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src/role_filter.py | kjkszpj/mifans | 0 | 36789 | <gh_stars>0
import pickle
data = pickle.load(open('../data/record.pk', 'rb'))
namedict = pickle.load(open('../data/namedict.pk', 'rb'))
rndict = {v:k for k, v in namedict.items()}
pickle.dump(rndict, open('../data/rndict.pk', 'wb'))
cnt_name = {v:[0, 0, 0] for v in namedict.values()}
for record in data:
for a in record[2]:
cnt_name[a][0] += 1
cnt_name[a][1] += 1
for a in record[4]:
cnt_name[a][0] += 1
cnt_name[a][2] += 1
for a in record[6]:
cnt_name[a][0] += 1
cnt_name[a][2] += 1
for a in record[8]:
cnt_name[a][0] += 1
cnt_name[a][2] += 1
a = [[v[2], k] for k, v in cnt_name.items()]
a.sort(reverse=True)
good_list = []
for cnt, id in a:
name = rndict[id]
ok = True
if name.find('@') != -1: ok = False
if name.upper().find('HACKINGTEAM') != -1: ok = True
if ok: good_list.append(id)
temp = []
for id in good_list:
if (cnt_name[id][1] >= 3 and cnt_name[id][2] >= 5):
print(cnt_name[id], id, rndict[id])
temp.append(id)
# print(len(temp))
# print(len(good_list))
# inf = open('../data/list.txt', 'r')
# lines = inf.readlines()
# print(len(lines))
# cnt = 0
# for line in lines:
# [idf, idt, time] = line.split(' ')
# idf = int(idf)
# idt = int(idt)
# time = int(time)
# if idf in temp and idt in temp:
# cnt += 1
# print(cnt)
# 3d display for initial list
tempf1 = open('../data/temp1.txt', 'w')
tempf2 = open('../data/temp2.txt', 'w')
tempf3 = open('../data/temp3.txt', 'w')
for k, v in cnt_name.items():
if k in temp:
tempf1.write('%d %d %d\n' % (v[0], v[1], v[2]))
elif k in good_list:
tempf3.write('%d %d %d\n' % (v[0], v[1], v[2]))
else:
tempf2.write('%d %d %d\n' % (v[0], v[1], v[2]))
print([rndict[k]] + v)
print(len(temp)) | [
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spotify_sort_by_release/__init__.py | mcieno/spotify-sort-by-release | 2 | 1613700 | <gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf8 -*-
from .spotify_sort_by_release import *
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Python/run_lfca_example.py | subond/lfca | 9 | 77941 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 16 17:58:52 2018
@author: Zhaoyi.Shen
"""
import sys
sys.path.append('/home/z1s/py/lib/')
from signal_processing import lfca
import numpy as np
import scipy as sp
from scipy import io
from matplotlib import pyplot as plt
filename = '/home/z1s/research/climate/LFCA_example/ERSST_1900_2016.mat'
mat = io.loadmat(filename)
lat_axis = mat['LAT_AXIS']
lon_axis = mat['LON_AXIS']
sst = mat['SST']
nlon = sst.shape[0]
nlat = sst.shape[1]
ntime = sst.shape[2]
time = np.arange(1900,2016.99,1/12.)
cutoff = 120
truncation = 30
#%%
mean_seasonal_cycle = np.zeros((nlon,nlat,12))
sst_anomalies = np.zeros((nlon,nlat,ntime))
for i in range(12):
mean_seasonal_cycle[...,i] = np.nanmean(sst[...,i:ntime:12],-1)
sst_anomalies[...,i:ntime:12] = sst[...,i:ntime:12] - mean_seasonal_cycle[...,i][...,np.newaxis]
#%%
s = sst_anomalies.shape
y, x = np.meshgrid(lat_axis,lon_axis)
area = np.cos(y*np.pi/180.)
area[np.where(np.isnan(np.mean(sst_anomalies,-1)))] = 0
#%%
domain = np.ones(area.shape)
domain[np.where(x<100)] = 0
domain[np.where((x<103) & (y<5))] = 0
domain[np.where((x<105) & (y<2))] = 0
domain[np.where((x<111) & (y<-6))] = 0
domain[np.where((x<114) & (y<-7))] = 0
domain[np.where((x<127) & (y<-8))] = 0
domain[np.where((x<147) & (y<-18))] = 0
domain[np.where(y>70)] = 0
domain[np.where((y>65) & ((x<175) | (x>200)))] = 0
domain[np.where(y<-45)] = 0
domain[np.where((x>260) & (y>17))] = 0
domain[np.where((x>270) & (y<=17) & (y>14))] = 0
domain[np.where((x>276) & (y<=14) & (y>9))] = 0
domain[np.where((x>290) & (y<=9))] = 0
#%%
order = 'C'
x = np.transpose(np.reshape(sst_anomalies,(s[0]*s[1],s[2]),order=order))
area_weights = np.transpose(np.reshape(area,(s[0]*s[1],1),order=order))
domain = np.transpose(np.reshape(domain,(s[0]*s[1],1),order=order))
icol_ret = np.where((area_weights!=0) & (domain!=0))
icol_disc = np.where((area_weights==0) | (domain==0))
x = x[:,icol_ret[1]]
area_weights = area_weights[:,icol_ret[1]]
normvec = np.transpose(area_weights)/np.sum(area_weights)
scale = np.sqrt(normvec)
#%%
lfcs, lfps, weights, r, pvar, pcs, eofs, ntr, pvar_slow, pvar_lfc, r_eofs, pvar_slow_eofs = \
lfca(x, cutoff, truncation, scale)
#%%
nins = np.size(icol_disc[1])
nrows = lfps.shape[0]
lfps_aug = np.zeros((nrows,lfps.shape[1]+nins))
lfps_aug[:] = np.nan
lfps_aug[:,icol_ret[1]] = lfps
nrows = eofs.shape[0]
eofs_aug = np.zeros((nrows,eofs.shape[1]+nins))
eofs_aug[:] = np.nan
eofs_aug[:,icol_ret[1]] = eofs
#%%
s1 = np.size(lon_axis)
s2 = np.size(lat_axis)
i = 0
pattern = np.reshape(lfps_aug[i,...],(s1,s2),order=order)
pattern[np.where(np.abs(pattern)>1.e5)] = np.nan
plt.figure()
plt.contourf(np.squeeze(lon_axis),np.squeeze(lat_axis),np.transpose(pattern),\
np.arange(-1,1.1,0.1),cmap=plt.cm.RdYlBu_r)
plt.figure()
plt.plot(lfcs[:,i]) | [
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sound.py | Artemia76/discord-audio-pipe | 0 | 47104 | import numpy as np
import sounddevice as sd
MME = 0
sd.default.channels = 2
sd.default.dtype = 'int16'
sd.default.latency = 'low'
sd.default.samplerate = 48000
class PCMStream:
def __init__(self):
self.stream = None
def read(self, num_bytes):
# frame is 4 bytes
frames = int(num_bytes / 4)
data = self.stream.read(frames)[0]
# convert to pcm format
return data.tobytes()
def change_device(self, num):
if (self.stream is not None):
self.stream.stop()
self.stream.close()
self.stream = sd.InputStream(device=num)
self.stream.start()
def query_devices():
index = 0
options = {}
for item in sd.query_devices():
# pip version only supports MME api
if (item.get('max_input_channels') > 0 and item.get('hostapi') == MME):
options[item.get('name')] = index
index += 1
return options
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ppg-bp/file_2.py | hao203/journal-file | 0 | 85421 | import pathlib
import random
import tensorflow as tf
data_root = pathlib.Path(r'C:\Users\<NAME>\Documents\data')
all_images_paths = list(data_root.glob('*/*'))
all_images_paths = [str(path) for path in all_images_paths]
random.shuffle(all_images_paths)
label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())
label_to_index = dict((name,index) for index,name in enumerate(label_names))
all_images_labels = [label_to_index[pathlib.Path(path).parent.name] for path in all_images_paths]
def preprocess_img(image):
image = tf.image.decode_jpeg(image,channels=3)
image = tf.image.resize(image,(28,28))
image /= 255.0
return image
def load_and_preprocess_image(path):
image = tf.io.read_file(path)
return preprocess_img(image)
AUTOTUNE = tf.data.experimental.AUTOTUNE
Batch_size = 15
size = int(len(all_images_paths) * 0.1)
path_ds_train = tf.data.Dataset.from_tensor_slices(all_images_paths[:-size])
image_ds_train = path_ds_train.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
label_ds_train = tf.data.Dataset.from_tensor_slices(tf.cast(all_images_labels[:-size],tf.int64))
ds_train = tf.data.Dataset.zip((image_ds_train,label_ds_train))
ds_train = ds_train.batch(Batch_size)
ds_train = ds_train.prefetch(buffer_size=AUTOTUNE)
path_ds_test = tf.data.Dataset.from_tensor_slices(all_images_paths[-size:])
image_ds_test = path_ds_test.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
label_ds_test = tf.data.Dataset.from_tensor_slices(tf.cast(all_images_labels[-size:],tf.int64))
ds_test = tf.data.Dataset.zip((image_ds_test,label_ds_test))
ds_test = ds_test.batch(Batch_size)
ds_test = ds_test.prefetch(buffer_size=AUTOTUNE)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(64, (3, 3), padding='same', strides=(1, 1), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2), padding='same', strides=(2, 2)))
model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', strides=(1, 1), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2), padding='same', strides=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(ds_train, epochs=10)
loss, acc = model.evaluate(ds_test)
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2
] |
src/model/exception/emote_fetch_error.py | konrad2508/kokomi-discord-bot | 2 | 6540 | class EmoteFetchError(Exception):
'''Exception stating that there was a problem while fetching emotes from a source.'''
| [
1,
770,
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12008,
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2
] |
013-Flareon 6 Reloadered/resolve.py | schommi/low-tech | 0 | 37362 | import itertools
secret = [
0x7A, 0x17, 0x08, 0x34, 0x17, 0x31, 0x3B, 0x25, 0x5B, 0x18, 0x2E, 0x3A, 0x15, 0x56, 0x0E, 0x11,
0x3E, 0x0D, 0x11, 0x3B, 0x24, 0x21, 0x31, 0x06, 0x3C, 0x26, 0x7C, 0x3C, 0x0D, 0x24, 0x16, 0x3A,
0x14, 0x79, 0x01, 0x3A, 0x18, 0x5A, 0x58, 0x73, 0x2E, 0x09, 0x00, 0x16, 0x00, 0x49, 0x22, 0x01,
0x40, 0x08, 0x0A, 0x14 ]
key = [0 for x in range(13)]
def decrypt():
result = ""
key_index = 0
for index in range(len(secret)):
result += chr(key [key_index] ^ secret[index])
key_index = (key_index + 1) % len (key)
return result
key[0] = ord ("@") ^ secret[-13]
key[1] = ord ("f") ^ secret[-12]
key[2] = ord ("l") ^ secret[-11]
key[3] = ord ("a") ^ secret[-10]
key[4] = ord ("r") ^ secret[-9]
key[5] = ord ("e") ^ secret[-8]
key[6] = ord ("-") ^ secret[-7]
key[7] = ord ("o") ^ secret[-6]
key[8] = ord ("n") ^ secret[-5]
key[9] = ord (".") ^ secret[-4]
key[10] = ord ("c") ^ secret[-3]
key[11] = ord ("o") ^ secret[-2]
key[12] = ord ("m") ^ secret[-1]
dbg = decrypt()
print(dbg)
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13,
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] |
src/utils/common_imaging.py | dovedevic/fullyautomatednutcracker | 5 | 1602110 | <filename>src/utils/common_imaging.py
import io
import requests
import random
from PIL import Image, ImageDraw, ImageFont
def resize(im, size):
bands = im.split()
bands = [b.resize(size, Image.LINEAR) for b in bands]
return Image.merge('RGBA', bands)
def resize_to_nearest(image, square_size):
bands = image.split()
width_diff = abs(image.width - square_size)
height_diff = abs(image.height - square_size)
if height_diff > width_diff:
size = (square_size, image.height * square_size // image.width)
else:
size = (image.width * square_size // image.height, square_size)
bands = [b.resize(size, Image.LINEAR) for b in bands]
return Image.merge('RGBA', bands)
def image_from_url(url, content_type):
try:
response = requests.get(url)
img = Image.open(io.BytesIO(response.content))
if content_type != 'image/gif':
img = img.convert('RGBA')
return img
except Exception as ex:
return None
def text_outline(pos: tuple, content: str, draw: ImageDraw, font: ImageFont, base_color: str, outline_color: str="black", outline_width: int=1):
draw.text((pos[0] - outline_width, pos[1] - outline_width), content, outline_color, font=font)
draw.text((pos[0], pos[1] - outline_width), content, outline_color, font=font)
draw.text((pos[0] + outline_width, pos[1] - outline_width), content, outline_color, font=font)
draw.text((pos[0] + outline_width, pos[1]), content, outline_color, font=font)
draw.text((pos[0] + outline_width, pos[1] + outline_width), content, outline_color, font=font)
draw.text((pos[0], pos[1] + outline_width), content, outline_color, font=font)
draw.text((pos[0] - outline_width, pos[1] + outline_width), content, outline_color, font=font)
draw.text((pos[0] - outline_width, pos[1]), content, outline_color, font=font)
draw.text(pos, content, base_color, font=font)
def hex_to_rgb(value):
value = value.lstrip('#')
lv = len(value)
return tuple(int(value[i:i+lv//3], 16) for i in range(0, lv, lv//3))
def hex_string_from_color_name(color_name: str):
return hex_colors[color_name.lower()] if color_name.lower() in hex_colors else None
def random_hex_color():
return random.choice(list(hex_colors.values()))
hex_colors = {
"amaranth": "E52B50",
"amber": "FFBF00",
"amethyst": "9966CC",
"apricot": "FBCEB1",
"aquamarine": "7FFFD4",
"azure": "007FFF",
"babyblue": "89CFF0",
"beige": "F5F5DC",
"brick": "CB4154",
"black": "000000",
"blue": "0000FF",
"bluegreen": "0095B6",
"blueviolet": "8A2BE2",
"blush": "DE5D83",
"bronze": "CD7F32",
"brown": "964B00",
"burgundy": "800020",
"byzantium": "702963",
"barmine": "960018",
"berise": "DE3163",
"cerulean": "007BA7",
"champagne": "F7E7CE",
"chartreuse": "7FFF00",
"chocolate": "7B3F00",
"cobalt": "0047AB",
"coffee": "6F4E37",
"copper": "B87333",
"coral": "FF7F50",
"crimson": "DC143C",
"cyan": "00FFFF",
"desert": "EDC9Af",
"electric": "7DF9FF",
"emerald": "50C878",
"erin": "00FF3F",
"gold": "FFD700",
"gray": "808080",
"grey": "808080",
"green": "008000",
"harlequin": "3FFF00",
"indigo": "4B0082",
"ivory": "FFFFF0",
"jade": "00A86B",
"jungle": "29AB87",
"lavender": "B57EDC",
"lemon": "FFF700",
"lilac": "C8A2C8",
"lime": "BFFF00",
"magenta": "FF00FF",
"rose": "FF00AF",
"maroon": "800000",
"mauve": "E0B0FF",
"navy": "000080",
"ochre": "CC7722",
"olive": "808000",
"orange": "FF6600",
"orangered": "FF4500",
"orchid": "DA70D6",
"peach": "FFE5B4",
"pear": "D1E231",
"periwinkle": "CCCCFF",
"persian": "1C39BB",
"pink": "FD6C9E",
"plum": "8E4585",
"prussian": "003153",
"puce": "CC8899",
"purple": "800080",
"raspberry": "E30B5C",
"red": "FF0000",
"redviolet": "C71585",
"ruby": "E0115F",
"salmon": "FA8072",
"sangria": "92000A",
"sapphire": "0F52BA",
"scarlet": "FF2400",
"silver": "C0C0C0",
"slate": "708090",
"spring": "A7FC00",
"tan": "D2B48C",
"taupe": "483C32",
"teal": "008080",
"turquoise": "40E0D0",
"ultramarine": "3F00FF",
"violet": "7F00FF",
"viridian": "40826D",
"white": "FFFFFF",
"yellow": "FFFF00",
}
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] |
lib/metrics.py | jarobyte91/post_ocr_correction | 3 | 104623 | <reponame>jarobyte91/post_ocr_correction<gh_stars>1-10
import pandas as pd
from Levenshtein import distance
import re
from tqdm.notebook import tqdm
def levenshtein(reference, hypothesis, progress_bar = False):
assert len(reference) == len(hypothesis)
text = zip(reference, hypothesis)
if progress_bar:
text = tqdm(text, total = len(reference))
d = [distance(r, h) for r, h in text]
output = pd.DataFrame({"reference":reference, "hypothesis":hypothesis})\
.assign(distance = lambda df: d)\
.assign(cer = lambda df: df.apply(lambda r: 100 * r["distance"] / max(len(r["reference"]), 1), axis = 1))
return output
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trove/guestagent/datastore/experimental/postgresql/service/process.py | cp16net/trove | 0 | 102940 | # Copyright (c) 2013 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from oslo_log import log as logging
from trove.common import cfg
from trove.common import utils
from trove.guestagent.common import operating_system
from trove.guestagent.datastore.experimental.postgresql.service.status import (
PgSqlAppStatus)
LOG = logging.getLogger(__name__)
CONF = cfg.CONF
PGSQL_SERVICE_CANDIDATES = ("postgresql",)
class PgSqlProcess(object):
"""Mixin that manages the PgSql process."""
def start_db(self, context):
self._enable_pgsql_on_boot()
"""Start the PgSql service."""
cmd = operating_system.service_discovery(PGSQL_SERVICE_CANDIDATES)
LOG.info(
_("{guest_id}: Starting database engine with command ({command}).")
.format(
guest_id=CONF.guest_id,
command=cmd['cmd_start'],
)
)
utils.execute_with_timeout(
*cmd['cmd_start'].split(),
timeout=30
)
def _enable_pgsql_on_boot(self):
try:
pgsql_service = operating_system.service_discovery(
PGSQL_SERVICE_CANDIDATES)
utils.execute_with_timeout(pgsql_service['cmd_enable'],
shell=True)
except KeyError:
LOG.exception(_("Error enabling PostgreSQL start on boot."))
raise RuntimeError("Service is not discovered.")
def _disable_pgsql_on_boot(self):
try:
pgsql_service = operating_system.service_discovery(
PGSQL_SERVICE_CANDIDATES)
utils.execute_with_timeout(pgsql_service['cmd_disable'],
shell=True)
except KeyError:
LOG.exception(_("Error disabling PostgreSQL start on boot."))
raise RuntimeError("Service is not discovered.")
def stop_db(self, context, do_not_start_on_reboot=False):
"""Stop the PgSql service."""
if do_not_start_on_reboot:
self._disable_pgsql_on_boot()
cmd = operating_system.service_discovery(PGSQL_SERVICE_CANDIDATES)
LOG.info(
_("{guest_id}: Stopping database engine with command ({command}).")
.format(
guest_id=CONF.guest_id,
command=cmd['cmd_stop'],
)
)
utils.execute_with_timeout(
*cmd['cmd_stop'].split(),
timeout=30
)
def restart(self, context):
"""Restart the PgSql service."""
LOG.info(
_("{guest_id}: Restarting database engine.").format(
guest_id=CONF.guest_id,
)
)
try:
PgSqlAppStatus.get().begin_restart()
self.stop_db(context)
self.start_db(context)
finally:
PgSqlAppStatus.get().end_install_or_restart()
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] |
tests/dialogs/wfDialComs.py | cefect/myCanFlood | 15 | 1604128 | <reponame>cefect/myCanFlood
'''
Created on Nov. 27, 2021
@author: cefect
commons for dialog workflow
'''
#==============================================================================
# imports---------------------------
#==============================================================================
#python standards
import os, logging, datetime, time, sys, traceback, unittest
from qgis.core import QgsCoordinateReferenceSystem, QgsVectorLayer, QgsProject
from PyQt5.QtWidgets import QApplication, QMainWindow
from wFlow.scripts import WorkFlow, Session
from hlpr.exceptions import Error
from hlpr.plug import plugLogger
from unittest import TestLoader
#===============================================================================
# #add a special hook tot he exception
#===============================================================================
def excepthook(exc_type, exc_value, exc_tb):
tb = "".join(traceback.format_exception(exc_type, exc_value, exc_tb))
print("error message:\n", tb)
QApplication.quit()
#===============================================================================
# logger
#===============================================================================
from hlpr.plug import plugLogger
from hlpr.logr import basic_logger
mod_logger = basic_logger()
class devPlugLogger(plugLogger):
"""wrapper to overwriting Qspecific methods with python logging"""
def getChild(self, new_childnm):
log_nm = '%s.%s'%(self.log_nm, new_childnm)
#build a new logger
child_log = devPlugLogger(self.parent,log_nm=log_nm)
return child_log
def _loghlp(self, #helper function for generalized logging
msg_raw, qlevel,
push=False, #treat as a push message on Qgis' bar
status=False, #whether to send to the status widget
):
#=======================================================================
# send message based on qlevel
#=======================================================================
msgDebug = '%s %s: %s'%(datetime.datetime.now().strftime('%d-%H.%M.%S'), self.log_nm, msg_raw)
if qlevel < 0: #file logger only
mod_logger.debug('D_%s'%msgDebug)
push, status = False, False #should never trip
else:#console logger
msg = '%s: %s'%(self.log_nm, msg_raw)
mod_logger.info(msg)
#Qgis bar
if push:
print('PUSH: ' +msg_raw)
class DSession(Session):
#placeholders expected on teh session by some dialogs
finv_vlay=None
iface = None
def __init__(self,
logger=None,
tag='devDial',
**kwargs
):
#add the exception hook
sys.excepthook = excepthook
if logger is None:
"""unlike the wflow session, plugin loggers use special methods for interfacing with QGIS"""
logger= devPlugLogger(self, log_nm='L')
super().__init__(logger=logger,tag=tag, **kwargs)
self.logger.info('finished DSession.init')
def run_suite(self,
workflow_l,
build_pickels = False,
get_tests=False,
**kwargs):
#=======================================================================
# defaults
#=======================================================================
log = self.logger.getChild('run_suite')
#=======================================================================
# user check
#=======================================================================
if build_pickels:
assert input('\n rebuild pickels on %i workflows? (y/n)'%len(workflow_l))=='y'
#=======================================================================
# loop and execute each workflow
#=======================================================================
first = True
for WorkFlow in workflow_l:
#===================================================================
# crs
#===================================================================
if not WorkFlow.crsid == self.crsid:
log.debug('crsid mismatch... switching to crs of workflow')
self.set_crs(WorkFlow.crsid, logger=log)
#=======================================================================
# #collect pars
#=======================================================================
"""similar to _get_wrkr().. but less flexible"""
for k in list(self.com_hndls) + self.flow_hndls:
kwargs[k] = getattr(self, k)
#init
kstr = ''.join(['\n %s: %s'%(k,v) for k,v in kwargs.items()]) #plot string
log.debug('building w/ %s'%kstr)
#===================================================================
# launch the flow
#===================================================================
with WorkFlow(logger=self.logger, session=self, **kwargs) as wflow:
wflow.pre()
wflow.D.launch()
wflow.post()
if build_pickels:
if first:
pick_d = dict()
pick_d[wflow.name] = wflow.write_pick()
if get_tests:
wflow.load_pick()
if first:
suite = unittest.TestSuite() #start the suite container
for tMethodName in TestLoader().getTestCaseNames(wflow.Test):
"""inits TestU only 1 test method per TeestU for now"""
suite.addTest(wflow.Test(tMethodName, runr=wflow))
#===================================================================
# wrap this workerse loop
#===================================================================
first=False
log.info('finished \'%s\''%WorkFlow.name)
#=======================================================================
# wrap
#=======================================================================
#attach results
if build_pickels:
self.pick_d = pick_d
log.info('wrote %i pickles'%len(pick_d))
if get_tests:
self.suite=suite
return self.out_dir
def run_wflow(self,
WorkFlow,
**kwargs):
#=======================================================================
# defaults
#=======================================================================
log = self.logger.getChild('run_wflow')
#===================================================================
# crs
#===================================================================
if not WorkFlow.crsid == self.crsid:
log.debug('crsid mismatch... switching to crs of workflow')
self.set_crs(WorkFlow.crsid, logger=log)
#=======================================================================
# #collect pars
#=======================================================================
"""similar to _get_wrkr().. but less flexible"""
for k in list(self.com_hndls) + self.flow_hndls:
kwargs[k] = getattr(self, k)
#init
kstr = ''.join(['\n %s: %s'%(k,v) for k,v in kwargs.items()]) #plot string
log.debug('building w/ %s'%kstr)
#===================================================================
# launch the flow
#===================================================================
with WorkFlow(logger=self.logger, session=self, **kwargs) as wflow:
log.info('\n PRE\n')
wflow.pre()
log.info('\n LAUNCH\n')
wflow.D.launch()
wflow.post()
sys.exit(self.qap.exec_()) #wrap
log.info('finished')
class DialWF(WorkFlow): #workflow to run on your dialog
name='DialWF'
pars_d = {}
DialogClass=None #replace with the dialog class you want to test on
crs = QgsCoordinateReferenceSystem('EPSG:4326')
def __init__(self,
DialogClass=None,
**kwargs):
super().__init__(**kwargs)
#=======================================================================
# setup the dialog
#=======================================================================
if DialogClass is None: DialogClass=self.DialogClass
self.D = DialogClass(None, session=self.session, plogger=self.logger)
self.res_d=dict()
def pre(self): #pre launch
pass #subclass to make your own
def post(self):
pass #subclass to create your own
def run_set(workflow_l,
**kwargs):
with DSession(**kwargs) as ses:
ses.run_suite(workflow_l)
out_dir = ses.out_dir
return out_dir
def open_dial(Workflow, #just open a dialog
**kwargs):
with DSession(**kwargs) as ses:
ses.run_wflow(Workflow)
out_dir = ses.out_dir
return out_dir
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] |
tests/management/commands/test_create_command.py | kaozdl/django-extensions | 0 | 17322 | # -*- coding: utf-8 -*-
import os
import shutil
from django.conf import settings
from django.core.management import call_command
from django.test import TestCase
from six import StringIO
try:
from unittest.mock import patch
except ImportError:
from mock import patch
class CreateCommandTests(TestCase):
"""Tests for create_command command."""
def setUp(self): # noqa
self.management_command_path = os.path.join(
settings.BASE_DIR, 'tests/testapp/management')
self.command_template_path = os.path.join(
settings.BASE_DIR, 'django_extensions/conf/command_template')
self.files = [
'__init__.py',
'commands/__init__.py',
'commands/sample.py',
]
def tearDown(self): # noqa
shutil.rmtree(self.management_command_path,
ignore_errors=True)
shutil.rmtree(os.path.join(self.command_template_path, '.hidden'),
ignore_errors=True)
test_pyc_path = os.path.join(self.command_template_path, 'test.pyc')
if os.path.isfile(test_pyc_path):
os.remove(test_pyc_path)
def _create_management_command_with_empty_files(self):
os.mkdir(self.management_command_path)
os.mkdir(os.path.join(self.management_command_path, 'commands'))
for f in self.files:
os.mknod(os.path.join(self.management_command_path, f))
def _create__pycache__in_command_template_directory(self):
os.mknod(os.path.join(self.command_template_path, 'test.pyc'))
def _create_hidden_directory_in_command_template_directory(self):
os.mkdir(os.path.join(self.command_template_path, '.hidden'))
@patch('sys.stdout', new_callable=StringIO)
def test_should_print_management_command_files_only_on_dry_run(self, m_stdout): # noqa
call_command('create_command', 'testapp', '--dry-run', verbosity=2)
for f in self.files:
filepath = os.path.join(self.management_command_path, f)
self.assertIn(filepath, m_stdout.getvalue())
self.assertFalse(os.path.isfile(filepath))
@patch('sys.stdout', new_callable=StringIO)
def test_should_create_management_command_files_and_print_filepaths(self, m_stdout): # noqa
call_command('create_command', 'testapp', verbosity=2)
for f in self.files:
filepath = os.path.join(self.management_command_path, f)
self.assertIn(filepath, m_stdout.getvalue())
self.assertTrue(os.path.isfile(filepath))
@patch('sys.stdout', new_callable=StringIO)
def test_should_print_that_filepaths_already_exists(self, m_stdout): # noqa
self._create_management_command_with_empty_files()
call_command('create_command', 'testapp', verbosity=2)
for f in self.files:
filepath = os.path.join(self.management_command_path, f)
self.assertIn(
'{} already exists'.format(filepath), m_stdout.getvalue())
self.assertTrue(os.path.isfile(filepath))
self.assertEqual(os.path.getsize(filepath), 0)
@patch('sys.stderr', new_callable=StringIO)
@patch('django_extensions.management.commands.create_command._make_writeable') # noqa
def test_should_print_error_on_OSError_exception(self, m__make_writeable, m_stderr): # noqa
m__make_writeable.side_effect = OSError
self._create__pycache__in_command_template_directory()
self._create_hidden_directory_in_command_template_directory()
call_command('create_command', 'testapp')
for f in self.files:
filepath = os.path.join(self.management_command_path, f)
self.assertIn("Notice: Couldn't set permission bits on {}. You're probably using an uncommon filesystem setup. No problem.\n".format(filepath), # noqa
m_stderr.getvalue())
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] |
commit_data.py | iwangjian/ByteCup2018 | 80 | 45700 | <filename>commit_data.py
import os
import argparse
def process_decoded(args):
if not os.path.exists(args.result_dir):
os.mkdir(args.result_dir)
punct = ["/", "`", "+", "-", ";", "-lrb-", "-rrb-", "``", "|", "~", """]
for file in os.listdir(args.decode_dir):
file_path = os.path.join(args.decode_dir, file)
file_id = int(str(file).split('.')[0]) + 1
res_file = str(file_id) + '.txt'
res_path = os.path.join(args.result_dir, res_file)
temp = []
with open(file_path, 'r') as fr:
text = fr.read().strip()
data = text.split(" ")
for word in data:
if not word in punct:
temp.append(word)
with open(res_path, 'w', encoding='utf-8') as fw:
fw.write(" ".join(temp))
fw.write('\n')
print("Finished: %s" % args.result_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert decoded files to commit files')
parser.add_argument('--decode_dir', action='store', required=True,
help='directory of decoded summaries')
parser.add_argument('--result_dir', action='store', required=True,
help='directory of submission')
args = parser.parse_args()
process_decoded(args)
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13,
2
] |
adminmgr/media/code/A3/task1/BD_1348_1576_1597_gO6b0h8.py | IamMayankThakur/test-bigdata | 9 | 179443 | import findspark
findspark.init()
import time
from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.sql.types import StructType, StructField, StringType
from pyspark.sql import Row,SQLContext
from pyspark.sql.functions import explode, split
import sys
import requests
def get_schema():
"""get instance usage schema."""
# Initialize columns for all string fields
columns = ["tweetid", "lang", "date",
"source", "len", "likes", "rts", "hashtags",
"usermentionnames", "usermentionid",
"name",
"place", "followers",
"friends"]
columns_struct_fields = [StructField(field_name, StringType(), True)
for field_name in columns]
schema = StructType(columns_struct_fields)
return schema
def aggregate_tweets_count(new_values, total_sum):
return sum(new_values) + (total_sum or 0)
def get_sql_context_instance(spark_context):
if ('sqlContextSingletonInstance' not in globals()):
globals()['sqlContextSingletonInstance'] = SQLContext(spark_context)
return globals()['sqlContextSingletonInstance']
def process_rdd(time, rdd):
try:
sql_context = get_sql_context_instance(rdd.context)
row_rdd = rdd.map(lambda w: Row(tweetid=w[0], hashtags=w[7]))
print(row_rdd)
hashtags_df = sql_context.createDataFrame(row_rdd)
hashtags_df.registerTempTable("allhashtags")
hashtag_counts_df = sql_context.sql("select tweetid, hashtags from allhashtags")
hashtag_counts_df.show()
except:
e = sys.exc_info()[0]
print("Error: %s" % e)
def tmp(x):
return (x.split(';')[0],1)
spark = SparkSession \
.builder \
.appName("BigData") \
.getOrCreate()
userSchema = get_schema()
csvDF = spark \
.readStream \
.option("sep", ";") \
.schema(userSchema) \
.csv("/stream")
csvDF1 = csvDF.select(explode(split(csvDF.hashtags, ",")).alias("Hashtags"))
csvDF1.createOrReplaceTempView("split")
csvSqlDF = spark.sql("select Hashtags, count(*) as count from split group by Hashtags order by count desc limit 5")
query = csvSqlDF.writeStream.format("console").outputMode("complete").start()
query.awaitTermination(100)
ssc = spark.sparkContext
ssc.stop()
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29900,
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29897,
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29889,
12597,
2677,
13,
893,
29883,
29889,
9847,
580,
13,
13,
2
] |
latihan1.py | mprwgt/labpy03 | 0 | 155669 | <filename>latihan1.py
print("masukan nilai N: 5 ")
import random
j= 5
a= 0
for i in range(j):
i = random.uniform(.0,.5)
a +=1
print("data ke :", a, "==>", i)
print("selesai") | [
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] |
test/programytest/config/sections/brain/test_dynamic.py | ItsPhant/program-y | 0 | 137825 | import unittest
from programy.config.file.yaml_file import YamlConfigurationFile
from programy.config.sections.brain.dynamic import BrainDynamicsConfiguration
from programy.config.sections.client.console import ConsoleConfiguration
class BrainDynamicsConfigurationTests(unittest.TestCase):
def test_with_data(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
dynamic:
variables:
gettime: programy.dynamic.variables.datetime.GetTime
sets:
number: programy.dynamic.sets.numeric.IsNumeric
roman: programy.dynamic.sets.roman.IsRomanNumeral
maps:
romantodec: programy.dynamic.maps.roman.MapRomanToDecimal
dectoroman: programy.dynamic.maps.roman.MapDecimalToRoman
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
dynamic_config = BrainDynamicsConfiguration()
dynamic_config.load_config_section(yaml, brain_config, ".")
def test_without_data(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
dynamic:
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
dynamic_config = BrainDynamicsConfiguration()
dynamic_config.load_config_section(yaml, brain_config, ".")
def test_with_no_data(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
dynamic_config = BrainDynamicsConfiguration()
dynamic_config.load_config_section(yaml, brain_config, ".")
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Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/dark_lang/views.py | osoco/better-ways-of-thinking-about-software | 3 | 21903 | """
Views file for the Darklang Django App
"""
from django.contrib.auth.decorators import login_required
from django.http import Http404
from django.shortcuts import redirect
from django.template.loader import render_to_string
from django.utils.decorators import method_decorator
from django.utils.translation import LANGUAGE_SESSION_KEY
from django.utils.translation import ugettext as _
from web_fragments.fragment import Fragment
from openedx.core.djangoapps.dark_lang import DARK_LANGUAGE_KEY
from openedx.core.djangoapps.dark_lang.models import DarkLangConfig
from openedx.core.djangoapps.plugin_api.views import EdxFragmentView
from openedx.core.djangoapps.user_api.preferences.api import delete_user_preference, set_user_preference
from openedx.core.djangoapps.util.user_messages import PageLevelMessages
LANGUAGE_INPUT_FIELD = 'preview_language'
class PreviewLanguageFragmentView(EdxFragmentView):
"""
View used when a user is attempting to change the preview language using Darklang.
Expected Behavior:
GET - returns a form for setting/resetting the user's dark language
POST - updates or clears the setting to the given dark language
"""
def render_to_fragment(self, request, course_id=None, **kwargs): # lint-amnesty, pylint: disable=arguments-differ, unused-argument
"""
Renders the language preview view as a fragment.
"""
html = render_to_string('dark_lang/preview-language-fragment.html', {})
return Fragment(html)
def create_base_standalone_context(self, request, fragment, **kwargs):
"""
Creates the base context for rendering a fragment as a standalone page.
"""
return {
'uses_bootstrap': True,
}
def standalone_page_title(self, request, fragment, **kwargs):
"""
Returns the page title for the standalone update page.
"""
return _('Preview Language Administration')
@method_decorator(login_required)
def get(self, request, *args, **kwargs):
"""
Renders the fragment to control the preview language.
"""
if not self._user_can_preview_languages(request.user):
raise Http404
return super().get(request, *args, **kwargs)
@method_decorator(login_required)
def post(self, request, **kwargs): # lint-amnesty, pylint: disable=unused-argument
"""
Accept requests to update the preview language.
"""
if not self._user_can_preview_languages(request.user):
raise Http404
action = request.POST.get('action', None)
if action == 'set_preview_language':
self._set_preview_language(request)
elif action == 'reset_preview_language':
self._clear_preview_language(request)
return redirect(request.path)
def _user_can_preview_languages(self, user):
"""
Returns true if the specified user can preview languages.
"""
if not DarkLangConfig.current().enabled:
return False
return user and not user.is_anonymous
def _set_preview_language(self, request):
"""
Sets the preview language for the current user.
"""
preview_language = request.POST.get(LANGUAGE_INPUT_FIELD, '')
if not preview_language.strip():
PageLevelMessages.register_error_message(request, _('Language not provided'))
return
set_user_preference(request.user, DARK_LANGUAGE_KEY, preview_language)
PageLevelMessages.register_success_message(
request,
_('Language set to {preview_language}').format(
preview_language=preview_language
)
)
def _clear_preview_language(self, request):
"""
Clears the preview language for the current user.
"""
delete_user_preference(request.user, DARK_LANGUAGE_KEY)
if LANGUAGE_SESSION_KEY in request.session:
del request.session[LANGUAGE_SESSION_KEY]
PageLevelMessages.register_success_message(
request,
_('Language reset to the default')
)
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Image2CAD/Core/Math/Constants.py | Elstargo00/image2cad-macos | 21 | 1606413 | <reponame>Elstargo00/image2cad-macos
# -*- coding: utf-8 -*-
"""
@author: <NAME>
Copyright (C) 2016, <NAME>.
Licensed under the Apache License 2.0. See LICENSE file in the project root for full license information.
"""
class Constants:
PI = 3.14159265358979
PRECISION = 0.000001
PIXEL_PRECISION = 1
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] |
src/cscli/views/__init__.py | ryohare/python-cscli | 0 | 95844 | <filename>src/cscli/views/__init__.py
from .hosts import HostsView
__all__ = [
"HostsView"
]
| [
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] |
containerchaos/main.py | containerchaos/containerchaos | 0 | 105797 | <filename>containerchaos/main.py
import subprocess
import time
import measure_response_time
import utils
URL = "http://localhost/"
def _run(cmd: str):
print("Running cmd: {}".format(cmd))
print(subprocess.run(cmd, shell=True, capture_output=True, cwd="../docker-nginx-flask-redis-postgres/"))
def _sleep(t: int = 5):
print("Sleeping {}s".format(t))
time.sleep(t)
def _cleanup():
print("Cleaning up old containers if they exist")
_run("docker stack rm webapp")
_run("docker-compose down")
def _setup():
print("Setting up swarm")
_run("docker-compose up -d")
_run("docker-compose down")
_run("docker swarm init")
def _deploy():
print("Deploying to swarm")
_run("docker stack deploy --compose-file docker-compose.yml webapp")
def main():
_cleanup()
_setup()
_deploy()
for i in range(100):
measure_response_time.measure_response_time(URL, "Baseline")
_cleanup()
_deploy()
print("Redo baseline, but dont save to csv") # To establish some API use
for i in range(100):
measure_response_time.measure_response_time(URL, "Baseline", write=False)
print("By CPU Usage")
containers = utils.get_containers("flaskapp")
c = utils.find_most_cpu(containers)
print("Stopping container {}".format(c))
utils.stop_container(c)
for i in range(100):
measure_response_time.measure_response_time(URL, "CPU Usage")
_cleanup()
if __name__ == '__main__':
main() | [
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] |
ooobuild/cssdyn/drawing/framework/__init__.py | Amourspirit/ooo_uno_tmpl | 0 | 85289 | <filename>ooobuild/cssdyn/drawing/framework/__init__.py
# coding: utf-8
#
# Copyright 2022 :Barry-Thomas-Paul: Moss
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http: // www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from ....dyn.drawing.framework.anchor_binding_mode import AnchorBindingMode as AnchorBindingMode
from ....dyn.drawing.framework.basic_pane_factory import BasicPaneFactory as BasicPaneFactory
from ....dyn.drawing.framework.basic_tool_bar_factory import BasicToolBarFactory as BasicToolBarFactory
from ....dyn.drawing.framework.basic_view_factory import BasicViewFactory as BasicViewFactory
from ....dyn.drawing.framework.border_type import BorderType as BorderType
from ....dyn.drawing.framework.configuration import Configuration as Configuration
from ....dyn.drawing.framework.configuration_change_event import ConfigurationChangeEvent as ConfigurationChangeEvent
from ....dyn.drawing.framework.configuration_controller import ConfigurationController as ConfigurationController
from ....dyn.drawing.framework.module_controller import ModuleController as ModuleController
from ....dyn.drawing.framework.resource_activation_mode import ResourceActivationMode as ResourceActivationMode
from ....dyn.drawing.framework.resource_id import ResourceId as ResourceId
from ....dyn.drawing.framework.tab_bar_button import TabBarButton as TabBarButton
from ....dyn.drawing.framework.x_configuration import XConfiguration as XConfiguration
from ....dyn.drawing.framework.x_configuration_change_listener import XConfigurationChangeListener as XConfigurationChangeListener
from ....dyn.drawing.framework.x_configuration_change_request import XConfigurationChangeRequest as XConfigurationChangeRequest
from ....dyn.drawing.framework.x_configuration_controller import XConfigurationController as XConfigurationController
from ....dyn.drawing.framework.x_configuration_controller_broadcaster import XConfigurationControllerBroadcaster as XConfigurationControllerBroadcaster
from ....dyn.drawing.framework.x_configuration_controller_request_queue import XConfigurationControllerRequestQueue as XConfigurationControllerRequestQueue
from ....dyn.drawing.framework.x_controller_manager import XControllerManager as XControllerManager
from ....dyn.drawing.framework.x_module_controller import XModuleController as XModuleController
from ....dyn.drawing.framework.x_pane import XPane as XPane
from ....dyn.drawing.framework.x_pane2 import XPane2 as XPane2
from ....dyn.drawing.framework.x_pane_border_painter import XPaneBorderPainter as XPaneBorderPainter
from ....dyn.drawing.framework.x_relocatable_resource import XRelocatableResource as XRelocatableResource
from ....dyn.drawing.framework.x_resource import XResource as XResource
from ....dyn.drawing.framework.x_resource_factory import XResourceFactory as XResourceFactory
from ....dyn.drawing.framework.x_resource_factory_manager import XResourceFactoryManager as XResourceFactoryManager
from ....dyn.drawing.framework.x_resource_id import XResourceId as XResourceId
from ....dyn.drawing.framework.x_tab_bar import XTabBar as XTabBar
from ....dyn.drawing.framework.x_tool_bar import XToolBar as XToolBar
from ....dyn.drawing.framework.x_view import XView as XView
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] |
nldrp/feature_extraction/pipeline/utterance_feature_loader.py | etzinis/nldrp | 6 | 56337 | <gh_stars>1-10
"""!
\brief Utterance level feature loader pipeline for loading the
extracted features in appropriate format for utterance calssification
from scikit models and kfold--leave one out pipeline.
@author <NAME> {<EMAIL>}
@copyright National Technical University of Athens
"""
import argparse
import numpy as np
import os
import sys
from sklearn.externals import joblib
nldrp_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'../../../')
sys.path.insert(0, nldrp_dir)
import nldrp.config
def get_args():
"""! Command line parser for Utterance level feature loader
pipeline"""
parser = argparse.ArgumentParser(
description='Utterance level feature loader pipeline' )
parser.add_argument("--dataset", type=str,
help="""The name of the dataset""",
required=True,
choices=['SAVEE'])
parser.add_argument("-i", "--save_dir", type=str,
help="""Where the corresponding binary file full of
data that will contain the dictionary for each speaker is
stored.
Another subdic for all the sentences with their ids
and a 1d numpy matrix for each one of them.
""",
default=nldrp.config.EXTRACTED_FEATURES_PATH )
parser.add_argument("--features_fusion_method", type=str,
help="""Linear or RQA nonlinear features or
their early
fusion by concatenation""",
default='rqa',
choices=['rqa', 'linear','fusion'])
parser.add_argument("-tau", type=int,
help="""Time Delay Ad-hoc""",
default=1)
parser.add_argument("--tau_est_method", type=str,
help="""How to estimate Time Delay (Using
an adhoc value as set or estimate AMI per
frame?)""",
default='ad_hoc',
choices=['ad_hoc', 'ami'])
parser.add_argument("-norm", type=str,
help="""Norm for computing in RPs""",
default='euclidean',
choices=["manhattan", "euclidean", "supremum"])
parser.add_argument("--thresh_method", type=str,
help="""How to threshold RPs""",
default='threshold',
choices=["threshold",
"threshold_std",
"recurrence_rate"])
parser.add_argument("-thresh", type=float,
help="""Value of threshold in (0,1)""",
default=0.1)
parser.add_argument("-fs", type=float,
help="""Sampling frequency Hz""",
default=44100)
parser.add_argument("--frame_duration", type=float,
help="""Frame duration in seconds""",
default=0.02)
args = parser.parse_args()
return args
def load(config):
exper_dat_name = ('{}-rqa-{}-tau-{}-{}-{}-{}-dur-{}-fs-{}'
'.dat'.format(
config['dataset'],
config['phase_space_method'],
config['time_lag'],
config['norm'],
config['thresh_method'],
config['thresh'],
config['frame_duration'],
config['fs']
))
utterance_save_dir = os.path.join(config['save_dir'], 'utterance/')
save_p = os.path.join(utterance_save_dir, exper_dat_name)
return joblib.load(save_p)
def convert_2_numpy_per_utterance(dataset_dic):
converted_dic = {}
for spkr in dataset_dic:
x_list = []
y_list = []
converted_dic[spkr] = {}
for id, el_dic in dataset_dic[spkr].items():
label = el_dic['y']
feat_vec = el_dic['x']
x_list.append(feat_vec)
y_list.append(label)
this_utt_array = np.array(x_list)
converted_dic[spkr]['x']=this_utt_array
converted_dic[spkr]['y']=y_list
return converted_dic
def load_and_convert(method,
config):
"""!
\brief Loads the dictionary form the specified folder and
converts it to a dic like the following
converted_dic = {
'speaker_1':{
'x':2d numpy array (n_utterances, n_features)
'y':list of strings corresponding to labels
}
...
'speaker_n':....
}
According to the specified method the 2d numpy matrix corresponds
to either linear | non-linear | concatenation of both feature sets
"""
if method == 'rqa':
loaded_dic = load(config)
else:
raise NotImplementedError('Fusion method: {} is not yet '
'implemented.'.format(method))
converted_dic = convert_2_numpy_per_utterance(loaded_dic)
return converted_dic
if __name__ == "__main__":
"""!brief Example of usage"""
args = get_args()
config = {
'dataset':args.dataset,
'save_dir':args.save_dir,
'phase_space_method':args.tau_est_method,
'time_lag':args.tau,
'embedding_dimension':3,
'norm':args.norm,
'thresh_method':args.thresh_method,
'thresh':args.thresh,
'l_min':2,
'v_min':2,
'w_min':1,
'frame_duration':args.frame_duration,
'frame_stride':args.frame_duration / 2.0,
'fs':args.fs
}
converted_feats_dic = load_and_convert(args.features_fusion_method,
config)
print converted_feats_dic.values() | [
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5975,
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2
] |
gaphor/RAAML/stpa/connectors.py | Texopolis/gaphor | 867 | 15054 | <filename>gaphor/RAAML/stpa/connectors.py<gh_stars>100-1000
from gaphor.diagram.connectors import Connector
from gaphor.diagram.presentation import Classified
from gaphor.RAAML.raaml import RelevantTo
from gaphor.RAAML.stpa import RelevantToItem
from gaphor.SysML.requirements.connectors import DirectedRelationshipPropertyPathConnect
@Connector.register(Classified, RelevantToItem)
class RelevantToConnect(DirectedRelationshipPropertyPathConnect):
relation_type = RelevantTo
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] |
213. House Robber II.py | minyoungan/coding-interview | 1 | 96837 | <filename>213. House Robber II.py
class Solution:
def rob(self, nums: List[int]) -> int:
def recur(arr):
memo = {}
def dfs(i):
if i in memo:
return memo[i]
if i >= len(arr):
return 0
cur = max(arr[i] + dfs(i + 2), dfs(i + 1))
memo[i] = cur
return cur
return dfs(0)
return max(nums[0], recur(nums[0:-1]), recur(nums[1:]))
class Solution:
def rob(self, nums: List[int]) -> int:
skip_first = self._helper(nums[1:])
skp_last = self._helper(nums[:-1])
return max(nums[0], skip_first, skp_last)
def _helper(self, nums: List[int]) -> int:
one = 0
two = 0
for n in nums:
tmp = two
two = max(one + n, two)
one = tmp
return two
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697,
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13,
632,
13,
4706,
736,
1023,
13,
2
] |
project_euler_20.py | andrazm123/Project-Euler | 0 | 127525 | def fakulteta(n):
if n == 0:
return 1
else:
return n * fakulteta(n - 1)
def vsota_stevk_fakultete(n):
seznam = [int(d) for d in str(fakulteta(n))]
vsota = 0
for i in range(0, len(seznam)):
vsota = vsota + seznam[i]
return(vsota)
print(vsota_stevk_fakultete(100)) | [
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baselines/deepq/assembly/data/plot_result.py | DengYuelin/baselines-assembly | 1 | 28392 | # -*- coding: utf-8 -*-
"""
# @Time : 24/10/18 2:40 PM
# @Author : <NAME>
# @FileName: plot_result.py
# @Software: PyCharm
# @Github ： https://github.com/hzm2016
"""
import collections
import matplotlib.pyplot as plt
import numpy as np
import pickle
import copy as cp
from baselines.deepq.assembly.src.value_functions import *
"""=================================Plot result====================================="""
# YLABEL = ['$F_x(N)$', '$F_y(N)$', '$F_z(N)$', '$M_x(Nm)$', '$M_y(Nm)$', '$M_z(Nm)$']
YLABEL = ['$F_x$(N)', '$F_y$(N)', '$F_z$(N)', '$M_x$(Nm)', '$M_y$(Nm)', '$M_z$(Nm)']
Title = ["X axis force", "Y axis force", "Z axis force",
"X axis moment", "Y axis moment", "Z axis moment"]
High = np.array([40, 40, 0, 5, 5, 5, 542, -36, 188, 5, 5, 5])
Low = np.array([-40, -40, -40, -5, -5, -5, 538, -42, 192, -5, -5, -5])
scale = np.array([40, 40, 40, 5, 5, 5])
"""================================================================================="""
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
def plot(result_path):
plt.figure(figsize=(15, 15), dpi=100)
plt.title('Search Result')
prediction_result = np.load(result_path)
for i in range(len(prediction_result)):
for j in range(6):
line = prediction_result[:, j]
# plt.subplot(2, 3, j+1)
plt.plot(line)
plt.ylabel(YLABEL[j])
plt.xlabel('steps')
plt.legend(YLABEL)
plt.show()
def plot_force_and_moment(path_2, path_3):
V_force = np.load(path_2)
V_state = np.load(path_3)
plt.figure(figsize=(15, 10), dpi=100)
plt.title("Search Result of Force", fontsize=20)
plt.plot(V_force[:100])
plt.xlabel("Steps", fontsize=20)
plt.ylabel("F(N)", fontsize=20)
plt.legend(labels=['Fx', 'Fy', 'Fz', 'Mx', 'My', 'Mz'], loc='best', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.figure(figsize=(15, 10), dpi=100)
plt.title("Search Result of State", fontsize=20)
plt.plot(V_state[:100] - [539.88427, -38.68679, 190.03184, 179.88444, 1.30539, 0.21414])
plt.xlabel("Steps", fontsize=20)
plt.ylabel("Coordinate", fontsize=20)
plt.legend(labels=['x', 'y', 'z', 'rx', 'ry', 'rz'], loc='best', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()
def plot_reward(reward_path):
reward = np.load(reward_path)
print(reward[0])
plt.figure(figsize=(15, 15), dpi=100)
plt.title('Episode Reward')
plt.plot(np.arange(len(reward) - 1), np.array(reward[1:]))
plt.ylabel('Episode Reward')
plt.xlabel('Episodes')
plt.show()
def plot_raw_data(path_1):
data = np.load(path_1)
force_m = np.zeros((len(data), 12))
plt.figure(figsize=(20, 20), dpi=100)
plt.tight_layout(pad=3, w_pad=0.5, h_pad=1.0)
plt.subplots_adjust(left=0.065, bottom=0.1, right=0.995, top=0.9, wspace=0.2, hspace=0.2)
plt.title("True Data")
for j in range(len(data)):
force_m[j] = data[j, 0]
k = -1
for i in range(len(data)):
if data[i, 1] == 0:
print("===========================================")
line = force_m[k+1:i+1]
print(line)
k = i
for j in range(6):
plt.subplot(2, 3, j + 1)
plt.plot(line[:, j])
# plt.plot(line[:, 0])
if j == 1:
plt.ylabel(YLABEL[j], fontsize=17.5)
plt.xlabel('steps', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
else:
plt.ylabel(YLABEL[j], fontsize=20)
plt.xlabel('steps', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
i += 1
def plot_continuous_data(path):
raw_data = np.load(path)
plt.figure(figsize=(20, 15))
plt.title('Episode Reward')
plt.tight_layout(pad=3, w_pad=0.5, h_pad=1.0)
plt.subplots_adjust(left=0.1, bottom=0.15, right=0.98, top=0.9, wspace=0.23, hspace=0.22)
# plt.subplots_adjust(left=0.065, bottom=0.1, right=0.995, top=0.9, wspace=0.2, hspace=0.2)
data = np.zeros((len(raw_data), 12))
for j in range(len(raw_data)):
data[j] = raw_data[j, 0]
for j in range(6):
plt.subplot(2, 3, j + 1)
plt.plot(data[:, j]*scale[j], linewidth=2.5)
# plt.ylabel(YLABEL[j], fontsize=18)
if j>2:
plt.xlabel('steps', fontsize=30)
plt.title(YLABEL[j], fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
# plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.2)
plt.savefig('raw_data.pdf')
plt.show()
def compute_true_return(path):
raw_data = np.load(path)
# print(raw_data)
clock = 0
G = 0.
past_gammas = []
past_cumulants = []
all_G = []
for i in range(len(raw_data)):
observation, action, done, action_probability = raw_data[i]
if done == False:
gamma = 0.99
else:
gamma = 0.
past_gammas.append(gamma)
past_cumulants.append(1)
if done == False:
clock += 1
G = 0
all_G.append(cp.deepcopy(G))
else:
print('clock', clock)
for j in reversed(range(0, clock + 1)):
G *= past_gammas[j]
G += past_cumulants[j]
all_G.append(cp.deepcopy(G))
clock = 0
past_cumulants = []
past_gammas = []
print(len(raw_data))
plt.figure(figsize=(20, 15))
plt.plot(all_G[300:400])
plt.show()
return all_G
# Plot the true prediction and true value
def plot_different_gamma_data(path):
f = open(path, 'rb')
titles = ['$\gamma = 0.4$', '$\gamma = 0.8$', '$\gamma = 0.96$', '$\gamma =1.0$']
# true_data = compute_true_return('prediction_result_different_gamma.npy')
# f = open('../data/learning_result_policy', 'rb')
# plot_value_functions = ['Move down Fy', 'Move down Fx', 'Move down Fz', 'Move down Mx', 'Move down My', 'Move down Mz']
plot_value_functions = ['Move down step', 'Move down step 2', 'Move down step 3', 'Move down step 4']
# plot_value_functions = ['Move down Fx', 'Move down Fx 1', 'Move down Fx 2', 'Move down Fx 3']
raw_data = pickle.load(f)
plt.figure(figsize=(20, 15))
plt.tight_layout(pad=3, w_pad=1., h_pad=0.5)
plt.subplots_adjust(left=0.1, bottom=0.15, right=0.98, top=0.9, wspace=0.23, hspace=0.23)
# legend = sorted([key for key in plot_value_functions.keys()])
# print(legend)
# print(value_functions.keys())
for j, key in enumerate(plot_value_functions):
plt.subplot(2, 2, j + 1)
# print(list(raw_data[('GTD(1)', 'Hindsight Error')][key]))
# plt.plot(np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[:], linewidth=2.5)
# plt.plot(true_data[300:])
plt.plot(np.array(raw_data[('GTD(0)', 'UDE')][key])[600:], linewidth=2.75)
# print('true value', np.array(raw_data[('GTD(0)', 'UDE')][key])[300:400])
# plt.plot(np.array(raw_data[('GTD(0)', 'TD Error')][key])[600:], linewidth=2.5)
# print('old prediction', np.array(raw_data[('GTD(0)', 'TD Error')][key])[300:400])
plt.plot(np.array(raw_data[('GTD(0)', 'Prediction')][key])[600:], linewidth=2.75)
# plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[300:] - np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[300:], linewidth=2.5)
# plt.legend('True value', 'Prediction value')
plt.title(titles[j], fontsize=30)
if j > 1:
plt.xlabel('steps', fontsize=30)
plt.ylabel('Number of steps', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
# plt.savefig('different_gamma.pdf')
plt.show()
# Plot the true prediction and true value
def chinese_plot_different_gamma_data(path):
f = open(path, 'rb')
titles = ['$\gamma = 0.4$', '$\gamma = 0.8$', '$\gamma = 0.96$', '$\gamma =1.0$']
# true_data = compute_true_return('prediction_result_different_gamma.npy')
# f = open('../data/learning_result_policy', 'rb')
# plot_value_functions = ['Move down Fy', 'Move down Fx', 'Move down Fz', 'Move down Mx', 'Move down My', 'Move down Mz']
plot_value_functions = ['Move down step', 'Move down step 2', 'Move down step 3', 'Move down step 4']
# plot_value_functions = ['Move down Fx', 'Move down Fx 1', 'Move down Fx 2', 'Move down Fx 3']
raw_data = pickle.load(f)
plt.figure(figsize=(20, 12), dpi=1000)
plt.tight_layout(pad=3, w_pad=1., h_pad=0.5)
plt.subplots_adjust(left=0.08, bottom=0.12, right=0.98, top=0.95, wspace=0.23, hspace=0.33)
# legend = sorted([key for key in plot_value_functions.keys()])
# print(legend)
# print(value_functions.keys())
for j, key in enumerate(plot_value_functions):
plt.subplot(2, 2, j + 1)
# print(list(raw_data[('GTD(1)', 'Hindsight Error')][key]))
# plt.plot(np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[:], linewidth=2.5)
# plt.plot(true_data[300:])
plt.plot(np.array(raw_data[('GTD(0)', 'UDE')][key])[600:], linewidth=2.75)
# print('true value', np.array(raw_data[('GTD(0)', 'UDE')][key])[300:400])
# plt.plot(np.array(raw_data[('GTD(0)', 'TD Error')][key])[600:], linewidth=2.5)
# print('old prediction', np.array(raw_data[('GTD(0)', 'TD Error')][key])[300:400])
plt.plot(np.array(raw_data[('GTD(0)', 'Prediction')][key])[600:], linewidth=2.75)
# plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[300:] - np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[300:], linewidth=2.5)
# plt.legend('True value', 'Prediction value')
plt.title(titles[j], fontsize=36)
if j > 1:
plt.xlabel('搜索步数', fontsize=36)
plt.ylabel('预测周期', fontsize=36)
plt.xticks([0, 50, 100, 150, 200], fontsize=36)
plt.yticks(fontsize=36)
plt.savefig('./figure/pdf/chinese_different_gamma.pdf')
# plt.show()
def chinese_plot_compare_raw_data(path1, path2):
raw_data = np.load(path1)
raw_data_1 = np.load(path2)
plt.figure(figsize=(20, 12), dpi=1000)
plt.title('Episode Reward')
plt.tight_layout(pad=3, w_pad=0.5, h_pad=1.0)
plt.subplots_adjust(left=0.08, bottom=0.08, right=0.98, top=0.95, wspace=0.33, hspace=0.15)
data = np.zeros((len(raw_data), 12))
for j in range(len(raw_data)):
data[j] = raw_data[j, 0]
data_1 = np.zeros((len(raw_data_1), 12))
for j in range(len(raw_data_1)):
data_1[j] = raw_data_1[j, 0]
for j in range(6):
plt.subplot(2, 3, j + 1)
plt.plot(data[:100, j], linewidth=2.5, color='r', linestyle='--')
plt.plot(data_1[:100, j], linewidth=2.5, color='b')
# plt.ylabel(YLABEL[j], fontsize=18)
if j>2:
plt.xlabel('搜索步数', fontsize=38)
plt.title(YLABEL[j], fontsize=38)
plt.xticks(fontsize=38)
plt.yticks(fontsize=38)
# plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.2)
plt.savefig('./figure/pdf/chinese_raw_data.pdf')
# plt.show()
# Plot the true prediction and true value
def chinese_plot_different_policy_data(path, name):
f = open(path, 'rb')
# true_data = compute_true_return('prediction_result_different_gamma.npy')
# f = open('../data/learning_result_policy', 'rb')
plot_value_functions = ['Move down Fx', 'Move down Fy', 'Move down Fz', 'Move down Mx', 'Move down My', 'Move down Mz']
# plot_value_functions = ['Move down step', 'Move down step 2', 'Move down step 3', 'Move down step 4']
# plot_value_functions = ['Move down Fx', 'Move down Fx 1', 'Move down Fx 2', 'Move down Fx 3']
raw_data = pickle.load(f)
plt.figure(figsize=(20, 12), dpi=1000)
plt.title('Episode Reward')
plt.tight_layout(pad=3, w_pad=0.5, h_pad=1.0)
plt.subplots_adjust(left=0.1, bottom=0.1, right=0.98, top=0.95, wspace=0.33, hspace=0.25)
# plt.subplots_adjust(left=0.1, bottom=0.12, right=0.98, top=0.94, wspace=0.23, hspace=0.33)
# legend = sorted([key for key in plot_value_functions.keys()])
# print(legend)
# print(value_functions.keys())
for j, key in enumerate(plot_value_functions):
plt.subplot(2, 3, j + 1)
# print(list(raw_data[('GTD(1)', 'Hindsight Error')][key]))
# plt.plot(np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[400:]*scale[j], linewidth=2.5)
# plt.plot(true_data[300:])
plt.plot(np.array(raw_data[('GTD(1)', 'UDE')][key])[1000:]*scale[j], linewidth=2.5)
# print('true value', np.array(raw_data[('GTD(0)', 'UDE')][key])[300:400])
# plt.plot(np.array(raw_data[('GTD(0)', 'TD Error')][key])[600:], linewidth=2.5, color='r')
# print('old prediction', np.array(raw_data[('GTD(0)', 'TD Error')][key])[300:400])
plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[1000:]*scale[j], linewidth=2.5)
# plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[300:] - np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[300:], linewidth=2.5)
# plt.legend('True value', 'Prediction value')
plt.title(YLABEL[j], fontsize=38)
if j > 2:
plt.xlabel('搜索步数', fontsize=38)
plt.xticks([0, 50, 100, 150, 200], fontsize=38)
plt.yticks(fontsize=38)
plt.savefig('./figure/pdf/chinese_' + name +'.pdf')
# plt.show()
# Plot the true prediction and true value
def plot_different_policy_data(path):
f = open(path, 'rb')
# true_data = compute_true_return('prediction_result_different_gamma.npy')
# f = open('../data/learning_result_policy', 'rb')
plot_value_functions = ['Move down Fx', 'Move down Fy', 'Move down Fz', 'Move down Mx', 'Move down My', 'Move down Mz']
# plot_value_functions = ['Move down step', 'Move down step 2', 'Move down step 3', 'Move down step 4']
# plot_value_functions = ['Move down Fx', 'Move down Fx 1', 'Move down Fx 2', 'Move down Fx 3']
raw_data = pickle.load(f)
plt.figure(figsize=(20, 12), dpi=1000)
plt.title('Episode Reward')
plt.tight_layout(pad=3, w_pad=1.0, h_pad=1.0)
plt.subplots_adjust(left=0.1, bottom=0.15, right=0.98, top=0.9, wspace=0.23, hspace=0.23)
# legend = sorted([key for key in plot_value_functions.keys()])
# print(legend)
# print(value_functions.keys())
for j, key in enumerate(plot_value_functions):
plt.subplot(2, 3, j + 1)
# print(list(raw_data[('GTD(1)', 'Hindsight Error')][key]))
# plt.plot(np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[400:]*scale[j], linewidth=2.5)
# plt.plot(true_data[300:])
plt.plot(np.array(raw_data[('GTD(1)', 'UDE')][key])[1000:]*scale[j], linewidth=2.5)
# print('true value', np.array(raw_data[('GTD(0)', 'UDE')][key])[300:400])
# plt.plot(np.array(raw_data[('GTD(0)', 'TD Error')][key])[600:], linewidth=2.5, color='r')
# print('old prediction', np.array(raw_data[('GTD(0)', 'TD Error')][key])[300:400])
plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[1000:]*scale[j], linewidth=2.5)
# plt.plot(np.array(raw_data[('GTD(1)', 'Prediction')][key])[300:] - np.array(raw_data[('GTD(1)', 'Hindsight Error')][key])[300:], linewidth=2.5)
# plt.legend('True value', 'Prediction value')
plt.title(YLABEL[j], fontsize=30)
if j > 2:
plt.xlabel('steps', fontsize=30)
plt.xticks([0, 50, 100, 150, 200], fontsize=25)
plt.yticks(fontsize=25)
plt.savefig('./figure/pdf/chinese_different_policies_b.pdf')
# plt.show()
if __name__ == "__main__":
# force = np.load('./search_force.npy')
# state = np.load('./search_state.npy')
# print(np.max(force, axis=0))
# print(np.min(force, axis=0))
# print(np.max(state, axis=0))
# print(np.min(state, axis=0))
# plot('./search_state.npy')
# plot('./search_force.npy')
# plot_reward('./episode_rewards.npy')
# data = np.load('prediction_result.npy')
# print(data[:, 2])
# plot_continuous_data('prediction_result_different_gamma_six_force.npy')
# f = open('../data/learning_result', 'rb')
# y = pickle.load(f)
# data = y[('GTD(1)', 'Hindsight Error')]['Move down Fz']
# print(data)
# plt.figure(figsize=(15, 15), dpi=100)
# plt.title('Search Result')
#
# plt.plot(data)
# plt.ylabel(YLABEL[0])
# plt.xlabel('steps')
# plt.legend(YLABEL)
# plt.show()
# compute_true_return('prediction_result_different_gamma.npy')
# plot_true_data('learning_result_six_force_gamma_0.9')
# plot_true_data('learning_result_different_gamma')
# plot_different_gamma_data('learning_result_different_policy')
"""=============================== plot different policy ===================================== """
# plot_different_policy_data('learning_result_six_force_gamma_0.9')
# chinese_plot_different_policy_data('learning_result_six_force_gamma_0.9')
# plot_different_policy_data('learning_result_different_policy_new_3')
chinese_plot_different_policy_data('learning_result_different_policy_new_3', 'off_policy_3')
# chinese_plot_different_policy_data('learning_result_different_policy')
# chinese_plot_different_policy_data('learning_result_different_policy')
"""=============================== plot different gamma ======================================== """
# plot_different_gamma_data('learning_result_different_gamma_new')
# chinese_plot_different_gamma_data('learning_result_different_gamma_new') | [
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1495,
13,
1678,
17735,
353,
6024,
4535,
4283,
353,
29871,
29900,
29889,
29946,
29938,
742,
525,
4535,
4283,
353,
29871,
29900,
29889,
29947,
29938,
742,
525,
4535,
4283,
353,
29871,
29900,
29889,
29929,
29953,
29938,
742,
525,
4535,
4283,
353,
29896,
29889,
29900,
29938,
2033,
13,
1678,
396,
1565,
29918,
1272,
353,
10272,
29918,
3009,
29918,
2457,
877,
11965,
2463,
29918,
2914,
29918,
29881,
15622,
29918,
4283,
29889,
29876,
2272,
1495,
13,
1678,
396,
285,
353,
1722,
877,
6995,
1272,
29914,
21891,
29918,
2914,
29918,
22197,
742,
525,
6050,
1495,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29891,
742,
525,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29920,
742,
525,
16619,
1623,
341,
29916,
742,
525,
16619,
1623,
1619,
742,
525,
16619,
1623,
341,
29920,
2033,
13,
1678,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
4331,
742,
525,
16619,
1623,
4331,
29871,
29906,
742,
525,
16619,
1623,
4331,
29871,
29941,
742,
525,
16619,
1623,
4331,
29871,
29946,
2033,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29916,
29871,
29896,
742,
525,
16619,
1623,
383,
29916,
29871,
29906,
742,
525,
16619,
1623,
383,
29916,
29871,
29941,
2033,
13,
1678,
10650,
29918,
1272,
353,
5839,
280,
29889,
1359,
29898,
29888,
29897,
13,
1678,
14770,
29889,
4532,
29898,
1003,
2311,
7607,
29906,
29900,
29892,
29871,
29896,
29945,
876,
13,
1678,
14770,
29889,
29873,
523,
29918,
2680,
29898,
8305,
29922,
29941,
29892,
281,
29918,
8305,
29922,
29896,
1696,
298,
29918,
8305,
29922,
29900,
29889,
29945,
29897,
13,
1678,
14770,
29889,
1491,
26762,
29918,
328,
5143,
29898,
1563,
29922,
29900,
29889,
29896,
29892,
5970,
29922,
29900,
29889,
29896,
29945,
29892,
1492,
29922,
29900,
29889,
29929,
29947,
29892,
2246,
29922,
29900,
29889,
29929,
29892,
281,
3493,
29922,
29900,
29889,
29906,
29941,
29892,
298,
3493,
29922,
29900,
29889,
29906,
29941,
29897,
13,
1678,
396,
15983,
353,
12705,
4197,
1989,
363,
1820,
297,
6492,
29918,
1767,
29918,
12171,
29889,
8149,
580,
2314,
13,
1678,
396,
1596,
29898,
26172,
29897,
13,
1678,
396,
1596,
29898,
1767,
29918,
12171,
29889,
8149,
3101,
13,
1678,
363,
432,
29892,
1820,
297,
26985,
29898,
5317,
29918,
1767,
29918,
12171,
1125,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29906,
29892,
29871,
29906,
29892,
432,
718,
29871,
29896,
29897,
13,
4706,
396,
1596,
29898,
1761,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
12622,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
7503,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
3009,
29918,
1272,
29961,
29941,
29900,
29900,
29901,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29955,
29945,
29897,
13,
4706,
396,
1596,
877,
3009,
995,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
1596,
877,
1025,
18988,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29955,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
17531,
448,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
26172,
877,
5574,
995,
742,
525,
23084,
2463,
995,
1495,
13,
4706,
14770,
29889,
3257,
29898,
23545,
793,
29961,
29926,
1402,
4079,
2311,
29922,
29941,
29900,
29897,
13,
4706,
565,
432,
1405,
29871,
29896,
29901,
13,
9651,
14770,
29889,
29916,
1643,
877,
24530,
742,
4079,
2311,
29922,
29941,
29900,
29897,
13,
4706,
14770,
29889,
29891,
1643,
877,
4557,
310,
6576,
742,
4079,
2311,
29922,
29941,
29900,
29897,
13,
4706,
14770,
29889,
486,
7358,
29898,
5657,
2311,
29922,
29906,
29945,
29897,
13,
4706,
14770,
29889,
3637,
7358,
29898,
5657,
2311,
29922,
29906,
29945,
29897,
13,
1678,
396,
14770,
29889,
7620,
1003,
877,
29881,
15622,
29918,
4283,
29889,
5140,
1495,
13,
1678,
14770,
29889,
4294,
580,
13,
13,
13,
29937,
18399,
278,
1565,
18988,
322,
1565,
995,
13,
1753,
521,
8233,
29918,
5317,
29918,
29881,
15622,
29918,
4283,
29918,
1272,
29898,
2084,
1125,
13,
1678,
285,
353,
1722,
29898,
2084,
29892,
525,
6050,
1495,
13,
1678,
17735,
353,
6024,
4535,
4283,
353,
29871,
29900,
29889,
29946,
29938,
742,
525,
4535,
4283,
353,
29871,
29900,
29889,
29947,
29938,
742,
525,
4535,
4283,
353,
29871,
29900,
29889,
29929,
29953,
29938,
742,
525,
4535,
4283,
353,
29896,
29889,
29900,
29938,
2033,
13,
1678,
396,
1565,
29918,
1272,
353,
10272,
29918,
3009,
29918,
2457,
877,
11965,
2463,
29918,
2914,
29918,
29881,
15622,
29918,
4283,
29889,
29876,
2272,
1495,
13,
1678,
396,
285,
353,
1722,
877,
6995,
1272,
29914,
21891,
29918,
2914,
29918,
22197,
742,
525,
6050,
1495,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29891,
742,
525,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29920,
742,
525,
16619,
1623,
341,
29916,
742,
525,
16619,
1623,
1619,
742,
525,
16619,
1623,
341,
29920,
2033,
13,
1678,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
4331,
742,
525,
16619,
1623,
4331,
29871,
29906,
742,
525,
16619,
1623,
4331,
29871,
29941,
742,
525,
16619,
1623,
4331,
29871,
29946,
2033,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29916,
29871,
29896,
742,
525,
16619,
1623,
383,
29916,
29871,
29906,
742,
525,
16619,
1623,
383,
29916,
29871,
29941,
2033,
13,
1678,
10650,
29918,
1272,
353,
5839,
280,
29889,
1359,
29898,
29888,
29897,
13,
1678,
14770,
29889,
4532,
29898,
1003,
2311,
7607,
29906,
29900,
29892,
29871,
29896,
29906,
511,
270,
1631,
29922,
29896,
29900,
29900,
29900,
29897,
13,
1678,
14770,
29889,
29873,
523,
29918,
2680,
29898,
8305,
29922,
29941,
29892,
281,
29918,
8305,
29922,
29896,
1696,
298,
29918,
8305,
29922,
29900,
29889,
29945,
29897,
13,
1678,
14770,
29889,
1491,
26762,
29918,
328,
5143,
29898,
1563,
29922,
29900,
29889,
29900,
29947,
29892,
5970,
29922,
29900,
29889,
29896,
29906,
29892,
1492,
29922,
29900,
29889,
29929,
29947,
29892,
2246,
29922,
29900,
29889,
29929,
29945,
29892,
281,
3493,
29922,
29900,
29889,
29906,
29941,
29892,
298,
3493,
29922,
29900,
29889,
29941,
29941,
29897,
13,
1678,
396,
15983,
353,
12705,
4197,
1989,
363,
1820,
297,
6492,
29918,
1767,
29918,
12171,
29889,
8149,
580,
2314,
13,
1678,
396,
1596,
29898,
26172,
29897,
13,
1678,
396,
1596,
29898,
1767,
29918,
12171,
29889,
8149,
3101,
13,
13,
1678,
363,
432,
29892,
1820,
297,
26985,
29898,
5317,
29918,
1767,
29918,
12171,
1125,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29906,
29892,
29871,
29906,
29892,
432,
718,
29871,
29896,
29897,
13,
4706,
396,
1596,
29898,
1761,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
12622,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
7503,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
3009,
29918,
1272,
29961,
29941,
29900,
29900,
29901,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29955,
29945,
29897,
13,
4706,
396,
1596,
877,
3009,
995,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
1596,
877,
1025,
18988,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29955,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
17531,
448,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
26172,
877,
5574,
995,
742,
525,
23084,
2463,
995,
1495,
13,
13,
4706,
14770,
29889,
3257,
29898,
23545,
793,
29961,
29926,
1402,
4079,
2311,
29922,
29941,
29953,
29897,
13,
13,
4706,
565,
432,
1405,
29871,
29896,
29901,
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2680,
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29892,
281,
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29922,
29900,
29889,
29945,
29892,
298,
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29889,
29900,
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13,
1678,
14770,
29889,
1491,
26762,
29918,
328,
5143,
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1563,
29922,
29900,
29889,
29900,
29947,
29892,
5970,
29922,
29900,
29889,
29900,
29947,
29892,
1492,
29922,
29900,
29889,
29929,
29947,
29892,
2246,
29922,
29900,
29889,
29929,
29945,
29892,
281,
3493,
29922,
29900,
29889,
29941,
29941,
29892,
298,
3493,
29922,
29900,
29889,
29896,
29945,
29897,
13,
1678,
848,
353,
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29889,
3298,
359,
3552,
2435,
29898,
1610,
29918,
1272,
511,
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29906,
876,
13,
1678,
363,
432,
297,
3464,
29898,
2435,
29898,
1610,
29918,
1272,
22164,
13,
4706,
848,
29961,
29926,
29962,
353,
10650,
29918,
1272,
29961,
29926,
29892,
29871,
29900,
29962,
13,
13,
1678,
848,
29918,
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353,
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29889,
3298,
359,
3552,
2435,
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1610,
29918,
1272,
29918,
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511,
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29906,
876,
13,
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363,
432,
297,
3464,
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2435,
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1610,
29918,
1272,
29918,
29896,
22164,
13,
4706,
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29918,
29896,
29961,
29926,
29962,
353,
10650,
29918,
1272,
29918,
29896,
29961,
29926,
29892,
29871,
29900,
29962,
13,
13,
1678,
363,
432,
297,
3464,
29898,
29953,
1125,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29906,
29892,
29871,
29941,
29892,
432,
718,
29871,
29896,
29897,
13,
4706,
14770,
29889,
5317,
29898,
1272,
7503,
29896,
29900,
29900,
29892,
432,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29892,
2927,
2433,
29878,
742,
6276,
342,
1508,
2433,
489,
1495,
13,
4706,
14770,
29889,
5317,
29898,
1272,
29918,
29896,
7503,
29896,
29900,
29900,
29892,
432,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29892,
2927,
2433,
29890,
1495,
13,
4706,
396,
14770,
29889,
29891,
1643,
29898,
29979,
24461,
6670,
29961,
29926,
1402,
4079,
2311,
29922,
29896,
29947,
29897,
13,
4706,
565,
432,
29958,
29906,
29901,
13,
9651,
14770,
29889,
29916,
1643,
877,
233,
147,
159,
31836,
233,
176,
168,
30354,
742,
4079,
2311,
29922,
29941,
29947,
29897,
13,
4706,
14770,
29889,
3257,
29898,
29979,
24461,
6670,
29961,
29926,
1402,
4079,
2311,
29922,
29941,
29947,
29897,
13,
4706,
14770,
29889,
486,
7358,
29898,
5657,
2311,
29922,
29941,
29947,
29897,
13,
4706,
14770,
29889,
3637,
7358,
29898,
5657,
2311,
29922,
29941,
29947,
29897,
13,
13,
1678,
396,
14770,
29889,
1491,
26762,
29918,
328,
5143,
29898,
1563,
29922,
29900,
29889,
29896,
29892,
5970,
29922,
29900,
29889,
29896,
29892,
1492,
29922,
29900,
29889,
29929,
29892,
2246,
29922,
29900,
29889,
29929,
29892,
281,
3493,
29922,
29900,
29889,
29946,
29892,
298,
3493,
29922,
29900,
29889,
29906,
29897,
13,
1678,
14770,
29889,
7620,
1003,
877,
6904,
4532,
29914,
5140,
29914,
305,
8233,
29918,
1610,
29918,
1272,
29889,
5140,
1495,
13,
1678,
396,
14770,
29889,
4294,
580,
13,
13,
13,
29937,
18399,
278,
1565,
18988,
322,
1565,
995,
13,
1753,
521,
8233,
29918,
5317,
29918,
29881,
15622,
29918,
22197,
29918,
1272,
29898,
2084,
29892,
1024,
1125,
13,
13,
1678,
285,
353,
1722,
29898,
2084,
29892,
525,
6050,
1495,
13,
1678,
396,
1565,
29918,
1272,
353,
10272,
29918,
3009,
29918,
2457,
877,
11965,
2463,
29918,
2914,
29918,
29881,
15622,
29918,
4283,
29889,
29876,
2272,
1495,
13,
1678,
396,
285,
353,
1722,
877,
6995,
1272,
29914,
21891,
29918,
2914,
29918,
22197,
742,
525,
6050,
1495,
13,
1678,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29891,
742,
525,
16619,
1623,
383,
29920,
742,
525,
16619,
1623,
341,
29916,
742,
525,
16619,
1623,
1619,
742,
525,
16619,
1623,
341,
29920,
2033,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
4331,
742,
525,
16619,
1623,
4331,
29871,
29906,
742,
525,
16619,
1623,
4331,
29871,
29941,
742,
525,
16619,
1623,
4331,
29871,
29946,
2033,
13,
1678,
396,
6492,
29918,
1767,
29918,
12171,
353,
6024,
16619,
1623,
383,
29916,
742,
525,
16619,
1623,
383,
29916,
29871,
29896,
742,
525,
16619,
1623,
383,
29916,
29871,
29906,
742,
525,
16619,
1623,
383,
29916,
29871,
29941,
2033,
13,
1678,
10650,
29918,
1272,
353,
5839,
280,
29889,
1359,
29898,
29888,
29897,
13,
1678,
14770,
29889,
4532,
29898,
1003,
2311,
7607,
29906,
29900,
29892,
29871,
29896,
29906,
511,
270,
1631,
29922,
29896,
29900,
29900,
29900,
29897,
13,
1678,
14770,
29889,
3257,
877,
29923,
12907,
390,
809,
538,
1495,
13,
1678,
14770,
29889,
29873,
523,
29918,
2680,
29898,
8305,
29922,
29941,
29892,
281,
29918,
8305,
29922,
29900,
29889,
29945,
29892,
298,
29918,
8305,
29922,
29896,
29889,
29900,
29897,
13,
1678,
14770,
29889,
1491,
26762,
29918,
328,
5143,
29898,
1563,
29922,
29900,
29889,
29896,
29892,
5970,
29922,
29900,
29889,
29896,
29892,
1492,
29922,
29900,
29889,
29929,
29947,
29892,
2246,
29922,
29900,
29889,
29929,
29945,
29892,
281,
3493,
29922,
29900,
29889,
29941,
29941,
29892,
298,
3493,
29922,
29900,
29889,
29906,
29945,
29897,
13,
1678,
396,
14770,
29889,
1491,
26762,
29918,
328,
5143,
29898,
1563,
29922,
29900,
29889,
29896,
29892,
5970,
29922,
29900,
29889,
29896,
29906,
29892,
1492,
29922,
29900,
29889,
29929,
29947,
29892,
2246,
29922,
29900,
29889,
29929,
29946,
29892,
281,
3493,
29922,
29900,
29889,
29906,
29941,
29892,
298,
3493,
29922,
29900,
29889,
29941,
29941,
29897,
13,
1678,
396,
15983,
353,
12705,
4197,
1989,
363,
1820,
297,
6492,
29918,
1767,
29918,
12171,
29889,
8149,
580,
2314,
13,
1678,
396,
1596,
29898,
26172,
29897,
13,
1678,
396,
1596,
29898,
1767,
29918,
12171,
29889,
8149,
3101,
13,
1678,
363,
432,
29892,
1820,
297,
26985,
29898,
5317,
29918,
1767,
29918,
12171,
1125,
13,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29906,
29892,
29871,
29941,
29892,
432,
718,
29871,
29896,
29897,
13,
4706,
396,
1596,
29898,
1761,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
12622,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
29961,
29946,
29900,
29900,
29901,
14178,
7052,
29961,
29926,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
3009,
29918,
1272,
29961,
29941,
29900,
29900,
29901,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29896,
29900,
29900,
29900,
29901,
14178,
7052,
29961,
29926,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
1596,
877,
3009,
995,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
29965,
2287,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29953,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29892,
2927,
2433,
29878,
1495,
13,
4706,
396,
1596,
877,
1025,
18988,
742,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29900,
29897,
742,
525,
24495,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
29946,
29900,
29900,
2314,
13,
4706,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29896,
29900,
29900,
29900,
29901,
14178,
7052,
29961,
29926,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
5317,
29898,
9302,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
23084,
2463,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
17531,
448,
7442,
29889,
2378,
29898,
1610,
29918,
1272,
29961,
877,
23799,
29928,
29898,
29896,
29897,
742,
525,
29950,
12772,
523,
4829,
1495,
3816,
1989,
2314,
29961,
29941,
29900,
29900,
29901,
1402,
1196,
2103,
29922,
29906,
29889,
29945,
29897,
13,
4706,
396,
14770,
29889,
26172,
877,
5574,
995,
742,
525,
23084,
2463,
995,
1495,
13,
4706,
14770,
29889,
3257,
29898,
29979,
24461,
6670,
29961,
29926,
1402,
4079,
2311,
29922,
29941,
29947,
29897,
13,
4706,
565,
432,
1405,
29871,
29906,
29901,
13,
9651,
14770,
29889,
29916,
1643,
877,
233,
147,
159,
31836,
233,
176,
168,
30354,
742,
4079,
2311,
29922,
29941,
29947,
29897,
13,
4706,
14770,
29889,
486,
7358,
4197,
29900,
29892,
29871,
29945,
29900,
29892,
29871,
29896,
29900,
29900,
29892,
29871,
29896,
29945,
29900,
29892,
29871,
29906,
29900,
29900,
1402,
4079,
2311,
29922,
29941,
29947,
29897,
13,
4706,
14770,
29889,
3637,
7358,
29898,
5657,
2311,
29922,
29941,
29947,
29897,
13,
13,
1678,
14770,
29889,
7620,
1003,
877,
6904,
4532,
29914,
5140,
29914,
305,
8233,
29918,
29915,
718,
1024,
718,
4286,
5140,
1495,
13,
1678,
396,
14770,
29889,
4294,
580,
13,
13,
13,
29937,
18399,
278,
1565,
18988,
322,
1565,
995,
13,
1753,
6492,
29918,
29881,
15622,
29918,
22197,
29918,
1272,
29898,
2084,
1125,
13,
1678,
285,
353,
1722,
29898,
2084,
29892,
525,
6050,
1495,
13,
1678,
396,
1565,
29918,
1272,
353,
10272,
29918,
3009,
29918,
2457,
877,
11965,
2463,
29918,
2914,
29918,
29881,
15622,
29918,
4283,
29889,
29876,
2272,
1495,
13,
1678,
396,
285,
353,
1722,
877,
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samcnet/simple.py | binarybana/samcnet | 12 | 98816 | <gh_stars>10-100
import numpy as np
import scipy.stats as st
class Simple():
def __init__(self, truemu=2.0, sigma=1.0, N=20, mu0=0.0, tau=5.0):
self.x = 0.0
self.old_x = 0.0
self.truemu = truemu
self.sigma = sigma
self.N = N
self.mu0 = mu0
self.tau = tau
self.data = st.norm.rvs(loc=self.truemu, scale=self.sigma, size=self.N)
self.postmode = (self.mu0/self.tau**2 + self.data.mean()/self.sigma**2) \
/ (1/self.tau**2 + 1/self.sigma**2)
print "Postmode: %f" % self.postmode
def propose(self):
self.old_x = self.x
self.x += np.random.normal(scale=3.0)
def copy(self):
return self.x
def save_to_db(self):
return self.x
def reject(self):
self.x = self.old_x
def energy(self):
sum = 0.0
# prior
sum -= st.norm.logpdf(self.x, loc=self.mu0, scale=self.tau)
# likelihood
sum -= st.norm.logpdf(self.data, loc=self.x, scale=self.sigma).sum()
return sum
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] |
management/forms.py | packmas13/registration | 1 | 166191 | <reponame>packmas13/registration
from django import forms
from django.utils.translation import gettext_lazy as _
class SendSupportEmailForm(forms.Form):
SUBJECT_CHOICES = [
"",
_("Participant management"),
_("Feedback / Suggestion"),
_("Bug report"),
_("Other"),
]
sender = forms.CharField(label=_("Sender"), disabled=True)
subject = forms.ChoiceField(
label=_("Subject"), choices=zip(SUBJECT_CHOICES, SUBJECT_CHOICES)
)
message = forms.CharField(
label=_("Message"), widget=forms.Textarea(attrs={"cols": 32, "rows": 4})
)
def __init__(self, user, *args, **kwargs):
super().__init__(*args, **kwargs)
if not self.initial:
self.initial["sender"] = "{} {} <{}>".format(
user.first_name, user.last_name, user.email
)
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] |
exec.py | developerHaneum/listBug | 2 | 24096 | <filename>exec.py
from clint.textui import *
def Write():
i = 0
while True:
i += 1
text = input(colored.cyan("%d: "%i))
if text == "q":
sys.exit(0)
def Print():
print(colored.cyan("-- Bug list --"))
print(colored.cyan("-- ") + colored.red("<q:quit>") + colored.cyan(" --"))
Write()
Print()
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buildutil/main.py | TediCreations/buildutils | 0 | 4573 | #!/usr/bin/env python3
import os
import argparse
import subprocess
if __name__ == '__main__':
from version import __version__
from configParser import ConfigParser
else:
from .version import __version__
from .configParser import ConfigParser
def command(cmd):
"""Run a shell command"""
subprocess.call(cmd, shell=True)
"""
cmd_split = cmd.split()
process = subprocess.Popen(cmd_split,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
stdout, stderr = process.communicate()
return stdout, stderr
"""
def main():
absFilePath = os.path.dirname(os.path.abspath(__file__))
cwdPath = os.path.abspath(os.getcwd())
parser = argparse.ArgumentParser(
prog="buildutil",
description="Assembly/C/C++ utility to build embedded systems",
epilog="Author: <NAME>",
fromfile_prefix_chars='@')
# parser.add_argument('-v', '--verbose',
# action='store_true',
# help='an optional argument')
"""
parser.add_argument('Path',
metavar='path',
type=str,
default=cwdPath,
help='the config filepath')
"""
parser.add_argument(
'-d', '--directory',
type=str,
default=cwdPath,
help='the config filepath')
parser.add_argument(
'-v', '--version',
action='store_true',
help='get the version of the build system')
# parser.add_argument(
# '-f',
# '--file',
# help='A readable file',
# metavar='FILE',
# type=argparse.FileType('r'),
# default=None)
cmd_parser = parser.add_subparsers(dest='cmd', description="")
parser_build = cmd_parser.add_parser(
'build',
help="build the project")
parser_get_version = cmd_parser.add_parser(
'get_version',
help="try to get the version from git")
# parser_get_version.add_argument(
# '-a', '--alpha',
# dest='alpha',
# help='try to get the version')
# Execute parse_args()
args = parser.parse_args()
subcommand = parser.parse_args().cmd
if args.version is True:
print(f"version: {__version__}")
exit(0)
# if subcommand is None or subcommand == "build":
if subcommand == "build":
makefilePath = os.path.join(absFilePath, "conf/make/Makefile")
command(f"make -f {makefilePath}")
elif subcommand == "get_version":
print("version")
else:
ConfigParser()
print("fuck")
return
# Working directory
wd = os.path.abspath(args.directory)
print(f"File: {absFilePath}")
print(F"CWD: {cwdPath}")
print(F"Working directory: {wd}")
print(F"makefile path: {makefilePath}")
print()
command(f"make -f {makefilePath}")
if __name__ == '__main__':
main()
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tests/unit/test_categories.py | aeskincbr/kesher-backend | 1 | 177437 | <reponame>aeskincbr/kesher-backend
import json
from pytest_mock import MockerFixture
from service import report_category_handler
def test_get_report_categories_list(mocker: MockerFixture):
response = report_category_handler.get_report_categories_list(mocker.MagicMock(), mocker.MagicMock())
body = json.loads(response["body"])
assert body is not None | [
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tests/testConverters.py | ttm/musicLegacy | 2 | 90994 | <filename>tests/testConverters.py
import musicLegacy as m
import importlib
#from IPython.lib.deepreload import reload as dreload
importlib.reload(m.converters)
importlib.reload(m)
#dreload(m,exclude="pytz")
co=m.BasicConverter()
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radssh/shell.py | Eli-Tarrago/radssh | 39 | 57028 | #
# Copyright (c) 2014, 2016, 2018, 2020 LexisNexis Risk Data Management Inc.
#
# This file is part of the RadSSH software package.
#
# RadSSH is free software, released under the Revised BSD License.
# You are permitted to use, modify, and redsitribute this software
# according to the Revised BSD License, a copy of which should be
# included with the distribution as file LICENSE.txt
#
'''
Python wrapper for parallel execution shell
===========================================
*** This module should be run, not imported ***
Usage: ```python -m radssh.shell host [...]```
Will read settings from /etc/radssh_config, and supplement with ~/.radssh_config.
Settings may also be provided on the command line, using the form --keyword=value.
'''
import sys
import os
import time
import socket
import pprint
import readline
import atexit
import logging
from . import ssh
from . import config
from .console import RadSSHConsole, monochrome
try:
from . import star_commands as star
import radssh.plugins
except ImportError:
class NullStarCommands(object):
'''Use stub if plugins or star_commands can not be loaded'''
@classmethod
def call(*args, **kwargs):
print('Plugins directory not found - *commands disabled')
star_help = call
star_info = call
commands = {'*help': star_help}
star = NullStarCommands()
# Try using colorama when running on Windows
if sys.platform.startswith('win'):
try:
import colorama
colorama.initialise.init()
except Exception as e:
print('Unable to support ANSI escape sequences via colorama module')
print(e)
sys.exit(1)
# Ensure ~/.ssh directory exists, with sensible permissions
try:
os.mkdir(os.path.expanduser('~/.ssh'), 0o700)
except OSError:
pass
################################################################################
command_listeners = []
def shell(cluster, logdir=None, playbackfile=None, defaults=None):
'''Very basic interactive shell'''
if not defaults:
defaults = config.load_default_settings()
while True:
try:
if playbackfile:
try:
cmd = next(playbackfile)
print('%s %s' % (defaults['shell.prompt'], cmd.strip()))
except StopIteration:
return
else:
try:
cmd = input('%s ' % defaults['shell.prompt'])
except KeyboardInterrupt:
print('\n<Ctrl-C> during input\nUse EOF (<Ctrl-D>) or *exit to exit shell\n')
continue
# Feed command line to any registered listeners from plugins
for feed in command_listeners:
feed_result = feed(cmd)
if feed_result:
if defaults['show_altered_commands'] == 'on':
cluster.console.message('Command modified from "%s" to "%s"' % (cmd, feed_result))
cmd = str(feed_result)
if logdir:
with open(os.path.join(logdir, 'session.commands'), 'a') as f:
f.write('%s\n' % cmd)
args = cmd.split()
if len(args) > 0:
if os.path.basename(args[0]) == 'sudo' and len(args) > 1:
initial_command = os.path.basename(args[1])
else:
initial_command = os.path.basename(args[0])
if initial_command in defaults['commands.forbidden'].split(','):
print('You really don\'t want to run %s without a TTY, do you?' % initial_command)
continue
if initial_command in defaults['commands.restricted'].split(','):
print('STOP! "%s" is listed as a restricted command (Potentially dangerous)' % initial_command)
print('and requires explicit confirmation before running.')
print('Please double check all parameters, just to be sure...')
print(' >>>', cmd)
confirm = input('Enter \'100%\' if completely sure: ')
if confirm != '100%':
continue
if args[0].startswith('#'):
# Comment
continue
if args[0].startswith('*'):
ret = star.call(cluster, logdir, cmd)
cluster.console.join()
if isinstance(ret, ssh.Cluster):
cluster.console.message('Switched cluster from %r to %r' % (cluster, ret))
cluster = ret
continue
r = cluster.run_command(cmd)
if logdir:
cluster.log_result(logdir, encoding=defaults['character_encoding'])
# Quick summary report, if jobs failed
failures = {}
completions = []
completion_time = 0.0
for k, job in r.items():
v = job.result
if job.completed:
if v.return_code == 0:
completions.append(str(k))
completion_time += job.end_time - job.start_time
else:
failures.setdefault(v.return_code, []).append(str(k))
else:
failures.setdefault(None, []).append(str(k))
if failures:
print('\nSummary of return codes:')
for k, v in [(0, completions)] + list(failures.items()):
if len(v) > 5:
print(k, '\t- (%d hosts)' % len(v))
else:
print(k, '\t-', sorted(v))
if completions:
print('Average completion time for %d hosts: %fs' % (len(completions), (completion_time / len(completions))))
except KeyboardInterrupt:
print('Ctrl-C during command preparation - command aborted.')
except EOFError as e:
print(e)
break
print('Shell exiting')
cluster.close_connections()
################################################################################
# Readline/libedit command completion
# Supports *commands, executables (LOCAL), and path (REMOTE) completion
class radssh_tab_handler(object):
'''Class wrapper for readline TAB key completion'''
def __init__(self, cluster, star):
# Need access to the cluster object to get SFTP service
# for remote path completion, and the star command dictionary
# to know what *commands are available.
self.cluster = cluster
self.star = star
try:
self.using_libedit = ('libedit' in readline.__doc__)
except TypeError:
# pyreadline (windows) readline.__doc__ is None (not iterable)
self.using_libedit = False
self.completion_choices = []
readline.set_completer()
readline.set_completer(self.complete)
readline.set_completer_delims(' \t\n/*')
if self.using_libedit:
readline.parse_and_bind('bind ^I rl_complete')
else:
readline.parse_and_bind('tab: complete')
def complete_star_command(self, lead_in, text, state):
if state == 0:
# Rebuild cached list of choices that match
# Reset list to empty (choices = [] would reference local, not persistent list)
del self.completion_choices[:]
for choice in self.star.commands.keys():
if choice.startswith(lead_in):
self.completion_choices.append(choice + ' ')
# Discrepancy with readline/libedit and handling of leading *
if self.using_libedit:
return self.completion_choices[state]
else:
return self.completion_choices[state][1:]
def complete_executable(self, lead_in, text, state):
if state == 0:
del self.completion_choices[:]
for path_dir in os.environ['PATH'].split(os.path.pathsep):
try:
for f in os.listdir(path_dir):
try:
if os.path.isdir(os.path.join(path_dir, f)):
continue
st = os.stat(os.path.join(path_dir, f))
if (st.st_mode & 0o111) and f.startswith(text):
self.completion_choices.append(f + ' ')
except OSError:
continue
except OSError:
continue
self.completion_choices.append(None)
return self.completion_choices[state]
def complete_remote_path(self, lead_in, text, state):
if state == 0:
del self.completion_choices[:]
for t in self.cluster.connections.values():
if t.is_authenticated():
break
else:
print('No authenticated connections')
raise RuntimeError('Tab Completion unavailable')
s = t.open_sftp_client()
parent = os.path.dirname(lead_in)
partial = os.path.basename(lead_in)
if not parent:
parent = './'
for x in s.listdir(parent):
if x.startswith(partial):
full_path = os.path.join(parent, x)
try:
# See if target is a directory, and append '/' if it is
s.chdir(full_path)
x += '/'
full_path += '/'
except Exception:
pass
if self.using_libedit:
self.completion_choices.append(full_path)
else:
self.completion_choices.append(x)
self.completion_choices.append(None)
return self.completion_choices[state]
def complete_local_path(self, lead_in, text, state):
if state == 0:
del self.completion_choices[:]
parent = os.path.dirname(lead_in)
partial = os.path.basename(lead_in)
if not parent:
parent = './'
for x in os.listdir(parent):
if x.startswith(partial):
full_path = os.path.join(parent, x)
if os.path.isdir(full_path):
# See if target is a directory, and append '/' if it is
x += '/'
full_path += '/'
if self.using_libedit:
self.completion_choices.append(full_path)
else:
self.completion_choices.append(x)
self.completion_choices.append(None)
return self.completion_choices[state]
def complete(self, text, state):
buffer = readline.get_line_buffer()
lead_in = buffer[:readline.get_endidx()].split()[-1]
try:
if buffer.startswith('*') and ' ' in buffer:
# See if *command has custom tab completion
star_command = self.star.commands.get(buffer.split()[0], None)
if star_command and star_command.tab_completion:
return star_command.tab_completion(self, buffer, lead_in, text, state)
if lead_in.startswith('*'):
# User needs help completing *command...
return self.complete_star_command(lead_in, text, state)
else:
# Default behavior - remote file path completion
return self.complete_remote_path(lead_in, text, state)
except Exception:
raise
################################################################################
# Workaround for https://github.com/radssh/radssh/issues/32
# Newer GNU Readline library raise false errno value that the Python
# wrapper reraises as IOError. https://bugs.python.org/issue10350 not
# being backported to Python 2.7, so handle it with more code...
def safe_write_history_file(filename):
# To avoid false negative, use stat() to test the file modification times
try:
readline.write_history_file(filename)
except IOError as e:
# Ignore this exception if we wrote out the history file recently
try:
post = os.stat(filename).st_mtime
if post > time.time() - 3:
logging.debug('Ignoring "%s" writing history file', str(e))
except Exception:
raise e
################################################################################
def radssh_shell_main():
args = sys.argv[1:]
defaults = config.load_settings()
# Keep command line options separately, for reuse in sshconfig defaults
cmdline_options = config.command_line_settings(args, defaults.get('user.settings'))
defaults.update(cmdline_options)
if 'socket.timeout' in defaults:
socket.setdefaulttimeout(float(defaults['socket.timeout']))
# Setup Logging
logformat = '%(asctime)s %(levelname)-8s [%(name)s:%(thread)08X] %(message)s'
logdir = os.path.expanduser(time.strftime(defaults.get('logdir', '')))
if logdir:
if not os.path.exists(logdir):
os.mkdir(logdir)
logging.basicConfig(filename=os.path.join(logdir, 'radssh.log'),
format=logformat)
else:
logging.basicConfig(format=logformat)
pass
try:
logging.getLogger().setLevel(getattr(logging, defaults['loglevel'].upper()))
except AttributeError:
raise RuntimeError('RadSSH setting "loglevel" should be set to one of [CRITICAL,ERROR,WARNING,INFO,DEBUG] instead of "%s"', defaults['loglevel'])
logger = logging.getLogger('radssh')
# Make an AuthManager to handle user authentication
a = ssh.AuthManager(defaults['username'],
auth_file=os.path.expanduser(defaults['authfile']),
try_auth_none=(defaults['try_auth_none'] == 'on'))
# Load Plugins to aid in host lookups and add *commands dynamically
loaded_plugins = {}
exe_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
system_plugin_dir = os.path.join(exe_dir, 'plugins')
disable_plugins = defaults['disable_plugins'].split(',')
plugin_dirs = [x for x in defaults['plugins'].split(';') if x]
plugin_dirs.append(system_plugin_dir)
for x in plugin_dirs:
plugin_dir = os.path.abspath(os.path.expanduser(x))
if not os.path.exists(plugin_dir):
continue
for module in sorted(os.listdir(plugin_dir)):
if module.endswith('.py') and not module.startswith('__'):
plugin = module[:-3]
# Skip modules found in more that 1 location, and ones explicitly disabled
if plugin in loaded_plugins or plugin in disable_plugins:
continue
try:
logger.info('Loading plugin module: %s', plugin)
this_plugin = radssh.plugins.load_plugin(os.path.join(plugin_dir, module))
if hasattr(this_plugin, 'settings'):
prefix = 'plugin.%s.' % plugin
user_settings = {}
user_settings = dict([(k[len(prefix):], v) for k, v in defaults.items() if k.startswith(prefix)])
logger.info('Updating settings for plugin %s with: %s', plugin, user_settings)
this_plugin.settings.update(user_settings)
if hasattr(this_plugin, 'init'):
logger.debug('Calling init method for plugin: %s', plugin)
this_plugin.init(defaults=defaults, auth=a, plugins=loaded_plugins, star_commands=star.commands, shell=shell)
if hasattr(this_plugin, 'star_commands'):
logger.debug('Registering *commands for plugin: %s %s', plugin, this_plugin.star_commands.keys())
star.commands.update(this_plugin.star_commands)
if hasattr(this_plugin, 'command_listener'):
command_listeners.append(this_plugin.command_listener)
loaded_plugins[plugin] = this_plugin
except Exception as e:
logger.error('Failed to load plugin (%s): %s', plugin, repr(e))
# Use command line args as connect list, or give user option to supply list now
if not args:
print('No command line arguments given.')
print('You can connect to a number of hosts by hostname or IP')
if loaded_plugins:
print('You can also give symbolic names that can be translated by')
print('the following loaded plugins:')
for module, plugin in loaded_plugins.items():
try:
lookup_doc = plugin.lookup.__doc__
print(module, plugin.__doc__)
print('\t%s' % lookup_doc)
try:
plugin.banner()
except AttributeError:
pass
except AttributeError:
pass
connect_list = input('Enter a list of connection destinations: ').split()
else:
connect_list = args
if not connect_list:
sys.exit(0)
# Do the connections if needed, offer names to plugin lookup() functions
hosts = []
for arg in connect_list:
for helper, resolver in loaded_plugins.items():
if hasattr(resolver, 'lookup'):
try:
cluster = resolver.lookup(arg)
if cluster:
logger.debug('%s expanded by %s', arg, helper)
for label, host, conn in cluster:
if conn:
hosts.append((label, conn))
else:
hosts.append((label, host))
break
except Exception as e:
logger.error('Exception looking up %s via %s: %r', arg, helper, e)
cluster = None
else:
hosts.append((arg, None))
# Almost done with all the preliminary setup steps...
if defaults['loglevel'] not in ('CRITICAL', 'ERROR'):
print('*** Parallel Shell ***')
print('Using AuthManager:', a)
print('Logging to %s' % logdir)
pprint.pprint(defaults, indent=4)
print()
star.star_help()
# Create a RadSSHConsole instance for screen output
job_buffer = int(defaults['stalled_job_buffer'])
console_name = defaults['shell.console']
console = None
if '.' in console_name:
# Try finding formatter as module.function from loaded plugins
logger.info('Attempting to load custom console formatter: %s', console_name)
module_name, function_name = console_name.split('.', 1)
try:
custom_formatter = getattr(loaded_plugins[module_name], function_name)
console = RadSSHConsole(formatter=custom_formatter, retain_recent=job_buffer)
except KeyError:
logger.error('Plugin not loaded for shell.console formatter %s', console_name)
except AttributeError:
logger.error('Plugin formatter not found for shell.console formatter %s', console_name)
except Exception as e:
logger.error('Exception on console formatter %s: %r', console_name, e)
# Fallback to a standard console if plugin provided one did not load
if console is None:
if not sys.stdout.isatty() or console_name == 'monochrome':
console = RadSSHConsole(formatter=monochrome, retain_recent=job_buffer)
else:
console = RadSSHConsole(retain_recent=job_buffer)
# Finally, we are able to create the Cluster
print('Connecting to %d hosts...' % len(hosts))
cluster = ssh.Cluster(hosts, auth=a, console=console, defaults=defaults)
ready, disabled, failed_auth, failed_connect, dropped = cluster.connection_summary()
if defaults['loglevel'] not in ('CRITICAL', 'ERROR'):
star.star_info(cluster, logdir, '', [])
else:
# If cluster is not 100% connected, let user know even if loglevel is not low enough
if any((failed_auth, failed_connect, dropped)):
print('There were problems connecting to some nodes:')
if failed_connect:
print(' %d nodes failed to connect' % failed_connect)
if failed_auth:
print(' %d nodes failed authentication' % failed_auth)
if dropped:
print(' %d dropped connections' % dropped)
print(' Use "*info" for connection details.')
if ready == 1 and disabled + failed_auth + failed_connect + dropped == 0:
# Cluster size of one - check if auto_tty is set
if defaults['auto_tty'] == 'on' and 'star_tty' in loaded_plugins:
print('Auto-invoking *tty for a cluster size of 1')
loaded_plugins['star_tty'].settings['prompt_delay'] = "0.0"
star.call(cluster, logdir, '*tty')
# cluster.console.join()
cluster.close_connections()
raise SystemExit("Session complete")
# Command line history support
if defaults.get('historyfile'):
histfile = os.path.expanduser(defaults['historyfile'])
try:
readline.read_history_file(histfile)
except IOError:
pass
readline.set_history_length(int(os.environ.get('HISTSIZE', 1000)))
if sys.version_info.major == 2:
# Workaround #32 - fix not backported to Python 2.X
atexit.register(safe_write_history_file, histfile)
else:
atexit.register(readline.write_history_file, histfile)
# Add TAB completion for *commands and remote file paths
tab_completion = radssh_tab_handler(cluster, star)
# With the cluster object, start interactive session
shell(cluster=cluster, logdir=logdir, defaults=defaults)
if __name__ == '__main__':
radssh_shell_main()
| [
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396,
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toontown/hood/GenericAnimatedBuilding.py | TheFamiliarScoot/open-toontown | 99 | 196320 | <reponame>TheFamiliarScoot/open-toontown<gh_stars>10-100
from toontown.hood import GenericAnimatedProp
class GenericAnimatedBuilding(GenericAnimatedProp.GenericAnimatedProp):
def __init__(self, node):
GenericAnimatedProp.GenericAnimatedProp.__init__(self, node)
def enter(self):
if base.config.GetBool('buildings-animate', False):
GenericAnimatedProp.GenericAnimatedProp.enter(self)
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Plots/Bar/NCL_bar_2.py | learn2free/GeoCAT-examples | 1 | 22387 | """
NCL_bar_2.py
===============
This script illustrates the following concepts:
- Drawing bars instead of curves in an XY plot
- Changing the aspect ratio of a bar plot
- Drawing filled bars up or down based on a Y reference value
- Setting the minimum/maximum value of the Y axis in a bar plot
- Using named colors to indicate a fill color
- Creating array of dates to use as x-axis tick labels
- Creating a main title
See following URLs to see the reproduced NCL plot & script:
- Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/bar_2.ncl
- Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/bar_2_lg.png
"""
import geocat.datafiles as gdf
import matplotlib.pyplot as plt
###############################################################################
# Import packages:
import numpy as np
import xarray as xr
from geocat.viz import util as gvutil
###############################################################################
# Read in data:
# Open a netCDF data file using xarray default engine and load the data into xarrays
ds = xr.open_dataset(gdf.get("netcdf_files/soi.nc"))
dsoik = ds.DSOI_KET
date = ds.date
num_months = np.shape(date)[0]
# Dates in the file are represented by year and month (YYYYMM)
# representing them fractionally will make ploting the data easier
# This produces the same results as NCL's yyyymm_to_yyyyfrac() function
date_frac = np.empty_like(date)
for n in np.arange(0, num_months, 1):
yyyy = int(date[n] / 100)
mon = (date[n] / 100 - yyyy) * 100
date_frac[n] = yyyy + (mon - 1) / 12
###############################################################################
# Plot
# Generate figure (set its size (width, height) in inches) and axes
plt.figure(figsize=(12, 6))
ax = plt.axes()
# Create a list of colors based on the color bar values
colors = ['red' if (value > 0) else 'blue' for value in dsoik[::8]]
plt.bar(date_frac[::8],
dsoik[::8],
align='edge',
edgecolor='black',
color=colors,
width=8 / 12,
linewidth=.6)
# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
x_minor_per_major=4,
y_minor_per_major=5,
labelsize=20)
# Use geocat.viz.util convenience function to set axes parameters
gvutil.set_axes_limits_and_ticks(ax,
ylim=(-3, 3),
yticks=np.linspace(-3, 3, 7),
yticklabels=np.linspace(-3, 3, 7),
xlim=(date_frac[40], date_frac[-16]),
xticks=np.linspace(1900, 1980, 5))
# Use geocat.viz.util convenience function to set titles and labels
gvutil.set_titles_and_labels(ax,
maintitle="Darwin Southern Oscillation Index",
ylabel='Anomalies',
maintitlefontsize=28,
labelfontsize=20)
plt.show()
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] |
scheduler/views.py | saketbairoliya2/salescheduler | 0 | 46239 | from django.contrib.auth import login, authenticate
from django.contrib.auth.forms import UserCreationForm
from django.shortcuts import render, redirect
from django.http import HttpResponse, HttpResponseRedirect, HttpResponseBadRequest
from django.contrib.auth.decorators import login_required
from django.views.decorators.http import require_GET, require_POST
from .forms import AppointmentSlotsForm, DemoScheduleForm, CalendarAttachForm
from .models import DemoAppointment
from oauth2client.contrib import xsrfutil
import httplib2
from oauth2client.client import OAuth2WebServerFlow
from googleapiclient.discovery import build
from django.conf import settings
import datetime
FLOW=OAuth2WebServerFlow(
client_id=settings.G_CLIENT_ID,
client_secret=settings.G_CLIENT_SECRET,
scope=['https://www.googleapis.com/auth/calendar'],
redirect_uri=settings.G_REDIRECT_URI
)
@login_required
def index(request):
if request.method == 'POST':
form = AppointmentSlotsForm(request.POST)
if form.is_valid():
form.save(request.user)
return render(request, 'scheduler/slot_booked.html')
else:
form = AppointmentSlotsForm()
calendar_attached = False
if request.user.caleder.first() != None:
calendar_attached = True
return render(request, 'scheduler/landing.html',
{'form':form, 'attach_calender':calendar_attached})
def signup(request):
if request.method == 'POST':
form = UserCreationForm(request.POST)
if form.is_valid():
form.save()
username = form.cleaned_data.get('username')
raw_password = form.cleaned_data.get('password1')
user = authenticate(username=username, password=<PASSWORD>)
login(request, user)
return redirect('scheduler:index')
else:
form = UserCreationForm()
return render(request, 'registration/signup.html', {'form': form})
def available_slots(request, year=None, month=None, day=None):
if year == None or month == None or day == None:
return render(request, 'scheduler/schedule_demo.html')
if request.method == 'GET':
form = DemoScheduleForm(year=int(year),month=int(month), day=int(day))
return render(request, 'scheduler/schedule_demo.html', {'form':form})
if request.method == 'POST':
form = DemoScheduleForm(request.POST, year=int(year),month=int(month), day=int(day))
if form.is_valid():
form.save()
appointments = DemoAppointment.objects.all()
return render(request, 'scheduler/request_calls.html', {'appointments':appointments})
return render(request, 'scheduler/schedule_demo.html')
def google_calender_permission(request):
FLOW.params['state'] = xsrfutil.generate_token(settings.SECRET_KEY,
request.user)
FLOW.params['access_type'] = 'offline'
authorize_url = FLOW.step1_get_authorize_url()
return HttpResponseRedirect(authorize_url)
def google_auth_return(request):
if not xsrfutil.validate_token(settings.SECRET_KEY, request.GET.get('state').encode("utf-8"),request.user):
return HttpResponseBadRequest()
credential = FLOW.step2_exchange(request.GET)
#print(credential.to_json())
http = httplib2.Http()
http = credential.authorize(http)
service = build("calendar", "v3", http=http)
calendar_data = service.calendarList().list().execute()
for calendar in calendar_data['items']:
if 'primary' in calendar and calendar['primary'] == True:
form = CalendarAttachForm({'calender_id':calendar['id'], 'refresh_token':credential.refresh_token})
print('here')
if form.is_valid():
print('here')
form.save(request.user)
print(form.errors)
return redirect('scheduler:index')
return redirect('scheduler:index')
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877,
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14952,
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] |
src/__init__.py | smkell/pyfbl | 0 | 51303 | from flags import Flags
class PositionElgibility(Flags):
""" Bitwise enum for marking a player's egibility.abs
"""
catcher = ()
first_base = ()
second_base = ()
third_base = ()
short_stop = ()
outfield = ()
left_field = ()
center_field = ()
right_field = ()
designated_hitter = ()
pitcher = ()
starting_pitcher = ()
relief_pitcher = () | [
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api/files/api/app/google_billing.py | trackit/trackit-legacy | 2 | 116840 | import json
from app import app
from app.msol_util import checksum, day_allocations
from app.gcloud.buckets import get_billing_files, download_billing_file
from models import db, User, GoogleCloudIdentity
from datetime import datetime, time
from time import strptime, mktime
import oauth2client
import httplib2
import msol_util
import apiclient
import flask
import json
import re
class GoogleCloudException(Exception):
pass
class GoogleCloudBillingException(GoogleCloudException):
pass
class GoogleCloudBillingValueException(GoogleCloudBillingException):
pass
class GoogleCloudBillingFormatException(GoogleCloudBillingException):
pass
def parse_billing_json(json_billing_data):
try:
if type(json_billing_data) == str:
return json.loads(json_billing_data)
elif type(json_billing_data) == file:
return json.load(json_billing_data)
except ValueError:
raise GoogleCloudBillingFormatException('Failed to parse JSON data.')
def get_billing_record_raw(billing_file, billing_data):
if type(billing_data) == list:
for record in billing_data:
yield record
else:
raise GoogleCloudBillingFormatException('Billing data is not a list.')
def get_all_line_items(identity, billing_bucket, since=None):
first_month_string = since.strftime('%Y-%m') if since else ''
for raw_file in get_billing_files(identity, billing_bucket):
match = re.search(r'\d{4}-\d{2}-\d{2}', raw_file['name'])
file_month_string = datetime.strptime(match.group(), '%Y-%m-%d').strftime('%Y-%m')
if file_month_string < first_month_string:
continue
print raw_file['name']
response, billing_data = download_billing_file(identity, raw_file)
billing_json = parse_billing_json(billing_data)
for record in get_billing_record_raw(raw_file, billing_json):
yield record
class ResourceByDayAccumulator(object):
def __init__(self):
self._resource_by_day = {}
self._time_cache = CachedTimeParse()
def __call__(self, line_item):
rid = line_item['lineItemId']
description = line_item['description']
account = line_item['accountId']
project_number = line_item['projectNumber']
project_name = project_number if not 'projectName' in line_item else line_item['projectName']
usage_start = self._time_cache[line_item['startTime']]
usage_end = self._time_cache[line_item['endTime']]
cost = parse_float(line_item['cost']['amount'])
credits = 0
if 'credits' in line_item:
for credit in line_item['credits']:
credits += parse_float(credit['amount'])
day = usage_end.date()
key = (day, rid, project_number)
if key not in self._resource_by_day:
self._resource_by_day[key] = dict(description=description,
project_name=project_name,
product=rid.rsplit('/', 1)[0],
date=day,
cost=cost + credits,
account=account,
rid=rid,
id=checksum(account, *map(str, key)))
else:
self._resource_by_day[key]['cost'] += cost
self._resource_by_day[key]['cost'] += credits
def results(self):
for by_day in self._resource_by_day.itervalues():
by_day = dict(by_day)
yield by_day
def nonempty_parse(parser):
def parse(val):
if val:
return parser(val)
return parse
parse_json = nonempty_parse(lambda v: json.loads(v))
parse_time = nonempty_parse(lambda v: datetime.fromtimestamp(mktime(strptime(v.rsplit('-', 1)[0], '%Y-%m-%dT%H:%M:%S'))))
parse_float = nonempty_parse(float)
parse_str = nonempty_parse(lambda v: v)
class CachedTimeParse(dict):
def __missing__(self, time_str):
parsed = parse_time(time_str)
self[time_str] = parsed
return parsed
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] |
renderer.py | shrionit/MyGameEngine | 0 | 96328 | import cv2
from glfw.GLFW import *
from OpenGL.GL import *
from PIL import Image
from model.loader import *
from model.model import *
from shader import *
from tool.gui import *
from tool.mmath import *
from tool.color import *
shaderProp = {
'renderprop': DProp({
'isvisible': True,
'img': 0,
'gamma': 1.0
}),
'lightprop': DProp({
'isvisible': True,
'mode': ['blinn', 'phong'],
'shininess': 16.0,
'specDamp': 5.0,
'lightcolor': Color3(1.0, 1.0, 1.0),
'ambientcolor': Color3(1.0, 1.0, 1.0)
}),
'kernelprop': DProp({
'isvisible': True,
'row': [[0.0]*3]*3
})
}
data = [
1.0, 1.0, 1.0, # 0 Bottom Left
1.0, -1.0, 1.0, # 1 Top Left
-1.0, -1.0, 1.0, # 2 Top Right
-1.0, 1.0, 1.0 # 3 Bottom Right
]
# data = [e/2.0 for e in data]
indices = [
0, 1, 2,
2, 0, 3
]
texcord = [
1.0, 1.0, # 0
1.0, 0.0, # 1
0.0, 0.0, # 2
0.0, 1.0, # 3
]
data = np.array(data, dtype=np.float32)
indices = np.array(indices, dtype=np.int32)
texcord = np.array(texcord, dtype=np.float32)
loader = Loader()
cv = cv2.VideoCapture(0)
class Renderer:
def __init__(self, camera, window, impl):
self.camera = camera
self.projection = camera.createProjectionMatrix()
self.window = window
self.window_width, self.window_height = glfwGetWindowSize(self.window)
self.entities = 0
self.shader = 0
self.hasIndex = False
self.framebuffer = glGenFramebuffers(1)
self.renderbuffer = glGenRenderbuffers(1)
self.framebuffer_attachment = glGenTextures(1)
self.initFrameBuffer()
self.viewer = impl
self.viewer.gengui(shaderProp)
def initFrameBuffer(self):
# binding frame buffer
glBindFramebuffer(GL_FRAMEBUFFER, self.framebuffer)
# binding attachment texture
glBindTexture(GL_TEXTURE_2D, self.framebuffer_attachment)
# binding render buffer (a direct rendered frame storage without conversion to img format)
glBindRenderbuffer(GL_RENDERBUFFER, self.renderbuffer)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, self.window_width, self.window_height,
0, GL_RGB, GL_UNSIGNED_BYTE, None)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
# glTexImage2D(
# GL_TEXTURE_2D, 0, GL_DEPTH24_STENCIL8, self.window_width, self.window_height, 0,
# GL_DEPTH_STENCIL, GL_UNSIGNED_INT, None
# )
glBindTexture(GL_TEXTURE_2D, 0)
glFramebufferTexture2D(
GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, GL_TEXTURE_2D, self.framebuffer_attachment, 0)
glRenderbufferStorage(
GL_RENDERBUFFER, GL_DEPTH24_STENCIL8, self.window_width, self.window_height)
glBindRenderbuffer(GL_RENDERBUFFER, 0)
glFramebufferRenderbuffer(
GL_FRAMEBUFFER, GL_DEPTH_STENCIL_ATTACHMENT, GL_RENDERBUFFER, self.renderbuffer)
if glCheckFramebufferStatus(GL_FRAMEBUFFER) != GL_FRAMEBUFFER_COMPLETE:
print("ERROR: Frame Buffer is Not complete")
glBindFramebuffer(GL_FRAMEBUFFER, 0)
def process(self, entities: Entity, shader: Shader, hasIndex=False, bgcolor=glm.vec4(0.1, 0.1, 0.1, 1.0)):
self.scene_shader = Shader('scene_frag', 'scene_vert')
self.scene_shader.attach()
print('Program LOG: \n', glGetProgramInfoLog(
self.scene_shader.shaderProgram))
self.scene = loader.loadRaw(
data, indices, texture=self.framebuffer_attachment, texcoord=texcord, vert_size=3)
self.scene_shader.detach()
self.entities = entities
self.shader = shader
self.hasIndex = hasIndex
self.shader.attach()
self.shader.loadProjectionMatrix(self.projection)
self.bgcolor = bgcolor
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
glEnable(GL_DEPTH_TEST)
glEnable(GL_BLEND)
glCullFace(GL_BACK)
print(
f'frame_shader => {self.scene_shader.shaderProgram}, scene_shader => {self.shader.shaderProgram}')
self.viewer.giveshader(
DProp({
'frame_shader': self.scene_shader,
'scene_shader': self.shader
})
)
print('framebf = > ', self.framebuffer_attachment)
self.viewer.newframe(self.framebuffer_attachment)
def assignCamToTex(self, texid):
ret, frame = cv.read()
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
newimage = Image.fromarray(img)
newimg_data = newimage.transpose(
Image.FLIP_TOP_BOTTOM).convert('RGBA').tobytes()
glBindTexture(GL_TEXTURE_2D, texid)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, newimage.width,
newimage.height, 0, GL_RGBA, GL_UNSIGNED_BYTE, newimg_data)
def processinput(self):
if glfwGetKey(self.window, eval('GLFW_KEY_UP')) == GLFW_PRESS:
shaderProp['lightprop'].specDamp += 1
self.shader.putSingleValueAt(
'damp', shaderProp['lightprop'].specDamp)
if glfwGetKey(self.window, GLFW_KEY_DOWN) == GLFW_PRESS:
shaderProp['lightprop'].specDamp -= 1
self.shader.putSingleValueAt(
'damp', shaderProp['lightprop'].specDamp)
def renderframe(self):
glEnable(GL_CULL_FACE)
self.shader.attach()
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
self.projection = self.camera.createProjectionMatrix()
self.shader.loadProjectionMatrix(self.projection)
glClearColor(*self.bgcolor)
self.processinput()
for entity in self.entities:
self.shader.putDataInUniformLocation('time', glfwGetTime())
view = self.camera.createViewMatrix()
self.shader.loadViewMatrix(glm.value_ptr(view))
self.camera.movement()
trans = entity.updatemat(createTransformationMatrix)
self.shader.loadTransformationMatrix(glm.value_ptr(
trans
))
if self.shader._default_frag != entity.entity_shader:
self.shader.updateShader(entity.entity_shader)
entity.bind()
if entity.id == 'dummy':
entity.setrotation(glm.vec3(0.0, 0.2*glfwGetTime(), 0.0))
entity.updateTex(self.framebuffer_attachment)
mode = entity.getmode()
if mode == 'WIRE':
glPolygonMode(GL_FRONT_AND_BACK, GL_LINE)
mode = GL_TRIANGLES
elif mode == 'VERT':
glPolygonMode(GL_FRONT_AND_BACK, GL_FILL)
glPointSize(3.0)
mode = GL_POINTS
else:
glPolygonMode(GL_FRONT_AND_BACK, GL_FILL)
mode = GL_TRIANGLES
if not self.hasIndex:
glDrawArrays(
mode, 0, entity.getmodel().v_count()
)
else:
glDrawElements(
mode, entity.getmodel().i_count(),
GL_UNSIGNED_INT, None
)
entity.unbind()
def renderscene(self):
glEnable(GL_FRAMEBUFFER_SRGB)
glDisable(GL_CULL_FACE)
self.scene_shader.attach()
self.scene.bind()
glDisable(GL_DEPTH_TEST)
glDrawElements(
GL_TRIANGLES, self.scene.i_count(),
GL_UNSIGNED_INT, None
)
self.viewer.show()
self.scene.unbind()
glEnable(GL_DEPTH_TEST)
def dispose(self):
self.scene_shader.stop()
self.shader.stop()
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devilry/project/common/devilry_test_runner.py | devilry/devilry-django | 29 | 160005 | <reponame>devilry/devilry-django
import warnings
from django.test.runner import DiscoverRunner
#from django.utils.deprecation import RemovedInDjango20Warning
class DevilryTestRunner(DiscoverRunner):
def setup_test_environment(self, **kwargs):
# warnings.filterwarnings('ignore', category=RemovedInDjango)
super(DevilryTestRunner, self).setup_test_environment(**kwargs)
warnings.filterwarnings('ignore', category=DeprecationWarning)
warnings.filterwarnings('ignore', category=ResourceWarning)
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agent/python/lib/agentdb.py | herry13/nuri | 0 | 111420 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# (c) 2014, Herry <<EMAIL>>
#
# This file is part of Nuri
#
# Nuri is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# Nuri is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with Ansible. If not, see <http://www.gnu.org/licenses/>.
import sys
import json
import urlparse
import sqlite3 as sqlite
# table "agents":
# - name VARCHAR
# - address VARCHAR
# - port INT
#
class AgentDB(object):
tablename = 'agents'
def __init__(self, config):
self.db_file = config['db_file']
self.conn = sqlite.connect(config['db_file'])
self.logger = config['logger']
# check table existence
with self.conn:
if not self.__table_exists():
self.conn.cursor().execute("CREATE TABLE " + self.tablename + "(name VARCHAR, address VARCHAR, port INT)")
self.conn.commit()
if not self.__table_exists():
raise Exception('cannot create table ' + self.tablename)
def __table_exists(self):
return (len(self.__query("SELECT name FROM sqlite_master WHERE type='table' AND name='" + self.tablename + "'")) > 0)
def __query(self, sql):
with self.conn:
return self.conn.cursor().execute(sql).fetchall()
def __get(self, where=''):
sql = 'SELECT name,address,port FROM ' + self.tablename
sql += ' WHERE ' + where if len(where) > 0 else ''
agents = {}
for row in self.__query(sql):
agents[row[0]] = {'sfpAddress': row[1], 'sfpPort': row[2]}
return agents
# Add agents
def add(self, agents):
with self.conn:
c = self.conn.cursor()
for key in agents:
address = str(agents[key]['sfpAddress'])
port = str(int(agents[key]['sfpPort']))
c.execute('INSERT INTO ' + self.tablename + " VALUES ('" + key + "','" + address + "'," + port + ")")
self.conn.commit()
# return return by name
def get_by_name(self, name):
where = "name='" + name + "'"
return self.__get(where)
# Return all agents
def get_all(self):
return self.__get()
# delete all agents
def delete_all(self):
with self.conn:
c = self.conn.cursor()
c.execute("DELETE FROM " + self.tablename)
self.conn.commit()
# delete agent by name
def delete_by_name(self, name):
c = self.conn.cursor()
c.execute("DELETE FROM " + self.tablename + " WHERE name='" + name + "'")
self.conn.commit()
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12,
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1311,
29889,
13082,
29889,
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580,
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2
] |
illud/ansi/escape_codes/control.py | AustinScola/illud | 1 | 117352 | """ANSI escape code prefixes for control escape codes."""
ESCAPE = '\x1b'
CONTROL_SEQUENCE_INTRODUCER = ESCAPE + '['
| [
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] |
agc/agc007/agc007a.py | c-yan/atcoder | 1 | 19495 | H, W = map(int, input().split())
A = [input() for _ in range(H)]
if H + W - 1 == sum(a.count('#') for a in A):
print('Possible')
else:
print('Impossible')
| [
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libs/clustermatch/settings.py | miltondp/clustermatch-gene-expr | 0 | 1613851 | """
General settings. This file is intended to be modified by the user. Each entry
also provides an alternative way to specify its value using an environment
variable.
"""
# Instead of changing this file, you can also use the environment variable name
# specified for each entry (environment variables supersede these settings).
# Specifies the main directory where all data and results generated are stored.
# When setting up the environment for the first time, input data will be
# automatically downloaded into a subfolder of ROOT_DIR.
#
# Default: if not specified (None), it defaults to the 'cm_gene_expr' subfolder
# in the temporary directory of the operating system (i.e. '/tmp/cm_gene_expr'
# in Unix systems).
# Environment variable: CM_ROOT_DIR
ROOT_DIR = None
# Specifies the directory where the manuscript git repository was
# cloned/downloaded. If None, manuscript figures and other related files will
# not be generated.
#
# Default: None
# Environment variable: CM_MANUSCRIPT_DIR
MANUSCRIPT_DIR = None
#
# CPU usage
#
# Amount of cores to use for general usage.
#
# Default: half of available cores.
# Environment variable: CM_N_JOBS
N_JOBS = None
# Number of cores to use for low-computational tasks (IO, etc). This number
# can be greater than N_JOBS.
#
# Default: same as N_JOBS.
# Environment variable: CM_N_JOBS_LOW
N_JOBS_LOW = None
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genesis/__main__.py | gjolly/genesis | 1 | 181855 | <reponame>gjolly/genesis<gh_stars>1-10
import genesis.build
if __name__ == '__main__':
genesis.build.main()
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bcs-ui/backend/helm/toolkit/deployer.py | jamesgetx/bk-bcs | 0 | 193054 | <gh_stars>0
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
import contextlib
import logging
import tempfile
from pathlib import Path
from typing import Dict, List, NewType, Optional, Union
import attr
from celery import shared_task
from django.utils.crypto import get_random_string
from backend.helm.helm import models
from backend.helm.helm.providers.constants import PUBLIC_REPO_URL
from backend.helm.toolkit.kubehelm.helm import KubeHelmClient
from backend.helm.toolkit.kubehelm.options import RawFlag
from backend.packages.blue_krill.data_types.enum import StructuredEnum
from backend.utils import client as bcs_client
from backend.utils.error_codes import error_codes
logger = logging.getLogger(__name__)
ArgsDict = NewType('ArgsDict', Dict[str, Union[str, List[RawFlag]]])
VALUESFILE_KEY = '--valuesfile'
class HelmOperation(str, StructuredEnum):
INSTALL = 'install'
UPGRADE = 'upgrade'
UNINSTALL = 'uninstall'
ROLLBACK = 'rollback'
@attr.s(auto_attribs=True)
class ReleaseArgs:
"""HelmDeployer 的执行参数.
options 有序示例 [
{"--valuesfile": {"file_name": "", "file_content": ""}},
{"--set": "a=1"},
{"--valuesfile": {"file_name": "", "file_content": ""}}
{"--post-renderer": {"bcs_inject_data": ""}}
]
其他参考官方 options https://helm.sh/docs/helm/helm_install/#options
"""
project_id: str
cluster_id: str
# release name
name: str
namespace: str
operator: str
chart_url: str
options: List[RawFlag] = attr.field(factory=list)
@classmethod
def from_dict(cls, init_data: Dict) -> 'ReleaseArgs':
field_names = [f.name for f in attr.fields(cls)]
return cls(**{k: v for k, v in init_data.items() if k in field_names})
class HelmDeployer:
"""
only for helm3 use absolute url as chart args
"""
def __init__(self, access_token: str, helm_args: ReleaseArgs):
self.access_token = access_token
self.helm_args = helm_args
def install(self):
self.run_with_helm(HelmOperation.INSTALL)
def upgrade(self):
self.run_with_helm(HelmOperation.UPGRADE)
def uninstall(self):
self.run_with_helm(HelmOperation.UNINSTALL)
def run_with_helm(self, op: str):
"""执行 Helm 命令
:param op: helm 操作, 如 install, upgrade 等
"""
err_msg = ''
with bcs_client.make_helm_client(
project_id=self.helm_args.project_id,
cluster_id=self.helm_args.cluster_id,
access_token=self.access_token,
) as (client, err):
if err is not None:
err_msg = f'make helm client failed for cluster({self.helm_args.cluster_id}): {err}'
if not err_msg:
try:
self._run_with_helm(client, op)
except Exception as e:
err_msg = f'helm {op} failed in cluster({self.helm_args.cluster_id}): {e}'
if err_msg:
raise error_codes.ComponentError(err_msg)
def _run_with_helm(self, helm_client: KubeHelmClient, op: str):
self._inject_auth_options()
with self._reconfigure_values_and_post_renderer():
if op == HelmOperation.UNINSTALL:
helm_client.uninstall(self.helm_args.name, self.helm_args.namespace)
elif op in [HelmOperation.INSTALL, HelmOperation.UPGRADE]:
helm_client.do_install_or_upgrade(
op,
self.helm_args.name,
self.helm_args.namespace,
self.helm_args.chart_url,
self.helm_args.options,
)
else:
raise NotImplementedError(f'unsupported op {op}')
def _inject_auth_options(self):
"""注入用户凭证"""
# chart_url 如 'http://repo.example.com/bcs-public-project/helm-public-repo/charts/xxx-operator-0.5.0.tgz'
chart_url = self.helm_args.chart_url
repo_url = chart_url.partition('/charts/')[0]
if repo_url == PUBLIC_REPO_URL: # 平台公共仓库不需要auth信息
return
repository = models.Repository.objects.filter(
url__startswith=repo_url, project_id=self.helm_args.project_id
).first()
if not repository: # 非平台chart repo url，由用户决定--username xxx --password xxx
return
credentials = models.RepositoryAuth.objects.get(repo=repository).credentials_decoded
self.helm_args.options.extend(
[{'--username': credentials['username']}, {'--password': credentials['password']}]
)
@contextlib.contextmanager
def _reconfigure_values_and_post_renderer(self):
"""整理 values, 增加 post-renderer
TODO 增加 ytt 的 post-renderer 支持
"""
idx_list = self._find_valuesfile()
if not idx_list:
yield
else:
# 如果 options 包含 --valuesfile, 需要生成临时 values 文件
with tempfile.TemporaryDirectory() as temp_dir:
# 为了保留原始 options 的顺序，通过找到索引重新赋值
for idx in idx_list:
# --valuesfile 非 helm 官方 flag, 需要转换成 --values
self.helm_args.options[idx] = {
'--values': self._write_values(temp_dir, self.helm_args.options[idx][VALUESFILE_KEY])
}
yield
def _find_valuesfile(self) -> List[int]:
"""查找 options 中 --valuesfile 的配置位置"""
idx_list = []
for idx, flag in enumerate(self.helm_args.options):
if isinstance(flag, dict) and VALUESFILE_KEY in flag.keys():
idx_list.append(idx)
return idx_list
def _write_values(self, temp_dir: str, valuesfile: Dict[str, str]) -> str:
"""将 valuesfile 中的内容写入文件
:param valuesfile: {'file_name': '', 'file_content': ''}
:return 写入的文件位置
"""
path = Path(temp_dir) / valuesfile['file_name']
path.write_text(valuesfile['file_content'])
return str(path.resolve())
def make_valuesfile_flag(values: str, file_name: Optional[str] = None) -> RawFlag:
return RawFlag(
{VALUESFILE_KEY: {'file_content': values, 'file_name': file_name or f'{get_random_string(12)}.yaml'}}
)
@shared_task
def helm_install(access_token: str, release_args: ArgsDict) -> Optional[str]:
try:
deployer = HelmDeployer(access_token=access_token, helm_args=ReleaseArgs.from_dict(release_args))
deployer.install()
except Exception as e:
return _generate_err_msg(HelmOperation.INSTALL, release_args, e)
@shared_task
def helm_upgrade(access_token: str, release_args: ArgsDict) -> Optional[str]:
try:
deployer = HelmDeployer(access_token=access_token, helm_args=ReleaseArgs.from_dict(release_args))
deployer.upgrade()
except Exception as e:
return _generate_err_msg(HelmOperation.UPGRADE, release_args, e)
@shared_task
def helm_uninstall(access_token: str, release_args: ArgsDict) -> Optional[str]:
try:
deployer = HelmDeployer(access_token=access_token, helm_args=ReleaseArgs.from_dict(release_args))
deployer.uninstall()
except Exception as e:
err_msg = _generate_err_msg(HelmOperation.UNINSTALL, release_args, e)
# 忽略 release: not found 的错误
if 'release: not found' in err_msg:
return
return err_msg
def _generate_err_msg(op: str, release_args: ArgsDict, e: Exception) -> str:
err_msg = (
f"helm {op} {release_args['name']} -n {release_args['namespace']} failed "
f"in cluster({release_args['cluster_id']}): {e}"
)
logger.error(err_msg)
return err_msg
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] |
hw1/deco.py | Tymeade/otus-python | 0 | 35253 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from functools import update_wrapper, wraps
def disable(func):
"""
Disable a decorator by re-assigning the decorator's name
to this function. For example, to turn off memoization:
>>> memo = disable
"""
return func
def decorator(decorator_func):
"""
Decorate a decorator so that it inherits the docstrings
and stuff from the function it's decorating.
"""
return update_wrapper(decorator_func, 1)
def countcalls(func):
"""Decorator that counts calls made to the function decorated."""
@wraps(func)
def wrapped(*args, **kwargs):
wrapped.calls += 1
return func(*args, **kwargs)
wrapped.calls = 0
return wrapped
def memo(func):
"""
Memoize a function so that it caches all return values for
faster future lookups.
"""
memory = {}
@wraps(func)
def decorated(*args):
if args in memory:
return memory[args]
answer = func(*args)
memory[args] = answer
return answer
return decorated
def n_ary(func):
"""
Given binary function f(x, y), return an n_ary function such
that f(x, y, z) = f(x, f(y,z)), etc. Also allow f(x) = x.
"""
@wraps(func)
def wrapped(*args):
if len(args) == 1:
return args[0]
if len(args) == 2:
return func(*args)
return func(args[0], wrapped(*args[1:]))
return wrapped
def trace(ident):
"""Trace calls made to function decorated.
@trace("____")
def fib(n):
....
>>> fib(3)
--> fib(3)
____ --> fib(2)
________ --> fib(1)
________ <-- fib(1) == 1
________ --> fib(0)
________ <-- fib(0) == 1
____ <-- fib(2) == 2
____ --> fib(1)
____ <-- fib(1) == 1
<-- fib(3) == 3
"""
def deco(func):
@wraps(func)
def wrapped(*args, **kwargs):
arguments = [str(a) for a in args] + ["%s=%s" % (key, value) for key, value in kwargs.iteritems()]
argument_string = ",".join(arguments)
func_name = "%s(%s)" % (func.__name__, argument_string)
wrapped.call_level += 1
print ident * wrapped.call_level, "-->", func_name
answer = func(*args, **kwargs)
print ident * wrapped.call_level, "<--", func_name, "==", answer
wrapped.call_level -= 1
return answer
wrapped.call_level = 0
return wrapped
return deco
@memo
@countcalls
@n_ary
def foo(a, b):
return a + b
@countcalls
@memo
@n_ary
def bar(a, b):
return a * b
@countcalls
@trace("####")
@memo
def fib(n):
"""Some doc"""
return 1 if n <= 1 else fib(n-1) + fib(n-2)
def main():
print foo(4, 3)
print foo(4, 3, 2)
print foo(4, 3)
print "foo was called", foo.calls, "times"
print bar(4, 3)
print bar(4, 3, 2)
print bar(4, 3, 2, 1)
print "bar was called", bar.calls, "times"
print fib.__doc__
fib(3)
print fib.calls, 'calls made'
if __name__ == '__main__':
main()
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mitzasql/db/connection.py | vladbalmos/mitzasql | 69 | 165577 | # Copyright (c) 2021 <NAME> <<EMAIL>>
# Author: <NAME> <<EMAIL>>
# See LICENSE file
import time
from datetime import datetime
import copy
import urwid
import mysql.connector
from mysql.connector import (errorcode, errors)
from ..logger import (LoggerMixin, logger)
class Connection(LoggerMixin):
SIGNAL_EXCEPTION = 'exception'
QUERY_LOG = []
def __init__(self, connection_data, session_name=None):
self.set_log_prefix('Connection')
self.is_tcp = False
self.database = None
self._retry_count = 0
self.con = self._create_mysql_connection(connection_data)
self._connection_data = connection_data
try:
self.session_name = connection_data['name']
except KeyError:
self.session_name = session_name
urwid.register_signal(self.__class__, [self.SIGNAL_EXCEPTION])
def _create_mysql_connection(self, connection_data):
kwargs = {
'user': connection_data['username'],
'password': connection_data['password'],
'connection_timeout': 5,
'autocommit': True
}
protocol, host = self._extract_host(connection_data['host'])
if protocol == 'tcp':
kwargs['host'] = host
kwargs['port'] = connection_data['port']
self.is_tcp = True
else:
kwargs['unix_socket'] = host
if 'database' in connection_data and len(connection_data['database']) > 0:
kwargs['database'] = connection_data['database']
self.database = connection_data['database']
kwargs['get_warnings'] = True
con = mysql.connector.connect(**kwargs)
self._retry_count = 0
return con
@property
def fresh(self):
con_data = copy.deepcopy(self._connection_data)
if self.database:
con_data['database'] = self.database
elif 'database' in con_data:
del con_data['database']
con = self.__class__(con_data, self.session_name)
return con
def _retry_connection(self):
self.log_info("Disconnected. Retrying mysql connection...")
if self._retry_count >= 5:
return False
try:
self.con = self._create_mysql_connection(self._connection_data)
if self.database:
self.change_db(self.database)
return True
except errors.OperationalError as e:
self._retry_count += 1
time.sleep(1)
return self._retry_connection()
def _extract_host(self, host):
if host.startswith('unix://'):
protocol = 'unix'
hostname = host[7:]
else:
protocol = 'tcp'
hostname = host[6:]
return (protocol, hostname)
@property
def host(self):
protocol, host = self._extract_host(self._connection_data['host'])
return host
@property
def port(self):
return self._connection_data['port']
def close(self):
return self.con.close()
@property
def cursor(self):
return self.con.cursor(buffered=True)
@property
def dict_cursor(self):
return self.con.cursor(buffered=True, dictionary=True)
def query(self, query, params=None, dictionary=False, multi=False):
start = time.time()
self.log_debug('Query: %s. Params: %s', str(query), str(params))
try:
if dictionary is False:
cursor = self.cursor
else:
cursor = self.dict_cursor
result = cursor.execute(query, params=params, multi=multi)
if multi == True:
return result
return cursor
except errors.OperationalError as e:
# Retry connection if exception is "MySQL Connection not available"
self.log_exception('Query exception: %s', e)
if self._retry_connection():
return self.query(query, params=params, dictionary=dictionary, multi=multi)
raise e
except errors.Error as e:
self.log_exception('Query exception: %s', e)
raise e
finally:
now = time.time()
duration = now - start
Connection.QUERY_LOG.append((datetime.now(), query, params, duration))
def change_db(self, name):
self.database = name
query = 'USE `{0}`'.format(name);
self.query(query);
def is_fatal_error(self, error):
if isinstance(error, errors.OperationalError):
return True
if isinstance(error, errors.InterfaceError):
if error.msg == 'Use cmd_query_iter for statements with multiple queries.':
return False
if error.msg == 'Use multi=True when executing multiple statements':
return False
return True
if isinstance(errors, errors.NotSupportedError):
return True
return False
@classmethod
def factory(cls, connection_data, session_name=None):
try:
instance = cls(connection_data, session_name)
except mysql.connector.Error as err:
logger.exception('Connection exception %s', err)
return (None, err)
return (instance, None)
@classmethod
def test(cls, connection_data):
connection, err = cls.factory(connection_data)
if connection is None:
return (False, str(err))
connection.close()
return (True, None)
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] |
main.py | iandaamerican/ItsSpresDiscordBot | 0 | 98513 | import os
import requests
import json
import discord
from discord.ext import commands
client = commands.Bot(command_prefix=commands.when_mentioned_or("d/","D/"),help_command=None)
#api key references
my_secret = os.environ['TOKEN']
apiSecret = os.environ['apexApi']
#notify me when bot has come online
@client.event
async def on_ready():
print('bot is now online!!!!')
@client.event
async def on_message(message):
msg_content = message.content.lower()
#bot command
atCommandMap = ["!map"]
if any(word in msg_content for word in atCommandMap):
r = requests.get(f"https://api.mozambiquehe.re/maprotation?version=2&auth={apiSecret}")
if r is None:
print("api is not working")
else:
j=r.json()
#battleRoyale Maps
currentMapName = j["battle_royale"]["current"]["map"]
nextMapName = j["battle_royale"]["next"]["map"]
timeRemaining = j["battle_royale"]["current"]["remainingMins"]
await message.channel.send(f"Current Map: {currentMapName}\nNext Map: {nextMapName}\nTime Remaining: {timeRemaining}")
client.run(my_secret)
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migration/lib/metadata_view.py | HumanCellAtlas/migration-service | 0 | 140636 | <gh_stars>0
import json
from .schema_reference import SchemaReference
class MetadataView(dict):
"""
A view representing the data that exists in a metadata file. The contents are stored as a dictionary with a
specific pointer to the metadata schema that was responsible for generating the view of the data as it is currently.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
@staticmethod
def from_file(path_to_metadata_json_file: str):
with open(path_to_metadata_json_file, 'r') as metadata_file:
metadata_dict = json.load(metadata_file)
return MetadataView(**metadata_dict)
@property
def schema_reference(self) -> SchemaReference:
return SchemaReference(self['describedBy'])
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13.py | jakerella/advent2020 | 0 | 171386 | import sys
from helpers import read_data_file
print('\n************* Day 13 *************\n')
lines = read_data_file(sys.argv[1])
delay = int(lines[0])
bus_ids = list(map(lambda x:int(x) if x.isdigit() else x, lines[1].split(',')))
print(delay, bus_ids)
def part1():
next_bus = None
wait_time = 0
timestamp = delay
while next_bus is None:
for bus_id in filter(lambda x: type(x) is int, bus_ids):
if timestamp % bus_id == 0:
next_bus = bus_id
wait_time = timestamp - delay
timestamp += 1
print('bus', next_bus, 'will be here in', wait_time, ':', next_bus * wait_time)
def part2():
def get_bus(b):
if b == 'x':
return None
return (b, bus_ids.index(b))
bus_schedule = list(filter(lambda x: x != None, list(map(get_bus, bus_ids))))
# print(bus_schedule)
min_interval = bus_schedule[0][0]
timestamp = 0
for bus, index in bus_schedule[1:]:
while True:
if (timestamp + index) % bus == 0:
min_interval = bus * min_interval
break
timestamp += min_interval
print('start timestamp', timestamp)
part1()
part2()
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bronzeDataBuilder.py | lucaspettit/GifTheRipper | 0 | 62557 | import giphypop
import requests
from os import path, makedirs
from PIL import Image, ImagePalette
import io
import argparse
import json
import time
from Utils import init_dirs
limit = 100
giphy = giphypop.Giphy(api_key='')
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--src', type=str, required=True, help='Path to .JSON file with search terms')
parser.add_argument('--dest', type=str, required=True, help='Destination directory')
parser.add_argument('--limit', type=int, default=100, help='Limit the number of GIFs for each search')
parser.add_argument('--api-key', type=str, required=True, help='GIPHY API key')
args = parser.parse_args()
# verify limit value
if args.limit <= 0:
raise ValueError('parse_args: Invalid number for \'limit\'. Must be >= 1.')
# unpack api
if path.isfile(args.api_key):
ext = path.splitext(args.api_key)[-1]
if ext == '.json':
j = json.load(args.api_key)
if 'api' not in j:
raise ValueError('parse_args: Cannot find API. JSON file does not contain key \'api\'.')
args.api_key = j['api']
elif ext == '.txt':
with open(args.api_key) as f:
args.api_key = f.readline().strip('\n').strip()
else:
raise ValueError('parse_args: Cannot find API. Unrecognized file extension')
return parser.parse_args()
# function takes a GIF and returns a list of images
def gif2jpg(gifbytes):
try:
img = Image.open(io.BytesIO(gifbytes))
except IOError:
return []
i = 0
palette = img.getpalette()
images = []
try:
while True:
img.putpalette(palette)
next_image = Image.new("RGBA", img.size)
next_image.paste(img)
images.append(next_image.copy())
i += 1
img.seek(i)
except EOFError:
pass
return images
# load and validate arguments
args = parse_args()
dirnames = init_dirs()
logdir = dirnames['log']
resdir = dirnames['res']
configdir = dirnames['config']
if not path.isdir(args.dest):
makedirs(args.dest)
rootdir = args.dest
setname = path.basename(path.splitext(args.dest)[0])
logfilename = path.join(logdir, '%s-Log.txt' % setname)
configfilename = path.join(configdir, '%s-Config.json' % setname)
# load search words (type = list)
with open(args.src) as f:
categories = json.load(f)
if not isinstance(categories, dict):
raise ValueError('Cannot infer .json format')
# load config file
if path.isfile(configfilename):
with open(configfilename) as f:
config = json.load(f)
# mesh them together
config_titles = list(config.keys())
for config_tile in config_titles:
for category_title in categories.keys():
if category_title not in config:
config[category_title] = 0
else:
idx = config_titles.index(category_title)
del config_titles[idx]
for title in config_titles:
del config[title]
# create new config file
else:
config = {
title: {
term: 0 for term in terms
} for title, terms in categories.items()
}
with open(configfilename, 'w') as f:
json.dump(config, f)
categories = config
if not path.isfile(logfilename):
open(logfilename, 'w').close()
for category, terms in categories.items():
# create new directory if needed
outdir = path.join(rootdir, category)
if not path.isdir(outdir):
makedirs(outdir)
# write log file
logstr = 'category: %s\n' % category
with open(logfilename, 'a+') as f:
f.write(logstr)
print(logstr[:-1])
for term, start_index in terms.items():
logstr = ' term : %s\n' % term
logstr += ' start index: %d\n' % start_index
with open(logfilename, 'a+') as f:
f.write(logstr)
print(logstr[:-1])
# query for each term
start_t = time.time()
success = 0
num_url_failed = 0
num_save_failed = 0
for i, gif in enumerate(giphy.search(term, limit=limit)):
if (i + 1) % 10 == 0:
end_t = time.time()
logstr = ' ------------------------\n'
logstr += ' current index : %d\n' % (i + start_index)
logstr += ' gifs downloaded: %d\n' % success
logstr += ' url fails : %d\n' % num_url_failed
logstr += ' save fails : %d\n' % num_save_failed
logstr += ' time : %.2f\n' % (end_t - start_t)
with open(logfilename, 'a+') as f:
f.write(logstr)
print(logstr[:-1])
success = 0
num_url_failed = 0
num_save_failed = 0
# save config
config[category][term] = i + start_index
with open(configfilename, 'w') as f:
json.dump(config, f, indent=2)
start_t = time.time()
try:
url = gif.media_url
try:
r = requests.get(url)
data = r.content
filename = '%s_%d.gif' % (term, i)
open(path.join(outdir, filename), "wb+").write(data)
success += 1
except Exception as e:
num_save_failed += 1
except Exception as e:
num_url_failed += 1
print('done')
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Guitar Training Remote/music_theory/mode.py | keremkoseoglu/Python-Library | 3 | 31316 | import random
from music_theory.note import Note
class Mode:
def __init__(self):
self._modes = [
# Major oriented
"Ionian",
"Dorian",
"Phrygian",
"Lydian",
"Mixo",
"Aeolian",
"Locrian",
# Melodic minor oriented
"Jazz minor",
"Dorian b2",
"Lydian aug",
"Lydian dom",
"Mixo b6",
"Half dim",
"Altered"
]
def get_mode_list(self) -> []:
return self._modes
def get_random_mode(self) -> str:
random_note = Note().get_random_note()
random_mode = self.get_random_mode_type()
return random_note + " " + random_mode
def get_random_mode_type(self) -> str:
i = random.randint(0, len(self._modes) - 1)
return self._modes[i]
def get_random_modes(self, count: int, with_note=True) -> []:
output = []
note_obj = Note()
for c in range(count):
if with_note:
random_note = note_obj.get_random_note() + " "
else:
random_note = ""
random_mode = self.get_random_mode_type()
random_result = random_note + random_mode
output.append(random_result)
return output
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magma/uniquification.py | leonardt/magma | 167 | 183523 | from dataclasses import dataclass
from enum import Enum, auto
from magma.is_definition import isdefinition
from magma.logging import root_logger
from magma.passes import DefinitionPass
_logger = root_logger()
class MultipleDefinitionException(Exception):
pass
class UniquificationMode(Enum):
WARN = auto()
ERROR = auto()
UNIQUIFY = auto()
@dataclass(frozen=True)
class _HashStruct:
defn_repr: str
is_verilog: bool
verilog_str: bool
inline_verilog: tuple
def _make_hash_struct(definition):
repr_ = repr(definition)
inline_verilog = tuple()
for inline_str, connect_references in definition.inline_verilog_strs:
connect_references = tuple(connect_references.items())
inline_verilog += (inline_str, connect_references)
if hasattr(definition, "verilogFile") and definition.verilogFile:
return _HashStruct(repr_, True, definition.verilogFile, inline_verilog)
return _HashStruct(repr_, False, "", inline_verilog)
def _hash(definition):
hash_struct = _make_hash_struct(definition)
return hash(hash_struct)
class UniquificationPass(DefinitionPass):
def __init__(self, main, mode):
super().__init__(main)
self.mode = mode
self.seen = {}
self.original_names = {}
def _rename(self, ckt, new_name):
assert ckt not in self.original_names
self.original_names[ckt] = ckt.name
type(ckt).rename(ckt, new_name)
def __call__(self, definition):
for module in definition.bind_modules:
self._run(module)
name = definition.name
key = _hash(definition)
seen = self.seen.setdefault(name, {})
if key not in seen:
if self.mode is UniquificationMode.UNIQUIFY and len(seen) > 0:
suffix = "_unq" + str(len(seen))
new_name = name + suffix
self._rename(definition, new_name)
seen[key] = [definition]
else:
if self.mode is not UniquificationMode.UNIQUIFY:
assert seen[key][0].name == name
elif name != seen[key][0].name:
new_name = seen[key][0].name
self._rename(definition, new_name)
seen[key].append(definition)
def run(self):
super().run()
duplicated = []
for name, definitions in self.seen.items():
if len(definitions) > 1:
duplicated.append((name, definitions))
UniquificationPass.handle(duplicated, self.mode)
@staticmethod
def handle(duplicated, mode):
if len(duplicated):
msg = f"Multiple definitions: {[name for name, _ in duplicated]}"
if mode is UniquificationMode.ERROR:
error(msg)
raise MultipleDefinitionException([name for name, _ in duplicated])
elif mode is UniquificationMode.WARN:
warning(msg)
def _get_mode(mode_or_str):
if isinstance(mode_or_str, str):
try:
return UniquificationMode[mode_or_str]
except KeyError as e:
modes = [k for k in UniquificationMode.__members__]
raise ValueError(f"Valid uniq. modes are {modes}")
if isinstance(mode_or_str, UniquificationMode):
return mode_or_str
raise NotImplementedError(f"Unsupported type: {type(mode_or_str)}")
def reset_names(original_names):
for ckt, original_name in original_names.items():
type(ckt).rename(ckt, original_name)
# This pass runs uniquification according to @mode and returns a dictionary
# mapping any renamed circuits to their original names. If @mode is ERROR or
# WARN the returned dictionary should be empty.
def uniquification_pass(circuit, mode_or_str):
mode = _get_mode(mode_or_str)
pass_ = UniquificationPass(circuit, mode)
pass_.run()
return pass_.original_names
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] |
lib/PyAMF-0.7.2/pyamf/tests/remoting/__init__.py | MiCHiLU/google_appengine_sdk | 790 | 141857 | # Copyright (c) The PyAMF Project.
# See LICENSE.txt for details.
"""
Remoting tests.
@since: 0.1.0
"""
| [
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fable/fable_sources/fable/test/tst_show_calls.py | hickerson/bbn | 4 | 112983 | from __future__ import division
def run(args):
assert len(args) == 0
import libtbx.load_env
from cStringIO import StringIO
import os
op = os.path
t_dir = libtbx.env.under_dist(
module_name="fable", path="test/valid", test=op.isdir)
excluded_file_names = set("""\
blockdata_unnamed.f
""".splitlines())
from fable.command_line import show_calls
for file_name in os.listdir(t_dir):
if (not file_name.endswith(".f")): continue
if (file_name in excluded_file_names): continue
sys.stdout = StringIO()
show_calls.run(args=[op.join(t_dir, file_name)])
sys.stdout = sys.__stdout__
print "OK"
if (__name__ == "__main__"):
import sys
run(args=sys.argv[1:])
| [
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13,
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978,
1649,
1275,
376,
1649,
3396,
1649,
29908,
1125,
13,
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13,
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29889,
19218,
29961,
29896,
29901,
2314,
13,
2
] |
pytorch_toolkit/face_recognition/model/common.py | AnastasiaaSenina/openvino_training_extensions | 1 | 2316 | <reponame>AnastasiaaSenina/openvino_training_extensions
"""
Copyright (c) 2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from abc import abstractmethod
from functools import partial
import torch.nn as nn
class ModelInterface(nn.Module):
"""Abstract class for models"""
@abstractmethod
def set_dropout_ratio(self, ratio):
"""Sets dropout ratio of the model"""
@abstractmethod
def get_input_res(self):
"""Returns input resolution"""
from .rmnet_angular import RMNetAngular
from .mobilefacenet import MobileFaceNet
from .landnet import LandmarksNet
from .se_resnet_angular import SEResNetAngular
from .shufflenet_v2_angular import ShuffleNetV2Angular
from .backbones.se_resnet import se_resnet50, se_resnet101, se_resnet152
from .backbones.resnet import resnet50
from .backbones.se_resnext import se_resnext50, se_resnext101, se_resnext152
models_backbones = {'rmnet': RMNetAngular,
'mobilenetv2': MobileFaceNet,
'mobilenetv2_2x': partial(MobileFaceNet, width_multiplier=2.0),
'mobilenetv2_1_5x': partial(MobileFaceNet, width_multiplier=1.5),
'resnet50': partial(SEResNetAngular, base=resnet50),
'se_resnet50': partial(SEResNetAngular, base=se_resnet50),
'se_resnet101': partial(SEResNetAngular, base=se_resnet101),
'se_resnet152': partial(SEResNetAngular, base=se_resnet152),
'se_resnext50': partial(SEResNetAngular, base=se_resnext50),
'se_resnext101': partial(SEResNetAngular, base=se_resnext101),
'se_resnext152': partial(SEResNetAngular, base=se_resnext152),
'shufflenetv2': ShuffleNetV2Angular}
models_landmarks = {'landnet': LandmarksNet}
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] |
benchmark/bench_asynckafka.py | askuratovsky/asynckafka | 31 | 24075 | <gh_stars>10-100
import asyncio
from asynckafka import Producer, Consumer
import config
import utils
loop = asyncio.get_event_loop()
async def fill_topic_with_messages():
producer = Producer(
brokers=config.KAFKA_URL,
rdk_producer_config=config.RDK_PRODUCER_CONFIG,
rdk_topic_config=config.RDK_TOPIC_CONFIG,
)
producer.start()
messages_consumed = 0
print(f"Preparing benchmark. Filling topic {config.TOPIC} with "
f"{config.MESSAGE_NUMBER} messages of {config.MESSAGE_BYTES} bytes "
f"each one.")
await asyncio.sleep(0.1)
with utils.Timer() as timer:
for _ in range(config.MESSAGE_NUMBER):
messages_consumed += 1
await producer.produce(config.TOPIC, config.MESSAGE)
producer.stop()
print(f"The producer time to send the messages is {timer.interval} "
f"seconds.")
utils.print_statistics(timer.interval)
async def consume_the_messages_stream_consumer():
stream_consumer = Consumer(
brokers=config.KAFKA_URL,
topics=[config.TOPIC],
rdk_consumer_config=config.RDK_CONSUMER_CONFIG,
rdk_topic_config=config.RDK_TOPIC_CONFIG
)
stream_consumer.start()
messages_consumed = 0
print("Starting to consume the messages.")
with utils.Timer() as timer:
async for message in stream_consumer:
messages_consumed += 1
if messages_consumed == config.MESSAGE_NUMBER:
stream_consumer.stop()
print(f"The time used to consume the messages is {timer.interval} "
f"seconds.")
utils.print_statistics(timer.interval)
async def main_coro():
await fill_topic_with_messages()
await consume_the_messages_stream_consumer()
if __name__ == "__main__":
loop.run_until_complete(main_coro())
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2
] |
core/storage/job/gae_models_test.py | aman-roy/oppia | 2 | 147275 | <filename>core/storage/job/gae_models_test.py
# Copyright 2018 The Oppia Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for Oppia job models."""
from __future__ import absolute_import # pylint: disable=import-only-modules
from __future__ import unicode_literals # pylint: disable=import-only-modules
from core.platform import models
from core.tests import test_utils
(base_models, job_models) = models.Registry.import_models(
[models.NAMES.base_model, models.NAMES.job])
class JobModelTest(test_utils.GenericTestBase):
"""Tests for Oppia job models."""
def test_get_deletion_policy(self):
self.assertEqual(
job_models.JobModel.get_deletion_policy(),
base_models.DELETION_POLICY.NOT_APPLICABLE)
def test_is_cancelable(self):
"""The job is cancelable if its status is either queued or started."""
job = job_models.JobModel(
id='MyJobId', status_code=job_models.STATUS_CODE_NEW)
self.assertFalse(job.is_cancelable)
job.status_code = job_models.STATUS_CODE_QUEUED
self.assertTrue(job.is_cancelable)
job.status_code = job_models.STATUS_CODE_STARTED
self.assertTrue(job.is_cancelable)
job.status_code = job_models.STATUS_CODE_FAILED
self.assertFalse(job.is_cancelable)
job.status_code = job_models.STATUS_CODE_CANCELED
self.assertFalse(job.is_cancelable)
class JobModelSetUpJobsTest(test_utils.GenericTestBase):
"""Tests for Oppia job models with setUp."""
def setUp(self):
super(JobModelSetUpJobsTest, self).setUp()
job_models.JobModel(
id='MyJobId1', job_type='JobType1',
status_code=job_models.STATUS_CODE_FAILED).put()
job_models.JobModel(
id='MyJobId2', job_type='JobType2',
status_code=job_models.STATUS_CODE_STARTED).put()
job_models.JobModel(
id='MyJobId3', job_type='JobType2',
status_code=job_models.STATUS_CODE_COMPLETED).put()
def test_get_all_unfinished_jobs(self):
self.assertEqual(
job_models.JobModel.get_all_unfinished_jobs(3),
[job_models.JobModel.get_by_id('MyJobId2')])
def test_get_unfinished_jobs(self):
self.assertEqual(
job_models.JobModel.get_unfinished_jobs('JobType1').fetch(1), [])
self.assertEqual(
job_models.JobModel.get_unfinished_jobs('JobType2').fetch(1),
[job_models.JobModel.get_by_id('MyJobId2')])
def test_do_unfinished_jobs_exist(self):
self.assertFalse(job_models.JobModel.do_unfinished_jobs_exist(
'JobType1'))
self.assertTrue(job_models.JobModel.do_unfinished_jobs_exist(
'JobType2'))
job2 = job_models.JobModel.get('MyJobId2', strict=True)
job2.status_code = job_models.STATUS_CODE_COMPLETED
job2.put()
self.assertFalse(job_models.JobModel.do_unfinished_jobs_exist(
'JobType2'))
class ContinuousComputationModelTest(test_utils.GenericTestBase):
"""Tests for Oppia continuous computation models."""
def test_get_deletion_policy(self):
self.assertEqual(
job_models.ContinuousComputationModel.get_deletion_policy(),
base_models.DELETION_POLICY.NOT_APPLICABLE)
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] |
tests/potential/EamPotential/Al__born_exp_fs/test____init__.py | eragasa/pypospack | 4 | 12330 | import pytest
from pypospack.potential import EamPotential
symbols = ['Al']
func_pair_name = "bornmayer"
func_density_name = "eam_dens_exp"
func_embedding_name = "fs"
expected_parameter_names_pair_potential = []
expected_parameter_names_density_function = []
expected_parameter_names_embedding_function = []
expected_parameter_names = [
'p_AlAl_phi0', 'p_AlAl_gamma', 'p_AlAl_r0',
'd_Al_rho0', 'd_Al_beta', 'd_Al_r0',
'e_Al_F0', 'e_Al_p', 'e_Al_q', 'e_Al_F1', 'e_Al_rho0']
print(80*'-')
print("func_pair_name={}".format(func_pair_name))
print("func_density_name={}".format(func_density_name))
print("func_embedding_name={}".format(func_density_name))
print(80*'-')
def test____init__():
obj_pot = EamPotential(
symbols=symbols,
func_pair=func_pair_name,
func_density=func_density_name,
func_embedding=func_embedding_name)
assert type(obj_pot) is EamPotential
assert obj_pot.potential_type == 'eam'
assert type(obj_pot.symbols) is list
assert len(obj_pot.symbols) == len(symbols)
for i,v in enumerate(symbols):
obj_pot.symbols[i] = v
assert obj_pot.is_charge is False
assert type(obj_pot.parameter_names) is list
assert len(obj_pot.parameter_names) == len(expected_parameter_names)
for i,v in enumerate(expected_parameter_names):
obj_pot.parameter_names = v
if __name__ == "__main__":
# CONSTRUCTOR TEST
pot = EamPotential(symbols=symbols,
func_pair=func_pair_name,
func_density=func_density_name,
func_embedding=func_embedding_name)
print('pot.potential_type == {}'.format(\
pot.potential_type))
print('pot.symbols == {}'.format(\
pot.symbols))
print('pot.parameter_names == {}'.format(\
pot.parameter_names))
print('pot.is_charge == {}'.format(\
pot.is_charge))
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videoanalyst/evaluation/got_benchmark/datasets/lasot.py | TragedyN/SiamFCpp | 737 | 1616108 | from __future__ import absolute_import, print_function
import glob
import json
import os
import pickle
from typing import Dict
import numpy as np
from loguru import logger
from tqdm import tqdm
_VALID_SUBSETS = ['train', 'test']
class LaSOT(object):
r"""`LaSOT <https://cis.temple.edu/lasot/>`_ Datasets.
Publication:
``LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking``,
<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>,
<NAME>, <NAME>, and <NAME>., CVPR 2019.
Args:
root_dir (string): Root directory of dataset where sequence
folders exist.
subset (string, optional): Specify ``train`` or ``test``
subset of LaSOT.
"""
data_dict = {subset: dict() for subset in _VALID_SUBSETS}
def __init__(self,
root_dir,
subset='test',
return_meta=False,
check_integrity=True,
cache_path=None,
ignore_cache=False):
super(LaSOT, self).__init__()
subset = subset.split('_')
assert set(subset).issubset({'train', 'test'}), 'Unknown subset.'
self.root_dir = root_dir
self.subset = subset
self.return_meta = return_meta
# check seems useless, disabled
# if check_integrity:
# self._check_integrity(root_dir)
self.cache_path = cache_path
self.ignore_cache = ignore_cache
self.anno_files = sorted(
glob.glob(os.path.join(root_dir, '*/*/groundtruth.txt')))
self.seq_dirs = [
os.path.join(os.path.dirname(f), 'img') for f in self.anno_files
]
self.seq_names = [
os.path.basename(os.path.dirname(f)) for f in self.anno_files
]
# load subset sequence names
split_file = os.path.join(os.path.dirname(__file__), 'lasot.json')
with open(split_file, 'r') as f:
splits = json.load(f)
self.splits = splits
self.seq_names = []
for s in subset:
self.seq_names.extend(splits[s])
# Former seq_dirs/anno_files have been replaced by caching mechanism.
# See _ensure_cache for detail.
# image and annotation paths
# self.seq_dirs = [os.path.join(
# root_dir, n[:n.rfind('-')], n, 'img')
# for n in self.seq_names]
# self.anno_files = [os.path.join(
# os.path.dirname(d), 'groundtruth.txt')
# for d in self.seq_dirs]
self._ensure_cache()
self.seq_names = [
k for subset in self.subset
for k, _ in LaSOT.data_dict[subset].items()
]
self.seq_names = sorted(self.seq_names)
self.seq_datas = {
k: v
for subset in self.subset
for k, v in LaSOT.data_dict[subset].items()
}
def __getitem__(self, index):
r"""
Args:
index (integer or string): Index or name of a sequence.
Returns:
tuple: (img_files, anno) if ``return_meta`` is False, otherwise
(img_files, anno, meta), where ``img_files`` is a list of
file names, ``anno`` is a N x 4 (rectangles) numpy array, while
``meta`` is a dict contains meta information about the sequence.
"""
# if isinstance(index, six.string_types):
# if not index in self.seq_names:
# raise Exception('Sequence {} not found.'.format(index))
# index = self.seq_names.index(index)
if isinstance(index, int):
index = self.seq_names[index]
seq_data = self.seq_datas[index]
img_files = seq_data["img_files"]
anno = seq_data["anno"]
meta = seq_data["meta"]
# img_files = sorted(glob.glob(os.path.join(
# self.seq_dirs[index], '*.jpg')))
# anno = np.loadtxt(self.anno_files[index], delimiter=',')
if self.return_meta:
meta = self._fetch_meta(self.seq_dirs[index])
return img_files, anno, meta
else:
return img_files, anno
def __len__(self):
return len(self.seq_names)
def _check_integrity(self, root_dir):
seq_names = os.listdir(root_dir)
seq_names = [n for n in seq_names if not n[0] == '.']
if os.path.isdir(root_dir) and len(seq_names) > 0:
# check each sequence folder
for seq_name in seq_names:
seq_dir = os.path.join(root_dir, seq_name)
if not os.path.isdir(seq_dir):
print('Warning: sequence %s not exists.' % seq_name)
else:
# dataset not exists
raise Exception('Dataset not found or corrupted.')
def _fetch_meta(self, seq_dir):
seq_dir = os.path.dirname(seq_dir)
meta = {}
# attributes
for att in ['full_occlusion', 'out_of_view']:
att_file = os.path.join(seq_dir, att + '.txt')
meta[att] = np.loadtxt(att_file, delimiter=',')
# nlp
nlp_file = os.path.join(seq_dir, 'nlp.txt')
with open(nlp_file, 'r') as f:
meta['nlp'] = f.read().strip()
return meta
def _ensure_cache(self):
"""Perform all overheads related to cache (building/loading/check)
"""
# check if subset cache already exists in LaSOT.data_dict and is valid w.r.t. list.txt
if self._check_cache_for_current_subset():
return
# load subset cache into LaSOT.data_dict
cache_path = self._get_cache_path(cache_path=self.cache_path)
self.cache_path = cache_path
if all([os.path.isfile(p)
for p in self.cache_path.values()]) and not self.ignore_cache:
logger.info("{}: cache file exists: {} ".format(
LaSOT.__name__, cache_path))
self._load_cache_for_current_subset(cache_path)
if self._check_cache_for_current_subset():
logger.info(
"{}: record check has been processed and validity is confirmed for cache file: {} "
.format(LaSOT.__name__, cache_path))
return
else:
logger.info(
"{}: cache file {} not valid, rebuilding cache...".format(
LaSOT.__name__, cache_path))
# build subset cache in LaSOT.data_dict and cache to storage
self._build_cache_for_current_subset()
logger.info("{}: current cache file: {} ".format(
LaSOT.__name__, self.cache_path))
logger.info(
"{}: need to clean this cache file if you move dataset directory".
format(LaSOT.__name__))
logger.info(
"{}: consider cleaning this cache file in case of erros such as FileNotFoundError or IOError"
.format(LaSOT.__name__))
def _get_cache_path(self, cache_path: Dict[str, str] = None):
r"""Ensure cache_path.
If cache_path does not exist, turn to default set: root_dir/subset.pkl.
"""
if (cache_path is None) or any(
[not os.path.isfile(cache_path) for p in cache_path.values()]):
logger.info(
"{}: passed cache file {} invalid, change to default cache path"
.format(LaSOT.__name__, cache_path))
cache_path = {
subset: os.path.join(self.root_dir, subset + ".pkl")
for subset in self.subset
}
return cache_path
def _check_cache_for_current_subset(self) -> bool:
r""" check if LaSOT.data_dict[subset] exists and contains all record in seq_names
"""
is_valid_data_dict = all([subset in LaSOT.data_dict for subset in self.subset]) and \
(set([seq_name for subset in self.subset for seq_name in LaSOT.data_dict[subset].keys()]) == set(self.seq_names))
return is_valid_data_dict
def _build_cache_for_current_subset(self):
r"""Build cache for current subset (self.subset)
"""
root_dir = self.root_dir
subset = self.subset
for s in subset:
logger.info("{}: start loading {}".format(LaSOT.__name__, s))
seq_names = self.splits[s]
for seq_name in tqdm(seq_names):
seq_dir = os.path.join(root_dir, seq_name[:seq_name.rfind('-')],
seq_name)
img_files, anno, meta = self.load_single_sequence(seq_dir)
LaSOT.data_dict[s][seq_name] = dict(img_files=img_files,
anno=anno,
meta=meta)
with open(self.cache_path[s], "wb") as f:
pickle.dump(LaSOT.data_dict[s], f)
logger.info("{}: dump cache file to {}".format(
LaSOT.__name__, self.cache_path[s]))
def _load_cache_for_current_subset(self, cache_path: Dict[str, str]):
for subset in self.subset:
assert os.path.exists(
cache_path[subset]
), "cache_path does not exist: %s " % cache_path[subset]
with open(cache_path[subset], "rb") as f:
LaSOT.data_dict[subset] = pickle.load(f)
logger.info("{}: loaded cache file {}".format(
LaSOT.__name__, cache_path[subset]))
def load_single_sequence(self, seq_dir):
img_files = sorted(glob.glob(os.path.join(seq_dir, 'img/*.jpg')))
anno = np.loadtxt(os.path.join(seq_dir, "groundtruth.txt"),
delimiter=',')
assert len(img_files) == len(anno)
if self.return_meta:
meta = self._fetch_meta(seq_dir)
return img_files, anno, meta
else:
return img_files, anno, None
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2
] |
subscriptions/urls.py | praekeltfoundation/seed-stage-based-messaging | 1 | 144523 | <reponame>praekeltfoundation/seed-stage-based-messaging
from django.conf.urls import include, url
from rest_framework import routers
from . import views
router = routers.DefaultRouter()
router.register(r"subscriptions", views.SubscriptionViewSet)
router.register(r"failed-tasks", views.FailedTaskViewSet)
router.register(r"behind-subscriptions", views.BehindSubscriptionViewSet)
# Wire up our API using automatic URL routing.
# Additionally, we include login URLs for the browseable API.
urlpatterns = [
url(r"^api/v1/", include(router.urls)),
url(
r"^api/v1/subscriptions/(?P<subscription_id>.+)/send$",
views.SubscriptionSend.as_view(),
name="subscription-send",
),
url(
r"^api/v1/subscriptions/(?P<subscription_id>.+)/resend$",
views.SubscriptionResend.as_view(),
),
url(r"^api/v1/subscriptions/request$", views.SubscriptionRequest.as_view()),
url(r"^api/v1/user/token/$", views.UserView.as_view(), name="create-user-token"),
url(r"^api/v1/runsendestimate$", views.DailyEstimateRun.as_view()),
]
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2
] |
cdk/consoleme_ecs_service/nested_stacks/vpc_stack.py | avishayil/consoleme-ecs-service | 2 | 23622 | """
VPC stack for running ConsoleMe on ECS
"""
import urllib.request
from aws_cdk import (
aws_ec2 as ec2,
core as cdk
)
class VPCStack(cdk.NestedStack):
"""
VPC stack for running ConsoleMe on ECS
"""
def __init__(self, scope: cdk.Construct, id: str, **kwargs) -> None:
super().__init__(scope, id, **kwargs)
# VPC and security groups
vpc = ec2.Vpc(
self, 'Vpc',
max_azs=2
)
consoleme_sg = ec2.SecurityGroup(
self,
'LBSG',
vpc=vpc,
description='Consoleme ECS service load balancer security group',
allow_all_outbound=True
)
# Open ingress to the deploying computer public IP
my_ip_cidr = urllib.request.urlopen(
'http://checkip.amazonaws.com').read().decode('utf-8').strip() + '/32'
consoleme_sg.add_ingress_rule(
peer=ec2.Peer.ipv4(cidr_ip=my_ip_cidr),
connection=ec2.Port.tcp(port=443),
description='Allow HTTPS traffic'
)
redis_sg = ec2.SecurityGroup(
self,
'ECSG',
vpc=vpc,
description='Consoleme Redis security group',
allow_all_outbound=True
)
redis_sg.connections.allow_from(consoleme_sg, port_range=ec2.Port.tcp(
port=6379), description='Allow ingress from ConsoleMe containers')
self.vpc = vpc
self.redis_sg = redis_sg
self.consoleme_sg = consoleme_sg
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model_vc.py | casperwang/autovc | 2 | 166832 | import torch
import pdb
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_uniform_(
self.linear_layer.weight,
gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, x):
return self.linear_layer(x)
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert(kernel_size % 2 == 1)
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
class Encoder(nn.Module):
"""Encoder module:
"""
def __init__(self, dim_neck, dim_emb, freq): #Set up 5x1 ConvNorm * 3 and BLSTM * 2
# What is dim_emb ?
super(Encoder, self).__init__()
self.dim_neck = dim_neck #What is dim_neck? #irene: 這個參數用在61行，那個位置代表lstm的hidden size
self.freq = freq #What is freq? #irene: 參考paper 4.2，freq是downsampling/upsampling留下的取timestamp的frequency
convolutions = []
for i in range(3):
conv_layer = nn.Sequential(
ConvNorm(80+dim_emb if i==0 else 512,
512,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
nn.BatchNorm1d(512)) #Not so sure about what this is
#irene: 針對這次要訓練的batch做normalization，network中要加batchnorm的目的主要是要讓
# 該batch的每一筆feature有一樣的scaling，如果feature因為scale差很多，對output的影響
# 不一樣，做backpropagation時算得的gradient會不同，造成訓練困難
# 詳細可以參考李宏毅老師的課程影片 https://www.youtube.com/watch?v=BZh1ltr5Rkg
# OK 已了解!
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions) #Turns the list of Models into one big model I think
#irene: yes, 按照list裡的順序合成一個module
self.lstm = nn.LSTM(512, dim_neck, 2, batch_first=True, bidirectional=True) #Adds BLSTM * 2
def forward(self, x, c_org):
#what is x and c_org?
#irene: 呼叫Encoder時，執行forward的input，encoder的input 要trace code找一下這兩個個別是什麼
# 可是Encoder.forward()感覺是我們要在train.py自己呼叫的function?
#What is the preprocessing on the 3 lines below for?
#irene: 前兩行基本上是把它變成吃進model的 dimension, pytorch是 BxCxHxW
# (B: batch size, C: channel, H: height, W: weight)
# 第三行是把這兩個input concatenate，變成真的input
#pdb.set_trace()
x = x.squeeze(1).transpose(2,1)
c_org = c_org.unsqueeze(-1).expand(-1, -1, x.size(-1))
x = torch.cat((x, c_org), dim=1)
for conv in self.convolutions: #Apply the convolutions to x
x = F.relu(conv(x)) #F: is from python.nn functional #irene: should be torch.nn.functional
x = x.transpose(1, 2) #why transpose? #irene: I guess transpose back to the original format, there is a transpose in line 77
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x) #What is _?
#irene: self.lstm will return 2 values, but you don't need the second one,
# so you can use _ to represent immaterial variable
out_forward = outputs[:, :, :self.dim_neck]
out_backward = outputs[:, :, self.dim_neck:]
codes = []
for i in range(0, outputs.size(1), self.freq):
codes.append(torch.cat((out_forward[:,i+self.freq-1,:],out_backward[:,i,:]), dim=-1)) #???
# I think it is to combine the forward output and backward output of LSTM
return codes
class Decoder(nn.Module):
"""Decoder module:
"""
def __init__(self, dim_neck, dim_emb, dim_pre): #dim_neck seems to be Down1 and Down2? dim_emb seems to be E_S, dim_neck looks to be E_C
#There should be Up1, Up2 and Copy (from the paper)
super(Decoder, self).__init__()
self.lstm1 = nn.LSTM(dim_neck*2+dim_emb, dim_pre, 1, batch_first=True) #Why is there an extra LSTM?
#irene: 對應decoder裡面的第一個lstm，what do you mean extra?
convolutions = []
for i in range(3): # 5x1 ConvNorm * 3
conv_layer = nn.Sequential(
ConvNorm(dim_pre,
dim_pre,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
nn.BatchNorm1d(dim_pre))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
self.lstm2 = nn.LSTM(dim_pre, 1024, 2, batch_first=True) #only 2 LSTMs?
#The paper mentions a 1x1 Conv of size 80, four 5x1 ConvNorms with size 512, and a 5x1 ConvNorm with size 80. Where did they go :(
#irene: postnet
self.linear_projection = LinearNorm(1024, 80) #I assume this is the 5x1 ConvNorm? Or does this encompass the whole architecture mentioned on line 108?
#irene: yes
def forward(self, x): #I think it's run with input x, but there are supposed to be 3 inputs to the decoder, are they already concatenated beforehand?
#irene: yes, they are already concatenated before fed to decoder. line 215
#self.lstm1.flatten_parameters()
x, _ = self.lstm1(x)
x = x.transpose(1, 2)
for conv in self.convolutions:
x = F.relu(conv(x))
x = x.transpose(1, 2)
outputs, _ = self.lstm2(x)
decoder_output = self.linear_projection(outputs)
return decoder_output
class Postnet(nn.Module):
"""Postnet
- Five 1-d convolution with 512 channels and kernel size 5
"""
def __init__(self):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(80, 512,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(512))
)
for i in range(1, 5 - 1):
self.convolutions.append(
nn.Sequential(
ConvNorm(512,
512,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(512))
)
self.convolutions.append(
nn.Sequential(
ConvNorm(512, 80,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='linear'),
nn.BatchNorm1d(80))
)
def forward(self, x):
for i in range(len(self.convolutions) - 1):
x = torch.tanh(self.convolutions[i](x))
x = self.convolutions[-1](x)
return x
class Generator(nn.Module):
"""Generator network."""
def __init__(self, dim_neck, dim_emb, dim_pre, freq): #Meanings of parameters still unclear :(
#irene: the parameters for encoder and decoder, explained above
super(Generator, self).__init__()
self.encoder = Encoder(dim_neck, dim_emb, freq)
self.decoder = Decoder(dim_neck, dim_emb, dim_pre)
self.postnet = Postnet()
def forward(self, x, c_org, c_trg):
#c_org = style original?
#c_trg = style
codes = self.encoder(x, c_org)
if c_trg is None:
return torch.cat(codes, dim=-1)
tmp = []
for code in codes:
tmp.append(code.unsqueeze(1).expand(-1,int(x.size(1)/len(codes)),-1))
code_exp = torch.cat(tmp, dim=1)
#pdb.set_trace()
encoder_outputs = torch.cat((code_exp, c_trg.unsqueeze(1).expand(-1,x.size(1),-1)), dim=-1)
mel_outputs = self.decoder(encoder_outputs)
mel_outputs_postnet = self.postnet(mel_outputs.transpose(2,1))
mel_outputs_postnet = mel_outputs + mel_outputs_postnet.transpose(2,1)
#pdb.set_trace()
#print("About to print")
#mel_outputs = mel_outputs.unsqueeze(1) #Channel (I think?)
#mel_outputs_postnet = mel_outputs_postnet.unsqueeze(1) #Same as above
#print("Currently in last line of model")
#pdb.set_trace()
return mel_outputs, mel_outputs_postnet, torch.cat(codes, dim=-1)
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] |
q_learning/backup_output_net1_discount_0_batch_400_random_mines/qnetwork.py | jakejhansen/minesweeper_solver | 32 | 161044 | <gh_stars>10-100
"""
This module contains a function to update another neural network by copying
all trainable variables and a class for a deep Q network.
"""
import os
import numpy as np
import tensorflow as tf
# Based on
# https://github.com/tomrunia/DeepReinforcementLearning-Atari
def update_target_network(sess, network_name_train, network_name_target):
"""This helper method copies all the trainable weights and biases from
one DeepQNetwork to another. This method is used for synchronisation
of the train and target Q-networks
Parameters
----------
sess: tensorflow session.
The current session in tensorflow.
network_name_train: str
Name of Q-Network for training.s
network_name_target: str
Name of Q-Network for predicting target Q-values
Returns
-------
None
"""
# Retrieve current variable scope and set reuse flag to True
tf.get_variable_scope().reuse_variables()
# Get list of values in "network_name_train" in the collection
# GraphKeys.TRAINABLE_VARIABLES
vars_source = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope=network_name_train
)
copy_ops = [] # Where we copy the variables to
check_ops = [] # Contains a list of booleans determining if variables are equal
for v in vars_source:
# Note the [0:-2] to cut of the device placement
v_source = tf.get_variable(v.name[0:-2])
# Remove variable prefix (network name)
var_name = v.name[v.name.find("/"):]
v_target = tf.get_variable(network_name_target + var_name[0:-2])
copy_ops.append(v_target.assign(v_source))
check_ops.append(tf.equal(v_target, v_source))
# Actual copying all the variables, check if the values are equal
sess.run(copy_ops)
check_res = sess.run(check_ops)
for res in check_res:
if not np.all(res): # Failed to copy variables
raise ValueError("Verification of tf.equal(var_train, var_target) failed.")
class DeepQNetwork:
"""A class for replay memories used during training of deep Q-networks.
Parameters
----------
replay_capacity : int
The maximum number of samples in replay memory
batch_size : Bool
The number of frames to return.
Attributes:
network_name : str
The name of the network.
trainable : bool
If this network is supposed to be trained.
True for the Q-network we train and False for the targer Q-network.
model_file : str
The name of the file containing the trained model parameters.
output_dir : str
The name of the output directory.
input_size : int
The size of the screen input.
input_depth : int
The input depth, this is the number of frames used, expects 4.
filter_sizes: :obj: 'list' of :obj: 'int'
A list containing the filter sizes in conv layers.
filter_strides: :obj: 'list' of :obj: 'int'
A list containing the filter strides in conv layers.
num_filters: :obj: 'list' of :obj: 'int'
A list containing the number of filters in conv layers.
num_hidden: :obj: 'list' of :obj: 'int'
A list containing the number of hidden units in feedforward layers.
num_actions: int
The number of actions available.
dueling_type: str
The type of dueling.
batch_size: int
Size of the training batch.
learning_rate_base: float
The start learning rate.
learning_rate_decay: float
The decay rate of the learning rate.
We decay every larning_rate_iteration.
learning_rate_step: int
The number of iterations to anneal the learning rate.
learning_rate_min: float
The minimum learning rate.
clip_delta: bool
If True use error clipping.
batch_accumulator: str
Either "mean" or "sum", determines if we reduce sum or mean of
delta_square
"""
def __init__(self, params, num_actions, network_name, trainable):
self.params = params
# Network information
self.network_name = network_name
self.trainable = trainable
# Paths and models
self.model_file = params.model_file
self.output_dir = params.output_dir
# Input to the Q-network
self.input_height = params.input_height # 84
self.input_width = params.input_width # 84
self.env_type = params.env
if self.env_type == 'minesweeper':
self.input_depth = params.nchannels # 4
else:
self.input_depth = params.history_length # 4
self.network_type = params.network_type
# CNN layer architecture
self.filter_sizes = params.filter_sizes # 8, 4, 3
self.filter_strides = params.filter_strides # 4, 2, 1
self.num_filters = params.num_filters # 32, 64, 64
self.num_hidden = params.num_hidden # 512
self.num_actions = num_actions # depends on game
self.dueling_type = params.dueling_type
# Training Parameters
self.batch_size = params.batch_size # 32
self.learning_rate_base = params.learning_rate # 0.0025
self.learning_rate_decay = params.learning_rate_decay # 0.96
self.learning_rate_step = params.learning_rate_step # 450000
self.learning_rate_min = params.learning_rate_minimum # 0.00025
self.discount = params.discount # 0.99
self.network_update_rate = params.network_update_rate # 10000
self.clip_delta = params.clip_delta # 1.0
self.batch_accumulator = params.batch_accumulator # mean or sum
# Init TensorFlow graph
self._init_graph()
def _init_graph(self):
"""Initializes the computational graph in Tensorflow and builds the
neural network.
If trainable is True, we also build the loss part of the graph.
Returns
-------
None
"""
# Initialize operation for creating/writing summary to disk
self.train_summary_op = None
self.eval_summary_op = None
self.train_summary_writer = None
with tf.variable_scope(self.network_name):
self.global_iteration = tf.Variable(0, name='global_iteration',
trainable=False)
# Placeholders
self.pl_screens = tf.placeholder(tf.float32,
shape=[None,
self.input_height,
self.input_width,
self.input_depth],
name="screens")
# For feedforward networks
self.pl_flat = tf.placeholder(tf.float32,
shape=[None,
self.input_height*self.input_width*self.input_depth],
name="flat")
# Contain q-targest for all batches per action, None for batches
self.pl_qtargets = tf.placeholder(tf.float32, shape=[None, self.num_actions],
name="qtargets")
self.pl_actions = tf.placeholder(tf.int64, shape=[None],
name="actions")
self.pl_actions_target = tf.placeholder(tf.int64, shape=[None], # Actions from train-network used to select q-values in target network
name="actions_target")
self.pl_rewards = tf.placeholder(tf.float32, shape=[None],
name="rewards")
self.pl_dones = tf.placeholder(tf.float32, shape=[None],
name="dones")
# Convert strings to lists
self.filter_sizes = list(map(int, self.filter_sizes.split(",")))
self.filter_strides = list(map(int, self.filter_strides.split(",")))
self.num_filters = list(map(int, self.num_filters.split(",")))
self.qvalues = self._build_cnn()
# Initialize the loss function if we have the trainable Q-network.
# Otherwise all parameters are copied.
if self.trainable:
self._build_loss()
# Learning rate decay
# When training a model, it is often recommended to lower the
# learning rate as the training progresses.
# This function applies an exponential decay function to a
# provided initial learning rate.
# Uses global_iteration to compute the decayed learning rate.
self.learning_rate = tf.train.exponential_decay(
self.learning_rate_base, self.global_iteration,
self.learning_rate_step, self.learning_rate_decay,
staircase=True
)
self.learning_rate = tf.maximum(self.learning_rate,
self.learning_rate_min)
# Adam Optimizer
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate,
epsilon=1.5e-4) # From Rainbow paper
self.train_op_optimizer = self.optimizer.minimize(
self.loss,
global_step=self.global_iteration # Steps refer to something else in reinforcement, so we call iteration
)
# Keep track of running average of the loss when minimizing
# the loss
# tf.control_dependencies means that
# self.train_op = tf.group(self.loss_moving_avg_op)
# is only run after self.loss_moving_avg_op has been executed
with tf.control_dependencies([self.train_op_optimizer]):
self.train_op = tf.group(self.loss_moving_avg_op)
def _build_cnn(self):
"""Builds a convolutional network. When we add new layers, it is
important to set trainable=self.trainable since these parameters needs
to be copied to the target nework and it uses the set of trianable.
Returns
-------
:obj: 'ndarray' of :obj: 'float'
The q-values in the output as a list, corresponding to each possible
action.
"""
with tf.variable_scope("Network"):
if self.network_type == 1:
conv1 = tf.layers.conv2d(
inputs=self.pl_screens,
filters=18,
kernel_size=[5, 5],
strides=(1, 1),
activation=tf.nn.relu,
padding="same", # Same as in keras
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="conv1"
)
conv2 = tf.layers.conv2d(
inputs=conv1,
filters=36,
kernel_size=[3, 3],
strides=(1,1),
activation=tf.nn.relu,
padding="same", # Same as in keras
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="conv2"
)
elif self.network_type == 2:
conv1 = tf.layers.conv2d(
inputs=self.pl_screens,
filters=16,
kernel_size=[4, 4],
strides=(1, 1),
activation=tf.nn.relu,
padding="valid", # Same as in keras
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="conv1"
)
conv2 = tf.layers.conv2d(
inputs=conv1,
filters=32,
kernel_size=[3, 3],
strides=(1,1),
activation=tf.nn.relu,
padding="same", # Same as in keras
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="conv2"
)
conv3 = tf.layers.conv2d(
inputs=conv2,
filters=32,
kernel_size=[2, 2],
strides=(1,1),
activation=tf.nn.relu,
padding="valid", # Same as in keras
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="conv3"
)
# To implement dueling, see https://github.com/dbobrenko/reinforceflow/blob/master/reinforceflow/nets/dueling.py
if self.dueling_type is None:
if self.network_type == 1:
conv_flatten = tf.contrib.layers.flatten(
inputs=conv2
)
fc1 = tf.layers.dense(
inputs=conv_flatten,
units=288,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="fc1"
)
fc2 = tf.layers.dense(
inputs=fc1,
units=220,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="fc2"
)
fc3 = tf.layers.dense(
inputs=fc2,
units=220,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="fc3"
)
qvalues = tf.layers.dense(
inputs=fc3,
units=self.num_actions,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable
)
if self.network_type == 2:
conv_flatten = tf.contrib.layers.flatten(
inputs=conv3
)
fc1 = tf.layers.dense(
inputs=conv_flatten,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="fc1"
)
qvalues = tf.layers.dense(
inputs=fc1,
units=self.num_actions,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable
)
# Dueling is enabled
else:
#We take the output from the final convolutional layer and
# split it into separate advantage and value streams.
if self.network_type == 1:
streamAC, streamVC = tf.split(conv2,2,3)
elif self.network_type == 2:
streamAC, streamVC = tf.split(conv3,2,3)
streamA = tf.contrib.layers.flatten(streamAC)
streamV = tf.contrib.layers.flatten(streamVC)
qvalues = self._add_dueling(streamA, streamV)
return qvalues
def _add_dueling(self, streamA, streamV):
"""Adds dueling to a network.
See:
https://github.com/devsisters/DQN-tensorflow/blob/master/dqn/agent.py
https://github.com/dbobrenko/reinforceflow/blob/master/reinforceflow/nets/dueling.py
https://github.com/devsisters/DQN-tensorflow/blob/master/dqn/agent.py
https://github.com/gokhanettin/dddqn-tf/blob/master/dddqn.py
https://github.com/awjuliani/DeepRL-Agents/blob/master/Double-Dueling-DQN.ipynb
And the original paper
https://arxiv.org/pdf/1511.06581.pdf
Parameters
----------
streamA :obj: 'ndarray' of :obj: 'float'
The input to the advantage part.
streamV :obj: 'ndarray' of :obj: 'float'
The input to value part.
(Deprecated)
net_input :obj: 'ndarray' of :obj: 'float'
The input to the part of the network implementing dueling
Returns
-------
:obj: 'ndarray' of :obj: 'float'
The q-values in the output as a list, corresponding to each possible
action.
"""
adv_in = tf.layers.dense(
inputs=streamA,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="adv_in")
adv_out = tf.layers.dense(
inputs=adv_in,
units=self.num_actions,
activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="adv_out")
value_in = tf.layers.dense(
inputs = streamV,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="value_in")
value_out = tf.layers.dense(
inputs=value_in,
units=1,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(seed=self.params.seed),
bias_initializer= tf.constant_initializer(self.params.bias_init),
trainable=self.trainable,
name="value_out")
# caculate the Q(s,a;theta)
# dueling_type == 'avg'
# Q(s,a;theta) = V(s;theta) + (A(s,a;theta)-Avg_a(A(s,a;theta)))
# dueling_type == 'max'
# Q(s,a;theta) = V(s;theta) + (A(s,a;theta)-max_a(A(s,a;theta)))
# dueling_type == 'naive'
# Q(s,a;theta) = V(s;theta) + A(s,a;theta)
if self.dueling_type == 'naive':
qvalues = value_out + adv_out
# Mean is what is suggested in the paper
elif self.dueling_type == 'mean':
qvalues = tf.add(value_out,
tf.subtract(adv_out,
tf.reduce_mean(adv_out, 1, keep_dims=True)))
elif self.dueling_type == 'max':
qvalues = tf.add(value_out,
tf.subtract(adv_out,
tf.reduce_max(adv_out, 1, keep_dims=True)))
else:
raise ValueError("Unknown dueling type '%s'. Available: 'naive', \
'mean', 'max'." % self.dueling_type)
return qvalues
def _build_loss(self):
"""Build the loss function for the neural network.
"""
with tf.variable_scope("loss"):
# Compute y_j = r_j + discount*max_qvalue
self.tf_discount = tf.constant(self.discount)
#self.pl_qtargets = tf.reduce_max(self.pl_qtargets, reduction_indices=[1])
# self.pl_actions_train
#self.pl_qtargets = self.pl_qtargets[range(self.batch_size), self.pl_actions_target]
# Get Q-values for the argmax actions from actions train
# For this, we use the one-hot encoding
self.actions_one_hot_target = tf.one_hot(
self.pl_actions_target, self.num_actions, 1.0, 0.0
)
# Finally, we compute the q-vaue of the target network
# for the actions which maximixed the
self.qtarget = tf.reduce_sum(
tf.multiply(
self.pl_qtargets,
self.actions_one_hot_target),
reduction_indices=1
)
if self.env_type == 'atari':
# In atari, done is bad since the games should last as long as possible
# for mine-sweeper, done is ambigious, and we represent loss by a strong negative
# reward, and win is simply progressing by a certain number of steps
self.qtarget = tf.add(
self.pl_rewards,
tf.multiply(1.0-self.pl_dones,
tf.multiply(self.tf_discount, self.qtarget))
)
elif self.env_type == 'minesweeper':
self.qtarget = tf.add(
self.pl_rewards,
tf.multiply(self.tf_discount, self.qtarget)
)
# Select Q-values for given actions
# The actions are a 1D array, we one-hot encode with the number of actions
# last two are on-value, off-value
# Then we just have a matrix with 1s where an action was performed
self.actions_one_hot = tf.one_hot(
self.pl_actions, self.num_actions, 1.0, 0.0
)
# Below we obtain the loss by taking the sum of squares difference
# between the target and prediction Q values.
# Compute finally sum along all rows by summing column elements
self.qvalue_pred = tf.reduce_sum(
tf.multiply(
self.qvalues,
self.actions_one_hot),
reduction_indices=1
)
# Difference between target (true) and predicted Q-network output
self.delta = tf.subtract(self.qtarget, self.qvalue_pred)
if self.clip_delta:
# Perform clipping of the error term,
# default clipping is to (-1, +1) range
self.delta_square = tf.losses.huber_loss(self.qtarget, self.qvalue_pred)
else:
# No error clipping
# td-error
self.delta_square = tf.square(self.delta)
# Actual loss
if self.batch_accumulator == "sum":
self.loss = tf.reduce_sum(self.delta_square)
else:
self.loss = tf.reduce_mean(self.delta_square)
# Running average of the loss for TensorBoard
self.loss_moving_avg = tf.train.ExponentialMovingAverage(decay=0.999)
self.loss_moving_avg_op = self.loss_moving_avg.apply([self.loss])
def build_summary_writer(self, sess):
"""Build summary writer of the progress.
"""
# Compute the average Q-value
avg_qvalues_train = tf.reduce_mean(self.qvalues)
avg_qvalues_target = tf.reduce_mean(tf.reduce_mean(self.pl_qtargets))
avg_reward_batch = tf.reduce_mean(tf.reduce_mean(self.pl_rewards))
# Summaries for training
training_summaries = [
tf.summary.scalar("train/qvalues_train_avg", avg_qvalues_train),
tf.summary.scalar("train/qvalues_target_avg", avg_qvalues_target),
tf.summary.scalar("train/avg_reward_batch", avg_reward_batch),
tf.summary.scalar("train/loss", self.loss),
tf.summary.scalar("train/loss_average", self.loss_moving_avg.average(self.loss)),
tf.summary.scalar("train/learning_rate", self.learning_rate),
tf.summary.histogram("train/delta", self.delta)
]
training_summaries_merged = tf.summary.merge(training_summaries)
# Environment related summaries
with tf.variable_scope("environment"):
self.avg_reward_per_game = tf.Variable(0.0, trainable=False, name="avg_reward_per_game")
self.max_reward_per_game = tf.Variable(0.0, trainable=False, name="max_reward_per_game")
self.avg_moves_per_game = tf.Variable(0.0, trainable=False, name="avg_moves_per_game")
self.num_games_played = tf.Variable(0.0, trainable=False, name="num_games_played")
self.moves = tf.Variable(0.0, trainable=False, name="num_moves_played")
self.total_reward_replay = tf.Variable(0.0, trainable=False, name="reward_in_replay_memory")
self.actions_random = tf.Variable(0.0, trainable=False, name="num_actions_random")
self.actions_greedy = tf.Variable(0.0, trainable=False, name="num_actions_greedy")
environment_summaries = [
tf.summary.scalar("environment/avg_reward_per_game", self.avg_reward_per_game),
tf.summary.scalar("environment/max_reward_per_game", self.max_reward_per_game),
tf.summary.scalar("environment/num_games_played", self.num_games_played),
tf.summary.scalar("environment/moves", self.moves),
tf.summary.scalar("environment/avg_moves_per_game", self.avg_moves_per_game),
tf.summary.scalar("environment/reward_in_replay_memory", self.total_reward_replay),
tf.summary.scalar("actions/num_actions_random", self.actions_random),
tf.summary.scalar("actions/num_actions_greedy", self.actions_greedy),
#tf.summary.image("screens", self.pl_screens, max_outputs=10) # This only works with atari for some reason
]
environment_summaries_merged = tf.summary.merge(environment_summaries)
# Environment related summaries
with tf.variable_scope("evaluation"):
self.eval_rewards = tf.Variable(0.0, trainable=False, name="total_reward")
self.eval_win_rate = tf.Variable(0.0, trainable=False, name="win_rate")
self.eval_num_rewards = tf.Variable(0.0, trainable=False, name="num_rewards")
self.eval_max_reward = tf.Variable(0.0, trainable=False, name="max_reward")
self.eval_num_episodes = tf.Variable(0.0, trainable=False, name="num_episodes")
self.eval_actions = tf.Variable(np.zeros(self.num_actions), trainable=False, name="actions")
evaluation_summaries = [
tf.summary.scalar("evaluation/total_reward", self.eval_rewards),
tf.summary.scalar("evaluation/win_rate", self.eval_win_rate),
tf.summary.scalar("evaluation/num_rewards", self.eval_num_rewards),
tf.summary.scalar("evaluation/max_reward", self.eval_max_reward),
tf.summary.scalar("evaluation/num_episodes", self.eval_num_episodes),
tf.summary.histogram("evaluation/actions", self.eval_actions)
]
# Evaluation Summaries for TensorBoard
self.eval_summary_op = tf.summary.merge(evaluation_summaries)
# Training summaries for TensorBoard
self.train_summary_op = tf.summary.merge([training_summaries_merged,
environment_summaries_merged])
train_summary_dir = os.path.join(self.output_dir, "summaries_" + self.params.game)
self.train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
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obs_movieconverter.py | zackees/productivity_cmds | 0 | 90120 | <filename>obs_movieconverter.py<gh_stars>0
""" Converts obs movies to a format usable by imovie """
import os
import sys
from shutil import copyfile
files = sorted([f for f in os.listdir('.') if '.mp4' in f and '2020' in f])
out_files = ['new_' + f + '.mov' for f in files]
rtn_codes = []
for count, f in enumerate(files):
input = f
out = out_files[count]
cmd = 'ffmpeg -i ' + input + ' -c:a aac -c:v libx264 -crf 20 -preset fast -f mov ' + out
#cmd = 'handbrakecli -i ' + input + ' -o ' + out + ' -e x264 -q 20 -B 160 --aencoder mp3'
print(cmd)
rtn = os.system(cmd)
rtn = 0
rtn_codes.append(rtn)
any_failed = False
for count, rtn in enumerate(rtn_codes):
if rtn != 0:
print('Exited because of error code ' + str(rtn) + ' in file ' + files[count])
if any_failed:
print('One or more files failed, aborting file replacement')
sys.exit(0)
for count, f in enumerate(files):
copyfile(out_files[count], f)
#os.system('handbrakecli -i 2020-11-24_18-12-06.mp4 -o movie.mp4 -e x264 -q 20 -B 160') | [
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english/data_processing/lessons/code/graphplot.py | hrutkabence/tutorials | 0 | 1606876 | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Draw time series from csv files or lists
Data series come from the columns of the csv files multi columns can be used from
the same file. One column for x values (it can be numerical or date/time data) and
more columns for y1, y2, .., yn. Alternatively lists can be given with the values.
As many charts will be drawn as many y columns given.
If more input files are given multiple lines will be drawn in the same subfigure.
It is poissible to define different number of y columns for the input csv files.
The input csv files may have different number of rows and columns. column indeces are
zero based.
The data series are given by a list with maximum 6 elementst
1st the name of the csv file separated by ';' and
list of column ordinal numbers first is for x, the followings are for y values
(0 base index), the same column can be repeated
x column can be numerical or date/time, y columns are numerical
or complex list [ x1, [y11, y12], x2, [y21 y22]]
2nd list of multipliers for the columns, default 1 for each column
3rd list of offsets for the columns, default 0 for each column
4th list of simple format specifiers for y values, default is matplotlib defaults
5th list of legend labels for y values, default filename:column
Here is a simple example with three input files and two subplots
first.csv:
1;0.34;2.56
2;0.58;1.43
3;1.02;1.1
second.csv:
A;1.2;0.86;0.55;6.54
B;1.9;1.7;0.72;5.78
C;2.4;1.45;0.4;1.34
D;2.8;0.86;.88;5.12
third.csv
1;0.75;1.8
2;2.1;2.5
3;1.8;3.1
titles = ["line1", "line2", "points"]
units = ["m", "mm", "degree"]
data_series = [[['first.csv', [0, 1, 2]], [1, 1, 0.56], [0, 0, 0], ['g--', 'r'], ['l1', 'l2']],
[['second.csv', [1, 3, 2]], [1, 1, 1], [0, 0, 0], ['', ''], ['b1', 'b2']],
[['third.csv', [0, 2]], [1, 0.75], [0, -0.3], ['b+']]]
g = GraphPlot(titles, units, data_series)
g.draw()
"""
import csv
import os.path
import re
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
class GraphPlot:
""" class to plot a graph
:param titles: list of titles for subplots
:param units: units for aces
:param data_series: list of list of titles, units and for each data file, filename column numbers, scales, offsets, formats, labels; scales, offset, formats and labels are optional
"""
SEPARATOR = ";" # update for your input
def __init__(self, titles, units, data_series):
""" initialize instance """
self.titles = titles
self.units = units
self.x = []
self.y = []
self.fmts = []
self.labels = []
for serie in data_series:
scales = [1] * len(serie[0][1]) # default scales
if len(serie) > 1:
scales = serie[1]
offsets = [0] * len(serie[0][1]) # default offsets
if len(serie) > 2:
offsets = serie[2]
if isinstance(serie[0][0], str):
act_x, act_y = self.load(serie[0][0], serie[0][1], scales, offsets)
else:
act_x = serie[0][0]
act_y = serie[0][1]
self.x.append(act_x)
self.y.append(act_y)
fmt = [''] * len(serie[0][1]) # default formats
if len(serie) > 3:
fmt = serie[3]
if isinstance(serie[0][0], str):
label = ["{}:{}".format(serie[0][0], str(col))
for col in serie[0][1][1:]]
else:
label = [str(col+1) for col in range(len(serie[1][1:]))]
if len(serie) > 4:
label = serie[4]
self.labels.append(label)
self.fmts.append(fmt)
try:
self.main_title, _ = os.path.splitext(os.path.basename(data_series[0][0]))
except Exception:
self.main_title, _ = os.path.splitext(os.path.basename(__file__))
def draw(self):
""" draw multi graph """
rows = max([len(yi) for yi in self.y])
fig = plt.figure()
fig.canvas.set_window_title(self.main_title)
for ind in range(rows):
ax = plt.subplot(rows, 1, ind+1)
for i in range(len(self.x)):
if len(self.y[i]) > ind:
if isinstance(self.x[i][0], datetime):
plt.plot_date(self.x[i], self.y[i][ind], self.fmts[i][ind],
label=self.labels[i][ind])
else:
plt.plot(self.x[i], self.y[i][ind], self.fmts[i][ind],
label=self.labels[i][ind])
plt.xticks(rotation=45)
plt.xlabel(self.units[0])
plt.ylabel(self.units[ind+1])
plt.grid()
plt.legend()
ax.set_title(self.titles[ind])
fig.tight_layout()
plt.show()
fig.savefig(self.main_title + '.png')
@staticmethod
def load(fname, cols, scales, offsets):
""" load input data
:param fname: name of csv input file
:param cols: ordinal column numbers to use
:param scales: multipliers for columns
:param offsets: offsets for columns
:returns tuple x and y values (multiple y series as list)
"""
data = []
with open(fname, newline='') as f:
reader = csv.reader(f, delimiter=GraphPlot.SEPARATOR)
for row in reader:
data.append(row)
if re.match("[0-9]{4}-[0-9]{2}-[0-9]{2}", data[0][cols[0]]):
x = [datetime.strptime(row[cols[0]], '%Y-%m-%d %H:%M:%S.%f')
for row in data]
else:
x = [float(row[cols[0]]) * scales[0] + offsets[0] for row in data]
y = []
for i in range(1, len(cols)):
y.append([float(row[cols[i]]) * scales[i] + offsets[i] for row in data])
return (x, y)
if __name__ == "__main__":
from sys import argv
from math import (sin, cos, pi)
DEMO_ID = 1
if len(argv) > 1:
DEMO_ID = int(argv[1])
if DEMO_ID == 1:
TITLES = ["line1", "line2", "points"]
UNITS = ["m", "mm", "degree", "m"]
X1 = [1, 2, 3, 4, 5, 6]
Y11 = [0.34, 0.58, 1.02, 1.21, 1.52, 1.61]
Y12 = [2.56, 1.43, 1.1, 0.8, 0.48, 0.67]
X2 = [1.2, 1.9, 2.4, 2.8, 3.5, 5.8]
Y21 = [0.86, 1.7, 1.45, 0.86, 1.2, 3.0]
Y22 = [0.55, 0.72, 0.4, 0.88, 0.99, 2.0]
# x3 == x1
Y31 = [1.8, 2.5, 3.1, 2.6, 2.3, 2.8]
DATA_SERIES = [[[X1, [Y11, Y12, Y12]],
[1, 1, 0.56, 1], [0, 0, 0, 1],
['g--', 'r', 'ro'], ['l1', 'l2', 'l2']],
[[X2, [Y22, Y21, Y22]],
[1, 1, 1, 0.75], [0, 0, 0, -0.5],
['', '', 'yx'], ['b1', 'b2', 'b1']],
[[X1, [Y31]], [1, 0.75], [0, -0.3], ['b+']]]
G = GraphPlot(TITLES, UNITS, DATA_SERIES)
G.draw()
elif DEMO_ID == 2:
TITLES = ["trigonometry"]
UNITS = ["fok", "-", "-"]
DATA_SERIES = [[['test/sin_cos.csv', [0, 2]], [1, 1], [0, 0],
[''], ['sin']],
[['test/sin_cos.csv', [0, 3]], [1, 1], [0, 0],
[''], ['cos']]]
G = GraphPlot(TITLES, UNITS, DATA_SERIES)
G.draw()
elif DEMO_ID == 3:
TITLES = ["trigonometry"]
UNITS = ["fok", "-", "-"]
X = list(range(0, 370, 10))
Y1 = [sin(xi / 180 * pi) for xi in range(0, 370, 10)]
Y2 = [cos(xi / 180 * pi) for xi in range(0, 370, 10)]
DATA_SERIES = [[[X, [Y1]], [0, 2], [1, 1], [0, 0],
[''], ['sin']],
[[X, [Y2]], [0, 3], [1, 1], [0, 0],
[''], ['cos']]]
G = GraphPlot(TITLES, UNITS, DATA_SERIES)
G.draw()
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] |
65-Spam-filter/main.py | PawelZabinski/ocr-code-challenges-files | 0 | 30962 | # Spam filter
# Take a list of dishes from a menu and add “spam” to them. See https://en.wikipedia.org/wiki/Spam_(Monty_Python).
def main():
try:
dishCount = int(input('Enter number of dishes in menu > '))
dishes = [input(f'Enter Dish No.{i + 1} > ') for i in range(dishCount)]
dishes = [f'{dish} spam' for dish in dishes]
for dish in dishes:
print(dish)
except:
print('Invalid dish count. Please enter a positive integer')
if __name__ == '__main__':
main() | [
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] |
arpym/tools/forward_selection.py | dpopadic/arpmRes | 6 | 178733 | <filename>arpym/tools/forward_selection.py<gh_stars>1-10
# -*- coding: utf-8 -*-
import numpy as np
def forward_selection(g, n_, k_=None):
"""For details, see here.
Parameters
----------
g : function
n_ : int
k_ : int, optional
Returns
-------
s_star_fwd : list, shape(k_, 1:k_)
"""
if k_ is None:
k_ = n_
# Step 0: Initialize
j = np.arange(1, n_ + 1)
s_star_forward = np.array([], dtype='int')
for k in range(k_):
# Step 1: Determine the best element in j
n_star = np.argmax([g(np.append(s_star_forward, n))
for n in j])
# Step 2: Append the best element in j to the selection and remove it from j
s_star_forward = np.append(s_star_forward, j[n_star])
j = np.delete(j, n_star)
return [s_star_forward[:k] for k in range(1, k_ + 1)]
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asset_manager/assets/Asset.py | miquel-vv/asset_manager | 0 | 1605739 | from .errors import OutOfRangeError
from .PriceCleaner import PriceCleaner
from ..mappers.PriceMapper import PriceMapper
from ..mappers.OrderMapper import OrderMapper
from ..connectors import KrakenConnector
import pandas as pd
import datetime
class Asset:
def __init__(self, asset_id):
self.asset_id = asset_id
self.price_mapper = PriceMapper()
self.order_mapper = OrderMapper()
self.prices=None
self.span=None
self.order_book=None
def get_last_price_date(self):
last_date = self.price_mapper.get_last_saved_date(self.asset_id)
if self.prices is not None:
last_date_prices = self.prices.index[-1]
if last_date_prices >= last_date:
last_date = last_date_prices
else:
self.prices = None
if last_date is None:
return datetime.datetime(2020, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)
return last_date
def get_first_price_date(self):
first_date = self.price_mapper.get_first_saved_date(self.asset_id)
if first_date is None:
OutOfRangeError("No prices have been saved yet for asset: {0}".format(self.asset_id))
return first_date
def in_range(self, start_date=None, end_date=None):
first_date = self.get_first_price_date()
last_date = self.get_last_price_date()
start_date=first_date if start_date is None else start_date
end_date=last_date if end_date is None else end_date
if start_date<first_date or start_date>last_date:
return False
if end_date<first_date or end_date>last_date:
return False
return True
def load_prices(self, start_date, end_date):
self.prices = self.price_mapper.get_prices(self.asset_id)
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
return self.asset_id == other.asset_id
def __hash__(self):
return self.asset_id.__hash__()
def clean_prices(self):
if self.span is None:
raise AttributeError("Span must be set before cleaning the prices.")
self.prices.sort_index(inplace=True)
price_cleaner = PriceCleaner(self.prices, datetime.timedelta(minutes=self.span))
self.prices = price_cleaner.get_cleaned_prices()
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ukbot/rules/image.py | danmichaelo/UKBot | 2 | 175098 | <gh_stars>1-10
# encoding=utf-8
# vim: fenc=utf-8 et sw=4 ts=4 sts=4 ai
import re
from mwclient.errors import InvalidPageTitle
import urllib
import logging
from ..common import _
from ..contributions import UserContribution
from .rule import Rule
from .decorators import family
logger = logging.getLogger(__name__)
class ImageRule(Rule):
rule_name = 'image'
def __init__(self, sites, template, trans=None):
Rule.__init__(self, sites, template, trans)
# Points for added images that are also uploaded by the same user, but not marked as 'own work'.
self.points_own = self.get_param('own', datatype=float)
# Points for added images that are also uploaded by the same user, and also marked as 'own work'.
self.points_ownwork = self.get_param('ownwork', datatype=float)
# Max number of images already present in the article. Set this to 0 to only give points
# for addition of images to articles that do not have images from before.
self.maxinitialcount = self.get_param('maxinitialcount', datatype=int)
# Find localized File: prefixes for all sites
self.file_prefixes = set([prefix for site in self.sites.sites.values() for prefix in site.file_prefixes])
# Keep a statistic for total number of images added
self.total = 0
# Compile regexps for match images
prefixes = r'(?:%s)' % '|'.join(['%s:' % x for x in self.file_prefixes])
suffixes = r'\.(?:svg|png|jpe?g|gif|tiff)'
imagematcher = r"""
(?:
(?:=|\||^)%(prefixes)s? # "=File:", "=", "|File:", "|", ...
| %(prefixes)s
)
( # start capture
[^\}\]\[=\|$\n]*?
%(suffixes)s
) # end capture
""" % {'prefixes': prefixes, 'suffixes': suffixes}
logger.debug('ImageRule regexp: %s', imagematcher)
self.imagematcher = re.compile(imagematcher, flags=re.IGNORECASE | re.MULTILINE | re.VERBOSE)
self.extlinkmatcher = re.compile(r'https?://[^ \n]*?' + suffixes, flags=re.IGNORECASE)
def get_images(self, txt):
txt = self.extlinkmatcher.sub('', txt) # remove external links to images
# return len(re.findall(imagematcher, txt, flags=re.IGNORECASE))
for img in self.imagematcher.finditer(txt):
yield img.group(1).strip()
@staticmethod
def get_image_uploader(rev, filename):
try:
image = rev.article().site().images[filename]
except InvalidPageTitle:
logger.error('Image filename "%s" is invalid, ignoring this file', filename)
return
imageinfo = image.imageinfo
if len(imageinfo) > 0: # seems like image.exists only checks locally
try:
uploader = imageinfo['user']
except KeyError:
logger.error("Could not locate user for file '%s' in rev. %s ",
filename, rev.revid)
return
logger.debug("File '%s' uploaded by '%s', revision made by '%s'",
filename, uploader, rev.username)
return uploader
@staticmethod
def get_image_credit(rev, filename):
credit = ''
extrainfo = rev.article().site().api('query', prop='imageinfo', titles=u'File:{}'.format(filename),
iiprop='extmetadata')
try:
for pageid, page in extrainfo['query']['pages'].items():
credit = page['imageinfo'][0]['extmetadata']['Credit']['value']
except KeyError:
logger.debug("Could not read credit info for file '%s'", filename)
return credit
@family('wikipedia.org', 'wikibooks.org')
def test(self, rev):
imgs_before = list(self.get_images(rev.parenttext))
imgs_after = list(self.get_images(rev.text))
added = set(imgs_after).difference(set(imgs_before))
if len(added) == 0:
return
counters = {'ownwork': [], 'own': [], 'other': []}
for filename in added:
filename = urllib.parse.unquote(filename)
uploader = self.get_image_uploader(rev, filename)
if uploader is None:
logger.warning("File '%s' does not exist or is invalid", filename)
continue
if uploader == rev.username:
credit = self.get_image_credit(rev, filename)
if 'int-own-work' in credit or 'Itse otettu valokuva' in credit:
logger.debug("File '%s' identified as own work.", filename)
counters['ownwork'].append(filename)
else:
logger.debug("File '%s' identified as own upload, but not own work.", filename)
counters['own'].append(filename)
else:
logger.debug("File '%s' not identified as own upload or own work.", filename)
counters['other'].append(filename)
# Update statistic
self.total += len(counters['own']) + len(counters['ownwork']) + len(counters['other'])
# If maxinitialcount is 0, only the first image counts.
# If an user adds both an own image and an image by someone else,
# we should make sure to credit the own image, not the other.
# We therefore process the own images first.
points = 0
for n, img in enumerate(added):
if self.maxinitialcount is None or len(imgs_before) + n <= self.maxinitialcount:
if img in counters['ownwork']:
points += self.points_ownwork or self.points_own or self.points
elif img in counters['own']:
points += self.points_own or self.points
else:
points += self.points
yield UserContribution(rev=rev, rule=self, points=points,
description=_('images') % {'images': len(added)})
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8346,
2396,
7431,
29898,
23959,
26972,
13,
2
] |
run_exp.py | louisfaury/gnn-es | 3 | 172141 |
from distributions.distribution_factory import create_distribution
from objectives.objective_factory import create_objective
from samplers.sampler_factory import create_sampler
from algorithms.algo_factory import create_algorithm
import os
import argparse
import pprint as pp
import json
import tensorflow as tf
import numpy as np
def run_experiments(config):
seeds = config["seeds"]
dimension = config["dimensions"]
max_eval_per_run = config["max_eval_per_run"]
outputs_dict = dict()
outputs_config_dict = dict()
outputs_config_dict["objective"] = config["objective"]
outputs_config_dict["distribution"] = config["distribution"]
outputs_config_dict["algo"] = config["algo"]
outputs_config_dict["sampler"] = config["sampler"]
outputs_dict["config"] = outputs_config_dict
output_results_dict = dict()
for seed in seeds:
results = run_one_experiment(config, dimension, seed, max_eval_per_run)
output_results_dict[seed] = results
outputs_dict["results"] = output_results_dict
return outputs_dict
def run_one_experiment(config, output_size, seed, max_evals):
np.random.seed(seed)
tf.reset_default_graph()
tf.set_random_seed(seed)
session = tf.Session()
with session.as_default():
distribution_config = config["distribution"]
distribution = create_distribution(distribution_config, output_size)
sampler_config = config["sampler"]
sampler = create_sampler(sampler_config, distribution)
algo_config = config["algo"]
algo = create_algorithm(algo_config, distribution)
objective_config = config["objective"]
objective = create_objective(objective_config, output_size)
session.run(tf.global_variables_initializer())
results = dict()
evals = 0
iters = 0
while evals<max_evals:
samples = dict()
queries, new_evals = sampler.sample()
evals += new_evals
scores = objective.f(queries)
samples["data"] = queries
samples["cost"] = scores
diagnostic = algo.fit(samples)
diagnostic["evals"] = evals
print_diagnostics(iters, diagnostic)
iters += 1
results[evals] = diagnostic
return results
def print_diagnostics(iteration, diagnostics):
print("Iteration %i" % iteration)
print("=================")
for key in diagnostics.keys():
print(key,":", diagnostics[key])
print("=================")
print("\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ES with Invertible Networks')
parser.add_argument('--config_name',
help='Name for the configuration file',
type=str)
parser.add_argument('--output_name',
help='Name for the result file',
type=str)
args = vars(parser.parse_args())
pp.pprint(args)
config_file_dir = 'config'
if not os.path.isdir(config_file_dir):
os.makedirs(config_file_dir)
output_file_dir = 'logs'
if not os.path.isdir(output_file_dir):
os.makedirs(output_file_dir)
config_file_name = args["config_name"]
config_file = os.path.join(config_file_dir, config_file_name)
with open(config_file, 'r') as f:
config = json.load(f)
results = run_experiments(config)
output_file_name = args["output_name"]
output_file = os.path.join(output_file_dir, output_file_name)
with open(output_file, 'w') as f:
json.dump(results, f)
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4 kyu/Memoized Log Cutting.py | mwk0408/codewars_solutions | 6 | 113055 | <reponame>mwk0408/codewars_solutions<gh_stars>1-10
def cut_log(p, n):
maximum=[0]
for j in range(1, n+1):
comp=-float("inf")
for i in range(1, j+1): # Two nested loops = Θ(n^2)
comp=max(p[i-1+1]+maximum[j-i], comp)
maximum.append(comp)
return maximum[-1] | [
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src/npi/visualization/training.py | NancyFulda/towards-neural-programming-interfaces | 0 | 137110 | from matplotlib import pyplot as plt
def make_training_plots(
title: str, save_file_path: str, epoch_data, test_data, test_data_generated=None
):
"""
Make plots for training progress of NPI network
Arguments:
epoch_losses -- List of tuples: (epoch, avg_epoch_loss)
test_losses -- List of tuples: (epoch, avg_test_loss)
"""
# Get epochs for tests into a list.
test_epochs = list(map(lambda x: x[0], test_data))
test_values = list(map(lambda x: x[1], test_data))
fig1, ax1 = plt.subplots()
ax1.plot(list(map(lambda x: x[0], epoch_data)), label="train")
ax1.plot(test_epochs, test_values, label="test")
if test_data_generated:
ax1.plot(test_epochs, list(map(lambda x: x[0], test_data_generated)), label="generated test")
ax1.set_xlabel("Epoch")
ax1.set_ylabel("Average")
ax1.set_title(title)
plt.legend()
plt.draw()
fig1.savefig(f"{save_file_path}visualization_{title.replace(' ', '_')}.png")
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bundle/completor.vim/pythonx/completers/cpp.py | wonwooddo/vim_setup | 0 | 102644 | # -*- coding: utf-8 -*-
import re
import os
from completor import Completor
from completor.compat import to_bytes
word_patten = re.compile('\w+$')
trigger = re.compile('(\.|->|#|::)\s*(\w*)$')
def sanitize(menu):
if not menu:
return menu
# type
menu = menu.replace(b'[#', b'').replace(b'#]', b' ')
# argument
menu = menu.replace(b'<#', b'').replace(b'#>', b'')
return menu
class Clang(Completor):
filetype = 'cpp'
args_file = '.clang_complete'
def format_cmd(self):
row, col = self.cursor
tempfile = self.tempname
match = trigger.search(self.input_data)
if match:
start, _ = match.span()
sign, _ = match.groups()
col = start + (2 if sign in ('->', '::') else 1)
elif not word_patten.search(self.input_data):
return []
args = [
self.get_option('clang_binary') or 'clang',
'-fsyntax-only',
'-I',
self.current_directory,
]
args.extend(self.parse_config(self.args_file))
complatfile = tempfile
if os.getenv('MSYSTEM') is not None:
cmd = 'cygpath -a -w {}'.format(complatfile)
complatfile = os.popen(cmd).read()
complatfile = complatfile.strip().replace('\\', '\\\\')
args.extend([
'-Xclang',
'-code-completion-macros',
'-Xclang',
'-code-completion-at={}:{}:{}'.format(complatfile, row, col + 1),
tempfile
])
return args
# items: list of bytes
def parse(self, items):
match = trigger.search(self.input_data)
if match:
_, prefix = match.groups()
else:
match = word_patten.search(self.input_data)
if not match:
return []
prefix = match.group()
prefix = to_bytes(prefix)
res = []
for item in items:
if not item.startswith(b'COMPLETION:'):
continue
parts = [e.strip() for e in item.split(b':')]
if len(parts) < 2 or not parts[1].startswith(prefix):
continue
data = {'word': parts[1], 'dup': 1, 'menu': ''}
if len(parts) > 2:
if parts[1] == b'Pattern':
subparts = parts[2].split(b' ', 1)
data['word'] = subparts[0]
if len(subparts) > 1:
data['menu'] = subparts[1]
else:
data['menu'] = b':'.join(parts[2:])
func_sig = sanitize(data['menu'])
data['menu'] = func_sig
# Show function signature in the preview window
data['info'] = func_sig
res.append(data)
return res
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ui/notice.py | cfpb/regulations-xml-parser | 4 | 190977 | <reponame>cfpb/regulations-xml-parser<gh_stars>1-10
from dateutil import parser as date_parser
from lxml import etree
from inflect import engine
from operator import itemgetter
class Notice:
def __init__(self, filename):
self.filename = filename
self.terms = []
self.modified = False
self.effective_date = None
with open(filename, 'r') as f:
reg_xml = f.read()
parser = etree.XMLParser(huge_tree=True)
self.tree = etree.fromstring(reg_xml, parser)
reg = self.tree.find('{eregs}preamble')
if reg is not None:
self.document_number = reg.find('{eregs}documentNumber').text
else:
changeset = self.tree.find('{eregs}changeset')
if changeset:
self.document_number = changeset.get('rightDocumentNumber')
else:
raise ValueError('Not a RegML file!')
eff_date = self.tree.find('.//{eregs}effectiveDate').text
self.effective_date = date_parser.parse(eff_date)
def __str__(self):
return '<Notice: {}>'.format(self.document_number)
def extract_terms(self):
definitions = self.tree.findall('.//{eregs}def')
self.terms = []
inf = engine()
for defn in definitions:
defined_in = defn.find('../..').get('label')
term = defn.get('term')
plural = inf.plural(term)
self.terms.append((term, defined_in, self.document_number))
self.terms.append((plural, defined_in, self.document_number))
self.terms.sort(key=itemgetter(0))
@property
def defined_terms(self):
if not self.terms:
self.extract_terms()
return self.terms
def replace_node(self, label, new_element):
current_element = self.tree.find('.//{}[@label="{}"]'.format(new_element.tag, label))
if not current_element:
current_element = self.tree.find('.//{{eregs}}{}[@label="{}"]'.format(new_element.tag, label))
if not current_element:
raise ValueError('Element {} not found!'.format(new_element.tag))
current_element_parent = current_element.find('..')
current_element_parent.replace(current_element, new_element)
self.modified = True
def save(self):
with open(self.filename, 'w') as f:
f.write(etree.tostring(self.tree, pretty_print=True, encoding='UTF-8'))
self.modified = False | [
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tests/test_hopfield_net.py | lantunes/cellpylib | 124 | 162762 | import unittest
import cellpylib as cpl
import numpy as np
import os
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
class TestHopfieldNet(unittest.TestCase):
def test_hopfield_net(self):
np.random.seed(0)
# patterns for training
zero = [
0, 1, 1, 1, 0,
1, 0, 0, 0, 1,
1, 0, 0, 0, 1,
1, 0, 0, 0, 1,
1, 0, 0, 0, 1,
0, 1, 1, 1, 0,
0, 0, 0, 0, 0]
one = [
0, 1, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 0, 0, 0]
two = [
1, 1, 1, 0, 0,
0, 0, 0, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 0, 0,
1, 0, 0, 0, 0,
1, 1, 1, 1, 1,
0, 0, 0, 0, 0]
# replace the zeroes with -1 to make these vectors bipolar instead of binary
one = [-1 if x == 0 else x for x in one]
two = [-1 if x == 0 else x for x in two]
zero = [-1 if x == 0 else x for x in zero]
P = [zero, one, two]
hopfield_net = cpl.HopfieldNet(num_cells=35)
hopfield_net.train(P)
expected_weights = self._convert_to_ndarray("hopfield_net_weights.txt")
np.testing.assert_equal(expected_weights, hopfield_net.W)
expected_activities = self._convert_to_ndarray("hopfield_net.ca")
half_two = [
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 1, 1, 0, 0,
1, 0, 0, 0, 0,
1, 1, 1, 1, 1,
0, 0, 0, 0, 0]
half_two = [-1 if x == 0 else x for x in half_two]
cellular_automaton = np.array([half_two])
cellular_automaton = cpl.evolve(cellular_automaton, timesteps=155,
apply_rule=hopfield_net.apply_rule, r=hopfield_net.r)
np.testing.assert_equal(expected_activities, cellular_automaton)
def _convert_to_ndarray(self, filename, dtype=int):
with open(os.path.join(THIS_DIR, 'resources', filename), 'r') as content_file:
content = content_file.read()
content = content.replace('[[', '')
content = content.replace(']]', '')
content = content.replace('[', '')
content = content.replace('],', ';')
content = [[dtype(i) for i in x.split(',')] for x in content.split(';')]
return np.array(content) | [
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] |
NatualLangageProcessing/solution/2_1.py | vavalm/AI-Frameworks | 0 | 77058 | <reponame>vavalm/AI-Frameworks
ir = 0
print("Liste des tokens racinisé de la première ligne : " + train_array[0])
rw = data_train_OHE.getrow(ir)
pd.DataFrame([(v, vocabulary[v], k) for k,v in zip(rw.data,rw.indices)], columns=["indices","token","weight"]) | [
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] |
battlesim.py | Shashwat986/BattleSim | 0 | 178563 | <filename>battlesim.py
import random
from collections import defaultdict
matching = defaultdict(int,{
('P','K'):3,
('K','G'):3,
('G','L'):3,
('L','P'):3,
('K','P'):-3,
('G','K'):-3,
('L','G'):-3,
('P','L'):-3
})
def dieroll(n):
return sum([random.randrange(6) + 1 for i in range(n)])
class Player(object):
def __init__(self,tough,sta,stre,coun):
self.t = tough
self.stam = sta
self.stre = stre
self.coun = coun
self.HP = tough * 6 * 4
self.maxHP = self.HP
self.degree = 1
def move(self):
s = random.randrange(5 + 10*self.degree + 1)
d = 5 + 10*self.degree - s
t = random.choice(['P','K','L','G'])
return s,d,t
def status(self):
if self.HP > self.t * 6 * 3: return 0
if self.HP > self.t * 6 * 2: return 2
if self.HP > self.t * 6 * 1: return 4
if self.HP > self.t * 6 * 0: return 6
class Fight(object):
def __init__(self, p1,p2):
self.underdog = (p1.t + p2.t + p1.stam + p2.stam) * 6
self.p1 = p1
self.p2 = p2
def calcdmg(self,p1,p2):
atk_ = dieroll(p1.stre) + s1 + matching[(t1,t2)]
def_ = dieroll(p2.coun) + s2
if atk_ > def_ :
p2.HP -= d1
#def dmg(pa,pd,sa,da,ta,sd,dd,td)
def fight(self):
ctr = 1
p1 = self.p1
p2 = self.p2
while (p1.HP > 0 and p2.HP > 0):
s1,d1,t1 = p1.move()
s2,d2,t2 = p2.move()
# 1's attack
atk_ = dieroll(p1.stre) + s1 + matching[(t1,t2)]
def_ = dieroll(p2.coun) + s2
if atk_ > def_ :
p2.HP -= d1 + dieroll(p1.stre)
else:
print "Counter 1!"
# 2's attack
atk_ = dieroll(p2.stre) + s2 + matching[(t2,t1)]
def_ = dieroll(p1.coun) + s1
if atk_ > def_ :
p1.HP -= d2 + dieroll(p2.stre)
else:
print "Counter 2!"
# Recovery
p1.HP += dieroll(p1.stam)
if p1.HP > p1.maxHP: p1.HP = p1.maxHP
p2.HP += dieroll(p2.stam)
if p2.HP > p2.maxHP: p2.HP = p2.maxHP
# Degree
a = dieroll(1)
if a > 4:
if dieroll(1) > p1.status():
p1.degree += 1
if a == 1:
if dieroll(1) > 3:
p1.degree -= 1
else:
p1.degree -= 2
a = dieroll(1)
if a > 4:
if dieroll(1) > p2.status():
p2.degree += 1
if a == 1:
if dieroll(1) > 3:
p2.degree -= 1
else:
p2.degree -= 2
if p1.degree < 0: p1.degree = 0
if p2.degree < 0: p2.degree = 0
if p1.degree > 5: p1.degree = 5
if p2.degree > 5: p2.degree = 5
#print "h"
if p1.HP - p2.HP > self.underdog:
print "Underdog"
p2.degree = 5
if p2.HP - p1.HP > self.underdog:
print "Underdog"
p1.degree = 5
print "Turn {}: P1 sd = {}/{}; P2 sd = {}/{}".format(ctr,s1,d1, s2,d2)
print "Turn {}: P1 HP = {}; P2 HP = {}".format(ctr,p1.HP, p2.HP)
print "Turn {}: P1 dg = {}; P2 dg = {}".format(ctr,p1.degree, p2.degree)
print
ctr += 1
print "Player {} Wins!".format(1 if p1.HP > p2.HP else 2)
return 1 if p1.HP > p2.HP else 2, ctr
ctr = 0
ctrc = 0
N = 100
for i in range(N):
p1 = Player(4,2,2,2)
p2 = Player(2,3,2,3)
print "Starting conditions: P1 = {}, {} ; P2 = {}, {}".format(p1.HP,p1.degree,p2.HP,p2.degree)
f=Fight(p1,p2)
print "Underdog:",f.underdog
print
p,c = f.fight()
ctr += p
ctrc += c
print ctr * 1.0/N
print ctrc * 1.0/N
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] |
devopsipy/decorators.py | kharnam/devopsipy | 0 | 3109 | """
Module to contain Pywork decorators
"""
__author__ = '<NAME>'
import re
import time
import itertools
import logging
log = logging.getLogger(__name__)
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setup.py | microsoft/ATAC | 9 | 159161 | from distutils.core import setup
setup(
name='ATAC',
version='0.1.0',
author='<NAME>',
author_email='<EMAIL>',
package_dir={'':'src'},
packages=['atac'],
url='https://github.com/chinganc/atac',
license='MIT LICENSE',
description='ATAC code',
long_description=open('README.md').read(),
install_requires=[
"garage==2021.3.0",
"gym==0.17.2",],
extras_require={
'mujoco200': ["mujoco_py==2.0.2.8", "d4rl @ git+https://github.com/chinganc/d4rl@master#egg=d4rl"],
'mujoco210': ["d4rl @ git+https://github.com/rail-berkeley/d4rl@master#egg=d4rl"]}
)
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hwtLib/tests/types/union_test.py | optical-o/hwtLib | 24 | 79620 | <filename>hwtLib/tests/types/union_test.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import unittest
from hwt.hdl.types.bits import Bits
from hwt.hdl.types.struct import HStruct
from hwt.hdl.types.union import HUnion
from hwtLib.types.ctypes import uint8_t, uint16_t, int8_t, uint32_t
from pyMathBitPrecise.bit_utils import mask
class UnionTC(unittest.TestCase):
def test_assertMembersSameSize(self):
t = HUnion(
(uint8_t, "a"),
(uint8_t, "b"),
(uint8_t, "c"),
(uint8_t, "d"),
)
self.assertEqual(t.bit_length(), 8)
with self.assertRaises(TypeError):
HUnion(
(uint16_t, "a"),
(uint8_t, "b"),
)
def test_assertNoPadding(self):
with self.assertRaises(AssertionError):
HUnion(
(uint8_t, None),
(uint8_t, "b"),
)
def test_value_simple(self):
t = HUnion(
(uint8_t, "unsigned"),
(int8_t, "signed"),
)
v = t.from_py(None)
v.unsigned = mask(8)
self.assertEqual(int(v.signed), -1)
v.signed = 0
self.assertEqual(int(v.unsigned), 0)
def test_value_struct_and_bits(self):
t = HUnion(
(uint16_t, "bits"),
(HStruct(
(uint8_t, "lower"),
(uint8_t, "upper"),
), "struct"),
)
v = t.from_py(None)
v.struct.upper = 1
self.assertEqual(v.bits.val, 1 << 8)
self.assertEqual(v.bits.vld_mask, mask(8) << 8)
v.struct.lower = 1
self.assertEqual(v.bits.val, (1 << 8) | 1)
self.assertEqual(v.bits.vld_mask, mask(16))
v.bits = 2
self.assertEqual(int(v.struct.lower), 2)
self.assertEqual(int(v.struct.upper), 0)
def test_value_array_and_bits(self):
t = HUnion(
(uint32_t, "bits"),
(uint8_t[4], "arr"),
)
v = t.from_py(None)
b = (4 << (3 * 8)) | (3 << (2 * 8)) | (2 << 8) | 1
v.bits = b
for i, item in enumerate(v.arr):
self.assertEqual(int(item), i + 1)
self.assertEqual(int(v.bits), b)
def test_value_array_toArray(self):
t = HUnion(
(uint16_t[2], "arr16b"),
(int8_t[4], "arr8b"),
)
v = t.from_py(None)
for i in range(len(v.arr16b)):
v.arr16b[i] = i + 1
for i, item in enumerate(v.arr8b):
if (i + 1) % 2 == 0:
v = 0
else:
v = i // 2 + 1
self.assertEqual(int(item), v)
def test_value_array_of_struct_to_bits(self):
t = HUnion(
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr"),
(Bits(24 * 3), "bits")
)
v = t.from_py(None)
for i in range(len(v.arr)):
v.arr[i] = {"a": i + 1,
"b": (i + 1) * 3
}
self.assertEqual(int(v.bits),
1
| 3 << 16
| 2 << 24
| 6 << (24 + 16)
| 3 << (2 * 24)
| 9 << (2 * 24 + 16))
def test_hunion_type_eq(self):
t0 = HUnion(
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr"),
(Bits(24 * 3), "bits")
)
t1 = HUnion(
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr"),
(Bits(24 * 3), "bits")
)
self.assertEqual(t0, t1)
self.assertEqual(t1, t0)
t1 = HUnion(
(Bits(24 * 3), "bits"),
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr")
)
self.assertEqual(t0, t1)
self.assertEqual(t1, t0)
t1 = HUnion(
(uint32_t, "bits"),
(uint8_t[4], "arr"),
)
self.assertNotEqual(t0, t1)
self.assertNotEqual(t1, t0)
t1 = HUnion(
(Bits(24 * 3), "bbits"),
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr")
)
self.assertNotEqual(t0, t1)
self.assertNotEqual(t1, t0)
t1 = Bits(24 * 3)
self.assertNotEqual(t0, t1)
self.assertNotEqual(t1, t0)
t1 = HUnion(
(Bits(24 * 3, signed=False), "bits"),
(HStruct(
(uint16_t, "a"),
(uint8_t, "b"),
)[3], "arr")
)
self.assertNotEqual(t0, t1)
self.assertNotEqual(t1, t0)
if __name__ == '__main__':
suite = unittest.TestSuite()
# suite.addTest(UnionTC('testValue'))
suite.addTest(unittest.makeSuite(UnionTC))
runner = unittest.TextTestRunner(verbosity=3)
runner.run(suite)
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] |
python_scripts/write_BCR_html.py | cregan727/ubiquitous-rotary-phone | 0 | 67729 | import altair as alt
import pandas as pd
import numpy as np
import sys
TRUSTTSV = sys.argv[1]
CELLNUM = sys.argv[2]
BCRTEMP = """
<div style="width: 100%; height: 700px; position: relative; clear:both;overflow: hidden; white-space:nowrap; padding-top: 10px;clear:both;">
<div style="width: 110%, position: absolute; clear:both;">
<p style="text-align: left; color: #094D92; font-size: 30px"> BCR STATS: </p>
</div>
FREQHIST
<div style="width: 50%; float: right; padding-top: 50px;">
TABLE
</div>
DotPLOT
</div>
"""
# colnames for chain 1 and 2 - for B cells, the heavy chain is chain 1, and light chain is chain 2
TRUST_columns = ["V_gene",
"D_gene",
"J_gene",
"C_gene",
"cdr3_nt",
"cdr3_aa",
"read_cnt",
"consensus_id",
"CDR3_germline_similarity",
"consensus_full_length"]
TRUSTaligned = pd.read_csv(TRUSTTSV,
delimiter='\t',
index_col='#barcode')
Bcells = TRUSTaligned[TRUSTaligned['cell_type'] == 'B']
print(CELLNUM)
CELLNUM = int(CELLNUM)
print(type(CELLNUM))
# Calculate the percentages of heavy, light, and paired chains found in the B cells
No_BCR = CELLNUM - len(Bcells.index)
L_only = len(Bcells[(Bcells['chain1'] == "*") & (Bcells['chain2'] != "*")])
H_only = len(Bcells[(Bcells['chain2'] == "*") & (Bcells['chain1'] != "*")])
paired = len(Bcells[(Bcells['chain1'] != "*") & (Bcells['chain2'] != "*")])
BCR_stats = pd.DataFrame([No_BCR, L_only, H_only, paired, CELLNUM],
index=['No BCR',
"Light Chain Only",
"Heavy Chain Only",
"Paired Chains",
"Total"],
columns=['Number of Cells'])
BCR_stats['Percent of Cells'] = (BCR_stats['Number of Cells']*100/CELLNUM)
BCR_stats['Percent of Cells'] = round(BCR_stats['Percent of Cells'],
2).astype("str")+"%"
BCRSTATSTABLE = BCR_stats.to_html()
BCRSTATSTABLE = BCRSTATSTABLE.replace(
""" border="1" """, " ").replace(
"text-align: right", "text-align: left")
# split the heavy and light chain info out of its csv form
Bcells = Bcells.join(pd.DataFrame(Bcells.chain1.str.split(",").tolist(),
columns=['H_'+x for x in TRUST_columns],
index=Bcells.index))
Bcells = Bcells.join(pd.DataFrame(Bcells.chain2.str.split(",").tolist(),
columns=['L_'+x for x in TRUST_columns],
index=Bcells.index))
Bcells = Bcells.drop(columns=['chain1',
'chain2',
'secondary_chain1',
'secondary_chain2'])
# calculate frequencies for freq histogram
lightchainaa = pd.DataFrame(Bcells.groupby(
'L_cdr3_aa').size(), columns=['freq'])
lightchainaa['chain'] = 'light'
heavychainaa = pd.DataFrame(Bcells.groupby(
'H_cdr3_aa').size(), columns=['freq'])
heavychainaa['chain'] = 'heavy'
aa_freq = pd.concat([lightchainaa, heavychainaa])
freqhist = alt.Chart(aa_freq).mark_bar(
color='reds').encode(
alt.X('freq',
bin=alt.Bin(step=.9999),
title='Number of Cells the CDR3 Amino Acid Sequence is Observed in'),
y=alt.Y('count()',
title="Number of CDR3 AA sequences"),
color=alt.Color('chain',
scale=alt.Scale(scheme='viridis'))).properties(
width='container',
height=200,
title='Frequency Histogram')
# create the dataframe with the dotplot data
pair_B = Bcells[(Bcells['H_V_gene'] != "*") & (Bcells['L_V_gene'] != "*")]
dotplot_data = pair_B[['H_C_gene', 'L_C_gene']]
dotplot_data = pd.DataFrame(dotplot_data.groupby(
["H_C_gene", "L_C_gene"]).size())
dotplot_data = dotplot_data.unstack()
NaN = np.nan
dotplot_data.loc['IGHG3'] = [NaN] * len(dotplot_data.columns)
dotplot_data.loc['IGHG4'] = [NaN] * len(dotplot_data.columns)
dotplot_data.loc['IGHA'] = [NaN] * len(dotplot_data.columns)
dotplot_data.loc['IGHD'] = [NaN] * len(dotplot_data.columns)
dotplot_data.loc['IGHE'] = [NaN] * len(dotplot_data.columns)
# Heavy/Light dotplot
source = dotplot_data.melt(ignore_index=False, col_level='L_C_gene')
print(source)
source["H_C_gene"] = source.index
source['Light Chain'] = np.where(["IGK" in x for x in source['L_C_gene']],
"Kappa", "Lambda")
print(source.index.tolist())
dotplot_BCR = alt.Chart(source).mark_circle().encode(
x=alt.X('H_C_gene',
scale=alt.Scale(
domain=np.sort(source.index.tolist())),
title='Heavy Chain'),
y=alt.Y('L_C_gene',
title='Light Chain'),
size='value',
color=alt.Color('Light Chain', scale=alt.Scale(scheme='viridis')),
tooltip=['value', 'Light Chain']
).properties(
width='container',
height=200,
title="Heavy and Light Chain usage for cells with Paired Data",
).interactive()
bcr_dp_html = dotplot_BCR.to_html()
print(bcr_dp_html)
bcr_dp_html = bcr_dp_html[bcr_dp_html.find("<div id"):bcr_dp_html.find("</body>")]
print(bcr_dp_html)
bcr_dp_html = bcr_dp_html.replace("vis", "vis_dp_b")
bcr_dp_html = bcr_dp_html.replace(
"""<div id="vis_dp_b"></div>""",
"""<div id="vis_dp_b" style="width: 100%; position: absolute; clear:both; overflow: hidden; white-space:nowrap; padding-top: 300px; padding-bottom: 20px"></div>""")
freq_hist_html = freqhist.to_html()
freq_hist_html = freq_hist_html[freq_hist_html.find("<div id"):freq_hist_html.find("</body>")]
freq_hist_html = freq_hist_html.replace("vis", "vis_fq_b")
freq_hist_html = freq_hist_html.replace(
"""<div id="vis_fq_b"></div>""",
"""<div id="vis_fq_b" style="width: 50%; height: 50%; float: left; position: absolute; clear:both; overflow: hidden; white-space:nowrap"></div>""")
BCRTEMP = BCRTEMP.replace("FREQHIST", freq_hist_html)
BCRTEMP = BCRTEMP.replace("TABLE", BCRSTATSTABLE)
BCRTEMP = BCRTEMP.replace("DotPLOT", bcr_dp_html)
# final output
f = open("summary.html", "r")
htmlfile = f.read()
f.close()
htmlfile = htmlfile.replace('ADD_BCR_INFO', BCRTEMP)
f = open("summary.html", 'w')
f.write(htmlfile)
f.close()
print(np.sort(source.index.tolist()))
print("Done adding to html")
print(htmlfile)
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src/tasks/__init__.py | luke-james/setup-config | 0 | 87252 | <reponame>luke-james/setup-config<gh_stars>0
from . import setup_tasks | [
1,
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] |
cogs/utils/classes/LohiEmbed.py | Sendouc/lohi-python | 0 | 45735 | # https://discordapp.com/developers/docs/resources/channel#embed-limits
import discord
from ..lists import (
weapons_english_internal,
abilities_short_to_emoji,
top_500_emoji,
weapons_to_emoji,
)
# Cheers to Lean
class LohiEmbed:
def __init__(
self,
title="\uFEFF",
description="\uFEFF",
color=0x60DD8E,
url=None,
footer="\uFEFF",
):
self.title = title
self.description = description
self.color = color
self.url = url
self.fields = []
self.footer = footer
def add_field(self, name="\uFEFF", value="\uFEFF", inline=True):
self.fields.append((name, value, inline))
def add_weapon_build_fields(self, builds):
for build in builds:
title = build["title"]
discord_tag = f"{build['discord_user']['username']}#{build['discord_user']['discriminator']}"
if title:
title = f"{title} by {discord_tag}"
else:
title = f"{discord_tag}"
ability_arrs = [build["headgear"], build["clothing"], build["shoes"]]
ability_str = ""
for arr in ability_arrs:
for count, ability in enumerate(arr):
ability_str += abilities_short_to_emoji[ability]
if count == 0:
ability_str += "|"
ability_str += "\n"
self.add_field(title, ability_str, False)
if len(self.fields) == 0:
self.add_field(value="No builds found!")
def get_embeds(self):
title = self.title[: 256 - 8]
desc = self.description[:2048]
used_up = len(title) + len(desc)
embed = discord.Embed(
title=title, description=desc, color=self.color, url=self.url
)
embed.set_footer(text=self.footer)
returned_embeds = []
for i in range(len(self.fields)):
name, value, inline = self.fields[i]
name = name[:256]
value = value[:1024]
additional = len(name) + len(value)
if i % 25 == 24 or used_up + additional > 6000:
returned_embeds.append(embed)
title = self.title + " (cont.)"
embed = discord.Embed(
title=title, description=desc, color=self.color, url=self.url
)
embed.set_footer(text=self.footer)
used_up = len(title) + len(desc)
used_up += additional
embed.add_field(name=name, value=value, inline=inline)
returned_embeds.append(embed)
return returned_embeds
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] |
main_pro_window.py | Great-special/Trade_Copier | 0 | 154058 | <filename>main_pro_window.py
import tkinter as tk
from Slave_copier import *
from tkinter import ttk
# Creating the Copier User InterFace program
_window = tk.Tk()
_window.title('Trade Copier beta - Client')
_window.minsize(450,600)
_window.maxsize(450,600)
upper_frame = tk.Frame(_window, height=200, bg='red')
upper_frame.pack(fill=tk.X, pady=5)
middle_frame = tk.Frame(_window, height=200, bg='blue')
middle_frame.pack(fill=tk.X)
lower_frame = tk.Frame(_window, height=200, bg='green')
lower_frame.pack(fill=tk.X, pady=5)
# Connecting login to interFace
def connect_account():
startMT5(Account.get(), TradeServer.get(), Password.get())
# Account number or Login
global Account
Account = tk.IntVar()
acc_details_label = tk.Label(upper_frame, text='Account Details', font=('bold', 12), pady=5).grid(row=0, column=1, columnspan=2, pady=10)
account_label = tk.Label(upper_frame, text='Login:', font=('bold', 13), padx=10)
account_label.grid(row=1, column=0, sticky=tk.W, pady=5, padx=10)
account_entry = tk.Entry(upper_frame, textvariable=Account, width=30)
account_entry.grid(row=1, column=1, sticky=tk.W, pady=5)
account_entry.focus()
# Trade Server or Account Server
global TradeServer
TradeServer = tk.StringVar()
tradeserver_label = tk.Label(upper_frame, text='Server:', font=('bold', 13), padx=15)
tradeserver_label.grid(row=2, column=0, sticky=tk.W, padx=10)
tradeserver_entry = tk.Entry(upper_frame, textvariable=TradeServer, width=30)
tradeserver_entry.grid(row=2, column=1, sticky=tk.W)
# Password
global Password
Password = tk.StringVar()
pword_label = tk.Label(upper_frame, text='Password:', font=('bold', 13), padx=10)
pword_label.grid(row=3, column=0, sticky=tk.W, pady=10, padx=10)
pword_entry = tk.Entry(upper_frame, textvariable=Password, width=30)
pword_entry.grid(row=3, column=1, sticky=tk.W, pady=10)
# Connect Button
start_MT5_btn = tk.Button(upper_frame, text='Connect Account', width=20, command=connect_account)
start_MT5_btn.grid(row=4, column=1, pady=5, padx=15, columnspan=2)
#open positions
pos_details_label = tk.Label(middle_frame, text='Position Details', font=('bold', 12), pady=5).grid(row=0, column=1, columnspan=3, pady=5, padx=150)
pos_list = tk.Listbox(middle_frame, height=10, width=70,border=0)
pos_list.grid(row=1, column=1, columnspan=2, padx=10, pady=10)
#General info
gen_info = tk.Label(lower_frame, text='General Info', font=('bold', 12), pady=5).grid(row=0, column=1, columnspan=2, padx=70, pady=10)
conn_server_btn = tk.Button(lower_frame, text='Connect Server', width=15, command=startServer, padx=10)
conn_server_btn.grid(row=1, column=0, columnspan=2, padx=10, pady=5, sticky=tk.W)
# close_pos_btn = tk.Button(lower_frame, text='Close Position', width=13, command=closeOpenPosition, padx=10)
# close_pos_btn.grid(row=1, column=3, sticky=tk.E, padx=10)
info_bar_label = tk.Label(lower_frame, text='Info Bar', font=('bold', 12), pady=5).grid(row=2, column=0, sticky=tk.W, padx=10)
info_bar_box = tk.Listbox(lower_frame, height=3, width=30).grid(row=2, column=1, padx=5, pady=10)
print('[STARTING] Program Window')
_window.mainloop()
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] |
2019_Challenges/Week 03-21/JimPriorWordSearch.py | cohpy/DoJo-Challenges | 2 | 75472 | """
<NAME>'s solution to the DoJo problem of March 21,2019.
"""
from itertools import chain
def is_word_in_array(word, array):
left_to_right_strings = (''.join(row) for row in array)
top_to_bottom_strings = (''.join(column) for column in zip(*array))
all_strings = chain(left_to_right_strings, top_to_bottom_strings)
return any (word in candidate for candidate in all_strings)
def is_word_in_array2(word, array):
rows = map(lambda row: ''.join(row), array)
columns = map(lambda column: ''.join(column), zip(*array))
return (
any(map(lambda row: word in row, rows))
or
any(map(lambda column: word in column, columns))
)
def is_word_in_array3(word, array):
return any (
word in s
for s in [
''.join(row) for row in array
] + [
''.join(column) for column in zip(*array)
]
)
if __name__ == '__main__':
matrix = [
['F', 'A', 'C', 'I', ],
['O', 'B', 'O', 'P', ],
['A', 'N', 'O', 'B', ],
['M', 'A', 'S', 'S', ],
]
print(is_word_in_array('FOAM', matrix))
print(is_word_in_array('MASS', matrix))
print(is_word_in_array2('FOAM', matrix))
print(is_word_in_array2('MASS', matrix))
print(is_word_in_array3('FOAM', matrix))
print(is_word_in_array3('MASS', matrix))
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876,
13,
2
] |
mumblr/templatetags/mumblr_tags.py | hmarr/django-mumblr | 35 | 28461 | <reponame>hmarr/django-mumblr
from django.template import Library, Node, TemplateSyntaxError
import re
from mumblr.entrytypes import EntryType
register = Library()
class LatestEntriesNode(Node):
def __init__(self, num, var_name):
self.num = int(num or 10)
self.var_name = var_name
def render(self, context):
context[self.var_name] = list(EntryType.live_entries()[:self.num])
return ''
@register.tag
def get_latest_entries(parser, token):
# Usage:
# {% get_latest_entries as entries %} (default 10 entries)
# (or {% get_latest_entries 7 as entries %} for 7 entries)
# {% for entry in entries %}
# <li>{{ entry.title }}</li>
# {% endfor %}
tag_name, contents = token.contents.split(None, 1)
match = re.search(r'(\d+\s+)?as\s+([A-z_][A-z0-9_]+)', contents)
if not match:
raise TemplateSyntaxError("%r tag syntax error" % tag_name)
num, var_name = match.groups()
return LatestEntriesNode(num, var_name)
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source/lib/waflibv2.py | subanishaik27/aws-waf | 678 | 70171 | ######################################################################################################################
# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# #
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance #
# with the License. A copy of the License is located at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES #
# OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions #
# and limitations under the License. #
######################################################################################################################
# import boto3
# from botocore.config import Config
from botocore.exceptions import ClientError
from ipaddress import ip_address
from backoff import on_exception, expo, full_jitter
from lib.boto3_util import create_client
API_CALL_NUM_RETRIES = 5
MAX_TIME = 20
client = create_client('wafv2')
class WAFLIBv2(object):
def __init__(self):
return
# Parse arn into ip_set_id
def arn_to_id(self, arn):
if arn == None:
return None
tmp = arn.split('/')
return tmp.pop()
# Determine network version for source_ip
def which_ip_version(self, log, source_ip):
if source_ip == None:
return None
try:
source_ip = source_ip.strip()
ip_type = "IPV%s"%ip_address(source_ip).version
return ip_type
except Exception as e:
log.error("Source ip %s is not IPV4 or IPV6.", str(source_ip))
log.error(str(e))
return None
# Append correct cidr to source_ip
def set_ip_cidr(self, log, source_ip):
if source_ip == None:
return None
try:
source_ip = source_ip.strip()
ip_type = "IPV%s"%ip_address(source_ip).version
except Exception as e:
log.error("Source ip %s is not IPV4 or IPV6.", str(source_ip))
log.error(str(e))
return None
ip_class = "32" if ip_type == "IPV4" else "128"
return str(source_ip)+"/"+str(ip_class)
# Retrieve IPSet given an ip_set_id
def get_ip_set_by_id(self, log, scope, name, ip_set_id):
try:
log.debug("[waflib:get_ip_set_by_id] Start")
response = client.get_ip_set(
Scope=scope,
Name=name,
Id=ip_set_id
)
log.debug("[waflib:get_ip_set_by_id] got ip set: \n{}.".format(response))
log.debug("[waflib:get_ip_set_by_id] End")
return response
except Exception as e:
log.error("[waflib:get_ip_set_by_id] Failed to get IPSet %s", str(ip_set_id))
log.error(str(e))
return None
# Retrieve IPSet given an ip set arn
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def get_ip_set(self, log, scope, name, arn):
try:
log.info("[waflib:get_ip_set] Start")
ip_set_id = self.arn_to_id(arn)
response = client.get_ip_set(
Scope=scope,
Name=name,
Id=ip_set_id
)
log.info("[waflib:get_ip_set] End")
return response
except Exception as e:
log.error("Failed to get IPSet %s", str(ip_set_id))
log.error(str(e))
return None
# Retrieve addresses based on ip_set_id
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def get_addresses(self, log, scope, name, arn):
try:
response = self.get_ip_set(log, scope, name, arn)
addresses = response["IPSet"]["Addresses"]
return addresses
except Exception as e:
log.error("Failed to get addresses for ARN %s", str(arn))
log.error(str(e))
return None
# Update addresses in an IPSet using ip set id
@on_exception(expo, client.exceptions.WAFOptimisticLockException,
max_time=MAX_TIME,
jitter=full_jitter,
max_tries=API_CALL_NUM_RETRIES)
def update_ip_set_by_id(self, log, scope, name, ip_set_id, addresses, lock_token, description):
log.debug("[waflib:update_ip_set_by_id] Start")
try:
response = client.update_ip_set(
Scope=scope,
Name=name,
Id=ip_set_id,
Addresses=addresses,
LockToken=lock_token,
Description=description
)
log.debug("[waflib:update_ip_set_by_id] update ip set response: \n{}.".format(response))
log.debug("[waflib:update_ip_set_by_id] End")
return response
# Get the latest ip set and retry updating api call when OptimisticLockException occurs
except ClientError as ex:
exception_type = ex.response['Error']['Code']
if exception_type in ['OptimisticLockException']:
log.info("[waflib:update_ip_set_by_id] OptimisticLockException detected. Get the latest ip set and retry updating ip set.")
ip_set = self.get_ip_set_by_id(log, scope, name, ip_set_id)
lock_token = ip_set['LockToken']
response = client.update_ip_set(
Scope=scope,
Name=name,
Id=ip_set_id,
Addresses=addresses,
LockToken=lock_token,
Description=description
)
log.debug("[waflib:update_ip_set_id] End")
return response
except Exception as e:
log.error(e)
log.error("[waflib:update_ip_set_by_id] Failed to update IPSet: %s", str(ip_set_id))
return None
# Update addresses in an IPSet using ip set arn
@on_exception(expo, client.exceptions.WAFOptimisticLockException,
max_time=MAX_TIME,
jitter=full_jitter,
max_tries=API_CALL_NUM_RETRIES)
def update_ip_set(self, log, scope, name, ip_set_arn, addresses):
log.info("[waflib:update_ip_set] Start")
if (ip_set_arn is None or name is None):
log.error("No IPSet found for: %s ", str(ip_set_arn))
return None
try:
# convert from arn to ip_set_id
ip_set_id = self.arn_to_id(ip_set_arn)
# retrieve the ipset to get a locktoken
ip_set = self.get_ip_set(log, scope, name, ip_set_arn)
lock_token = ip_set['LockToken']
description = ip_set['IPSet']['Description']
log.info("Updating IPSet with description: %s, lock token: %s", str(description), str(lock_token))
response = client.update_ip_set(
Scope=scope,
Name=name,
Description=description,
Id=ip_set_id,
Addresses=addresses,
LockToken=lock_token
)
new_ip_set = self.get_ip_set(log, scope, name, ip_set_id)
log.debug("[waflib:update_ip_set] update ip set response:\n{}".format(response))
log.info("[waflib:update_ip_set] End")
return new_ip_set
except Exception as e:
log.error(e)
log.error("Failed to update IPSet: %s", str(ip_set_id))
return None
# Put Log Configuration for webacl
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def put_logging_configuration(self, log, web_acl_arn, delivery_stream_arn):
try:
response = client.put_logging_configuration(
LoggingConfiguration={
'ResourceArn': web_acl_arn,
'LogDestinationConfigs': [delivery_stream_arn]
}
)
return response
except Exception as e:
log.error("Failed to configure log for WebAcl: %s", str(web_acl_arn))
log.error(str(e))
return None
# Delete Log Configuration for webacl
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def delete_logging_configuration(self, log, web_acl_arn):
try:
response = client.delete_logging_configuration(
ResourceArn=web_acl_arn
)
return response
except Exception as e:
log.error("Failed to delete log for WebAcl: %s", str(web_acl_arn))
log.error(str(e))
return None
# List webacls
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def list_web_acls(self, log, scope):
try:
response = client.list_web_acls(
Scope=scope
)
return response
except Exception as e:
log.error("Failed to list WebAcld in scope: %s", str(scope))
log.error(str(e))
return None
# log when retry is stopped
# def give_up_retry(self, log, e):
# log.error("Giving up retry after %s times.",str(API_CALL_NUM_RETRIES))
# log.error(e)
#################################################################
# Following functions only used for testing, not in WAF Solution
#################################################################
@on_exception(expo,
(client.exceptions.WAFInternalErrorException,
client.exceptions.WAFOptimisticLockException,
client.exceptions.WAFLimitsExceededException),
max_time=MAX_TIME)
def create_ip_set(self, log, scope, name, description, version, addresses):
try:
response = client.create_ip_set(
Scope=scope,
Name=name,
Description=description,
IPAddressVersion=version,
Addresses=addresses
)
return response
except Exception as e:
log.error("Failed to create IPSet: %s", str(name))
log.error(str(e))
return None
@on_exception(expo,
(client.exceptions.WAFInternalErrorException,
client.exceptions.WAFOptimisticLockException,
client.exceptions.WAFAssociatedItemException),
max_time=MAX_TIME)
def delete_ip_set(self, log, scope, name, ip_set_id):
try:
response = self.get_ip_set(log, scope, name, ip_set_id)
if response is not None:
lock_token = response['LockToken']
response = client.delete_ip_set(
Scope=scope,
Name=name,
LockToken=lock_token,
Id=ip_set_id
)
return response
except Exception as e:
log.error("Failed to delete IPSet: %s", str(name))
log.error(str(e))
return None
@on_exception(expo, client.exceptions.WAFInternalErrorException, max_time=MAX_TIME)
def list_ip_sets(self, log, scope, marker=None):
try:
response = None
if marker == None:
response = client.list_ip_sets(
Scope=scope,
Limit=50
)
else:
response = client.list_ip_sets(
Scope=scope,
NextMarker=marker,
Limit=50
)
return response
except Exception as e:
log.error("Failed to list IPSets in scope: %s", str(scope))
log.error(str(e))
return None | [
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learner2/pupil/migrations/0012_sessiondata.py | Arunkumar99/learners | 3 | 28049 | <reponame>Arunkumar99/learners
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pupil', '0011_questions_set_id'),
]
operations = [
migrations.CreateModel(
name='SessionData',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('user_id', models.IntegerField(default=0)),
('session_count', models.IntegerField(default=0)),
('result', models.CharField(max_length=10)),
('level', models.IntegerField(default=0)),
('performance1', models.ImageField(upload_to=b'')),
('performance2', models.ImageField(upload_to=b'')),
('performance3', models.ImageField(upload_to=b'')),
('csv_file', models.FileField(upload_to=b'')),
],
),
]
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testproject/users/serializers.py | joshua-hashimoto/django-channels-jwt-auth-middleware | 9 | 161776 | <filename>testproject/users/serializers.py
from django.contrib.auth import get_user_model
from dj_rest_auth.serializers import UserDetailsSerializer
from rest_framework.serializers import ModelSerializer, SerializerMethodField
User = get_user_model()
class UserSerializer(ModelSerializer):
profile_image = SerializerMethodField()
class Meta:
model = User
fields = (
'id',
'profile_image',
'username',
'email',
)
def get_profile_image(self, obj):
if (profile_image := obj.profile_image):
return profile_image.url
else:
return None
class CustomUserDetailSerializer(UserDetailsSerializer):
class Meta(UserDetailsSerializer.Meta):
fields = (
'id',
'profile_image',
'username',
'email',
)
def update(self, instance, validated_data):
"""
Note:
change this method if you want to update user model.
in the case of this application this is not needed because
it already uses CustomUser. However it is worth keeping memo.
"""
return super().update(instance, validated_data)
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custom_components/hacs/operational/setup_actions/sensor.py | sob/hass-conf | 27 | 1610939 | <gh_stars>10-100
""""Starting setup task: Sensor"."""
from homeassistant.helpers import discovery
from custom_components.hacs.const import DOMAIN
from custom_components.hacs.share import get_hacs
from ...enums import HacsSetupTask
async def async_add_sensor():
"""Async wrapper for add sensor"""
hacs = get_hacs()
hacs.log.info("Setup task %s", HacsSetupTask.SENSOR)
if hacs.configuration.config_type == "yaml":
hacs.hass.async_create_task(
discovery.async_load_platform(
hacs.hass, "sensor", DOMAIN, {}, hacs.configuration.config
)
)
else:
hacs.hass.async_add_job(
hacs.hass.config_entries.async_forward_entry_setup(
hacs.configuration.config_entry, "sensor"
)
)
| [
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firebase-gist.py | darwin/firebase-gist | 1 | 1575 | from firebase import firebase
import os
import datetime
import json
import logging
from boto.s3.connection import S3Connection
from boto.s3.key import Key
from github3 import login
firebase_url = os.environ['FIREBASE_DB']
firebase_secret = os.environ['FIREBASE_SECRET']
firebase_path = os.environ['FIREBASE_PATH']
firebase_username = os.environ['FIREBASE_USERNAME'] # not checked ATM
gh_token = os.environ['GH_TOKEN']
gh_gist = os.environ['GH_GIST']
gh_fname = os.environ['GH_FNAME']
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def connect_firebase():
f = firebase.FirebaseApplication(firebase_url, None)
f.authentication = firebase.FirebaseAuthentication(firebase_secret, firebase_username, admin=True)
return f
logger.info('==================================')
logger.info('Fetching firebase data')
f = connect_firebase()
data = f.get(firebase_path, None)
new_content = json.dumps(data, ensure_ascii=False, indent=2, sort_keys=True)
logger.info('Reading existing gist')
gh = login(token=gh_token)
gist = gh.gist(gh_gist)
old_content = ""
for f in gist.iter_files():
if f.filename == gh_fname:
old_content = f.content
break
if old_content == new_content:
logger.info('No changes detected')
else:
logger.info('Updating gist with new content')
gist.edit(files={
gh_fname: {
"content": new_content
}
})
logger.info('Done.') | [
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notebooks/main.py | Reasmey/adsi_beer_app | 0 | 189027 | <gh_stars>0
from fastapi import FastAPI
from starlette.responses import JSONResponse
from joblib import load
import pandas as pd
from fastapi.encoders import jsonable_encoder
#from pydantic import BaseModel
from torch.utils.data import DataLoader
from model import format_features, apply_scaler, single_tensor, ClassificationEmbdNN, get_model, predict
app = FastAPI()
@app.get("/")
def read_root():
return {"Displaying a brief description of the project objectives, list of endpoints, expected input parameters and output format of the model, link to the Github repo related to this project"}
@app.get('/health', status_code=200)
def healthcheck():
return 'GMM Clustering is all ready to go!'
@app.post("/beer/type/")
async def predict_beer_style(brewery_name: int, review_aroma: int, review_appearance: int, review_palate: int, review_taste: int):
features = format_features(brewery_name,review_aroma, review_appearance, review_palate, review_taste)
obs = pd.DataFrame(features)
# scale num cols
obs = apply_scaler(obs)
#transform to embed object
obs_tensor = single_tensor(obs)
#predict on embed obj
model = get_model()
#return predictions as text string
answer = predict(obs_tensor, model, single=True)
return answer
@app.post("/beers/type/")
async def predict_beer_style_multi(brewery_name: int, review_aroma: int, review_appearance: int, review_palate: int, review_taste: int):
features = format_features(brewery_name,review_aroma, review_appearance, review_palate, review_taste)
obs = pd.DataFrame(features)
# scale num cols
obs = apply_scaler(obs)
#transform to embed object
obs_tensor = single_tensor(obs)
#predict on embed obj
model = get_model()
#return predictions as text string
answer = predict(obs_tensor, model)
return answer
@app.get("/model/architecture/")
async def print_model():
model = get_model()
return model
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RegonAPI/converters.py | damianwasik98/RegonAPI | 10 | 39684 | <reponame>damianwasik98/RegonAPI
"""
Converters of codes
"""
from string import digits
from .exceptions import RegonConvertionError
from . import validators
REGON9_WEIGHTS = [8, 9, 2, 3, 4, 5, 6, 7]
REGON14_WEIGHTS = [2, 4, 8, 5, 0, 9, 7, 3, 6, 1, 2, 4, 8]
def regon8_to_9(regon8):
"""Convert REGON8 to REGON9
Parameters
----------
regon8 : str
REGON8
Returns
-------
str
REGON9
Raises
------
RegonConvertionError
If regon8 is not valid
"""
if not validators.is_valid_regon8(regon8):
raise RegonConvertionError(regon8)
a, b = list(regon8), REGON9_WEIGHTS
a = list(map(lambda x: int(x), a))
last_digit = sum(list(map(lambda x: x[0] * x[1], zip(a, b)))) % 11
regon9 = "{regon8}{last_digit}".format(regon8=regon8, last_digit=last_digit)
return regon9
def regon13_to_14(regon13):
"""Convert REGON13 to REGON14
Parameters
----------
regon13 : str
REGON13
Returns
-------
str
REGON14
Raises
------
RegonConvertionError
If regon13 is not valid
"""
if not validators.is_valid_regon13(regon13):
raise RegonConvertionError(regon13)
a, b = list(regon13), REGON14_WEIGHTS
a = list(map(lambda x: int(x), a))
last_digit = sum(list(map(lambda x: x[0] * x[1], zip(a, b)))) % 11
regon14 = "{regon13}{last_digit}".format(regon13=regon13, last_digit=last_digit)
return regon14
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qrand/caches/cache.py | LaurentAjdnik/qrand | 18 | 16435 | <filename>qrand/caches/cache.py<gh_stars>10-100
## _____ _____
## | __ \| __ \ AUTHOR: <NAME>
## | |__) | |__) | ---------------------------------
## | ___/| _ / DATE: May 18, 2021
## | | | | \ \ ---------------------------------
## |_| |_| \_\ https://github.com/pedrorrivero
##
## Copyright 2021 <NAME>
##
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
##
## http://www.apache.org/licenses/LICENSE-2.0
##
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
from abc import ABC, abstractmethod
###############################################################################
## BIT CACHE INTERFACE
###############################################################################
class BitCache(ABC):
"""
BitCache FIFO (first in, first out) data structure.
Attributes
----------
size: int
The number of bits currently stored in the BitCache.
Methods
-------
dump() -> str
Outputs all the contents in the cache without erasing.
flush() -> None:
Erases the cache.
pop(n: int) -> str:
Returns a size `n` bitstring removing it from the top of the cache.
push(bitstring: str) -> None:
Inserts bitstring at the end of the cache.
"""
############################### PUBLIC API ###############################
@property
@abstractmethod
def size(self) -> int:
"""
The number of bits currently stored in the BitCache.
"""
pass
@abstractmethod
def dump(self) -> str:
"""
Outputs all the contents in the cache without erasing.
Returns
-------
out: str
Cache contents.
"""
pass
@abstractmethod
def flush(self) -> None:
"""
Erases the cache.
"""
pass
@abstractmethod
def pop(self, num_bits: int) -> str:
"""
Returns a size `n` bitstring removing it from the top of the cache.
Parameters
----------
num_bits: int
Number of bits to retrieve.
Returns
-------
out: str
Size `n` bitstring.
Raises
------
TypeError
If input is not int.
ValueError
If input is less than one.
RuntimeError
If input is greater than cache size.
"""
pass
@abstractmethod
def push(self, bitstring: str) -> None:
"""
Inserts bitstring at the end of the cache.
Parameters
----------
bitstring: str
The bitstring to insert.
Raises
------
TypeError
If input bitstring is not str.
ValueError
If input bitstring is not a valid bitstring.
"""
pass
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2586,
1807,
29889,
13,
4706,
9995,
13,
4706,
1209,
13,
2
] |
saleor/core/__init__.py | X10project/rob_photography | 3 | 164256 | <reponame>X10project/rob_photography
TOKEN_PATTERN = ('(?P<token>[0-9a-z]{8}-[0-9a-z]{4}-[0-9a-z]{4}-[0-9a-z]{4}'
'-[0-9a-z]{12})')
| [
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] |
Raspberry_Pi_Animated_Gif_Player/code.py | albinger/Adafruit_Learning_System_Guides | 0 | 31062 | # SPDX-FileCopyrightText: 2021 <NAME> for Adafruit Industries
#
# SPDX-License-Identifier: MIT
import usb_cdc
import rotaryio
import board
import digitalio
serial = usb_cdc.data
encoder = rotaryio.IncrementalEncoder(board.ROTA, board.ROTB)
button = digitalio.DigitalInOut(board.SWITCH)
button.switch_to_input(pull=digitalio.Pull.UP)
last_position = None
button_state = False
while True:
position = encoder.position
if last_position is None or position != last_position:
serial.write(bytes(str(position) + ",", "utf-8"))
last_position = position
print(button.value)
if not button.value and not button_state:
button_state = True
if button.value and button_state:
serial.write(bytes("click,", "utf-8"))
button_state = False
| [
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2826,
29918,
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] |
Modulo 3/Lista11/17.py | BelfortJoao/Programacao-1 | 2 | 173999 | <gh_stars>1-10
# “é múltiplo de 3” ou “não é múltiplo de 3”.
Num1 = int(input("Escreva um Numero: "))
if Num1 % 3 > 0:
print("não é múltiplo de 3")
else:
print("é múltiplo de 3") | [
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] |
main/util/dto.py | emhayusa/4d_api | 0 | 54539 | from flask_restplus import Namespace, fields
class StatusDto:
api = Namespace('status', description='status related operations')
schema = api.model('status', {
'id': fields.Integer(dump_only=True),
'status_name': fields.String(required=True, description='status name')
})
entry = api.model('status_entry', {
'status_name': fields.String(required=True, description='status name')
})
class TypeDto:
api = Namespace('type', description='type related operations')
schema = api.model('type', {
'id': fields.Integer(dump_only=True),
'type_name': fields.String(required=True, description='type name')
})
entry = api.model('type_entry', {
'type_name': fields.String(required=True, description='type name')
})
class AreaDto:
api = Namespace('area', description='area related operations')
schema = api.model('area', {
'id': fields.Integer(dump_only=True),
'area_name': fields.String(required=True, description='area_name')
})
entry = api.model('area_entry', {
'area_name': fields.String(required=True, description='area_name')
})
class UserDto:
api = Namespace('user', description='user related operations')
schema = api.model('user', {
'email': fields.String(required=True, description='user email address'),
'username': fields.String(required=True, description='user username'),
'public_id': fields.String(description='user Identifier'),
'type' : fields.Nested(TypeDto().schema, required=True),
'area' : fields.Nested(AreaDto().schema, required=True)
})
user = api.model('user', {
'type_id' : fields.Integer(required=True, description='type id'),
'area_id' : fields.Integer(required=True, description='area id'),
'email': fields.String(required=True, description='user email address'),
'username': fields.String(required=True, description='user username'),
'password': fields.String(required=True, description='user password')
})
class AuthDto:
api = Namespace('auth', description='authentication related operations')
user_auth = api.model('auth_details', {
'username': fields.String(required=True, description='The username'),
'password': fields.String(required=True, description='The user password ')
})
class GmlDto:
api = Namespace('gml', description='gml related operations')
schema = api.model('gml', {
'id': fields.Integer(dump_only=True),
#'contributor' : fields.Nested(UserDto().schema, required=True),
'contributor': fields.String(required=True, description='contributor username'),
'building_name': fields.String(required=True, description='building name'),
'city_gml_file': fields.String(required=True, description='city gml file name'),
'uploaded_on': fields.DateTime(required=True, description='uploaded time'),
'verified_on': fields.DateTime(required=True, description='verified time'),
'validator': fields.String(required=True, description='validator username'),
#'validator' : fields.Nested(UserDto().schema, required=True),
'reason': fields.String(required=True, description='reason'),
'area' : fields.Nested(AreaDto().schema, required=True),
'status' : fields.Nested(StatusDto().schema, required=True)
})
entry = api.model('gml_entry', {
'contributor': fields.String(required=True, description='contributor username'),
'building_name': fields.String(required=True, description='building name'),
'city_gml_file': fields.String(required=True, description='city gml file name'),
'area_id': fields.Integer(required=True, description='area id')
})
verify = api.model('gml_verify', {
'id': fields.Integer(required=True, description='id'),
'validator': fields.String(required=True, description='validator username'),
'status_id': fields.Integer(required=True, description='status id'),
'reason': fields.String(required=False, description='reason')
})
class CityGmlDto:
api = Namespace('city_gml', description='city gml related operations')
schema = api.model('city_gml', {
'id': fields.Integer(dump_only=True),
'contributor': fields.String(required=True, description='contributor username'),
'city_gml_file': fields.String(required=True, description='city gml file name'),
'uploaded_on': fields.DateTime(required=True, description='uploaded time'),
'verified_on': fields.DateTime(required=True, description='verified time'),
'validator': fields.String(required=True, description='validator username'),
'reason': fields.String(required=True, description='reason'),
'area' : fields.Nested(AreaDto().schema, required=True),
'status' : fields.Nested(StatusDto().schema, required=True)
})
entry = api.model('city_gml_entry', {
'contributor': fields.String(required=True, description='contributor username'),
'city_gml_file': fields.String(required=True, description='city gml file name'),
'area_id': fields.Integer(required=True, description='area id')
})
verify = api.model('city_gml_verify', {
'id': fields.Integer(required=True, description='id'),
'validator': fields.String(required=True, description='validator username'),
'status_id': fields.Integer(required=True, description='status id'),
'reason': fields.String(required=False, description='reason')
})
class BuildingsDto:
api = Namespace('buildings', description='buildings related operations')
schema = api.model('buildings', {
'id': fields.Integer(dump_only=True),
'gml_id': fields.String(required=True, description='gml_id'),
'city_gml' : fields.Nested(CityGmlDto().schema, required=True),
})
entry = api.model('buildings_entry', {
'list_gml_id': fields.List(fields.String(required=True, description='gml id')),
'city_gml_id': fields.Integer(required=True, description='city gml id')
})
class MonitoringAttributeDto:
api = Namespace('monitoring', description='monitoring attribute related operations')
schema = api.model('monitoring_attribute', {
'id': fields.Integer(dump_only=True),
'timestamp': fields.DateTime(required=True, description='timestamp'),
'name': fields.String(required=True, description='name'),
'function': fields.String(required=True, description='function'),
'height': fields.Float(required=True, description='height'),
'buildings' : fields.Nested(BuildingsDto().schema, required=True),
'contributor': fields.String(required=True, description='contributor username'),
'verified_on': fields.DateTime(required=True, description='verified time'),
'validator': fields.String(required=True, description='validator username'),
'reason': fields.String(required=True, description='reason'),
'status' : fields.Nested(StatusDto().schema, required=True)
})
entry = api.model('monitoring_attribute_entry', {
'name': fields.String(required=True, description='name'),
'function': fields.String(required=True, description='function'),
'height': fields.Float(required=True, description='height'),
'user_id' : fields.String(required=True, description='user public id'),
'gml_id' : fields.String(required=True, description='building gml id')
})
verify = api.model('monitoring_attribute_verify', {
'id': fields.Integer(required=True, description='id'),
'validator': fields.String(required=True, description='validator username'),
'status_id': fields.Integer(required=True, description='status id'),
'reason': fields.String(required=False, description='reason')
})
class MonitoringPhotoDto:
api = Namespace('monitoring', description='monitoring photo related operations')
schema = api.model('monitoring_photo', {
'id': fields.Integer(dump_only=True),
'timestamp': fields.DateTime(required=True, description='timestamp'),
'photo_file': fields.String(required=True, description='photo_file'),
'buildings' : fields.Nested(BuildingsDto().schema, required=True),
'contributor': fields.String(required=True, description='contributor username'),
'verified_on': fields.DateTime(required=True, description='verified time'),
'validator': fields.String(required=True, description='validator username'),
'reason': fields.String(required=True, description='reason'),
'status' : fields.Nested(StatusDto().schema, required=True)
})
entry = api.model('monitoring_photo_entry', {
'photo_file': fields.String(required=True, description='photo_file'),
'user_id' : fields.String(required=True, description='user public id'),
'gml_id' : fields.String(required=True, description='building gml id')
})
verify = api.model('monitoring_photo_verify', {
'id': fields.Integer(required=True, description='id'),
'validator': fields.String(required=True, description='validator username'),
'status_id': fields.Integer(required=True, description='status id'),
'reason': fields.String(required=False, description='reason')
})
class MonitoringVideoDto:
api = Namespace('monitoring', description='monitoring video related operations')
schema = api.model('monitoring_video', {
'id': fields.Integer(dump_only=True),
'timestamp': fields.DateTime(required=True, description='timestamp'),
'video_file': fields.String(required=True, description='video_file'),
'buildings' : fields.Nested(BuildingsDto().schema, required=True),
'contributor': fields.String(required=True, description='contributor username'),
'verified_on': fields.DateTime(required=True, description='verified time'),
'validator': fields.String(required=True, description='validator username'),
'reason': fields.String(required=True, description='reason'),
'status' : fields.Nested(StatusDto().schema, required=True)
})
entry = api.model('monitoring_video_entry', {
'video_file': fields.String(required=True, description='video_file'),
'user_id' : fields.String(required=True, description='user public id'),
'gml_id' : fields.String(required=True, description='building gml id')
})
verify = api.model('monitoring_video_verify', {
'id': fields.Integer(required=True, description='id'),
'validator': fields.String(required=True, description='validator username'),
'status_id': fields.Integer(required=True, description='status id'),
'reason': fields.String(required=False, description='reason')
})
class CityObjectDto:
api = Namespace('cityobject', description='city object related operations') | [
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29889,
7798,
29898,
12403,
29922,
5574,
29892,
6139,
2433,
4882,
1178,
5477,
13,
4706,
525,
23147,
2396,
4235,
29889,
1231,
29898,
12403,
29922,
8824,
29892,
6139,
2433,
23147,
1495,
13,
1678,
5615,
13,
13,
1990,
4412,
2061,
29928,
517,
29901,
13,
1678,
7882,
353,
14706,
3535,
877,
12690,
3318,
742,
6139,
2433,
12690,
1203,
4475,
6931,
1495,
2
] |
Subsets and Splits