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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
13cde4f4ec9916ff8a799bc071fde32fd8bf29b3
| 2,461 |
py
|
Python
|
plotter.py
|
StrangeTcy/pathnet-pytorch
|
58c8088b992ad2f36b843186d93edc872d547c7b
|
[
"BSD-3-Clause"
] | 86 |
2017-04-05T13:03:13.000Z
|
2022-03-28T12:38:48.000Z
|
plotter.py
|
StrangeTcy/pathnet-pytorch
|
58c8088b992ad2f36b843186d93edc872d547c7b
|
[
"BSD-3-Clause"
] | 7 |
2017-04-30T20:59:46.000Z
|
2019-02-09T10:56:40.000Z
|
plotter.py
|
StrangeTcy/pathnet-pytorch
|
58c8088b992ad2f36b843186d93edc872d547c7b
|
[
"BSD-3-Clause"
] | 21 |
2017-04-05T23:42:39.000Z
|
2021-11-17T21:17:22.000Z
|
import argparse
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--mnist', action='store_true', default=False,
help='open mnist result')
args = parser.parse_args()
try:
if args.mnist:
f = open(os.path.join('./result/result_mnist.pickle'))
result = pickle.load(f)
f.close()
pathnet_first = []
pathnet_second = []
for res in result:
pathnet_first.append(res[2])
pathnet_second.append(res[3])
subplot('111', pathnet_first, pathnet_second,'MNIST')
plt.show()
else:
f = open(os.path.join('./result/result_cifar_svhn.pickle'))
result = pickle.load(f)
f.close()
cifar_first = []
cifar_second = []
svhn_first = []
svhn_second = []
for res in result:
if res[0] == 'pathnet_cifar_first':
cifar_first.append(res[2])
svhn_second.append(res[3])
else:
svhn_first.append(res[2])
cifar_second.append(res[3])
subplot('211', cifar_first, cifar_second,'CIFAR-10')
subplot('212', svhn_first, svhn_second,'cSVHN')
plt.show()
except IOError:
print("Result file does not exist")
| 28.952941 | 95 | 0.603007 |
13ce074cf333bc82bde9c49d1dbfefb77ad96d57
| 715 |
py
|
Python
|
kindler/solver/optimizer.py
|
mingruimingrui/kindler
|
8a9c2278b607a167b0ce827b218e54949a1120e7
|
[
"MIT"
] | null | null | null |
kindler/solver/optimizer.py
|
mingruimingrui/kindler
|
8a9c2278b607a167b0ce827b218e54949a1120e7
|
[
"MIT"
] | null | null | null |
kindler/solver/optimizer.py
|
mingruimingrui/kindler
|
8a9c2278b607a167b0ce827b218e54949a1120e7
|
[
"MIT"
] | null | null | null |
import torch
| 22.34375 | 67 | 0.601399 |
13ce147cc376d9100195efcbf75606622c35be95
| 2,805 |
py
|
Python
|
platypus/tests/test_operators.py
|
sctiwari/EZFF_ASE
|
94710d4cf778ff2db5e6df0cd6d10d92e1b98afe
|
[
"MIT"
] | 2 |
2021-05-10T16:28:50.000Z
|
2021-12-15T04:03:34.000Z
|
platypus/tests/test_operators.py
|
sctiwari/EZFF_ASE
|
94710d4cf778ff2db5e6df0cd6d10d92e1b98afe
|
[
"MIT"
] | null | null | null |
platypus/tests/test_operators.py
|
sctiwari/EZFF_ASE
|
94710d4cf778ff2db5e6df0cd6d10d92e1b98afe
|
[
"MIT"
] | null | null | null |
# Copyright 2015-2018 David Hadka
#
# This file is part of Platypus, a Python module for designing and using
# evolutionary algorithms (EAs) and multiobjective evolutionary algorithms
# (MOEAs).
#
# Platypus is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Platypus is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Platypus. If not, see <http://www.gnu.org/licenses/>.
import unittest
from mock import patch
from ..core import Problem, Solution
from ..types import Permutation
from ..operators import Swap
| 35.961538 | 74 | 0.636364 |
13cea3eb1b8257abf1a1958d34086a311a9082d4
| 6,241 |
py
|
Python
|
fusion_net/bilinear_sampler.py
|
ClovisChen/LearningCNN
|
cd9102a3d71f602024558d818039f5b759c92fa5
|
[
"Apache-2.0"
] | null | null | null |
fusion_net/bilinear_sampler.py
|
ClovisChen/LearningCNN
|
cd9102a3d71f602024558d818039f5b759c92fa5
|
[
"Apache-2.0"
] | null | null | null |
fusion_net/bilinear_sampler.py
|
ClovisChen/LearningCNN
|
cd9102a3d71f602024558d818039f5b759c92fa5
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-are not covered by the UCLB ACP-A Licence,
from __future__ import absolute_import, division, print_function
import tensorflow as tf
def bilinear_sampler_1d_h(input_images, x_offset, wrap_mode='border', name='bilinear_sampler', **kwargs):
'''
: x_offset--X
: x, , , exsamples:[1,2,3] --> [1,1,2,2,3,3]
'''
# get_disp,.
def _transform(input_images, x_offset):
'''
meshgridXY
exsamples:
_width=3linspace(0.0,_width_f-1.0,_width)[ 0., 1., 2.]height
>>> x = tf.linspace(0.0, 2.0, 3)
>>> sess.run(x)
array([0., 1., 2. ], dtype = float32)
>>> x = tf.linspace(0.0, 2.0, 3)
>>> y = tf.linspace(0.0, 4.0, 5)
>>> x_t, y_t = tf.meshgrid(x, y)
>>> sess.run(x_t)
array([0., 1., 2.],
[0., 1., 2.],
[0., 1., 2.],
[0., 1., 2.],
[0., 1., 2.]], dtype=float32)
>>> sess.run(y_t)
array([0., 0., 0.],
[1., 1., 1.],
[2., 2., 2.],
[3., 3., 3.],
[4., 4., 4.]], dtype=float32)
>>> x_t_flat = tf.reshape(x_t, (1, -1))
>>> y_t_flat = tf.reshape(y_t, (1, -1))
>>> sess.run(x_t_flat)
array([[0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.]], dtype=float32)
>>> sess.run(y_t_flat)
array([[0., 0., 0., 1., 1., 1., 2., 2., 2., 3., 3., 3., 4., 4., 4.]], dtype=float32)
>>> x_t_flat = tf.tile(x_t_flat, tf.stack([2,1]))
>>> sess.run(x_t_flat)
arraay([[0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.], [0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.]], dtype=float32)
>>> x_t_flat = tf.reshape(x_t_flat, (1, -1))
>>> sess.run(x_t_flat)
array([[0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.]], dtype=float32)
'''
with tf.variable_scope('transform'):
# grid of (x_t, y_t, 1), eq (1) in ref [1]
x_t, y_t = tf.meshgrid(tf.linspace(0.0, _width_f - 1.0, _width),
tf.linspace(0.0 , _height_f - 1.0 , _height))
x_t_flat = tf.reshape(x_t, (1, -1))
y_t_flat = tf.reshape(y_t, (1, -1))
x_t_flat = tf.tile(x_t_flat, tf.stack([_num_batch, 1]))
y_t_flat = tf.tile(y_t_flat, tf.stack([_num_batch, 1]))
x_t_flat = tf.reshape(x_t_flat, [-1])
y_t_flat = tf.reshape(y_t_flat, [-1])
x_t_flat = x_t_flat + tf.reshape(x_offset, [-1]) * _width_f
input_transformed = _interpolate(input_images, x_t_flat, y_t_flat)
output = tf.reshape(
input_transformed, tf.stack([_num_batch, _height, _width, _num_channels]))
return output
with tf.variable_scope(name):
'''
[num_batch, height, width, num_channels]
'''
_num_batch = tf.shape(input_images)[0]
_height = tf.shape(input_images)[1]
_width = tf.shape(input_images)[2]
_num_channels = tf.shape(input_images)[3]
_height_f = tf.cast(_height, tf.float32)
_width_f = tf.cast(_width, tf.float32)
_wrap_mode = wrap_mode
output = _transform(input_images, x_offset)
return output
| 41.331126 | 155 | 0.497196 |
13cebdf8d097569317951da787d81aebd898d39b
| 7,125 |
py
|
Python
|
Supernovae.py
|
adamamiller/iptf16hvw-1
|
d674114e94b5b20398d2e4208b55eb8e2394dce9
|
[
"MIT"
] | null | null | null |
Supernovae.py
|
adamamiller/iptf16hvw-1
|
d674114e94b5b20398d2e4208b55eb8e2394dce9
|
[
"MIT"
] | null | null | null |
Supernovae.py
|
adamamiller/iptf16hvw-1
|
d674114e94b5b20398d2e4208b55eb8e2394dce9
|
[
"MIT"
] | 1 |
2018-08-21T15:17:48.000Z
|
2018-08-21T15:17:48.000Z
|
#import relevant libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from astropy.io import ascii
import json
from IPython.display import display, Image
from specutils import Spectrum1D
from astropy import units
from scipy.optimize import curve_fit
from scipy.interpolate import interp1d
import scipy.integrate as integrate
from astropy.time import Time
from Supernovae import *
#speed of light (km/s)
c = 3e5
#Define class to hold releveant information for spectra data
#Create Supernovae class to store Spectral objects
#define function that converts wavlengths to restframe and corrects flux for redshift, and normalizes flux
def Unpack_Spectra(Spectra, z, normalization = [5000,6000]):
'''
Args:
Spectra - one epoch of spectral data in JSON format from OSN
z (float) - redshift of SN
normalizationn (list) - 2 item list containing boundaries of region used for normalization
Returns:
Pandas DataFrame - 2 column dataframe: wavelength and flux
Flux is corrected for redshift and normalized
Wavelength is converted to SN restframe
'''
#Extract Wavelengths
wavelengths = [float(x[0]) for x in Spectra]
#Extract Fluxes
fluxes = [float(x[1]) for x in Spectra]
#correct fluxes for redshift
fluxes = [correct_flux(flux, z) for flux in fluxes]
#Extract fluxes in normalization range
rel_flux_range = [x for x in Spectra if (float(x[0])>normalization[0]) & (float(x[0])<normalization[1])]
#Make sure there rel_flux_range isnt empty
if len(rel_flux_range) == 0:
#print('No wavelengths in normalization region, not including spectra')
return None
#Calculate average flux in this range
flux_sum = 0
for x in rel_flux_range:
flux_sum += float(x[1])
average_flux = flux_sum / float(len(rel_flux_range))
#Normalize flux
fluxes = [float(flux) / average_flux for flux in fluxes]
#convert wavelength to restframe
wavelengths = [wavelength / float(1 + z) for wavelength in wavelengths]
#store in pandas dataframe
df = pd.DataFrame()
df['Flux'] = fluxes
df['Wavelength'] = wavelengths
return df
def correct_flux(flux_obs, z):
'''
Args:
flux_obs (int) - observed flux
z (int) - redshift
Returns:
int - redshift corrected flux
'''
flux_emit = (z * flux_obs) + flux_obs
return flux_emit
#Define function to get relevant spectra from OSN JSON data file
def create_SN_object(JSON, MJD_max, z):
'''
Function to create Supernovae object for given JSON data file from OSN
Args:
JSON (str) - path to OSN JSON file of interest
MJD_max (int) - number of days past maximum brightness
phase (int) - phase for spectra of interest
Returns:
Supernovae - Supernovae object with spectra list filled
'''
supernovae = Supernovae(str(JSON[0:-5]), z, MJD_max)
#Load OSN json data
file = open('../Data/OSN_data/' + str(JSON))
json_data = json.load(file)
spectra_data = json_data[JSON[0:-5]]['spectra']
spectra_data = np.array(spectra_data)
for i in range(len(spectra_data)):
spectra = Spectra(spectra_data[i]['data'], float(spectra_data[i]['time']) / (1+z), z, MJD_max)
if spectra.data is None:
continue
else:
supernovae.store_spectra(spectra)
return supernovae
#Define function to convert calendar date to MJD
def convert_date_toMJD(date):
'''
Args:
date (str) - string of calendar date (e.g. '2002-8-17')
Returns:
float - MJD value of given calendar date
'''
t = Time(date)
t.format = 'mjd'
return t.value
#Define function to calculate absorption velocities
def calc_abs_velc(restframe, dopplershifted):
'''
Args:
restframe (float) - restframe wavelength of absorption
dopplershifted (float) - dopplershifted wavelength of absorption
Returns:
float - corresponding absorption velocity
'''
velocity = ((restframe - dopplershifted) / np.float(restframe))* c
return velocity
| 28.27381 | 108 | 0.629474 |
13cfe935b3ae2ffb5842e273c8ff7ca4cd002f6d
| 2,753 |
py
|
Python
|
userbot/plugins/alive.py
|
iraqis1/irqis
|
d95303c48b5f15dbe814454a48d847e838793713
|
[
"MIT"
] | null | null | null |
userbot/plugins/alive.py
|
iraqis1/irqis
|
d95303c48b5f15dbe814454a48d847e838793713
|
[
"MIT"
] | null | null | null |
userbot/plugins/alive.py
|
iraqis1/irqis
|
d95303c48b5f15dbe814454a48d847e838793713
|
[
"MIT"
] | null | null | null |
"""Check if userbot alive. If you change these, you become the gayest gay such that even the gay world will disown you."""
import asyncio
from telethon import events
from telethon.tl.types import ChannelParticipantsAdmins
from platform import uname
from userbot import ALIVE_NAME
from userbot.utils import admin_cmd
DEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else "No name set yet nibba, check pinned in @XtraTgBot"
| 26.219048 | 123 | 0.521976 |
13d20c58618bae1fd9184241e64ff9b913dd727d
| 9,313 |
py
|
Python
|
connector/ADBConnector.py
|
qiutongxue/ArknightsAutoHelper
|
6b97b289e9ea4e5e3f39561ef8c2217657f6ff60
|
[
"MIT"
] | 1 |
2020-12-16T06:19:02.000Z
|
2020-12-16T06:19:02.000Z
|
connector/ADBConnector.py
|
qiutongxue/ArknightsAutoHelper
|
6b97b289e9ea4e5e3f39561ef8c2217657f6ff60
|
[
"MIT"
] | null | null | null |
connector/ADBConnector.py
|
qiutongxue/ArknightsAutoHelper
|
6b97b289e9ea4e5e3f39561ef8c2217657f6ff60
|
[
"MIT"
] | null | null | null |
import os
import logging.config
from random import randint
import zlib
import struct
import socket
import time
from PIL import Image
import config
# from config import ADB_ROOT, ADB_HOST, SCREEN_SHOOT_SAVE_PATH, ShellColor, CONFIG_PATH,enable_adb_host_auto_detect, ADB_SERVER
from .ADBClientSession import ADBClientSession
from util.socketutil import recvall
from . import revconn
# from numpy import average, dot, linalg
logger = logging.getLogger(__name__)
| 36.521569 | 163 | 0.57962 |
13d25057738843cced8f3d82852dabf41375fb9a
| 754 |
py
|
Python
|
redshift_upload/base_utilities.py
|
douglassimonsen/redshift_upload
|
e549c770538f022c0b90a983ca056f3e9c16c643
|
[
"MIT"
] | null | null | null |
redshift_upload/base_utilities.py
|
douglassimonsen/redshift_upload
|
e549c770538f022c0b90a983ca056f3e9c16c643
|
[
"MIT"
] | 1 |
2022-03-12T03:50:55.000Z
|
2022-03-12T03:50:55.000Z
|
redshift_upload/base_utilities.py
|
douglassimonsen/redshift_upload
|
e549c770538f022c0b90a983ca056f3e9c16c643
|
[
"MIT"
] | null | null | null |
import inspect
import os
from pathlib import Path
| 30.16 | 98 | 0.624668 |
13d2e35293cf4b36df0d8aa584ec383e80a8a174
| 263 |
py
|
Python
|
main.py
|
Gloriel621/MgallManager
|
7d5c02ab6bdc2f6c6922d4a7e021faef33d868bb
|
[
"MIT"
] | 9 |
2021-12-22T11:37:23.000Z
|
2022-03-09T02:25:35.000Z
|
main.py
|
Gloriel621/MgallManager
|
7d5c02ab6bdc2f6c6922d4a7e021faef33d868bb
|
[
"MIT"
] | 4 |
2021-12-16T14:26:01.000Z
|
2022-02-16T02:05:41.000Z
|
main.py
|
Gloriel621/MgallManager
|
7d5c02ab6bdc2f6c6922d4a7e021faef33d868bb
|
[
"MIT"
] | 1 |
2021-12-22T12:59:57.000Z
|
2021-12-22T12:59:57.000Z
|
import sys
from PyQt5.QtWidgets import QApplication
from gui import MgallManager
if __name__ == "__main__":
main()
| 16.4375 | 43 | 0.69962 |
13d3859368c4908cf2a507d1bd62d989795acc1a
| 54,009 |
py
|
Python
|
pysc2/lib/actions.py
|
javierrcc522/starcraft2_api_machineLear
|
5833ba1344ab5445c4f09fafc33e6058070ebe6c
|
[
"Apache-2.0"
] | 2 |
2020-04-30T09:07:25.000Z
|
2021-03-21T22:58:22.000Z
|
pysc2/lib/actions.py
|
javierrcc522/starcraft2_api_machineLear
|
5833ba1344ab5445c4f09fafc33e6058070ebe6c
|
[
"Apache-2.0"
] | null | null | null |
pysc2/lib/actions.py
|
javierrcc522/starcraft2_api_machineLear
|
5833ba1344ab5445c4f09fafc33e6058070ebe6c
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Define the static list of types and actions for SC2."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numbers
import six
from pysc2.lib import point
from s2clientprotocol import spatial_pb2 as sc_spatial
from s2clientprotocol import ui_pb2 as sc_ui
def move_camera(action, minimap):
"""Move the camera."""
minimap.assign_to(action.action_feature_layer.camera_move.center_minimap)
def select_point(action, select_point_act, screen):
"""Select a unit at a point."""
select = action.action_feature_layer.unit_selection_point
screen.assign_to(select.selection_screen_coord)
select.type = select_point_act
def select_rect(action, select_add, screen, screen2):
"""Select units within a rectangle."""
select = action.action_feature_layer.unit_selection_rect
out_rect = select.selection_screen_coord.add()
screen_rect = point.Rect(screen, screen2)
screen_rect.tl.assign_to(out_rect.p0)
screen_rect.br.assign_to(out_rect.p1)
select.selection_add = bool(select_add)
def select_idle_worker(action, select_worker):
"""Select an idle worker."""
action.action_ui.select_idle_worker.type = select_worker
def select_army(action, select_add):
"""Select the entire army."""
action.action_ui.select_army.selection_add = select_add
def select_warp_gates(action, select_add):
"""Select all warp gates."""
action.action_ui.select_warp_gates.selection_add = select_add
def select_larva(action):
"""Select all larva."""
action.action_ui.select_larva.SetInParent() # Adds the empty proto field.
def select_unit(action, select_unit_act, select_unit_id):
"""Select a specific unit from the multi-unit selection."""
select = action.action_ui.multi_panel
select.type = select_unit_act
select.unit_index = select_unit_id
def control_group(action, control_group_act, control_group_id):
"""Act on a control group, selecting, setting, etc."""
select = action.action_ui.control_group
select.action = control_group_act
select.control_group_index = control_group_id
def unload(action, unload_id):
"""Unload a unit from a transport/bunker/nydus/etc."""
action.action_ui.cargo_panel.unit_index = unload_id
def build_queue(action, build_queue_id):
"""Cancel a unit in the build queue."""
action.action_ui.production_panel.unit_index = build_queue_id
def cmd_quick(action, ability_id, queued):
"""Do a quick command like 'Stop' or 'Stim'."""
action_cmd = action.action_feature_layer.unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
def cmd_screen(action, ability_id, queued, screen):
"""Do a command that needs a point on the screen."""
action_cmd = action.action_feature_layer.unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
screen.assign_to(action_cmd.target_screen_coord)
def cmd_minimap(action, ability_id, queued, minimap):
"""Do a command that needs a point on the minimap."""
action_cmd = action.action_feature_layer.unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
minimap.assign_to(action_cmd.target_minimap_coord)
def autocast(action, ability_id):
"""Toggle autocast."""
action.action_ui.toggle_autocast.ability_id = ability_id
# The list of known types.
TYPES = Arguments.types(
screen=ArgumentType.point(),
minimap=ArgumentType.point(),
screen2=ArgumentType.point(),
queued=ArgumentType.enum([False, True]), # (now vs add to queue)
control_group_act=ArgumentType.enum([
sc_ui.ActionControlGroup.Recall,
sc_ui.ActionControlGroup.Set,
sc_ui.ActionControlGroup.Append,
sc_ui.ActionControlGroup.SetAndSteal,
sc_ui.ActionControlGroup.AppendAndSteal,
]),
control_group_id=ArgumentType.scalar(10),
select_point_act=ArgumentType.enum([
sc_spatial.ActionSpatialUnitSelectionPoint.Select,
sc_spatial.ActionSpatialUnitSelectionPoint.Toggle,
sc_spatial.ActionSpatialUnitSelectionPoint.AllType,
sc_spatial.ActionSpatialUnitSelectionPoint.AddAllType,
]),
select_add=ArgumentType.enum([False, True]), # (select vs select_add)
select_unit_act=ArgumentType.enum([
sc_ui.ActionMultiPanel.SingleSelect,
sc_ui.ActionMultiPanel.DeselectUnit,
sc_ui.ActionMultiPanel.SelectAllOfType,
sc_ui.ActionMultiPanel.DeselectAllOfType,
]),
select_unit_id=ArgumentType.scalar(500), # Depends on current selection.
select_worker=ArgumentType.enum([
sc_ui.ActionSelectIdleWorker.Set,
sc_ui.ActionSelectIdleWorker.Add,
sc_ui.ActionSelectIdleWorker.All,
sc_ui.ActionSelectIdleWorker.AddAll,
]),
build_queue_id=ArgumentType.scalar(10), # Depends on current build queue.
unload_id=ArgumentType.scalar(500), # Depends on the current loaded units.
)
# Which argument types do each function need?
FUNCTION_TYPES = {
no_op: [],
move_camera: [TYPES.minimap],
select_point: [TYPES.select_point_act, TYPES.screen],
select_rect: [TYPES.select_add, TYPES.screen, TYPES.screen2],
select_unit: [TYPES.select_unit_act, TYPES.select_unit_id],
control_group: [TYPES.control_group_act, TYPES.control_group_id],
select_idle_worker: [TYPES.select_worker],
select_army: [TYPES.select_add],
select_warp_gates: [TYPES.select_add],
select_larva: [],
unload: [TYPES.unload_id],
build_queue: [TYPES.build_queue_id],
cmd_quick: [TYPES.queued],
cmd_screen: [TYPES.queued, TYPES.screen],
cmd_minimap: [TYPES.queued, TYPES.minimap],
autocast: [],
}
# Which ones need an ability?
ABILITY_FUNCTIONS = {cmd_quick, cmd_screen, cmd_minimap, autocast}
# Which ones require a point?
POINT_REQUIRED_FUNCS = {
False: {cmd_quick, autocast},
True: {cmd_screen, cmd_minimap, autocast}}
always = lambda _: True
def str(self, space=False):
"""String version. Set space=True to line them all up nicely."""
return "%s/%s (%s)" % (str(self.id).rjust(space and 4),
self.name.ljust(space and 50),
"; ".join(str(a) for a in self.args))
class Functions(object):
"""Represents the full set of functions.
Can't use namedtuple since python3 has a limit of 255 function arguments, so
build something similar.
"""
# pylint: disable=line-too-long
FUNCTIONS = Functions([
Function.ui_func(0, "no_op", no_op),
Function.ui_func(1, "move_camera", move_camera),
Function.ui_func(2, "select_point", select_point),
Function.ui_func(3, "select_rect", select_rect),
Function.ui_func(4, "select_control_group", control_group),
Function.ui_func(5, "select_unit", select_unit,
lambda obs: obs.ui_data.HasField("multi")),
Function.ui_func(6, "select_idle_worker", select_idle_worker,
lambda obs: obs.player_common.idle_worker_count > 0),
Function.ui_func(7, "select_army", select_army,
lambda obs: obs.player_common.army_count > 0),
Function.ui_func(8, "select_warp_gates", select_warp_gates,
lambda obs: obs.player_common.warp_gate_count > 0),
Function.ui_func(9, "select_larva", select_larva,
lambda obs: obs.player_common.larva_count > 0),
Function.ui_func(10, "unload", unload,
lambda obs: obs.ui_data.HasField("cargo")),
Function.ui_func(11, "build_queue", build_queue,
lambda obs: obs.ui_data.HasField("production")),
# Everything below here is generated with gen_actions.py
Function.ability(12, "Attack_screen", cmd_screen, 3674),
Function.ability(13, "Attack_minimap", cmd_minimap, 3674),
Function.ability(14, "Attack_Attack_screen", cmd_screen, 23, 3674),
Function.ability(15, "Attack_Attack_minimap", cmd_minimap, 23, 3674),
Function.ability(16, "Attack_AttackBuilding_screen", cmd_screen, 2048, 3674),
Function.ability(17, "Attack_AttackBuilding_minimap", cmd_minimap, 2048, 3674),
Function.ability(18, "Attack_Redirect_screen", cmd_screen, 1682, 3674),
Function.ability(19, "Scan_Move_screen", cmd_screen, 19, 3674),
Function.ability(20, "Scan_Move_minimap", cmd_minimap, 19, 3674),
Function.ability(21, "Behavior_BuildingAttackOff_quick", cmd_quick, 2082),
Function.ability(22, "Behavior_BuildingAttackOn_quick", cmd_quick, 2081),
Function.ability(23, "Behavior_CloakOff_quick", cmd_quick, 3677),
Function.ability(24, "Behavior_CloakOff_Banshee_quick", cmd_quick, 393, 3677),
Function.ability(25, "Behavior_CloakOff_Ghost_quick", cmd_quick, 383, 3677),
Function.ability(26, "Behavior_CloakOn_quick", cmd_quick, 3676),
Function.ability(27, "Behavior_CloakOn_Banshee_quick", cmd_quick, 392, 3676),
Function.ability(28, "Behavior_CloakOn_Ghost_quick", cmd_quick, 382, 3676),
Function.ability(29, "Behavior_GenerateCreepOff_quick", cmd_quick, 1693),
Function.ability(30, "Behavior_GenerateCreepOn_quick", cmd_quick, 1692),
Function.ability(31, "Behavior_HoldFireOff_quick", cmd_quick, 3689),
Function.ability(32, "Behavior_HoldFireOff_Ghost_quick", cmd_quick, 38, 3689),
Function.ability(33, "Behavior_HoldFireOff_Lurker_quick", cmd_quick, 2552, 3689),
Function.ability(34, "Behavior_HoldFireOn_quick", cmd_quick, 3688),
Function.ability(35, "Behavior_HoldFireOn_Ghost_quick", cmd_quick, 36, 3688),
Function.ability(36, "Behavior_HoldFireOn_Lurker_quick", cmd_quick, 2550, 3688),
Function.ability(37, "Behavior_PulsarBeamOff_quick", cmd_quick, 2376),
Function.ability(38, "Behavior_PulsarBeamOn_quick", cmd_quick, 2375),
Function.ability(39, "Build_Armory_screen", cmd_screen, 331),
Function.ability(40, "Build_Assimilator_screen", cmd_screen, 882),
Function.ability(41, "Build_BanelingNest_screen", cmd_screen, 1162),
Function.ability(42, "Build_Barracks_screen", cmd_screen, 321),
Function.ability(43, "Build_Bunker_screen", cmd_screen, 324),
Function.ability(44, "Build_CommandCenter_screen", cmd_screen, 318),
Function.ability(45, "Build_CreepTumor_screen", cmd_screen, 3691),
Function.ability(46, "Build_CreepTumor_Queen_screen", cmd_screen, 1694, 3691),
Function.ability(47, "Build_CreepTumor_Tumor_screen", cmd_screen, 1733, 3691),
Function.ability(48, "Build_CyberneticsCore_screen", cmd_screen, 894),
Function.ability(49, "Build_DarkShrine_screen", cmd_screen, 891),
Function.ability(50, "Build_EngineeringBay_screen", cmd_screen, 322),
Function.ability(51, "Build_EvolutionChamber_screen", cmd_screen, 1156),
Function.ability(52, "Build_Extractor_screen", cmd_screen, 1154),
Function.ability(53, "Build_Factory_screen", cmd_screen, 328),
Function.ability(54, "Build_FleetBeacon_screen", cmd_screen, 885),
Function.ability(55, "Build_Forge_screen", cmd_screen, 884),
Function.ability(56, "Build_FusionCore_screen", cmd_screen, 333),
Function.ability(57, "Build_Gateway_screen", cmd_screen, 883),
Function.ability(58, "Build_GhostAcademy_screen", cmd_screen, 327),
Function.ability(59, "Build_Hatchery_screen", cmd_screen, 1152),
Function.ability(60, "Build_HydraliskDen_screen", cmd_screen, 1157),
Function.ability(61, "Build_InfestationPit_screen", cmd_screen, 1160),
Function.ability(62, "Build_Interceptors_quick", cmd_quick, 1042),
Function.ability(63, "Build_Interceptors_autocast", autocast, 1042),
Function.ability(64, "Build_MissileTurret_screen", cmd_screen, 323),
Function.ability(65, "Build_Nexus_screen", cmd_screen, 880),
Function.ability(66, "Build_Nuke_quick", cmd_quick, 710),
Function.ability(67, "Build_NydusNetwork_screen", cmd_screen, 1161),
Function.ability(68, "Build_NydusWorm_screen", cmd_screen, 1768),
Function.ability(69, "Build_PhotonCannon_screen", cmd_screen, 887),
Function.ability(70, "Build_Pylon_screen", cmd_screen, 881),
Function.ability(71, "Build_Reactor_quick", cmd_quick, 3683),
Function.ability(72, "Build_Reactor_screen", cmd_screen, 3683),
Function.ability(73, "Build_Reactor_Barracks_quick", cmd_quick, 422, 3683),
Function.ability(74, "Build_Reactor_Barracks_screen", cmd_screen, 422, 3683),
Function.ability(75, "Build_Reactor_Factory_quick", cmd_quick, 455, 3683),
Function.ability(76, "Build_Reactor_Factory_screen", cmd_screen, 455, 3683),
Function.ability(77, "Build_Reactor_Starport_quick", cmd_quick, 488, 3683),
Function.ability(78, "Build_Reactor_Starport_screen", cmd_screen, 488, 3683),
Function.ability(79, "Build_Refinery_screen", cmd_screen, 320),
Function.ability(80, "Build_RoachWarren_screen", cmd_screen, 1165),
Function.ability(81, "Build_RoboticsBay_screen", cmd_screen, 892),
Function.ability(82, "Build_RoboticsFacility_screen", cmd_screen, 893),
Function.ability(83, "Build_SensorTower_screen", cmd_screen, 326),
Function.ability(84, "Build_SpawningPool_screen", cmd_screen, 1155),
Function.ability(85, "Build_SpineCrawler_screen", cmd_screen, 1166),
Function.ability(86, "Build_Spire_screen", cmd_screen, 1158),
Function.ability(87, "Build_SporeCrawler_screen", cmd_screen, 1167),
Function.ability(88, "Build_Stargate_screen", cmd_screen, 889),
Function.ability(89, "Build_Starport_screen", cmd_screen, 329),
Function.ability(90, "Build_StasisTrap_screen", cmd_screen, 2505),
Function.ability(91, "Build_SupplyDepot_screen", cmd_screen, 319),
Function.ability(92, "Build_TechLab_quick", cmd_quick, 3682),
Function.ability(93, "Build_TechLab_screen", cmd_screen, 3682),
Function.ability(94, "Build_TechLab_Barracks_quick", cmd_quick, 421, 3682),
Function.ability(95, "Build_TechLab_Barracks_screen", cmd_screen, 421, 3682),
Function.ability(96, "Build_TechLab_Factory_quick", cmd_quick, 454, 3682),
Function.ability(97, "Build_TechLab_Factory_screen", cmd_screen, 454, 3682),
Function.ability(98, "Build_TechLab_Starport_quick", cmd_quick, 487, 3682),
Function.ability(99, "Build_TechLab_Starport_screen", cmd_screen, 487, 3682),
Function.ability(100, "Build_TemplarArchive_screen", cmd_screen, 890),
Function.ability(101, "Build_TwilightCouncil_screen", cmd_screen, 886),
Function.ability(102, "Build_UltraliskCavern_screen", cmd_screen, 1159),
Function.ability(103, "BurrowDown_quick", cmd_quick, 3661),
Function.ability(104, "BurrowDown_Baneling_quick", cmd_quick, 1374, 3661),
Function.ability(105, "BurrowDown_Drone_quick", cmd_quick, 1378, 3661),
Function.ability(106, "BurrowDown_Hydralisk_quick", cmd_quick, 1382, 3661),
Function.ability(107, "BurrowDown_Infestor_quick", cmd_quick, 1444, 3661),
Function.ability(108, "BurrowDown_InfestorTerran_quick", cmd_quick, 1394, 3661),
Function.ability(109, "BurrowDown_Lurker_quick", cmd_quick, 2108, 3661),
Function.ability(110, "BurrowDown_Queen_quick", cmd_quick, 1433, 3661),
Function.ability(111, "BurrowDown_Ravager_quick", cmd_quick, 2340, 3661),
Function.ability(112, "BurrowDown_Roach_quick", cmd_quick, 1386, 3661),
Function.ability(113, "BurrowDown_SwarmHost_quick", cmd_quick, 2014, 3661),
Function.ability(114, "BurrowDown_Ultralisk_quick", cmd_quick, 1512, 3661),
Function.ability(115, "BurrowDown_WidowMine_quick", cmd_quick, 2095, 3661),
Function.ability(116, "BurrowDown_Zergling_quick", cmd_quick, 1390, 3661),
Function.ability(117, "BurrowUp_quick", cmd_quick, 3662),
Function.ability(118, "BurrowUp_autocast", autocast, 3662),
Function.ability(119, "BurrowUp_Baneling_quick", cmd_quick, 1376, 3662),
Function.ability(120, "BurrowUp_Baneling_autocast", autocast, 1376, 3662),
Function.ability(121, "BurrowUp_Drone_quick", cmd_quick, 1380, 3662),
Function.ability(122, "BurrowUp_Hydralisk_quick", cmd_quick, 1384, 3662),
Function.ability(123, "BurrowUp_Hydralisk_autocast", autocast, 1384, 3662),
Function.ability(124, "BurrowUp_Infestor_quick", cmd_quick, 1446, 3662),
Function.ability(125, "BurrowUp_InfestorTerran_quick", cmd_quick, 1396, 3662),
Function.ability(126, "BurrowUp_InfestorTerran_autocast", autocast, 1396, 3662),
Function.ability(127, "BurrowUp_Lurker_quick", cmd_quick, 2110, 3662),
Function.ability(128, "BurrowUp_Queen_quick", cmd_quick, 1435, 3662),
Function.ability(129, "BurrowUp_Queen_autocast", autocast, 1435, 3662),
Function.ability(130, "BurrowUp_Ravager_quick", cmd_quick, 2342, 3662),
Function.ability(131, "BurrowUp_Ravager_autocast", autocast, 2342, 3662),
Function.ability(132, "BurrowUp_Roach_quick", cmd_quick, 1388, 3662),
Function.ability(133, "BurrowUp_Roach_autocast", autocast, 1388, 3662),
Function.ability(134, "BurrowUp_SwarmHost_quick", cmd_quick, 2016, 3662),
Function.ability(135, "BurrowUp_Ultralisk_quick", cmd_quick, 1514, 3662),
Function.ability(136, "BurrowUp_Ultralisk_autocast", autocast, 1514, 3662),
Function.ability(137, "BurrowUp_WidowMine_quick", cmd_quick, 2097, 3662),
Function.ability(138, "BurrowUp_Zergling_quick", cmd_quick, 1392, 3662),
Function.ability(139, "BurrowUp_Zergling_autocast", autocast, 1392, 3662),
Function.ability(140, "Cancel_quick", cmd_quick, 3659),
Function.ability(141, "Cancel_AdeptPhaseShift_quick", cmd_quick, 2594, 3659),
Function.ability(142, "Cancel_AdeptShadePhaseShift_quick", cmd_quick, 2596, 3659),
Function.ability(143, "Cancel_BarracksAddOn_quick", cmd_quick, 451, 3659),
Function.ability(144, "Cancel_BuildInProgress_quick", cmd_quick, 314, 3659),
Function.ability(145, "Cancel_CreepTumor_quick", cmd_quick, 1763, 3659),
Function.ability(146, "Cancel_FactoryAddOn_quick", cmd_quick, 484, 3659),
Function.ability(147, "Cancel_GravitonBeam_quick", cmd_quick, 174, 3659),
Function.ability(148, "Cancel_LockOn_quick", cmd_quick, 2354, 3659),
Function.ability(149, "Cancel_MorphBroodlord_quick", cmd_quick, 1373, 3659),
Function.ability(150, "Cancel_MorphGreaterSpire_quick", cmd_quick, 1221, 3659),
Function.ability(151, "Cancel_MorphHive_quick", cmd_quick, 1219, 3659),
Function.ability(152, "Cancel_MorphLair_quick", cmd_quick, 1217, 3659),
Function.ability(153, "Cancel_MorphLurker_quick", cmd_quick, 2333, 3659),
Function.ability(154, "Cancel_MorphLurkerDen_quick", cmd_quick, 2113, 3659),
Function.ability(155, "Cancel_MorphMothership_quick", cmd_quick, 1848, 3659),
Function.ability(156, "Cancel_MorphOrbital_quick", cmd_quick, 1517, 3659),
Function.ability(157, "Cancel_MorphOverlordTransport_quick", cmd_quick, 2709, 3659),
Function.ability(158, "Cancel_MorphOverseer_quick", cmd_quick, 1449, 3659),
Function.ability(159, "Cancel_MorphPlanetaryFortress_quick", cmd_quick, 1451, 3659),
Function.ability(160, "Cancel_MorphRavager_quick", cmd_quick, 2331, 3659),
Function.ability(161, "Cancel_MorphThorExplosiveMode_quick", cmd_quick, 2365, 3659),
Function.ability(162, "Cancel_NeuralParasite_quick", cmd_quick, 250, 3659),
Function.ability(163, "Cancel_Nuke_quick", cmd_quick, 1623, 3659),
Function.ability(164, "Cancel_SpineCrawlerRoot_quick", cmd_quick, 1730, 3659),
Function.ability(165, "Cancel_SporeCrawlerRoot_quick", cmd_quick, 1732, 3659),
Function.ability(166, "Cancel_StarportAddOn_quick", cmd_quick, 517, 3659),
Function.ability(167, "Cancel_StasisTrap_quick", cmd_quick, 2535, 3659),
Function.ability(168, "Cancel_Last_quick", cmd_quick, 3671),
Function.ability(169, "Cancel_HangarQueue5_quick", cmd_quick, 1038, 3671),
Function.ability(170, "Cancel_Queue1_quick", cmd_quick, 304, 3671),
Function.ability(171, "Cancel_Queue5_quick", cmd_quick, 306, 3671),
Function.ability(172, "Cancel_QueueAddOn_quick", cmd_quick, 312, 3671),
Function.ability(173, "Cancel_QueueCancelToSelection_quick", cmd_quick, 308, 3671),
Function.ability(174, "Cancel_QueuePasive_quick", cmd_quick, 1831, 3671),
Function.ability(175, "Cancel_QueuePassiveCancelToSelection_quick", cmd_quick, 1833, 3671),
Function.ability(176, "Effect_Abduct_screen", cmd_screen, 2067),
Function.ability(177, "Effect_AdeptPhaseShift_screen", cmd_screen, 2544),
Function.ability(178, "Effect_AutoTurret_screen", cmd_screen, 1764),
Function.ability(179, "Effect_BlindingCloud_screen", cmd_screen, 2063),
Function.ability(180, "Effect_Blink_screen", cmd_screen, 3687),
Function.ability(181, "Effect_Blink_Stalker_screen", cmd_screen, 1442, 3687),
Function.ability(182, "Effect_ShadowStride_screen", cmd_screen, 2700, 3687),
Function.ability(183, "Effect_CalldownMULE_screen", cmd_screen, 171),
Function.ability(184, "Effect_CausticSpray_screen", cmd_screen, 2324),
Function.ability(185, "Effect_Charge_screen", cmd_screen, 1819),
Function.ability(186, "Effect_Charge_autocast", autocast, 1819),
Function.ability(187, "Effect_ChronoBoost_screen", cmd_screen, 261),
Function.ability(188, "Effect_Contaminate_screen", cmd_screen, 1825),
Function.ability(189, "Effect_CorrosiveBile_screen", cmd_screen, 2338),
Function.ability(190, "Effect_EMP_screen", cmd_screen, 1628),
Function.ability(191, "Effect_Explode_quick", cmd_quick, 42),
Function.ability(192, "Effect_Feedback_screen", cmd_screen, 140),
Function.ability(193, "Effect_ForceField_screen", cmd_screen, 1526),
Function.ability(194, "Effect_FungalGrowth_screen", cmd_screen, 74),
Function.ability(195, "Effect_GhostSnipe_screen", cmd_screen, 2714),
Function.ability(196, "Effect_GravitonBeam_screen", cmd_screen, 173),
Function.ability(197, "Effect_GuardianShield_quick", cmd_quick, 76),
Function.ability(198, "Effect_Heal_screen", cmd_screen, 386),
Function.ability(199, "Effect_Heal_autocast", autocast, 386),
Function.ability(200, "Effect_HunterSeekerMissile_screen", cmd_screen, 169),
Function.ability(201, "Effect_ImmortalBarrier_quick", cmd_quick, 2328),
Function.ability(202, "Effect_ImmortalBarrier_autocast", autocast, 2328),
Function.ability(203, "Effect_InfestedTerrans_screen", cmd_screen, 247),
Function.ability(204, "Effect_InjectLarva_screen", cmd_screen, 251),
Function.ability(205, "Effect_KD8Charge_screen", cmd_screen, 2588),
Function.ability(206, "Effect_LockOn_screen", cmd_screen, 2350),
Function.ability(207, "Effect_LocustSwoop_screen", cmd_screen, 2387),
Function.ability(208, "Effect_MassRecall_screen", cmd_screen, 3686),
Function.ability(209, "Effect_MassRecall_Mothership_screen", cmd_screen, 2368, 3686),
Function.ability(210, "Effect_MassRecall_MothershipCore_screen", cmd_screen, 1974, 3686),
Function.ability(211, "Effect_MedivacIgniteAfterburners_quick", cmd_quick, 2116),
Function.ability(212, "Effect_NeuralParasite_screen", cmd_screen, 249),
Function.ability(213, "Effect_NukeCalldown_screen", cmd_screen, 1622),
Function.ability(214, "Effect_OracleRevelation_screen", cmd_screen, 2146),
Function.ability(215, "Effect_ParasiticBomb_screen", cmd_screen, 2542),
Function.ability(216, "Effect_PhotonOvercharge_screen", cmd_screen, 2162),
Function.ability(217, "Effect_PointDefenseDrone_screen", cmd_screen, 144),
Function.ability(218, "Effect_PsiStorm_screen", cmd_screen, 1036),
Function.ability(219, "Effect_PurificationNova_screen", cmd_screen, 2346),
Function.ability(220, "Effect_Repair_screen", cmd_screen, 3685),
Function.ability(221, "Effect_Repair_autocast", autocast, 3685),
Function.ability(222, "Effect_Repair_Mule_screen", cmd_screen, 78, 3685),
Function.ability(223, "Effect_Repair_Mule_autocast", autocast, 78, 3685),
Function.ability(224, "Effect_Repair_SCV_screen", cmd_screen, 316, 3685),
Function.ability(225, "Effect_Repair_SCV_autocast", autocast, 316, 3685),
Function.ability(226, "Effect_Salvage_quick", cmd_quick, 32),
Function.ability(227, "Effect_Scan_screen", cmd_screen, 399),
Function.ability(228, "Effect_SpawnChangeling_quick", cmd_quick, 181),
Function.ability(229, "Effect_SpawnLocusts_screen", cmd_screen, 2704),
Function.ability(230, "Effect_Spray_screen", cmd_screen, 3684),
Function.ability(231, "Effect_Spray_Protoss_screen", cmd_screen, 30, 3684),
Function.ability(232, "Effect_Spray_Terran_screen", cmd_screen, 26, 3684),
Function.ability(233, "Effect_Spray_Zerg_screen", cmd_screen, 28, 3684),
Function.ability(234, "Effect_Stim_quick", cmd_quick, 3675),
Function.ability(235, "Effect_Stim_Marauder_quick", cmd_quick, 253, 3675),
Function.ability(236, "Effect_Stim_Marauder_Redirect_quick", cmd_quick, 1684, 3675),
Function.ability(237, "Effect_Stim_Marine_quick", cmd_quick, 380, 3675),
Function.ability(238, "Effect_Stim_Marine_Redirect_quick", cmd_quick, 1683, 3675),
Function.ability(239, "Effect_SupplyDrop_screen", cmd_screen, 255),
Function.ability(240, "Effect_TacticalJump_screen", cmd_screen, 2358),
Function.ability(241, "Effect_TimeWarp_screen", cmd_screen, 2244),
Function.ability(242, "Effect_Transfusion_screen", cmd_screen, 1664),
Function.ability(243, "Effect_ViperConsume_screen", cmd_screen, 2073),
Function.ability(244, "Effect_VoidRayPrismaticAlignment_quick", cmd_quick, 2393),
Function.ability(245, "Effect_WidowMineAttack_screen", cmd_screen, 2099),
Function.ability(246, "Effect_WidowMineAttack_autocast", autocast, 2099),
Function.ability(247, "Effect_YamatoGun_screen", cmd_screen, 401),
Function.ability(248, "Hallucination_Adept_quick", cmd_quick, 2391),
Function.ability(249, "Hallucination_Archon_quick", cmd_quick, 146),
Function.ability(250, "Hallucination_Colossus_quick", cmd_quick, 148),
Function.ability(251, "Hallucination_Disruptor_quick", cmd_quick, 2389),
Function.ability(252, "Hallucination_HighTemplar_quick", cmd_quick, 150),
Function.ability(253, "Hallucination_Immortal_quick", cmd_quick, 152),
Function.ability(254, "Hallucination_Oracle_quick", cmd_quick, 2114),
Function.ability(255, "Hallucination_Phoenix_quick", cmd_quick, 154),
Function.ability(256, "Hallucination_Probe_quick", cmd_quick, 156),
Function.ability(257, "Hallucination_Stalker_quick", cmd_quick, 158),
Function.ability(258, "Hallucination_VoidRay_quick", cmd_quick, 160),
Function.ability(259, "Hallucination_WarpPrism_quick", cmd_quick, 162),
Function.ability(260, "Hallucination_Zealot_quick", cmd_quick, 164),
Function.ability(261, "Halt_quick", cmd_quick, 3660),
Function.ability(262, "Halt_Building_quick", cmd_quick, 315, 3660),
Function.ability(263, "Halt_TerranBuild_quick", cmd_quick, 348, 3660),
Function.ability(264, "Harvest_Gather_screen", cmd_screen, 3666),
Function.ability(265, "Harvest_Gather_Drone_screen", cmd_screen, 1183, 3666),
Function.ability(266, "Harvest_Gather_Mule_screen", cmd_screen, 166, 3666),
Function.ability(267, "Harvest_Gather_Probe_screen", cmd_screen, 298, 3666),
Function.ability(268, "Harvest_Gather_SCV_screen", cmd_screen, 295, 3666),
Function.ability(269, "Harvest_Return_quick", cmd_quick, 3667),
Function.ability(270, "Harvest_Return_Drone_quick", cmd_quick, 1184, 3667),
Function.ability(271, "Harvest_Return_Mule_quick", cmd_quick, 167, 3667),
Function.ability(272, "Harvest_Return_Probe_quick", cmd_quick, 299, 3667),
Function.ability(273, "Harvest_Return_SCV_quick", cmd_quick, 296, 3667),
Function.ability(274, "HoldPosition_quick", cmd_quick, 18),
Function.ability(275, "Land_screen", cmd_screen, 3678),
Function.ability(276, "Land_Barracks_screen", cmd_screen, 554, 3678),
Function.ability(277, "Land_CommandCenter_screen", cmd_screen, 419, 3678),
Function.ability(278, "Land_Factory_screen", cmd_screen, 520, 3678),
Function.ability(279, "Land_OrbitalCommand_screen", cmd_screen, 1524, 3678),
Function.ability(280, "Land_Starport_screen", cmd_screen, 522, 3678),
Function.ability(281, "Lift_quick", cmd_quick, 3679),
Function.ability(282, "Lift_Barracks_quick", cmd_quick, 452, 3679),
Function.ability(283, "Lift_CommandCenter_quick", cmd_quick, 417, 3679),
Function.ability(284, "Lift_Factory_quick", cmd_quick, 485, 3679),
Function.ability(285, "Lift_OrbitalCommand_quick", cmd_quick, 1522, 3679),
Function.ability(286, "Lift_Starport_quick", cmd_quick, 518, 3679),
Function.ability(287, "Load_screen", cmd_screen, 3668),
Function.ability(288, "Load_Bunker_screen", cmd_screen, 407, 3668),
Function.ability(289, "Load_Medivac_screen", cmd_screen, 394, 3668),
Function.ability(290, "Load_NydusNetwork_screen", cmd_screen, 1437, 3668),
Function.ability(291, "Load_NydusWorm_screen", cmd_screen, 2370, 3668),
Function.ability(292, "Load_Overlord_screen", cmd_screen, 1406, 3668),
Function.ability(293, "Load_WarpPrism_screen", cmd_screen, 911, 3668),
Function.ability(294, "LoadAll_quick", cmd_quick, 3663),
Function.ability(295, "LoadAll_CommandCenter_quick", cmd_quick, 416, 3663),
Function.ability(296, "Morph_Archon_quick", cmd_quick, 1766),
Function.ability(297, "Morph_BroodLord_quick", cmd_quick, 1372),
Function.ability(298, "Morph_Gateway_quick", cmd_quick, 1520),
Function.ability(299, "Morph_GreaterSpire_quick", cmd_quick, 1220),
Function.ability(300, "Morph_Hellbat_quick", cmd_quick, 1998),
Function.ability(301, "Morph_Hellion_quick", cmd_quick, 1978),
Function.ability(302, "Morph_Hive_quick", cmd_quick, 1218),
Function.ability(303, "Morph_Lair_quick", cmd_quick, 1216),
Function.ability(304, "Morph_LiberatorAAMode_quick", cmd_quick, 2560),
Function.ability(305, "Morph_LiberatorAGMode_screen", cmd_screen, 2558),
Function.ability(306, "Morph_Lurker_quick", cmd_quick, 2332),
Function.ability(307, "Morph_LurkerDen_quick", cmd_quick, 2112),
Function.ability(308, "Morph_Mothership_quick", cmd_quick, 1847),
Function.ability(309, "Morph_OrbitalCommand_quick", cmd_quick, 1516),
Function.ability(310, "Morph_OverlordTransport_quick", cmd_quick, 2708),
Function.ability(311, "Morph_Overseer_quick", cmd_quick, 1448),
Function.ability(312, "Morph_PlanetaryFortress_quick", cmd_quick, 1450),
Function.ability(313, "Morph_Ravager_quick", cmd_quick, 2330),
Function.ability(314, "Morph_Root_screen", cmd_screen, 3680),
Function.ability(315, "Morph_SpineCrawlerRoot_screen", cmd_screen, 1729, 3680),
Function.ability(316, "Morph_SporeCrawlerRoot_screen", cmd_screen, 1731, 3680),
Function.ability(317, "Morph_SiegeMode_quick", cmd_quick, 388),
Function.ability(318, "Morph_SupplyDepot_Lower_quick", cmd_quick, 556),
Function.ability(319, "Morph_SupplyDepot_Raise_quick", cmd_quick, 558),
Function.ability(320, "Morph_ThorExplosiveMode_quick", cmd_quick, 2364),
Function.ability(321, "Morph_ThorHighImpactMode_quick", cmd_quick, 2362),
Function.ability(322, "Morph_Unsiege_quick", cmd_quick, 390),
Function.ability(323, "Morph_Uproot_quick", cmd_quick, 3681),
Function.ability(324, "Morph_SpineCrawlerUproot_quick", cmd_quick, 1725, 3681),
Function.ability(325, "Morph_SporeCrawlerUproot_quick", cmd_quick, 1727, 3681),
Function.ability(326, "Morph_VikingAssaultMode_quick", cmd_quick, 403),
Function.ability(327, "Morph_VikingFighterMode_quick", cmd_quick, 405),
Function.ability(328, "Morph_WarpGate_quick", cmd_quick, 1518),
Function.ability(329, "Morph_WarpPrismPhasingMode_quick", cmd_quick, 1528),
Function.ability(330, "Morph_WarpPrismTransportMode_quick", cmd_quick, 1530),
Function.ability(331, "Move_screen", cmd_screen, 16),
Function.ability(332, "Move_minimap", cmd_minimap, 16),
Function.ability(333, "Patrol_screen", cmd_screen, 17),
Function.ability(334, "Patrol_minimap", cmd_minimap, 17),
Function.ability(335, "Rally_Units_screen", cmd_screen, 3673),
Function.ability(336, "Rally_Units_minimap", cmd_minimap, 3673),
Function.ability(337, "Rally_Building_screen", cmd_screen, 195, 3673),
Function.ability(338, "Rally_Building_minimap", cmd_minimap, 195, 3673),
Function.ability(339, "Rally_Hatchery_Units_screen", cmd_screen, 212, 3673),
Function.ability(340, "Rally_Hatchery_Units_minimap", cmd_minimap, 212, 3673),
Function.ability(341, "Rally_Morphing_Unit_screen", cmd_screen, 199, 3673),
Function.ability(342, "Rally_Morphing_Unit_minimap", cmd_minimap, 199, 3673),
Function.ability(343, "Rally_Workers_screen", cmd_screen, 3690),
Function.ability(344, "Rally_Workers_minimap", cmd_minimap, 3690),
Function.ability(345, "Rally_CommandCenter_screen", cmd_screen, 203, 3690),
Function.ability(346, "Rally_CommandCenter_minimap", cmd_minimap, 203, 3690),
Function.ability(347, "Rally_Hatchery_Workers_screen", cmd_screen, 211, 3690),
Function.ability(348, "Rally_Hatchery_Workers_minimap", cmd_minimap, 211, 3690),
Function.ability(349, "Rally_Nexus_screen", cmd_screen, 207, 3690),
Function.ability(350, "Rally_Nexus_minimap", cmd_minimap, 207, 3690),
Function.ability(351, "Research_AdeptResonatingGlaives_quick", cmd_quick, 1594),
Function.ability(352, "Research_AdvancedBallistics_quick", cmd_quick, 805),
Function.ability(353, "Research_BansheeCloakingField_quick", cmd_quick, 790),
Function.ability(354, "Research_BansheeHyperflightRotors_quick", cmd_quick, 799),
Function.ability(355, "Research_BattlecruiserWeaponRefit_quick", cmd_quick, 1532),
Function.ability(356, "Research_Blink_quick", cmd_quick, 1593),
Function.ability(357, "Research_Burrow_quick", cmd_quick, 1225),
Function.ability(358, "Research_CentrifugalHooks_quick", cmd_quick, 1482),
Function.ability(359, "Research_Charge_quick", cmd_quick, 1592),
Function.ability(360, "Research_ChitinousPlating_quick", cmd_quick, 265),
Function.ability(361, "Research_CombatShield_quick", cmd_quick, 731),
Function.ability(362, "Research_ConcussiveShells_quick", cmd_quick, 732),
Function.ability(363, "Research_DrillingClaws_quick", cmd_quick, 764),
Function.ability(364, "Research_ExtendedThermalLance_quick", cmd_quick, 1097),
Function.ability(365, "Research_GlialRegeneration_quick", cmd_quick, 216),
Function.ability(366, "Research_GraviticBooster_quick", cmd_quick, 1093),
Function.ability(367, "Research_GraviticDrive_quick", cmd_quick, 1094),
Function.ability(368, "Research_GroovedSpines_quick", cmd_quick, 1282),
Function.ability(369, "Research_HiSecAutoTracking_quick", cmd_quick, 650),
Function.ability(370, "Research_HighCapacityFuelTanks_quick", cmd_quick, 804),
Function.ability(371, "Research_InfernalPreigniter_quick", cmd_quick, 761),
Function.ability(372, "Research_InterceptorGravitonCatapult_quick", cmd_quick, 44),
Function.ability(373, "Research_MagFieldLaunchers_quick", cmd_quick, 766),
Function.ability(374, "Research_MuscularAugments_quick", cmd_quick, 1283),
Function.ability(375, "Research_NeosteelFrame_quick", cmd_quick, 655),
Function.ability(376, "Research_NeuralParasite_quick", cmd_quick, 1455),
Function.ability(377, "Research_PathogenGlands_quick", cmd_quick, 1454),
Function.ability(378, "Research_PersonalCloaking_quick", cmd_quick, 820),
Function.ability(379, "Research_PhoenixAnionPulseCrystals_quick", cmd_quick, 46),
Function.ability(380, "Research_PneumatizedCarapace_quick", cmd_quick, 1223),
Function.ability(381, "Research_ProtossAirArmor_quick", cmd_quick, 3692),
Function.ability(382, "Research_ProtossAirArmorLevel1_quick", cmd_quick, 1565, 3692),
Function.ability(383, "Research_ProtossAirArmorLevel2_quick", cmd_quick, 1566, 3692),
Function.ability(384, "Research_ProtossAirArmorLevel3_quick", cmd_quick, 1567, 3692),
Function.ability(385, "Research_ProtossAirWeapons_quick", cmd_quick, 3693),
Function.ability(386, "Research_ProtossAirWeaponsLevel1_quick", cmd_quick, 1562, 3693),
Function.ability(387, "Research_ProtossAirWeaponsLevel2_quick", cmd_quick, 1563, 3693),
Function.ability(388, "Research_ProtossAirWeaponsLevel3_quick", cmd_quick, 1564, 3693),
Function.ability(389, "Research_ProtossGroundArmor_quick", cmd_quick, 3694),
Function.ability(390, "Research_ProtossGroundArmorLevel1_quick", cmd_quick, 1065, 3694),
Function.ability(391, "Research_ProtossGroundArmorLevel2_quick", cmd_quick, 1066, 3694),
Function.ability(392, "Research_ProtossGroundArmorLevel3_quick", cmd_quick, 1067, 3694),
Function.ability(393, "Research_ProtossGroundWeapons_quick", cmd_quick, 3695),
Function.ability(394, "Research_ProtossGroundWeaponsLevel1_quick", cmd_quick, 1062, 3695),
Function.ability(395, "Research_ProtossGroundWeaponsLevel2_quick", cmd_quick, 1063, 3695),
Function.ability(396, "Research_ProtossGroundWeaponsLevel3_quick", cmd_quick, 1064, 3695),
Function.ability(397, "Research_ProtossShields_quick", cmd_quick, 3696),
Function.ability(398, "Research_ProtossShieldsLevel1_quick", cmd_quick, 1068, 3696),
Function.ability(399, "Research_ProtossShieldsLevel2_quick", cmd_quick, 1069, 3696),
Function.ability(400, "Research_ProtossShieldsLevel3_quick", cmd_quick, 1070, 3696),
Function.ability(401, "Research_PsiStorm_quick", cmd_quick, 1126),
Function.ability(402, "Research_RavenCorvidReactor_quick", cmd_quick, 793),
Function.ability(403, "Research_RavenRecalibratedExplosives_quick", cmd_quick, 803),
Function.ability(404, "Research_ShadowStrike_quick", cmd_quick, 2720),
Function.ability(405, "Research_Stimpack_quick", cmd_quick, 730),
Function.ability(406, "Research_TerranInfantryArmor_quick", cmd_quick, 3697),
Function.ability(407, "Research_TerranInfantryArmorLevel1_quick", cmd_quick, 656, 3697),
Function.ability(408, "Research_TerranInfantryArmorLevel2_quick", cmd_quick, 657, 3697),
Function.ability(409, "Research_TerranInfantryArmorLevel3_quick", cmd_quick, 658, 3697),
Function.ability(410, "Research_TerranInfantryWeapons_quick", cmd_quick, 3698),
Function.ability(411, "Research_TerranInfantryWeaponsLevel1_quick", cmd_quick, 652, 3698),
Function.ability(412, "Research_TerranInfantryWeaponsLevel2_quick", cmd_quick, 653, 3698),
Function.ability(413, "Research_TerranInfantryWeaponsLevel3_quick", cmd_quick, 654, 3698),
Function.ability(414, "Research_TerranShipWeapons_quick", cmd_quick, 3699),
Function.ability(415, "Research_TerranShipWeaponsLevel1_quick", cmd_quick, 861, 3699),
Function.ability(416, "Research_TerranShipWeaponsLevel2_quick", cmd_quick, 862, 3699),
Function.ability(417, "Research_TerranShipWeaponsLevel3_quick", cmd_quick, 863, 3699),
Function.ability(418, "Research_TerranStructureArmorUpgrade_quick", cmd_quick, 651),
Function.ability(419, "Research_TerranVehicleAndShipPlating_quick", cmd_quick, 3700),
Function.ability(420, "Research_TerranVehicleAndShipPlatingLevel1_quick", cmd_quick, 864, 3700),
Function.ability(421, "Research_TerranVehicleAndShipPlatingLevel2_quick", cmd_quick, 865, 3700),
Function.ability(422, "Research_TerranVehicleAndShipPlatingLevel3_quick", cmd_quick, 866, 3700),
Function.ability(423, "Research_TerranVehicleWeapons_quick", cmd_quick, 3701),
Function.ability(424, "Research_TerranVehicleWeaponsLevel1_quick", cmd_quick, 855, 3701),
Function.ability(425, "Research_TerranVehicleWeaponsLevel2_quick", cmd_quick, 856, 3701),
Function.ability(426, "Research_TerranVehicleWeaponsLevel3_quick", cmd_quick, 857, 3701),
Function.ability(427, "Research_TunnelingClaws_quick", cmd_quick, 217),
Function.ability(428, "Research_WarpGate_quick", cmd_quick, 1568),
Function.ability(429, "Research_ZergFlyerArmor_quick", cmd_quick, 3702),
Function.ability(430, "Research_ZergFlyerArmorLevel1_quick", cmd_quick, 1315, 3702),
Function.ability(431, "Research_ZergFlyerArmorLevel2_quick", cmd_quick, 1316, 3702),
Function.ability(432, "Research_ZergFlyerArmorLevel3_quick", cmd_quick, 1317, 3702),
Function.ability(433, "Research_ZergFlyerAttack_quick", cmd_quick, 3703),
Function.ability(434, "Research_ZergFlyerAttackLevel1_quick", cmd_quick, 1312, 3703),
Function.ability(435, "Research_ZergFlyerAttackLevel2_quick", cmd_quick, 1313, 3703),
Function.ability(436, "Research_ZergFlyerAttackLevel3_quick", cmd_quick, 1314, 3703),
Function.ability(437, "Research_ZergGroundArmor_quick", cmd_quick, 3704),
Function.ability(438, "Research_ZergGroundArmorLevel1_quick", cmd_quick, 1189, 3704),
Function.ability(439, "Research_ZergGroundArmorLevel2_quick", cmd_quick, 1190, 3704),
Function.ability(440, "Research_ZergGroundArmorLevel3_quick", cmd_quick, 1191, 3704),
Function.ability(441, "Research_ZergMeleeWeapons_quick", cmd_quick, 3705),
Function.ability(442, "Research_ZergMeleeWeaponsLevel1_quick", cmd_quick, 1186, 3705),
Function.ability(443, "Research_ZergMeleeWeaponsLevel2_quick", cmd_quick, 1187, 3705),
Function.ability(444, "Research_ZergMeleeWeaponsLevel3_quick", cmd_quick, 1188, 3705),
Function.ability(445, "Research_ZergMissileWeapons_quick", cmd_quick, 3706),
Function.ability(446, "Research_ZergMissileWeaponsLevel1_quick", cmd_quick, 1192, 3706),
Function.ability(447, "Research_ZergMissileWeaponsLevel2_quick", cmd_quick, 1193, 3706),
Function.ability(448, "Research_ZergMissileWeaponsLevel3_quick", cmd_quick, 1194, 3706),
Function.ability(449, "Research_ZerglingAdrenalGlands_quick", cmd_quick, 1252),
Function.ability(450, "Research_ZerglingMetabolicBoost_quick", cmd_quick, 1253),
Function.ability(451, "Smart_screen", cmd_screen, 1),
Function.ability(452, "Smart_minimap", cmd_minimap, 1),
Function.ability(453, "Stop_quick", cmd_quick, 3665),
Function.ability(454, "Stop_Building_quick", cmd_quick, 2057, 3665),
Function.ability(455, "Stop_Redirect_quick", cmd_quick, 1691, 3665),
Function.ability(456, "Stop_Stop_quick", cmd_quick, 4, 3665),
Function.ability(457, "Train_Adept_quick", cmd_quick, 922),
Function.ability(458, "Train_Baneling_quick", cmd_quick, 80),
Function.ability(459, "Train_Banshee_quick", cmd_quick, 621),
Function.ability(460, "Train_Battlecruiser_quick", cmd_quick, 623),
Function.ability(461, "Train_Carrier_quick", cmd_quick, 948),
Function.ability(462, "Train_Colossus_quick", cmd_quick, 978),
Function.ability(463, "Train_Corruptor_quick", cmd_quick, 1353),
Function.ability(464, "Train_Cyclone_quick", cmd_quick, 597),
Function.ability(465, "Train_DarkTemplar_quick", cmd_quick, 920),
Function.ability(466, "Train_Disruptor_quick", cmd_quick, 994),
Function.ability(467, "Train_Drone_quick", cmd_quick, 1342),
Function.ability(468, "Train_Ghost_quick", cmd_quick, 562),
Function.ability(469, "Train_Hellbat_quick", cmd_quick, 596),
Function.ability(470, "Train_Hellion_quick", cmd_quick, 595),
Function.ability(471, "Train_HighTemplar_quick", cmd_quick, 919),
Function.ability(472, "Train_Hydralisk_quick", cmd_quick, 1345),
Function.ability(473, "Train_Immortal_quick", cmd_quick, 979),
Function.ability(474, "Train_Infestor_quick", cmd_quick, 1352),
Function.ability(475, "Train_Liberator_quick", cmd_quick, 626),
Function.ability(476, "Train_Marauder_quick", cmd_quick, 563),
Function.ability(477, "Train_Marine_quick", cmd_quick, 560),
Function.ability(478, "Train_Medivac_quick", cmd_quick, 620),
Function.ability(479, "Train_MothershipCore_quick", cmd_quick, 1853),
Function.ability(480, "Train_Mutalisk_quick", cmd_quick, 1346),
Function.ability(481, "Train_Observer_quick", cmd_quick, 977),
Function.ability(482, "Train_Oracle_quick", cmd_quick, 954),
Function.ability(483, "Train_Overlord_quick", cmd_quick, 1344),
Function.ability(484, "Train_Phoenix_quick", cmd_quick, 946),
Function.ability(485, "Train_Probe_quick", cmd_quick, 1006),
Function.ability(486, "Train_Queen_quick", cmd_quick, 1632),
Function.ability(487, "Train_Raven_quick", cmd_quick, 622),
Function.ability(488, "Train_Reaper_quick", cmd_quick, 561),
Function.ability(489, "Train_Roach_quick", cmd_quick, 1351),
Function.ability(490, "Train_SCV_quick", cmd_quick, 524),
Function.ability(491, "Train_Sentry_quick", cmd_quick, 921),
Function.ability(492, "Train_SiegeTank_quick", cmd_quick, 591),
Function.ability(493, "Train_Stalker_quick", cmd_quick, 917),
Function.ability(494, "Train_SwarmHost_quick", cmd_quick, 1356),
Function.ability(495, "Train_Tempest_quick", cmd_quick, 955),
Function.ability(496, "Train_Thor_quick", cmd_quick, 594),
Function.ability(497, "Train_Ultralisk_quick", cmd_quick, 1348),
Function.ability(498, "Train_VikingFighter_quick", cmd_quick, 624),
Function.ability(499, "Train_Viper_quick", cmd_quick, 1354),
Function.ability(500, "Train_VoidRay_quick", cmd_quick, 950),
Function.ability(501, "Train_WarpPrism_quick", cmd_quick, 976),
Function.ability(502, "Train_WidowMine_quick", cmd_quick, 614),
Function.ability(503, "Train_Zealot_quick", cmd_quick, 916),
Function.ability(504, "Train_Zergling_quick", cmd_quick, 1343),
Function.ability(505, "TrainWarp_Adept_screen", cmd_screen, 1419),
Function.ability(506, "TrainWarp_DarkTemplar_screen", cmd_screen, 1417),
Function.ability(507, "TrainWarp_HighTemplar_screen", cmd_screen, 1416),
Function.ability(508, "TrainWarp_Sentry_screen", cmd_screen, 1418),
Function.ability(509, "TrainWarp_Stalker_screen", cmd_screen, 1414),
Function.ability(510, "TrainWarp_Zealot_screen", cmd_screen, 1413),
Function.ability(511, "UnloadAll_quick", cmd_quick, 3664),
Function.ability(512, "UnloadAll_Bunker_quick", cmd_quick, 408, 3664),
Function.ability(513, "UnloadAll_CommandCenter_quick", cmd_quick, 413, 3664),
Function.ability(514, "UnloadAll_NydasNetwork_quick", cmd_quick, 1438, 3664),
Function.ability(515, "UnloadAll_NydusWorm_quick", cmd_quick, 2371, 3664),
Function.ability(516, "UnloadAllAt_screen", cmd_screen, 3669),
Function.ability(517, "UnloadAllAt_minimap", cmd_minimap, 3669),
Function.ability(518, "UnloadAllAt_Medivac_screen", cmd_screen, 396, 3669),
Function.ability(519, "UnloadAllAt_Medivac_minimap", cmd_minimap, 396, 3669),
Function.ability(520, "UnloadAllAt_Overlord_screen", cmd_screen, 1408, 3669),
Function.ability(521, "UnloadAllAt_Overlord_minimap", cmd_minimap, 1408, 3669),
Function.ability(522, "UnloadAllAt_WarpPrism_screen", cmd_screen, 913, 3669),
Function.ability(523, "UnloadAllAt_WarpPrism_minimap", cmd_minimap, 913, 3669),
])
# pylint: enable=line-too-long
# Some indexes to support features.py and action conversion.
ABILITY_IDS = collections.defaultdict(set) # {ability_id: {funcs}}
for func in FUNCTIONS:
if func.ability_id >= 0:
ABILITY_IDS[func.ability_id].add(func)
ABILITY_IDS = {k: frozenset(v) for k, v in six.iteritems(ABILITY_IDS)}
FUNCTIONS_AVAILABLE = {f.id: f for f in FUNCTIONS if f.avail_fn}
| 57.825482 | 100 | 0.750745 |
13d4be5ff6607a768ff6f008bc6f55355c95eab1
| 3,209 |
py
|
Python
|
pywick/meters/aucmeter.py
|
ashishpatel26/pywick
|
1afffd1c21c2b188836d3599e802146182757bb5
|
[
"MIT"
] | 2 |
2020-11-28T07:56:09.000Z
|
2021-11-08T09:30:39.000Z
|
pywick/meters/aucmeter.py
|
ashishpatel26/pywick
|
1afffd1c21c2b188836d3599e802146182757bb5
|
[
"MIT"
] | null | null | null |
pywick/meters/aucmeter.py
|
ashishpatel26/pywick
|
1afffd1c21c2b188836d3599e802146182757bb5
|
[
"MIT"
] | null | null | null |
import numbers
from . import meter
import numpy as np
import torch
| 38.662651 | 91 | 0.606108 |
13d5b4c27424ce3f3958c70f0a0828815649d42f
| 2,971 |
py
|
Python
|
homeassistant/components/zha/core/channels/lighting.py
|
liangleslie/core
|
cc807b4d597daaaadc92df4a93c6e30da4f570c6
|
[
"Apache-2.0"
] | 30,023 |
2016-04-13T10:17:53.000Z
|
2020-03-02T12:56:31.000Z
|
homeassistant/components/zha/core/channels/lighting.py
|
liangleslie/core
|
cc807b4d597daaaadc92df4a93c6e30da4f570c6
|
[
"Apache-2.0"
] | 24,710 |
2016-04-13T08:27:26.000Z
|
2020-03-02T12:59:13.000Z
|
homeassistant/components/zha/core/channels/lighting.py
|
liangleslie/core
|
cc807b4d597daaaadc92df4a93c6e30da4f570c6
|
[
"Apache-2.0"
] | 11,956 |
2016-04-13T18:42:31.000Z
|
2020-03-02T09:32:12.000Z
|
"""Lighting channels module for Zigbee Home Automation."""
from __future__ import annotations
from contextlib import suppress
from zigpy.zcl.clusters import lighting
from .. import registries
from ..const import REPORT_CONFIG_DEFAULT
from .base import ClientChannel, ZigbeeChannel
| 33.382022 | 76 | 0.693369 |
13d60376951a923005fa671870c17c7889bfb96b
| 6,140 |
py
|
Python
|
pf/queue.py
|
PiRAT4/py-pf
|
7ffdd0a283d4a36fc4c473433d5f79a84eeb5d31
|
[
"BSD-3-Clause"
] | null | null | null |
pf/queue.py
|
PiRAT4/py-pf
|
7ffdd0a283d4a36fc4c473433d5f79a84eeb5d31
|
[
"BSD-3-Clause"
] | null | null | null |
pf/queue.py
|
PiRAT4/py-pf
|
7ffdd0a283d4a36fc4c473433d5f79a84eeb5d31
|
[
"BSD-3-Clause"
] | null | null | null |
"""Classes to represent Packet Filter's queueing schedulers and statistics."""
import pf._struct
from pf._base import PFObject
from pf.constants import *
from pf._utils import rate2str
__all__ = ["ServiceCurve",
"FlowQueue",
"PFQueue",
"PFQueueStats"]
| 29.95122 | 79 | 0.548534 |
13d646c36a3c1b8a275d4d104aa95c23081163a3
| 2,378 |
py
|
Python
|
sdk/python/pulumi_azure_native/labservices/v20181015/__init__.py
|
pulumi-bot/pulumi-azure-native
|
f7b9490b5211544318e455e5cceafe47b628e12c
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/labservices/v20181015/__init__.py
|
pulumi-bot/pulumi-azure-native
|
f7b9490b5211544318e455e5cceafe47b628e12c
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/labservices/v20181015/__init__.py
|
pulumi-bot/pulumi-azure-native
|
f7b9490b5211544318e455e5cceafe47b628e12c
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
# Export this package's modules as members:
from ._enums import *
from .environment import *
from .environment_setting import *
from .gallery_image import *
from .get_environment import *
from .get_environment_setting import *
from .get_gallery_image import *
from .get_global_user_environment import *
from .get_global_user_operation_batch_status import *
from .get_global_user_operation_status import *
from .get_global_user_personal_preferences import *
from .get_lab import *
from .get_lab_account import *
from .get_lab_account_regional_availability import *
from .get_user import *
from .lab import *
from .lab_account import *
from .list_global_user_environments import *
from .list_global_user_labs import *
from .user import *
from ._inputs import *
from . import outputs
_register_module()
| 38.983607 | 102 | 0.710681 |
13d760267b20f874fc4b087de72759e81f401445
| 6,123 |
py
|
Python
|
servicedirectory/src/sd-api/users/tests/tests_serializers.py
|
ealogar/servicedirectory
|
fb4f4bfa8b499b93c03af589ef2f34c08a830b17
|
[
"Apache-2.0"
] | null | null | null |
servicedirectory/src/sd-api/users/tests/tests_serializers.py
|
ealogar/servicedirectory
|
fb4f4bfa8b499b93c03af589ef2f34c08a830b17
|
[
"Apache-2.0"
] | null | null | null |
servicedirectory/src/sd-api/users/tests/tests_serializers.py
|
ealogar/servicedirectory
|
fb4f4bfa8b499b93c03af589ef2f34c08a830b17
|
[
"Apache-2.0"
] | null | null | null |
'''
(c) Copyright 2013 Telefonica, I+D. Printed in Spain (Europe). All Rights
Reserved.
The copyright to the software program(s) is property of Telefonica I+D.
The program(s) may be used and or copied only with the express written
consent of Telefonica I+D or in accordance with the terms and conditions
stipulated in the agreement/contract under which the program(s) have
been supplied.
'''
from unittest import TestCase
from mock import MagicMock, patch
from commons.json_schema_validator.schema_reader import SchemaField
from commons.json_schema_validator.schema_reader import SchemaReader
from users.serializers import UserCollectionSerializer
| 52.784483 | 114 | 0.6634 |
13d7896d6d799cba6c0e766504d5f3eea5f2e531
| 3,124 |
py
|
Python
|
Web/notifyXAPI/app/src/users/views.py
|
abs0lut3pwn4g3/RootersCTF2019-challenges
|
397a6fad0b03e55541df06e5103172ae850cd4e5
|
[
"MIT"
] | 14 |
2019-10-13T07:38:04.000Z
|
2022-02-13T09:03:50.000Z
|
Web/notifyXAPI/app/src/users/views.py
|
abs0lut3pwn4g3/RootersCTF2019-challenges
|
397a6fad0b03e55541df06e5103172ae850cd4e5
|
[
"MIT"
] | 1 |
2019-10-13T07:35:13.000Z
|
2019-10-13T08:22:48.000Z
|
Web/notifyXAPI/app/src/users/views.py
|
abs0lut3pwn4g3/RootersCTF2019-challenges
|
397a6fad0b03e55541df06e5103172ae850cd4e5
|
[
"MIT"
] | 4 |
2019-10-13T08:21:43.000Z
|
2022-01-09T16:39:33.000Z
|
''' User views '''
from datetime import timedelta
from flask import request, jsonify, make_response, redirect, json, render_template
from flask_jwt_extended import (create_access_token, jwt_required)
from flask_restful import Resource
from flask_login import login_user, current_user
from sqlalchemy.exc import IntegrityError, InvalidRequestError
from src import db, api
from .models import User
from .schemas import UserSchema
api.add_resource(UserLoginResource, '/login/', endpoint='login')
api.add_resource(UserRegisterResource, '/register/', endpoint='register')
| 40.571429 | 124 | 0.588348 |
13d7ee99520201ddee21c6a144be012df62e596b
| 14,211 |
py
|
Python
|
querybuilder/tests/window_tests.py
|
wesokes/django-query-builder
|
3cc53d33ee0a4ada515635e4be631167a774b457
|
[
"MIT"
] | 110 |
2015-01-28T17:38:38.000Z
|
2022-02-17T02:51:55.000Z
|
querybuilder/tests/window_tests.py
|
wesokes/django-query-builder
|
3cc53d33ee0a4ada515635e4be631167a774b457
|
[
"MIT"
] | 31 |
2015-02-17T19:57:29.000Z
|
2021-08-30T03:44:55.000Z
|
querybuilder/tests/window_tests.py
|
wesokes/django-query-builder
|
3cc53d33ee0a4ada515635e4be631167a774b457
|
[
"MIT"
] | 33 |
2015-02-05T22:08:46.000Z
|
2021-12-06T08:12:49.000Z
|
from querybuilder.fields import (
RankField, RowNumberField, DenseRankField, PercentRankField, CumeDistField, NTileField, LagField,
LeadField, FirstValueField, LastValueField, NthValueField, NumStdDevField
)
from querybuilder.query import QueryWindow, Query
from querybuilder.tests.models import Order
from querybuilder.tests.query_tests import QueryTestCase, get_comparison_str
| 33.916468 | 119 | 0.521779 |
13d92122cdb041cef0407b7567e1db27fbf5978f
| 676 |
py
|
Python
|
emoji/coffee.py
|
wbprice/ojimoji
|
7b1a8b5ed0062d1d52e151e7412e1131e3de7924
|
[
"MIT"
] | null | null | null |
emoji/coffee.py
|
wbprice/ojimoji
|
7b1a8b5ed0062d1d52e151e7412e1131e3de7924
|
[
"MIT"
] | null | null | null |
emoji/coffee.py
|
wbprice/ojimoji
|
7b1a8b5ed0062d1d52e151e7412e1131e3de7924
|
[
"MIT"
] | null | null | null |
import numpy
h = .25
s = 1
bitmap = numpy.array([
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0],
[0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0],
[0,0,1,1,1,0,1,0,1,1,1,0,0,1,0,0],
[0,0,1,1,0,1,0,1,0,1,1,0,0,1,0,0],
[0,0,1,1,1,0,1,0,1,1,1,0,1,0,0,0],
[0,0,1,1,0,1,0,1,0,1,1,1,0,0,0,0],
[0,0,1,1,1,0,1,0,1,1,1,0,0,0,0,0],
[0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0],
[0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])
| 30.727273 | 39 | 0.426036 |
13d981ac5c1effe03cfb8f11663b3250b5130bd2
| 4,546 |
py
|
Python
|
dumpcode/npzbdt.py
|
gkfthddk/keras
|
46d96c65d69c39df298800336bbb4d867a2561fb
|
[
"MIT"
] | null | null | null |
dumpcode/npzbdt.py
|
gkfthddk/keras
|
46d96c65d69c39df298800336bbb4d867a2561fb
|
[
"MIT"
] | null | null | null |
dumpcode/npzbdt.py
|
gkfthddk/keras
|
46d96c65d69c39df298800336bbb4d867a2561fb
|
[
"MIT"
] | null | null | null |
import numpy as np
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import KFold
import scipy.stats as sts
import xgboost as xgb
from xiter import *
import pandas as pd
import argparse
from datetime import datetime
parser=argparse.ArgumentParser()
parser.add_argument("--end",type=float,default=100000.,help='end ratio')
parser.add_argument("--save",type=str,default="test_",help='save name')
parser.add_argument("--network",type=str,default="rnn",help='network name on symbols/')
parser.add_argument("--right",type=str,default="/scratch/yjdata/gluon100_img",help='which train sample (qq,gg,zq,zg)')
parser.add_argument("--pt",type=int,default=200,help='pt range pt~pt*1.1')
parser.add_argument("--ptmin",type=float,default=0.,help='pt range pt~pt*1.1')
parser.add_argument("--ptmax",type=float,default=2.,help='pt range pt~pt*1.1')
parser.add_argument("--epochs",type=int,default=10,help='num epochs')
parser.add_argument("--batch_size",type=int,default=100000,help='batch_size')
parser.add_argument("--loss",type=str,default="categorical_crossentropy",help='network name on symbols/')
parser.add_argument("--gpu",type=int,default=0,help='gpu number')
parser.add_argument("--isz",type=int,default=0,help='0 or z or not')
parser.add_argument("--eta",type=float,default=0.,help='end ratio')
parser.add_argument("--etabin",type=float,default=1,help='end ratio')
parser.add_argument("--unscale",type=int,default=0,help='end ratio')
args=parser.parse_args()
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
batch_size=args.batch_size
params = {
'max_depth': sts.randint(1,6),
'learning_rate': sts.uniform(0.0010,0.500),
'n_estimators': sts.randint(10,101)
}
model=xgb.XGBClassifier(objective='binary:logistic',tree_method="gpu_hist")
if(args.isz==1):
if(args.etabin==1):
loaded=np.load("zqmixed{}pteta.npz".format(args.pt))
print("zqmixed{}pteta.npz".format(args.pt))
else:
loaded=np.load("zqmixed{}pt.npz".format(args.pt))
print("zqmixed{}pt.npz".format(args.pt))
elif(args.isz==-1):
if(args.etabin==1):
loaded=np.load("qqmixed{}pteta.npz".format(args.pt))
print("qqmixed{}pteta.npz".format(args.pt))
else:
loaded=np.load("qqmixed{}pt.npz".format(args.pt))
print("qqmixed{}pt.npz".format(args.pt))
elif(args.isz==0):
if(args.etabin==1):
if(args.unscale==1):
loaded=np.load("unscalemixed{}pteta.npz".format(args.pt))
else:
loaded=np.load("mixed{}pteta.npz".format(args.pt))
print("etabin 1")
else:
if(args.unscale==1):
loaded=np.load("unscalemixed{}pt.npz".format(args.pt))
else:
loaded=np.load("mixed{}pt.npz".format(args.pt))
print("etabin 2.4")
data=loaded["bdtset"][:,:5]
label=loaded["label"]
line=int(30000)
endline=int(40000)
if(len(label)<40000):
line=int(len(label)*3./4.)
endline=len(label)
X=data[0:line]
vx=data[line:endline]
Y=label[0:line]
vy=label[line:endline]
Y=np.array(Y)[:,0]
folds = 3
param_comb = 100
skf = KFold(n_splits=folds, shuffle = True, random_state = 173)
#skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)
random_search = RandomizedSearchCV(model, param_distributions=params, n_iter=param_comb, scoring='log_loss', n_jobs=6, cv=skf.split(X,Y), verbose=3, random_state=173 )
# Here we go
start_time = timer(None) # timing starts from this point for "start_time" variable
random_search.fit(X, Y)
timer(start_time)
#print(random_search.predict(X[:10]))
#print('\n All results:')
#print(random_search.cv_results_)
#print('\n Best estimator:')
#print(random_search.best_estimator_)
print('\n Best normalized gini score for %d-fold search with %d parameter combinations:' % (folds, param_comb))
print(random_search.best_score_ * 2 - 1)
#print('\n Best hyperparameters:')
#print(random_search.best_params_)
results = pd.DataFrame(random_search.cv_results_)
results.to_csv('xgb/{}-{}.csv'.format(args.save,args.pt), index=False)
#random_search.best_estimator_.save_model("bdt-{}.dat".format(args.pt))
| 38.525424 | 167 | 0.720853 |
13dbe7e355868c585eddb2ca7609fa83a2860aed
| 12,384 |
py
|
Python
|
rlenv/StockTradingEnv0.py
|
watchsea/RL-Stock
|
53bd13a1bd1760e082c6db2ad9b010adbc3a767b
|
[
"MIT"
] | null | null | null |
rlenv/StockTradingEnv0.py
|
watchsea/RL-Stock
|
53bd13a1bd1760e082c6db2ad9b010adbc3a767b
|
[
"MIT"
] | null | null | null |
rlenv/StockTradingEnv0.py
|
watchsea/RL-Stock
|
53bd13a1bd1760e082c6db2ad9b010adbc3a767b
|
[
"MIT"
] | null | null | null |
import random
import json
import gym
from gym import spaces
import pandas as pd
import numpy as np
MAX_ACCOUNT_BALANCE = 2147483647
MAX_NUM_SHARES = 2147483647
MAX_SHARE_PRICE = 5000
MAX_VOLUME = 1000e8
MAX_AMOUNT = 3e10
MAX_OPEN_POSITIONS = 5
MAX_STEPS = 20000
MAX_DAY_CHANGE = 1
INITIAL_ACCOUNT_BALANCE = 10000
DATA_HIS_PERIOD = 5
# position constant
FLAT = 0 # no position
LONG = 1 # buy position
SHORT = 2 # sell position
# action constant
HOLD = 0
BUY = 1
SELL = 2
| 41.69697 | 127 | 0.581476 |
13dd331f73864377a1d0952e862e552f50ab90bf
| 2,053 |
py
|
Python
|
processing/manager.py
|
mrfleap/us-population-heatmap
|
e3f1c5d8294716ff491c7b8b40adb77929f9aeee
|
[
"MIT"
] | null | null | null |
processing/manager.py
|
mrfleap/us-population-heatmap
|
e3f1c5d8294716ff491c7b8b40adb77929f9aeee
|
[
"MIT"
] | null | null | null |
processing/manager.py
|
mrfleap/us-population-heatmap
|
e3f1c5d8294716ff491c7b8b40adb77929f9aeee
|
[
"MIT"
] | null | null | null |
import json
import os
import pathlib
import time
from tqdm import tqdm
from aggregator import aggregate
from download import DOWNLOAD_PATH, download_files, unzip_files
from tqdm.contrib.concurrent import process_map
if __name__ == "__main__":
main()
| 27.013158 | 103 | 0.632245 |
13ddb1c02b1ed46330b061450969494a2a48af52
| 794 |
py
|
Python
|
gen_data.py
|
kshoji6011/vehicleai
|
135de71cce65f4a61b42c49493ed356f2d512d6c
|
[
"MIT"
] | null | null | null |
gen_data.py
|
kshoji6011/vehicleai
|
135de71cce65f4a61b42c49493ed356f2d512d6c
|
[
"MIT"
] | null | null | null |
gen_data.py
|
kshoji6011/vehicleai
|
135de71cce65f4a61b42c49493ed356f2d512d6c
|
[
"MIT"
] | null | null | null |
from PIL import Image
import os, glob
import numpy as np
from sklearn import model_selection
classes = ["car", "bycycle", "motorcycle", "pedestrian"]
num_class = len(classes)
image_size = 50
#
X = []
Y = []
for index, classlabel in enumerate(classes):
photos_dir = "./" + classlabel
files = glob.glob(photos_dir + "/*.jpg")
for i, file in enumerate(files):
if i >=237: break
image = Image.open(file)
image = image.convert("RGB")
image = image.resize((image_size, image_size))
data = np.asarray(image) / 255
X.append(data)
Y.append(index)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, Y)
xy = (X_train, X_test, y_train, y_test)
np.save("./vehicle.npy", xy)
| 24.8125 | 73 | 0.644836 |
13de3e4f691cb4b36cd9750b30e5106c02f72fb9
| 724 |
py
|
Python
|
app/main.py
|
immortel32/Sword_Sorcery_Story_Generator
|
7978dfc335813362b2d94c455b970f58421123c8
|
[
"MIT"
] | 2 |
2021-04-01T00:50:22.000Z
|
2021-04-01T02:18:45.000Z
|
app/main.py
|
immortel32/Sword_Sorcery_Story_Generator
|
7978dfc335813362b2d94c455b970f58421123c8
|
[
"MIT"
] | 1 |
2021-04-01T21:39:44.000Z
|
2021-04-01T21:39:44.000Z
|
app/main.py
|
immortel32/Sword_Sorcery_Story_Generator
|
7978dfc335813362b2d94c455b970f58421123c8
|
[
"MIT"
] | 1 |
2021-04-01T01:03:33.000Z
|
2021-04-01T01:03:33.000Z
|
from services import waypoint_scenarios, quest_scenarios
from services.build_campaign import Campaign
from log_setup import log
if __name__ == "__main__":
number_waypoint_scenario = waypoint_scenarios.get_number_of_waypoint_scenarios()
log.info(f"We have {number_waypoint_scenario} waypoint available")
number_quests_available = quest_scenarios.get_number_of_quest_scenarios()
log.info(f"We have {number_quests_available} quests available")
random_waypoint_scenario = waypoint_scenarios.get_random_scenario(10)
random_quest = quest_scenarios.get_random_scenario(1)
campaign = Campaign()
campaign.build_campaign(
waypoint_list=random_waypoint_scenario, quest_list=random_quest
)
| 42.588235 | 84 | 0.809392 |
13ded3828a8c037ea4aa78b91386fb78512809eb
| 326 |
py
|
Python
|
tests/test-recipes/metadata/ignore_some_prefix_files/run_test.py
|
mbargull/conda-build
|
ebc56f48196774301863fecbe98a32a7ded6eb7e
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test-recipes/metadata/ignore_some_prefix_files/run_test.py
|
mbargull/conda-build
|
ebc56f48196774301863fecbe98a32a7ded6eb7e
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test-recipes/metadata/ignore_some_prefix_files/run_test.py
|
mbargull/conda-build
|
ebc56f48196774301863fecbe98a32a7ded6eb7e
|
[
"BSD-3-Clause"
] | null | null | null |
import os
pkgs = os.path.join(os.environ["ROOT"], "pkgs")
info_dir = os.path.join(pkgs, "conda-build-test-ignore-some-prefix-files-1.0-0", "info")
has_prefix_file = os.path.join(info_dir, "has_prefix")
print(info_dir)
assert os.path.isfile(has_prefix_file)
with open(has_prefix_file) as f:
assert "test2" not in f.read()
| 32.6 | 88 | 0.733129 |
13defa0932ab990d6739afd72d573b29bcd8a6e3
| 2,473 |
py
|
Python
|
distill.py
|
Lukeming-tsinghua/Interpretable-NN-for-IBD-diagnosis
|
5fb0fae774e010cdd6b63ff487a4528f0397647d
|
[
"MIT"
] | null | null | null |
distill.py
|
Lukeming-tsinghua/Interpretable-NN-for-IBD-diagnosis
|
5fb0fae774e010cdd6b63ff487a4528f0397647d
|
[
"MIT"
] | null | null | null |
distill.py
|
Lukeming-tsinghua/Interpretable-NN-for-IBD-diagnosis
|
5fb0fae774e010cdd6b63ff487a4528f0397647d
|
[
"MIT"
] | null | null | null |
import os
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import classification_report
from torch.optim import Adam
from tqdm import tqdm
from data import DataIteratorDistill
from loss import FocalLoss
from model import CNN
from torchtext import data, vocab
from args import get_args, print_args
from config import ConfigBinaryClassification
from config import ConfigBinaryClassificationDistill
from config import ConfigTripleClassification
if __name__ == "__main__":
args = get_args()
print_args(args)
if args.class_num == 2:
cfg = ConfigBinaryClassificationDistill()
elif args.class_num == 3:
cfg = ConfigTripleClassification()
else:
raise ValueError("wrong class num")
device = torch.device("cuda:%d" % args.cuda)
Data = DataIteratorDistill(config=cfg, train_batchsize=args.batch_size)
model = torch.load("checkpoints/CNN-29", map_location=device)
optimizer = Adam(model.parameters(), lr=args.lr)
criterion = FocalLoss(classes=args.class_num, device=device).to(device)
criterion_kv = nn.KLDivLoss().to(device)
alpha = 0.2
T = 2
for epoch in range(args.epoch_num):
print(epoch)
for sample in Data.train_iter:
model.train()
optimizer.zero_grad()
output = model(sample.text.permute(1, 0).to(device))
loss_f = criterion(output, sample.label.to(device))
output = F.log_softmax(output/T, 1)
score = torch.cat((sample.pred0.unsqueeze(1).to(device),
sample.pred1.unsqueeze(1).to(device)), dim=1)
score = F.softmax(score/T,1)
loss_kv = criterion_kv(output, score.to(device)) * T * T
loss = alpha * loss_f + (1 - alpha) * loss_kv
#print(loss_f.item(), loss_kv.item())
loss.backward()
optimizer.step()
with torch.no_grad():
model.eval()
preds = []
labels = []
for sample in Data.valid_iter:
output = model(sample.text.permute(1, 0).to(device))
p = output.argmax(1).cpu().tolist()
l = sample.label.tolist()
preds += p
labels += l
report = classification_report(preds, labels)
print(report)
torch.save(model, os.path.join(args.save_dir, args.save_config + str(epoch)))
| 34.347222 | 89 | 0.632835 |
13df4c0e986f7c76ecd11c6a6721e985d305104d
| 7,597 |
py
|
Python
|
tests/TALTests/HTMLTests/TALAttributesTestCases.py
|
janbrohl/SimpleTAL
|
f5a3ddd9a74cf9af7356bb431513e3534717802d
|
[
"BSD-3-Clause"
] | 5 |
2015-11-20T12:17:04.000Z
|
2021-03-19T13:49:33.000Z
|
tests/TALTests/HTMLTests/TALAttributesTestCases.py
|
mar10/SimpleTAL
|
f5a3ddd9a74cf9af7356bb431513e3534717802d
|
[
"BSD-3-Clause"
] | 5 |
2015-09-20T12:55:23.000Z
|
2018-05-12T10:34:20.000Z
|
tests/TALTests/HTMLTests/TALAttributesTestCases.py
|
mar10/SimpleTAL
|
f5a3ddd9a74cf9af7356bb431513e3534717802d
|
[
"BSD-3-Clause"
] | 1 |
2022-01-24T13:37:38.000Z
|
2022-01-24T13:37:38.000Z
|
#!/usr/bin/python
# -*- coding: iso-8859-1 -*-
# Copyright (c) 2016, Jan Brohl <[email protected]>
# All rights reserved.
# See LICENSE.txt
# Copyright (c) 2004 Colin Stewart (http://www.owlfish.com/)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
# IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# If you make any bug fixes or feature enhancements please let me know!
"""
Unit test cases.
"""
from __future__ import unicode_literals
import unittest
import os
import io
import logging
import logging.config
from simpletal import simpleTAL, simpleTALES
if (os.path.exists("logging.ini")):
logging.config.fileConfig("logging.ini")
else:
logging.basicConfig()
if __name__ == '__main__':
unittest.main()
| 46.042424 | 162 | 0.65618 |
13dfb252487b62555c998999e6abd56dcb22612c
| 562 |
py
|
Python
|
iseq_prof/fasta.py
|
EBI-Metagenomics/iseq-prof
|
ca41a0f3aa1e70e59648bdc08b36da1ec76220ad
|
[
"MIT"
] | null | null | null |
iseq_prof/fasta.py
|
EBI-Metagenomics/iseq-prof
|
ca41a0f3aa1e70e59648bdc08b36da1ec76220ad
|
[
"MIT"
] | null | null | null |
iseq_prof/fasta.py
|
EBI-Metagenomics/iseq-prof
|
ca41a0f3aa1e70e59648bdc08b36da1ec76220ad
|
[
"MIT"
] | null | null | null |
from pathlib import Path
from typing import List
from fasta_reader import FASTAItem, FASTAWriter, read_fasta
__all__ = ["downsample"]
| 29.578947 | 71 | 0.715302 |
13e04df35258103610f99481901f9649956a3c76
| 209 |
py
|
Python
|
src/data_settings.py
|
DhruvSrikanth/TSLA-React
|
2ce4edb6b21ec1a301047124cfda5bb30deb3a90
|
[
"MIT"
] | null | null | null |
src/data_settings.py
|
DhruvSrikanth/TSLA-React
|
2ce4edb6b21ec1a301047124cfda5bb30deb3a90
|
[
"MIT"
] | null | null | null |
src/data_settings.py
|
DhruvSrikanth/TSLA-React
|
2ce4edb6b21ec1a301047124cfda5bb30deb3a90
|
[
"MIT"
] | null | null | null |
# API keys
# YF_API_KEY = "YRVHVLiFAt3ANYZf00BXr2LHNfZcgKzdWVmsZ9Xi" # yahoo finance api key
TICKER = "TSLA"
INTERVAL = "1m"
PERIOD = "1d"
LOOK_BACK = 30 # hard limit to not reach rate limit of 100 per day
| 20.9 | 81 | 0.732057 |
13e067146d5c409e953e8fe9a97ca674f7b0976f
| 2,217 |
py
|
Python
|
ymir/backend/src/ymir_controller/controller/utils/invoker_mapping.py
|
phoenix-xhuang/ymir
|
537d3ac389c4a365ce4daef431c95b42ddcd5b1b
|
[
"Apache-2.0"
] | 64 |
2021-11-15T03:48:00.000Z
|
2022-03-25T07:08:46.000Z
|
ymir/backend/src/ymir_controller/controller/utils/invoker_mapping.py
|
phoenix-xhuang/ymir
|
537d3ac389c4a365ce4daef431c95b42ddcd5b1b
|
[
"Apache-2.0"
] | 35 |
2021-11-23T04:14:35.000Z
|
2022-03-26T09:03:43.000Z
|
ymir/backend/src/ymir_controller/controller/utils/invoker_mapping.py
|
phoenix-xhuang/ymir
|
537d3ac389c4a365ce4daef431c95b42ddcd5b1b
|
[
"Apache-2.0"
] | 57 |
2021-11-11T10:15:40.000Z
|
2022-03-29T07:27:54.000Z
|
from controller.invoker import (
invoker_cmd_branch_checkout,
invoker_cmd_branch_commit,
invoker_cmd_branch_create,
invoker_cmd_branch_delete,
invoker_cmd_branch_list,
invoker_cmd_evaluate,
invoker_cmd_filter,
invoker_cmd_gpu_info,
invoker_cmd_inference,
invoker_cmd_init,
invoker_cmd_label_add,
invoker_cmd_label_get,
invoker_cmd_log,
invoker_cmd_merge,
invoker_cmd_pull_image,
invoker_cmd_repo_check,
invoker_cmd_repo_clear,
invoker_cmd_sampling,
invoker_cmd_terminate,
invoker_cmd_user_create,
invoker_task_factory,
)
from proto import backend_pb2
RequestTypeToInvoker = {
backend_pb2.CMD_BRANCH_CHECKOUT: invoker_cmd_branch_checkout.BranchCheckoutInvoker,
backend_pb2.CMD_BRANCH_CREATE: invoker_cmd_branch_create.BranchCreateInvoker,
backend_pb2.CMD_BRANCH_DEL: invoker_cmd_branch_delete.BranchDeleteInvoker,
backend_pb2.CMD_BRANCH_LIST: invoker_cmd_branch_list.BranchListInvoker,
backend_pb2.CMD_COMMIT: invoker_cmd_branch_commit.BranchCommitInvoker,
backend_pb2.CMD_EVALUATE: invoker_cmd_evaluate.EvaluateInvoker,
backend_pb2.CMD_FILTER: invoker_cmd_filter.FilterBranchInvoker,
backend_pb2.CMD_GPU_INFO_GET: invoker_cmd_gpu_info.GPUInfoInvoker,
backend_pb2.CMD_INFERENCE: invoker_cmd_inference.InferenceCMDInvoker,
backend_pb2.CMD_INIT: invoker_cmd_init.InitInvoker,
backend_pb2.CMD_LABEL_ADD: invoker_cmd_label_add.LabelAddInvoker,
backend_pb2.CMD_LABEL_GET: invoker_cmd_label_get.LabelGetInvoker,
backend_pb2.CMD_LOG: invoker_cmd_log.LogInvoker,
backend_pb2.CMD_MERGE: invoker_cmd_merge.MergeInvoker,
backend_pb2.CMD_PULL_IMAGE: invoker_cmd_pull_image.ImageHandler,
backend_pb2.CMD_TERMINATE: invoker_cmd_terminate.CMDTerminateInvoker,
backend_pb2.CMD_REPO_CHECK: invoker_cmd_repo_check.RepoCheckInvoker,
backend_pb2.CMD_REPO_CLEAR: invoker_cmd_repo_clear.RepoClearInvoker,
backend_pb2.REPO_CREATE: invoker_cmd_init.InitInvoker,
backend_pb2.TASK_CREATE: invoker_task_factory.CreateTaskInvokerFactory,
backend_pb2.USER_CREATE: invoker_cmd_user_create.UserCreateInvoker,
backend_pb2.CMD_SAMPLING: invoker_cmd_sampling.SamplingInvoker,
}
| 43.470588 | 87 | 0.834461 |
13e0862967d39493794d0afe2c6e9020dad2d87c
| 777 |
py
|
Python
|
tests/utils/date_utils.py
|
asuol/worky
|
362257e77486af05941cc977055c01e49b09a2dd
|
[
"MIT"
] | null | null | null |
tests/utils/date_utils.py
|
asuol/worky
|
362257e77486af05941cc977055c01e49b09a2dd
|
[
"MIT"
] | null | null | null |
tests/utils/date_utils.py
|
asuol/worky
|
362257e77486af05941cc977055c01e49b09a2dd
|
[
"MIT"
] | null | null | null |
from datetime import datetime, timedelta
due_date_format = '%Y-%m-%d'
datepicker_date_format = '%m%d%Y'
| 25.064516 | 78 | 0.776062 |
13e0ca25df4bf90f8ba82de1f47aa08f14078d33
| 5,304 |
py
|
Python
|
numba/roc/tests/hsapy/test_gufuncbuilding.py
|
luk-f-a/numba
|
3a682bd827e416335e3574bc7b10f0ec69adb701
|
[
"BSD-2-Clause",
"BSD-3-Clause"
] | 76 |
2020-07-06T14:44:05.000Z
|
2022-02-14T15:30:21.000Z
|
numba/roc/tests/hsapy/test_gufuncbuilding.py
|
luk-f-a/numba
|
3a682bd827e416335e3574bc7b10f0ec69adb701
|
[
"BSD-2-Clause",
"BSD-3-Clause"
] | 108 |
2020-08-17T22:38:26.000Z
|
2021-12-06T09:44:14.000Z
|
numba/roc/tests/hsapy/test_gufuncbuilding.py
|
luk-f-a/numba
|
3a682bd827e416335e3574bc7b10f0ec69adb701
|
[
"BSD-2-Clause",
"BSD-3-Clause"
] | 11 |
2020-07-12T16:18:07.000Z
|
2022-02-05T16:48:35.000Z
|
import numpy as np
from numba.roc.vectorizers import HsaGUFuncVectorize
from numba.roc.dispatch import HSAGenerializedUFunc
from numba import guvectorize
import unittest
if __name__ == '__main__':
unittest.main()
| 31.951807 | 75 | 0.532805 |
13e10f247a53a809b100dc05b97804f51f30b05a
| 463 |
py
|
Python
|
server/form/mongo.py
|
SRM-IST-KTR/ossmosis
|
06e375dfdd67f91286ffbcb13e04b6543585d8ad
|
[
"MIT"
] | 6 |
2021-07-04T07:59:17.000Z
|
2021-07-04T14:41:00.000Z
|
server/form/mongo.py
|
SRM-IST-KTR/ossmosis
|
06e375dfdd67f91286ffbcb13e04b6543585d8ad
|
[
"MIT"
] | null | null | null |
server/form/mongo.py
|
SRM-IST-KTR/ossmosis
|
06e375dfdd67f91286ffbcb13e04b6543585d8ad
|
[
"MIT"
] | 1 |
2022-02-15T13:31:46.000Z
|
2022-02-15T13:31:46.000Z
|
import os
from pymongo import MongoClient
from dotenv import load_dotenv
if __name__ == "__main__":
pass
| 21.045455 | 52 | 0.637149 |
13e1722808236b6aeebbdaf4b408b6e5d0b9cadb
| 738 |
py
|
Python
|
control-flow/solution/file_hosts.py
|
giserh/book-python
|
ebd4e70cea1dd56986aa8efbae3629ba3f1ba087
|
[
"MIT"
] | 1 |
2019-01-02T15:04:08.000Z
|
2019-01-02T15:04:08.000Z
|
control-flow/solution/file_hosts.py
|
giserh/book-python
|
ebd4e70cea1dd56986aa8efbae3629ba3f1ba087
|
[
"MIT"
] | null | null | null |
control-flow/solution/file_hosts.py
|
giserh/book-python
|
ebd4e70cea1dd56986aa8efbae3629ba3f1ba087
|
[
"MIT"
] | null | null | null |
FILE = r'../src/etc-hosts.txt'
hostnames = []
try:
with open(FILE, encoding='utf-8') as file:
content = file.readlines()
except FileNotFoundError:
print('File does not exist')
except PermissionError:
print('Permission denied')
for line in content:
if line.startswith('#'):
continue
if line.isspace():
continue
line = line.strip().split()
ip = line[0]
hosts = line[1:]
for record in hostnames:
if record['ip'] == ip:
record['hostnames'].update(hosts)
break
else:
hostnames.append({
'hostnames': set(hosts),
'protocol': 'IPv4' if '.' in ip else 'IPv6',
'ip': ip,
})
print(hostnames)
| 19.421053 | 56 | 0.550136 |
13e172837fb61cdf3f4fe7914fb4c72959b8c9f8
| 497 |
py
|
Python
|
zindi/docs/utils/n_subimissions_per_day.py
|
eaedk/testing-zindi-package
|
5aef7375a629b328fa8ecf9c4559e2897611a1e9
|
[
"MIT"
] | 6 |
2020-09-07T17:56:49.000Z
|
2021-12-09T12:34:01.000Z
|
zindi/docs/utils/n_subimissions_per_day.py
|
eaedk/testing-zindi-package
|
5aef7375a629b328fa8ecf9c4559e2897611a1e9
|
[
"MIT"
] | 2 |
2021-02-24T11:56:48.000Z
|
2021-03-30T14:40:45.000Z
|
zindi/docs/utils/n_subimissions_per_day.py
|
eaedk/testing-zindi-package
|
5aef7375a629b328fa8ecf9c4559e2897611a1e9
|
[
"MIT"
] | 4 |
2020-11-01T00:06:36.000Z
|
2021-11-19T19:20:52.000Z
|
def n_subimissions_per_day( url, headers ):
"""Get the number of submissions we can make per day for the selected challenge.
Parameters
----------
url : {'prize', 'points', 'knowledge' , 'all'}, default='all'
The reward of the challenges for top challengers.
headers : dictionary ,
The headers of the request.
Returns
-------
n_sub : int, default=0 : Means error during info retrieval.
The number of submissions we can make per day.
"""
| 35.5 | 84 | 0.629779 |
13e274ea10a3039f67d424845564d23d8affb74d
| 2,875 |
py
|
Python
|
algo/test/test_maximum_cut.py
|
ssavinash1/Algorithm_stanford
|
f2588b6bcac2b0858e78b819e6e8402109e80ee2
|
[
"MIT"
] | 24 |
2016-03-21T07:53:54.000Z
|
2020-06-29T12:16:36.000Z
|
algo/test/test_maximum_cut.py
|
ssavinash1/Algorithm_stanford
|
f2588b6bcac2b0858e78b819e6e8402109e80ee2
|
[
"MIT"
] | 5 |
2015-09-29T17:12:36.000Z
|
2020-03-26T20:51:56.000Z
|
algo/test/test_maximum_cut.py
|
ssavinash1/Algorithm_stanford
|
f2588b6bcac2b0858e78b819e6e8402109e80ee2
|
[
"MIT"
] | 12 |
2016-05-24T16:48:32.000Z
|
2020-10-02T12:22:09.000Z
|
# -*- coding: utf-8 -*-
import unittest
from src.graph import Graph
from src.maximum_cut import maximum_cut, maximum_cut_for_bipartite_graph
| 33.823529 | 79 | 0.420522 |
13e34fac65209ea50100d76fe6090282f3d8e3b4
| 5,788 |
py
|
Python
|
gdb/print-avs-rbtree.py
|
kemonats/avs_commons
|
ecce4edf5376d132e3686af227c9adf22ce1090e
|
[
"Apache-2.0"
] | 4 |
2016-11-04T12:55:32.000Z
|
2019-03-21T15:07:58.000Z
|
gdb/print-avs-rbtree.py
|
kemonats/avs_commons
|
ecce4edf5376d132e3686af227c9adf22ce1090e
|
[
"Apache-2.0"
] | 5 |
2015-02-11T09:34:36.000Z
|
2021-04-19T08:51:50.000Z
|
gdb/print-avs-rbtree.py
|
kemonats/avs_commons
|
ecce4edf5376d132e3686af227c9adf22ce1090e
|
[
"Apache-2.0"
] | 17 |
2015-12-17T10:32:09.000Z
|
2022-02-14T10:58:39.000Z
|
# -*- coding: utf-8 -*-
#
# Copyright 2021 AVSystem <[email protected]>
#
# 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.
# installation: append "source PATH_TO_THIS_SCRIPT" to ~/.gdbinit
import gdb
PrintAvsRbtreeSubtree()
PrintAvsRbtree()
PrintAvsRbtreeNode()
| 37.102564 | 146 | 0.593988 |
13e3573ef0ab92fc261e9835b12be4ef8345103f
| 1,372 |
py
|
Python
|
hour17/PythonGroup.py
|
sampx/mongodb-practice
|
0698b21b7da57693ba4146384c8ad65530b0066b
|
[
"MIT"
] | null | null | null |
hour17/PythonGroup.py
|
sampx/mongodb-practice
|
0698b21b7da57693ba4146384c8ad65530b0066b
|
[
"MIT"
] | null | null | null |
hour17/PythonGroup.py
|
sampx/mongodb-practice
|
0698b21b7da57693ba4146384c8ad65530b0066b
|
[
"MIT"
] | null | null | null |
from pymongo import MongoClient
if __name__=="__main__":
mongo = MongoClient('mongodb://localhost:27017/')
db = mongo['words']
collection = db['word_stats']
firstIsALastIsVowel(collection)
firstLetterTotals(collection)
| 39.2 | 69 | 0.54519 |
13e4eede3d6e6a6be8776d50a9b969b677e1d046
| 5,115 |
py
|
Python
|
packnet_sfm/models/model_utils.py
|
pection/packnet-sfm
|
d5673567b649e6bfda292c894cacdeb06aa80913
|
[
"MIT"
] | 1 |
2022-02-22T06:19:02.000Z
|
2022-02-22T06:19:02.000Z
|
packnet_sfm/models/model_utils.py
|
pection/packnet-sfm
|
d5673567b649e6bfda292c894cacdeb06aa80913
|
[
"MIT"
] | null | null | null |
packnet_sfm/models/model_utils.py
|
pection/packnet-sfm
|
d5673567b649e6bfda292c894cacdeb06aa80913
|
[
"MIT"
] | null | null | null |
# Copyright 2020 Toyota Research Institute. All rights reserved.
from packnet_sfm.utils.image import flip_lr, interpolate_scales
from packnet_sfm.utils.misc import filter_dict
from packnet_sfm.utils.types import is_tensor, is_list, is_numpy
def flip(tensor, flip_fn):
"""
Flip tensors or list of tensors based on a function
Parameters
----------
tensor : torch.Tensor or list[torch.Tensor] or list[list[torch.Tensor]]
Tensor to be flipped
flip_fn : Function
Flip function
Returns
-------
tensor : torch.Tensor or list[torch.Tensor] or list[list[torch.Tensor]]
Flipped tensor or list of tensors
"""
if not is_list(tensor):
return flip_fn(tensor)
else:
if not is_list(tensor[0]):
return [flip_fn(val) for val in tensor]
else:
return [[flip_fn(v) for v in val] for val in tensor]
def merge_outputs(*outputs):
"""
Merges model outputs for logging
Parameters
----------
outputs : tuple of dict
Outputs to be merged
Returns
-------
output : dict
Dictionary with a "metrics" key containing a dictionary with various metrics and
all other keys that are not "loss" (it is handled differently).
"""
ignore = ['loss'] # Keys to ignore
combine = ['metrics'] # Keys to combine
merge = {key: {} for key in combine}
for output in outputs:
# Iterate over all keys
for key, val in output.items():
# Combine these keys
if key in combine:
for sub_key, sub_val in output[key].items():
assert sub_key not in merge[key].keys(), \
'Combining duplicated key {} to {}'.format(sub_key, key)
merge[key][sub_key] = sub_val
# Ignore these keys
elif key not in ignore:
assert key not in merge.keys(), \
'Adding duplicated key {}'.format(key)
merge[key] = val
return merge
def stack_batch(batch):
"""
Stack multi-camera batches (B,N,C,H,W becomes BN,C,H,W)
Parameters
----------
batch : dict
Batch
Returns
-------
batch : dict
Stacked batch
"""
# If there is multi-camera information
if len(batch['rgb'].shape) == 5:
assert batch['rgb'].shape[0] == 1, 'Only batch size 1 is supported for multi-cameras'
# Loop over all keys
for key in batch.keys():
# If list, stack every item
if is_list(batch[key]):
if is_tensor(batch[key][0]) or is_numpy(batch[key][0]):
batch[key] = [sample[0] for sample in batch[key]]
# Else, stack single item
else:
batch[key] = batch[key][0]
return batch
def flip_batch_input(batch):
"""
Flip batch input information (copies data first)
Parameters
----------
batch : dict
Batch information
Returns
-------
batch : dict
Flipped batch
"""
# Flip tensors
for key in filter_dict(batch, [
'rgb', 'rgb_context',
'input_depth', 'input_depth_context',
]):
batch[key] = flip(batch[key], flip_lr)
# Flip intrinsics
for key in filter_dict(batch, [
'intrinsics'
]):
batch[key] = batch[key].clone()
batch[key][:, 0, 2] = batch['rgb'].shape[3] - batch[key][:, 0, 2]
# Return flipped batch
return batch
def flip_output(output):
"""
Flip output information
Parameters
----------
output : dict
Dictionary of model outputs (e.g. with keys like 'inv_depths' and 'uncertainty')
Returns
-------
output : dict
Flipped output
"""
# Flip tensors
for key in filter_dict(output, [
'uncertainty', 'logits_semantic', 'ord_probability',
'inv_depths', 'inv_depths_context', 'inv_depths1', 'inv_depths2',
'pred_depth', 'pred_depth_context', 'pred_depth1', 'pred_depth2',
'pred_inv_depth', 'pred_inv_depth_context', 'pred_inv_depth1', 'pred_inv_depth2',
]):
output[key] = flip(output[key], flip_lr)
return output
def upsample_output(output, mode='nearest', align_corners=None):
"""
Upsample multi-scale outputs to full resolution.
Parameters
----------
output : dict
Dictionary of model outputs (e.g. with keys like 'inv_depths' and 'uncertainty')
mode : str
Which interpolation mode is used
align_corners: bool or None
Whether corners will be aligned during interpolation
Returns
-------
output : dict
Upsampled output
"""
for key in filter_dict(output, [
'inv_depths', 'uncertainty'
]):
output[key] = interpolate_scales(
output[key], mode=mode, align_corners=align_corners)
for key in filter_dict(output, [
'inv_depths_context'
]):
output[key] = [interpolate_scales(
val, mode=mode, align_corners=align_corners) for val in output[key]]
return output
| 28.259669 | 93 | 0.585728 |
13e58721f5e2c382c66ef605548dfd06ad0e1f44
| 1,363 |
py
|
Python
|
utils/stg/min_jerk_traj.py
|
dgerod/more-dmps
|
4dc886a138f289532b2672537f91ff857448ad27
|
[
"BSD-2-Clause"
] | 7 |
2017-12-23T13:43:33.000Z
|
2021-08-21T14:50:55.000Z
|
utils/stg/min_jerk_traj.py
|
dgerod/more-dmps
|
4dc886a138f289532b2672537f91ff857448ad27
|
[
"BSD-2-Clause"
] | 1 |
2016-08-21T05:05:32.000Z
|
2016-08-21T05:05:32.000Z
|
utils/stg/min_jerk_traj.py
|
dgerod/more-dmps
|
4dc886a138f289532b2672537f91ff857448ad27
|
[
"BSD-2-Clause"
] | 2 |
2018-02-11T02:01:32.000Z
|
2019-09-16T01:43:43.000Z
|
'''
Created on 25.07.2012
@author: karl
'''
| 24.781818 | 96 | 0.533382 |
13e5f9585262ddf6bd1e3799d8611f18effa881a
| 40 |
py
|
Python
|
tests/__init__.py
|
karanrampal/triplet-loss
|
b62008dedbf8640ccf0dc359b5aadd5e8b0ab134
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
karanrampal/triplet-loss
|
b62008dedbf8640ccf0dc359b5aadd5e8b0ab134
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
karanrampal/triplet-loss
|
b62008dedbf8640ccf0dc359b5aadd5e8b0ab134
|
[
"MIT"
] | null | null | null |
# To make directory as a python package
| 20 | 39 | 0.775 |
13e74a7f98e6571f4fc714e2743e38c7eafbf58e
| 2,607 |
py
|
Python
|
orthoexon/tests/test_util.py
|
jessicalettes/orthoexon
|
463ad1908364c602cf75dbddb0b16a42f4100a36
|
[
"BSD-3-Clause"
] | null | null | null |
orthoexon/tests/test_util.py
|
jessicalettes/orthoexon
|
463ad1908364c602cf75dbddb0b16a42f4100a36
|
[
"BSD-3-Clause"
] | null | null | null |
orthoexon/tests/test_util.py
|
jessicalettes/orthoexon
|
463ad1908364c602cf75dbddb0b16a42f4100a36
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_orthoexon
----------------------------------
Tests for `orthoexon` module.
"""
import os
import pytest
def test_separate_with_quotes(exon_id_with_quotes):
from orthoexon.util import separate
test = separate(exon_id_with_quotes)
true = "ENSE00001229068"
assert test == true
def test_separate(exon_id):
from orthoexon.util import separate
test = separate(exon_id)
true = "ENSE00001229068"
assert test == true
def test_translate(exon_id, human_fasta, human_gtf_database):
from orthoexon.util import translate
from orthoexon.util import separate
for index, species1gene in enumerate(human_gtf_database.features_of_type('gene')):
species1gffutilsgeneid = str(species1gene['gene_id'])
species1geneid = separate(species1gffutilsgeneid)
for exon in human_gtf_database.children(species1geneid,
featuretype='CDS',
order_by='start'):
if exon_id == exon:
test = translate(exon, human_fasta)
break
break
true = 'MAEDADMRNELEEMQRRADQLADE'
assert test == true
# def test_getsequence(exon, human_gtf_database):
# from orthoexon.util import getsequence
#
# test = getsequence(exon, human_gtf_database)
# true = 'ATGGCCGAAGACGCAGACATGCGCAATGAGCTGGAGGAGATGCAGCGAAGGGCTGACCAGTT' \
# 'GGCTGATGAG'
#
# assert test == true
# def test_make_sequence_array(finalsequencedf):
# from orthoexon.util import make_sequence_array
#
# test = make_sequence_array(finalsequencedf)
# true = ......
#
# assert test == true
| 23.917431 | 86 | 0.678558 |
13e8cb3c3e9246762451e5cb22ca74f1dccc3db7
| 2,656 |
py
|
Python
|
predict_recognition.py
|
yeyupiaoling/Kersa-Speaker-Recognition
|
7ccf42c006f42ff6074ad3937e44a0dfa68c6d33
|
[
"Apache-2.0"
] | 42 |
2020-07-12T13:21:13.000Z
|
2021-07-01T01:06:12.000Z
|
predict_recognition.py
|
yeyupiaoling/VoiceprintRecognition-Keras
|
7ccf42c006f42ff6074ad3937e44a0dfa68c6d33
|
[
"Apache-2.0"
] | 3 |
2020-08-19T06:16:02.000Z
|
2020-11-02T02:16:56.000Z
|
predict_recognition.py
|
yeyupiaoling/Kersa-Speaker-Recognition
|
7ccf42c006f42ff6074ad3937e44a0dfa68c6d33
|
[
"Apache-2.0"
] | 12 |
2020-07-15T14:33:51.000Z
|
2021-05-24T03:55:04.000Z
|
import argparse
import os
import shutil
import time
import numpy as np
from utils import model, utils
from utils.record import RecordAudio
parser = argparse.ArgumentParser()
parser.add_argument('--audio_db', default='audio_db/', type=str, help='')
parser.add_argument('--threshold', default=0.7, type=float, help='')
parser.add_argument('--model_path', default=r'models/resnet34-56.h5', type=str, help='')
args = parser.parse_args()
person_feature = []
person_name = []
#
network_eval = model.vggvox_resnet2d_icassp(input_dim=(257, None, 1), mode='eval')
#
network_eval.load_weights(os.path.join(args.model_path), by_name=True)
print('==> successfully loading model {}.'.format(args.model_path))
#
#
#
#
if __name__ == '__main__':
load_audio_db(args.audio_db)
record_audio = RecordAudio()
while True:
select_fun = int(input("01"))
if select_fun == 0:
audio_path = record_audio.record()
name = input("")
if name == '': continue
register(audio_path, name)
elif select_fun == 1:
audio_path = record_audio.record()
name, p = recognition(audio_path)
if p > args.threshold:
print("%s%f" % (name, p))
else:
print("")
else:
print('')
| 28.255319 | 103 | 0.628765 |
13e90500b4879323474a6761d01dbd32906a8b6c
| 6,853 |
py
|
Python
|
cubedash/_product.py
|
vconrado/datacube-explorer
|
ccb9a9a42e5dd16e2b0325a1f881b080bb2806e6
|
[
"Apache-2.0"
] | null | null | null |
cubedash/_product.py
|
vconrado/datacube-explorer
|
ccb9a9a42e5dd16e2b0325a1f881b080bb2806e6
|
[
"Apache-2.0"
] | null | null | null |
cubedash/_product.py
|
vconrado/datacube-explorer
|
ccb9a9a42e5dd16e2b0325a1f881b080bb2806e6
|
[
"Apache-2.0"
] | null | null | null |
import logging
from datetime import timedelta
from flask import Blueprint, Response, abort, redirect, url_for
from cubedash import _model, _utils, _utils as utils
_LOG = logging.getLogger(__name__)
bp = Blueprint("product", __name__)
def _iso8601_duration(tdelta: timedelta):
"""
Format a timedelta as an iso8601 duration
>>> _iso8601_duration(timedelta(seconds=0))
'PT0S'
>>> _iso8601_duration(timedelta(seconds=1))
'PT1S'
>>> _iso8601_duration(timedelta(seconds=23423))
'PT6H30M23S'
>>> _iso8601_duration(timedelta(seconds=4564564556))
'P52830DT14H35M56S'
"""
all_secs = tdelta.total_seconds()
secs = int(all_secs % 60)
h_m_s = (
int(all_secs // 3600 % 24),
int(all_secs // 60 % 60),
secs if secs % 1 != 0 else int(secs),
)
parts = ["P"]
days = int(all_secs // 86400)
if days:
parts.append(f"{days}D")
if any(h_m_s):
parts.append("T")
if all_secs:
for val, name in zip(h_m_s, ["H", "M", "S"]):
if val:
parts.append(f"{val}{name}")
else:
parts.append("T0S")
return "".join(parts)
| 27.522088 | 86 | 0.620604 |
13e960260e528ba2d92aa2f22ba8b6f12cf5bbfe
| 1,923 |
py
|
Python
|
litex_boards/platforms/sipeed_tang_nano.py
|
ozbenh/litex-boards
|
f18b10d1edb4e162a77972e2e9c5bad54ca00788
|
[
"BSD-2-Clause"
] | null | null | null |
litex_boards/platforms/sipeed_tang_nano.py
|
ozbenh/litex-boards
|
f18b10d1edb4e162a77972e2e9c5bad54ca00788
|
[
"BSD-2-Clause"
] | null | null | null |
litex_boards/platforms/sipeed_tang_nano.py
|
ozbenh/litex-boards
|
f18b10d1edb4e162a77972e2e9c5bad54ca00788
|
[
"BSD-2-Clause"
] | null | null | null |
#
# This file is part of LiteX-Boards.
#
# Copyright (c) 2021 Florent Kermarrec <[email protected]>
# SPDX-License-Identifier: BSD-2-Clause
# Board diagram/pinout:
# https://user-images.githubusercontent.com/1450143/133655492-532d5e9a-0635-4889-85c9-68683d06cae0.png
# http://dl.sipeed.com/TANG/Nano/HDK/Tang-NANO-2704(Schematic).pdf
from migen import *
from litex.build.generic_platform import *
from litex.build.gowin.platform import GowinPlatform
from litex.build.openfpgaloader import OpenFPGALoader
# IOs ----------------------------------------------------------------------------------------------
_io = [
# Clk / Rst
("clk24", 0, Pins("35"), IOStandard("LVCMOS33")),
# Leds
("user_led", 0, Pins("16"), IOStandard("LVCMOS33")),
("user_led", 1, Pins("17"), IOStandard("LVCMOS33")),
("user_led", 2, Pins("18"), IOStandard("LVCMOS33")),
# Buttons.
("user_btn", 0, Pins("15"), IOStandard("LVCMOS33")),
("user_btn", 0, Pins("14"), IOStandard("LVCMOS33")),
# Serial
("serial", 0,
Subsignal("tx", Pins("8")),
Subsignal("rx", Pins("9")),
IOStandard("LVCMOS33")
),
]
# Connectors ---------------------------------------------------------------------------------------
_connectors = []
# Platform -----------------------------------------------------------------------------------------
| 32.05 | 115 | 0.573583 |
13eaaea3944207924e8d3d8e8f4c1a6a0ee51732
| 10,525 |
py
|
Python
|
nm_cavia/rl/metalearner.py
|
anon-6994/nm-metarl
|
45c8798c2139d8c200cc7a398331c1b98a0dccec
|
[
"MIT"
] | null | null | null |
nm_cavia/rl/metalearner.py
|
anon-6994/nm-metarl
|
45c8798c2139d8c200cc7a398331c1b98a0dccec
|
[
"MIT"
] | null | null | null |
nm_cavia/rl/metalearner.py
|
anon-6994/nm-metarl
|
45c8798c2139d8c200cc7a398331c1b98a0dccec
|
[
"MIT"
] | null | null | null |
import torch
from torch.distributions.kl import kl_divergence
from torch.nn.utils.convert_parameters import (vector_to_parameters,
parameters_to_vector)
from rl_utils.optimization import conjugate_gradient
from rl_utils.torch_utils import (weighted_mean, detach_distribution, weighted_normalize)
| 40.171756 | 120 | 0.624323 |
13eac7b96baca5f54a52331a42d7e035d905943e
| 2,321 |
py
|
Python
|
request/management/commands/purgerequests.py
|
hramezani/django-request
|
4b9c7b22f26338d2c93110477aa44041b1c5ddb4
|
[
"BSD-2-Clause"
] | 373 |
2016-04-22T21:18:41.000Z
|
2022-03-31T23:13:31.000Z
|
request/management/commands/purgerequests.py
|
hramezani/django-request
|
4b9c7b22f26338d2c93110477aa44041b1c5ddb4
|
[
"BSD-2-Clause"
] | 128 |
2016-04-22T21:30:55.000Z
|
2022-03-08T20:24:44.000Z
|
request/management/commands/purgerequests.py
|
hramezani/django-request
|
4b9c7b22f26338d2c93110477aa44041b1c5ddb4
|
[
"BSD-2-Clause"
] | 79 |
2016-04-25T08:44:56.000Z
|
2022-03-17T01:41:27.000Z
|
from datetime import timedelta
from dateutil.relativedelta import relativedelta
from django.core.management.base import BaseCommand, CommandError
from django.utils import timezone
from ...models import Request
DURATION_OPTIONS = {
'hours': lambda amount: timezone.now() - timedelta(hours=amount),
'days': lambda amount: timezone.now() - timedelta(days=amount),
'weeks': lambda amount: timezone.now() - timedelta(weeks=amount),
'months': lambda amount: timezone.now() + relativedelta(months=-amount),
'years': lambda amount: timezone.now() + relativedelta(years=-amount),
}
try:
# to keep backward Python 2 compatibility
input = raw_input
except NameError:
pass
| 30.142857 | 88 | 0.623007 |
13ebac2480a38d6864e7de863e165c5d96c0e1f7
| 10,928 |
py
|
Python
|
tests/test_crypto.py
|
kimtaehong/PythonForWindows
|
d04eed1754e2e23474213b89580d68e1b73c3fe4
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_crypto.py
|
kimtaehong/PythonForWindows
|
d04eed1754e2e23474213b89580d68e1b73c3fe4
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_crypto.py
|
kimtaehong/PythonForWindows
|
d04eed1754e2e23474213b89580d68e1b73c3fe4
|
[
"BSD-3-Clause"
] | null | null | null |
import pytest
import windows.crypto
import windows.generated_def as gdef
import windows.crypto.generation
from .pfwtest import *
pytestmark = pytest.mark.usefixtures('check_for_gc_garbage')
TEST_CERT = b"""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"""
## Cert info:
# Name: PythonForWindowsTest
# Serial: '1b 8e 94 cb 0b 3e eb b6 41 39 f3 c9 09 b1 6b 46'
TEST_PFX_PASSWORD = "TestPassword"
TEST_PFX = b"""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==
"""
PFW_TEST_TMP_KEY_CONTAINER = "PythonForWindowsTMPContainerTest"
RANDOM_CERTIF_NAME = b"PythonForWindowsGeneratedRandomCertifTest"
RANDOM_PFX_PASSWORD = "PythonForWindowsGeneratedRandomPFXPassword"
# str(windows.crypto.encrypt(TEST_CERT, "Hello crypto")).encode("base64")
# Target serial == TEST_CERT.Serial == 1b 8e 94 cb 0b 3e eb b6 41 39 f3 c9 09 b1 6b 46
TEST_CRYPTMSG = b"""MIIBJAYJKoZIhvcNAQcDoIIBFTCCARECAQAxgc0wgcoCAQAwMzAfMR0wGwYDVQQDExRQeXRob25G
b3JXaW5kb3dzVGVzdAIQG46Uyws+67ZBOfPJCbFrRjANBgkqhkiG9w0BAQcwAASBgA1fwFY8w4Bb
fOMer94JhazbJxaUnV305QzF27w4GwNQ2UIpl9KWJoJJaF7azU3nVhP33agAxlxmr9fP48B6DeE1
pbu1jX9tEWlTJC6O0TmKcRPjblEaU6VJXXlpKlKZCmwCUuHR9VtcXGnxEU1Hy7FmHM96lvDRmYQT
Y0MnRJLyMDwGCSqGSIb3DQEHATAdBglghkgBZQMEASoEEEdEGEzKBrDO/zC8z6q6HLaAEGbjGCay
s6u32YhUxQ4/QhI="""
| 45.344398 | 132 | 0.811585 |
13ec7c22478f599c0c77b64f3478dbd3b142fa61
| 8,135 |
py
|
Python
|
cdci_data_analysis/analysis/plot_tools.py
|
andreatramacere/cdci_data_analysis
|
8ae34a7252d6baf011a3b99fbe4f6e624b63d7df
|
[
"MIT"
] | null | null | null |
cdci_data_analysis/analysis/plot_tools.py
|
andreatramacere/cdci_data_analysis
|
8ae34a7252d6baf011a3b99fbe4f6e624b63d7df
|
[
"MIT"
] | null | null | null |
cdci_data_analysis/analysis/plot_tools.py
|
andreatramacere/cdci_data_analysis
|
8ae34a7252d6baf011a3b99fbe4f6e624b63d7df
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import, division, print_function
from builtins import (bytes, str, open, super, range,
zip, round, input, int, pow, object, map, zip)
__author__ = "Andrea Tramacere"
import numpy as np
from astropy import wcs
from bokeh.layouts import row, widgetbox,gridplot
from bokeh.models import CustomJS, Slider,HoverTool,ColorBar,LinearColorMapper,LabelSet,ColumnDataSource
from bokeh.embed import components
from bokeh.plotting import figure
from bokeh.palettes import Plasma256
| 31.288462 | 132 | 0.547019 |
13edd551b75e71fd96ef5d443c0c54bbec028a56
| 844 |
py
|
Python
|
test/unit/test_testaid_unit_pathlist.py
|
RebelCodeBase/testaid
|
998c827b826fe4374ecf0a234fef61a975e2fcd7
|
[
"Apache-2.0"
] | 17 |
2019-08-04T09:29:19.000Z
|
2020-05-16T02:25:20.000Z
|
test/unit/test_testaid_unit_pathlist.py
|
RebelCodeBase/testaid
|
998c827b826fe4374ecf0a234fef61a975e2fcd7
|
[
"Apache-2.0"
] | 12 |
2019-07-19T22:20:42.000Z
|
2020-01-20T06:45:38.000Z
|
test/unit/test_testaid_unit_pathlist.py
|
RebelCodeBase/testaid
|
998c827b826fe4374ecf0a234fef61a975e2fcd7
|
[
"Apache-2.0"
] | 3 |
2019-08-08T18:18:13.000Z
|
2019-10-07T13:46:03.000Z
|
from pathlib import Path
from testaid.pathlist import PathList
| 29.103448 | 73 | 0.726303 |
13ee6c7b533dc2afb81ce7087fea00c547679671
| 22,147 |
py
|
Python
|
tests/unit/zhmcclient/test_hba.py
|
vkpro-forks/python-zhmcclient
|
eab2dca37cb417d03411450dabf72805214b5ca0
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/zhmcclient/test_hba.py
|
vkpro-forks/python-zhmcclient
|
eab2dca37cb417d03411450dabf72805214b5ca0
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/zhmcclient/test_hba.py
|
vkpro-forks/python-zhmcclient
|
eab2dca37cb417d03411450dabf72805214b5ca0
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2016-2017 IBM Corp. 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.
"""
Unit tests for _hba module.
"""
from __future__ import absolute_import, print_function
import pytest
import re
import copy
from zhmcclient import Client, Hba, HTTPError, NotFound
from zhmcclient_mock import FakedSession
from tests.common.utils import assert_resources
# Object IDs and names of our faked HBAs:
HBA1_OID = 'hba 1-oid'
HBA1_NAME = 'hba 1'
HBA2_OID = 'hba 2-oid'
HBA2_NAME = 'hba 2'
# URIs and Object IDs of elements referenced in HBA properties:
FCP1_OID = 'fake-fcp1-oid'
PORT11_OID = 'fake-port11-oid'
PORT11_URI = '/api/adapters/{}/storage-ports/{}'.format(FCP1_OID, PORT11_OID)
def test_hba_delete_create_same_name(self):
"""Test Hba.delete() followed by Hba.create() with same name."""
# Add a faked HBA to be tested and another one
faked_hba = self.add_hba1()
hba_name = faked_hba.name
self.add_hba2()
# Construct the input properties for a third HBA with same name
part3_props = copy.deepcopy(faked_hba.properties)
part3_props['description'] = 'Third HBA'
# Set the status of the faked partition
self.faked_partition.properties['status'] = 'stopped' # deletable
hba_mgr = self.partition.hbas
hba = hba_mgr.find(name=hba_name)
# Execute the deletion code to be tested.
hba.delete()
# Check that the HBA no longer exists
with pytest.raises(NotFound):
hba_mgr.find(name=hba_name)
# Execute the creation code to be tested.
hba_mgr.create(part3_props)
# Check that the HBA exists again under that name
hba3 = hba_mgr.find(name=hba_name)
description = hba3.get_property('description')
assert description == 'Third HBA'
def test_hba_update_name(self):
"""Test Hba.update_properties() with 'name' property."""
# Add a faked HBA
faked_hba = self.add_hba1()
hba_name = faked_hba.name
# Set the status of the faked partition
self.faked_partition.properties['status'] = 'stopped' # updatable
hba_mgr = self.partition.hbas
hba = hba_mgr.find(name=hba_name)
new_hba_name = "new-" + hba_name
# Execute the code to be tested
hba.update_properties(properties={'name': new_hba_name})
# Verify that the resource is no longer found by its old name, using
# list() (this does not use the name-to-URI cache).
hbas_list = hba_mgr.list(
filter_args=dict(name=hba_name))
assert len(hbas_list) == 0
# Verify that the resource is no longer found by its old name, using
# find() (this uses the name-to-URI cache).
with pytest.raises(NotFound):
hba_mgr.find(name=hba_name)
# Verify that the resource object already reflects the update, even
# though it has not been refreshed yet.
assert hba.properties['name'] == new_hba_name
# Refresh the resource object and verify that it still reflects the
# update.
hba.pull_full_properties()
assert hba.properties['name'] == new_hba_name
# Verify that the resource can be found by its new name, using find()
new_hba_find = hba_mgr.find(name=new_hba_name)
assert new_hba_find.properties['name'] == new_hba_name
# Verify that the resource can be found by its new name, using list()
new_hbas_list = hba_mgr.list(
filter_args=dict(name=new_hba_name))
assert len(new_hbas_list) == 1
new_hba_list = new_hbas_list[0]
assert new_hba_list.properties['name'] == new_hba_name
| 34.658842 | 77 | 0.557457 |
13ef6d428251649b315fbe8757c2d7336d7471a8
| 367 |
py
|
Python
|
compiler-rt/test/asan/TestCases/Windows/lit.local.cfg.py
|
medismailben/llvm-project
|
e334a839032fe500c3bba22bf976ab7af13ce1c1
|
[
"Apache-2.0"
] | 2,338 |
2018-06-19T17:34:51.000Z
|
2022-03-31T11:00:37.000Z
|
compiler-rt/test/asan/TestCases/Windows/lit.local.cfg.py
|
medismailben/llvm-project
|
e334a839032fe500c3bba22bf976ab7af13ce1c1
|
[
"Apache-2.0"
] | 3,740 |
2019-01-23T15:36:48.000Z
|
2022-03-31T22:01:13.000Z
|
compiler-rt/test/asan/TestCases/Windows/lit.local.cfg.py
|
medismailben/llvm-project
|
e334a839032fe500c3bba22bf976ab7af13ce1c1
|
[
"Apache-2.0"
] | 500 |
2019-01-23T07:49:22.000Z
|
2022-03-30T02:59:37.000Z
|
root = getRoot(config)
# We only run a small set of tests on Windows for now.
# Override the parent directory's "unsupported" decision until we can handle
# all of its tests.
if root.host_os in ['Windows']:
config.unsupported = False
else:
config.unsupported = True
| 24.466667 | 76 | 0.73842 |
13efdb45818b7da3afae845201256a86d37c940d
| 4,302 |
py
|
Python
|
Lib/test/libregrtest/utils.py
|
oskomorokhov/cpython
|
c0e11a3ceb9427e09db4224f394c7789bf6deec5
|
[
"0BSD"
] | 5 |
2017-08-25T04:31:30.000Z
|
2022-03-22T15:01:56.000Z
|
Lib/test/libregrtest/utils.py
|
oskomorokhov/cpython
|
c0e11a3ceb9427e09db4224f394c7789bf6deec5
|
[
"0BSD"
] | 20 |
2021-03-25T12:52:42.000Z
|
2022-03-01T02:02:03.000Z
|
Lib/test/libregrtest/utils.py
|
oskomorokhov/cpython
|
c0e11a3ceb9427e09db4224f394c7789bf6deec5
|
[
"0BSD"
] | 3 |
2020-04-13T14:41:31.000Z
|
2022-03-02T18:56:32.000Z
|
import math
import os.path
import sys
import textwrap
from test import support
def printlist(x, width=70, indent=4, file=None):
"""Print the elements of iterable x to stdout.
Optional arg width (default 70) is the maximum line length.
Optional arg indent (default 4) is the number of blanks with which to
begin each line.
"""
blanks = ' ' * indent
# Print the sorted list: 'x' may be a '--random' list or a set()
print(textwrap.fill(' '.join(str(elt) for elt in sorted(x)), width,
initial_indent=blanks, subsequent_indent=blanks),
file=file)
orig_unraisablehook = None
| 22.761905 | 75 | 0.600418 |
13f22ca29da54a0f4486e1f8539ee236d259fc1e
| 5,960 |
py
|
Python
|
efetch_server/plugins/fa_sqlite/fa_sqlite_ajax.py
|
Syrkadian/efetch
|
120ac963507d54998beecfd8b8cd85ad123e6e54
|
[
"Apache-2.0"
] | 38 |
2015-08-18T00:29:16.000Z
|
2021-12-06T15:53:47.000Z
|
efetch_server/plugins/fa_sqlite/fa_sqlite_ajax.py
|
Syrkadian/efetch
|
120ac963507d54998beecfd8b8cd85ad123e6e54
|
[
"Apache-2.0"
] | 20 |
2016-03-18T02:20:27.000Z
|
2020-04-09T22:16:42.000Z
|
efetch_server/plugins/fa_sqlite/fa_sqlite_ajax.py
|
Syrkadian/efetch
|
120ac963507d54998beecfd8b8cd85ad123e6e54
|
[
"Apache-2.0"
] | 8 |
2016-08-23T14:59:15.000Z
|
2020-04-09T21:43:25.000Z
|
"""
AJAX for SQLite Viewer plugin
"""
from yapsy.IPlugin import IPlugin
from flask import Response, jsonify
import json
import logging
import sqlite3
| 35.266272 | 133 | 0.51896 |
13f2af320410f86bedc3a6ddc8c44eb547f14053
| 252 |
py
|
Python
|
raspagem/random/lista_cidades.py
|
sslppractice/propython
|
fa470c3bf0dcfbb26037146d77c7491596cabb26
|
[
"MIT"
] | null | null | null |
raspagem/random/lista_cidades.py
|
sslppractice/propython
|
fa470c3bf0dcfbb26037146d77c7491596cabb26
|
[
"MIT"
] | null | null | null |
raspagem/random/lista_cidades.py
|
sslppractice/propython
|
fa470c3bf0dcfbb26037146d77c7491596cabb26
|
[
"MIT"
] | null | null | null |
import requests, json
url = 'http://educacao.dadosabertosbr.com/api/cidades/ce'
cidades = requests.get(url).content
cidades = cidades.decode('utf-8')
cidades = json.loads(cidades)
for cidade in cidades:
codigo, nome = cidade.split(':')
print(nome)
| 22.909091 | 57 | 0.734127 |
13f387c5b382d039623c959bbf8db86a94b8f10b
| 29 |
py
|
Python
|
mezzanine/__init__.py
|
startupgrind/mezzanine
|
23d24a07c69bf8f02d60148b0b8da6c76bc5061e
|
[
"BSD-2-Clause"
] | null | null | null |
mezzanine/__init__.py
|
startupgrind/mezzanine
|
23d24a07c69bf8f02d60148b0b8da6c76bc5061e
|
[
"BSD-2-Clause"
] | 3 |
2019-02-18T07:52:41.000Z
|
2021-03-31T03:51:11.000Z
|
mezzanine/__init__.py
|
startupgrind/mezzanine
|
23d24a07c69bf8f02d60148b0b8da6c76bc5061e
|
[
"BSD-2-Clause"
] | null | null | null |
__version__ = "4.3.1.post1"
| 9.666667 | 27 | 0.655172 |
13f3a6d9012ba4c4473a1ffb1f1db1418326ee1f
| 7,566 |
py
|
Python
|
src/autonomous/purepursuit.py
|
Sloomey/DeepSpace2019
|
dda035c0ac100209b03a2ff04d86df09c6de9a85
|
[
"MIT"
] | null | null | null |
src/autonomous/purepursuit.py
|
Sloomey/DeepSpace2019
|
dda035c0ac100209b03a2ff04d86df09c6de9a85
|
[
"MIT"
] | null | null | null |
src/autonomous/purepursuit.py
|
Sloomey/DeepSpace2019
|
dda035c0ac100209b03a2ff04d86df09c6de9a85
|
[
"MIT"
] | null | null | null |
import math
from constants import Constants
from utils import vector2d
from wpilib import SmartDashboard as Dash
from autonomous import pursuitpoint
| 50.10596 | 178 | 0.655432 |
13f4c5d6b839fc74a59e3720afa044833541c6ea
| 8,661 |
py
|
Python
|
esphome/voluptuous_schema.py
|
TheEggi/esphomeyaml
|
98e8cc1edc7b29891e8100eb484922e5c2d4fc33
|
[
"MIT"
] | null | null | null |
esphome/voluptuous_schema.py
|
TheEggi/esphomeyaml
|
98e8cc1edc7b29891e8100eb484922e5c2d4fc33
|
[
"MIT"
] | null | null | null |
esphome/voluptuous_schema.py
|
TheEggi/esphomeyaml
|
98e8cc1edc7b29891e8100eb484922e5c2d4fc33
|
[
"MIT"
] | null | null | null |
import difflib
import itertools
import voluptuous as vol
from esphome.py_compat import string_types
# pylint: disable=protected-access, unidiomatic-typecheck
| 43.305 | 99 | 0.564831 |
13f5928fe05ccf64858c18af5eff2188153c32e0
| 20,738 |
py
|
Python
|
semisupervised/DensityPeaks.py
|
dpr1005/Semisupervised-learning-and-instance-selection-methods
|
646d9e729c85322e859928e71a3241f2aec6d93d
|
[
"MIT"
] | 3 |
2021-12-10T09:04:18.000Z
|
2022-01-22T15:03:19.000Z
|
semisupervised/DensityPeaks.py
|
dpr1005/Semisupervised-learning-and-instance-selection-methods
|
646d9e729c85322e859928e71a3241f2aec6d93d
|
[
"MIT"
] | 107 |
2021-12-02T07:43:11.000Z
|
2022-03-31T11:02:46.000Z
|
semisupervised/DensityPeaks.py
|
dpr1005/Semisupervised-learning-and-instance-selection-methods
|
646d9e729c85322e859928e71a3241f2aec6d93d
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @Filename: DensityPeaks.py
# @Author: Daniel Puente Ramrez
# @Time: 5/3/22 09:55
# @Version: 4.0
import math
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors
from sklearn.preprocessing import LabelEncoder
from sklearn.semi_supervised import SelfTrainingClassifier
from sklearn.svm import SVC
from instance_selection import ENN
from .utils import split
| 35.268707 | 86 | 0.580384 |
13f713d62e74a1cd787ec98b134812d16f5287ea
| 933 |
py
|
Python
|
N-aryTreeLevelOrderTraversal429.py
|
Bit64L/LeetCode-Python-
|
64847cbb1adcaca4561b949e8acc52e8e031a6cb
|
[
"MIT"
] | null | null | null |
N-aryTreeLevelOrderTraversal429.py
|
Bit64L/LeetCode-Python-
|
64847cbb1adcaca4561b949e8acc52e8e031a6cb
|
[
"MIT"
] | null | null | null |
N-aryTreeLevelOrderTraversal429.py
|
Bit64L/LeetCode-Python-
|
64847cbb1adcaca4561b949e8acc52e8e031a6cb
|
[
"MIT"
] | null | null | null |
"""
# Definition for a Node.
"""
node2 = TreeNode(2, [])
node3 = TreeNode(3, [])
children = [node2, node3]
node1 = TreeNode(1, children)
solution = Solution()
print(solution.levelOrder(node1))
| 20.733333 | 39 | 0.485531 |
13f7593938a4204f0e27844ca0c493ca0b47ec5f
| 16,444 |
py
|
Python
|
plugin.video.team.milhanos/websocket/_core.py
|
akuala/REPO.KUALA
|
ea9a157025530d2ce8fa0d88431c46c5352e89d4
|
[
"Apache-2.0"
] | 2 |
2018-11-02T19:55:30.000Z
|
2020-08-14T02:22:20.000Z
|
venv/lib/python3.5/site-packages/websocket/_core.py
|
dukakisxyz/wifiportal21-map
|
1f1917c2f3c2987f7a88cc537d7c50449d144ea0
|
[
"MIT"
] | null | null | null |
venv/lib/python3.5/site-packages/websocket/_core.py
|
dukakisxyz/wifiportal21-map
|
1f1917c2f3c2987f7a88cc537d7c50449d144ea0
|
[
"MIT"
] | 3 |
2019-12-17T20:47:00.000Z
|
2021-02-11T19:03:59.000Z
|
"""
websocket - WebSocket client library for Python
Copyright (C) 2010 Hiroki Ohtani(liris)
This library 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 2.1 of the License, or (at your option) any later version.
This library 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
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor,
Boston, MA 02110-1335 USA
"""
from __future__ import print_function
import six
import socket
if six.PY3:
from base64 import encodebytes as base64encode
else:
from base64 import encodestring as base64encode
import struct
import threading
# websocket modules
from ._exceptions import *
from ._abnf import *
from ._socket import *
from ._utils import *
from ._url import *
from ._logging import *
from ._http import *
from ._handshake import *
from ._ssl_compat import *
"""
websocket python client.
=========================
This version support only hybi-13.
Please see http://tools.ietf.org/html/rfc6455 for protocol.
"""
def create_connection(url, timeout=None, class_=WebSocket, **options):
"""
connect to url and return websocket object.
Connect to url and return the WebSocket object.
Passing optional timeout parameter will set the timeout on the socket.
If no timeout is supplied,
the global default timeout setting returned by getdefauttimeout() is used.
You can customize using 'options'.
If you set "header" list object, you can set your own custom header.
>>> conn = create_connection("ws://echo.websocket.org/",
... header=["User-Agent: MyProgram",
... "x-custom: header"])
timeout: socket timeout time. This value is integer.
if you set None for this value,
it means "use default_timeout value"
class_: class to instantiate when creating the connection. It has to implement
settimeout and connect. It's __init__ should be compatible with
WebSocket.__init__, i.e. accept all of it's kwargs.
options: "header" -> custom http header list or dict.
"cookie" -> cookie value.
"origin" -> custom origin url.
"host" -> custom host header string.
"http_proxy_host" - http proxy host name.
"http_proxy_port" - http proxy port. If not set, set to 80.
"http_no_proxy" - host names, which doesn't use proxy.
"http_proxy_auth" - http proxy auth information.
tuple of username and password.
default is None
"enable_multithread" -> enable lock for multithread.
"sockopt" -> socket options
"sslopt" -> ssl option
"subprotocols" - array of available sub protocols.
default is None.
"skip_utf8_validation" - skip utf8 validation.
"socket" - pre-initialized stream socket.
"""
sockopt = options.pop("sockopt", [])
sslopt = options.pop("sslopt", {})
fire_cont_frame = options.pop("fire_cont_frame", False)
enable_multithread = options.pop("enable_multithread", False)
skip_utf8_validation = options.pop("skip_utf8_validation", False)
websock = class_(sockopt=sockopt, sslopt=sslopt,
fire_cont_frame=fire_cont_frame,
enable_multithread=enable_multithread,
skip_utf8_validation=skip_utf8_validation, **options)
websock.settimeout(timeout if timeout is not None else getdefaulttimeout())
websock.connect(url, **options)
return websock
| 33.490835 | 90 | 0.589698 |
13f7ddb9a41846fb5799958db3fabf2ed4eeb64a
| 3,105 |
py
|
Python
|
vaccine_card/logistic/models.py
|
Unanimad/lais_046_2020_etapa_2
|
630efc6b25a580be44b6cd50be6744a01221a2c4
|
[
"Apache-2.0"
] | null | null | null |
vaccine_card/logistic/models.py
|
Unanimad/lais_046_2020_etapa_2
|
630efc6b25a580be44b6cd50be6744a01221a2c4
|
[
"Apache-2.0"
] | null | null | null |
vaccine_card/logistic/models.py
|
Unanimad/lais_046_2020_etapa_2
|
630efc6b25a580be44b6cd50be6744a01221a2c4
|
[
"Apache-2.0"
] | null | null | null |
from django.db import models
from vaccine_card.vaccination.models import Vaccine
| 36.104651 | 111 | 0.738808 |
13f954a55ebaa879400311cfe5c32a3993b29137
| 12,933 |
py
|
Python
|
test/test_rimuhosting.py
|
shenoyn/libcloud
|
bd902992a658b6a99193d69323e051ffa7388253
|
[
"Apache-2.0"
] | 1 |
2015-11-08T12:59:27.000Z
|
2015-11-08T12:59:27.000Z
|
test/test_rimuhosting.py
|
shenoyn/libcloud
|
bd902992a658b6a99193d69323e051ffa7388253
|
[
"Apache-2.0"
] | null | null | null |
test/test_rimuhosting.py
|
shenoyn/libcloud
|
bd902992a658b6a99193d69323e051ffa7388253
|
[
"Apache-2.0"
] | null | null | null |
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# libcloud.org licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright 2009 RedRata Ltd
from libcloud.drivers.rimuhosting import RimuHostingNodeDriver
from test import MockHttp
from test import MockHttp, TestCaseMixin
import unittest
import httplib
| 45.22028 | 380 | 0.540478 |
13f9663c3671ee791e1374fc1c550b7438edff48
| 1,033 |
py
|
Python
|
tests/base_tests/polygon_tests/test_contains.py
|
lycantropos/gon
|
b3f811ece5989d1623b17d633a84071fbff6dd69
|
[
"MIT"
] | 10 |
2020-07-18T12:55:52.000Z
|
2022-03-20T07:09:10.000Z
|
tests/base_tests/polygon_tests/test_contains.py
|
lycantropos/gon
|
b3f811ece5989d1623b17d633a84071fbff6dd69
|
[
"MIT"
] | 52 |
2019-07-11T16:59:01.000Z
|
2022-03-29T19:41:59.000Z
|
tests/base_tests/polygon_tests/test_contains.py
|
lycantropos/gon
|
b3f811ece5989d1623b17d633a84071fbff6dd69
|
[
"MIT"
] | 1 |
2020-03-22T12:56:07.000Z
|
2020-03-22T12:56:07.000Z
|
from typing import Tuple
from hypothesis import given
from gon.base import (Point,
Polygon)
from tests.utils import (equivalence,
implication)
from . import strategies
| 26.487179 | 72 | 0.708616 |
b913259774170b0ae117752589cf379fac40286c
| 4,139 |
py
|
Python
|
easyidp/core/tests/test_class_reconsproject.py
|
HowcanoeWang/EasyIDP
|
0d0a0df1287e3c15cda17e8e4cdcbe05f21f7272
|
[
"MIT"
] | null | null | null |
easyidp/core/tests/test_class_reconsproject.py
|
HowcanoeWang/EasyIDP
|
0d0a0df1287e3c15cda17e8e4cdcbe05f21f7272
|
[
"MIT"
] | null | null | null |
easyidp/core/tests/test_class_reconsproject.py
|
HowcanoeWang/EasyIDP
|
0d0a0df1287e3c15cda17e8e4cdcbe05f21f7272
|
[
"MIT"
] | null | null | null |
import os
import numpy as np
import pytest
import easyidp
from easyidp.core.objects import ReconsProject, Points
from easyidp.io import metashape
module_path = os.path.join(easyidp.__path__[0], "io/tests")
| 42.234694 | 112 | 0.723846 |
b9139bb412d2bf193e04d2282744a0d621a61a94
| 2,413 |
py
|
Python
|
withings_api/const.py
|
tiloc/python_withings_api
|
64c9706ab70c93e4c54cc843a778ecd3f9960980
|
[
"MIT"
] | null | null | null |
withings_api/const.py
|
tiloc/python_withings_api
|
64c9706ab70c93e4c54cc843a778ecd3f9960980
|
[
"MIT"
] | null | null | null |
withings_api/const.py
|
tiloc/python_withings_api
|
64c9706ab70c93e4c54cc843a778ecd3f9960980
|
[
"MIT"
] | null | null | null |
"""Constant values."""
STATUS_SUCCESS = (0,)
STATUS_AUTH_FAILED = (100, 101, 102, 200, 401)
STATUS_INVALID_PARAMS = (
201,
202,
203,
204,
205,
206,
207,
208,
209,
210,
211,
212,
213,
216,
217,
218,
220,
221,
223,
225,
227,
228,
229,
230,
234,
235,
236,
238,
240,
241,
242,
243,
244,
245,
246,
247,
248,
249,
250,
251,
252,
254,
260,
261,
262,
263,
264,
265,
266,
267,
271,
272,
275,
276,
283,
284,
285,
286,
287,
288,
290,
293,
294,
295,
297,
300,
301,
302,
303,
304,
321,
323,
324,
325,
326,
327,
328,
329,
330,
331,
332,
333,
334,
335,
336,
337,
338,
339,
340,
341,
342,
343,
344,
345,
346,
347,
348,
349,
350,
351,
352,
353,
380,
381,
382,
400,
501,
502,
503,
504,
505,
506,
509,
510,
511,
523,
532,
3017,
3018,
3019,
)
STATUS_UNAUTHORIZED = (214, 277, 2553, 2554, 2555)
STATUS_ERROR_OCCURRED = (
215,
219,
222,
224,
226,
231,
233,
237,
253,
255,
256,
257,
258,
259,
268,
269,
270,
273,
274,
278,
279,
280,
281,
282,
289,
291,
292,
296,
298,
305,
306,
308,
309,
310,
311,
312,
313,
314,
315,
316,
317,
318,
319,
320,
322,
370,
371,
372,
373,
374,
375,
383,
391,
402,
516,
517,
518,
519,
520,
521,
525,
526,
527,
528,
529,
530,
531,
533,
602,
700,
1051,
1052,
1053,
1054,
2551,
2552,
2556,
2557,
2558,
2559,
3000,
3001,
3002,
3003,
3004,
3005,
3006,
3007,
3008,
3009,
3010,
3011,
3012,
3013,
3014,
3015,
3016,
3020,
3021,
3022,
3023,
3024,
5000,
5001,
5005,
5006,
6000,
6010,
6011,
9000,
10000,
)
STATUS_TIMEOUT = (522,)
STATUS_BAD_STATE = (524,)
STATUS_TOO_MANY_REQUESTS = (601,)
| 9.690763 | 50 | 0.390385 |
b9141fcf42d65abf107a484255f641db4d6e639b
| 3,249 |
py
|
Python
|
examples/canvas/bezier.py
|
sirpercival/kivy
|
29ef854a200e6764aae60ea29324379c69d271a3
|
[
"MIT"
] | 2 |
2015-10-26T12:35:37.000Z
|
2020-11-26T12:06:09.000Z
|
examples/canvas/bezier.py
|
sirpercival/kivy
|
29ef854a200e6764aae60ea29324379c69d271a3
|
[
"MIT"
] | null | null | null |
examples/canvas/bezier.py
|
sirpercival/kivy
|
29ef854a200e6764aae60ea29324379c69d271a3
|
[
"MIT"
] | 3 |
2015-07-18T11:03:59.000Z
|
2018-03-17T01:32:42.000Z
|
#!/usr/bin/env python
from kivy.app import App
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.slider import Slider
from kivy.graphics import Color, Bezier, Line
if __name__ == '__main__':
Main().run()
| 33.84375 | 82 | 0.544783 |
b915c0a9f384ec67869ab91a6425fc1e66fbe2a2
| 2,698 |
py
|
Python
|
tests/bugs/core_6489_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
tests/bugs/core_6489_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
tests/bugs/core_6489_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
#coding:utf-8
#
# id: bugs.core_6489
# title: User without ALTER ANY ROLE privilege can use COMMENT ON ROLE
# decription:
# Test creates two users: one of them has no any rights, second is granted with 'alter any role' privilege.
# First user ('junior') must not have ability to add comment to rdb$admin role, but second ('senior') must
# be able to set comment to any string and make it null.
#
# Confirmed bug on 4.0.0.2384, 3.0.8.33425
# Checked on: 4.0.0.2387, 3.0.8.33426 -- all OK.
#
# NOTE:
# phrase '-Effective user is ...' presents only in FB 4.x and is suppressed here.
#
# tracker_id: CORE-6489
# min_versions: ['3.0.8']
# versions: 3.0.8
# qmid: None
import pytest
from firebird.qa import db_factory, isql_act, Action
# version: 3.0.8
# resources: None
substitutions_1 = [('ROLE_DESCR_BLOB_ID .*', ''), ('[\t ]+', ' '), ('(-)?Effective user is.*', '')]
init_script_1 = """"""
db_1 = db_factory(sql_dialect=3, init=init_script_1)
test_script_1 = """
create or alter user tmp$c6489_junior password '123' using plugin Srp;
create or alter user tmp$c6489_senior password '456' using plugin Srp;
commit;
grant alter any role to user tmp$c6489_senior;
commit;
connect '$(DSN)' user tmp$c6489_junior password '123';
comment on role rdb$admin is 'Comment by tmp$c6489_junior';
commit;
connect '$(DSN)' user tmp$c6489_senior password '456';
comment on role rdb$admin is 'Comment by tmp$c6489_senior';
commit;
set list on;
select r.rdb$description as role_descr_blob_id from rdb$roles r where r.rdb$role_name = upper('rdb$admin');
commit;
comment on role rdb$admin is null;
commit;
connect '$(DSN)' user 'SYSDBA' password 'masterkey';
drop user tmp$c6489_junior using plugin Srp;
drop user tmp$c6489_senior using plugin Srp;
commit;
"""
act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1)
expected_stdout_1 = """
Comment by tmp$c6489_senior
"""
expected_stderr_1 = """
Statement failed, SQLSTATE = 28000
unsuccessful metadata update
-COMMENT ON RDB$ADMIN failed
-no permission for ALTER access to ROLE RDB$ADMIN
-Effective user is TMP$C6489_JUNIOR
"""
| 32.902439 | 124 | 0.648258 |
b915eeed88fbfbe46318454fd21bc9db43d6d639
| 6,023 |
py
|
Python
|
utils/utils_bbox.py
|
MasoonZhang/FasterRConvMixer
|
a7a17d00f716a28a5b301088053e00840c222524
|
[
"MIT"
] | null | null | null |
utils/utils_bbox.py
|
MasoonZhang/FasterRConvMixer
|
a7a17d00f716a28a5b301088053e00840c222524
|
[
"MIT"
] | null | null | null |
utils/utils_bbox.py
|
MasoonZhang/FasterRConvMixer
|
a7a17d00f716a28a5b301088053e00840c222524
|
[
"MIT"
] | 1 |
2022-03-14T05:29:42.000Z
|
2022-03-14T05:29:42.000Z
|
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.ops import nms
| 45.628788 | 136 | 0.381205 |
b9160a13d47cfdacbbfdb45a0590f6674809ddbe
| 96 |
py
|
Python
|
lib/python/test/__init__.py
|
woozhijun/cat
|
3d523202c38e37b1a2244b26d4336ebbea5db001
|
[
"Apache-2.0"
] | 17,318 |
2015-01-03T03:02:07.000Z
|
2022-03-31T02:43:28.000Z
|
lib/python/test/__init__.py
|
MrCoderYu/cat
|
674bd9ab70267dd6fc74879e4344af77397f4acd
|
[
"Apache-2.0"
] | 1,162 |
2015-01-04T08:23:49.000Z
|
2022-03-31T15:38:04.000Z
|
lib/python/test/__init__.py
|
MrCoderYu/cat
|
674bd9ab70267dd6fc74879e4344af77397f4acd
|
[
"Apache-2.0"
] | 5,520 |
2015-01-03T03:02:07.000Z
|
2022-03-31T16:16:56.000Z
|
#!/usr/bin/env python
# encoding: utf-8
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
| 13.714286 | 31 | 0.729167 |
b918984647c67e09bce945847905654d35530277
| 15,886 |
py
|
Python
|
tests/test_pyclipper.py
|
odidev/pyclipper
|
3de54fa4c4d5b8efeede364fbe69336f935f88f2
|
[
"MIT"
] | null | null | null |
tests/test_pyclipper.py
|
odidev/pyclipper
|
3de54fa4c4d5b8efeede364fbe69336f935f88f2
|
[
"MIT"
] | null | null | null |
tests/test_pyclipper.py
|
odidev/pyclipper
|
3de54fa4c4d5b8efeede364fbe69336f935f88f2
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
"""
Tests for Pyclipper wrapper library.
"""
from __future__ import print_function
from unittest2 import TestCase, main
import sys
if sys.version_info < (3,):
integer_types = (int, long)
else:
integer_types = (int,)
import pyclipper
# Example polygons from http://www.angusj.com/delphi/clipper.php
PATH_SUBJ_1 = [[180, 200], [260, 200], [260, 150], [180, 150]] # square, orientation is False
PATH_SUBJ_2 = [[215, 160], [230, 190], [200, 190]] # triangle
PATH_CLIP_1 = [[190, 210], [240, 210], [240, 130], [190, 130]] # square
PATH_SIGMA = [[300, 400], [100, 400], [200, 300], [100, 200], [300, 200]] # greek letter sigma
PATTERN = [[4, -6], [6, -6], [-4, 6], [-6, 6]]
INVALID_PATH = [[1, 1], ] # less than 2 vertices
def test_execute(self):
solution = self.pc.Execute(*self.default_args)
self.assertEqual(len(solution), 2)
def test_execute2(self):
solution = self.pc.Execute2(*self.default_args)
self.assertIsInstance(solution, pyclipper.PyPolyNode)
self.check_pypolynode(solution)
def test_execute_empty(self):
pc = pyclipper.Pyclipper()
with self.assertRaises(pyclipper.ClipperException):
pc.Execute(pyclipper.CT_UNION,
pyclipper.PFT_NONZERO,
pyclipper.PFT_NONZERO)
def test_clear(self):
self.pc.Clear()
with self.assertRaises(pyclipper.ClipperException):
self.pc.Execute(*self.default_args)
def test_exact_results(self):
"""
Test whether coordinates passed into the library are returned exactly, if they are not affected by the
operation.
"""
pc = pyclipper.Pyclipper()
# Some large triangle.
path = [[[0, 1], [0, 0], [15 ** 15, 0]]]
pc.AddPaths(path, pyclipper.PT_SUBJECT, True)
result = pc.Execute(pyclipper.PT_CLIP, pyclipper.PFT_EVENODD, pyclipper.PFT_EVENODD)
assert result == path
def check_pypolynode(self, node):
self.assertTrue(len(node.Contour) == 0 or len(node.Contour) > 2)
# check vertex coordinate, should not be an iterable (in that case
# that means that node.Contour is a list of paths, should be path
if node.Contour:
self.assertFalse(hasattr(node.Contour[0][0], '__iter__'))
for child in node.Childs:
self.check_pypolynode(child)
class TestPyclipperOffset(TestCase):
class TestScalingFactorWarning(TestCase):
class TestScalingFunctions(TestCase):
scale = 2 ** 31
path = [(0, 0), (1, 1)]
paths = [path] * 3
class TestNonStandardNumbers(TestCase):
def _do_solutions_match(paths_1, paths_2, factor=None):
if len(paths_1) != len(paths_2):
return False
paths_1 = [_modify_vertices(p, multiplier=factor, converter=round if factor else None) for p in paths_1]
paths_2 = [_modify_vertices(p, multiplier=factor, converter=round if factor else None) for p in paths_2]
return all(((p_1 in paths_2) for p_1 in paths_1))
def _modify_vertices(path, addend=0.0, multiplier=1.0, converter=None):
path = path[:]
return [[convert_coordinate(c) for c in v] for v in path]
def run_tests():
main()
if __name__ == '__main__':
run_tests()
| 34.914286 | 111 | 0.660078 |
b918bb151415e9d667f5286334be578684cceb18
| 10,754 |
py
|
Python
|
espoem_facts/facts.py
|
emre/espoem_facts
|
0d7164dcfe8a82e1f142929b1e00c3a85f29f101
|
[
"MIT"
] | null | null | null |
espoem_facts/facts.py
|
emre/espoem_facts
|
0d7164dcfe8a82e1f142929b1e00c3a85f29f101
|
[
"MIT"
] | null | null | null |
espoem_facts/facts.py
|
emre/espoem_facts
|
0d7164dcfe8a82e1f142929b1e00c3a85f29f101
|
[
"MIT"
] | 1 |
2018-07-19T18:44:09.000Z
|
2018-07-19T18:44:09.000Z
|
FACTS = ['espoem multiplied by zero still equals espoem.',
'There is no theory of evolution. Just a list of creatures espoem has allowed to live.',
'espoem does not sleep. He waits.',
'Alexander Graham Bell had three missed calls from espoem when he invented the telephone.',
'espoem is the reason why Waldo is hiding.',
'espoem can slam a revolving door.',
"espoem isn't lifting himself up when doing a pushup; he's pushing the earth down.",
"espoem' hand is the only hand that can beat a Royal Flush.",
'espoem made a Happy Meal cry.',
"espoem doesn't need Twitter; he's already following you.",
'espoem once won an underwater breathing contest with a fish.While urinating, espoem is easily capable of welding titanium.',
'In an act of great philanthropy, espoem made a generous donation to the American Cancer Society. He donated 6,000 dead bodies for scientific research.',
'espoem once one a game of connect four in 3 moves.',
"Google won't search for espoem because it knows you don't find espoem, he finds you.",
'espoem? favourite cut of meat is the roundhouse.',
'It is scientifically impossible for espoem to have had a mortal father. The most popular theory is that he went back in time and fathered himself.',
'espoem had to stop washing his clothes in the ocean. The tsunamis were killing people.',
'Pluto is actually an orbiting group of British soldiers from the American Revolution who entered space after the espoem gave them a roundhouse kick to the face.',
'In the Words of Julius Caesar, Veni, Vidi, Vici, espoem. Translation: I came, I saw, and I was roundhouse-kicked inthe face by espoem.',
"espoem doesn't look both ways before he crosses the street... he just roundhouses any cars that get too close.",
'Human cloning is outlawed because of espoem, because then it would be possible for a espoem roundhouse kick to meet another espoem roundhouse kick. Physicists theorize that this contact would end the universe.',
'Using his trademark roundhouse kick, espoem once made a fieldgoal in RJ Stadium in Tampa Bay from the 50 yard line of Qualcomm stadium in San Diego.',
'espoem played Russian Roulette with a fully loaded gun and won.',
"espoem roundhouse kicks don't really kill people. They wipe out their entire existence from the space-time continuum.",
"espoem' testicles do not produce sperm. They produce tiny white ninjas that recognize only one mission: seek and destroy.",
'MacGyver immediately tried to make a bomb out of some Q-Tips and Gatorade, but espoem roundhouse-kicked him in the solar plexus. MacGyver promptly threw up his own heart.',
'Not everyone that espoem is mad at gets killed. Some get away. They are called astronauts.',
'espoem can drink an entire gallon of milk in thirty-seven seconds.',
'If you spell espoem in Scrabble, you win. Forever.',
"When you say no one's perfect, espoem takes this as a personal insult.",
"espoem invented Kentucky Fried Chicken's famous secret recipe with eleven herbs and spices. Nobody ever mentions the twelfth ingredient: Fear.",
'espoem can skeletize a cow in two minutes.',
'espoem eats lightning and shits out thunder.',
'In a fight between Batman and Darth Vader, the winner would be espoem.',
"The phrase 'dead ringer' refers to someone who sits behind espoem in a movie theater and forgets to turn their cell phone off.",
"It is said that looking into espoem' eyes will reveal your future. Unfortunately, everybody's future is always the same: death by a roundhouse-kick to the face.",
"espoem's log statements are always at the FATAL level.",
'espoem can win in a game of Russian roulette with a fully loaded gun.',
'Nothing can escape the gravity of a black hole, except for espoem. espoem eats black holes. They taste like chicken.',
'There is no theory of evolution, just a list of creatures espoem allows to live.',
'A study showed the leading causes of death in the United States are: 1. Heart disease, 2. espoem, 3. Cancer',
'Everybody loves Raymond. Except espoem.',
'Noah was the only man notified before espoem relieved himself in the Atlantic Ocean.',
'In a tagteam match, espoem was teamed with Hulk Hogan against King Kong Bundy and Andre The Giant. He pinned all 3 at the same time.',
"Nobody doesn't like Sara Lee. Except espoem.",
"espoem never has to wax his skis because they're always slick with blood.",
'espoem ordered a Big Mac at Burger King, and got one.',
'espoem owns a chain of fast-food restaurants throughout the southwest. They serve nothing but barbecue-flavored ice cream and Hot Pockets.',
"espoem's database has only one table, 'Kick', which he DROPs frequently.",
"espoem built a time machine and went back in time to stop the JFK assassination. As Oswald shot, espoem met all three bullets with his beard, deflecting them. JFK's head exploded out of sheer amazement.",
'espoem can write infinite recursion functions, and have them return.',
'When espoem does division, there are no remainders.',
'We live in an expanding universe. All of it is trying to get away from espoem.',
'espoem cannot love, he can only not kill.',
'espoem knows the value of NULL, and he can sort by it too.',
'There is no such thing as global warming. espoem was cold, so he turned the sun up.',
'The best-laid plans of mice and men often go awry. Even the worst-laid plans of espoem come off without a hitch.',
'When espoem goes to donate blood, he declines the syringe, and instead requests a hand gun and a bucket.',
'espoem can solve the Towers of Hanoi in one move.',
'All roads lead to espoem. And by the transitive property, a roundhouse kick to the face.',
'If you were somehow able to land a punch on espoem your entire arm would shatter upon impact. This is only in theory, since, come on, who in their right mind would try this?',
'One time, at band camp, espoem ate a percussionist.',
'Product Owners never argue with espoem after he demonstrates the DropKick feature.',
'espoem can read from an input stream.',
'The original draft of The Lord of the Rings featured espoem instead of Frodo Baggins. It was only 5 pages long, as espoem roundhouse-kicked Sauron?s ass halfway through the first chapter.',
"If, by some incredible space-time paradox, espoem would ever fight himself, he'd win. Period.",
'When taking the SAT, write espoem for every answer. You will score over 8000.',
'When in a bar, you can order a drink called a espoem. It is also known as a Bloody Mary, if your name happens to be Mary.',
'espoem causes the Windows Blue Screen of Death.',
'espoem went out of an infinite loop.',
'When Bruce Banner gets mad, he turns into the Hulk. When the Hulk gets mad, he turns into espoem.',
'espoem insists on strongly-typed programming languages.',
'espoem can blow bubbles with beef jerky.',
"espoem is widely predicted to be first black president. If you're thinking to yourself, But espoem isn't black, then you are dead wrong. And stop being a racist.",
'espoem once went skydiving, but promised never to do it again. One Grand Canyon is enough.',
"Godzilla is a Japanese rendition of espoem's first visit to Tokyo.",
'espoem has the greatest Poker-Face of all time. He won the 1983 World Series of Poker, despite holding only a Joker, a Get out of Jail Free Monopoloy card, a 2 of clubs, 7 of spades and a green #4 card from the game UNO.',
'Teenage Mutant Ninja Turtles is based on a true story: espoem once swallowed a turtle whole, and when he crapped it out, the turtle was six feet tall and had learned karate.',
"If you try to kill -9 espoem's programs, it backfires.",
"espoem' Penis is a third degree blackbelt, and an honorable 32nd-degree mason.",
'In ancient China there is a legend that one day a child will be born from a dragon, grow to be a man, and vanquish evil from the land. That man is not espoem, because espoem killed that man.',
'espoem can dereference NULL.',
'All arrays espoem declares are of infinite size, because espoem knows no bounds.',
'The pen is mighter than the sword, but only if the pen is held by espoem.',
"espoem doesn't step on toes. espoem steps on necks.",
'The truth will set you free. Unless espoem has you, in which case, forget it buddy!',
'Simply by pulling on both ends, espoem can stretch diamonds back into coal.',
'espoem does not style his hair. It lays perfectly in place out of sheer terror.',
'espoem once participated in the running of the bulls. He walked.',
'Never look a gift espoem in the mouth, because he will bite your damn eyes off.',
"If you Google search espoem getting his ass kicked you will generate zero results. It just doesn't happen.",
'espoem can unit test entire applications with a single assert.',
'On his birthday, espoem randomly selects one lucky child to be thrown into the sun.',
"Little known medical fact: espoem invented the Caesarean section when he roundhouse-kicked his way out of his monther's womb.",
"No one has ever spoken during review of espoem' code and lived to tell about it.",
'The First rule of espoem is: you do not talk about espoem.',
'Fool me once, shame on you. Fool espoem once and he will roundhouse kick you in the face.',
"espoem doesn't read books. He stares them down until he gets the information he wants.",
"The phrase 'balls to the wall' was originally conceived to describe espoem entering any building smaller than an aircraft hangar.",
"Someone once tried to tell espoem that roundhouse kicks aren't the best way to kick someone. This has been recorded by historians as the worst mistake anyone has ever made.",
'Along with his black belt, espoem often chooses to wear brown shoes. No one has DARED call him on it. Ever.',
'Whiteboards are white because espoem scared them that way.',
'espoem drives an ice cream truck covered in human skulls.',
"Every time espoem smiles, someone dies. Unless he smiles while he's roundhouse kicking someone in the face. Then two people die."]
| 102.419048 | 232 | 0.702343 |
b918c27f2b168efd69908773e44475244b686dd0
| 3,379 |
py
|
Python
|
imageproc_OE_IF_quant/2_annotate_extracted_cells.py
|
hshayya/2022_Shayya_UPR_Guidance
|
b9a305a147a105c3ac9c0173e06b94f66e4a6102
|
[
"MIT"
] | null | null | null |
imageproc_OE_IF_quant/2_annotate_extracted_cells.py
|
hshayya/2022_Shayya_UPR_Guidance
|
b9a305a147a105c3ac9c0173e06b94f66e4a6102
|
[
"MIT"
] | null | null | null |
imageproc_OE_IF_quant/2_annotate_extracted_cells.py
|
hshayya/2022_Shayya_UPR_Guidance
|
b9a305a147a105c3ac9c0173e06b94f66e4a6102
|
[
"MIT"
] | null | null | null |
import xml.etree.ElementTree as ET
import csv
import os
import re
from ij import IJ
from loci.plugins.in import ImporterOptions
from loci.plugins import BF
from ij.plugin import ImagesToStack
from ij import io
#Records metadata (x,y location) for cells that were extracted with 1_find_extract_cells.py
#metadata will be used in subsequent analysis to cluster cells from similar locations on the section -> semi-quantiative, local, analysis
def parse_cellcounter_to_dict(fpath):
'''Parse Cell-Counter Xml file to Dictionary
Inputs:
fpath (str) path to xml file on disk
Values:
(dict). Keys 'x_cal', 'y_cal' = (float) calibrations in each axis.
Keys '1'-'8' = (lists) of tuples containing cell positions in the form (x,y)
'''
tree = ET.parse(fpath)
cells_dict = {}
cells_dict['x_cal'] = float(tree.find('./Image_Properties/X_Calibration').text)
cells_dict['y_cal'] = float(tree.find('./Image_Properties/Y_Calibration').text)
rt = tree.find('Marker_Data') #re-root the tree
for type_ in rt.iter('Marker_Type'):
cells = []
for marker_ in type_.iter('Marker'):
cells.append((int(marker_[0].text), int(marker_[1].text)))
#
cells_dict[type_.find('Type').text] = cells
return cells_dict
#Load Xml Files
xml_locs = ['/path/to/xml/files'] #same as used in find_extract_cells
xml_files = [os.path.join(base_, f) for base_ in xml_locs for f in os.listdir(base_) if f[-3:] == 'xml' and f[0] != '.']
#Work through each xml file
f_out_path = '/path/to/annotation/out.tsv'
with open(f_out_path,'w') as fout:
fout.write('\t'.join(['cell','x_um','y_um']))
for e,xml_ in enumerate(xml_files):
print 'Working on file: ' + os.path.split(xml_)[1] + '...' + str(e+1) + '/' + str(len(xml_files))
#Find the orig .nd2 file, copied from find_extract_cells.py, see that code for more details.
orig_f_name = re.search('(?<=CellCounter_).*(?=\\-Downsampled)', os.path.split(xml_)[1]).group() + '.nd2'
search_dir = '/'.join(os.path.split(xml_)[0].split('/')[:-1])
files_found = [os.path.join(root, f) for (root, dirs, files) in os.walk(search_dir) for f in files if f == orig_f_name]
if len(files_found) == 1:
fullres_image = files_found[0]
else:
print "Could not find fullres image."
raise ValueError('Found 0 or >1 matching file')
#Generate the original inputs that were passed to extract_cells
input_item = (re.search('(?<=_).*',orig_f_name[:-4]).group(), {'fullres':fullres_image, 'counter':parse_cellcounter_to_dict(xml_)})
input_dict = input_item
types_of_interest={'7':'tdtom','8':'gfp'}
#Copied from the "Extract Cells", recovering positional info and writing to disk instead of extracting cell -> small image.
anim, vals = input_dict
#Loop through Cells and Annotate.
for cell_type, cell_label in types_of_interest.iteritems():
print 'Working on cell_type ' + cell_label
for i in range(len(vals['counter'][cell_type])):
print 'Iteration ' + str(i+1) + '/' + str(len(vals['counter'][cell_type]))
#Convert Px Downsampled -> Px Full Res
x_full_px = vals['counter'][cell_type][i][0] * vals['counter']['x_cal'] #in um
y_full_px = vals['counter'][cell_type][i][1] * vals['counter']['y_cal'] #in um
#Write Information
out_title = '_'.join([anim, cell_label, str(i)])
fout.write('\n' + '\t'.join([out_title, str(x_full_px), str(y_full_px)]))
#Final tsv of form cell_label,x,y.
| 40.710843 | 137 | 0.693992 |
b919ab13ac46e733a617fc950c062280033c20b8
| 439 |
py
|
Python
|
MAEnv/env_SingleCatchPigs/test_SingleCatchPigs.py
|
Abluceli/Multi-agent-Reinforcement-Learning-Algorithms
|
15810a559e2f2cf9e5fcb158c083f9e9dd6012fc
|
[
"MIT"
] | 5 |
2020-05-25T03:08:09.000Z
|
2022-02-27T05:57:28.000Z
|
MAEnv/env_SingleCatchPigs/test_SingleCatchPigs.py
|
Abluceli/Multi-agent-Reinforcement-Learning-Algorithms
|
15810a559e2f2cf9e5fcb158c083f9e9dd6012fc
|
[
"MIT"
] | 1 |
2020-12-22T01:35:36.000Z
|
2022-01-28T01:51:06.000Z
|
MAEnv/env_SingleCatchPigs/test_SingleCatchPigs.py
|
Abluceli/Multi-agent-Reinforcement-Learning-Algorithms
|
15810a559e2f2cf9e5fcb158c083f9e9dd6012fc
|
[
"MIT"
] | 1 |
2020-05-06T01:56:55.000Z
|
2020-05-06T01:56:55.000Z
|
from env_SingleCatchPigs import EnvSingleCatchPigs
import random
env = EnvSingleCatchPigs(7)
max_iter = 10000
env.set_agent_at([2, 2], 0)
env.set_pig_at([4, 4], 0)
for i in range(max_iter):
print("iter= ", i)
env.render()
action = random.randint(0, 4)
print('action is', action)
reward, done = env.step(action)
print('reward', reward, 'done', done)
if reward > 0:
print('catch the pig', reward, done)
| 24.388889 | 50 | 0.658314 |
b91c1523d70c0416c1afa5a4c6a25a3d2f1e426b
| 3,417 |
py
|
Python
|
eust/tables/data.py
|
rasmuse/eust
|
2138076d52c0ffa20fba10e4e0319dd50c4e8a91
|
[
"MIT"
] | 1 |
2021-03-14T04:06:02.000Z
|
2021-03-14T04:06:02.000Z
|
eust/tables/data.py
|
rasmuse/eust
|
2138076d52c0ffa20fba10e4e0319dd50c4e8a91
|
[
"MIT"
] | 9 |
2019-04-29T09:01:39.000Z
|
2021-11-15T17:48:36.000Z
|
eust/tables/data.py
|
rasmuse/eust
|
2138076d52c0ffa20fba10e4e0319dd50c4e8a91
|
[
"MIT"
] | 1 |
2019-10-23T08:56:33.000Z
|
2019-10-23T08:56:33.000Z
|
# -*- coding: utf-8 -*-
import re
import gzip
import pandas as pd
import numpy as np
from eust.core import _download_file, conf
_DIMENSION_NAME_RE = re.compile(r"^[a-z_0-9]+$")
_YEAR_RE = re.compile(r"^(1|2)[0-9]{3}$")
_TSV_GZ_FILENAME = "data.tsv.gz"
_HDF_FILENAME = "data.h5"
_HDF_TABLE_PATH = "eurostat_table"
| 26.695313 | 78 | 0.654375 |
b91ce0003a23729f5cf4b45b783933c9e0cd6696
| 22,196 |
py
|
Python
|
utils.py
|
fatemehtd/Echo-SyncNet
|
ebb280e83a67b31436c4cfa420f9c06a92ac8c12
|
[
"MIT"
] | 6 |
2021-03-19T16:55:30.000Z
|
2022-03-15T08:41:56.000Z
|
utils.py
|
matiasmolinas/Echo-SyncNet
|
f7f81ead7a24d7574c0668df3765ef58fd71d54d
|
[
"MIT"
] | 3 |
2021-10-01T22:15:44.000Z
|
2022-03-25T03:12:47.000Z
|
utils.py
|
matiasmolinas/Echo-SyncNet
|
f7f81ead7a24d7574c0668df3765ef58fd71d54d
|
[
"MIT"
] | 3 |
2021-03-19T16:55:35.000Z
|
2022-02-03T10:40:48.000Z
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from config import CONFIG
import json
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
import io
import math
import os
import time
from absl import flags
from absl import logging
from easydict import EasyDict
import matplotlib
matplotlib.use('Agg')
FLAGS = flags.FLAGS
def visualize_batch(data, global_step, batch_size, num_steps):
"""Visualizes a batch."""
frames = data['frames']
frames_list = tf.unstack(frames, num=num_steps, axis=1)
frames_summaries = tf.concat(frames_list, axis=2)
batch_list = tf.split(frames_summaries, batch_size, axis=0)
batch_summaries = tf.concat(batch_list, axis=1)
tf.summary.image('train_batch', batch_summaries, step=global_step)
def visualize_nearest_neighbours(model, data, global_step, batch_size,
num_steps, num_frames_per_step, split):
"""Visualize nearest neighbours in embedding space."""
# Set learning_phase to False to use models in inference mode.
tf.keras.backend.set_learning_phase(0)
cnn = model['cnn']
emb = model['emb']
if 'tcn' in CONFIG.TRAINING_ALGO:
cnn_feats = get_cnn_feats(
cnn, data, training=False, num_steps=2 * num_steps)
emb_feats = emb(cnn_feats, 2 * num_steps)
emb_feats = tf.stack(
tf.split(emb_feats, 2 * num_steps, axis=0)[::2], axis=1)
else:
cnn_feats = get_cnn_feats(cnn, data, training=False)
emb_feats = emb(cnn_feats, num_steps)
emb_feats = tf.stack(tf.split(emb_feats, num_steps, axis=0), axis=1)
query_feats = emb_feats[0]
if CONFIG.OPTICALFLOW:
frames = data['video_frames']
else:
frames = data['frames']
image_list = tf.unstack(frames, num=batch_size, axis=0)
if 'tcn' in CONFIG.TRAINING_ALGO:
im_list = [image_list[0]
[num_frames_per_step - 1::num_frames_per_step][::2]]
else:
im_list = [image_list[0][num_frames_per_step - 1::num_frames_per_step]]
sim_matrix = np.zeros(
(batch_size-1, num_steps, num_steps), dtype=np.float32)
for i in range(1, batch_size):
candidate_feats = emb_feats[i]
if 'tcn' in CONFIG.TRAINING_ALGO:
img_list = tf.unstack(image_list[i], num=2 * num_steps * num_frames_per_step,
axis=0)[num_frames_per_step - 1::num_frames_per_step][::2]
else:
img_list = tf.unstack(image_list[i], num=num_steps * num_frames_per_step,
axis=0)[num_frames_per_step - 1::num_frames_per_step]
nn_img_list = []
for j in range(num_steps):
curr_query_feats = tf.tile(query_feats[j:j+1], [num_steps, 1])
mean_squared_distance = tf.reduce_mean(
tf.math.squared_difference(curr_query_feats, candidate_feats), axis=1)
sim_matrix[i-1, j] = softmax(-1.0 * mean_squared_distance)
nn_img_list.append(img_list[tf.argmin(mean_squared_distance)])
nn_img = tf.stack(nn_img_list, axis=0)
im_list.append(nn_img)
summary_im = tf.expand_dims(tf.concat([vstack(im) for im in im_list],
axis=0), axis=0)
tf.summary.image('%s/nn' % split, summary_im, step=global_step)
# Convert sim_matrix to float32 as summary_image doesn't take float64
sim_matrix = sim_matrix.astype(np.float32)
tf.summary.image('%s/similarity_matrix' % split,
np.expand_dims(sim_matrix, axis=3), step=global_step)
def gen_cycles(num_cycles, batch_size, cycle_len):
"""Generate cycles for alignment."""
random_cycles = random_choice_noreplace(
num_cycles, batch_size)[:, :cycle_len]
return random_cycles
def get_warmup_lr(lr, global_step, lr_params):
"""Returns learning rate during warm up phase."""
if lr_params.NUM_WARMUP_STEPS > 0:
global_steps_int = tf.cast(global_step, tf.int32)
warmup_steps_int = tf.constant(
lr_params.NUM_WARMUP_STEPS, dtype=tf.int32)
global_steps_float = tf.cast(global_steps_int, tf.float32)
warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
warmup_percent_done = global_steps_float / warmup_steps_float
warmup_lr = lr_params.INITIAL_LR * warmup_percent_done
is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
lr = (1.0 - is_warmup) * lr + is_warmup * warmup_lr
return lr
# Minimally adapted from Tensorflow object_detection code.
def get_lr_fn(optimizer_config):
"""Returns function that provides current learning rate based on config.
NOTE: This returns a function as in Eager we need to call assign to update
the learning rate.
Args:
optimizer_config: EasyDict, contains params required to initialize the
learning rate and the learning rate decay function.
Returns:
lr_fn: function, this can be called to return the current learning rate
based on the provided config.
Raises:
ValueError: in case invalid params have been passed in the config.
"""
lr_params = optimizer_config.LR
# pylint: disable=g-long-lambda
if lr_params.DECAY_TYPE == 'exp_decay':
elif lr_params.DECAY_TYPE == 'manual':
lr_step_boundaries = [int(x)
for x in lr_params.MANUAL_LR_STEP_BOUNDARIES]
f = lr_params.MANUAL_LR_DECAY_RATE
learning_rate_sequence = [(lr_params.INITIAL_LR) * f**p
for p in range(len(lr_step_boundaries) + 1)]
elif lr_params.DECAY_TYPE == 'fixed':
elif lr_params.DECAY_TYPE == 'poly':
else:
raise ValueError('Learning rate decay type %s not supported. Only support'
'the following decay types: fixed, exp_decay, manual,'
'and poly.')
return (lambda lr, global_step: get_warmup_lr(lr_fn(lr, global_step),
global_step, lr_params))
def get_optimizer(optimizer_config, learning_rate):
"""Returns optimizer based on config and learning rate."""
if optimizer_config.TYPE == 'AdamOptimizer':
opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)
elif optimizer_config.TYPE == 'MomentumOptimizer':
opt = tf.keras.optimizers.SGD(
learning_rate=learning_rate, momentum=0.9)
else:
raise ValueError('Optimizer %s not supported. Only support the following'
'optimizers: AdamOptimizer, MomentumOptimizer .')
return opt
def get_lr_opt_global_step():
"""Intializes learning rate, optimizer and global step."""
optimizer = get_optimizer(CONFIG.OPTIMIZER, CONFIG.OPTIMIZER.LR.INITIAL_LR)
global_step = optimizer.iterations
learning_rate = optimizer.learning_rate
return learning_rate, optimizer, global_step
def restore_ckpt(logdir, **ckpt_objects):
"""Create and restore checkpoint (if one exists on the path)."""
# Instantiate checkpoint and restore from any pre-existing checkpoint.
# Since model is a dict we can insert multiple modular networks in this dict.
checkpoint = tf.train.Checkpoint(**ckpt_objects)
ckpt_manager = tf.train.CheckpointManager(
checkpoint,
directory=logdir,
max_to_keep=10,
keep_checkpoint_every_n_hours=1)
status = checkpoint.restore(ckpt_manager.latest_checkpoint)
return ckpt_manager, status, checkpoint
def setup_train_dir(logdir, overwrite=False, force_train=True):
"""Setups directory for training."""
tf.io.gfile.makedirs(logdir)
config_path = os.path.join(logdir, 'config.json')
if not os.path.exists(config_path) or overwrite:
logging.info(
'Using the existing passed in config as no config.json file exists in '
'%s', logdir)
with tf.io.gfile.GFile(config_path, 'w') as config_file:
config = dict([(k, to_dict(v)) for k, v in CONFIG.items()])
json.dump(config, config_file, sort_keys=True, indent=4)
else:
logging.info(
'Using config from config.json that exists in %s.', logdir)
with tf.io.gfile.GFile(config_path, 'r') as config_file:
config_dict = json.load(config_file)
CONFIG.update(config_dict)
train_logs_dir = os.path.join(logdir, 'train.logs')
if os.path.exists(train_logs_dir) and not force_train:
raise ValueError('You might be overwriting a directory that already '
'has train_logs. Please provide a new logdir name in '
'config or pass --force_train while launching script.')
tf.io.gfile.makedirs(train_logs_dir)
def setup_eval_dir(logdir, config_timeout_seconds=1):
"""Setups directory for evaluation."""
tf.io.gfile.makedirs(logdir)
tf.io.gfile.makedirs(os.path.join(logdir, 'eval_logs'))
config_path = os.path.join(logdir, 'config.json')
while not tf.io.gfile.exists(config_path):
logging.info('Waiting for config to exist. Going to sleep '
' %s for secs.', config_timeout_seconds)
time.sleep(config_timeout_seconds)
while True:
with tf.io.gfile.GFile(config_path, 'r') as config_file:
config_dict = json.load(config_file)
if config_dict is None:
time.sleep(config_timeout_seconds)
else:
break
CONFIG.update(config_dict)
def get_data(iterator):
"""Return a data dict which contains all the requested sequences."""
data = iterator.get_next()
return data, data['chosen_steps'], data['seq_lens']
def get_embeddings_dataset(model, iterator, frames_per_batch,
keep_data=False, optical_flow=False, keep_labels=True,
max_embs=None, callbacks=[]):
"""Get embeddings from a one epoch iterator."""
keep_labels = keep_labels and CONFIG.DATA.FRAME_LABELS
num_frames_per_step = CONFIG.DATA.NUM_STEPS
cnn = model['cnn']
emb = model['emb']
embs_list = []
labels_list = []
steps_list = []
seq_lens_list = []
names_list = []
seq_labels_list = []
if keep_data:
frames_list = []
if optical_flow:
frame_original_list = []
n = 0
# Make Recurrent Layers stateful, set batch size.
# We do this as we are embedding the whole sequence and that can take
# more than one batch to be passed and we don't want to automatically
# reset hidden states after each batch.
if CONFIG.MODEL.EMBEDDER_TYPE == 'convgru':
for gru_layer in emb.gru_layers:
gru_layer.stateful = True
gru_layer.input_spec[0].shape = [1, ]
while cond(n):
try:
print(n)
embs = []
labels = []
steps = []
seq_lens = []
names = []
seq_labels = []
if keep_data:
frames = []
if optical_flow:
frame_original = []
# Reset GRU states for each video.
if CONFIG.MODEL.EMBEDDER_TYPE == 'convgru':
for gru_layer in emb.gru_layers:
gru_layer.reset_states()
data, chosen_steps, seq_len = get_data(iterator)
seq_len = seq_len.numpy()[0]
num_batches = int(math.ceil(float(seq_len)/frames_per_batch))
for i in range(num_batches):
if (i + 1) * frames_per_batch > seq_len:
num_steps = seq_len - i * frames_per_batch
else:
num_steps = frames_per_batch
curr_idx = i * frames_per_batch
curr_data = {}
for k, v in data.items():
# Need to do this as some modalities might not exist.
if len(v.shape) > 1 and v.shape[1] != 0:
idxes = get_indices(curr_idx, num_steps, seq_len)
curr_data[k] = tf.gather(v, idxes, axis=1)
else:
curr_data[k] = v
cnn_feats = get_cnn_feats(cnn, curr_data,
num_steps=num_frames_per_step * num_steps,
training=False)
emb_feats = emb(cnn_feats, num_steps)
logging.debug('On sequence number %d, frames embedded %d', n,
curr_idx + num_steps)
# np.save(tf.io.gfile.GFile('/air/team/saman/test_weights_old.npy', 'w'), cnn.weights[0].numpy())
# np.save(tf.io.gfile.GFile('/air/team/saman/test_batch_old.npy', 'w'), curr_data["frames"])
# np.save(tf.io.gfile.GFile('/air/team/saman/test_cnn_old.npy', 'w'), cnn_feats.numpy())
# np.save(tf.io.gfile.GFile('/air/team/saman/test_emb_old.npy', 'w'), emb_feats.numpy())
embs.append(emb_feats.numpy())
for f in callbacks:
f(np.concatenate(embs), data, chosen_steps, seq_len)
steps.append(chosen_steps.numpy()[0])
seq_lens.append(seq_len * [seq_len])
all_labels = data['frame_labels'].numpy()[0]
name = data['name'].numpy()[0]
names.append(seq_len * [name])
seq_label = data['seq_labels'].numpy()[0]
seq_labels.append(seq_len * [seq_label])
labels.append(all_labels)
embs = np.concatenate(embs, axis=0)
labels = np.concatenate(labels, axis=0)
steps = np.concatenate(steps, axis=0)
seq_lens = np.concatenate(seq_lens, axis=0)
names = np.concatenate(names, axis=0)
seq_labels = np.concatenate(seq_labels, axis=0)
if keep_data:
frames.append(data['frames'].numpy()[0])
frames = np.concatenate(frames, axis=0)
if optical_flow:
frame_original.append(data['video_frames'].numpy()[0])
frame_original = np.concatenate(frame_original, axis=0)
if keep_labels:
labels = labels[~np.isnan(embs).any(axis=1)]
assert len(embs) == len(labels)
seq_labels = seq_labels[~np.isnan(embs).any(axis=1)]
names = names[~np.isnan(embs).any(axis=1)]
seq_lens = seq_lens[~np.isnan(embs).any(axis=1)]
steps = steps[~np.isnan(embs).any(axis=1)]
if keep_data:
frames = frames[~np.isnan(embs).any(axis=1)]
if optical_flow:
frame_original = frame_original[~np.isnan(embs).any(axis=1)]
embs = embs[~np.isnan(embs).any(axis=1)]
assert len(embs) == len(seq_lens)
assert len(embs) == len(steps)
assert len(names) == len(steps)
embs_list.append(embs)
if keep_labels:
labels_list.append(labels)
seq_labels_list.append(seq_labels)
steps_list.append(steps)
seq_lens_list.append(seq_lens)
names_list.append(names)
if keep_data:
frames_list.append(frames)
if optical_flow:
frame_original_list.append(frame_original)
n += 1
except tf.errors.OutOfRangeError:
logging.info('Finished embedding the dataset.')
break
dataset = {'embs': embs_list,
'seq_lens': seq_lens_list,
'steps': steps_list,
'names': names_list,
'seq_labels': seq_labels_list}
if keep_data:
dataset['frames'] = frames_list
if optical_flow:
dataset['frames_original'] = frame_original_list
if keep_labels:
dataset['labels'] = labels_list
# Reset statefulness to recurrent layers for other evaluation tasks.
if CONFIG.MODEL.EMBEDDER_TYPE == 'convgru':
for gru_layer in emb.gru_layers:
gru_layer.stateful = False
return dataset
def gen_plot(x, y):
"""Create a pyplot, save to buffer and return TB compatible image."""
plt.figure()
plt.plot(x, y)
plt.title('Val Accuracy')
plt.ylim(0, 1)
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
# Convert PNG buffer to TF image
image = tf.image.decode_png(buf.getvalue(), channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
return image
def set_learning_phase(f):
"""Sets the correct learning phase before calling function f."""
def wrapper(*args, **kwargs):
"""Calls the function f after setting proper learning phase."""
if 'training' not in kwargs:
raise ValueError('Function called with set_learning_phase decorator which'
' does not have training argument.')
training = kwargs['training']
if training:
# Set learning_phase to True to use models in training mode.
tf.keras.backend.set_learning_phase(1)
else:
# Set learning_phase to False to use models in inference mode.
tf.keras.backend.set_learning_phase(0)
return f(*args, **kwargs)
return wrapper
| 36.748344 | 113 | 0.621193 |
b91d0a28a2d3c169f55ef3fbe14306db5438a499
| 8,468 |
py
|
Python
|
UnityPy/classes/Sprite.py
|
dblack2056/UnityPy
|
303291e46ddfbf266131237e59e6b1b5c46a9ca4
|
[
"MIT"
] | null | null | null |
UnityPy/classes/Sprite.py
|
dblack2056/UnityPy
|
303291e46ddfbf266131237e59e6b1b5c46a9ca4
|
[
"MIT"
] | null | null | null |
UnityPy/classes/Sprite.py
|
dblack2056/UnityPy
|
303291e46ddfbf266131237e59e6b1b5c46a9ca4
|
[
"MIT"
] | null | null | null |
from enum import IntEnum
from .Mesh import BoneWeights4, SubMesh, VertexData
from .NamedObject import NamedObject
from .PPtr import PPtr, save_ptr
from ..export import SpriteHelper
from ..enums import SpriteMeshType
from ..streams import EndianBinaryWriter
class SpriteVertex:
def __init__(self, reader):
version = reader.version
self.pos = reader.read_vector3()
if version[:2] <= (4, 3): # 4.3 and down
self.uv = reader.read_vector2()
| 34.563265 | 90 | 0.599906 |
b91e27e8ce2a32cb1f2fa0c55d35f35399d00f99
| 11,123 |
py
|
Python
|
eazy/filters.py
|
albertfxwang/eazy-py
|
bcfd8a1e49f077adc794202871345542ab29800b
|
[
"MIT"
] | null | null | null |
eazy/filters.py
|
albertfxwang/eazy-py
|
bcfd8a1e49f077adc794202871345542ab29800b
|
[
"MIT"
] | null | null | null |
eazy/filters.py
|
albertfxwang/eazy-py
|
bcfd8a1e49f077adc794202871345542ab29800b
|
[
"MIT"
] | null | null | null |
import numpy as np
import os
from astropy.table import Table
from . import utils
__all__ = ["FilterDefinition", "FilterFile", "ParamFilter"]
VEGA_FILE = os.path.join(utils.path_to_eazy_data(),
'alpha_lyr_stis_008.fits')
VEGA = Table.read(VEGA_FILE)
for c in VEGA.colnames:
VEGA[c] = VEGA[c].astype(float)
class FilterFile:
def __init__(self, file='FILTER.RES.latest', path='./'):
"""
Read a EAZY filter file.
.. plot::
:include-source:
import matplotlib.pyplot as plt
from eazy.filters import FilterFile
res = FilterFile(path=None)
print(len(res.filters))
bp = res[205]
print(bp)
fig, ax = plt.subplots(1,1,figsize=(6,4))
ax.plot(bp.wave, bp.throughput, label=bp.name.split()[0])
ax.set_xlabel('wavelength, Angstroms')
ax.set_ylabel('throughput')
ax.legend()
ax.grid()
fig.tight_layout(pad=0.5)
"""
if path is None:
file_path = os.path.join(os.getenv('EAZYCODE'), 'filters', file)
else:
file_path = os.path.join(path, file)
with open(file_path, 'r') as fp:
lines = fp.readlines()
self.filename = file_path
filters = []
wave = []
trans = []
header = ''
for line in lines:
if 'lambda_c' in line:
if len(wave) > 0:
# Make filter from lines already read in
new_filter = FilterDefinition(name=header,
wave=np.cast[float](wave),
throughput=np.cast[float](trans))
# new_filter.name = header
# new_filter.wave = np.cast[float](wave)
# new_filter.throughput = np.cast[float](trans)
filters.append(new_filter)
# Initialize filter
header = ' '.join(line.split()[1:])
wave = []
trans = []
else:
lspl = np.cast[float](line.split())
wave.append(lspl[1])
trans.append(lspl[2])
# last one
# new_filter = FilterDefinition()
# new_filter.name = header
# new_filter.wave = np.cast[float](wave)
# new_filter.throughput = np.cast[float](trans)
new_filter = FilterDefinition(name=header,
wave=np.cast[float](wave),
throughput=np.cast[float](trans))
filters.append(new_filter)
self.filters = filters
def __getitem__(self, i1):
"""
Return unit-indexed filter, e.g., 161 = 2mass-j
"""
return self.filters[i1-1]
def names(self, verbose=True):
"""
Print the filter names.
"""
if verbose:
for i in range(len(self.filters)):
print('{0:5d} {1}'.format(i+1, self.filters[i].name))
else:
string_list = ['{0:5d} {1}\n'.format(i+1, self.filters[i].name) for i in range(len(self.filters))]
return string_list
def write(self, file='xxx.res', verbose=True):
"""
Dump the filter information to a filter file.
"""
fp = open(file,'w')
for filter in self.filters:
fp.write('{0:6d} {1}\n'.format(len(filter.wave), filter.name))
for i in range(len(filter.wave)):
fp.write('{0:6d} {1:.5e} {2:.5e}\n'.format(i+1, filter.wave[i], filter.throughput[i]))
fp.close()
string_list = self.names(verbose=False)
fp = open(file+'.info', 'w')
fp.writelines(string_list)
fp.close()
if verbose:
print('Wrote <{0}[.info]>'.format(file))
def search(self, search_string, case=False, verbose=True):
"""
Search filter names for ``search_string``. If ``case`` is True, then
match case.
"""
import re
if not case:
search_string = search_string.upper()
matched = []
for i in range(len(self.filters)):
filt_name = self.filters[i].name
if not case:
filt_name = filt_name.upper()
if re.search(search_string, filt_name) is not None:
if verbose:
print('{0:5d} {1}'.format(i+1, self.filters[i].name))
matched.append(i)
return np.array(matched)
class ParamFilter(FilterDefinition):
| 30.225543 | 134 | 0.507687 |
b91e9c056c9dab4c7981c513788ac7b746223cf5
| 672 |
py
|
Python
|
LeetCode/106.py
|
KevinTMtz/CompetitiveProgramming
|
0bf8a297c404073df707b6d7b06965b055ccd872
|
[
"MIT"
] | 1 |
2020-12-08T02:01:18.000Z
|
2020-12-08T02:01:18.000Z
|
LeetCode/106.py
|
KevinTMtz/CompetitiveProgramming
|
0bf8a297c404073df707b6d7b06965b055ccd872
|
[
"MIT"
] | null | null | null |
LeetCode/106.py
|
KevinTMtz/CompetitiveProgramming
|
0bf8a297c404073df707b6d7b06965b055ccd872
|
[
"MIT"
] | null | null | null |
#
# LeetCode
#
# Problem - 106
# URL - https://leetcode.com/problems/construct-binary-tree-from-inorder-and-postorder-traversal/
#
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
| 24 | 97 | 0.651786 |
b91f064ec51160dd5a168a0ea9d44e81a3af31b7
| 44,880 |
py
|
Python
|
evalml/automl/automl_search.py
|
skvorekn/evalml
|
2cbfa344ec3fdc0fb0f4a0f1093811135b9b97d8
|
[
"BSD-3-Clause"
] | null | null | null |
evalml/automl/automl_search.py
|
skvorekn/evalml
|
2cbfa344ec3fdc0fb0f4a0f1093811135b9b97d8
|
[
"BSD-3-Clause"
] | null | null | null |
evalml/automl/automl_search.py
|
skvorekn/evalml
|
2cbfa344ec3fdc0fb0f4a0f1093811135b9b97d8
|
[
"BSD-3-Clause"
] | null | null | null |
import copy
import time
from collections import defaultdict
import cloudpickle
import numpy as np
import pandas as pd
import woodwork as ww
from sklearn.model_selection import BaseCrossValidator
from .pipeline_search_plots import PipelineSearchPlots
from evalml.automl.automl_algorithm import IterativeAlgorithm
from evalml.automl.callbacks import log_error_callback
from evalml.automl.engine import SequentialEngine
from evalml.automl.utils import (
check_all_pipeline_names_unique,
get_default_primary_search_objective,
make_data_splitter
)
from evalml.exceptions import AutoMLSearchException, PipelineNotFoundError
from evalml.model_family import ModelFamily
from evalml.objectives import (
get_core_objectives,
get_non_core_objectives,
get_objective
)
from evalml.pipelines import (
MeanBaselineRegressionPipeline,
ModeBaselineBinaryPipeline,
ModeBaselineMulticlassPipeline,
TimeSeriesBaselineBinaryPipeline,
TimeSeriesBaselineMulticlassPipeline,
TimeSeriesBaselineRegressionPipeline
)
from evalml.pipelines.components.utils import get_estimators
from evalml.pipelines.utils import make_pipeline
from evalml.preprocessing import split_data
from evalml.problem_types import ProblemTypes, handle_problem_types
from evalml.tuners import SKOptTuner
from evalml.utils import convert_to_seconds, infer_feature_types
from evalml.utils.logger import (
get_logger,
log_subtitle,
log_title,
time_elapsed,
update_pipeline
)
logger = get_logger(__file__)
def train_pipelines(self, pipelines):
"""Train a list of pipelines on the training data.
This can be helpful for training pipelines once the search is complete.
Arguments:
pipelines (list(PipelineBase)): List of pipelines to train.
Returns:
Dict[str, PipelineBase]: Dictionary keyed by pipeline name that maps to the fitted pipeline.
Note that the any pipelines that error out during training will not be included in the dictionary
but the exception and stacktrace will be displayed in the log.
"""
return self._engine.train_batch(pipelines)
def score_pipelines(self, pipelines, X_holdout, y_holdout, objectives):
"""Score a list of pipelines on the given holdout data.
Arguments:
pipelines (list(PipelineBase)): List of pipelines to train.
X_holdout (ww.DataTable, pd.DataFrame): Holdout features.
y_holdout (ww.DataTable, pd.DataFrame): Holdout targets for scoring.
objectives (list(str), list(ObjectiveBase)): Objectives used for scoring.
Returns:
Dict[str, Dict[str, float]]: Dictionary keyed by pipeline name that maps to a dictionary of scores.
Note that the any pipelines that error out during scoring will not be included in the dictionary
but the exception and stacktrace will be displayed in the log.
"""
return self._engine.score_batch(pipelines, X_holdout, y_holdout, objectives)
| 50.145251 | 234 | 0.660561 |
b9206e8febc3abecc98cfdec65d8f8f8f61e43fc
| 782 |
py
|
Python
|
graphql_social_auth/mutations.py
|
deepsourcelabs/django-graphql-social-auth
|
a0cc7715144dc289ccb4d2430e7c3b94fc1dffba
|
[
"MIT"
] | 1 |
2021-09-03T11:55:33.000Z
|
2021-09-03T11:55:33.000Z
|
graphql_social_auth/mutations.py
|
deepsourcelabs/django-graphql-social-auth
|
a0cc7715144dc289ccb4d2430e7c3b94fc1dffba
|
[
"MIT"
] | null | null | null |
graphql_social_auth/mutations.py
|
deepsourcelabs/django-graphql-social-auth
|
a0cc7715144dc289ccb4d2430e7c3b94fc1dffba
|
[
"MIT"
] | null | null | null |
import graphene
from graphql_jwt.decorators import setup_jwt_cookie
from . import mixins, types
from .decorators import social_auth
| 25.225806 | 68 | 0.726343 |
b92225fd1fc48f3b53478df0ef2d1501b1d04475
| 1,625 |
py
|
Python
|
yellowbrick/regressor/base.py
|
Juan0001/yellowbrick-docs-zh
|
36275d9704fc2a946c5bec5f802106bb5281efd1
|
[
"Apache-2.0"
] | 20 |
2018-03-24T02:29:20.000Z
|
2022-03-03T05:01:40.000Z
|
yellowbrick/regressor/base.py
|
Juan0001/yellowbrick-docs-zh
|
36275d9704fc2a946c5bec5f802106bb5281efd1
|
[
"Apache-2.0"
] | 4 |
2018-03-20T12:01:17.000Z
|
2019-04-07T16:02:19.000Z
|
yellowbrick/regressor/base.py
|
Juan0001/yellowbrick-docs-zh
|
36275d9704fc2a946c5bec5f802106bb5281efd1
|
[
"Apache-2.0"
] | 5 |
2018-03-17T08:18:57.000Z
|
2019-11-15T02:20:20.000Z
|
# yellowbrick.regressor.base
# Base classes for regressor Visualizers.
#
# Author: Rebecca Bilbro <[email protected]>
# Author: Benjamin Bengfort <[email protected]>
# Created: Fri Jun 03 10:30:36 2016 -0700
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: base.py [7d3f5e6] [email protected] $
"""
Base classes for regressor Visualizers.
"""
##########################################################################
## Imports
##########################################################################
from ..utils import isregressor
from ..base import ScoreVisualizer
from ..exceptions import YellowbrickTypeError
## Packages for export
__all__ = [
"RegressionScoreVisualizer",
]
##########################################################################
## Regression Visualization Base Object
##########################################################################
| 30.660377 | 79 | 0.582154 |
b92247a49fd2631992a5eddee925c5305320a529
| 2,941 |
py
|
Python
|
contrib/stack/stripmapStack/crossmul.py
|
falkamelung/isce2
|
edea69d4b6216f4ac729eba78f12547807a2751a
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
contrib/stack/stripmapStack/crossmul.py
|
falkamelung/isce2
|
edea69d4b6216f4ac729eba78f12547807a2751a
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
contrib/stack/stripmapStack/crossmul.py
|
falkamelung/isce2
|
edea69d4b6216f4ac729eba78f12547807a2751a
|
[
"ECL-2.0",
"Apache-2.0"
] | 1 |
2021-06-05T16:39:25.000Z
|
2021-06-05T16:39:25.000Z
|
#!/usr/bin/env python3
import os
import argparse
import logging
import isce
import isceobj
from components.stdproc.stdproc import crossmul
from iscesys.ImageUtil.ImageUtil import ImageUtil as IU
def createParser():
'''
Command Line Parser.
'''
parser = argparse.ArgumentParser( description='Generate offset field between two Sentinel swaths')
parser.add_argument('-m', '--master', type=str, dest='master', required=True,
help='Master image')
parser.add_argument('-s', '--slave', type=str, dest='slave', required=True,
help='Slave image')
parser.add_argument('-o', '--outdir', type=str, dest='prefix', default='crossmul',
help='Prefix of output int and amp files')
parser.add_argument('-a', '--alks', type=int, dest='azlooks', default=1,
help='Azimuth looks')
parser.add_argument('-r', '--rlks', type=int, dest='rglooks', default=1,
help='Range looks')
return parser
if __name__ == '__main__':
main()
'''
Main driver.
'''
| 27.485981 | 102 | 0.682761 |
b92338655b37aa1b9646d78826676f4639eac7d3
| 550 |
py
|
Python
|
27. Remove Element/solution2.py
|
sunshot/LeetCode
|
8f6503201831055f1d49ed3abb25be44a13ec317
|
[
"MIT"
] | null | null | null |
27. Remove Element/solution2.py
|
sunshot/LeetCode
|
8f6503201831055f1d49ed3abb25be44a13ec317
|
[
"MIT"
] | null | null | null |
27. Remove Element/solution2.py
|
sunshot/LeetCode
|
8f6503201831055f1d49ed3abb25be44a13ec317
|
[
"MIT"
] | null | null | null |
from typing import List
if __name__== '__main__':
solution = Solution()
nums = [3,2,2,3]
val = 3
ans = solution.removeElement(nums, val)
# print(ans)
print(nums[:ans])
| 23.913043 | 62 | 0.461818 |
b923cd998b5a122c2fa8e86b09305b2b291d6507
| 3,873 |
py
|
Python
|
platformio/commands/home/run.py
|
Granjow/platformio-core
|
71ae579bc07b2e11fec16acda482dea04bc3a359
|
[
"Apache-2.0"
] | 4,744 |
2016-11-28T14:37:47.000Z
|
2022-03-31T12:35:56.000Z
|
platformio/commands/home/run.py
|
Granjow/platformio-core
|
71ae579bc07b2e11fec16acda482dea04bc3a359
|
[
"Apache-2.0"
] | 3,424 |
2016-11-27T22:45:41.000Z
|
2022-03-31T21:40:03.000Z
|
platformio/commands/home/run.py
|
Granjow/platformio-core
|
71ae579bc07b2e11fec16acda482dea04bc3a359
|
[
"Apache-2.0"
] | 576 |
2016-12-01T18:48:22.000Z
|
2022-03-30T02:27:35.000Z
|
# Copyright (c) 2014-present PlatformIO <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from urllib.parse import urlparse
import click
import uvicorn
from starlette.applications import Starlette
from starlette.middleware import Middleware
from starlette.responses import PlainTextResponse
from starlette.routing import Mount, Route, WebSocketRoute
from starlette.staticfiles import StaticFiles
from starlette.status import HTTP_403_FORBIDDEN
from platformio.commands.home.rpc.handlers.account import AccountRPC
from platformio.commands.home.rpc.handlers.app import AppRPC
from platformio.commands.home.rpc.handlers.ide import IDERPC
from platformio.commands.home.rpc.handlers.misc import MiscRPC
from platformio.commands.home.rpc.handlers.os import OSRPC
from platformio.commands.home.rpc.handlers.piocore import PIOCoreRPC
from platformio.commands.home.rpc.handlers.project import ProjectRPC
from platformio.commands.home.rpc.server import WebSocketJSONRPCServerFactory
from platformio.compat import aio_get_running_loop
from platformio.exception import PlatformioException
from platformio.package.manager.core import get_core_package_dir
from platformio.proc import force_exit
def run_server(host, port, no_open, shutdown_timeout, home_url):
contrib_dir = get_core_package_dir("contrib-piohome")
if not os.path.isdir(contrib_dir):
raise PlatformioException("Invalid path to PIO Home Contrib")
ws_rpc_factory = WebSocketJSONRPCServerFactory(shutdown_timeout)
ws_rpc_factory.addObjectHandler(AccountRPC(), namespace="account")
ws_rpc_factory.addObjectHandler(AppRPC(), namespace="app")
ws_rpc_factory.addObjectHandler(IDERPC(), namespace="ide")
ws_rpc_factory.addObjectHandler(MiscRPC(), namespace="misc")
ws_rpc_factory.addObjectHandler(OSRPC(), namespace="os")
ws_rpc_factory.addObjectHandler(PIOCoreRPC(), namespace="core")
ws_rpc_factory.addObjectHandler(ProjectRPC(), namespace="project")
path = urlparse(home_url).path
routes = [
WebSocketRoute(path + "wsrpc", ws_rpc_factory, name="wsrpc"),
Route(path + "__shutdown__", shutdown_server, methods=["POST"]),
Mount(path, StaticFiles(directory=contrib_dir, html=True), name="static"),
]
if path != "/":
routes.append(Route("/", protected_page))
uvicorn.run(
Starlette(
middleware=[Middleware(ShutdownMiddleware)],
routes=routes,
on_startup=[
lambda: click.echo(
"PIO Home has been started. Press Ctrl+C to shutdown."
),
lambda: None if no_open else click.launch(home_url),
],
),
host=host,
port=port,
log_level="warning",
)
| 38.73 | 88 | 0.737155 |
b924107dfd6ae9e56411cce662afa3db86b021e5
| 11,450 |
py
|
Python
|
appengine/components/components/machine_provider/rpc_messages.py
|
stefb965/luci-py
|
e0a8a5640c4104e5c90781d833168aa8a8d1f24d
|
[
"Apache-2.0"
] | 1 |
2017-10-30T15:08:10.000Z
|
2017-10-30T15:08:10.000Z
|
appengine/components/components/machine_provider/rpc_messages.py
|
stefb965/luci-py
|
e0a8a5640c4104e5c90781d833168aa8a8d1f24d
|
[
"Apache-2.0"
] | null | null | null |
appengine/components/components/machine_provider/rpc_messages.py
|
stefb965/luci-py
|
e0a8a5640c4104e5c90781d833168aa8a8d1f24d
|
[
"Apache-2.0"
] | 1 |
2020-07-05T19:54:40.000Z
|
2020-07-05T19:54:40.000Z
|
# Copyright 2015 The LUCI Authors. All rights reserved.
# Use of this source code is governed under the Apache License, Version 2.0
# that can be found in the LICENSE file.
"""Messages for the Machine Provider API."""
# pylint: disable=unused-wildcard-import, wildcard-import
from protorpc import messages
from components.machine_provider.dimensions import *
from components.machine_provider.instructions import *
from components.machine_provider.policies import *
| 38.945578 | 79 | 0.773537 |
b925f7b3126896a3611797c97e1fa8d0eee2234c
| 564 |
py
|
Python
|
webscraping.py
|
carvalho-fdec/DesafioDSA
|
fec9742bd77ddc3923ed616b6511cce87de48968
|
[
"MIT"
] | null | null | null |
webscraping.py
|
carvalho-fdec/DesafioDSA
|
fec9742bd77ddc3923ed616b6511cce87de48968
|
[
"MIT"
] | null | null | null |
webscraping.py
|
carvalho-fdec/DesafioDSA
|
fec9742bd77ddc3923ed616b6511cce87de48968
|
[
"MIT"
] | null | null | null |
# webscraping test
import urllib.request
from bs4 import BeautifulSoup
with urllib.request.urlopen('http://www.netvasco.com.br') as url:
page = url.read()
#print(page)
print(url.geturl())
print(url.info())
print(url.getcode())
# Analise o html na varivel 'page' e armazene-o no formato Beautiful Soup
soup = BeautifulSoup(page, 'html.parser')
#print(soup.prettify())
print(soup.title)
print(soup.title.string)
print(soup.title.name)
soup_a = soup.find_all('a')[:10]
for a in soup_a:
print(a.get('href'))
print(a.get_text())
| 18.193548 | 74 | 0.687943 |
b9270600c4aae588202efc6c296f0228f4d2527a
| 21,441 |
py
|
Python
|
tensorboard/backend/event_processing/data_provider_test.py
|
hongxu-jia/tensorboard
|
98d4dadc61fd5a0580bed808653c59fb37748893
|
[
"Apache-2.0"
] | 1 |
2021-01-07T14:58:47.000Z
|
2021-01-07T14:58:47.000Z
|
tensorboard/backend/event_processing/data_provider_test.py
|
hongxu-jia/tensorboard
|
98d4dadc61fd5a0580bed808653c59fb37748893
|
[
"Apache-2.0"
] | null | null | null |
tensorboard/backend/event_processing/data_provider_test.py
|
hongxu-jia/tensorboard
|
98d4dadc61fd5a0580bed808653c59fb37748893
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Unit tests for `tensorboard.backend.event_processing.data_provider`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import numpy as np
from tensorboard import context
from tensorboard.backend.event_processing import data_provider
from tensorboard.backend.event_processing import (
plugin_event_multiplexer as event_multiplexer,
)
from tensorboard.compat.proto import summary_pb2
from tensorboard.data import provider as base_provider
from tensorboard.plugins.graph import metadata as graph_metadata
from tensorboard.plugins.histogram import metadata as histogram_metadata
from tensorboard.plugins.histogram import summary_v2 as histogram_summary
from tensorboard.plugins.scalar import metadata as scalar_metadata
from tensorboard.plugins.scalar import summary_v2 as scalar_summary
from tensorboard.plugins.image import metadata as image_metadata
from tensorboard.plugins.image import summary_v2 as image_summary
from tensorboard.util import tensor_util
import tensorflow.compat.v1 as tf1
import tensorflow.compat.v2 as tf
tf1.enable_eager_execution()
if __name__ == "__main__":
tf.test.main()
| 39.559041 | 80 | 0.583741 |
b927180a3b55091e89983dcae5d96dd47f1373ae
| 4,172 |
py
|
Python
|
extras/amld/cloud/quickdraw_rnn/task.py
|
luyang1210/tensorflow
|
948324f4cafdc97ae51c0e44fc1c28677a6e2e8a
|
[
"Apache-2.0"
] | 1 |
2019-04-28T15:46:45.000Z
|
2019-04-28T15:46:45.000Z
|
extras/amld/cloud/quickdraw_rnn/task.py
|
luyang1210/tensorflow
|
948324f4cafdc97ae51c0e44fc1c28677a6e2e8a
|
[
"Apache-2.0"
] | null | null | null |
extras/amld/cloud/quickdraw_rnn/task.py
|
luyang1210/tensorflow
|
948324f4cafdc97ae51c0e44fc1c28677a6e2e8a
|
[
"Apache-2.0"
] | 1 |
2020-11-18T04:43:33.000Z
|
2020-11-18T04:43:33.000Z
|
"""Experiment wrapper for training on Cloud ML."""
import argparse, glob, os
import tensorflow as tf
# From this package.
import model
def generate_experiment_fn(data_dir, train_batch_size, eval_batch_size,
train_steps, eval_steps, cell_size, hidden,
**experiment_args):
"""Returns experiment_fn for a RNN classifier.
Args:
data_dir: Where {train,eval}-* tf.train.Example datasets can be found.
train_batch_size: Batch size during training.
train_batch_size: Batch size during evaluation.
train_steps: Number of training steps.
eval_steps: Number of evaluation steps.
cell_size: LSTM cell size.
hidden: Number of units in hidden layers (note that None means "use default"
wich is equivalent to [] -- see code in model).
experiment_args: Additional arguments when `tf.contrib.learn.Experiment`
is instantiated.
"""
classes = tf.gfile.Open('%s/labels.txt' % data_dir).read().splitlines()
n_classes = len(classes)
params = tf.contrib.training.HParams(
cell_size=cell_size,
hidden=hidden or None, # Default is empty list.
)
config = tf.contrib.learn.RunConfig()
return _experiment_fn
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_dir',
help='GCS or local path to training data',
required=True
)
parser.add_argument(
'--train_batch_size',
help='Batch size for training steps',
type=int,
default=100
)
parser.add_argument(
'--eval_batch_size',
help='Batch size for evaluation steps',
type=int,
default=100
)
parser.add_argument(
'--train_steps',
help='Steps to run the training job for.',
type=int,
default=10000
)
parser.add_argument(
'--eval_steps',
help='Number of steps to run evalution for at each checkpoint',
default=100,
type=int
)
parser.add_argument(
'--output_dir',
help='GCS location to write checkpoints and export models',
required=True
)
parser.add_argument(
'--job-dir',
help='this model ignores this field, but it is required by gcloud',
default='junk'
)
parser.add_argument(
'--eval_delay_secs',
help='How long to wait before running first evaluation',
default=10,
type=int
)
parser.add_argument(
'--min_eval_frequency',
help='Minimum number of training steps between evaluations',
default=1,
type=int
)
# Hyper parameters.
parser.add_argument(
'--cell_size',
help='LSTM cell size.',
default=256,
type=int
)
parser.add_argument(
'--hidden',
help='Units in hidden layers.',
default=(),
nargs='+',
type=int
)
args = parser.parse_args()
arguments = args.__dict__
# unused args provided by service
arguments.pop('job_dir', None)
arguments.pop('job-dir', None)
output_dir = arguments.pop('output_dir')
# Run the training job
tf.contrib.learn.learn_runner.run(
generate_experiment_fn(**arguments), output_dir)
| 28.972222 | 81 | 0.613375 |
b92725db4e0f08b5ebf9656b39a1e567c20d5ffb
| 150 |
py
|
Python
|
A/116A.py
|
johnggo/Codeforces-Solutions
|
4127ae6f72294b5781fb94c42b69cfef570aae42
|
[
"MIT"
] | 1 |
2020-08-25T19:59:11.000Z
|
2020-08-25T19:59:11.000Z
|
A/116A.py
|
johnggo/Codeforces-Solutions
|
4127ae6f72294b5781fb94c42b69cfef570aae42
|
[
"MIT"
] | null | null | null |
A/116A.py
|
johnggo/Codeforces-Solutions
|
4127ae6f72294b5781fb94c42b69cfef570aae42
|
[
"MIT"
] | null | null | null |
# Time: 310 ms
# Memory: 1664 KB
n = int(input())
e = 0
s = 0
for i in range(n):
s =s- eval(input().replace(' ', '-'))
e = max(e, s)
print(e)
| 15 | 41 | 0.506667 |
b9299565a87f9a052852f5ae8225680eeeb2de61
| 1,923 |
py
|
Python
|
tests/test_serialize.py
|
aferrall/redner
|
be52e4105140f575f153d640ba889eb6e6015616
|
[
"MIT"
] | 1,146 |
2018-11-11T01:47:18.000Z
|
2022-03-31T14:11:03.000Z
|
tests/test_serialize.py
|
Awcrr/redner
|
b4f57037af26b720d916bbaf26103a3499101a9f
|
[
"MIT"
] | 177 |
2018-11-13T22:48:25.000Z
|
2022-03-30T07:19:29.000Z
|
tests/test_serialize.py
|
Awcrr/redner
|
b4f57037af26b720d916bbaf26103a3499101a9f
|
[
"MIT"
] | 127 |
2018-11-11T02:32:17.000Z
|
2022-03-31T07:24:03.000Z
|
import pyredner
import numpy as np
import torch
cam = pyredner.Camera(position = torch.tensor([0.0, 0.0, -5.0]),
look_at = torch.tensor([0.0, 0.0, 0.0]),
up = torch.tensor([0.0, 1.0, 0.0]),
fov = torch.tensor([45.0]), # in degree
clip_near = 1e-2, # needs to > 0
resolution = (256, 256),
fisheye = False)
mat_grey = pyredner.Material(\
diffuse_reflectance = \
torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()))
materials = [mat_grey]
shape_triangle = pyredner.Shape(\
vertices = torch.tensor([[-1.7, 1.0, 0.0], [1.0, 1.0, 0.0], [-0.5, -1.0, 0.0]],
device = pyredner.get_device()),
indices = torch.tensor([[0, 1, 2]], dtype = torch.int32,
device = pyredner.get_device()),
uvs = None,
normals = None,
material_id = 0)
shape_light = pyredner.Shape(\
vertices = torch.tensor([[-1.0, -1.0, -7.0],
[ 1.0, -1.0, -7.0],
[-1.0, 1.0, -7.0],
[ 1.0, 1.0, -7.0]], device = pyredner.get_device()),
indices = torch.tensor([[0, 1, 2],[1, 3, 2]],
dtype = torch.int32, device = pyredner.get_device()),
uvs = None,
normals = None,
material_id = 0)
shapes = [shape_triangle, shape_light]
light = pyredner.AreaLight(shape_id = 1,
intensity = torch.tensor([20.0,20.0,20.0]))
area_lights = [light]
scene = pyredner.Scene(cam, shapes, materials, area_lights)
scene_state_dict = scene.state_dict()
scene = pyredner.Scene.load_state_dict(scene_state_dict)
scene_args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 16,
max_bounces = 1)
render = pyredner.RenderFunction.apply
img = render(0, *scene_args)
pyredner.imwrite(img.cpu(), 'results/test_serialize/img.exr')
| 34.339286 | 83 | 0.560582 |
b92a551001bac345f595f68ea0440f1231ad8e57
| 2,302 |
py
|
Python
|
src/zope/publisher/tests/test_requestdataproperty.py
|
Shoobx/zope.publisher
|
790e82045d7ae06146bd8c5e27139555b9ec1641
|
[
"ZPL-2.1"
] | 3 |
2016-11-18T08:58:09.000Z
|
2021-02-01T06:13:45.000Z
|
src/zope/publisher/tests/test_requestdataproperty.py
|
Shoobx/zope.publisher
|
790e82045d7ae06146bd8c5e27139555b9ec1641
|
[
"ZPL-2.1"
] | 42 |
2015-06-02T19:26:10.000Z
|
2022-03-15T07:24:03.000Z
|
src/zope/publisher/tests/test_requestdataproperty.py
|
Shoobx/zope.publisher
|
790e82045d7ae06146bd8c5e27139555b9ec1641
|
[
"ZPL-2.1"
] | 7 |
2015-04-03T09:29:31.000Z
|
2021-06-07T14:47:45.000Z
|
##############################################################################
#
# Copyright (c) 2001, 2002 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Request Data-Property Tests
"""
from unittest import TestCase, makeSuite
from zope.interface.common.tests.basemapping \
import testIEnumerableMapping, testIReadMapping
from zope.publisher.base \
import RequestDataProperty, RequestDataGetter, RequestDataMapper
_marker = object()
def test_suite():
return makeSuite(Test)
| 27.404762 | 78 | 0.614683 |
b92a9fd2163ca676afa6df078248d3bd1b2d8259
| 146 |
py
|
Python
|
tools/scoring/dimensions/__init__.py
|
ahemphill/digitalbuildings
|
56a03b0055f9f771c3ed0a962f6bfb2b1d968947
|
[
"Apache-2.0"
] | null | null | null |
tools/scoring/dimensions/__init__.py
|
ahemphill/digitalbuildings
|
56a03b0055f9f771c3ed0a962f6bfb2b1d968947
|
[
"Apache-2.0"
] | null | null | null |
tools/scoring/dimensions/__init__.py
|
ahemphill/digitalbuildings
|
56a03b0055f9f771c3ed0a962f6bfb2b1d968947
|
[
"Apache-2.0"
] | null | null | null |
""" Enable import """
from os import path
import sys
sys.path.append(
path.abspath(path.join('tools', 'validators', 'instance_validator')))
| 18.25 | 73 | 0.69863 |
b92b002b9d57e933962f9291a749b365792c1b9a
| 1,444 |
py
|
Python
|
src/thornfield/caches/cache_compression_decorator.py
|
drorvinkler/thornfield
|
3c5bb8afaa96097bc71cccb119394a0f351d828f
|
[
"MIT"
] | 2 |
2020-11-24T13:27:14.000Z
|
2020-11-24T13:29:40.000Z
|
src/thornfield/caches/cache_compression_decorator.py
|
drorvinkler/thornfield
|
3c5bb8afaa96097bc71cccb119394a0f351d828f
|
[
"MIT"
] | 1 |
2020-11-24T13:33:45.000Z
|
2020-11-24T15:10:41.000Z
|
src/thornfield/caches/cache_compression_decorator.py
|
drorvinkler/thornfield
|
3c5bb8afaa96097bc71cccb119394a0f351d828f
|
[
"MIT"
] | null | null | null |
from typing import Callable, AnyStr, Optional
from zlib import compress as default_compress, decompress as default_decompress
from .cache import Cache
from ..constants import NOT_FOUND
| 29.469388 | 79 | 0.628809 |
b92be50c97841e71ffe31a7d7baa405cc9ba5537
| 38,846 |
py
|
Python
|
manim/mobject/vector_field.py
|
kdkasad/manim
|
249b1dcab0f18a43e953b5fda517734084c0a941
|
[
"MIT"
] | 2 |
2021-12-07T14:25:07.000Z
|
2021-12-09T14:16:10.000Z
|
manim/mobject/vector_field.py
|
kdkasad/manim
|
249b1dcab0f18a43e953b5fda517734084c0a941
|
[
"MIT"
] | 3 |
2021-09-15T08:11:29.000Z
|
2021-10-06T02:00:03.000Z
|
manim/mobject/vector_field.py
|
kdkasad/manim
|
249b1dcab0f18a43e953b5fda517734084c0a941
|
[
"MIT"
] | 3 |
2020-04-10T20:38:06.000Z
|
2020-09-30T03:03:45.000Z
|
"""Mobjects representing vector fields."""
__all__ = [
"VectorField",
"ArrowVectorField",
"StreamLines",
]
import itertools as it
import random
from math import ceil, floor
from typing import Callable, Iterable, Optional, Sequence, Tuple, Type
import numpy as np
from colour import Color
from PIL import Image
from .. import config
from ..animation.composition import AnimationGroup, Succession
from ..animation.creation import Create
from ..animation.indication import ShowPassingFlash
from ..animation.update import UpdateFromAlphaFunc
from ..constants import OUT, RIGHT, UP
from ..mobject.geometry import Vector
from ..mobject.mobject import Mobject
from ..mobject.types.vectorized_mobject import VGroup, VMobject
from ..utils.bezier import interpolate, inverse_interpolate
from ..utils.color import BLUE_E, GREEN, RED, YELLOW, color_to_rgb, rgb_to_color
from ..utils.deprecation import deprecated_params
from ..utils.rate_functions import ease_out_sine, linear
from ..utils.simple_functions import sigmoid
from .types.opengl_vectorized_mobject import OpenGLVMobject
DEFAULT_SCALAR_FIELD_COLORS: list = [BLUE_E, GREEN, YELLOW, RED]
def nudge_submobjects(
self,
dt: float = 1,
substeps: int = 1,
pointwise: bool = False,
) -> "VectorField":
"""Apply a nudge along the vector field to all submobjects.
Parameters
----------
dt
A scalar to the amount the mobject is moved along the vector field.
The actual distance is based on the magnitude of the vector field.
substeps
The amount of steps the whole nudge is divided into. Higher values
give more accurate approximations.
pointwise
Whether to move the mobject along the vector field. See :meth:`nudge` for details.
Returns
-------
VectorField
This vector field.
"""
for mob in self.submobjects:
self.nudge(mob, dt, substeps, pointwise)
return self
def get_nudge_updater(
self,
speed: float = 1,
pointwise: bool = False,
) -> Callable[[Mobject, float], Mobject]:
"""Get an update function to move a :class:`~.Mobject` along the vector field.
When used with :meth:`~.Mobject.add_updater`, the mobject will move along the vector field, where its speed is determined by the magnitude of the vector field.
Parameters
----------
speed
At `speed=1` the distance a mobject moves per second is equal to the magnitude of the vector field along its path. The speed value scales the speed of such a mobject.
pointwise
Whether to move the mobject along the vector field. See :meth:`nudge` for details.
Returns
-------
Callable[[Mobject, float], Mobject]
The update function.
"""
return lambda mob, dt: self.nudge(mob, dt * speed, pointwise=pointwise)
def start_submobject_movement(
self,
speed: float = 1,
pointwise: bool = False,
) -> "VectorField":
"""Start continuously moving all submobjects along the vector field.
Calling this method multiple times will result in removing the previous updater created by this method.
Parameters
----------
speed
The speed at which to move the submobjects. See :meth:`get_nudge_updater` for details.
pointwise
Whether to move the mobject along the vector field. See :meth:`nudge` for details.
Returns
-------
VectorField
This vector field.
"""
self.stop_submobject_movement()
self.submob_movement_updater = lambda mob, dt: mob.nudge_submobjects(
dt * speed,
pointwise=pointwise,
)
self.add_updater(self.submob_movement_updater)
return self
def stop_submobject_movement(self) -> "VectorField":
"""Stops the continuous movement started using :meth:`start_submobject_movement`.
Returns
-------
VectorField
This vector field.
"""
self.remove_updater(self.submob_movement_updater)
self.submob_movement_updater = None
return self
def get_colored_background_image(self, sampling_rate: int = 5) -> Image.Image:
"""Generate an image that displays the vector field.
The color at each position is calculated by passing the positing through a
series of steps:
Calculate the vector field function at that position, map that vector to a
single value using `self.color_scheme` and finally generate a color from
that value using the color gradient.
Parameters
----------
sampling_rate
The stepsize at which pixels get included in the image. Lower values give
more accurate results, but may take a long time to compute.
Returns
-------
Image.Imgae
The vector field image.
"""
if self.single_color:
raise ValueError(
"There is no point in generating an image if the vector field uses a single color.",
)
ph = int(config["pixel_height"] / sampling_rate)
pw = int(config["pixel_width"] / sampling_rate)
fw = config["frame_width"]
fh = config["frame_height"]
points_array = np.zeros((ph, pw, 3))
x_array = np.linspace(-fw / 2, fw / 2, pw)
y_array = np.linspace(fh / 2, -fh / 2, ph)
x_array = x_array.reshape((1, len(x_array)))
y_array = y_array.reshape((len(y_array), 1))
x_array = x_array.repeat(ph, axis=0)
y_array.repeat(pw, axis=1) # TODO why not y_array = y_array.repeat(...)?
points_array[:, :, 0] = x_array
points_array[:, :, 1] = y_array
rgbs = np.apply_along_axis(self.pos_to_rgb, 2, points_array)
return Image.fromarray((rgbs * 255).astype("uint8"))
def get_vectorized_rgba_gradient_function(
self,
start: float,
end: float,
colors: Iterable,
):
"""
Generates a gradient of rgbas as a numpy array
Parameters
----------
start
start value used for inverse interpolation at :func:`~.inverse_interpolate`
end
end value used for inverse interpolation at :func:`~.inverse_interpolate`
colors
list of colors to generate the gradient
Returns
-------
function to generate the gradients as numpy arrays representing rgba values
"""
rgbs = np.array([color_to_rgb(c) for c in colors])
return func
class ArrowVectorField(VectorField):
"""A :class:`VectorField` represented by a set of change vectors.
Vector fields are always based on a function defining the :class:`~.Vector` at every position.
The values of this functions is displayed as a grid of vectors.
By default the color of each vector is determined by it's magnitude.
Other color schemes can be used however.
Parameters
----------
func
The function defining the rate of change at every position of the vector field.
color
The color of the vector field. If set, position-specific coloring is disabled.
color_scheme
A function mapping a vector to a single value. This value gives the position in the color gradient defined using `min_color_scheme_value`, `max_color_scheme_value` and `colors`.
min_color_scheme_value
The value of the color_scheme function to be mapped to the first color in `colors`. Lower values also result in the first color of the gradient.
max_color_scheme_value
The value of the color_scheme function to be mapped to the last color in `colors`. Higher values also result in the last color of the gradient.
colors
The colors defining the color gradient of the vector field.
x_range
A sequence of x_min, x_max, delta_x
y_range
A sequence of y_min, y_max, delta_y
z_range
A sequence of z_min, z_max, delta_z
three_dimensions
Enables three_dimensions. Default set to False, automatically turns True if
z_range is not None.
length_func
The function determining the displayed size of the vectors. The actual size
of the vector is passed, the returned value will be used as display size for the
vector. By default this is used to cap the displayed size of vectors to reduce the clutter.
opacity
The opacity of the arrows.
vector_config
Additional arguments to be passed to the :class:`~.Vector` constructor
kwargs : Any
Additional arguments to be passed to the :class:`~.VGroup` constructor
Examples
--------
.. manim:: BasicUsage
:save_last_frame:
class BasicUsage(Scene):
def construct(self):
func = lambda pos: ((pos[0] * UR + pos[1] * LEFT) - pos) / 3
self.add(ArrowVectorField(func))
.. manim:: SizingAndSpacing
class SizingAndSpacing(Scene):
def construct(self):
func = lambda pos: np.sin(pos[0] / 2) * UR + np.cos(pos[1] / 2) * LEFT
vf = ArrowVectorField(func, x_range=[-7, 7, 1])
self.add(vf)
self.wait()
length_func = lambda x: x / 3
vf2 = ArrowVectorField(func, x_range=[-7, 7, 1], length_func=length_func)
self.play(vf.animate.become(vf2))
self.wait()
.. manim:: Coloring
:save_last_frame:
class Coloring(Scene):
def construct(self):
func = lambda pos: pos - LEFT * 5
colors = [RED, YELLOW, BLUE, DARK_GRAY]
min_radius = Circle(radius=2, color=colors[0]).shift(LEFT * 5)
max_radius = Circle(radius=10, color=colors[-1]).shift(LEFT * 5)
vf = ArrowVectorField(
func, min_color_scheme_value=2, max_color_scheme_value=10, colors=colors
)
self.add(vf, min_radius, max_radius)
"""
def get_vector(self, point: np.ndarray):
"""Creates a vector in the vector field.
The created vector is based on the function of the vector field and is
rooted in the given point. Color and length fit the specifications of
this vector field.
Parameters
----------
point
The root point of the vector.
kwargs : Any
Additional arguments to be passed to the :class:`~.Vector` constructor
"""
output = np.array(self.func(point))
norm = np.linalg.norm(output)
if norm != 0:
output *= self.length_func(norm) / norm
vect = Vector(output, **self.vector_config)
vect.shift(point)
if self.single_color:
vect.set_color(self.color)
else:
vect.set_color(self.pos_to_color(point))
return vect
class StreamLines(VectorField):
"""StreamLines represent the flow of a :class:`VectorField` using the trace of moving agents.
Vector fields are always based on a function defining the vector at every position.
The values of this functions is displayed by moving many agents along the vector field
and showing their trace.
Parameters
----------
func
The function defining the rate of change at every position of the vector field.
color
The color of the vector field. If set, position-specific coloring is disabled.
color_scheme
A function mapping a vector to a single value. This value gives the position in the color gradient defined using `min_color_scheme_value`, `max_color_scheme_value` and `colors`.
min_color_scheme_value
The value of the color_scheme function to be mapped to the first color in `colors`. Lower values also result in the first color of the gradient.
max_color_scheme_value
The value of the color_scheme function to be mapped to the last color in `colors`. Higher values also result in the last color of the gradient.
colors
The colors defining the color gradient of the vector field.
x_range
A sequence of x_min, x_max, delta_x
y_range
A sequence of y_min, y_max, delta_y
z_range
A sequence of z_min, z_max, delta_z
three_dimensions
Enables three_dimensions. Default set to False, automatically turns True if
z_range is not None.
noise_factor
The amount by which the starting position of each agent is altered along each axis. Defaults to :code:`delta_y / 2` if not defined.
n_repeats
The number of agents generated at each starting point.
dt
The factor by which the distance an agent moves per step is stretched. Lower values result in a better approximation of the trajectories in the vector field.
virtual_time
The time the agents get to move in the vector field. Higher values therefore result in longer stream lines. However, this whole time gets simulated upon creation.
max_anchors_per_line
The maximum number of anchors per line. Lines with more anchors get reduced in complexity, not in length.
padding
The distance agents can move out of the generation area before being terminated.
stroke_width
The stroke with of the stream lines.
opacity
The opacity of the stream lines.
Examples
--------
.. manim:: BasicUsage
:save_last_frame:
class BasicUsage(Scene):
def construct(self):
func = lambda pos: ((pos[0] * UR + pos[1] * LEFT) - pos) / 3
self.add(StreamLines(func))
.. manim:: SpawningAndFlowingArea
:save_last_frame:
class SpawningAndFlowingArea(Scene):
def construct(self):
func = lambda pos: np.sin(pos[0]) * UR + np.cos(pos[1]) * LEFT + pos / 5
stream_lines = StreamLines(
func, x_range=[-3, 3, 0.2], y_range=[-2, 2, 0.2], padding=1
)
spawning_area = Rectangle(width=6, height=4)
flowing_area = Rectangle(width=8, height=6)
labels = [Tex("Spawning Area"), Tex("Flowing Area").shift(DOWN * 2.5)]
for lbl in labels:
lbl.add_background_rectangle(opacity=0.6, buff=0.05)
self.add(stream_lines, spawning_area, flowing_area, *labels)
"""
def create(
self,
lag_ratio: Optional[float] = None,
run_time: Optional[Callable[[float], float]] = None,
**kwargs
) -> AnimationGroup:
"""The creation animation of the stream lines.
The stream lines appear in random order.
Parameters
----------
lag_ratio
The lag ratio of the animation.
If undefined, it will be selected so that the total animation length is 1.5 times the run time of each stream line creation.
run_time
The run time of every single stream line creation. The runtime of the whole animation might be longer due to the `lag_ratio`.
If undefined, the virtual time of the stream lines is used as run time.
Returns
-------
:class:`~.AnimationGroup`
The creation animation of the stream lines.
Examples
--------
.. manim:: StreamLineCreation
class StreamLineCreation(Scene):
def construct(self):
func = lambda pos: (pos[0] * UR + pos[1] * LEFT) - pos
stream_lines = StreamLines(
func,
color=YELLOW,
x_range=[-7, 7, 1],
y_range=[-4, 4, 1],
stroke_width=3,
virtual_time=1, # use shorter lines
max_anchors_per_line=5, # better performance with fewer anchors
)
self.play(stream_lines.create()) # uses virtual_time as run_time
self.wait()
"""
if run_time is None:
run_time = self.virtual_time
if lag_ratio is None:
lag_ratio = run_time / 2 / len(self.submobjects)
animations = [
Create(line, run_time=run_time, **kwargs) for line in self.stream_lines
]
random.shuffle(animations)
return AnimationGroup(*animations, lag_ratio=lag_ratio)
def start_animation(
self,
warm_up=True,
flow_speed: float = 1,
time_width: float = 0.3,
rate_func: Callable[[float], float] = linear,
line_animation_class: Type[ShowPassingFlash] = ShowPassingFlash,
**kwargs
) -> None:
"""Animates the stream lines using an updater.
The stream lines will continuously flow
Parameters
----------
warm_up : bool, optional
If `True` the animation is initialized line by line. Otherwise it starts with all lines shown.
flow_speed
At `flow_speed=1` the distance the flow moves per second is equal to the magnitude of the vector field along its path. The speed value scales the speed of this flow.
time_width
The proportion of the stream line shown while being animated
rate_func
The rate function of each stream line flashing
line_animation_class
The animation class being used
Examples
--------
.. manim:: ContinuousMotion
class ContinuousMotion(Scene):
def construct(self):
func = lambda pos: np.sin(pos[0] / 2) * UR + np.cos(pos[1] / 2) * LEFT
stream_lines = StreamLines(func, stroke_width=3, max_anchors_per_line=30)
self.add(stream_lines)
stream_lines.start_animation(warm_up=False, flow_speed=1.5)
self.wait(stream_lines.virtual_time / stream_lines.flow_speed)
"""
for line in self.stream_lines:
run_time = line.duration / flow_speed
line.anim = line_animation_class(
line,
run_time=run_time,
rate_func=rate_func,
time_width=time_width,
**kwargs,
)
line.anim.begin()
line.time = random.random() * self.virtual_time
if warm_up:
line.time *= -1
self.add(line.anim.mobject)
self.add_updater(updater)
self.flow_animation = updater
self.flow_speed = flow_speed
self.time_width = time_width
def end_animation(self) -> AnimationGroup:
"""End the stream line animation smoothly.
Returns an animation resulting in fully displayed stream lines without a noticeable cut.
Returns
-------
:class:`~.AnimationGroup`
The animation fading out the running stream animation.
Raises
------
ValueError
if no stream line animation is running
Examples
--------
.. manim:: EndAnimation
class EndAnimation(Scene):
def construct(self):
func = lambda pos: np.sin(pos[0] / 2) * UR + np.cos(pos[1] / 2) * LEFT
stream_lines = StreamLines(
func, stroke_width=3, max_anchors_per_line=5, virtual_time=1, color=BLUE
)
self.add(stream_lines)
stream_lines.start_animation(warm_up=False, flow_speed=1.5, time_width=0.5)
self.wait(1)
self.play(stream_lines.end_animation())
"""
if self.flow_animation is None:
raise ValueError("You have to start the animation before fading it out.")
max_run_time = self.virtual_time / self.flow_speed
creation_rate_func = ease_out_sine
creation_staring_speed = creation_rate_func(0.001) * 1000
creation_run_time = (
max_run_time / (1 + self.time_width) * creation_staring_speed
)
# creation_run_time is calculated so that the creation animation starts at the same speed
# as the regular line flash animation but eases out.
dt = 1 / config["frame_rate"]
animations = []
self.remove_updater(self.flow_animation)
self.flow_animation = None
for line in self.stream_lines:
create = Create(
line,
run_time=creation_run_time,
rate_func=creation_rate_func,
)
if line.time <= 0:
animations.append(
Succession(
UpdateFromAlphaFunc(
line,
hide_and_wait,
run_time=-line.time / self.flow_speed,
),
create,
),
)
self.remove(line.anim.mobject)
line.anim.finish()
else:
remaining_time = max_run_time - line.time / self.flow_speed
animations.append(
Succession(
UpdateFromAlphaFunc(
line,
finish_updater_cycle,
run_time=remaining_time,
),
create,
),
)
return AnimationGroup(*animations)
# TODO: Variant of StreamLines that is able to respond to changes in the vector field function
| 36.96099 | 185 | 0.57298 |
b92c7cbb70fbc4dd2dec20c24e021d0f6405bd12
| 19,900 |
py
|
Python
|
marshmallow_dataclass/__init__.py
|
dan-starkware/marshmallow_dataclass
|
25c3e041d8c6a87d740984e57a5bd29b768afbf8
|
[
"MIT"
] | null | null | null |
marshmallow_dataclass/__init__.py
|
dan-starkware/marshmallow_dataclass
|
25c3e041d8c6a87d740984e57a5bd29b768afbf8
|
[
"MIT"
] | null | null | null |
marshmallow_dataclass/__init__.py
|
dan-starkware/marshmallow_dataclass
|
25c3e041d8c6a87d740984e57a5bd29b768afbf8
|
[
"MIT"
] | null | null | null |
"""
This library allows the conversion of python 3.7's :mod:`dataclasses`
to :mod:`marshmallow` schemas.
It takes a python class, and generates a marshmallow schema for it.
Simple example::
from marshmallow import Schema
from marshmallow_dataclass import dataclass
@dataclass
class Point:
x:float
y:float
point = Point(x=0, y=0)
point_json = Point.Schema().dumps(point)
Full example::
from marshmallow import Schema
from dataclasses import field
from marshmallow_dataclass import dataclass
import datetime
@dataclass
class User:
birth: datetime.date = field(metadata= {
"required": True # A parameter to pass to marshmallow's field
})
website:str = field(metadata = {
"marshmallow_field": marshmallow.fields.Url() # Custom marshmallow field
})
Schema: ClassVar[Type[Schema]] = Schema # For the type checker
"""
import inspect
from enum import EnumMeta
from functools import lru_cache
from typing import (
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
cast,
overload,
)
import dataclasses
import marshmallow
import typing_inspect
__all__ = ["dataclass", "add_schema", "class_schema", "field_for_schema", "NewType"]
NoneType = type(None)
_U = TypeVar("_U")
# Whitelist of dataclass members that will be copied to generated schema.
MEMBERS_WHITELIST: Set[str] = {"Meta"}
# Max number of generated schemas that class_schema keeps of generated schemas. Removes duplicates.
MAX_CLASS_SCHEMA_CACHE_SIZE = 1024
# _cls should never be specified by keyword, so start it with an
# underscore. The presence of _cls is used to detect if this
# decorator is being called with parameters or not.
def dataclass(
_cls: Type[_U] = None,
*,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool = False,
base_schema: Optional[Type[marshmallow.Schema]] = None,
):
"""
This decorator does the same as dataclasses.dataclass, but also applies :func:`add_schema`.
It adds a `.Schema` attribute to the class object
:param base_schema: marshmallow schema used as a base class when deriving dataclass schema
>>> @dataclass
... class Artist:
... name: str
>>> Artist.Schema
<class 'marshmallow.schema.Artist'>
>>> from typing import ClassVar
>>> from marshmallow import Schema
>>> @dataclass(order=True) # preserve field order
... class Point:
... x:float
... y:float
... Schema: ClassVar[Type[Schema]] = Schema # For the type checker
...
>>> Point.Schema().load({'x':0, 'y':0}) # This line can be statically type checked
Point(x=0.0, y=0.0)
"""
# dataclass's typing doesn't expect it to be called as a function, so ignore type check
dc = dataclasses.dataclass( # type: ignore
_cls, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen
)
if _cls is None:
return lambda cls: add_schema(dc(cls), base_schema)
return add_schema(dc, base_schema)
def add_schema(_cls=None, base_schema=None):
"""
This decorator adds a marshmallow schema as the 'Schema' attribute in a dataclass.
It uses :func:`class_schema` internally.
:param type cls: The dataclass to which a Schema should be added
:param base_schema: marshmallow schema used as a base class when deriving dataclass schema
>>> class BaseSchema(marshmallow.Schema):
... def on_bind_field(self, field_name, field_obj):
... field_obj.data_key = (field_obj.data_key or field_name).upper()
>>> @add_schema(base_schema=BaseSchema)
... @dataclasses.dataclass
... class Artist:
... names: Tuple[str, str]
>>> artist = Artist.Schema().loads('{"NAMES": ["Martin", "Ramirez"]}')
>>> artist
Artist(names=('Martin', 'Ramirez'))
"""
return decorator(_cls) if _cls else decorator
def class_schema(
clazz: type, base_schema: Optional[Type[marshmallow.Schema]] = None
) -> Type[marshmallow.Schema]:
"""
Convert a class to a marshmallow schema
:param clazz: A python class (may be a dataclass)
:param base_schema: marshmallow schema used as a base class when deriving dataclass schema
:return: A marshmallow Schema corresponding to the dataclass
.. note::
All the arguments supported by marshmallow field classes can
be passed in the `metadata` dictionary of a field.
If you want to use a custom marshmallow field
(one that has no equivalent python type), you can pass it as the
``marshmallow_field`` key in the metadata dictionary.
>>> import typing
>>> Meters = typing.NewType('Meters', float)
>>> @dataclasses.dataclass()
... class Building:
... height: Optional[Meters]
... name: str = dataclasses.field(default="anonymous")
... class Meta:
... ordered = True
...
>>> class_schema(Building) # Returns a marshmallow schema class (not an instance)
<class 'marshmallow.schema.Building'>
>>> @dataclasses.dataclass()
... class City:
... name: str = dataclasses.field(metadata={'required':True})
... best_building: Building # Reference to another dataclasses. A schema will be created for it too.
... other_buildings: List[Building] = dataclasses.field(default_factory=lambda: [])
...
>>> citySchema = class_schema(City)()
>>> city = citySchema.load({"name":"Paris", "best_building": {"name": "Eiffel Tower"}})
>>> city
City(name='Paris', best_building=Building(height=None, name='Eiffel Tower'), other_buildings=[])
>>> citySchema.load({"name":"Paris"})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'best_building': ['Missing data for required field.']}
>>> city_json = citySchema.dump(city)
>>> city_json['best_building'] # We get an OrderedDict because we specified order = True in the Meta class
OrderedDict([('height', None), ('name', 'Eiffel Tower')])
>>> @dataclasses.dataclass()
... class Person:
... name: str = dataclasses.field(default="Anonymous")
... friends: List['Person'] = dataclasses.field(default_factory=lambda:[]) # Recursive field
...
>>> person = class_schema(Person)().load({
... "friends": [{"name": "Roger Boucher"}]
... })
>>> person
Person(name='Anonymous', friends=[Person(name='Roger Boucher', friends=[])])
>>> @dataclasses.dataclass()
... class C:
... important: int = dataclasses.field(init=True, default=0)
... # Only fields that are in the __init__ method will be added:
... unimportant: int = dataclasses.field(init=False, default=0)
...
>>> c = class_schema(C)().load({
... "important": 9, # This field will be imported
... "unimportant": 9 # This field will NOT be imported
... }, unknown=marshmallow.EXCLUDE)
>>> c
C(important=9, unimportant=0)
>>> @dataclasses.dataclass
... class Website:
... url:str = dataclasses.field(metadata = {
... "marshmallow_field": marshmallow.fields.Url() # Custom marshmallow field
... })
...
>>> class_schema(Website)().load({"url": "I am not a good URL !"})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'url': ['Not a valid URL.']}
>>> @dataclasses.dataclass
... class NeverValid:
... @marshmallow.validates_schema
... def validate(self, data, **_):
... raise marshmallow.ValidationError('never valid')
...
>>> class_schema(NeverValid)().load({})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'_schema': ['never valid']}
>>> # noinspection PyTypeChecker
>>> class_schema(None) # unsupported type
Traceback (most recent call last):
...
TypeError: None is not a dataclass and cannot be turned into one.
>>> @dataclasses.dataclass
... class Anything:
... name: str
... @marshmallow.validates('name')
... def validates(self, value):
... if len(value) > 5: raise marshmallow.ValidationError("Name too long")
>>> class_schema(Anything)().load({"name": "aaaaaargh"})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'name': ['Name too long']}
"""
return _proxied_class_schema(clazz, base_schema)
def _field_by_supertype(
typ: Type,
default: marshmallow.missing,
newtype_supertype: Type,
metadata: dict,
base_schema: Optional[Type[marshmallow.Schema]],
) -> marshmallow.fields.Field:
"""
Return a new field for fields based on a super field. (Usually spawned from NewType)
"""
# Add the information coming our custom NewType implementation
typ_args = getattr(typ, "_marshmallow_args", {})
# Handle multiple validators from both `typ` and `metadata`.
# See https://github.com/lovasoa/marshmallow_dataclass/issues/91
new_validators: List[Callable] = []
for meta_dict in (typ_args, metadata):
if "validate" in meta_dict:
if marshmallow.utils.is_iterable_but_not_string(meta_dict["validate"]):
new_validators.extend(meta_dict["validate"])
elif callable(meta_dict["validate"]):
new_validators.append(meta_dict["validate"])
metadata["validate"] = new_validators if new_validators else None
metadata = {"description": typ.__name__, **typ_args, **metadata}
field = getattr(typ, "_marshmallow_field", None)
if field:
return field(**metadata)
else:
return field_for_schema(
newtype_supertype,
metadata=metadata,
default=default,
base_schema=base_schema,
)
def field_for_schema(
typ: type,
default=marshmallow.missing,
metadata: Mapping[str, Any] = None,
base_schema: Optional[Type[marshmallow.Schema]] = None,
) -> marshmallow.fields.Field:
"""
Get a marshmallow Field corresponding to the given python type.
The metadata of the dataclass field is used as arguments to the marshmallow Field.
:param typ: The type for which a field should be generated
:param default: value to use for (de)serialization when the field is missing
:param metadata: Additional parameters to pass to the marshmallow field constructor
:param base_schema: marshmallow schema used as a base class when deriving dataclass schema
>>> int_field = field_for_schema(int, default=9, metadata=dict(required=True))
>>> int_field.__class__
<class 'marshmallow.fields.Integer'>
>>> int_field.default
9
>>> field_for_schema(str, metadata={"marshmallow_field": marshmallow.fields.Url()}).__class__
<class 'marshmallow.fields.Url'>
"""
metadata = {} if metadata is None else dict(metadata)
if default is not marshmallow.missing:
metadata.setdefault("default", default)
# 'missing' must not be set for required fields.
if not metadata.get("required"):
metadata.setdefault("missing", default)
else:
metadata.setdefault("required", True)
# If the field was already defined by the user
predefined_field = metadata.get("marshmallow_field")
if predefined_field:
return predefined_field
# Generic types specified without type arguments
if typ is list:
typ = List[Any]
elif typ is dict:
typ = Dict[Any, Any]
# Base types
field = _field_by_type(typ, base_schema)
if field:
return field(**metadata)
if typ is Any:
metadata.setdefault("allow_none", True)
return marshmallow.fields.Raw(**metadata)
# Generic types
origin = typing_inspect.get_origin(typ)
if origin:
arguments = typing_inspect.get_args(typ, True)
# Override base_schema.TYPE_MAPPING to change the class used for generic types below
type_mapping = base_schema.TYPE_MAPPING if base_schema else {}
if origin in (list, List):
child_type = field_for_schema(arguments[0], base_schema=base_schema)
list_type = type_mapping.get(List, marshmallow.fields.List)
return list_type(child_type, **metadata)
if origin in (tuple, Tuple):
children = tuple(
field_for_schema(arg, base_schema=base_schema) for arg in arguments
)
tuple_type = type_mapping.get(Tuple, marshmallow.fields.Tuple)
return tuple_type(children, **metadata)
elif origin in (dict, Dict):
dict_type = type_mapping.get(Dict, marshmallow.fields.Dict)
return dict_type(
keys=field_for_schema(arguments[0], base_schema=base_schema),
values=field_for_schema(arguments[1], base_schema=base_schema),
**metadata,
)
elif typing_inspect.is_optional_type(typ):
subtyp = next(t for t in arguments if t is not NoneType) # type: ignore
# Treat optional types as types with a None default
metadata["default"] = metadata.get("default", None)
metadata["missing"] = metadata.get("missing", None)
metadata["required"] = False
return field_for_schema(subtyp, metadata=metadata, base_schema=base_schema)
elif typing_inspect.is_union_type(typ):
from . import union_field
return union_field.Union(
[
(
subtyp,
field_for_schema(
subtyp, metadata=metadata, base_schema=base_schema
),
)
for subtyp in arguments
],
**metadata,
)
# typing.NewType returns a function with a __supertype__ attribute
newtype_supertype = getattr(typ, "__supertype__", None)
if newtype_supertype and inspect.isfunction(typ):
return _field_by_supertype(
typ=typ,
default=default,
newtype_supertype=newtype_supertype,
metadata=metadata,
base_schema=base_schema,
)
# enumerations
if isinstance(typ, EnumMeta):
import marshmallow_enum
return marshmallow_enum.EnumField(typ, **metadata)
# Nested marshmallow dataclass
nested_schema = getattr(typ, "Schema", None)
# Nested dataclasses
forward_reference = getattr(typ, "__forward_arg__", None)
nested = (
nested_schema or forward_reference or class_schema(typ, base_schema=base_schema)
)
return marshmallow.fields.Nested(nested, **metadata)
def _base_schema(
clazz: type, base_schema: Optional[Type[marshmallow.Schema]] = None
) -> Type[marshmallow.Schema]:
"""
Base schema factory that creates a schema for `clazz` derived either from `base_schema`
or `BaseSchema`
"""
# Remove `type: ignore` when mypy handles dynamic base classes
# https://github.com/python/mypy/issues/2813
return BaseSchema
def _get_field_default(field: dataclasses.Field):
"""
Return a marshmallow default value given a dataclass default value
>>> _get_field_default(dataclasses.field())
<marshmallow.missing>
"""
# Remove `type: ignore` when https://github.com/python/mypy/issues/6910 is fixed
default_factory = field.default_factory # type: ignore
if default_factory is not dataclasses.MISSING:
return default_factory
elif field.default is dataclasses.MISSING:
return marshmallow.missing
return field.default
def NewType(
name: str,
typ: Type[_U],
field: Optional[Type[marshmallow.fields.Field]] = None,
**kwargs,
) -> Callable[[_U], _U]:
"""NewType creates simple unique types
to which you can attach custom marshmallow attributes.
All the keyword arguments passed to this function will be transmitted
to the marshmallow field constructor.
>>> import marshmallow.validate
>>> IPv4 = NewType('IPv4', str, validate=marshmallow.validate.Regexp(r'^([0-9]{1,3}\\.){3}[0-9]{1,3}$'))
>>> @dataclass
... class MyIps:
... ips: List[IPv4]
>>> MyIps.Schema().load({"ips": ["0.0.0.0", "grumble grumble"]})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'ips': {1: ['String does not match expected pattern.']}}
>>> MyIps.Schema().load({"ips": ["127.0.0.1"]})
MyIps(ips=['127.0.0.1'])
>>> Email = NewType('Email', str, field=marshmallow.fields.Email)
>>> @dataclass
... class ContactInfo:
... mail: Email = dataclasses.field(default="[email protected]")
>>> ContactInfo.Schema().load({})
ContactInfo(mail='[email protected]')
>>> ContactInfo.Schema().load({"mail": "grumble grumble"})
Traceback (most recent call last):
...
marshmallow.exceptions.ValidationError: {'mail': ['Not a valid email address.']}
"""
new_type.__name__ = name
new_type.__supertype__ = typ # type: ignore
new_type._marshmallow_field = field # type: ignore
new_type._marshmallow_args = kwargs # type: ignore
return new_type
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 34.133791 | 110 | 0.647538 |
b92e1fb5ed102dbd1d7dc2d4b0ef720e265a976f
| 1,045 |
py
|
Python
|
electrum_trc/scripts/txradar.py
|
TheSin-/electrum-trc
|
d2f5b15fd4399a9248cce0d63e20128f3f54e69c
|
[
"MIT"
] | 1 |
2019-08-20T18:05:32.000Z
|
2019-08-20T18:05:32.000Z
|
electrum_trc/scripts/txradar.py
|
TheSin-/electrum-trc
|
d2f5b15fd4399a9248cce0d63e20128f3f54e69c
|
[
"MIT"
] | 1 |
2022-03-14T19:45:31.000Z
|
2022-03-14T19:45:31.000Z
|
electrum_trc/scripts/txradar.py
|
TheSin-/electrum-trc
|
d2f5b15fd4399a9248cce0d63e20128f3f54e69c
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
import sys
import asyncio
from electrum_trc.network import filter_protocol, Network
from electrum_trc.util import create_and_start_event_loop, log_exceptions
try:
txid = sys.argv[1]
except:
print("usage: txradar txid")
sys.exit(1)
loop, stopping_fut, loop_thread = create_and_start_event_loop()
network = Network()
network.start()
asyncio.run_coroutine_threadsafe(f(), loop)
| 28.243243 | 99 | 0.675598 |
b92ef9143bb84fe6d37501129ff559d015cf231e
| 1,091 |
py
|
Python
|
jp.atcoder/dp/dp_g/24586988.py
|
kagemeka/atcoder-submissions
|
91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e
|
[
"MIT"
] | 1 |
2022-02-09T03:06:25.000Z
|
2022-02-09T03:06:25.000Z
|
jp.atcoder/dp/dp_g/24586988.py
|
kagemeka/atcoder-submissions
|
91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e
|
[
"MIT"
] | 1 |
2022-02-05T22:53:18.000Z
|
2022-02-09T01:29:30.000Z
|
jp.atcoder/dp/dp_g/24586988.py
|
kagemeka/atcoder-submissions
|
91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e
|
[
"MIT"
] | null | null | null |
import sys
import typing
import numpy as np
OJ = 'ONLINE_JUDGE'
if sys.argv[-1] == OJ:
from numba import i8, njit
from numba.pycc import CC
cc = CC('my_module')
fn = solve
signature = (i8, i8[:, :])
cc.export(
fn.__name__,
signature,
)(fn)
cc.compile()
exit(0)
from my_module import solve
main()
| 13.810127 | 30 | 0.505041 |
b930187de467bdc99d38231d4b217f6589a62613
| 2,039 |
py
|
Python
|
starteMessung.py
|
jkerpe/TroubleBubble
|
813ad797398b9f338f136bcb96c6c92186d92ebf
|
[
"MIT"
] | null | null | null |
starteMessung.py
|
jkerpe/TroubleBubble
|
813ad797398b9f338f136bcb96c6c92186d92ebf
|
[
"MIT"
] | null | null | null |
starteMessung.py
|
jkerpe/TroubleBubble
|
813ad797398b9f338f136bcb96c6c92186d92ebf
|
[
"MIT"
] | 1 |
2021-08-09T14:57:57.000Z
|
2021-08-09T14:57:57.000Z
|
from datetime import datetime
from pypylon import pylon
import nimmAuf
import smbus2
import os
import argparse
import bestimmeVolumen
from threading import Thread
import time
programmstart = time.time()
# Argumente parsen (bei Aufruf im Terminal z.B. 'starteMessung.py -n 100' eingeben)
ap = argparse.ArgumentParser(description="""Skript zum Aufnehmen von Bildern der Teststrecke und der
Volumenbestimmung von Luftblasen""")
ap.add_argument("-n", "--number", default=400, type=int, help="Anzahl an Frames die aufgenommen werden sollen. Default: 400 Bilder")
ap.add_argument("-fr", "--framerate", default=100, type=int, help="Framerate in fps. Richtwerte: <Flow 3 ml/s:50 fps, 3-6ml/s:100 fps, >6ml/s:200 fps; Default: 100 fps")
args = vars(ap.parse_args())
# Argumente des Parsers extrahieren
numberOfImagesToGrab = args['number']
framerate = args['framerate']
if __name__ == '__main__':
startzeit = time.time()
#Test ob Kamera angeschlossen ist
devices = pylon.TlFactory.GetInstance().EnumerateDevices()
if len(devices) == 0:
print("Keine Kamera angeschlossen oder Kamera woanders geffnet.")
return False
# Test ob Drucksensor angeschlossen ist
try:
bus = smbus2.SMBus(0)
bus.read_i2c_block_data(0x40, 0, 2) # 2 Bytes empfangen
except OSError:
print("Kein Drucksensor angeschlossen")
exit()
# Aus der aktuellen Zeit und den Parametern einen individuellen Ordnernamen generieren
dirname = f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}'
os.mkdir(dirname) # Ordner erstellen
print(f"Ordnername: {dirname}")
beginn = time.time()-programmstart
# Threads zum Aufnehmen und Verarbeiten starten
t_aufnahme = Thread(target=nimmAuf.starte, args=(dirname, numberOfImagesToGrab, framerate, startzeit))
t_tracke = Thread(target=bestimmeVolumen.tracke, args=(dirname, numberOfImagesToGrab))
t_aufnahme.start()
t_tracke.start()
t_aufnahme.join()
t_tracke.join()
| 34.559322 | 169 | 0.703776 |
b93050ad4c3c78860eb79accbddb8566a673cb7e
| 3,211 |
py
|
Python
|
application/services/decart.py
|
Sapfir0/web-premier-eye
|
f060b01e98a923374ea60360ba133caaa654b6c7
|
[
"MIT"
] | null | null | null |
application/services/decart.py
|
Sapfir0/web-premier-eye
|
f060b01e98a923374ea60360ba133caaa654b6c7
|
[
"MIT"
] | null | null | null |
application/services/decart.py
|
Sapfir0/web-premier-eye
|
f060b01e98a923374ea60360ba133caaa654b6c7
|
[
"MIT"
] | 1 |
2020-01-06T18:27:45.000Z
|
2020-01-06T18:27:45.000Z
|
import os
import tempfile
| 28.927928 | 117 | 0.62753 |
b9312660991c249b5bd6faf4ead63f4150e99b7e
| 4,915 |
py
|
Python
|
pysnmp/EXTREME-RTSTATS-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 11 |
2021-02-02T16:27:16.000Z
|
2021-08-31T06:22:49.000Z
|
pysnmp/EXTREME-RTSTATS-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 75 |
2021-02-24T17:30:31.000Z
|
2021-12-08T00:01:18.000Z
|
pysnmp/EXTREME-RTSTATS-MIB.py
|
agustinhenze/mibs.snmplabs.com
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
[
"Apache-2.0"
] | 10 |
2019-04-30T05:51:36.000Z
|
2022-02-16T03:33:41.000Z
|
#
# PySNMP MIB module EXTREME-RTSTATS-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/EXTREME-BASE-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 18:53:03 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
SingleValueConstraint, ValueSizeConstraint, ConstraintsUnion, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsUnion", "ValueRangeConstraint", "ConstraintsIntersection")
extremeAgent, = mibBuilder.importSymbols("EXTREME-BASE-MIB", "extremeAgent")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
Unsigned32, iso, Gauge32, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity, Bits, MibIdentifier, ModuleIdentity, Counter64, Counter32, NotificationType, Integer32, IpAddress, TimeTicks = mibBuilder.importSymbols("SNMPv2-SMI", "Unsigned32", "iso", "Gauge32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity", "Bits", "MibIdentifier", "ModuleIdentity", "Counter64", "Counter32", "NotificationType", "Integer32", "IpAddress", "TimeTicks")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
extremeRtStats = ModuleIdentity((1, 3, 6, 1, 4, 1, 1916, 1, 11))
if mibBuilder.loadTexts: extremeRtStats.setLastUpdated('9906240000Z')
if mibBuilder.loadTexts: extremeRtStats.setOrganization('Extreme Networks, Inc.')
extremeRtStatsTable = MibTable((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1), )
if mibBuilder.loadTexts: extremeRtStatsTable.setStatus('current')
extremeRtStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1), ).setIndexNames((0, "EXTREME-RTSTATS-MIB", "extremeRtStatsIndex"))
if mibBuilder.loadTexts: extremeRtStatsEntry.setStatus('current')
extremeRtStatsIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535))).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsIndex.setStatus('current')
extremeRtStatsIntervalStart = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 2), TimeTicks()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsIntervalStart.setStatus('current')
extremeRtStatsCRCAlignErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsCRCAlignErrors.setStatus('current')
extremeRtStatsUndersizePkts = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsUndersizePkts.setStatus('current')
extremeRtStatsOversizePkts = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsOversizePkts.setStatus('current')
extremeRtStatsFragments = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsFragments.setStatus('current')
extremeRtStatsJabbers = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsJabbers.setStatus('current')
extremeRtStatsCollisions = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsCollisions.setStatus('current')
extremeRtStatsTotalErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsTotalErrors.setStatus('current')
extremeRtStatsUtilization = MibTableColumn((1, 3, 6, 1, 4, 1, 1916, 1, 11, 1, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 10000))).setMaxAccess("readonly")
if mibBuilder.loadTexts: extremeRtStatsUtilization.setStatus('current')
mibBuilder.exportSymbols("EXTREME-RTSTATS-MIB", extremeRtStatsEntry=extremeRtStatsEntry, extremeRtStatsOversizePkts=extremeRtStatsOversizePkts, extremeRtStatsUndersizePkts=extremeRtStatsUndersizePkts, extremeRtStatsTable=extremeRtStatsTable, extremeRtStatsTotalErrors=extremeRtStatsTotalErrors, extremeRtStats=extremeRtStats, PYSNMP_MODULE_ID=extremeRtStats, extremeRtStatsCollisions=extremeRtStatsCollisions, extremeRtStatsCRCAlignErrors=extremeRtStatsCRCAlignErrors, extremeRtStatsJabbers=extremeRtStatsJabbers, extremeRtStatsIndex=extremeRtStatsIndex, extremeRtStatsUtilization=extremeRtStatsUtilization, extremeRtStatsIntervalStart=extremeRtStatsIntervalStart, extremeRtStatsFragments=extremeRtStatsFragments)
| 114.302326 | 713 | 0.790031 |
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