
22510,
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997,
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198,
197,
4798,
705,
29,
15,
6... | 2.191176 | 68 |
from django.conf.urls import url
from .views import *
app_name = "accounts"
urlpatterns = [
url(r"^signup/$", CustomSignupView.as_view(), name="custom_signup"),
url(r"^destroy/$", AjaxLogoutView.as_view(), name="destroy"),
url(r"^(?P<username>[\w.@+-]+)/$", ProfileView.as_view(), name="profile"),
]
| [
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764,
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1,
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796,
685,
198,
220,
220,
220,
19016,
7,
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1,
61,
12683,
929,
32... | 2.492063 | 126 |
#!/usr/bin/env python
Description: Load topology in Mininet
Author: James Hongyi Zeng (hyzeng_at_stanford.edu)
'''
from argparse import ArgumentParser
from socket import gethostbyname
from os import getuid
from mininet.log import lg, info
from mininet.cli import CLI
from mininet.net import Mininet
from mininet.topo import Topo
from mininet.link import Link, Intf
from mininet.node import Host, OVSKernelSwitch, Controller, RemoteController
class StanfordTopo( Topo ):
"Topology for Stanford backbone"
PORT_ID_MULTIPLIER = 1
INTERMEDIATE_PORT_TYPE_CONST = 1
OUTPUT_PORT_TYPE_CONST = 2
PORT_TYPE_MULTIPLIER = 10000
SWITCH_ID_MULTIPLIER = 100000
DUMMY_SWITCH_BASE = 1000
PORT_MAP_FILENAME = "data/port_map.txt"
TOPO_FILENAME = "data/backbone_topology.tf"
dummy_switches = set()
def __init__( self ):
# Read topology info
ports = self.load_ports(self.PORT_MAP_FILENAME)
links = self.load_topology(self.TOPO_FILENAME)
switches = ports.keys()
# Add default members to class.
super( StanfordTopo, self ).__init__()
# Create switch nodes
for s in switches:
self.add_switch( "s%s" % s )
# Wire up switches
self.create_links(links, ports)
# Wire up hosts
host_id = len(switches) + 1
for s in switches:
# Edge ports
for port in ports[s]:
self.add_host( "h%s" % host_id )
self.add_link( "h%s" % host_id, "s%s" % s, 0, port )
host_id += 1
# Consider all switches and hosts 'on'
# self.enable_all()
def load_ports(self, filename):
ports = {}
f = open(filename, 'r')
for line in f:
if not line.startswith("$") and line != "":
tokens = line.strip().split(":")
port_flat = int(tokens[1])
dpid = port_flat / self.SWITCH_ID_MULTIPLIER
port = port_flat % self.PORT_TYPE_MULTIPLIER
if dpid not in ports.keys():
ports[dpid] = set()
if port not in ports[dpid]:
ports[dpid].add(port)
f.close()
return ports
def load_topology(self, filename):
links = set()
f = open(filename, 'r')
for line in f:
if line.startswith("link"):
tokens = line.split('$')
src_port_flat = int(tokens[1].strip('[]').split(', ')[0])
dst_port_flat = int(tokens[7].strip('[]').split(', ')[0])
links.add((src_port_flat, dst_port_flat))
f.close()
return links
def create_links(self, links, ports):
'''Generate dummy switches
For example, interface A1 connects to B1 and C1 at the same time. Since
Mininet uses veth, which supports point to point communication only,
we need to manually create dummy switches
@param links link info from the file
@param ports port info from the file
'''
# First pass, find special ports with more than 1 peer port
first_pass = {}
for (src_port_flat, dst_port_flat) in links:
src_dpid = src_port_flat / self.SWITCH_ID_MULTIPLIER
dst_dpid = dst_port_flat / self.SWITCH_ID_MULTIPLIER
src_port = src_port_flat % self.PORT_TYPE_MULTIPLIER
dst_port = dst_port_flat % self.PORT_TYPE_MULTIPLIER
if (src_dpid, src_port) not in first_pass.keys():
first_pass[(src_dpid, src_port)] = set()
first_pass[(src_dpid, src_port)].add((dst_dpid, dst_port))
if (dst_dpid, dst_port) not in first_pass.keys():
first_pass[(dst_dpid, dst_port)] = set()
first_pass[(dst_dpid, dst_port)].add((src_dpid, src_port))
# Second pass, create new links for those special ports
dummy_switch_id = self.DUMMY_SWITCH_BASE
for (dpid, port) in first_pass.keys():
# Special ports!
if(len(first_pass[(dpid,port)])>1):
self.add_switch( "s%s" % dummy_switch_id )
self.dummy_switches.add(dummy_switch_id)
self.add_link( node1="s%s" % dpid, node2="s%s" % dummy_switch_id, port1=port, port2=1 )
dummy_switch_port = 2
for (dst_dpid, dst_port) in first_pass[(dpid,port)]:
first_pass[(dst_dpid, dst_port)].discard((dpid,port))
self.add_link( node1="s%s" % dummy_switch_id, node2="s%s" % dst_dpid, port1=dummy_switch_port, port2=dst_port)
ports[dst_dpid].discard(dst_port)
dummy_switch_port += 1
dummy_switch_id += 1
first_pass[(dpid,port)] = set()
ports[dpid].discard(port)
# Third pass, create the remaining links
for (dpid, port) in first_pass.keys():
for (dst_dpid, dst_port) in first_pass[(dpid,port)]:
self.add_link( node1="s%s" % dpid, node2="s%s" % dst_dpid, port1=port, port2=dst_port )
ports[dst_dpid].discard(dst_port)
ports[dpid].discard(port)
class StanfordMininet ( Mininet ):
def build( self ):
super( StanfordMininet, self ).build()
# FIXME: One exception... Dual links between yoza and yozb
# Need _manual_ modification for different topology files!!!
self.topo.add_link( node1="s%s" % 15, node2="s%s" % 16, port1=7, port2=4 )
def StanfordTopoTest( controller_ip, controller_port, dummy_controller_ip, dummy_controller_port ):
topo = StanfordTopo()
main_controller = lambda a: RemoteController( a, ip=controller_ip, port=controller_port)
net = StanfordMininet( topo=topo, switch=OVSKernelSwitch, controller=main_controller)
net.start()
# These switches should be set to a local controller..
dummy_switches = topo.dummy_switches
dummyClass = lambda a: RemoteController( a, ip=dummy_controller_ip, port=dummy_controller_port)
dummy_controller = net.addController( name='dummy_controller', controller=dummyClass)
dummy_controller.start()
for dpid in dummy_switches:
switch = net.nameToNode["s%s" % dpid]
switch.pause()
switch.start( [dummy_controller] )
# Turn on STP
for switchName in topo.switches():
switch = net.nameToNode[switchName]
cmd = "ovs-vsctl set Bridge %s stp_enable=true" % switch.name
switch.cmd(cmd)
switch.cmd('ovs-vsctl set Bridge s1 other_config:stp-priority=0x10')
CLI( net )
net.stop()
if __name__ == '__main__':
if getuid()!=0:
print "Please run this script as root / use sudo."
exit(-1)
lg.setLogLevel( 'info')
description = "Put Stanford backbone in Mininet"
parser = ArgumentParser(description=description)
parser.add_argument("-c", dest="controller_name",
default="localhost",
help="Controller's hostname or IP")
parser.add_argument("-p", dest="controller_port",type=int,
default=6633,
help="Controller's port")
parser.add_argument("-c2", dest="dummy_controller_name",
default="localhost",
help="Dummy controller's hostname or IP")
parser.add_argument("-p2", dest="dummy_controller_port",type=int,
default=6633,
help="Dummy ontroller's port")
args = parser.parse_args()
print description
print "Starting with primary controller %s:%d" % (args.controller_name, args.controller_port)
print "Starting with dummy controller %s:%d" % (args.dummy_controller_name, args.dummy_controller_port)
Mininet.init()
StanfordTopoTest(gethostbyname(args.controller_name), args.controller_port, gethostbyname(args.dummy_controller_name), args.dummy_controller_port)
| [
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14,
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628,
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6434,
25,
3700,
9764,
48111,
1168,
1516,
357,
12114,
89,
1516,
62,
265,
62,... | 2.087898 | 3,925 |
"""
Test a site with translated content.
Do not test titles as we remove the translation.
"""
import io
import os
import shutil
import lxml.html
import pytest
import nikola.plugins.command.init
from nikola import __main__
from .helper import cd
from .test_empty_build import ( # NOQA
test_archive_exists,
test_avoid_double_slash_in_rss,
test_check_files,
test_check_links,
test_index_in_sitemap,
)
def test_translated_titles(build, output_dir, other_locale):
"""Check that translated title is picked up."""
normal_file = os.path.join(output_dir, "pages", "1", "index.html")
translated_file = os.path.join(output_dir, other_locale, "pages", "1", "index.html")
# Files should be created
assert os.path.isfile(normal_file)
assert os.path.isfile(translated_file)
# And now let's check the titles
with io.open(normal_file, "r", encoding="utf8") as inf:
doc = lxml.html.parse(inf)
assert doc.find("//title").text == "Foo | Demo Site"
with io.open(translated_file, "r", encoding="utf8") as inf:
doc = lxml.html.parse(inf)
assert doc.find("//title").text == "Bar | Demo Site"
| [
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198,
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13,
198,
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198,
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198,
198,
11748,
300,
19875,
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... | 2.636569 | 443 |
import argparse
import os
import torch
import torch.nn.functional as F
from model_ST import *
import data
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import sys
from predict import evaluate_MA
from tensorboardX import SummaryWriter
# print model parameter
# Training settings
parser = argparse.ArgumentParser(description='Relation network for concurrent activity detection')
parser.add_argument('--BATCH_SIZE', type=int, default=256, help='Training batch size. Default=256')
parser.add_argument('--save_every', type=int, default=5, help='Save model every save_every epochs. Defualt=5')
parser.add_argument('--EPOCH', type=int, default=500, help='Number of epochs to train. Default=600')
parser.add_argument('--LR', type=float, default=0.001, help='Learning Rate. Default=0.001')
parser.add_argument('--TRAIN', action='store_true', default=True, help='Train or test? ')
parser.add_argument('--DEBUG', action='store_true', default=False, help='Debug mode (load less data)? Defualt=False')
parser.add_argument('--clip_grad', type=float, default=5.0, help='Gradient clipping parameter. Default=5,0')
parser.add_argument('--dataPath', type=str, default='/home/yi/PycharmProjects/relation_network/data/UCLA/new273',
help='path to the data folder')
parser.add_argument('--checkpoint', type=str, help='Checkpoint folder name under ./model/')
parser.add_argument('--verbose', type=int, default=1, help='Print verbose information? Default=True')
# model parameters
parser.add_argument('--n_input', type=int, default=37, help='Input feature vector size. Default=37')
parser.add_argument('--n_hidden', type=int, default=128, help='Hidden units for LSTM baseline. Default=128')
parser.add_argument('--n_layers', type=int, default=2, help='LSTM layer number. Default=2')
parser.add_argument('--n_class', type=int, default=12, help='Class label number. Default=12')
parser.add_argument('--use_lstm', action='store_true', default=True, help='Use LSTM for relation network classifier. Default=True')
parser.add_argument('--df', type=int, default=64, help='Relation feature dimension. Default=64')
parser.add_argument('--dk', type=int, default=8, help='Key feature dim. Default=8')
parser.add_argument('--nr', type=int, default=4, help='Multihead number. Default=4')
opt = parser.parse_args()
checkpoint_dir = './model/{}/'.format(opt.checkpoint)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
orig_stdout = sys.stdout
f = open(checkpoint_dir + '/parameter.txt', 'w')
sys.stdout = f
print(opt)
f.close()
sys.stdout = orig_stdout
# data preparation
train_dataset = data.ConActDataset(opt.dataPath)
test_dataset = data.ConActDataset(opt.dataPath, train=not opt.TRAIN)
writer = SummaryWriter()
# only take few sequences for debuging
debug_seq = 3
if opt.DEBUG:
train_data = []
for i in range(debug_seq):
input, labels = train_dataset[i]
train_data.append((input, labels))
print("%s loaded." % train_dataset.seq_list[i])
else:
print('Loading training data ----------------------')
train_data = []
train_labels = []
for i, (input, labels) in enumerate(train_dataset):
train_data.append((input, labels))
train_labels.append(labels)
print("%s loaded." % train_dataset.seq_list[i])
print('Loading testing data ----------------------')
test_data = []
for i, (input, labels) in enumerate(test_dataset):
test_data.append((input, labels))
print("%s loaded." % test_dataset.seq_list[i])
# for model_lstm
if opt.use_lstm:
rnn = RNN(opt.n_input, opt.n_hidden, opt.n_layers, opt.n_class, opt.BATCH_SIZE, opt.df, opt.dk, opt.nr).cuda() # use lstm as classifier
else:
rnn = RNN(opt.n_input, opt.n_hidden, opt.n_layers, opt.n_class, opt.use_lstm).cuda() # use fc as classifier
print(rnn.state_dict().keys())
optimizer = torch.optim.Adam(rnn.parameters(), lr=opt.LR)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.5) # set up scheduler
# Keep track of losses for plotting
best_loss = 10000
all_losses = []
current_loss = 3
FAA = [] # false area ration on test set
INTAP = [] # overall interval AP on test set
save_epoch = [] # list to save the model saving epoch
# train model
total_step = len(train_data)
for epoch in range(opt.EPOCH):
all_losses.append(current_loss)
current_loss = 0
for i, (input, labels) in enumerate(train_data):
optimizer.zero_grad()
feats = torch.from_numpy(input).float()
nframes, _ = input.shape
feats = feats.reshape(-1, nframes, 273).cuda()
#feats = feats.reshape(-1, nframes, opt.n_input*6).cuda()
# change label 0 to -1
labels[labels<1]=-1
labels = torch.from_numpy(labels)
labels = labels.float().cuda()
# Forward pass
outputs = rnn(feats)
outputs = torch.squeeze(outputs)
loss = F.mse_loss(outputs, labels)
# print model parameter if loss is NaN
if opt.verbose > 0:
if torch.isnan(loss):
print_model(rnn)
print('Epoch {}, step {}'.format(epoch+1, i+1))
raw_input("Press Enter to continue ...")
# Backward and optimize
loss.backward()
# This line is used to prevent the vanishing / exploding gradient problem
torch.nn.utils.clip_grad_norm_(rnn.parameters(), opt.clip_grad)
optimizer.step()
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch + 1, opt.EPOCH, i + 1, total_step, loss.item()))
current_loss = current_loss + loss.item()
writer.add_scalar('loss/loss', current_loss, epoch)
scheduler.step(current_loss) # update lr if needed
# save model parameters and loss figure
if ((epoch+1) % opt.save_every) == 0:
# compute false area on test set
if not opt.DEBUG:
false_area, overall_IAPlist = evaluate_MA(rnn, test_data)
FAA.append(torch.sum(false_area).item())
INTAP.append(overall_IAPlist[-2]) # get the interval AP at threshold 0.8
save_epoch.append(epoch+1)
if FAA[-1] == min(FAA):
# if has the minimum test error, save model
checkpoint_dir = './model/{}/'.format(opt.checkpoint)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if epoch > 100:
model_str = checkpoint_dir + 'net-best.pth'
torch.save(rnn, model_str)
checkpoint_dir = './model/{}/'.format(opt.checkpoint)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if opt.verbose == 2:
print('Making dir: {}'.format(checkpoint_dir))
model_str = checkpoint_dir + 'net-{}'.format(str(epoch+1))
if opt.verbose > 0:
print('Model saved to: {}.pth'.format(model_str))
if epoch >= 100:
torch.save(rnn, model_str+'.pth')
# save interval AP
np.savetxt(model_str + 'AP.csv', np.asarray(overall_IAPlist), fmt='%0.5f')
# save miss detection
np.savetxt(model_str + 'MD.txt', np.asarray(FAA), fmt='%0.5f')
# draw miss detection v.s. epoch figure
fig, ax1 = plt.subplots()
color = 'tab:red'
ax1.plot(range(epoch+1), all_losses, color=color)
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Loss', color=color)
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Miss detection area ratio', color=color)
ax2.plot(save_epoch, FAA, 'bd')
fig.savefig(model_str+'.png')
plt.close()
# draw intervalAP v.s. epoch figure
fig1, ax3 = plt.subplots()
color = 'tab:red'
ax3.plot(range(epoch+1), all_losses, color=color)
ax3.set_xlabel('Epochs')
ax3.set_ylabel('Loss', color=color)
ax4 = ax3.twinx()
color = 'tab:blue'
ax4.set_ylabel('Overall interval AP', color=color)
ax4.plot(save_epoch, INTAP, 'bd')
fig1.savefig(model_str+'_AP.png')
plt.close()
# print the loss on training set and evaluation metrics on test set to file
orig_stdout = sys.stdout
f = open(checkpoint_dir + '/loss.txt', 'w')
sys.stdout = f
print('Loss over epochs:')
print(all_losses)
if not opt.DEBUG:
print('Miss detection area ratio:')
print(FAA)
f.close()
sys.stdout = orig_stdout
| [
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... | 2.357964 | 3,654 |
"""Raised to quit."""
from illud.exception import IlludException
| [
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15931,
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628
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#!/usr/bin/env python
import cv2
import numpy as np
import os
import glob
import itertools
import json
from numpy.core.fromnumeric import argmax
#SECTION 1: UNDISTORT FISHEYE
#Read in OpenCV compatible instrinsics & distortion coeffs
COLOR_INTRINSIC = np.load('./savedCoeff/colorIntr.npy')
COLOR_DIST = np.load('./savedCoeff/colorDist.npy')
IR_INTRINSIC = np.load('./savedCoeff/irIntr.npy')
IR_DIST = np.load('./savedCoeff/irDist.npy')
print('Undistorting images-----------------')
imageDir = 'december_callibration_images'
ir_images = glob.glob('./' + imageDir + '/ir-*.png')
DIMS = (1024, 1024)
IDENTITY = np.eye(3)
for i in range(len(ir_images)):
ir_img = cv2.imread(ir_images[i], cv2.IMREAD_UNCHANGED)
new_K, roi = cv2.getOptimalNewCameraMatrix(IR_INTRINSIC, IR_DIST, DIMS, 1)
map1, map2 = cv2.initUndistortRectifyMap(IR_INTRINSIC, IR_DIST, IDENTITY, new_K, DIMS, cv2.CV_32FC1)
undistorted_ir_img = cv2.remap(ir_img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
#save the undistorted image
cv2.imwrite('./undistorted_december_ir_images/' + 'ir-' + str(i) + '.png', undistorted_ir_img)
| [
2,
48443,
14629,
14,
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14,
24330,
21015,
198,
198,
11748,
269,
85,
17,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
28686,
198,
11748,
15095,
198,
11748,
340,
861,
10141,
198,
11748,
33918,
198,
6738,
299,
32152,
13,
7295,
13,
... | 2.315574 | 488 |
import pytest
from hypothesis import given
from hypothesis.strategies import data
from numpy import array, array_equal
from tests.strategies import indices, tensor_data
from tinytorch.tensor.data import (
IndexingError,
TensorData,
broadcast_index,
shape_broadcast,
)
# Check basic properties of layout and strides.
def test_layout():
"Test basis properties of layout and strides"
data = [0] * 3 * 5
tensor_data = TensorData(data, (3, 5), (5, 1))
assert tensor_data.is_contiguous()
assert tensor_data.shape == (3, 5)
assert tensor_data.index((1, 0)) == 5
assert tensor_data.index((1, 2)) == 7
tensor_data = TensorData(data, (5, 3), (1, 5))
assert tensor_data.shape == (5, 3)
assert not tensor_data.is_contiguous()
data = [0] * 4 * 2 * 2
tensor_data = TensorData(data, (4, 2, 2))
assert tensor_data.strides == (4, 2, 1)
# Check basic properties of broadcasting.
def test_broadcast_index_smaller():
"Tests broadcast mapping between higher and lower dim tensors"
out_index = array([0, 0])
for big_index, expected_out_index in (
([0, 0, 0], [0, 0]),
([0, 0, 1], [0, 0]),
([0, 0, 2], [0, 0]),
([0, 1, 0], [1, 0]),
([0, 1, 1], [1, 0]),
([0, 1, 2], [1, 0]),
([1, 0, 0], [0, 0]),
([1, 0, 1], [0, 0]),
([1, 0, 2], [0, 0]),
([1, 1, 0], [1, 0]),
([1, 1, 1], [1, 0]),
([1, 1, 2], [1, 0]),
):
print(big_index, expected_out_index)
_broadcast_index(big_index=array(big_index))
assert array_equal(out_index, expected_out_index)
| [
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198,
6738,
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5254,
13,
2536,
2397,
444,
1330,
36525,
11,
11192,
273,
62,
7... | 2.251719 | 727 |
from django.apps import AppConfig
from terra_accounts.permissions_mixins import PermissionRegistrationMixin
| [
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42625,
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19816,
1040,
1330,
2448,
3411,
47133,
35608,
259,
628
] | 3.892857 | 28 |
#!/usr/bin/env python
# Copyright 2021 Jian Wu
# License: Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from typing import List, Union
from aps.tokenizer.base import TokenizerAbc, ApsTokenizer
| [
2,
48443,
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14,
8800,
14,
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21015,
198,
198,
2,
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2,
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25,
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362,
13,
15,
357,
4023,
1378,
2503,
13,
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13,
2398,
14,
677,
4541,
14,
43,
2149,
24290,
12,
17,
13,
15,
8,
198,
... | 2.888889 | 72 |
import os
from unittest import mock
import pytest
import requests
from constants import VALID_KEY
from utils import FunctionCalledException, function_called_raiser
from astrometry_net_client import Session
from astrometry_net_client.exceptions import APIKeyError, LoginFailedException
some_key = "somekey"
# Start of tests
| [
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3384,
4487,
1330,
15553,
34,
4262,
16922,
11,
2163,
62,
7174,
62,
430,
5847,... | 3.580645 | 93 |
import ast
import os.path
from typing import Iterable
from packaging.requirements import InvalidRequirement
from packaging.requirements import Requirement
from packaging.utils import canonicalize_name
from all_repos_depends.errors import DependsError
from all_repos_depends.types import Depends
NAME = 'python'
| [
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13,
8897,
18883,
1330,
9394,
24615,
198,
6738,
16846,
13,
26791,
1330,
4009... | 3.843373 | 83 |
from os import listdir
from os.path import isfile, join
default_applications = ['Utilities','App Store.app','Automator.app','Calculator.app','Calendar.app','Chess.app','Contacts.app','Dashboard.app','Dictionary.app','DVD Player.app','FaceTime.app','Font Book.app','iBooks.app','Image Capture.app','iTunes.app','Launchpad.app','Mail.app','Maps.app','Messages.app','Mission Control.app','Notes.app','Paste.app','Photo Booth.app','Photos.app','Preview.app','QuickTime Player.app','Reminders.app','Safari.app','Siri.app','Stickies.app','System Preferences.app','TextEdit.app','Time Machine.app','Utilities.app']
remaps = {
"iTerm.app": "iTerm2", # brew cask install iterm2 gives iTerm.app
"Alfred 3.app": "Alfred" # brew cask install alfred gives Alfred 3.app
}
mypath = "/Applications"
installed_applications = [f for f in listdir(mypath) if not isfile(join(mypath, f))]
cask_packages = Command('brew cask list').run().output.split()
mac_app_store_apps = Command('mas list').run().output.splitlines()
# collect applications that are not default ones.
user_applications = []
for x in installed_applications:
#first remap the names
if(x in remaps):
name = remaps[x]
else:
name = x
#then check if they are defaults
if name not in default_applications:
user_applications.append(name)
# determine which applications weren't installed via brew cask
unmanged_applications = []
for x in user_applications:
strip_dotapp = x[:-4] if (".app" in x) else x
trimmed = strip_dotapp.replace(" ", "-").lower()
is_casked = trimmed in cask_packages
is_mas = any(strip_dotapp in s for s in mac_app_store_apps)
# print('{} -> {}: {}|{}'.format(x, trimmed, is_casked, is_mas))
if(not is_casked and not is_mas):
unmanged_applications.append(x)
# print("-------------------")
print("You have {} default applications.".format(len(default_applications)))
print("Tou have {} brew cask applications.".format(len(cask_packages)))
print("Tou have {} app store applications.".format(len(mac_app_store_apps)))
print("You have {} user applications Applications not managed by brew cask or app store...\n------".format(len(unmanged_applications)))
for x in unmanged_applications:
print(x)
# print(mac_app_store_apps) | [
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13,
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318,
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38062,
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13,
1324,
41707,
9771,
3129,
1352,
13,... | 2.926219 | 759 |
"""StdoutItem class"""
from dataclasses import asdict, dataclass
from .hark_serialisable import HarkSerialisable, now_str
| [
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1330,
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32634,
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11,
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62,
2536,
628
] | 3.263158 | 38 |
# coding: utf-8
| [
2,
19617,
25,
3384,
69,
12,
23,
628
] | 2.125 | 8 |
"""
Contains database models
"""
from sqlalchemy import Column, ForeignKey, Integer, String, Float
from sqlalchemy.orm import relationship
from .database import Base
| [
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1330,
2776,
198,
198,
6738,
764,
48806,
1330,
7308,
628
] | 4.097561 | 41 |
# a = rwh_primes2(100)
# print(a)
# http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
""" Input n>=6, Returns a list of primes, 2 <= p < n """
print(sieve_for_primes_to(3))
print(sieve_for_primes_to(1))
print(sieve_for_primes_to(100))
| [
201,
198,
2,
257,
796,
374,
1929,
62,
1050,
999,
17,
7,
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8,
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198,
2,
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7,
64,
8,
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201,
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2,
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14,
6138,
507,
14,
1238,
3104,
36720,
14,
7217,
395,
12,
1014,
12,
... | 2.125 | 144 |
import numpy as np
import matplotlib.pyplot as plt
import copy
k = 4
ratio=0.95
# push the competitive center
if __name__ == '__main__':
competitive_k_means()
| [
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198,
198,
2,
4574,
262,
7606,
3641,
198,
198,
361,
11593,
3672,
834... | 2.704918 | 61 |
"""
Defines a Redshift class which encapsulates a database connection
and utility functions for managing that database.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os
import psycopg2
from shiftmanager.mixins import AdminMixin, ReflectionMixin, S3Mixin
from shiftmanager.memoized_property import memoized_property
| [
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834,
1330,
357,
48546,
62,
11748,
11,
7297,
11,
3601,
62,
... | 3.229508 | 122 |
#Python 3.4.3
#coding=gbk
# copy file wangyuxia 20160920
import sys, shutil, os, string
path = "E:\\test for qgis\\"
target_path = "E:\\test for qgis\\HourScale\\"
for i in range(2,31):
for j in range(0,24):
filename = 'N'+str(i).zfill(2)+str(j).zfill(2)
shutil.copyfile(path+'d_02.hdr',target_path+filename+'.hdr')
print("------------finished---------")
| [
2,
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13,
19,
13,
18,
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2,
66,
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28,
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346,
11,
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11,
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198,
6978,
796,
366,
36,
25,
6852,
... | 2.174157 | 178 |
import sys
from scipy.special import softmax
import torch.onnx
import onnxruntime as ort
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from pytorch2keras.converter import pytorch_to_keras
from models.faceboxes import FaceBoxes
input_dim = 1024
num_classes = 2
model_path = "weights/FaceBoxesProd.pth"
net = FaceBoxes('train', input_dim, num_classes)
def remove_prefix(state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
net = load_model(net, model_path, False)
net.eval()
net.to("cuda")
model_name = model_path.split("/")[-1].split(".")[0]
onnx_model_path = f"models/onnx/base-model.onnx"
# export ONNX model
dummy_input = torch.randn(1, 3, input_dim, input_dim).to("cuda")
torch.onnx.export(net, dummy_input, onnx_model_path, verbose=False, input_names=['input'], output_names=['output'])
"""
# try using pytorch2keras
keras_model = pytorch_to_keras(net, dummy_input, [(3, input_dim, input_dim)])
keras_model_path = f"models/onnx/base-model"
#keras_model.save(model_path)
# 0. print PyTorch outputs
out = net(dummy_input)
dummy_input = dummy_input.cpu().detach().numpy()
out = out.cpu().detach().numpy()
loc = out[:, :, 2:]
conf = out[:, :, :2]
scores = softmax(conf, axis=-1)
print(scores)
# 1. check if ONNX outputs are the same
ort_session = ort.InferenceSession(onnx_model_path)
input_name = ort_session.get_inputs()[0].name
out = ort_session.run(None, {input_name: dummy_input})[0]
loc = out[:, :, 2:]
conf = out[:, :, :2]
scores = softmax(conf, axis=-1)
print(scores)
# 2. check if Keras outputs are the same
keras_model_path = f"models/onnx/base-model"
keras_model = tf.keras.models.load_model(keras_model_path)
out = keras_model.predict(dummy_input)
loc = out[:, :, 2:]
conf = out[:, :, :2]
scores = softmax(conf, axis=-1)
print(scores)
# 3. check if intermediate results of Keras are the same
test_fn = K.function([keras_model.input], [keras_model.get_layer('334').output[0]])
test_out = test_fn(dummy_input)
print(np.round(np.array(test_out), 4)[:30])
"""
| [
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13,
261,
77,
87,
198,
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319,
77,
87,
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83,
198,
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299,
32152,
355,
45941,
198,
11748,
11192,
273,
11125,
355,
48700,
... | 2.572235 | 886 |
from setuptools import setup
setup(name='pysteamcmd',
version='0.1.2',
description='Python package to install and utilize steamcmd',
url='http://github.com/f0rkz/pysteamcmd',
author='f0rkz',
author_email='f0rkz@f0rkznet.net',
license='MIT',
packages=['pysteamcmd'],
install_requires=[],
zip_safe=False)
| [
6738,
900,
37623,
10141,
1330,
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198,
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40406,
7,
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220,
220,
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220,
220,
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15,
13,
16,
13,
17,
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198,
220,
220,
220,
220,
220,
6764,
11639,
37906,
5301,
284,
2721,
... | 2.265823 | 158 |
# Copyright (c) 2017, 2018 Jae-jun Kang
# See the file LICENSE for details.
from x2py.event_factory import EventFactory
from x2py.links.link_events import *
from x2py.links.strategy import ChannelStrategy
from x2py.util.trace import Trace
| [
2,
15069,
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66,
8,
2177,
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34521,
12,
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198,
2,
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8558,
22810,
198,
6738,
2124,
17,
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13,
28751,
... | 3.213333 | 75 |
import os
import sys
import json
import time
import tqdm
import socket
import subprocess
import numpy as np
import visdom
from typing import Tuple
from typing import Optional
| [
11748,
28686,
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355,
45941,
198,
198,
11748,
1490,
3438,
198,
198,
6738,
19720,
1330,
309,... | 3.714286 | 49 |
import os
import time
from sqlalchemy import create_engine, BigInteger, UnicodeText, Column, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy.exc import SQLAlchemyError
from aiogram import Bot, Dispatcher, executor, types
from aiogram.utils.exceptions import ChatNotFound
from dotenv import load_dotenv
load_dotenv()
# Database configuration
DB = os.getenv('DB_ADDR')
ENGINE = create_engine(DB)
Base = declarative_base()
Session = scoped_session(sessionmaker(bind=ENGINE))
# Bot configuration
USAGE = """\
/status -- show how many messages are pending
/receive -- receive pending messages
/send [user_id] -- reply to message to send it to given user
/drop -- drop all pending messages
/help -- shows this message
"""
TOKEN = os.getenv('BOT_TOKEN')
bot = Bot(token=TOKEN)
dp = Dispatcher(bot)
if __name__ == '__main__':
Base.metadata.create_all(ENGINE)
executor.start_polling(dp)
| [
11748,
28686,
198,
11748,
640,
198,
198,
6738,
44161,
282,
26599,
1330,
2251,
62,
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11,
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11,
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8206,
11,
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11,
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282,
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13,
2302,
13,
32446,
283,
876,
1330,
2377,
283,
876,
62,
86... | 3.139241 | 316 |
import numpy as np
import cvnn.layers as complex_layers
import tensorflow as tf
from pdb import set_trace
if __name__ == '__main__':
test_functional_api()
test_regression()
test_cifar()
| [
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900,
62,
40546,
628,
628,
628,
198,
361,
11593,
3672,
834,
6624,
... | 2.649351 | 77 |
"""Write a subset of keys from one CSV to another. Don't use lots of memory.
Usage:
%s <filename> <outputfile> [--columns=<columns>] [--htm] [--racol=<racol>] [--deccol=<deccol>] [--filtercol=<filtercol>]
%s (-h | --help)
%s --version
Options:
-h --help Show this screen.
--version Show version.
--columns=<columns> Comma separated (no spaces) columns.
--htm Generate HTM IDs and add to the column subset.
--racol=<racol> RA column, ignored if htm not specified [default: ra]
--deccol=<deccol> Declination column, ignored if htm not specified [default: dec]
--filtercol=<filtercol> Only write the row when this column is not blank.
"""
import sys
__doc__ = __doc__ % (sys.argv[0], sys.argv[0], sys.argv[0])
from docopt import docopt
from gkutils.commonutils import Struct, readGenericDataFile, cleanOptions
import csv
from gkhtm._gkhtm import htmName
if __name__ == '__main__':
main()
| [
37811,
16594,
257,
24637,
286,
8251,
422,
530,
44189,
284,
1194,
13,
2094,
470,
779,
6041,
286,
4088,
13,
198,
198,
28350,
25,
198,
220,
4064,
82,
1279,
34345,
29,
1279,
22915,
7753,
29,
685,
438,
28665,
82,
28,
27,
28665,
82,
37981... | 2.42823 | 418 |
"""
A binary tree is univalued if every node in the tree has the same value.
Return trueif and only if the given tree is univalued.
Example 1:
Input: [1,1,1,1,1,null,1]
Output: true
Example 2:
Input: [2,2,2,5,2]
Output: false
Note:
The number of nodes in the given tree will be in the range [1, 100].
Each node's value will be an integer in the range [0, 99].
"""
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None | [
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198,
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... | 2.557604 | 217 |
#!/usr/bin/env python
"""
_GetParentStatus_
MySQL implementation of DBSBufferFile.GetParentStatus
"""
from WMCore.Database.DBFormatter import DBFormatter
| [
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13... | 3.18 | 50 |