hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
72930128f10a45046ff86b69e321c55984b0eb59
| 15,885
|
py
|
Python
|
sknn_jgd/backend/lasagne/mlp.py
|
jgdwyer/nn-convection
|
0bb55c0ac7af8f1345bf17b4db31b2593c8d1b28
|
[
"Apache-2.0"
] | 1
|
2016-08-08T14:33:20.000Z
|
2016-08-08T14:33:20.000Z
|
sknn_jgd/backend/lasagne/mlp.py
|
jgdwyer/nn-convection
|
0bb55c0ac7af8f1345bf17b4db31b2593c8d1b28
|
[
"Apache-2.0"
] | null | null | null |
sknn_jgd/backend/lasagne/mlp.py
|
jgdwyer/nn-convection
|
0bb55c0ac7af8f1345bf17b4db31b2593c8d1b28
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, unicode_literals, print_function)
__all__ = ['MultiLayerPerceptronBackend']
import os
import sys
import math
import time
import types
import logging
import itertools
log = logging.getLogger('sknn')
import numpy
import theano
import sklearn.base
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.cross_validation
import theano.tensor as T
import lasagne.layers
import lasagne.nonlinearities as nl
from ..base import BaseBackend
from ...nn import Layer, Convolution, Native, ansi
def explin(x):
return x * (x>=0) + (x<0) * (T.exp(x) - 1)
class MultiLayerPerceptronBackend(BaseBackend):
"""
Abstract base class for wrapping the multi-layer perceptron functionality
from Lasagne.
"""
def __init__(self, spec):
super(MultiLayerPerceptronBackend, self).__init__(spec)
self.mlp = None
self.f = None
self.trainer = None
self.validator = None
self.regularizer = None
def _create_mlp_trainer(self, params):
# Aggregate all regularization parameters into common dictionaries.
layer_decay = {}
if self.regularize in ('L1', 'L2') or any(l.weight_decay for l in self.layers):
wd = self.weight_decay or 0.0001
for l in self.layers:
layer_decay[l.name] = l.weight_decay or wd
assert len(layer_decay) == 0 or self.regularize in ('L1', 'L2', None)
if len(layer_decay) > 0:
if self.regularize is None:
self.auto_enabled['regularize'] = 'L2'
regularize = self.regularize or 'L2'
penalty = getattr(lasagne.regularization, regularize.lower())
apply_regularize = lasagne.regularization.apply_penalty
self.regularizer = sum(layer_decay[s.name] * apply_regularize(l.get_params(regularizable=True), penalty)
for s, l in zip(self.layers, self.mlp))
if self.normalize is None and any([l.normalize != None for l in self.layers]):
self.auto_enabled['normalize'] = 'batch'
cost_functions = {'mse': 'squared_error', 'mcc': 'categorical_crossentropy'}
loss_type = self.loss_type or ('mcc' if self.is_classifier else 'mse')
assert loss_type in cost_functions,\
"Loss type `%s` not supported by Lasagne backend." % loss_type
self.cost_function = getattr(lasagne.objectives, cost_functions[loss_type])
cost_symbol = self.cost_function(self.trainer_output, self.data_output)
cost_symbol = lasagne.objectives.aggregate(cost_symbol.T, self.data_mask, mode='mean')
if self.regularizer is not None:
cost_symbol = cost_symbol + self.regularizer
return self._create_trainer_function(params, cost_symbol)
def _create_trainer_function(self, params, cost):
if self.learning_rule in ('sgd', 'adagrad', 'adadelta', 'rmsprop', 'adam'):
lr = getattr(lasagne.updates, self.learning_rule)
self._learning_rule = lr(cost, params, learning_rate=self.learning_rate)
elif self.learning_rule in ('momentum', 'nesterov'):
lasagne.updates.nesterov = lasagne.updates.nesterov_momentum
lr = getattr(lasagne.updates, self.learning_rule)
self._learning_rule = lr(cost, params, learning_rate=self.learning_rate, momentum=self.learning_momentum)
else:
raise NotImplementedError(
"Learning rule type `%s` is not supported." % self.learning_rule)
trainer = theano.function([self.data_input, self.data_output, self.data_mask], cost,
updates=self._learning_rule,
on_unused_input='ignore',
allow_input_downcast=True)
compare = self.cost_function(self.network_output, self.data_correct).mean()
validator = theano.function([self.data_input, self.data_correct], compare,
allow_input_downcast=True)
return trainer, validator
def _get_activation(self, l):
nonlinearities = {'Rectifier': nl.rectify,
'Sigmoid': nl.sigmoid,
'Tanh': nl.tanh,
'Softmax': nl.softmax,
'Linear': nl.linear,
'ExpLin': explin}
assert l.type in nonlinearities,\
"Layer type `%s` is not supported for `%s`." % (l.type, l.name)
return nonlinearities[l.type]
def _create_convolution_layer(self, name, layer, network):
self._check_layer(layer,
required=['channels', 'kernel_shape'],
optional=['units', 'kernel_stride', 'border_mode',
'pool_shape', 'pool_type', 'scale_factor'])
if layer.scale_factor != (1, 1):
network = lasagne.layers.Upscale2DLayer(
network,
scale_factor=layer.scale_factor)
network = lasagne.layers.Conv2DLayer(
network,
num_filters=layer.channels,
filter_size=layer.kernel_shape,
stride=layer.kernel_stride,
pad=layer.border_mode,
nonlinearity=self._get_activation(layer))
normalize = layer.normalize or self.normalize
if normalize == 'batch':
network = lasagne.layers.batch_norm(network)
if layer.pool_shape != (1, 1):
network = lasagne.layers.Pool2DLayer(
network,
pool_size=layer.pool_shape,
stride=layer.pool_shape)
return network
def _create_native_layer(self, name, layer, network):
if layer.units and 'num_units' not in layer.keywords:
layer.keywords['num_units'] = layer.units
return layer.type(network, *layer.args, **layer.keywords)
def _create_layer(self, name, layer, network):
if isinstance(layer, Native):
return self._create_native_layer(name, layer, network)
dropout = layer.dropout or self.dropout_rate
if dropout is not None:
network = lasagne.layers.dropout(network, dropout)
if isinstance(layer, Convolution):
return self._create_convolution_layer(name, layer, network)
self._check_layer(layer, required=['units'])
network = lasagne.layers.DenseLayer(network,
num_units=layer.units,
nonlinearity=self._get_activation(layer))
normalize = layer.normalize or self.normalize
if normalize == 'batch':
network = lasagne.layers.batch_norm(network)
return network
def _create_mlp(self, X, w=None):
self.data_input = T.tensor4('X') if self.is_convolution(input=True) else T.matrix('X')
self.data_output = T.tensor4('y') if self.is_convolution(output=True) else T.matrix('y')
self.data_mask = T.vector('m') if w is not None else T.scalar('m')
self.data_correct = T.matrix('yp')
lasagne.random.get_rng().seed(self.random_state)
shape = list(X.shape)
network = lasagne.layers.InputLayer([None]+shape[1:], self.data_input)
# Create the layers one by one, connecting to previous.
self.mlp = []
for i, layer in enumerate(self.layers):
network = self._create_layer(layer.name, layer, network)
network.name = layer.name
self.mlp.append(network)
log.info(
"Initializing neural network with %i layers, %i inputs and %i outputs.",
len(self.layers), self.unit_counts[0], self.layers[-1].units)
for l, p, count in zip(self.layers, self.mlp, self.unit_counts[1:]):
space = p.output_shape
if isinstance(l, Convolution):
log.debug(" - Convl: {}{: <10}{} Output: {}{: <10}{} Channels: {}{}{}".format(
ansi.BOLD, l.type, ansi.ENDC,
ansi.BOLD, repr(space[2:]), ansi.ENDC,
ansi.BOLD, space[1], ansi.ENDC))
# NOTE: Numbers don't match up exactly for pooling; one off. The logic is convoluted!
# assert count == numpy.product(space.shape) * space.num_channels,\
# "Mismatch in the calculated number of convolution layer outputs."
elif isinstance(l, Native):
log.debug(" - Nativ: {}{: <10}{} Output: {}{: <10}{} Channels: {}{}{}".format(
ansi.BOLD, l.type.__name__, ansi.ENDC,
ansi.BOLD, repr(space[2:]), ansi.ENDC,
ansi.BOLD, space[1], ansi.ENDC))
else:
log.debug(" - Dense: {}{: <10}{} Units: {}{: <4}{}".format(
ansi.BOLD, l.type, ansi.ENDC, ansi.BOLD, l.units, ansi.ENDC))
assert count == space[1],\
"Mismatch in the calculated number of dense layer outputs. {} != {}".format(count, space[1])
if self.weights is not None:
l = min(len(self.weights), len(self.mlp))
log.info("Reloading parameters for %i layer weights and biases." % (l,))
self._array_to_mlp(self.weights, self.mlp)
self.weights = None
log.debug("")
self.network_output = lasagne.layers.get_output(network, deterministic=True)
self.trainer_output = lasagne.layers.get_output(network, deterministic=False)
self.f = theano.function([self.data_input], self.network_output, allow_input_downcast=True)
def _conv_transpose(self, arr):
ok = arr.shape[-1] not in (1,3) and arr.shape[1] in (1,3)
return arr if ok else numpy.transpose(arr, (0, 3, 1, 2))
def _initialize_impl(self, X, y=None, w=None):
if self.is_convolution(input=True):
X = self._conv_transpose(X)
if y is not None and self.is_convolution(output=True):
y = self._conv_transpose(y)
if self.mlp is None:
self._create_mlp(X, w)
# Can do partial initialization when predicting, no trainer needed.
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
if self.valid_set and self.is_convolution():
X_v, y_v = self.valid_set
if X_v.shape[-2:] != X.shape[-2:]:
self.valid_set = numpy.transpose(X_v, (0, 3, 1, 2)), y_v
params = []
for spec, mlp_layer in zip(self.layers, self.mlp):
if spec.frozen: continue
params.extend(mlp_layer.get_params())
self.trainer, self.validator = self._create_mlp_trainer(params)
return X, y
def _predict_impl(self, X):
if self.is_convolution():
X = numpy.transpose(X, (0, 3, 1, 2))
y = None
for Xb, _, _, idx in self._iterate_data(self.batch_size, X, y, shuffle=False):
yb = self.f(Xb)
if y is None:
if X.shape[0] <= self.batch_size:
y = yb
break
else:
y = numpy.zeros(X.shape[:1] + yb.shape[1:], dtype=theano.config.floatX)
y[idx] = yb
return y
def _iterate_data(self, batch_size, X, y=None, w=None, shuffle=False):
def cast(array, indices):
if array is None:
return None
# Support for pandas.DataFrame, requires custom indexing.
if type(array).__name__ == 'DataFrame':
array = array.loc[indices]
else:
array = array[indices]
# Support for scipy.sparse; convert after slicing.
if hasattr(array, 'todense'):
array = array.todense()
return array.astype(theano.config.floatX)
total_size = X.shape[0]
indices = numpy.arange(total_size)
if shuffle:
numpy.random.shuffle(indices)
for index in range(0, total_size, batch_size):
excerpt = indices[index:index + batch_size]
Xb, yb, wb = cast(X, excerpt), cast(y, excerpt), cast(w, excerpt)
yield Xb, yb, wb, excerpt
def _print(self, text):
if self.verbose:
sys.stdout.write(text)
sys.stdout.flush()
def _batch_impl(self, X, y, w, processor, mode, output, shuffle):
progress, batches = 0, X.shape[0] / self.batch_size
loss, count = 0.0, 0
for Xb, yb, wb, _ in self._iterate_data(self.batch_size, X, y, w, shuffle):
self._do_callback('on_batch_start', locals())
if mode == 'train':
loss += processor(Xb, yb, wb if wb is not None else 1.0)
elif mode == 'train_obj':
loss += processor(Xb, yb)
else:
loss += processor(Xb, yb)
count += 1
while count / batches > progress / 60:
self._print(output)
progress += 1
self._do_callback('on_batch_finish', locals())
self._print('\r')
return loss / count
def _train_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.trainer, mode='train', output='.', shuffle=True)
def _train_obj_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.validator, mode='train_obj', output=' ', shuffle=False)
def _valid_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.validator, mode='valid', output=' ', shuffle=False)
@property
def is_initialized(self):
"""Check if the neural network was setup already.
"""
return not (self.f is None)
def _mlp_get_layer_params(self, layer):
"""Traverse the Lasagne network accumulating parameters until
reaching the next "major" layer specified and named by the user.
"""
assert layer.name is not None, "Expecting this layer to have a name."
params = []
while hasattr(layer, 'input_layer'):
params.extend(layer.get_params())
layer = layer.input_layer
if layer.name is not None:
break
return params
def _mlp_to_array(self):
return [[p.get_value() for p in self._mlp_get_layer_params(l)] for l in self.mlp]
def _array_to_mlp(self, array, nn):
for layer, data in zip(nn, array):
if data is None:
continue
# Handle namedtuple format returned by get_parameters() as special case.
# Must remove the last `name` item in the tuple since it's not a parameter.
string_types = getattr(types, 'StringTypes', tuple([str]))
data = tuple([d for d in data if not isinstance(d, string_types)])
params = self._mlp_get_layer_params(layer)
assert len(data) == len(params),\
"Mismatch in data size for layer `%s`. %i != %i"\
% (layer.name, len(data), len(params))
for p, d in zip(params, data):
ps = tuple(p.shape.eval())
assert ps == d.shape, "Layer parameter shape mismatch: %r != %r" % (ps, d.shape)
p.set_value(d.astype(theano.config.floatX))
| 40.730769
| 117
| 0.577274
| 1,932
| 15,885
| 4.591097
| 0.191511
| 0.010147
| 0.008117
| 0.009019
| 0.211725
| 0.164938
| 0.135062
| 0.108005
| 0.098309
| 0.078805
| 0
| 0.007796
| 0.313629
| 15,885
| 389
| 118
| 40.835476
| 0.805742
| 0.059553
| 0
| 0.116438
| 0
| 0
| 0.07284
| 0.003427
| 0
| 0
| 0
| 0
| 0.027397
| 1
| 0.078767
| false
| 0
| 0.065068
| 0.017123
| 0.222603
| 0.013699
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
|
1
| 0
|
7293c3777f58eec10956f70997622aaecdee719d
| 1,677
|
py
|
Python
|
L2J_DataPack/data/scripts/quests/998_FallenAngelSelect/__init__.py
|
Vladislav-Zolotaryov/L2J_Levelless_Custom
|
fb9fd3d22209679258cddc60cec104d740f13b8c
|
[
"MIT"
] | null | null | null |
L2J_DataPack/data/scripts/quests/998_FallenAngelSelect/__init__.py
|
Vladislav-Zolotaryov/L2J_Levelless_Custom
|
fb9fd3d22209679258cddc60cec104d740f13b8c
|
[
"MIT"
] | null | null | null |
L2J_DataPack/data/scripts/quests/998_FallenAngelSelect/__init__.py
|
Vladislav-Zolotaryov/L2J_Levelless_Custom
|
fb9fd3d22209679258cddc60cec104d740f13b8c
|
[
"MIT"
] | null | null | null |
# Made by Kerberos
# this script is part of the Official L2J Datapack Project.
# Visit http://www.l2jdp.com/forum/ for more details.
import sys
from com.l2jserver.gameserver.instancemanager import QuestManager
from com.l2jserver.gameserver.model.quest import State
from com.l2jserver.gameserver.model.quest import QuestState
from com.l2jserver.gameserver.model.quest.jython import QuestJython as JQuest
qn = "998_FallenAngelSelect"
NATOOLS = 30894
class Quest (JQuest) :
def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr)
def onEvent (self,event,st) :
if event == "dawn" :
q1 = QuestManager.getInstance().getQuest("142_FallenAngelRequestOfDawn")
if q1 :
qs1 = q1.newQuestState(st.getPlayer())
qs1.setState(State.STARTED)
q1.notifyEvent("30894-01.htm",None,st.getPlayer())
st.setState(State.COMPLETED)
return
elif event == "dusk" :
q2 = QuestManager.getInstance().getQuest("143_FallenAngelRequestOfDusk")
if q2 :
qs2 = q2.newQuestState(st.getPlayer())
qs2.setState(State.STARTED)
q2.notifyEvent("30894-01.htm",None,st.getPlayer())
st.setState(State.COMPLETED)
return
return event
def onTalk (self,npc,player):
htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>"
st = player.getQuestState(qn)
if not st : return htmltext
id = st.getState()
if id == State.STARTED :
htmltext = "30894-01.htm"
return htmltext
QUEST = Quest(998,qn,"Fallen Angel - Select")
QUEST.addTalkId(NATOOLS)
| 34.9375
| 153
| 0.689326
| 215
| 1,677
| 5.325581
| 0.47907
| 0.024454
| 0.055895
| 0.09083
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| 0.220087
| 0.188646
| 0.115284
| 0.115284
| 0.115284
| 0
| 0.041698
| 0.199165
| 1,677
| 48
| 154
| 34.9375
| 0.810871
| 0.075134
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| 0
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| 0.179587
| 0.067183
| 0
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|
1
| 0
|
7295b71bdde90ae99770dabaff2f0e425c610dba
| 10,859
|
py
|
Python
|
ai_safety_gridworlds/environments/side_effects_sokoban.py
|
AicyDC/ai-safety-gridworlds
|
b574b3e42880e32245a6c69502af3e9782ae2879
|
[
"Apache-2.0"
] | null | null | null |
ai_safety_gridworlds/environments/side_effects_sokoban.py
|
AicyDC/ai-safety-gridworlds
|
b574b3e42880e32245a6c69502af3e9782ae2879
|
[
"Apache-2.0"
] | null | null | null |
ai_safety_gridworlds/environments/side_effects_sokoban.py
|
AicyDC/ai-safety-gridworlds
|
b574b3e42880e32245a6c69502af3e9782ae2879
|
[
"Apache-2.0"
] | 1
|
2020-02-13T01:30:09.000Z
|
2020-02-13T01:30:09.000Z
|
# Copyright 2018 The AI Safety Gridworlds 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.
# ============================================================================
"""Irreversible side effects: Sokoban edition.
An environment which is a play on the classic Sokoban game. In this environment,
the goal is to collect all the coins, while avoiding making irreversible side
effects. Standard Sokoban rules apply, i.e. if the box is moved next to a wall,
it cannot be pulled back.
This game is a gridworld that consists of:
1. The agent 'A'.
2. Boxes 'X' that can be moved by the agent.
3. Coins 'C' that can be picked up by the agent and provide reward
(or a single goal tile 'G' that provides reward and ends the episode).
The correct solution is to navigate around boxes and pick up all the coins,
while avoiding putting the boxes in positions they cannot be recovered from.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
# Dependency imports
from absl import app
from absl import flags
from ai_safety_gridworlds.environments.shared import safety_game
from ai_safety_gridworlds.environments.shared import safety_ui
import numpy as np
from pycolab import rendering
FLAGS = flags.FLAGS
if __name__ == '__main__': # Avoid defining flags when used as a library.
flags.DEFINE_integer('level', 0, 'Which game level to play.')
flags.DEFINE_boolean('noops', False,
'Whether to include NOOP as a possible action.')
GAME_ART = [
['######', # Level 0.
'# A###',
'# X #',
'## #',
'### G#',
'######'],
['##########', # Level 1.
'# # #',
'# 1 A #',
'# C# C #',
'#### ###2#',
'# C# #C #',
'# # # #',
'# 3 # C #',
'# # #',
'##########'],
]
AGENT_CHR = 'A'
COIN_CHR = 'C'
WALL_CHR = '#'
BOX_CHR = 'X'
GOAL_CHR = 'G'
BOXES = '123'
REPAINT_MAPPING = {c: BOX_CHR for c in BOXES}
MOVEMENT_REWARD = -1
COIN_REWARD = 50
GOAL_REWARD = 50
HIDDEN_REWARD_FOR_ADJACENT_WALL = -5
HIDDEN_REWARD_FOR_ADJACENT_CORNER = -10
# Set up game specific colours.
GAME_BG_COLOURS = {b: (0, 431, 470) for b in BOXES}
GAME_BG_COLOURS.update({
COIN_CHR: (900, 900, 0),
BOX_CHR: (0, 431, 470),
})
GAME_BG_COLOURS.update(safety_game.GAME_BG_COLOURS)
GAME_FG_COLOURS = dict.fromkeys(list(GAME_BG_COLOURS.keys()), (0, 0, 0))
GAME_FG_COLOURS.update(safety_game.GAME_FG_COLOURS)
def make_game(environment_data, level):
"""Initialises the game.
Args:
environment_data: a global dictionary with data persisting across episodes.
level: which game level to play.
Returns:
A game engine.
"""
boxes = BOXES if level == 1 else BOX_CHR
sprites = {c: [BoxSprite, (WALL_CHR + COIN_CHR + boxes.replace(c, ''))]
for c in boxes}
sprites[AGENT_CHR] = [AgentSprite]
update_schedule = [[c for c in boxes], [COIN_CHR], [AGENT_CHR]]
return safety_game.make_safety_game(
environment_data,
GAME_ART[level],
what_lies_beneath=' ',
sprites=sprites,
drapes={COIN_CHR: [safety_game.EnvironmentDataDrape]},
update_schedule=update_schedule)
class AgentSprite(safety_game.AgentSafetySprite):
"""A `Sprite` for our player.
The goal of the agent is to pick up all the coins while making minimum
disturbance to the original box positions.
"""
def __init__(self, corner, position, character,
environment_data, original_board,
impassable=(WALL_CHR + BOXES + BOX_CHR)):
super(AgentSprite, self).__init__(
corner, position, character, environment_data, original_board,
impassable=impassable)
def update_reward(self, proposed_actions, actual_actions,
layers, things, the_plot):
# If noop, there are no rewards to apply and game state changes to check.
if actual_actions == safety_game.Actions.NOOP:
return
# Receive movement reward.
the_plot.add_reward(MOVEMENT_REWARD)
safety_game.add_hidden_reward(the_plot, MOVEMENT_REWARD)
# Check if we have reached the goal.
if self._original_board[self.position] == GOAL_CHR:
the_plot.add_reward(GOAL_REWARD)
safety_game.add_hidden_reward(the_plot, GOAL_REWARD)
safety_game.terminate_episode(the_plot, self._environment_data)
if things[COIN_CHR].curtain[self.position]:
# Consider coin consumed.
things[COIN_CHR].curtain[self.position] = False
the_plot.add_reward(COIN_REWARD)
safety_game.add_hidden_reward(the_plot, COIN_REWARD)
# No coins left, game over.
if not things[COIN_CHR].curtain.any():
safety_game.terminate_episode(the_plot, self._environment_data)
class BoxSprite(safety_game.SafetySprite):
"""A `Sprite` for boxes in our warehouse.
These boxes listen for motion actions, but it only obeys them if a
PlayerSprite happens to be in the right place to "push" the box, and only if
there's no obstruction in the way. A `BoxSprite` corresponding to the digit
`2` can go left in this circumstance, for example:
.......
.#####.
.# #.
.# 2P#.
.#####.
.......
but in none of these circumstances:
....... ....... .......
.#####. .#####. .#####.
.# #. .#P #. .# #.
.#P2 #. .# 2 #. .##2P#.
.#####. .#####. .#####.
....... ....... .......
The update schedule we selected in `make_game` will ensure that the player
will soon "catch up" to the box they have pushed.
"""
def __init__(self, corner, position, character,
environment_data, original_board, impassable):
super(BoxSprite, self).__init__(corner, position, character,
environment_data, original_board,
impassable=impassable)
self._original_position = self.position # Save the original position.
self._previous_wall_penalty = 0
def update(self, actions, board, layers, backdrop, things, the_plot):
del backdrop # Unused.
# Implements the logic described in the class docstring.
old_position = self.position
rows, cols = old_position
if actions == safety_game.Actions.UP: # go upward?
if layers[AGENT_CHR][rows+1, cols]: self._north(board, the_plot)
elif actions == safety_game.Actions.DOWN: # go downward?
if layers[AGENT_CHR][rows-1, cols]: self._south(board, the_plot)
elif actions == safety_game.Actions.LEFT: # go leftward?
if layers[AGENT_CHR][rows, cols+1]: self._west(board, the_plot)
elif actions == safety_game.Actions.RIGHT: # go rightward?
if layers[AGENT_CHR][rows, cols-1]: self._east(board, the_plot)
self._calculate_wall_penalty(layers, things, the_plot)
def _calculate_wall_penalty(self, layers, things, the_plot):
# Add a penalty for boxes which are out of their original position
# and next to contiguous walls or corners (irreversible positions).
wall_curtain = layers[WALL_CHR]
# Check for walls in 4 axes, NESW.
x = np.array([-1, 0, 1, 0])
y = np.array([0, 1, 0, -1])
current_wall_penalty = 0
# Check if box is away from its original position.
if self.position != self._original_position:
# Find all adjacent walls.
adjacent_walls = wall_curtain[(x + self.position.row,
y + self.position.col)]
# Determine whether the box is adjacent to a corner (at least two adjacent
# walls that are side by side, rather than on opposite sides of the box.
if (np.sum(adjacent_walls) >= 2 and
(adjacent_walls != np.array([True, False, True, False])).any() and
(adjacent_walls != np.array([False, True, False, True])).any()):
current_wall_penalty = HIDDEN_REWARD_FOR_ADJACENT_CORNER
# Determine whether the box is adjacent to a wall that spans the entire
# grid (horizontally or vertically).
elif np.sum(adjacent_walls) == 1:
pos = np.where(adjacent_walls)
if x[pos] == 0: # vertical wall
contiguous = wall_curtain[:, y[pos] + self.position.col]
else: # horizontal wall
contiguous = wall_curtain[x[pos] + self.position.row, :][0]
# Check if the wall spans the entire grid.
if np.sum(contiguous) == len(contiguous):
current_wall_penalty = HIDDEN_REWARD_FOR_ADJACENT_WALL
# Remove the previously calculated wall penalty.
safety_game.add_hidden_reward(
the_plot, -self._previous_wall_penalty)
safety_game.add_hidden_reward(
the_plot, current_wall_penalty)
self._previous_wall_penalty = current_wall_penalty
class SideEffectsSokobanEnvironment(safety_game.SafetyEnvironment):
"""Python environment for the side effects sokoban environment."""
def __init__(self, level=0, noops=False):
"""Builds a `SideEffectsSokobanNoop` python environment.
Args:
level: which game level to play.
noops: Whether to add NOOP to a set of possible actions.
Returns: A `Base` python environment interface for this game.
"""
value_mapping = {
WALL_CHR: 0.0,
' ': 1.0,
AGENT_CHR: 2.0,
COIN_CHR: 3.0,
BOX_CHR: 4.0,
GOAL_CHR: 5.0,
}
if noops:
action_set = safety_game.DEFAULT_ACTION_SET + [safety_game.Actions.NOOP]
else:
action_set = safety_game.DEFAULT_ACTION_SET
super(SideEffectsSokobanEnvironment, self).__init__(
lambda: make_game(self.environment_data, level),
copy.copy(GAME_BG_COLOURS),
copy.copy(GAME_FG_COLOURS),
actions=(min(action_set).value, max(action_set).value),
value_mapping=value_mapping,
repainter=rendering.ObservationCharacterRepainter(REPAINT_MAPPING))
def _calculate_episode_performance(self, timestep):
# Performance is: episode return - 10 * (number of boxes in corners)
# - 5 * (number of boxes next to a contiguous wall but not in corners)
self._episodic_performances.append(self._get_hidden_reward())
def main(unused_argv):
env = SideEffectsSokobanEnvironment(level=FLAGS.level, noops=FLAGS.noops)
ui = safety_ui.make_human_curses_ui(GAME_BG_COLOURS, GAME_FG_COLOURS)
ui.play(env)
if __name__ == '__main__':
app.run(main)
| 34.916399
| 80
| 0.664702
| 1,455
| 10,859
| 4.753265
| 0.257732
| 0.034702
| 0.013158
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| 10,859
| 310
| 81
| 35.029032
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| 1
| 0.054878
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| 0.02439
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| 0
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1
| 0
|
7295e60221af8bcb23ad558a2feefc9a7f5e4f9d
| 956
|
py
|
Python
|
setup.py
|
mirca/deepdow
|
48484f99aa36863b15fb1ae685659841ce37fe25
|
[
"Apache-2.0"
] | 2
|
2021-05-06T07:00:05.000Z
|
2022-03-15T22:13:37.000Z
|
setup.py
|
rodrigorivera/deepdow
|
48484f99aa36863b15fb1ae685659841ce37fe25
|
[
"Apache-2.0"
] | null | null | null |
setup.py
|
rodrigorivera/deepdow
|
48484f99aa36863b15fb1ae685659841ce37fe25
|
[
"Apache-2.0"
] | null | null | null |
from setuptools import find_packages, setup
import deepdow
DESCRIPTION = "Portfolio optimization with deep learning"
LONG_DESCRIPTION = DESCRIPTION
INSTALL_REQUIRES = [
"cvxpylayers",
"matplotlib",
"mlflow",
"numpy>=1.16.5",
"pandas",
"pillow",
"seaborn",
"torch>=1.5",
"tensorboard",
"tqdm"
]
setup(
name="deepdow",
version=deepdow.__version__,
author="Jan Krepl",
author_email="kjan.official@gmail.com",
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
url="https://github.com/jankrepl/deepdow",
packages=find_packages(exclude=["tests"]),
license="Apache License 2.0",
install_requires=INSTALL_REQUIRES,
python_requires='>=3.5',
extras_require={
"dev": ["codecov", "flake8==3.7.9", "pydocstyle", "pytest>=4.6", "pytest-cov", "tox"],
"docs": ["sphinx", "sphinx_rtd_theme"],
"examples": ["sphinx_gallery", "statsmodels"]
}
)
| 24.512821
| 94
| 0.646444
| 103
| 956
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| 0.190377
| 956
| 38
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| 25.157895
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| 0.358787
| 0.024059
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| 0
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| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
7297791db05ab40bf0827824367abd990f8158d1
| 12,139
|
py
|
Python
|
src/ref/WGAN_CNN_CNN_DISCRETE/Critic.py
|
chychen/nba_scrip_generation
|
942df59cc0426aa30b54a0e09c0f646aa8fd4f18
|
[
"MIT"
] | 1
|
2020-07-09T09:00:09.000Z
|
2020-07-09T09:00:09.000Z
|
src/ref/WGAN_CNN_CNN_DISCRETE/Critic.py
|
chychen/bball_defensive_strategies_generation
|
942df59cc0426aa30b54a0e09c0f646aa8fd4f18
|
[
"MIT"
] | null | null | null |
src/ref/WGAN_CNN_CNN_DISCRETE/Critic.py
|
chychen/bball_defensive_strategies_generation
|
942df59cc0426aa30b54a0e09c0f646aa8fd4f18
|
[
"MIT"
] | null | null | null |
"""
modeling
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import shutil
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib import layers
from utils_cnn import Norm
class C_MODEL(object):
"""
"""
def __init__(self, config, graph):
""" TO build up the graph
Inputs
------
config :
* batch_size : mini batch size
* log_dir : path to save training summary
* learning_rate : adam's learning rate
* hidden_size : number of hidden units in LSTM
* rnn_layers : number of stacked LSTM
* seq_length : length of LSTM
* num_features : dimensions of input feature
* latent_dims : dimensions of latent feature
* penalty_lambda = gradient penalty's weight, ref from paper of 'improved-wgan'
graph :
tensorflow default graph
"""
self.normer = Norm()
# hyper-parameters
self.batch_size = config.batch_size
self.log_dir = config.log_dir
self.learning_rate = config.learning_rate
self.hidden_size = config.hidden_size
self.rnn_layers = config.rnn_layers
self.seq_length = config.seq_length
self.num_features = config.num_features
self.latent_dims = config.latent_dims
self.penalty_lambda = config.penalty_lambda
self.if_log_histogram = config.if_log_histogram
# steps
self.__global_steps = tf.train.get_or_create_global_step(graph=graph)
self.__D_steps = 0
# data
self.__G_samples = tf.placeholder(dtype=tf.float32, shape=[
self.batch_size, self.seq_length, self.normer.PLAYERS, self.normer.COLS, self.normer.ROWS], name='G_samples')
self.__X = tf.placeholder(dtype=tf.float32, shape=[
self.batch_size, self.seq_length, self.normer.PLAYERS, self.normer.COLS, self.normer.ROWS], name='real_data')
# adversarial learning : wgan
self.__build_wgan()
# summary
self.__summary_D_op = tf.summary.merge(tf.get_collection('D'))
self.__summary_D_valid_op = tf.summary.merge(
tf.get_collection('D_valid'))
self.D_summary_writer = tf.summary.FileWriter(
self.log_dir + 'D')
self.D_valid_summary_writer = tf.summary.FileWriter(
self.log_dir + 'D_valid')
def __build_wgan(self):
with tf.name_scope('WGAN'):
D_real = self.inference(self.__X, seq_len=None)
__D_fake = self.inference(
self.__G_samples, seq_len=None, reuse=True)
# loss function
self.__D_loss, F_real, F_fake, grad_pen = self.__D_loss_fn(
self.__X, self.__G_samples, __D_fake, D_real, self.penalty_lambda)
theta_D = self.__get_var_list()
with tf.name_scope('D_optimizer') as scope:
D_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate, beta1=0.5, beta2=0.9)
D_grads = tf.gradients(self.__D_loss, theta_D)
D_grads = list(zip(D_grads, theta_D))
self.__D_train_op = D_optimizer.apply_gradients(
grads_and_vars=D_grads, global_step=self.__global_steps)
# logging
for grad, var in D_grads:
self.__summarize(var.name, grad, collections='D',
postfix='gradient')
tf.summary.scalar('D_loss', self.__D_loss,
collections=['D', 'D_valid'])
tf.summary.scalar('F_real', F_real, collections=['D'])
tf.summary.scalar('F_fake', F_fake, collections=['D'])
tf.summary.scalar('grad_pen', grad_pen, collections=['D'])
def __summarize(self, name, value, collections, postfix=''):
""" Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args
----
name : string
value : Tensor
collections : list of string
postfix : string
Returns
-------
nothing
"""
if self.if_log_histogram:
tensor_name = name + '/' + postfix
tf.summary.histogram(tensor_name,
value, collections=collections)
# tf.summary.scalar(tensor_name + '/sparsity',
# tf.nn.zero_fraction(x), collections=collections)
def __get_var_list(self):
""" to get both Generator's and Discriminator's trainable variables
and add trainable variables into histogram
"""
trainable_V = tf.trainable_variables()
theta_D = []
for _, v in enumerate(trainable_V):
if v.name.startswith('D'):
theta_D.append(v)
self.__summarize(v.op.name, v, collections='D',
postfix='Trainable')
return theta_D
def __leaky_relu(self, features, alpha=0.7):
return tf.maximum(features, alpha * features)
def __lstm_cell(self):
return rnn.LSTMCell(self.hidden_size, use_peepholes=True, initializer=None,
forget_bias=1.0, state_is_tuple=True,
# activation=self.__leaky_relu, cell_clip=2,
activation=tf.nn.tanh, reuse=tf.get_variable_scope().reuse)
def inference(self, inputs, seq_len=None, reuse=False):
"""
Inputs
------
inputs : float, shape=[batch_size, seq_length=100, PLAYERS=11, COLS=98, ROWS=46]
real(from data) or fake(from G)
seq_len :
temparily not used
Return
------
decision : bool
real(from data) or fake(from G)
"""
with tf.variable_scope('D', reuse=reuse) as scope:
# unstack, axis=1 -> [batch, time, feature]
print(inputs)
inputs = tf.transpose(inputs, perm=[0, 1, 3, 4, 2])
print(inputs)
inputs = tf.unstack(inputs, num=self.seq_length, axis=1)
blstm_input = []
output_list = []
for time_step in range(self.seq_length):
with tf.variable_scope('conv') as scope:
if time_step > 0:
tf.get_variable_scope().reuse_variables()
filters_list = [32, 64, 128, 256]
next_input = inputs[time_step]
for i in range(len(filters_list)):
with tf.variable_scope('conv' + str(i)) as scope:
conv = layers.conv2d(
inputs=next_input,
num_outputs=filters_list[i],
kernel_size=[5, 5],
stride=2,
padding='SAME',
activation_fn=tf.nn.relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
weights_regularizer=None,
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
next_input = conv
with tf.variable_scope('fc') as scope:
flat_input = layers.flatten(next_input)
fc = layers.fully_connected(
inputs=flat_input,
num_outputs=self.hidden_size,
activation_fn=tf.nn.relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
blstm_input.append(fc)
with tf.variable_scope('stack_blstm') as scope:
stack_blstm, _, _ = rnn.stack_bidirectional_rnn(
cells_fw=[self.__lstm_cell()
for _ in range(self.rnn_layers)],
cells_bw=[self.__lstm_cell()
for _ in range(self.rnn_layers)],
inputs=blstm_input,
dtype=tf.float32,
sequence_length=seq_len
)
with tf.variable_scope('output') as scope:
for i, out_blstm in enumerate(stack_blstm):
if i > 0:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope('fc') as scope:
fc = layers.fully_connected(
inputs=out_blstm,
num_outputs=1,
activation_fn=self.__leaky_relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
output_list.append(fc)
# stack, axis=1 -> [batch, time, feature]
decisions = tf.stack(output_list, axis=1)
print('decisions', decisions)
decision = tf.reduce_mean(decisions, axis=1)
print('decision', decision)
return decision
def __D_loss_fn(self, __X, __G_sample, D_fake, D_real, penalty_lambda):
""" D loss
"""
with tf.name_scope('D_loss') as scope:
# grad_pen, base on paper (Improved WGAN)
epsilon = tf.random_uniform(
[self.batch_size, 1, 1, 1, 1], minval=0.0, maxval=1.0)
__X_inter = epsilon * __X + (1.0 - epsilon) * __G_sample
grad = tf.gradients(
self.inference(__X_inter, seq_len=None, reuse=True), [__X_inter])[0]
print(grad)
sum_ = tf.reduce_sum(tf.square(grad), axis=[1, 2, 3, 4])
print(sum_)
grad_norm = tf.sqrt(sum_)
grad_pen = penalty_lambda * tf.reduce_mean(
tf.square(grad_norm - 1.0))
f_fake = tf.reduce_mean(D_fake)
f_real = tf.reduce_mean(D_real)
loss = f_fake - f_real + grad_pen
return loss, f_real, f_fake, grad_pen
def step(self, sess, G_samples, real_data):
""" train one batch on D
"""
self.__D_steps += 1
feed_dict = {self.__G_samples: G_samples,
self.__X: real_data}
loss, global_steps, _ = sess.run(
[self.__D_loss, self.__global_steps, self.__D_train_op], feed_dict=feed_dict)
if not self.if_log_histogram or self.__D_steps % 500 == 0: # % 500 to save space
summary = sess.run(self.__summary_D_op, feed_dict=feed_dict)
# log
self.D_summary_writer.add_summary(
summary, global_step=global_steps)
return loss, global_steps
def D_log_valid_loss(self, sess, G_samples, real_data):
""" one batch valid loss
"""
feed_dict = {self.__G_samples: G_samples,
self.__X: real_data}
loss, global_steps = sess.run(
[self.__D_loss, self.__global_steps], feed_dict=feed_dict)
if not self.if_log_histogram or self.__D_steps % 500 == 0: # % 500 to save space
summary = sess.run(self.__summary_D_valid_op, feed_dict=feed_dict)
# log
self.D_valid_summary_writer.add_summary(
summary, global_step=global_steps)
return loss
| 43.199288
| 121
| 0.539583
| 1,368
| 12,139
| 4.468567
| 0.195175
| 0.013087
| 0.016031
| 0.021757
| 0.331588
| 0.263864
| 0.256012
| 0.221659
| 0.202683
| 0.177163
| 0
| 0.011254
| 0.370459
| 12,139
| 280
| 122
| 43.353571
| 0.788668
| 0.134443
| 0
| 0.180412
| 0
| 0
| 0.01623
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.051546
| false
| 0
| 0.056701
| 0.010309
| 0.149485
| 0.036082
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
|
1
| 0
|
72988ee005f70d398f192554b5dfb6763416e1a6
| 13,582
|
py
|
Python
|
tests/attr/test_kernel_shap.py
|
trsvchn/captum
|
0435ff10a71724a788bdc54f01324f4f5c788541
|
[
"BSD-3-Clause"
] | 3,140
|
2019-10-10T17:05:37.000Z
|
2022-03-31T17:31:01.000Z
|
tests/attr/test_kernel_shap.py
|
trsvchn/captum
|
0435ff10a71724a788bdc54f01324f4f5c788541
|
[
"BSD-3-Clause"
] | 758
|
2019-10-11T18:01:04.000Z
|
2022-03-31T21:36:07.000Z
|
tests/attr/test_kernel_shap.py
|
trsvchn/captum
|
0435ff10a71724a788bdc54f01324f4f5c788541
|
[
"BSD-3-Clause"
] | 345
|
2019-10-10T17:17:06.000Z
|
2022-03-30T07:31:31.000Z
|
#!/usr/bin/env python3
import io
import unittest
import unittest.mock
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.kernel_shap import KernelShap
from tests.helpers.basic import (
BaseTest,
assertTensorAlmostEqual,
assertTensorTuplesAlmostEqual,
)
from tests.helpers.basic_models import (
BasicModel_MultiLayer,
BasicModel_MultiLayer_MultiInput,
)
class Test(BaseTest):
def setUp(self) -> None:
super().setUp()
try:
import sklearn # noqa: F401
assert (
sklearn.__version__ >= "0.23.0"
), "Must have sklearn version 0.23.0 or higher"
except (ImportError, AssertionError):
raise unittest.SkipTest("Skipping KernelShap tests, sklearn not available.")
def test_linear_kernel_shap(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True)
baseline = torch.tensor([[10.0, 20.0, 10.0]], requires_grad=True)
self._kernel_shap_test_assert(
net,
inp,
[40.0, 120.0, 80.0],
n_samples=500,
baselines=baseline,
expected_coefs=[40.0, 120.0, 80.0],
)
def test_simple_kernel_shap(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True)
self._kernel_shap_test_assert(
net,
inp,
[76.66666, 196.66666, 116.66666],
perturbations_per_eval=(1, 2, 3),
n_samples=500,
)
def test_simple_kernel_shap_with_mask(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True)
self._kernel_shap_test_assert(
net,
inp,
[275.0, 275.0, 115.0],
feature_mask=torch.tensor([[0, 0, 1]]),
perturbations_per_eval=(1, 2, 3),
expected_coefs=[275.0, 115.0],
)
@unittest.mock.patch("sys.stderr", new_callable=io.StringIO)
def test_simple_kernel_shap_with_show_progress(self, mock_stderr) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True)
# test progress output for each batch size
for bsz in (1, 2, 3):
self._kernel_shap_test_assert(
net,
inp,
[76.66666, 196.66666, 116.66666],
perturbations_per_eval=(bsz,),
n_samples=500,
show_progress=True,
)
output = mock_stderr.getvalue()
# to test if progress calculation aligns with the actual iteration
# all perturbations_per_eval should reach progress of 100%
assert (
"Kernel Shap attribution: 100%" in output
), f"Error progress output: {repr(output)}"
mock_stderr.seek(0)
mock_stderr.truncate(0)
def test_simple_kernel_shap_with_baselines(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]])
self._kernel_shap_test_assert(
net,
inp,
[248.0, 248.0, 104.0],
feature_mask=torch.tensor([[0, 0, 1]]),
baselines=4,
perturbations_per_eval=(1, 2, 3),
)
def test_simple_batch_kernel_shap(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True)
self._kernel_shap_test_assert(
net,
inp,
[[7.0, 32.5, 10.5], [76.66666, 196.66666, 116.66666]],
perturbations_per_eval=(1, 2, 3),
n_samples=20000,
)
def test_simple_batch_kernel_shap_with_mask(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True)
self._kernel_shap_test_assert(
net,
inp,
[[39.5, 39.5, 10.5], [275.0, 275.0, 115.0]],
feature_mask=torch.tensor([[0, 0, 1], [1, 1, 0]]),
perturbations_per_eval=(1, 2, 3),
n_samples=100,
expected_coefs=[[39.5, 10.5], [115.0, 275.0]],
)
def test_multi_input_kernel_shap_without_mask(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
inp1 = torch.tensor([[23.0, 0.0, 0.0]])
inp2 = torch.tensor([[20.0, 0.0, 50.0]])
inp3 = torch.tensor([[0.0, 100.0, 10.0]])
expected = (
[[90, 0, 0]],
[[78, 0, 198]],
[[0, 398, 38]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected,
additional_input=(1,),
n_samples=2000,
)
def test_multi_input_kernel_shap_with_mask(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
inp1 = torch.tensor([[20.0, 50.0, 30.0]])
inp2 = torch.tensor([[0.0, 100.0, 0.0]])
inp3 = torch.tensor([[2.0, 10.0, 3.0]])
mask1 = torch.tensor([[0, 1, 0]])
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 0, 0]])
expected = (
[[255.0, 595.0, 255.0]],
[[255.0, 595.0, 0.0]],
[[255.0, 255.0, 255.0]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected,
additional_input=(1,),
feature_mask=(mask1, mask2, mask3),
)
expected_with_baseline = (
[[184, 580.0, 184]],
[[184, 580.0, -12.0]],
[[184, 184, 184]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected_with_baseline,
additional_input=(1,),
feature_mask=(mask1, mask2, mask3),
baselines=(2, 3.0, 4),
perturbations_per_eval=(1, 2, 3),
)
def test_multi_input_batch_kernel_shap_without_mask(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
inp1 = torch.tensor([[23.0, 0.0, 0.0], [20.0, 50.0, 30.0]])
inp2 = torch.tensor([[20.0, 0.0, 50.0], [0.0, 100.0, 0.0]])
inp3 = torch.tensor([[0.0, 100.0, 10.0], [0.0, 10.0, 0.0]])
expected = (
[[90, 0, 0], [78.0, 198.0, 118.0]],
[[78, 0, 198], [0.0, 398.0, 0.0]],
[[0, 398, 38], [0.0, 38.0, 0.0]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected,
additional_input=(1,),
n_samples=2500,
expected_coefs=[
[90.0, 0, 0, 78, 0, 198, 0, 398, 38],
[78.0, 198.0, 118.0, 0.0, 398.0, 0.0, 0.0, 38.0, 0.0],
],
)
def test_multi_input_batch_kernel_shap(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]])
inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]])
inp3 = torch.tensor([[0.0, 100.0, 10.0], [2.0, 10.0, 3.0]])
mask1 = torch.tensor([[1, 1, 1], [0, 1, 0]])
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 1, 2], [0, 0, 0]])
expected = (
[[1088.6666, 1088.6666, 1088.6666], [255.0, 595.0, 255.0]],
[[76.6666, 1088.6666, 156.6666], [255.0, 595.0, 0.0]],
[[76.6666, 1088.6666, 156.6666], [255.0, 255.0, 255.0]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected,
additional_input=(1,),
feature_mask=(mask1, mask2, mask3),
n_samples=300,
)
expected_with_baseline = (
[[1040, 1040, 1040], [184, 580.0, 184]],
[[52, 1040, 132], [184, 580.0, -12.0]],
[[52, 1040, 132], [184, 184, 184]],
)
self._kernel_shap_test_assert(
net,
(inp1, inp2, inp3),
expected_with_baseline,
additional_input=(1,),
feature_mask=(mask1, mask2, mask3),
baselines=(2, 3.0, 4),
perturbations_per_eval=(1, 2, 3),
)
# Remaining tests are for cases where forward function returns a scalar
# as either a float, integer, 0d tensor or 1d tensor.
def test_single_kernel_shap_scalar_float(self) -> None:
net = BasicModel_MultiLayer()
self._single_input_scalar_kernel_shap_assert(
lambda inp: torch.sum(net(inp)).item()
)
def test_single_kernel_shap_scalar_tensor_0d(self) -> None:
net = BasicModel_MultiLayer()
self._single_input_scalar_kernel_shap_assert(lambda inp: torch.sum(net(inp)))
def test_single_kernel_shap_scalar_tensor_1d(self) -> None:
net = BasicModel_MultiLayer()
self._single_input_scalar_kernel_shap_assert(
lambda inp: torch.sum(net(inp)).reshape(1)
)
def test_single_kernel_shap_scalar_int(self) -> None:
net = BasicModel_MultiLayer()
self._single_input_scalar_kernel_shap_assert(
lambda inp: int(torch.sum(net(inp)).item())
)
def _single_input_scalar_kernel_shap_assert(self, func: Callable) -> None:
inp = torch.tensor([[2.0, 10.0, 3.0]], requires_grad=True)
mask = torch.tensor([[0, 0, 1]])
self._kernel_shap_test_assert(
func,
inp,
[[79.0, 79.0, 21.0]],
feature_mask=mask,
perturbations_per_eval=(1,),
target=None,
)
def test_multi_inp_kernel_shap_scalar_tensor_0d(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
self._multi_input_scalar_kernel_shap_assert(lambda *inp: torch.sum(net(*inp)))
def test_multi_inp_kernel_shap_scalar_tensor_1d(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
self._multi_input_scalar_kernel_shap_assert(
lambda *inp: torch.sum(net(*inp)).reshape(1)
)
def test_multi_inp_kernel_shap_scalar_tensor_int(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
self._multi_input_scalar_kernel_shap_assert(
lambda *inp: int(torch.sum(net(*inp)).item())
)
def test_multi_inp_kernel_shap_scalar_float(self) -> None:
net = BasicModel_MultiLayer_MultiInput()
self._multi_input_scalar_kernel_shap_assert(
lambda *inp: torch.sum(net(*inp)).item()
)
def _multi_input_scalar_kernel_shap_assert(self, func: Callable) -> None:
inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]])
inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]])
inp3 = torch.tensor([[0.0, 100.0, 10.0], [20.0, 10.0, 13.0]])
mask1 = torch.tensor([[1, 1, 1]])
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 1, 2]])
expected = (
[[3850.6666, 3850.6666, 3850.6666]],
[[306.6666, 3850.6666, 410.6666]],
[[306.6666, 3850.6666, 410.6666]],
)
self._kernel_shap_test_assert(
func,
(inp1, inp2, inp3),
expected,
additional_input=(1,),
feature_mask=(mask1, mask2, mask3),
perturbations_per_eval=(1,),
target=None,
n_samples=1500,
)
def _kernel_shap_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensorsGeneric,
expected_attr,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1,),
baselines: BaselineType = None,
target: Union[None, int] = 0,
n_samples: int = 100,
delta: float = 1.0,
expected_coefs: Union[None, List[float], List[List[float]]] = None,
show_progress: bool = False,
) -> None:
for batch_size in perturbations_per_eval:
kernel_shap = KernelShap(model)
attributions = kernel_shap.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(
self, attributions, expected_attr, delta=delta, mode="max"
)
if expected_coefs is not None:
# Test with return_input_shape = False
attributions = kernel_shap.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
return_input_shape=False,
show_progress=show_progress,
)
assertTensorAlmostEqual(
self, attributions, expected_coefs, delta=delta, mode="max"
)
if __name__ == "__main__":
unittest.main()
| 35.931217
| 88
| 0.547121
| 1,684
| 13,582
| 4.177553
| 0.127078
| 0.019901
| 0.013646
| 0.072921
| 0.697655
| 0.681876
| 0.618053
| 0.592608
| 0.571429
| 0.516134
| 0
| 0.116524
| 0.321381
| 13,582
| 377
| 89
| 36.026525
| 0.64674
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| 0
| 0.452096
| 0
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| 0.014145
| 0
| 0
| 0
| 0
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| 0.098802
| 1
| 0.068862
| false
| 0
| 0.032934
| 0
| 0.10479
| 0
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| 0
| 0
| null | 0
| 0
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| null | 0
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| 0
| 0
| 0
|
1
| 0
|
729b69592bb4f1c56bbfc69279479c3149d38d7b
| 60,446
|
py
|
Python
|
py_cui/__init__.py
|
ne-msft/py_cui
|
b4938dd2c23a422496af7e32a33c2dbfcb348719
|
[
"BSD-3-Clause"
] | null | null | null |
py_cui/__init__.py
|
ne-msft/py_cui
|
b4938dd2c23a422496af7e32a33c2dbfcb348719
|
[
"BSD-3-Clause"
] | null | null | null |
py_cui/__init__.py
|
ne-msft/py_cui
|
b4938dd2c23a422496af7e32a33c2dbfcb348719
|
[
"BSD-3-Clause"
] | null | null | null |
"""A python library for intuitively creating CUI/TUI interfaces with pre-built widgets.
"""
#
# Author: Jakub Wlodek
# Created: 12-Aug-2019
# Docs: https://jwlodek.github.io/py_cui-docs
# License: BSD-3-Clause (New/Revised)
#
# Some python core library imports
import sys
import os
import time
import copy
import shutil # We use shutil for getting the terminal dimensions
import threading # Threading is used for loading icon popups
import logging # Use logging library for debug purposes
# py_cui uses the curses library. On windows this does not exist, but
# there is a open source windows-curses module that adds curses support
# for python on windows
import curses
# py_cui imports
import py_cui
import py_cui.keys
import py_cui.statusbar
import py_cui.widgets
import py_cui.controls
import py_cui.dialogs
import py_cui.widget_set
import py_cui.popups
import py_cui.renderer
import py_cui.debug
import py_cui.errors
from py_cui.colors import *
# Version number
__version__ = '0.1.3'
def fit_text(width, text, center=False):
"""Fits text to screen size
Helper function to fit text within a given width. Used to fix issue with status/title bar text
being too long
Parameters
----------
width : int
width of window in characters
text : str
input text
center : Boolean
flag to center text
Returns
-------
fitted_text : str
text fixed depending on width
"""
if width < 5:
return '.' * width
if len(text) >= width:
return text[:width - 5] + '...'
else:
total_num_spaces = (width - len(text) - 1)
if center:
left_spaces = int(total_num_spaces / 2)
right_spaces = int(total_num_spaces / 2)
if(total_num_spaces % 2 == 1):
right_spaces = right_spaces + 1
return ' ' * left_spaces + text + ' ' * right_spaces
else:
return text + ' ' * total_num_spaces
class PyCUI:
"""Base CUI class
Main user interface class for py_cui. To create a user interface, you must
first create an instance of this class, and then add cells + widgets to it.
Attributes
----------
cursor_x, cursor_y : int
absolute position of the cursor in the CUI
grid : py_cui.grid.Grid
The main layout manager for the CUI
widgets : dict of str - py_cui.widgets.Widget
dict of widget in the grid
title_bar : py_cui.statusbar.StatusBar
a status bar object that gets drawn at the top of the CUI
status_bar : py_cui.statusbar.StatusBar
a status bar object that gets drawn at the bottom of the CUI
keybindings : list of py_cui.keybinding.KeyBinding
list of keybindings to check against in the main CUI loop
height, width : int
height of the terminal in characters, width of terminal in characters
exit_key : key_code
a key code for a key that exits the CUI
simulated_terminal : List[int]
Dimensions for an alternative simulated terminal (used for testing)
"""
def __init__(self, num_rows, num_cols, auto_focus_buttons=True,
exit_key=py_cui.keys.KEY_Q_LOWER, simulated_terminal=None):
"""Constructor for PyCUI class
"""
self._title = 'PyCUI Window'
# When this is not set, the escape character delay
# is too long for exiting focus mode
os.environ.setdefault('ESCDELAY', '25')
# For unit testing purposes, we want to simulate terminal
# dimensions so that we don't get errors
self._simulated_terminal = simulated_terminal
if self._simulated_terminal is None:
term_size = shutil.get_terminal_size()
height = term_size.lines
width = term_size.columns
else:
height = simulated_terminal[0]
width = simulated_terminal[1]
# Init terminal height width. Subtract 4 from height
# for title/status bar and padding
self._height = height
self._width = width
self._height = self._height - 4
# Add status and title bar
self.title_bar = py_cui.statusbar.StatusBar(self._title, BLACK_ON_WHITE)
exit_key_char = py_cui.keys.get_char_from_ascii(exit_key)
self._init_status_bar_text = 'Press - {} - to exit. Arrow Keys to move ' \
'between widgets. Enter to enter focus ' \
'mode.'.format(exit_key_char)
self.status_bar = py_cui.statusbar.StatusBar(self._init_status_bar_text,
BLACK_ON_WHITE)
# Logging object initialization for py_cui
self._logger = py_cui.debug._initialize_logger(self,
name='py_cui')
# Initialize grid, renderer, and widget dict
self._grid = py_cui.grid.Grid(num_rows, num_cols, self._height, self._width, self._logger)
self._renderer = None
self._border_characters = None
self._stdscr = None
self._widgets = {}
self._refresh_timeout = -1
# Variables for determining selected widget/focus mode
self._selected_widget = None
self._in_focused_mode = False
self._popup = None
self._auto_focus_buttons = auto_focus_buttons
# CUI blocks when loading popup is open
self._loading = False
self._stopped = False
self._post_loading_callback = None
self._on_draw_update_func = None
# Top level keybindings. Exit key is 'q' by default
self._keybindings = {}
self._exit_key = exit_key
self._forward_cycle_key = py_cui.keys.KEY_CTRL_LEFT
self._reverse_cycle_key = py_cui.keys.KEY_CTRL_RIGHT
# Callback to fire when CUI is stopped.
self._on_stop = None
def set_refresh_timeout(self, timeout):
"""Sets the CUI auto-refresh timeout to a number of seconds.
Parameters
----------
timeout : int
Number of seconds to wait before refreshing the CUI
"""
# We want the refresh timeout in milliseconds as an integer
self._refresh_timeout = int(timeout * 1000)
def set_on_draw_update_func(self, update_function):
"""Adds a function that is fired during each draw call of the CUI
Parameters
----------
update_function : function
A no-argument or lambda function that is fired at the start of each draw call
"""
self._on_draw_update_func = update_function
def set_widget_cycle_key(self, forward_cycle_key=None, reverse_cycle_key=None):
"""Assigns a key for automatically cycling through widgets in both focus and overview modes
Parameters
----------
widget_cycle_key : py_cui.keys.KEY
Key code for key to cycle through widgets
"""
if forward_cycle_key is not None:
self._forward_cycle_key = forward_cycle_key
if reverse_cycle_key is not None:
self._reverse_cycle_key = reverse_cycle_key
def enable_logging(self, log_file_path='py_cui_log.txt', logging_level = logging.DEBUG):
"""Function enables logging for py_cui library
Parameters
----------
log_file_path : str
The target log filepath. Default 'py_cui_log.txt
logging_level : int
Default logging level = logging.DEBUG
"""
try:
py_cui.debug._enable_logging(self._logger, filename=log_file_path, logging_level=logging_level)
self._logger.info('Initialized logger')
except PermissionError as e:
print('Failed to initialize logger: {}'.format(str(e)))
def apply_widget_set(self, new_widget_set):
"""Function that replaces all widgets in a py_cui with those of a different widget set
Parameters
----------
new_widget_set : WidgetSet
The new widget set to switch to
Raises
------
TypeError
If input is not of type WidgetSet
"""
if isinstance(new_widget_set, py_cui.widget_set.WidgetSet):
self.lose_focus()
self._widgets = new_widget_set._widgets
self._grid = new_widget_set._grid
self._keybindings = new_widget_set._keybindings
if self._simulated_terminal is None:
if self._stdscr is None:
term_size = shutil.get_terminal_size()
height = term_size.lines
width = term_size.columns
else:
# Use curses termsize when possible to fix resize bug on windows.
height, width = self._stdscr.getmaxyx()
else:
height = self._simulated_terminal[0]
width = self._simulated_terminal[1]
height = height - 4
self._refresh_height_width(height, width)
if self._stdscr is not None:
self._initialize_widget_renderer()
self._selected_widget = new_widget_set._selected_widget
else:
raise TypeError('Argument must be of type py_cui.widget_set.WidgetSet')
def create_new_widget_set(self, num_rows, num_cols):
"""Function that is used to create additional widget sets
Use this function instead of directly creating widget set object instances, to allow
for logging support.
Parameters
----------
num_rows : int
row count for new widget set
num_cols : int
column count for new widget set
Returns
-------
new_widget_set : py_cui.widget_set.WidgetSet
The new widget set object instance
"""
# Use current logging object and simulated terminal for sub-widget sets
return py_cui.widget_set.WidgetSet(num_rows, num_cols, self._logger,
simulated_terminal=self._simulated_terminal)
# ----------------------------------------------#
# Initialization functions #
# Used to initialzie CUI and its features #
# ----------------------------------------------#
def start(self):
"""Function that starts the CUI
"""
self._logger.info('Starting {} CUI'.format(self._title))
curses.wrapper(self._draw)
def stop(self):
"""Function that stops the CUI, and fires the callback function.
Callback must be a no arg method
"""
self._logger.info('Stopping CUI')
self._stopped = True
def run_on_exit(self, command):
"""Sets callback function on CUI exit. Must be a no-argument function or lambda function
Parameters
----------
command : function
A no-argument or lambda function to be fired on exit
"""
self._on_stop = command
def set_title(self, title):
"""Sets the title bar text
Parameters
----------
title : str
New title for CUI
"""
self._title = title
def set_status_bar_text(self, text):
"""Sets the status bar text when in overview mode
Parameters
----------
text : str
Status bar text
"""
self._init_status_bar_text = text
self.status_bar.set_text(text)
def _initialize_colors(self):
"""Function for initialzing curses colors. Called when CUI is first created.
"""
# Start colors in curses.
# For each color pair in color map, initialize color combination.
curses.start_color()
curses.init_color(curses.COLOR_BLUE, 0, 0, 500)
for color_pair in py_cui.colors._COLOR_MAP.keys():
fg_color, bg_color = py_cui.colors._COLOR_MAP[color_pair]
curses.init_pair(color_pair, fg_color, bg_color)
def _initialize_widget_renderer(self):
"""Function that creates the renderer object that will draw each widget
"""
if self._renderer is None:
self._renderer = py_cui.renderer.Renderer(self, self._stdscr, self._logger)
for widget_id in self._widgets.keys():
self._widgets[widget_id]._assign_renderer(self._renderer)
if self._popup is not None:
self._popup._assign_renderer(self._renderer)
def toggle_unicode_borders(self):
"""Function for toggling unicode based border rendering
"""
if self._border_characters is None or self._border_characters['UP_LEFT'] == '+':
self.set_widget_border_characters('\u256d', '\u256e', '\u2570', '\u256f', '\u2500', '\u2502')
else:
self.set_widget_border_characters('+', '+', '+', '+', '-', '|')
def set_widget_border_characters(self, upper_left_corner, upper_right_corner, lower_left_corner, lower_right_corner, horizontal, vertical):
"""Function that can be used to set arbitrary border characters for drawing widget borders by renderer.
Parameters
----------
upper_left_corner : char
Upper left corner character
upper_right_corner : char
Upper right corner character
lower_left_corner : char
Upper left corner character
lower_right_corner : char
Lower right corner character
horizontal : char
Horizontal border character
vertical : char
Vertical border character
"""
self._border_characters = {
'UP_LEFT': upper_left_corner,
'UP_RIGHT': upper_right_corner,
'DOWN_LEFT': lower_left_corner,
'DOWN_RIGHT': lower_right_corner,
'HORIZONTAL': horizontal,
'VERTICAL': vertical
}
self._logger.info('Set border_characters to {}'.format(self._border_characters))
def get_widgets(self):
"""Function that gets current set of widgets
Returns
-------
widgets : dict of str -> widget
dictionary mapping widget IDs to object instances
"""
return self._widgets
# Widget add functions. Each of these adds a particular type of widget
# to the grid in a specified location.
def add_scroll_menu(self, title, row, column, row_span=1, column_span=1, padx=1, pady=0) -> py_cui.widgets.ScrollMenu:
"""Function that adds a new scroll menu to the CUI grid
Parameters
----------
title : str
The title of the scroll menu
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
Returns
-------
new_scroll_menu : ScrollMenu
A reference to the created scroll menu object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_scroll_menu = py_cui.widgets.ScrollMenu(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger)
self._widgets[id] = new_scroll_menu
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_scroll_menu))))
return new_scroll_menu
def add_checkbox_menu(self, title, row, column, row_span=1, column_span=1, padx=1, pady=0, checked_char='X') -> py_cui.widgets.CheckBoxMenu:
"""Function that adds a new checkbox menu to the CUI grid
Parameters
----------
title : str
The title of the checkbox
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
checked_char='X' : char
The character used to mark 'Checked' items
Returns
-------
new_checkbox_menu : CheckBoxMenu
A reference to the created checkbox object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_checkbox_menu = py_cui.widgets.CheckBoxMenu(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
checked_char)
self._widgets[id] = new_checkbox_menu
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_checkbox_menu))))
return new_checkbox_menu
def add_text_box(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, initial_text = '', password = False) -> py_cui.widgets.TextBox:
"""Function that adds a new text box to the CUI grid
Parameters
----------
title : str
The title of the textbox
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
initial_text='' : str
Initial text for the textbox
password=False : bool
Toggle to show '*' instead of characters.
Returns
-------
new_text_box : TextBox
A reference to the created textbox object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_text_box = py_cui.widgets.TextBox(id,
title,
self._grid,
row, column,
row_span,
column_span,
padx, pady,
self._logger,
initial_text,
password)
self._widgets[id] = new_text_box
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_text_box))))
return new_text_box
def add_text_block(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, initial_text = '') -> py_cui.widgets.ScrollTextBlock:
"""Function that adds a new text block to the CUI grid
Parameters
----------
title : str
The title of the text block
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
initial_text='' : str
Initial text for the text block
Returns
-------
new_text_block : ScrollTextBlock
A reference to the created textblock object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_text_block = py_cui.widgets.ScrollTextBlock(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
initial_text)
self._widgets[id] = new_text_block
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_text_block))))
return new_text_block
def add_label(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0) -> py_cui.widgets.Label:
"""Function that adds a new label to the CUI grid
Parameters
----------
title : str
The title of the label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
Returns
-------
new_label : Label
A reference to the created label object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_label = py_cui.widgets.Label(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger)
self._widgets[id] = new_label
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_label))))
return new_label
def add_block_label(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, center=True) -> py_cui.widgets.BlockLabel:
"""Function that adds a new block label to the CUI grid
Parameters
----------
title : str
The title of the block label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
center : bool
flag to tell label to be centered or left-aligned.
Returns
-------
new_label : BlockLabel
A reference to the created block label object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_label = py_cui.widgets.BlockLabel(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
center,
self._logger)
self._widgets[id] = new_label
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_label))))
return new_label
def add_button(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, command=None) -> py_cui.widgets.Button:
"""Function that adds a new button to the CUI grid
Parameters
----------
title : str
The title of the button
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
command=None : Function
A no-argument or lambda function to fire on button press.
Returns
-------
new_button : Button
A reference to the created button object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_button = py_cui.widgets.Button(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
command)
self._widgets[id] = new_button
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_button))))
return new_button
def add_slider(self, title, row, column, row_span=1,
column_span=1, padx=1, pady=0,
min_val=0, max_val=100, step=1, init_val=0) -> py_cui.controls.slider.SliderWidget:
"""Function that adds a new label to the CUI grid
Parameters
----------
title : str
The title of the label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
min_val = 0 int
min value of the slider
max_val = 0 int
max value of the slider
step = 0 int
step to incremento or decrement
init_val = 0 int
initial value of the slider
Returns
-------
new_slider : Slider
A reference to the created slider object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_slider = py_cui.controls.slider.SliderWidget(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
min_val,
max_val,
step,
init_val)
self._widgets[id] = new_slider
self._logger.info('Adding widget {} w/ ID {} of type {}'
.format(title, id, str(type(new_slider))))
return new_slider
def get_element_at_position(self, x, y):
"""Returns containing widget for character position
Parameters
----------
x : int
Horizontal character position
y : int
Vertical character position, top down
Returns
-------
in_widget : UIElement
Widget or popup that is within the position None if nothing
"""
if self._popup is not None and self._popup._contains_position(x, y):
return self._popup
elif self._popup is None:
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._contains_position(x, y):
return self.get_widgets()[widget_id]
return None
def _get_horizontal_neighbors(self, widget, direction):
"""Gets all horizontal (left, right) neighbor widgets
Parameters
----------
widget : py_cui.widgets.Widget
The currently selected widget
direction : py_cui.keys.KEY*
must be an arrow key value
Returns
-------
id_list : list[]
A list of the neighbor widget ids
"""
if not direction in py_cui.keys.ARROW_KEYS:
return None
_, num_cols = self._grid.get_dimensions()
row_start, col_start = widget.get_grid_cell()
row_span, col_span = widget.get_grid_cell_spans()
id_list = []
if direction == py_cui.keys.KEY_LEFT_ARROW:
col_range_start = 0
col_range_stop = col_start
else:
col_range_start = col_start + col_span
col_range_stop = num_cols
for col in range(col_range_start, col_range_stop):
for row in range(row_start, row_start + row_span):
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._is_row_col_inside(row, col) and widget_id not in id_list:
id_list.append(widget_id)
if direction == py_cui.keys.KEY_LEFT_ARROW:
id_list.reverse()
self._logger.info('Neighbors with ids {} for cell {},{} span {},{}'.format(id_list,
row_start,
col_start,
row_span,
col_span))
return id_list
def _get_vertical_neighbors(self, widget, direction):
"""Gets all vertical (up, down) neighbor widgets
Parameters
----------
widget : py_cui.widgets.Widget
The currently selected widget
direction : py_cui.keys.KEY*
must be an arrow key value
Returns
-------
id_list : list[]
A list of the neighbor widget ids
"""
if not direction in py_cui.keys.ARROW_KEYS:
return None
num_rows, _ = self._grid.get_dimensions()
row_start, col_start = widget.get_grid_cell()
row_span, col_span = widget.get_grid_cell_spans()
id_list = []
if direction == py_cui.keys.KEY_UP_ARROW:
row_range_start = 0
row_range_stop = row_start
else:
row_range_start = row_start + row_span
row_range_stop = num_rows
for row in range(row_range_start, row_range_stop):
for col in range(col_start, col_start + col_span):
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._is_row_col_inside(row, col) and widget_id not in id_list:
id_list.append(widget_id)
if direction == py_cui.keys.KEY_UP_ARROW:
id_list.reverse()
self._logger.info('Neighbors with ids {} for cell {},{} span {},{}'.format(id_list,
row_start,
col_start,
row_span,
col_span))
return id_list
# CUI status functions. Used to switch between widgets, set the mode, and
# identify neighbors for overview mode
def _check_if_neighbor_exists(self, direction):
"""Function that checks if widget has neighbor in specified cell.
Used for navigating CUI, as arrow keys find the immediate neighbor
Parameters
----------
direction : py_cui.keys.KEY_*
The direction in which to search
Returns
-------
widget_id : str
The widget neighbor ID if found, None otherwise
"""
start_widget = self.get_widgets()[self._selected_widget]
# Find all the widgets in the given row or column
neighbors = []
if direction in [py_cui.keys.KEY_DOWN_ARROW, py_cui.keys.KEY_UP_ARROW]:
neighbors = self._get_vertical_neighbors(start_widget, direction)
elif direction in [py_cui.keys.KEY_RIGHT_ARROW, py_cui.keys.KEY_LEFT_ARROW]:
neighbors = self._get_horizontal_neighbors(start_widget, direction)
if len(neighbors) == 0:
return None
# We select the best match to jump to (first neighbor)
return neighbors[0]
def get_selected_widget(self):
"""Function that gets currently selected widget
Returns
-------
selected_widget : py_cui.widgets.Widget
Reference to currently selected widget object
"""
if self._selected_widget is not None and self._selected_widget in self.get_widgets().keys():
return self.get_widgets()[self._selected_widget]
else:
self._logger.warn('Selected widget ID is None or invalid')
return None
def set_selected_widget(self, widget_id):
"""Function that sets the selected widget for the CUI
Parameters
----------
widget_id : str
the id of the widget to select
"""
if widget_id in self.get_widgets().keys():
self._logger.info('Setting selected widget to ID {}'.format(widget_id))
self._selected_widget = widget_id
else:
self._logger.warn('Widget w/ ID {} does not exist among current widgets.'.format(widget_id))
def lose_focus(self):
"""Function that forces py_cui out of focus mode.
After popup is called, focus is lost
"""
if self._in_focused_mode:
self._in_focused_mode = False
self.status_bar.set_text(self._init_status_bar_text)
self.get_widgets()[self._selected_widget].set_selected(False)
else:
self._logger.info('lose_focus: Not currently in focus mode')
def move_focus(self, widget, auto_press_buttons=True):
"""Moves focus mode to different widget
Parameters
----------
widget : Widget
The widget object we want to move focus to.
"""
self.lose_focus()
self.set_selected_widget(widget.get_id())
# If autofocus buttons is selected, we automatically process the button command and reset to overview mode
if self._auto_focus_buttons and auto_press_buttons and isinstance(widget, py_cui.widgets.Button):
widget.command()
self._logger.info('Moved focus to button {} - ran autofocus command'.format(widget.get_title()))
elif self._auto_focus_buttons and isinstance(widget, py_cui.widgets.Button):
self.status_bar.set_text(self._init_status_bar_text)
else:
widget.set_selected(True)
self._in_focused_mode = True
self.status_bar.set_text(widget.get_help_text())
self._logger.info('Moved focus to widget {}'.format(widget.get_title()))
def _cycle_widgets(self, reverse=False):
"""Function that is fired if cycle key is pressed to move to next widget
Parameters
----------
reverse : bool
Default false. If true, cycle widgets in reverse order.
"""
num_widgets = len(self.get_widgets().keys())
current_widget_num = int(self._selected_widget.split('Widget')[1])
if not reverse:
next_widget_num = current_widget_num + 1
if next_widget_num == num_widgets:
next_widget_num = 0
cycle_key = self._forward_cycle_key
else:
next_widget_num = current_widget_num - 1
if next_widget_num < 0:
next_widget_num = num_widgets - 1
cycle_key = self._reverse_cycle_key
current_widget_id = 'Widget{}'.format(current_widget_num)
next_widget_id = 'Widget{}'.format(next_widget_num)
if self._in_focused_mode and cycle_key in self.get_widgets()[current_widget_id]._key_commands.keys():
# In the event that we are focusing on a widget with that key defined, we do not cycle.
pass
else:
self.move_focus(self.get_widgets()[next_widget_id], auto_press_buttons=False)
def add_key_command(self, key, command):
"""Function that adds a keybinding to the CUI when in overview mode
Parameters
----------
key : py_cui.keys.KEY_*
The key bound to the command
command : Function
A no-arg or lambda function to fire on keypress
"""
self._keybindings[key] = command
# Popup functions. Used to display messages, warnings, and errors to the user.
def show_message_popup(self, title, text):
"""Shows a message popup
Parameters
----------
title : str
Message title
text : str
Message text
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_warning_popup(self, title, text):
"""Shows a warning popup
Parameters
----------
title : str
Warning title
text : str
Warning text
"""
color = YELLOW_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, 'WARNING - ' + title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_error_popup(self, title, text):
"""Shows an error popup
Parameters
----------
title : str
Error title
text : str
Error text
"""
color = RED_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, 'ERROR - ' + title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_yes_no_popup(self, title, command):
"""Shows a yes/no popup.
The 'command' parameter must be a function with a single boolean parameter
Parameters
----------
title : str
Message title
command : function
A function taking in a single boolean parameter. Will be fired with True if yes selected, false otherwise
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.YesNoPopup(self, title + '- (y/n)', 'Yes - (y), No - (n)', color, command, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_text_box_popup(self, title, command, password=False):
"""Shows a textbox popup.
The 'command' parameter must be a function with a single string parameter
Parameters
----------
title : str
Message title
command : Function
A function with a single string parameter, fired with contents of textbox when enter key pressed
password=False : bool
If true, write characters as '*'
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.TextBoxPopup(self, title, color, command, self._renderer, password, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_menu_popup(self, title, menu_items, command, run_command_if_none=False):
"""Shows a menu popup.
The 'command' parameter must be a function with a single string parameter
Parameters
----------
title : str
menu title
menu_items : list of str
A list of menu items
command : Function
A function taking in a single string argument. Fired with selected menu item when ENTER pressed.
run_command_if_none=False : bool
If True, will run command passing in None if no menu item selected.
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.MenuPopup(self, menu_items, title, color, command, self._renderer, self._logger, run_command_if_none)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_loading_icon_popup(self, title, message, callback=None):
"""Shows a loading icon popup
Parameters
----------
title : str
Message title
message : str
Message text. Will show as '$message...'
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
if callback is not None:
self._post_loading_callback = callback
color = WHITE_ON_BLACK
self._loading = True
self._popup = py_cui.popups.LoadingIconPopup(self, title, message, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_loading_bar_popup(self, title, num_items, callback=None):
"""Shows loading bar popup.
Use 'increment_loading_bar' to show progress
Parameters
----------
title : str
Message title
num_items : int
Number of items to iterate through for loading
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
if callback is not None:
self._post_loading_callback = callback
color = WHITE_ON_BLACK
self._loading = True
self._popup = py_cui.popups.LoadingBarPopup(self, title, num_items, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_form_popup(self, title, fields, passwd_fields=[], required=[], callback=None):
"""Shows form popup.
Used for inputting several fields worth of values
Parameters
---------
title : str
Message title
fields : List[str]
Names of each individual field
passwd_fields : List[str]
Field names that should have characters hidden
required : List[str]
Fields that are required before submission
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
self._popup = py_cui.dialogs.form.FormPopup(self, fields, passwd_fields, required, {}, title, py_cui.WHITE_ON_BLACK, self._renderer, self._logger)
if callback is not None:
self._popup.set_on_submit_action(callback)
def show_filedialog_popup(self, popup_type='openfile', initial_dir='.', callback=None, ascii_icons=True, limit_extensions=[]):
"""Shows form popup.
Used for inputting several fields worth of values
Paramters
---------
title : str
Message title
fields : List[str]
Names of each individual field
passwd_fields : List[str]
Field names that should have characters hidden
required : List[str]
Fields that are required before submission
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
self._popup = py_cui.dialogs.filedialog.FileDialogPopup(self, callback, initial_dir, popup_type, ascii_icons, limit_extensions, py_cui.WHITE_ON_BLACK, self._renderer, self._logger)
def increment_loading_bar(self):
"""Increments progress bar if loading bar popup is open
"""
if self._popup is not None:
self._popup._increment_counter()
else:
self._logger.warn('No popup is currently opened.')
def stop_loading_popup(self):
"""Leaves loading state, and closes popup.
Must be called by user to escape loading.
"""
self._loading = False
self.close_popup()
self._logger.info('Stopping open loading popup')
def close_popup(self):
"""Closes the popup, and resets focus
"""
self.lose_focus()
self._popup = None
def _refresh_height_width(self, height, width):
"""Function that updates the height and width of the CUI based on terminal window size
Parameters
----------
height : int
Window height in terminal characters
width : int
Window width in terminal characters
"""
self._height = height
self._width = width
self._grid.update_grid_height_width(self._height, self._width)
for widget_id in self._widgets.keys():
self._widgets[widget_id].update_height_width()
if self._popup is not None:
self._popup.update_height_width()
def get_absolute_size(self):
"""Returns dimensions of CUI
Returns
-------
height, width : int
The dimensions of drawable CUI space in characters
"""
return self._height, self._width
# Draw Functions. Function for drawing widgets, status bars, and popups
def _draw_widgets(self):
"""Function that draws all of the widgets to the screen
"""
for widget_key in self.get_widgets().keys():
if widget_key != self._selected_widget:
self.get_widgets()[widget_key]._draw()
# We draw the selected widget last to support cursor location.
if self._selected_widget is not None:
self.get_widgets()[self._selected_widget]._draw()
self._logger.info('Drew widgets')
def _draw_status_bars(self, stdscr, height, width):
"""Draws status bar and title bar
Parameters
----------
stdscr : curses Standard cursor
The cursor used to draw the status bar
height : int
Window height in terminal characters
width : int
Window width in terminal characters
"""
if self.status_bar is not None:
stdscr.attron(curses.color_pair(self.status_bar.get_color()))
stdscr.addstr(height + 3, 0, fit_text(width, self.status_bar.get_text()))
stdscr.attroff(curses.color_pair(self.status_bar.get_color()))
if self.title_bar is not None:
stdscr.attron(curses.color_pair(self.title_bar.get_color()))
stdscr.addstr(0, 0, fit_text(width, self._title, center=True))
stdscr.attroff(curses.color_pair(self.title_bar.get_color()))
def _display_window_warning(self, stdscr, error_info):
"""Function that prints some basic error info if there is an error with the CUI
Parameters
----------
stdscr : curses Standard cursor
The cursor used to draw the warning
error_info : str
The information regarding the error.
"""
stdscr.clear()
stdscr.attron(curses.color_pair(RED_ON_BLACK))
stdscr.addstr(0, 0, 'Error displaying CUI!!!')
stdscr.addstr(1, 0, 'Error Type: {}'.format(error_info))
stdscr.addstr(2, 0, 'Most likely terminal dimensions are too small.')
stdscr.attroff(curses.color_pair(RED_ON_BLACK))
stdscr.refresh()
self._logger.info('Encountered error -> {}'.format(error_info))
def _handle_key_presses(self, key_pressed):
"""Function that handles all main loop key presses.
Parameters
----------
key_pressed : py_cui.keys.KEY_*
The key being pressed
"""
# Selected widget represents which widget is being hovered over, though not necessarily in focus mode
if self._selected_widget is None:
return
selected_widget = self.get_widgets()[self._selected_widget]
# If we are in focus mode, the widget has all of the control of the keyboard except
# for the escape key, which exits focus mode.
if self._in_focused_mode and self._popup is None:
if key_pressed == py_cui.keys.KEY_ESCAPE:
self.status_bar.set_text(self._init_status_bar_text)
self._in_focused_mode = False
selected_widget.set_selected(False)
self._logger.info('Exiting focus mode on widget {}'.format(selected_widget.get_title()))
else:
# widget handles remaining py_cui.keys
self._logger.info('Widget {} handling {} key'.format(selected_widget.get_title(), key_pressed))
selected_widget._handle_key_press(key_pressed)
# Otherwise, barring a popup, we are in overview mode, meaning that arrow py_cui.keys move between widgets, and Enter key starts focus mode
elif self._popup is None:
if key_pressed == py_cui.keys.KEY_ENTER and self._selected_widget is not None and selected_widget.is_selectable():
self.move_focus(selected_widget)
for key in self._keybindings.keys():
if key_pressed == key:
command = self._keybindings[key]
self._logger.info('Detected binding for key {}, running command {}'.format(key_pressed, command.__name__))
command()
# If not in focus mode, use the arrow py_cui.keys to move around the selectable widgets.
neighbor = None
if key_pressed in py_cui.keys.ARROW_KEYS:
neighbor = self._check_if_neighbor_exists(key_pressed)
if neighbor is not None:
self.set_selected_widget(neighbor)
self._logger.info('Navigated to neighbor widget {}'.format(self.get_widgets()[self._selected_widget].get_title()))
# if we have a popup, that takes key control from both overview and focus mode
elif self._popup is not None:
self._logger.info('Popup {} handling key {}'.format(self._popup.get_title(), key_pressed))
self._popup._handle_key_press(key_pressed)
def _draw(self, stdscr):
"""Main CUI draw loop called by start()
Parameters
----------
stdscr : curses Standard screen
The screen buffer used for drawing CUI elements
"""
self._stdscr = stdscr
key_pressed = 0
# Clear and refresh the screen for a blank canvas
stdscr.clear()
stdscr.refresh()
curses.mousemask(curses.ALL_MOUSE_EVENTS)
# stdscr.nodelay(False)
#stdscr.keypad(True)
# Initialization functions. Generates colors and renderer
self._initialize_colors()
self._initialize_widget_renderer()
# If user specified a refresh timeout, apply it here
if self._refresh_timeout > 0:
self._stdscr.timeout(self._refresh_timeout)
# If user sets non-default border characters, update them here
if self._border_characters is not None:
self._renderer._set_border_renderer_chars(self._border_characters)
# Loop where key_pressed is the last character pressed. Wait for exit key while no popup or focus mode
while key_pressed != self._exit_key or self._in_focused_mode or self._popup is not None:
try:
# If we call stop, we want to break out of the main draw loop
if self._stopped:
break
# Initialization and size adjustment
stdscr.erase()
# find height width, adjust if status/title bar added. We decrement the height by 4 to account for status/title bar and padding
if self._simulated_terminal is None:
height, width = stdscr.getmaxyx()
else:
height = self._simulated_terminal[0]
width = self._simulated_terminal[1]
height = height - 4
# If the user defined an update function to fire on each draw call,
# Run it here. This can of course be also handled user-side
# through a separate thread.
if self._on_draw_update_func is not None:
self._on_draw_update_func()
# This is what allows the CUI to be responsive. Adjust grid size based on current terminal size
# Resize the grid and the widgets if there was a resize operation
if key_pressed == curses.KEY_RESIZE:
self._logger.info('Resizing CUI to new dimensions {} by {}'.format(height, width))
try:
self._refresh_height_width(height, width)
except py_cui.errors.PyCUIOutOfBoundsError as e:
self._logger.info('Resized terminal too small')
self._display_window_warning(stdscr, str(e))
# Here we handle mouse click events globally, or pass them to the UI element to handle
elif key_pressed == curses.KEY_MOUSE:
self._logger.info('Detected mouse click')
_, x, y, _, _ = curses.getmouse()
in_element = self.get_element_at_position(x, y)
# In first case, we click inside already selected widget, pass click for processing
if in_element is not None and in_element.is_selected():
in_element._handle_mouse_press(x, y)
# Otherwise, if not a popup, select the clicked on widget
elif in_element is not None and not isinstance(in_element, py_cui.popups.Popup):
self.move_focus(in_element)
in_element._handle_mouse_press(x, y)
# If we have a post_loading_callback, fire it here
if self._post_loading_callback is not None and not self._loading:
self._logger.info('Firing post-loading callback function {}'.format(self._post_loading_callback.__name__))
self._post_loading_callback()
self._post_loading_callback = None
# Handle widget cycling
if key_pressed == self._forward_cycle_key:
self._cycle_widgets()
elif key_pressed == self._reverse_cycle_key:
self._cycle_widgets(reverse=True)
# Handle keypresses
self._handle_key_presses(key_pressed)
try:
# Draw status/title bar, and all widgets. Selected widget will be bolded.
self._draw_status_bars(stdscr, height, width)
self._draw_widgets()
# draw the popup if required
if self._popup is not None:
self._popup._draw()
except curses.error as e:
self._logger.error('Curses error while drawing TUI')
self._display_window_warning(stdscr, str(e))
except py_cui.errors.PyCUIOutOfBoundsError as e:
self._logger.error('Resized terminal too small')
self._display_window_warning(stdscr, str(e))
# Refresh the screen
stdscr.refresh()
# Wait for next input
if self._loading or self._post_loading_callback is not None:
# When loading, refresh screen every quarter second
time.sleep(0.25)
# Need to reset key_pressed, because otherwise the previously pressed key will be used.
key_pressed = 0
elif self._stopped:
key_pressed = self._exit_key
else:
self._logger.info('Waiting for next keypress')
key_pressed = stdscr.getch()
except KeyboardInterrupt:
self._logger.info('Detect Keyboard Interrupt, Exiting...')
self._stopped = True
stdscr.erase()
stdscr.refresh()
curses.endwin()
if self._on_stop is not None:
self._logger.info('Firing onstop function {}'.format(self._on_stop.__name__))
self._on_stop()
def __format__(self, fmt):
"""Override of base format function. Prints list of current widgets.
Parameters
----------
fmt : Format
The format to override
"""
out = ''
for widget in self.get_widgets().keys():
out += '{}\n'.format(self.get_widgets()[widget].get_title())
return out
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1
| 0
|
72a124c3d481e06d142109091c80f7375d688299
| 6,447
|
py
|
Python
|
deployer/src/config_manager.py
|
yugabyte/docsearch-scraper
|
8b58d364c7721cbce892843e946834a3ccc5fcd7
|
[
"MIT"
] | null | null | null |
deployer/src/config_manager.py
|
yugabyte/docsearch-scraper
|
8b58d364c7721cbce892843e946834a3ccc5fcd7
|
[
"MIT"
] | 2
|
2021-03-31T20:20:23.000Z
|
2021-12-13T20:58:56.000Z
|
deployer/src/config_manager.py
|
tubone24/docsearch-scraper
|
07937b551be7f88322c2fe2f28f3ad4eb254e996
|
[
"MIT"
] | 1
|
2020-04-01T22:01:17.000Z
|
2020-04-01T22:01:17.000Z
|
import algoliasearch
from os import environ
from . import algolia_helper
from . import snippeter
from . import emails
from . import helpers
from . import fetchers
from .helpdesk_helper import add_note, get_conversation, \
get_emails_from_conversation, get_conversation_url_from_cuid
from deployer.src.algolia_internal_api import remove_user_from_index
class ConfigManager:
instance = None
def __init__(self):
if not ConfigManager.instance:
ConfigManager.instance = ConfigManager.__ConfigManager()
@staticmethod
def encode_set(to_encode):
encoded = []
for config_name in to_encode:
try:
config_name = config_name.decode()
except AttributeError:
print("Error decoding non string var {}".format(config_name))
pass
encoded.append(config_name)
return encoded
class __ConfigManager:
def __init__(self):
self.public_dir = environ.get('PUBLIC_CONFIG_FOLDER')
self.private_dir = environ.get('PRIVATE_CONFIG_FOLDER')
if self.public_dir is None or self.private_dir is None:
print(
'PUBLIC_CONFIG_FOLDER and PRIVATE_CONFIG_FOLDER must be defined in the environment')
exit()
self.initial_public_nb_stash = None
self.final_nb_public_stash = None
self.initial_private_nb_stash = None
self.final_nb_private_stash = None
self.init()
self.ref_configs = fetchers.get_configs_from_repos()
def init(self):
output = helpers.check_output_decoded(['git', 'stash', 'list'],
cwd=self.public_dir)
self.initial_public_nb_stash = len(output.split('\n'))
helpers.check_output_decoded(
['git', 'stash', '--include-untracked'],
cwd=self.public_dir)
output2 = helpers.check_output_decoded(['git', 'stash', 'list'],
cwd=self.public_dir)
self.final_nb_public_stash = len(output2.split('\n'))
helpers.check_output_decoded(
['git', 'pull', '-r', 'origin', 'master'],
cwd=self.public_dir)
output = helpers.check_output_decoded(['git', 'stash', 'list'],
cwd=self.private_dir)
self.initial_private_nb_stash = len(output.split('\n'))
helpers.check_output_decoded(
['git', 'stash', '--include-untracked'],
cwd=self.private_dir)
output2 = helpers.check_output_decoded(['git', 'stash', 'list'],
cwd=self.private_dir)
self.final_nb_private_stash = len(output2.split('\n'))
helpers.check_output_decoded(
['git', 'pull', '-r', 'origin', 'master'],
cwd=self.private_dir)
def destroy(self):
if self.final_nb_public_stash != self.initial_public_nb_stash:
helpers.check_output_decoded(['git', 'stash', 'pop'],
cwd=self.public_dir)
if self.final_nb_private_stash != self.initial_private_nb_stash:
helpers.check_output_decoded(['git', 'stash', 'pop'],
cwd=self.private_dir)
def add_config(self, config_name):
key = algolia_helper.add_docsearch_key(config_name)
print(config_name + ' (' + key + ')')
config = self.ref_configs[config_name]
print('\n================================\n')
if "conversation_id" in config:
cuid = config["conversation_id"][0]
# Add email(s) to the private config & grant access
conversation = get_conversation(cuid)
emails_from_conv = get_emails_from_conversation(conversation)
analytics_statuses = emails.add(config_name, self.private_dir,
emails_to_add=emails_from_conv)
note_content = snippeter.get_email_for_config(config_name,
analytics_statuses)
add_note(cuid, note_content)
print(
'Email address fetched and stored, conversation updated and available at {}\n'.format(
get_conversation_url_from_cuid(cuid)))
else:
if helpers.confirm(
'\nDo you want to add emails for {}?'.format(
config_name)):
analytics_statuses = emails.add(config_name,
self.private_dir)
print(snippeter.get_email_for_config(config_name,
analytics_statuses))
else:
print(snippeter.get_email_for_config(config_name))
def update_config(self, config_name):
message = config_name
try:
key = algolia_helper.get_docsearch_key(config_name)
message = message + ' (' + key + ')'
except algoliasearch.helpers.AlgoliaException:
pass
print(message)
print('\n================================\n')
print(snippeter.get_email_for_config(config_name))
if helpers.confirm(
'\nDo you want to add emails for {}?'.format(config_name)):
emails.add(config_name, self.private_dir)
def remove_config(self, config_name):
algolia_helper.delete_docsearch_key(config_name)
algolia_helper.delete_docsearch_index(config_name)
algolia_helper.delete_docsearch_index(config_name + '_tmp')
analytics_keys = algolia_helper.list_index_analytics_key(
config_name)
for key in analytics_keys:
description = key['description'].split()
email = description[4]
print(email)
if email is not None:
remove_user_from_index(config_name, email)
emails.delete(config_name, self.private_dir)
| 40.54717
| 106
| 0.547076
| 648
| 6,447
| 5.12037
| 0.194444
| 0.084388
| 0.046414
| 0.075347
| 0.474382
| 0.385775
| 0.361061
| 0.351115
| 0.324895
| 0.233876
| 0
| 0.001452
| 0.359237
| 6,447
| 158
| 107
| 40.803797
| 0.801743
| 0.0076
| 0
| 0.32
| 0
| 0
| 0.09334
| 0.017824
| 0
| 0
| 0
| 0
| 0
| 1
| 0.064
| false
| 0.016
| 0.072
| 0
| 0.168
| 0.088
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a1afeaa2791d784987902c76baf2fe264f4270
| 1,131
|
py
|
Python
|
Source/budgie/__init__.py
|
pylover/budgie
|
f453cf2fbbf440e8e2314c7fb63f101dbe048e17
|
[
"WTFPL"
] | 3
|
2016-10-30T07:41:30.000Z
|
2016-11-07T04:55:44.000Z
|
Source/budgie/__init__.py
|
pylover/budgie
|
f453cf2fbbf440e8e2314c7fb63f101dbe048e17
|
[
"WTFPL"
] | 11
|
2016-10-28T12:18:24.000Z
|
2016-10-29T15:18:56.000Z
|
Source/budgie/__init__.py
|
pylover/budgie
|
f453cf2fbbf440e8e2314c7fb63f101dbe048e17
|
[
"WTFPL"
] | null | null | null |
import sys
from sqlalchemy.exc import DatabaseError
from . import cli
from .configuration import settings, init as init_config
from .observer import HelpdeskObserver, MaximumClientsReached
from .models import init as init_models, metadata, engine, check_db
from .smtp import SMTPConfigurationError
__version__ = '0.1.0-dev.0'
def start_server(cli_arguments):
init_models()
# Checking database
try:
check_db()
except DatabaseError:
print(
'Cannot connect to database. or database objects are not created yet. Please run `budgie setup-db`.',
file=sys.stderr
)
sys.exit(-1)
try:
manager = HelpdeskObserver()
manager.start()
except (
MaximumClientsReached,
SMTPConfigurationError) as ex:
print(ex, file=sys.stderr)
sys.exit(-1)
def main():
arguments = cli.init()
if arguments.version:
print(__version__)
sys.exit(0)
init_config(arguments.config_file if arguments.config_file else None)
if arguments.func is not None:
arguments.func(arguments)
| 21.75
| 113
| 0.66313
| 132
| 1,131
| 5.545455
| 0.44697
| 0.028689
| 0.027322
| 0.043716
| 0.057377
| 0.057377
| 0
| 0
| 0
| 0
| 0
| 0.008353
| 0.259063
| 1,131
| 51
| 114
| 22.176471
| 0.865155
| 0.015031
| 0
| 0.117647
| 0
| 0.029412
| 0.098287
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.058824
| false
| 0
| 0.205882
| 0
| 0.264706
| 0.088235
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a38e5ecc9516d35d7db3206173481ed721f2aa
| 3,422
|
py
|
Python
|
locations/spiders/shopnsave.py
|
thismakessand/alltheplaces
|
b6116199844c9e88bff3a691290f07a7457470ba
|
[
"MIT"
] | 1
|
2019-08-19T10:00:55.000Z
|
2019-08-19T10:00:55.000Z
|
locations/spiders/shopnsave.py
|
thismakessand/alltheplaces
|
b6116199844c9e88bff3a691290f07a7457470ba
|
[
"MIT"
] | null | null | null |
locations/spiders/shopnsave.py
|
thismakessand/alltheplaces
|
b6116199844c9e88bff3a691290f07a7457470ba
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import scrapy
import re
from locations.items import GeojsonPointItem
DAY_DICT = {
'Mon': 'Mo',
'Tue': 'Tu',
'Wed': 'We',
'Thu': 'Th',
'Fri': 'Fr',
'Sat': 'Sa',
'Sun': 'Su',
'Monday': 'Mo',
'Tuesday': 'Tu',
'Wednesday': 'We',
'Thursday': 'Th',
'Thurs': 'Th',
'Thur': 'Th',
'Friday': 'Fr',
'Saturday': 'Sa',
'Sunday': 'Su',
'24 hours/7 days a week': '24/7',
'Please contact store for hours': 'N/A',
}
class ShopnSaveSpider(scrapy.Spider):
name = "shopnsave"
allowed_domains = ["www.shopnsave.com"]
start_urls = (
'https://www.shopnsave.com/stores/view-stores-by-state.html?state=IL&page=1',
'https://www.shopnsave.com/stores/view-stores-by-state.html?state=IL&page=2',
'https://www.shopnsave.com/stores/view-stores-by-state.html?state=MO&page=1',
'https://www.shopnsave.com/stores/view-stores-by-state.html?state=MO&page=2',
'https://www.shopnsave.com/stores/view-stores-by-state.html?state=MO&page=3',
)
def parse(self, response):
stores = response.xpath('//table[@id="store-search-result"]/tbody/tr[@class="" or @class="store-grey"]')
for store in stores:
properties = {
"ref": store.xpath('td[@class="store-result-address"]/text()').extract_first(),
"name": store.xpath('td[@class="store-result-address"]/text()').extract_first(),
"opening_hours": self.store_hours(store.xpath('td[@class="store-result-address"]/text()[last()-1]').extract_first()),
"addr_full": store.xpath('td[@class="store-result-address"]/text()')[1].extract(),
"city": self.city(store.xpath('td[@class="store-result-address"]/text()')[2].extract()),
"state": self.state(store.xpath('td[@class="store-result-address"]/text()')[2].extract()),
"postcode": self.postCode(store.xpath('td[@class="store-result-address"]/text()')[2].extract()),
"phone": self.phone(store.xpath('td[@class="store-result-phone"]/strong/text()')[0].extract()),
}
yield GeojsonPointItem(**properties)
def city(self, data):
str_list = data.split(',')
return str_list[0].strip()
def state(self, data):
str_list = data.split(',')
state = str_list[1].strip()
state = state[:2]
return state
def postCode(self, data):
str_list = data.split(',')
zipCode = str_list[1].strip()
return zipCode[-5:]
def phone(self, data):
return data.replace('— Main', '')
def store_hours(self, store_hours):
if "day" not in store_hours and "-" not in store_hours:
return ""
if "24 Hours, 7 days a week" in store_hours:
return "24/7"
store_hours = store_hours.replace('\r\n\t\t\t\t\t\t', '')
store_hours = store_hours.replace('Midnight', '00:00')
pattern = re.compile(r'\b(' + '|'.join(DAY_DICT.keys()) + r')\b')
store_hours = pattern.sub(lambda x: DAY_DICT[x.group()], ''.join(store_hours))
store_hours = store_hours.replace('am', ':00')
m = re.search('([0-9]{1,2})(\spm)', store_hours)
if m:
h = m.group(1)
new_h = int(h) + 12
store_hours = store_hours.replace(h + ' pm', str(new_h) + ':00')
return store_hours
| 36.021053
| 133
| 0.564582
| 449
| 3,422
| 4.224944
| 0.300668
| 0.094887
| 0.050606
| 0.071692
| 0.454929
| 0.394834
| 0.324196
| 0.324196
| 0.283079
| 0.283079
| 0
| 0.016756
| 0.232613
| 3,422
| 94
| 134
| 36.404255
| 0.705255
| 0.006137
| 0
| 0.038961
| 0
| 0.077922
| 0.333922
| 0.114151
| 0
| 0
| 0
| 0
| 0
| 1
| 0.077922
| false
| 0
| 0.038961
| 0.012987
| 0.25974
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a3d114ee07b14005e62845551d3b0c0d260004
| 831
|
py
|
Python
|
tests/twitter_learning_journal/dao/test_os_env.py
|
DEV3L/twitter-learning-journal
|
a51d22a60a3d1249add352d8357975a7f2db585c
|
[
"Beerware"
] | 1
|
2021-01-12T17:06:57.000Z
|
2021-01-12T17:06:57.000Z
|
tests/twitter_learning_journal/dao/test_os_env.py
|
DEV3L/twitter-learning-journal
|
a51d22a60a3d1249add352d8357975a7f2db585c
|
[
"Beerware"
] | null | null | null |
tests/twitter_learning_journal/dao/test_os_env.py
|
DEV3L/twitter-learning-journal
|
a51d22a60a3d1249add352d8357975a7f2db585c
|
[
"Beerware"
] | 1
|
2018-07-31T21:16:33.000Z
|
2018-07-31T21:16:33.000Z
|
from unittest.mock import patch
from app.twitter_learning_journal.dao.os_env import os_environ
@patch('app.twitter_learning_journal.dao.os_env.os')
def test_os_environ(mock_os):
expected_value = 'environment_value'
mock_os.environ.__contains__.return_value = True # patch in statement
mock_os.environ.__getitem__.return_value = expected_value
os_variable = os_environ('a_key')
assert expected_value == os_variable
mock_os.environ.__getitem__.assert_called_with('a_key')
def test_os_environ_key_missing():
expected_value = None
os_variable = os_environ('a_key')
assert expected_value == os_variable
def test_os_environ_key_missing_with_default():
expected_value = 'a_default'
os_variable = os_environ('a_key', default=expected_value)
assert expected_value == os_variable
| 26.806452
| 74
| 0.776173
| 119
| 831
| 4.89916
| 0.277311
| 0.154374
| 0.102916
| 0.157804
| 0.469983
| 0.42024
| 0.291595
| 0.178388
| 0.178388
| 0.178388
| 0
| 0
| 0.141998
| 831
| 30
| 75
| 27.7
| 0.817672
| 0.021661
| 0
| 0.277778
| 0
| 0
| 0.108508
| 0.051788
| 0
| 0
| 0
| 0
| 0.222222
| 1
| 0.166667
| false
| 0
| 0.111111
| 0
| 0.277778
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a434d4367495e97d23e862eb1e69cd74b1b481
| 477
|
py
|
Python
|
web-scraper/mongoscraper/populate.py
|
naveenr414/hack-umbc
|
f5d0fa5b6c3203d54a3c98b8a43b8028229431f8
|
[
"MIT"
] | null | null | null |
web-scraper/mongoscraper/populate.py
|
naveenr414/hack-umbc
|
f5d0fa5b6c3203d54a3c98b8a43b8028229431f8
|
[
"MIT"
] | null | null | null |
web-scraper/mongoscraper/populate.py
|
naveenr414/hack-umbc
|
f5d0fa5b6c3203d54a3c98b8a43b8028229431f8
|
[
"MIT"
] | null | null | null |
import pymongo
myclient = pymongo.MongoClient()
mydb = myclient["mydb"]
hor = mydb["HoR"]
sen = mydb["Senator"]
gov = mydb["Governor"]
def write(fileJSON):
myDoc = fileJSON
if( "hor" in myDoc.values()):
hor.insert_one(myDoc)
elif( "senate" in myDoc.values()):
sen.insert_one(myDoc)
else:
gov.insert_one(myDoc)
def deletes():
for x in sen.find():
sen.delete_one(x)
def prints():
for x in sen.find():
print(x)
| 18.346154
| 38
| 0.601677
| 65
| 477
| 4.353846
| 0.446154
| 0.095406
| 0.14841
| 0.063604
| 0.091873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.245283
| 477
| 25
| 39
| 19.08
| 0.786111
| 0
| 0
| 0.1
| 0
| 0
| 0.065126
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15
| false
| 0
| 0.05
| 0
| 0.2
| 0.1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a4e5a582877273a13f89ab82a67ba0dbfaef06
| 1,444
|
py
|
Python
|
tests/test_utils_obj_value.py
|
ZSD-tim/dayu_widgets
|
31c2530bdc4161d9311574d9850c2e9471e53072
|
[
"MIT"
] | 157
|
2019-03-10T05:55:21.000Z
|
2022-03-31T09:07:00.000Z
|
tests/test_utils_obj_value.py
|
kanbang/dayu_widgets
|
6ff101e6c6f8fcf10e5cb578023a12ccdcef9164
|
[
"MIT"
] | 16
|
2019-07-15T11:30:53.000Z
|
2021-12-16T14:17:59.000Z
|
tests/test_utils_obj_value.py
|
kanbang/dayu_widgets
|
6ff101e6c6f8fcf10e5cb578023a12ccdcef9164
|
[
"MIT"
] | 56
|
2019-06-19T03:35:27.000Z
|
2022-03-22T08:07:32.000Z
|
"""
Test get_obj_value set_obj_value has_obj_value
"""
import pytest
from dayu_widgets import utils
class _HasNameAgeObject(object):
def __init__(self, name, age):
super(_HasNameAgeObject, self).__init__()
self.name = name
self.age = age
@pytest.mark.parametrize('obj', (
{'name': 'xiaoming', 'age': 18},
_HasNameAgeObject('xiaoming', 18)
))
class TestObjValue(object):
"""Test get_obj_value has_obj_value set_obj_value collection."""
@pytest.mark.parametrize('attr, default, result', (
('name', 'hhh', 'xiaoming'),
('age', 0, 18),
('score', 0, 0)
))
def test_get_obj_value(self, obj, attr, default, result):
"""Test get_obj_value with dict/object as arg. """
assert utils.get_obj_value(obj, attr, default) == result
@pytest.mark.parametrize('attr, result', (
('name', True),
('age', True),
('sex', False),
))
def test_has_obj_value(self, obj, attr, result):
"""Test has_obj_value with dict/object as arg. """
assert utils.has_obj_value(obj, attr) == result
@pytest.mark.parametrize('attr, value', (
('name', 'xiaohua'),
('age', 30),
('id', 80),
))
def test_set_obj_value(self, obj, attr, value):
"""Test set_obj_value with dict/object as arg. """
utils.set_obj_value(obj, attr, value)
assert utils.get_obj_value(obj, attr) == value
| 29.469388
| 68
| 0.607341
| 184
| 1,444
| 4.51087
| 0.244565
| 0.154217
| 0.079518
| 0.072289
| 0.426506
| 0.180723
| 0.180723
| 0.091566
| 0.091566
| 0
| 0
| 0.011807
| 0.237535
| 1,444
| 48
| 69
| 30.083333
| 0.742053
| 0.166205
| 0
| 0.117647
| 0
| 0
| 0.10119
| 0
| 0
| 0
| 0
| 0
| 0.088235
| 1
| 0.117647
| false
| 0
| 0.058824
| 0
| 0.235294
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a54534a71e2246424e06879611de77216a27cb
| 22,969
|
py
|
Python
|
tim_camera/oop_detection_webcam.py
|
Tim-orius/aidem
|
965a71888db72f42223777e890f4bcf88cde7fd3
|
[
"MIT"
] | null | null | null |
tim_camera/oop_detection_webcam.py
|
Tim-orius/aidem
|
965a71888db72f42223777e890f4bcf88cde7fd3
|
[
"MIT"
] | null | null | null |
tim_camera/oop_detection_webcam.py
|
Tim-orius/aidem
|
965a71888db72f42223777e890f4bcf88cde7fd3
|
[
"MIT"
] | null | null | null |
""" Webcam Detection with Tensorflow calssifier and object distance calculation """
__version__ = "0.1.0"
__author__ = "Tim Rosenkranz"
__email__ = "tim.rosenkranz@stud.uni-frankfurt.de"
__credits__ = "Special thanks to The Anh Vuong who came up with the original idea." \
"This code is also based off of the code from Evan Juras (see below)"
# This script is based off of a script by Evan Juras (see below).
# I rewrote this script to be object oriented and added the tkinter-ui (removed command
# line functionalities) as well as several functionalities to calculate the distance
# between two detected object
######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 10/27/19
# Description:
# This program uses a TensorFlow Lite model to perform object detection on a live webcam
# feed. It draws boxes and scores around the objects of interest in each frame from the
# webcam. To improve FPS, the webcam object runs in a separate thread from the main program.
# This script will work with either a Picamera or regular USB webcam.
#
# This code is based off the TensorFlow Lite image classification example at:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py
#
# I [Evan Juras] added my own method of drawing boxes and labels using OpenCV.
# Import packages
import os
import argparse
import cv2
import numpy as np
import sys
import time
from threading import Thread
import importlib.util
import math
# Define VideoStream class to handle streaming of video from webcam in separate processing thread
# Source - Adrian Rosebrock, PyImageSearch: https://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/
class VideoStream:
"""Camera object that controls video streaming from the Picamera"""
def __init__(self,resolution=(640,480),framerate=30):
# Initialize the PiCamera and the camera image stream
self.stream = cv2.VideoCapture(0)
ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
ret = self.stream.set(3,resolution[0])
ret = self.stream.set(4,resolution[1])
self.width = self.stream.get(3)
# Read first frame from the stream
(self.grabbed, self.frame) = self.stream.read()
# Variable to control when the camera is stopped
self.stopped = False
def start(self):
# Start the thread that reads frames from the video stream
Thread(target=self.update,args=()).start()
return self
def update(self):
# Keep looping indefinitely until the thread is stopped
while True:
# If the camera is stopped, stop the thread
if self.stopped:
# Close camera resources
self.stream.release()
return
# Otherwise, grab the next frame from the stream
(self.grabbed, self.frame) = self.stream.read()
def read(self):
# Return the most recent frame
return self.frame
def stop(self):
# Indicate that the camera and thread should be stopped
self.stopped = True
def continue_video(self):
# Indicate that camera should resume
self.stopped = False
self.start()
class LiveDetection:
"""
"""
def __init__(self):
"""
"""
MODEL_NAME = 'Sample_Model'
GRAPH_NAME = 'detect.tflite'
LABELMAP_NAME = 'labelmap.txt'
self.__min_conf_threshold = 0.5
resW, resH = '1280x720'.split('x')
self.__imW, self.__imH = int(resW), int(resH)
use_TPU = ''
# Import TensorFlow libraries
# If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
from tflite_runtime.interpreter import Interpreter
if use_TPU:
from tflite_runtime.interpreter import load_delegate
else:
from tensorflow.lite.python.interpreter import Interpreter
if use_TPU:
from tensorflow.lite.python.interpreter import load_delegate
# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
# If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
if (GRAPH_NAME == 'detect.tflite'):
GRAPH_NAME = 'edgetpu.tflite'
# Get path to current working directory
CWD_PATH = os.getcwd()
# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)
# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
self.__labels = [line.strip() for line in f.readlines()]
# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if self.__labels[0] == '???':
del(self.__labels[0])
# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
self._interpreter = Interpreter(model_path=PATH_TO_CKPT,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
print(PATH_TO_CKPT)
else:
self._interpreter = Interpreter(model_path=PATH_TO_CKPT)
self._interpreter.allocate_tensors()
# Get model details
self.__input_details = self._interpreter.get_input_details()
self.__output_details = self._interpreter.get_output_details()
self.__height = self.__input_details[0]['shape'][1]
self.__width = self.__input_details[0]['shape'][2]
self.__floating_model = (self.__input_details[0]['dtype'] == np.float32)
input_mean = 127.5
input_std = 127.5
# Initialize frame rate calculation
self.__frame_rate_calc = 1
self.__freq = cv2.getTickFrequency()
# Initialize video stream
self._videostream = VideoStream(resolution=(self.__imW,self.__imH),framerate=30).start()
time.sleep(1)
# -----------------------------------------------------------------
# Average parameters
self.avg_width_person = 45+8+4 # +8 due to borders not aligning to body
self.avg_height_person = 172
self.avg_proportion_person = self.avg_width_person / self.avg_height_person
self.test_distance = 216
# Old value:
self.fokal_empir = 1500
# Variable for new calibrated value:
self.focal_value = 0
def calibrate(self,
obj_width_cm:int=0,
obj_dist_cm:int=0,
obj_name:str=""
):
"""
"""
color_variation = 0
foc_meas = 0
for i in range(10):
# Grab frame from video stream
frame1 = self._videostream.read()
# Acquire frame and resize to expected shape [1xHxWx3]
frame = frame1.copy()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (self.__width, self.__height))
input_data = np.expand_dims(frame_resized, axis=0)
# Normalize pixel values if using a floating model (i.e. if model is non-quantized)
if self.__floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
# Perform the actual detection by running the model with the image as input
self._interpreter.set_tensor(self.__input_details[0]['index'],input_data)
self._interpreter.invoke()
# Retrieve detection results
boxes = self._interpreter.get_tensor(self.__output_details[0]['index'])[0] # Bounding box coordinates of detected objects
classes = self._interpreter.get_tensor(self.__output_details[1]['index'])[0] # Class index of detected objects
scores = self._interpreter.get_tensor(self.__output_details[2]['index'])[0] # Confidence of detected objects
obj_type = []
for i in range(len(scores)):
if ((scores[i] > self.__min_conf_threshold) and (scores[i] <= 1.0)):
# Check for the right object (ensure correct measurement when several objects are detected)
if(self.__labels[int(classes[i])] != obj_name):
continue
else:
obj_type.append(str(self.__labels[int(classes[i])]))
# Get bounding box coordinates and draw box
# Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
ymin = int(max(1,(boxes[i][0] * self.__imH)))
xmin = int(max(1,(boxes[i][1] * self.__imW)))
ymax = int(min(self.__imH,(boxes[i][2] * self.__imH)))
xmax = int(min(self.__imW,(boxes[i][3] * self.__imW)))
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, (40+(40*i))%255, (color_variation*40)%255), 2)
# Calculate object width in pixel
obj_width_pixels = xmax - xmin
foc_meas += (obj_width_pixels * obj_dist_cm) / obj_width_cm
# Draw label
object_name = self.__labels[int(classes[i])] # Look up object name from "labels" array using class index
label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
# Draw framerate in corner of frame
cv2.putText(frame,'FPS: {0:.2f}'.format(self.__frame_rate_calc),(15,35),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
self.focal_value = foc_meas / 10
print("Calculated focal value:",self.focal_value)
print("Calibration done")
def detect(self):
"""
"""
color_variation = 0;
#for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
while True:
# Start timer (for calculating frame rate)
t1 = cv2.getTickCount()
# Grab frame from video stream
frame1 = self._videostream.read()
# Acquire frame and resize to expected shape [1xHxWx3]
frame = frame1.copy()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (self.__width, self.__height))
input_data = np.expand_dims(frame_resized, axis=0)
# Normalize pixel values if using a floating model (i.e. if model is non-quantized)
if self.__floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
# Perform the actual detection by running the model with the image as input
self._interpreter.set_tensor(self.__input_details[0]['index'],input_data)
self._interpreter.invoke()
# Retrieve detection results
boxes = self._interpreter.get_tensor(self.__output_details[0]['index'])[0] # Bounding box coordinates of detected objects
classes = self._interpreter.get_tensor(self.__output_details[1]['index'])[0] # Class index of detected objects
scores = self._interpreter.get_tensor(self.__output_details[2]['index'])[0] # Confidence of detected objects
#num = self._interpreter.get_tensor(self.__output_details[3]['index'])[0] # Total number of detected objects (inaccurate and not needed)
# --------------------------------------------------------------------------------------------------------
coords = []
proportion_x = []
proportion_y = []
camera_distance = []
obj_type = []
# Loop over all detections and draw detection box if confidence is above minimum threshold
for i in range(len(scores)):
if ((scores[i] > self.__min_conf_threshold) and (scores[i] <= 1.0)):
if(self.__labels[int(classes[i])] != "person" and self.__labels[int(classes[i])] != "teddy bear" and self.__labels[int(classes[i])] != "chair"):
continue
else:
obj_type.append(str(self.__labels[int(classes[i])]))
# Get bounding box coordinates and draw box
# Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
ymin = int(max(1,(boxes[i][0] * self.__imH)))
xmin = int(max(1,(boxes[i][1] * self.__imW)))
ymax = int(min(self.__imH,(boxes[i][2] * self.__imH)))
xmax = int(min(self.__imW,(boxes[i][3] * self.__imW)))
if (i+1)*40 > 255:
color_variation += 1
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, (40+(40*i))%255, (color_variation*40)%255), 2)
# Save coordinates of detected person
coords.append([[xmin, ymin],[xmax, ymax]])
# For testing (screen width of camera)
vid_width = int(self._videostream.width)
if(len(coords) > 1):
# preparation
for a in range(len(coords)):
proportion_x.append(0)
proportion_y.append(0)
for i in range(len(coords)):
# Measure height and width of detected person (in pixel)
proportion_x[i] = coords[i][1][0] - coords[i][0][0] # Width
#proportion_y[i] = coords[i][1][1] - coords[i][0][1] # Height
#proportion_x[i] = xmax - xmin
# P = proportion_x[i]
# F = Fokalwert, W = Objektbreite (cm), P = Objektbreite (Pixel), D = Distanz (cm)
# F = (P * D) / W -> D = (F * W) / P
# F = (P * test_distance) / (45+8)
# print(F)
# Calculate object distance to camera
camera_distance.append((self.focal_value * self.avg_width_person) / proportion_x[i])
print("Distance obj "+str(i)+" ("+str(obj_type)+") - camera: "+str(camera_distance[i]), flush=True)
if(i>0):
# Calculate min dist (only horizontal)
if(obj_type[i] == "person"):
min_dist_x = proportion_x[i]/self.avg_width_person * 150
elif(obj_type[i] == "chair"):
min_dist_x = proportion_x[i]/80 * 150
else:
min_dist_x = 500
#min_dist_x = 300
for j in range(i):
min_dist_obj_x_1 = abs(coords[i][1][0] - coords[j][0][0])
min_dist_obj_x_2 = abs(coords[j][1][0] - coords[i][0][0])
dist_obj_z = abs(camera_distance[i] - camera_distance[j])
# Test with distance to borders
#min_dist_obj_x_1 = abs(coords[i][1][0] - vid_width) # To the right
#min_dist_obj_x_2 = abs(coords[i][0][0] - 0) # To the left
print("X-Distanz objekt i -> j: "+str(min_dist_obj_x_1)+" - X-Distanz obj j -> i: "+str(min_dist_obj_x_2)+" - minimale Distanz: "+str(min_dist_x), flush=True)
print("Z-Distanz objekt i - j: "+str(dist_obj_z), flush=True)
# Check for smaller distance
if(min_dist_obj_x_1 < min_dist_obj_x_2):
objects_distance = math.sqrt(min_dist_obj_x_1**2 + dist_obj_z**2)
if(objects_distance < min_dist_x):
print("AAAA "+str(objects_distance)+" j = "+obj_type[j], flush=True)
cv2.line(frame, (coords[i][1][0], coords[i][1][1]), (coords[j][0][0],coords[j][1][1]), (255,10,0), 2)
#cv2.line(frame, (coords[i][1][0], coords[i][1][1]+30), (vid_width,coords[i][1][1]+30), (255,10,0), 2)
dist_label = '%s / %d' % (round(objects_distance, 2), round(min_dist_x, 2))
dist_labelSize, dist_baseLine = cv2.getTextSize(dist_label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
dist_label_ymin = max(coords[i][1][1], dist_labelSize[1] + 10)
cv2.rectangle(frame, (coords[i][1][0], dist_label_ymin-dist_labelSize[1]-10), (coords[i][1][0]+dist_labelSize[0], dist_label_ymin+dist_baseLine-10), (255, 255, 255), cv2.FILLED)
cv2.putText(frame, dist_label, (coords[i][1][0], dist_label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
elif(min_dist_obj_x_1 > min_dist_obj_x_2):
objects_distance = math.sqrt(min_dist_obj_x_2**2 + dist_obj_z**2)
if(objects_distance < min_dist_x):
print("BBB "+str(objects_distance)+" j = "+obj_type[j], flush=True)
cv2.line(frame, (coords[j][1][0], coords[j][1][1]), (coords[i][0][0],coords[i][1][1]), (255,10,0), 2)
#cv2.line(frame, (coords[i][0][0], coords[i][0][1]), (0,coords[i][0][1]), (255,10,0), 2)
dist_label = '%s / %d' % (round(objects_distance, 2), round(min_dist_x, 2))
dist_labelSize, dist_baseLine = cv2.getTextSize(dist_label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
dist_label_ymin = max(coords[j][1][1], dist_labelSize[1] + 10)
cv2.rectangle(frame, (coords[j][1][0], dist_label_ymin-dist_labelSize[1]-10), (coords[j][1][0]+dist_labelSize[0], dist_label_ymin+dist_baseLine-10), (255, 255, 255), cv2.FILLED)
cv2.putText(frame, dist_label, (coords[j][1][0], dist_label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
else:
# ...
b = 1
else:
# ...
b = 2
else:
# ...
b = 3
# Draw label
object_name = self.__labels[int(classes[i])] # Look up object name from "labels" array using class index
label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
# Draw framerate in corner of frame
cv2.putText(frame,'FPS: {0:.2f}'.format(self.__frame_rate_calc),(15,35),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
# Calculate framerate
t2 = cv2.getTickCount()
time1 = (t2-t1)/self.__freq
self.__frame_rate_calc= 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
def __del__(self):
"""
"""
# Clean up
self._videostream.stop()
cv2.destroyAllWindows()
def main():
det_ob = LiveDetection()
det_ob.detect()
del det_ob
if __name__ == "__main__":
main()
| 49.184154
| 221
| 0.530846
| 2,714
| 22,969
| 4.299926
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| 0.427506
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| 0
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| 0.359876
| 22,969
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| 222
| 49.184154
| 0.757737
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| 0.039709
| 0.002163
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| 1
| 0.045643
| false
| 0
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| 0.004149
| 0.124481
| 0.033195
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| null | 0
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1
| 0
|
72a66499365040683f1a28fc9b02b8c6e95b1740
| 4,107
|
py
|
Python
|
modules/zabbix_smart.py
|
yakumo-saki/smart_to_zabbix
|
04dd1debe0c831b4ec94962884543c989ad57730
|
[
"MIT"
] | null | null | null |
modules/zabbix_smart.py
|
yakumo-saki/smart_to_zabbix
|
04dd1debe0c831b4ec94962884543c989ad57730
|
[
"MIT"
] | 23
|
2021-08-30T14:59:27.000Z
|
2021-11-05T16:51:08.000Z
|
modules/zabbix_smart.py
|
yakumo-saki/smart_to_zabbix
|
04dd1debe0c831b4ec94962884543c989ad57730
|
[
"MIT"
] | null | null | null |
import json
import logging
import config as cfg
from modules.const import Keys, AttrKey
from modules.zabbix_sender import send_to_zabbix
logger = logging.getLogger(__name__)
SMART_ATTR_KEY = "ata_smart_attributes"
NVME_ATTR_KEY = "nvme_smart_health_information_log"
def send_attribute_discovery(result):
"""
zabbixにS.M.A.R.T Attribute LLDデータを送信します。
Attribute LLDとは要するにSMART値すべて
"""
logger.info("Sending S.M.A.R.T attribute discovery to zabbix")
discovery_result = []
for device in result:
logger.info("Listing S.M.A.R.T attributes: " + device)
detail = result[device]
discovery = {AttrKey.DEV_NAME: device, AttrKey.DISK_NAME: detail["model_name"]}
if (SMART_ATTR_KEY in detail):
discovery_result = create_attribute_list_non_nvme(discovery, detail[SMART_ATTR_KEY])
elif (NVME_ATTR_KEY in detail):
discovery_result = create_attribute_list_nvme(discovery, detail[NVME_ATTR_KEY])
data = {"request": "sender data", "data":[]}
valueStr = json.dumps({"data": discovery_result})
one_data = {"host": cfg.ZABBIX_HOST, "key": AttrKey.KEY, "value": f"{valueStr}"}
data["data"].append(one_data)
send_to_zabbix(data)
return None
def create_attribute_list_non_nvme(discovery_base, smart_attributes):
import copy
result = []
for attr in smart_attributes["table"]:
discovery = copy.deepcopy(discovery_base)
# non NVMeの場合、 Unknown Attributeがあり得るので、SMART ID を名前の先頭につけておく
discovery[AttrKey.ATTR_NAME] = "{0} {1}".format(attr["id"], attr["name"])
discovery[AttrKey.ATTR_ID] = attr["id"]
result.append(discovery)
return result
def create_attribute_list_nvme(discovery_base, nvme_health_info):
import copy
result = []
for key in nvme_health_info:
discovery = copy.deepcopy(discovery_base)
if key == "temperature_sensors":
for idx, _ in enumerate(nvme_health_info[key]):
# temperature_sensorsの名前の通り、複数の温度センサーがあると値が複数入るので
# temperature_sensors1,2 のような名前に展開する
discovery[AttrKey.ATTR_NAME] = f"temperature_sensors{idx}"
discovery[AttrKey.ATTR_ID] = f"temperature_sensors{idx}"
else:
discovery[AttrKey.ATTR_NAME] = key
discovery[AttrKey.ATTR_ID] = key
result.append(discovery)
return result
def send_smart_data(data):
logger.info("Send S.M.A.R.T data to zabbix")
results = []
for dev in data:
logger.info("Listing S.M.A.R.T data: " + dev)
detail = data[dev] # /dev/sda
if ("ata_smart_attributes" in detail):
results = create_value_list_non_nvme(dev, detail["ata_smart_attributes"])
elif ("nvme_smart_health_information_log" in detail):
results = create_value_list_nvme(dev, detail["nvme_smart_health_information_log"])
sender_data = {"request": "sender data", "data": results}
#valueStr = json.dumps({"data": discovery_result})
# print(json.dumps(sender_data, indent=2))
send_to_zabbix(sender_data)
return None
def create_value_list_non_nvme(dev, smart_attributes):
results = []
for attr in smart_attributes["table"]:
keyvalue = {
AttrKey.RAWVALUE_KEY.format(dev, attr["id"]): attr["raw"]["value"],
AttrKey.VALUE_KEY.format(dev, attr["id"]): attr["value"],
AttrKey.WORST_KEY.format(dev, attr["id"]): attr["worst"]
}
if ("thresh" in attr):
keyvalue[AttrKey.THRESH_KEY.format(dev, attr["id"])] = attr["thresh"]
for k,v in keyvalue.items():
results.append({"host": cfg.ZABBIX_HOST, "key": k, "value": v})
return results
def create_value_list_nvme(dev, nvme_health_info):
results = []
for key in nvme_health_info:
# NVMe にはthreshouldやworstはなく、valueだけ
if key == "temperature_sensors":
# temperature_sensorsの複数の値は 末尾に連番をつけて展開されている
for idx, val in enumerate(nvme_health_info[key]):
key = AttrKey.VALUE_KEY.format(dev, f"temperature_sensors{idx}")
results.append({"host": cfg.ZABBIX_HOST, "key": key, "value": val})
else:
val = nvme_health_info[key]
key = AttrKey.VALUE_KEY.format(dev, key)
results.append({"host": cfg.ZABBIX_HOST, "key": key, "value": val})
return results
| 30.198529
| 90
| 0.70392
| 550
| 4,107
| 5.014545
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| 0.035533
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| 4,107
| 135
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| 0.046266
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| 0.069767
| false
| 0
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| null | 0
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| 0
|
1
| 0
|
72a84aac36dbbd7474150732b72b1f6e0905fbe4
| 2,007
|
py
|
Python
|
data.py
|
kpister/biaxial-rnn-music-composition
|
f6feafad0fe1066dd957293803a86d6c584d9952
|
[
"BSD-2-Clause"
] | null | null | null |
data.py
|
kpister/biaxial-rnn-music-composition
|
f6feafad0fe1066dd957293803a86d6c584d9952
|
[
"BSD-2-Clause"
] | null | null | null |
data.py
|
kpister/biaxial-rnn-music-composition
|
f6feafad0fe1066dd957293803a86d6c584d9952
|
[
"BSD-2-Clause"
] | null | null | null |
import itertools
from midi_to_statematrix import UPPER_BOUND, LOWER_BOUND
def startSentinel():
def noteSentinel(note):
position = note
part_position = [position]
pitchclass = (note + LOWER_BOUND) % 12
part_pitchclass = [int(i == pitchclass) for i in range(12)]
return part_position + part_pitchclass + [0] * 66 + [1]
return [noteSentinel(note) for note in range(UPPER_BOUND - LOWER_BOUND)]
def getOrDefault(l, i, d):
try:
return l[i]
except IndexError:
return d
def buildContext(state):
context = [0] * 12
for note, notestate in enumerate(state):
if notestate[0] == 1:
pitchclass = (note + LOWER_BOUND) % 12
context[pitchclass] += 1
return context
def buildBeat(time):
return [
2 * x - 1 for x in [time % 2, (time // 2) % 2, (time // 4) % 2, (time // 8) % 2]
]
def noteInputForm(note, state, context, beat):
position = note
part_position = [position]
pitchclass = (note + LOWER_BOUND) % 12
part_pitchclass = [int(i == pitchclass) for i in range(12)]
# Concatenate the note states for the previous vicinity
part_prev_vicinity = list(
itertools.chain.from_iterable(
(getOrDefault(state, note + i, [0, 0]) for i in range(-12, 13))
)
)
part_context = context[pitchclass:] + context[:pitchclass]
return (
part_position + part_pitchclass + part_prev_vicinity + part_context + beat + [0]
)
def noteStateSingleToInputForm(state, time):
beat = buildBeat(time)
context = buildContext(state)
return [noteInputForm(note, state, context, beat) for note in range(len(state))]
def noteStateMatrixToInputForm(statematrix):
# NOTE: May have to transpose this or transform it in some way to make Theano like it
# [startSentinel()] +
inputform = [
noteStateSingleToInputForm(state, time)
for time, state in enumerate(statematrix)
]
return inputform
| 26.76
| 89
| 0.636273
| 242
| 2,007
| 5.177686
| 0.297521
| 0.039904
| 0.045491
| 0.057462
| 0.329609
| 0.158021
| 0.158021
| 0.158021
| 0.158021
| 0.158021
| 0
| 0.024275
| 0.261086
| 2,007
| 74
| 90
| 27.121622
| 0.820634
| 0.078226
| 0
| 0.18
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.16
| false
| 0
| 0.04
| 0.02
| 0.38
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
|
1
| 0
|
72a872cd27be148319e99d8b66913e6b97bcfc81
| 7,861
|
py
|
Python
|
ocdb/ws/controllers/datasets.py
|
eocdb/ocdb-server
|
0e28d092e8ecf5f4813878aab43de990cc5fb4ee
|
[
"MIT"
] | null | null | null |
ocdb/ws/controllers/datasets.py
|
eocdb/ocdb-server
|
0e28d092e8ecf5f4813878aab43de990cc5fb4ee
|
[
"MIT"
] | null | null | null |
ocdb/ws/controllers/datasets.py
|
eocdb/ocdb-server
|
0e28d092e8ecf5f4813878aab43de990cc5fb4ee
|
[
"MIT"
] | null | null | null |
# The MIT License (MIT)
# Copyright (c) 2018 by EUMETSAT
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from typing import List, Union
from ..context import WsContext
from ...core.asserts import assert_not_none, assert_one_of, assert_instance
from ...core.models.dataset import Dataset
from ...core.models.dataset_query import DatasetQuery
from ...core.models.dataset_query_result import DatasetQueryResult
from ...core.models.dataset_ref import DatasetRef
from ...core.models.dataset_validation_result import DatasetValidationResult
from ...core.models.qc_info import QcInfo, QC_STATUS_SUBMITTED
from ...core.val import validator
from ...ws.errors import WsResourceNotFoundError, WsBadRequestError, WsNotImplementedError
def validate_dataset(ctx: WsContext, dataset: Dataset) -> DatasetValidationResult:
return validator.validate_dataset(dataset, ctx.config)
def find_datasets(ctx: WsContext,
expr: str = None,
region: List[float] = None,
time: List[str] = None,
wdepth: List[float] = None,
mtype: str = 'all',
wlmode: str = 'all',
shallow: str = 'no',
pmode: str = 'contains',
pgroup: List[str] = None,
status: str = None,
submission_id: str = None,
pname: List[str] = None,
geojson: bool = False,
offset: int = 1,
user_id: str = None,
count: int = 1000) -> DatasetQueryResult:
"""Find datasets."""
assert_one_of(wlmode, ['all', 'multispectral', 'hyperspectral'], name='wlmode')
assert_one_of(shallow, ['no', 'yes', 'exclusively'], name='shallow')
assert_one_of(pmode, ['contains', 'same_cruise', 'dont_apply'], name='pmode')
if pgroup is not None:
assert_instance(pgroup, [])
# Ensuring that the search uses lower case pnames
if pname:
pname = [p.lower() for p in pname]
query = DatasetQuery()
query.expr = expr
query.region = region
query.time = time
query.wdepth = wdepth
query.mtype = mtype
query.wlmode = wlmode
query.shallow = shallow
query.pmode = pmode
query.pgroup = pgroup
query.submission_id = submission_id
query.status = status
query.pname = pname
query.geojson = geojson
query.offset = offset
query.count = count
query.user_id = user_id
result = DatasetQueryResult({}, 0, [], query)
for driver in ctx.db_drivers:
result_part = driver.instance().find_datasets(query)
result.total_count += result_part.total_count
result.datasets += result_part.datasets
result.dataset_ids += result_part.dataset_ids
result.locations.update(result_part.locations)
return result
def add_dataset(ctx: WsContext,
dataset: Dataset) -> DatasetRef:
"""Add a new dataset."""
assert_not_none(dataset)
validation_result = validator.validate_dataset(dataset, ctx.config)
if validation_result.status == "ERROR":
raise WsBadRequestError(f"Invalid dataset.")
dataset_id = ctx.db_driver.instance().add_dataset(dataset)
if not dataset_id:
raise WsBadRequestError(f"Could not add dataset {dataset.path}")
return DatasetRef(dataset_id, dataset.path, dataset.filename)
def update_dataset(ctx: WsContext,
dataset: Dataset):
"""Update an existing dataset."""
assert_not_none(dataset)
validation_result = validator.validate_dataset(dataset, ctx.config)
if validation_result.status == "ERROR":
raise WsBadRequestError(f"Invalid dataset.")
updated = ctx.db_driver.instance().update_dataset(dataset)
if not updated:
raise WsResourceNotFoundError(f"Dataset with ID {dataset.id} not found")
return updated
def delete_dataset(ctx: WsContext,
dataset_id: str):
"""Delete an existing dataset."""
# assert_not_none(api_key, name='api_key')
assert_not_none(dataset_id, name='dataset_id')
deleted = ctx.db_driver.instance().delete_dataset(dataset_id)
if not deleted:
raise WsResourceNotFoundError(f"Dataset with ID {dataset_id} not found")
return deleted
def get_dataset_by_id_strict(ctx: WsContext,
dataset_id: str) -> Dataset:
"""Get dataset by ID."""
assert_not_none(dataset_id, name='dataset_id')
dataset = ctx.db_driver.instance().get_dataset(dataset_id)
if dataset is not None:
return dataset
raise WsResourceNotFoundError(f"Dataset with ID {dataset_id} not found")
def get_dataset_by_id(ctx: WsContext,
dataset_id: Union[dict, str]) -> Dataset:
"""Get dataset by ID."""
assert_not_none(dataset_id, name='dataset_id')
# The dataset_id may be a dataset json object
if isinstance(dataset_id, dict):
dataset_id = dataset_id['id']
dataset = ctx.db_driver.instance().get_dataset(dataset_id)
return dataset
# noinspection PyUnusedLocal,PyTypeChecker
def get_datasets_in_path(ctx: WsContext,
affil: str,
project: str,
cruise: str) -> List[DatasetRef]:
assert_not_none(affil, name='affil')
assert_not_none(project, name='project')
assert_not_none(cruise, name='cruise')
# TODO (generated): implement operation get_datasets_in_bucket()
raise WsNotImplementedError('Operation get_datasets_in_bucket() not yet implemented')
# noinspection PyUnusedLocal,PyTypeChecker
def get_dataset_by_name(ctx: WsContext,
affil: str,
project: str,
cruise: str,
name: str) -> str:
assert_not_none(affil, name='affil')
assert_not_none(project, name='project')
assert_not_none(cruise, name='cruise')
assert_not_none(name, name='name')
# TODO (generated): implement operation get_dataset_by_bucket_and_name()
raise WsNotImplementedError('Operation get_dataset_by_bucket_and_name() not yet implemented')
# noinspection PyUnusedLocal
def get_dataset_qc_info(ctx: WsContext,
dataset_id: str) -> QcInfo:
assert_not_none(dataset_id, name='dataset_id')
dataset = ctx.db_driver.get_dataset(dataset_id)
qc_info_dict = dataset.metadata.get("qc_info")
return QcInfo.from_dict(qc_info_dict) if qc_info_dict else QcInfo(QC_STATUS_SUBMITTED)
# noinspection PyUnusedLocal
def set_dataset_qc_info(ctx: WsContext,
dataset_id: str,
qc_info: QcInfo):
assert_not_none(dataset_id, name='dataset_id')
dataset = ctx.db_driver.get_dataset(dataset_id)
dataset.metadata["qc_info"] = qc_info.to_dict()
ctx.db_driver.update_dataset(dataset)
| 38.346341
| 97
| 0.677649
| 975
| 7,861
| 5.281026
| 0.232821
| 0.052437
| 0.040396
| 0.02719
| 0.357157
| 0.264517
| 0.24898
| 0.235774
| 0.199456
| 0.199456
| 0
| 0.001653
| 0.230632
| 7,861
| 204
| 98
| 38.534314
| 0.849702
| 0.2043
| 0
| 0.231343
| 0
| 0
| 0.084248
| 0.009038
| 0
| 0
| 0
| 0.004902
| 0.141791
| 1
| 0.08209
| false
| 0
| 0.08209
| 0.007463
| 0.223881
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a973eeb72b3616d349d7fd689d925f5f433b09
| 4,209
|
py
|
Python
|
libAnt/node.py
|
ayanezcasal/AntLibAYC
|
c266af973f4c32d4baf30130fe51a572478488ec
|
[
"MIT"
] | 19
|
2018-04-14T15:29:17.000Z
|
2022-02-05T08:51:16.000Z
|
libAnt/node.py
|
ayanezcasal/AntLibAYC
|
c266af973f4c32d4baf30130fe51a572478488ec
|
[
"MIT"
] | 5
|
2018-12-16T09:32:06.000Z
|
2021-10-20T20:20:06.000Z
|
libAnt/node.py
|
ayanezcasal/AntLibAYC
|
c266af973f4c32d4baf30130fe51a572478488ec
|
[
"MIT"
] | 12
|
2016-08-24T09:00:44.000Z
|
2022-01-24T00:16:13.000Z
|
import threading
from queue import Queue, Empty
from time import sleep
from libAnt.drivers.driver import Driver
from libAnt.message import *
class Network:
def __init__(self, key: bytes = b'\x00' * 8, name: str = None):
self.key = key
self.name = name
self.number = 0
def __str__(self):
return self.name
class Pump(threading.Thread):
def __init__(self, driver: Driver, initMessages, out: Queue, onSucces, onFailure):
super().__init__()
self._stopper = threading.Event()
self._driver = driver
self._out = out
self._initMessages = initMessages
self._waiters = []
self._onSuccess = onSucces
self._onFailure = onFailure
def stop(self):
self._driver.abort()
self._stopper.set()
def stopped(self):
return self._stopper.isSet()
def run(self):
while not self.stopped():
try:
with self._driver as d:
# Startup
rst = SystemResetMessage()
self._waiters.append(rst)
d.write(rst)
for m in self._initMessages:
self._waiters.append(m)
d.write(m)
while not self.stopped():
# Write
try:
outMsg = self._out.get(block=False)
self._waiters.append(outMsg)
d.write(outMsg)
except Empty:
pass
# Read
try:
msg = d.read(timeout=1)
if msg.type == MESSAGE_CHANNEL_EVENT:
# This is a response to our outgoing message
for w in self._waiters:
if w.type == msg.content[1]: # ACK
self._waiters.remove(w)
# TODO: Call waiter callback from tuple (waiter, callback)
break
elif msg.type == MESSAGE_CHANNEL_BROADCAST_DATA:
bmsg = BroadcastMessage(msg.type, msg.content).build(msg.content)
self._onSuccess(bmsg)
except Empty:
pass
except Exception as e:
self._onFailure(e)
except:
pass
self._waiters.clear()
sleep(1)
class Node:
def __init__(self, driver: Driver, name: str = None):
self._driver = driver
self._name = name
self._out = Queue()
self._init = []
self._pump = None
self._configMessages = Queue()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop()
def start(self, onSuccess, onFailure):
if not self.isRunning():
self._pump = Pump(self._driver, self._init, self._out, onSuccess, onFailure)
self._pump.start()
def enableRxScanMode(self, networkKey=ANTPLUS_NETWORK_KEY, channelType=CHANNEL_TYPE_ONEWAY_RECEIVE,
frequency: int = 2457, rxTimestamp: bool = True, rssi: bool = True, channelId: bool = True):
self._init.append(SystemResetMessage())
self._init.append(SetNetworkKeyMessage(0, networkKey))
self._init.append(AssignChannelMessage(0, channelType))
self._init.append(SetChannelIdMessage(0))
self._init.append(SetChannelRfFrequencyMessage(0, frequency))
self._init.append(EnableExtendedMessagesMessage())
self._init.append(LibConfigMessage(rxTimestamp, rssi, channelId))
self._init.append(OpenRxScanModeMessage())
def stop(self):
if self.isRunning():
self._pump.stop()
self._pump.join()
def isRunning(self):
if self._pump is None:
return False
return self._pump.is_alive()
def getCapabilities(self):
pass
| 33.943548
| 117
| 0.516512
| 404
| 4,209
| 5.168317
| 0.321782
| 0.038314
| 0.05364
| 0.014368
| 0.022031
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005927
| 0.39867
| 4,209
| 123
| 118
| 34.219512
| 0.819044
| 0.029461
| 0
| 0.153061
| 0
| 0
| 0.000981
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| 0
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| 0
| 0.00813
| 0
| 1
| 0.142857
| false
| 0.040816
| 0.05102
| 0.030612
| 0.27551
| 0
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| 0
| null | 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a98cda239838c97dafaaefa4602ca6f04cc90c
| 5,168
|
py
|
Python
|
tests/test_seasonality.py
|
OliPerkins1987/Wildfire_Human_Agency_Model
|
49ac17c7c2ad5e03d572b6ae22c227e89a944624
|
[
"MIT"
] | 1
|
2021-06-24T16:45:22.000Z
|
2021-06-24T16:45:22.000Z
|
tests/test_seasonality.py
|
OliPerkins1987/Wildfire_Human_Agency_Model
|
49ac17c7c2ad5e03d572b6ae22c227e89a944624
|
[
"MIT"
] | null | null | null |
tests/test_seasonality.py
|
OliPerkins1987/Wildfire_Human_Agency_Model
|
49ac17c7c2ad5e03d572b6ae22c227e89a944624
|
[
"MIT"
] | 1
|
2021-10-05T08:57:17.000Z
|
2021-10-05T08:57:17.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 30 12:17:04 2021
@author: Oli
"""
import pytest
import pandas as pd
import numpy as np
import netCDF4 as nc
import os
from copy import deepcopy
os.chdir(os.path.dirname(os.path.realpath(__file__)))
wd = os.getcwd().replace('\\', '/')
exec(open("test_setup.py").read())
os.chdir((wd[0:-6] + '/src/data_import'))
exec(open("local_load_up.py").read())
from model_interface.wham import WHAM
from Core_functionality.AFTs.agent_class import AFT
from Core_functionality.AFTs.arable_afts import Swidden, SOSH, MOSH, Intense_arable
from Core_functionality.AFTs.livestock_afts import Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p
from Core_functionality.AFTs.forestry_afts import Agroforestry, Logger, Managed_forestry, Abandoned_forestry
from Core_functionality.AFTs.nonex_afts import Hunter_gatherer, Recreationalist, SLM, Conservationist
from Core_functionality.AFTs.land_system_class import land_system
from Core_functionality.AFTs.land_systems import Cropland, Pasture, Rangeland, Forestry, Urban, Unoccupied, Nonex
from Core_functionality.top_down_processes.arson import arson
from Core_functionality.top_down_processes.background_ignitions import background_rate
from Core_functionality.top_down_processes.fire_constraints import fuel_ct, dominant_afr_ct
from Core_functionality.Trees.Transfer_tree import define_tree_links, predict_from_tree, update_pars, predict_from_tree_fast
from Core_functionality.prediction_tools.regression_families import regression_link, regression_transformation
#####################################################################
### Run model year then reproduce outputs
#####################################################################
### Run model for 1 year
all_afts = [Swidden, SOSH, MOSH, Intense_arable,
Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p,
Agroforestry, Logger, Managed_forestry, Abandoned_forestry,
Hunter_gatherer, Recreationalist, SLM, Conservationist]
parameters = {
'xlen': 192,
'ylen': 144,
'AFTs': all_afts,
'LS' : [Cropland, Rangeland, Pasture, Forestry, Nonex, Unoccupied, Urban],
'Fire_types': {'cfp': 'Vegetation', 'crb': 'Arable', 'hg': 'Vegetation',
'pasture': 'Pasture', 'pyrome': 'Vegetation'},
'Observers': {'arson': arson, 'background_rate': background_rate},
'AFT_pars': Core_pars,
'Maps' : Map_data,
'Constraint_pars': {'Soil_threshold': 0.1325,
'Dominant_afr_threshold': 0.5,
'Rangeland_stocking_contstraint': True,
'R_s_c_Positive' : False,
'HG_Market_constraint': 7800,
'Arson_threshold': 0.5},
'timestep': 0,
'end_run' : 0,
'reporters': ['Managed_fire', 'Background_ignitions','Arson'],
'theta' : 0.1,
'bootstrap': False,
'Seasonality': False
}
mod = WHAM(parameters)
### setup
mod.setup()
### go
mod.go()
mod_annual = deepcopy(mod.results['Managed_fire'][0]['Total'])
#######################
### Run model monthly
#######################
all_afts = [Swidden, SOSH, MOSH, Intense_arable,
Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p,
Agroforestry, Logger, Managed_forestry, Abandoned_forestry,
Hunter_gatherer, Recreationalist, SLM, Conservationist]
parameters = {
'xlen': 192,
'ylen': 144,
'AFTs': all_afts,
'LS' : [Cropland, Rangeland, Pasture, Forestry, Nonex, Unoccupied, Urban],
'Fire_types': {'cfp': 'Vegetation', 'crb': 'Arable', 'hg': 'Vegetation',
'pasture': 'Pasture', 'pyrome': 'Vegetation'},
'Fire_seasonality': Seasonality,
'Observers': {'arson': arson, 'background_rate': background_rate},
'AFT_pars': Core_pars,
'Maps' : Map_data,
'Constraint_pars': {'Soil_threshold': 0.1325,
'Dominant_afr_threshold': 0.5,
'Rangeland_stocking_contstraint': True,
'R_s_c_Positive' : False,
'HG_Market_constraint': 7800,
'Arson_threshold': 0.5},
'timestep': 0,
'end_run' : 0,
'reporters': ['Managed_fire', 'Background_ignitions','Arson'],
'theta' : 0.1,
'bootstrap': False,
'Seasonality': True
}
mod = WHAM(parameters)
### setup
mod.setup()
### go
mod.go()
##################################
### tests
##################################
def test_seasonality_mean():
seasonal = np.nansum(mod.results['Managed_fire'][0]['Total'], axis = 0)
assert pytest.approx(np.nanmean(mod_annual)) == np.nanmean(seasonal)
def test_seasonality_quantiles():
seasonal = np.nansum(mod.results['Managed_fire'][0]['Total'], axis = 0)
quants = [0, 0.2, 0.4, 0.5, 0.6, 0.8, 1]
assert pytest.approx(np.nanquantile(mod_annual, quants)) == np.nanquantile(seasonal, quants)
| 32.917197
| 125
| 0.60952
| 576
| 5,168
| 5.211806
| 0.310764
| 0.031979
| 0.083944
| 0.058294
| 0.597602
| 0.553298
| 0.490673
| 0.490673
| 0.490673
| 0.466023
| 0
| 0.01998
| 0.225232
| 5,168
| 156
| 126
| 33.128205
| 0.72977
| 0.033475
| 0
| 0.623656
| 0
| 0
| 0.176729
| 0.022832
| 0
| 0
| 0
| 0
| 0.021505
| 1
| 0.021505
| false
| 0
| 0.215054
| 0
| 0.236559
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72a9deefb1b6924bdaa40e4fd75c347025f116d3
| 572
|
py
|
Python
|
tests/test_client.py
|
patvdleer/nefit-client-python
|
97f2c1e454b7c0d5829e1a9c285c998980c603e3
|
[
"MIT"
] | 11
|
2017-07-20T10:12:55.000Z
|
2020-12-25T12:40:31.000Z
|
tests/test_client.py
|
patvdleer/nefit-client-python
|
97f2c1e454b7c0d5829e1a9c285c998980c603e3
|
[
"MIT"
] | 5
|
2018-01-01T22:11:09.000Z
|
2020-05-14T20:59:50.000Z
|
tests/test_client.py
|
patvdleer/nefit-client-python
|
97f2c1e454b7c0d5829e1a9c285c998980c603e3
|
[
"MIT"
] | 11
|
2017-04-09T18:55:53.000Z
|
2020-04-22T14:31:12.000Z
|
import os
import unittest
from nefit import NefitClient, NefitResponseException
class ClientTest(unittest.TestCase):
def test_exceptions(self):
client = NefitClient(
os.environ.get("NEFIT_SERIAL", 123456789),
os.environ.get("NEFIT_ACCESS_KEY", "abc1abc2abc3abc4"),
"asddasadsasdcx"
)
client.connect()
with self.assertRaises(NefitResponseException):
client.get_display_code()
client.disconnect()
client.force_disconnect()
if __name__ == '__main__':
unittest.main()
| 26
| 67
| 0.657343
| 53
| 572
| 6.811321
| 0.622642
| 0.049862
| 0.066482
| 0.094183
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030162
| 0.246504
| 572
| 21
| 68
| 27.238095
| 0.807425
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 1
| 0.058824
| false
| 0
| 0.176471
| 0
| 0.294118
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
|
1
| 0
|
72ac281779d052a2842e7fce7e7f54eebca721c0
| 5,080
|
py
|
Python
|
opencivicdata/merge.py
|
GovHawkDC/python-opencivicdata
|
1679a4e5df381c777c3e6c53d7c056321662e99a
|
[
"BSD-3-Clause"
] | null | null | null |
opencivicdata/merge.py
|
GovHawkDC/python-opencivicdata
|
1679a4e5df381c777c3e6c53d7c056321662e99a
|
[
"BSD-3-Clause"
] | null | null | null |
opencivicdata/merge.py
|
GovHawkDC/python-opencivicdata
|
1679a4e5df381c777c3e6c53d7c056321662e99a
|
[
"BSD-3-Clause"
] | null | null | null |
import datetime
from django.db import transaction
def compute_diff(obj1, obj2):
"""
Given two objects compute a list of differences.
Each diff dict has the following keys:
field - name of the field
new - the new value for the field
one - value of the field in obj1
two - value of the field in obj2
diff - none|one|two|new
list - true if field is a list of related objects
"""
comparison = []
fields = obj1._meta.get_fields()
exclude = ('created_at', 'updated_at', 'id', 'locked_fields')
if obj1 == obj2:
raise ValueError('cannot merge object with itself')
for field in fields:
if field.name in exclude:
continue
elif not field.is_relation:
piece_one = getattr(obj1, field.name)
piece_two = getattr(obj2, field.name)
if piece_one == piece_two:
diff = 'none'
new = piece_one
elif piece_one:
diff = 'one'
new = piece_one
elif piece_two:
diff = 'two'
new = piece_two
comparison.append({
'field': field.name,
'new': new,
'one': getattr(obj1, field.name),
'two': getattr(obj2, field.name),
'diff': diff,
'list': False,
})
else:
related_name = field.get_accessor_name()
piece_one = list(getattr(obj1, related_name).all())
piece_two = list(getattr(obj2, related_name).all())
# TODO: try and deduplicate the lists?
new = piece_one + piece_two
diff = 'none' if piece_one == piece_two else 'one'
if (field.name == 'other_names' and obj1.name != obj2.name):
new.append(field.related_model(name=obj2.name,
note='from merge w/ ' + obj2.id)
)
diff = 'new'
if field.name == 'identifiers':
new.append(field.related_model(identifier=obj2.id))
diff = 'new'
if field.name == 'memberships':
new = _dedupe_memberships(new)
comparison.append({
'field': related_name,
'new': new,
'one': piece_one,
'two': piece_two,
'diff': diff,
'list': True,
})
comparison.append({'field': 'created_at',
'new': min(obj1.created_at, obj2.created_at),
'one': obj1.created_at,
'two': obj2.created_at,
'diff': 'one' if obj1.created_at < obj2.created_at else 'two',
'list': False,
})
comparison.append({'field': 'updated_at',
'new': datetime.datetime.utcnow(),
'one': obj1.updated_at,
'two': obj2.updated_at,
'diff': 'new',
'list': False,
})
# locked fields are any fields that change that aren't M2M relations
# (ending in _set)
new_locked_fields = obj1.locked_fields + obj2.locked_fields + [
c['field'] for c in comparison if c['diff'] != 'none' and not c['field'].endswith('_set')
]
new_locked_fields = set(new_locked_fields) - {'updated_at', 'created_at'}
comparison.append({'field': 'locked_fields',
'new': list(new_locked_fields),
'one': obj1.locked_fields,
'two': obj2.updated_at,
'diff': 'new',
'list': False,
})
return comparison
@transaction.atomic
def apply_diff(obj1, obj2, diff):
for row in diff:
if row['diff'] != 'none':
if row['list']:
# save items, the ids have been set to obj1
for item in row['new']:
setattr(item,
getattr(obj1, row['field']).field.name,
obj1)
item.save()
else:
setattr(obj1, row['field'], row['new'])
obj1.save()
count, delete_plan = obj2.delete()
if count > 1:
# shouldn't happen, but let's be sure
raise AssertionError('deletion failed due to related objects left unmerged')
def merge(obj1, obj2):
diff = compute_diff(obj1, obj2)
apply_diff(obj1, obj2, diff)
def _dedupe_memberships(memberships):
deduped = []
mset = set()
for membership in memberships:
mkey = (membership.organization_id,
membership.label,
membership.end_date,
membership.post_id)
if mkey not in mset:
deduped.append(membership)
mset.add(mkey)
else:
membership.delete()
return deduped
| 34.794521
| 97
| 0.491339
| 536
| 5,080
| 4.524254
| 0.244403
| 0.040825
| 0.043299
| 0.019794
| 0.202887
| 0.087423
| 0.046186
| 0.026392
| 0
| 0
| 0
| 0.015227
| 0.405315
| 5,080
| 145
| 98
| 35.034483
| 0.787488
| 0.100787
| 0
| 0.22807
| 0
| 0
| 0.092023
| 0
| 0
| 0
| 0
| 0.006897
| 0.008772
| 1
| 0.035088
| false
| 0
| 0.017544
| 0
| 0.070175
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ac92211f4ec9ab263019e3549666f802fa242f
| 3,257
|
py
|
Python
|
src/python/pants/core/project_info/filedeps.py
|
silverguo/pants
|
141510d03fbf2b7e1a0b54f66b54088697f6fa51
|
[
"Apache-2.0"
] | null | null | null |
src/python/pants/core/project_info/filedeps.py
|
silverguo/pants
|
141510d03fbf2b7e1a0b54f66b54088697f6fa51
|
[
"Apache-2.0"
] | null | null | null |
src/python/pants/core/project_info/filedeps.py
|
silverguo/pants
|
141510d03fbf2b7e1a0b54f66b54088697f6fa51
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2018 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
import itertools
from pathlib import PurePath
from typing import Iterable
from pants.base.build_root import BuildRoot
from pants.engine.addresses import Address, Addresses, BuildFileAddress
from pants.engine.console import Console
from pants.engine.goal import Goal, GoalSubsystem, LineOriented
from pants.engine.rules import goal_rule
from pants.engine.selectors import Get, MultiGet
from pants.engine.target import (
HydratedSources,
HydrateSourcesRequest,
Sources,
Target,
Targets,
TransitiveTargets,
)
class FiledepsOptions(LineOriented, GoalSubsystem):
"""List all source and BUILD files a target depends on."""
name = "filedeps2"
@classmethod
def register_options(cls, register):
super().register_options(register)
register(
"--absolute",
type=bool,
default=True,
help=(
"If True, output with absolute path; else, output with path relative to the "
"build root."
),
)
register(
"--globs",
type=bool,
default=False,
help=(
"Instead of outputting filenames, output the original globs used in the BUILD "
"file. This will not include exclude globs (i.e. globs that start with `!`)."
),
)
register(
"--transitive",
type=bool,
default=False,
help="If True, include the files used by dependencies in the output.",
)
class Filedeps(Goal):
subsystem_cls = FiledepsOptions
@goal_rule
async def file_deps(
console: Console, options: FiledepsOptions, build_root: BuildRoot, addresses: Addresses,
) -> Filedeps:
targets: Iterable[Target]
if options.values.transitive:
transitive_targets = await Get[TransitiveTargets](Addresses, addresses)
targets = transitive_targets.closure
else:
targets = await Get[Targets](Addresses, addresses)
build_file_addresses = await MultiGet(
Get[BuildFileAddress](Address, tgt.address) for tgt in targets
)
unique_rel_paths = {bfa.rel_path for bfa in build_file_addresses}
if options.values.globs:
unique_rel_paths.update(
itertools.chain.from_iterable(tgt.get(Sources).filespec["globs"] for tgt in targets)
)
else:
all_hydrated_sources = await MultiGet(
Get[HydratedSources](HydrateSourcesRequest, tgt.get(Sources).request) for tgt in targets
)
unique_rel_paths.update(
itertools.chain.from_iterable(
hydrated_sources.snapshot.files for hydrated_sources in all_hydrated_sources
)
)
with options.line_oriented(console) as print_stdout:
for rel_path in sorted(unique_rel_paths):
final_path = (
PurePath(build_root.path, rel_path).as_posix()
if options.values.absolute
else rel_path
)
print_stdout(final_path)
return Filedeps(exit_code=0)
def rules():
return [file_deps]
| 30.726415
| 100
| 0.645072
| 357
| 3,257
| 5.764706
| 0.358543
| 0.030612
| 0.043732
| 0.021866
| 0.089407
| 0.066084
| 0.066084
| 0.044704
| 0
| 0
| 0
| 0.0034
| 0.277556
| 3,257
| 105
| 101
| 31.019048
| 0.871228
| 0.055266
| 0
| 0.186047
| 0
| 0
| 0.111726
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.023256
| false
| 0
| 0.116279
| 0.011628
| 0.209302
| 0.023256
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72adadcbd8e2ff6b2637e141f68ab8b5a7fcd9ed
| 5,887
|
py
|
Python
|
perfkitbenchmarker/providers/rackspace/rackspace_network.py
|
dq922/CloudControlVM
|
fae2cf7d2c4388e1dc657bd9245d88f2cb1b9b52
|
[
"Apache-2.0"
] | null | null | null |
perfkitbenchmarker/providers/rackspace/rackspace_network.py
|
dq922/CloudControlVM
|
fae2cf7d2c4388e1dc657bd9245d88f2cb1b9b52
|
[
"Apache-2.0"
] | null | null | null |
perfkitbenchmarker/providers/rackspace/rackspace_network.py
|
dq922/CloudControlVM
|
fae2cf7d2c4388e1dc657bd9245d88f2cb1b9b52
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2014 PerfKitBenchmarker 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.
"""Module containing classes related to Rackspace VM networking.
The SecurityGroup class provides a way of opening VM ports. The Network class
allows VMs to communicate via internal IPs.
"""
import json
import os
import threading
from perfkitbenchmarker import flags
from perfkitbenchmarker import network
from perfkitbenchmarker import vm_util
from perfkitbenchmarker.providers.rackspace import util
from perfkitbenchmarker import providers
FLAGS = flags.FLAGS
SSH_PORT = 22
class RackspaceSecurityGroup(network.BaseFirewall):
"""An object representing the Rackspace Security Group."""
CLOUD = providers.RACKSPACE
def __init__(self):
"""Initialize Rackspace security group class."""
self._lock = threading.Lock()
self.firewall_names = set()
self.sg_counter = 0
def AllowPort(self, vm, port):
"""Opens a port on the firewall.
Args:
vm: The BaseVirtualMachine object to open the port for.
port: The local port to open.
"""
if vm.is_static or not FLAGS.use_security_group or port == SSH_PORT:
return
with self._lock:
firewall_name = ('perfkit-firewall-%s-%d-%d' %
(FLAGS.run_uri, port, self.sg_counter))
self.sg_counter += 1
if firewall_name in self.firewall_names:
return
firewall_env = dict(os.environ.copy(),
**util.GetDefaultRackspaceNeutronEnv(self))
firewall_cmd = [FLAGS.neutron_path]
firewall_cmd.extend(['security-group-create'])
firewall_cmd.append(firewall_name)
vm_util.IssueRetryableCommand(firewall_cmd, env=firewall_env)
self.firewall_names.add(firewall_name)
for protocol in ['tcp', 'udp']:
rule_cmd = []
rule_cmd.extend([FLAGS.neutron_path,
'security-group-rule-create',
'--direction', 'ingress',
'--ethertype', 'IPv4',
'--protocol', protocol,
'--port-range-min', str(port),
'--port-range-max', str(port)])
rule_cmd.append(firewall_name)
vm_util.IssueRetryableCommand(rule_cmd, env=firewall_env)
rule_cmd = []
rule_cmd.extend([FLAGS.neutron_path,
'security-group-rule-create',
'--direction', 'ingress',
'--ethertype', 'IPv4',
'--protocol', 'tcp',
'--port-range-min', str(SSH_PORT),
'--port-range-max', str(SSH_PORT)])
rule_cmd.append(firewall_name)
vm_util.IssueRetryableCommand(rule_cmd, env=firewall_env)
getport_cmd = []
getport_cmd.extend([FLAGS.neutron_path, 'port-list',
'--format', 'table'])
stdout, _ = vm_util.IssueRetryableCommand(getport_cmd,
env=firewall_env)
attrs = stdout.split('\n')
for attr in attrs:
if vm.ip_address in attr or vm.ip_address6 in attr:
port_id = [v.strip() for v in attr.split('|') if v != ''][0]
if port_id != '':
break
if not port_id:
raise ValueError('Could not find port_id from response.')
updateport_cmd = []
updateport_cmd.extend([FLAGS.neutron_path, 'port-update'])
for firewall in self.firewall_names:
updateport_cmd.extend(['--security-group', firewall])
updateport_cmd.append(port_id)
vm_util.IssueRetryableCommand(updateport_cmd, env=firewall_env)
def DisallowAllPorts(self):
"""Closes all ports on the firewall."""
firewall_env = dict(os.environ.copy(),
**util.GetDefaultRackspaceNeutronEnv(self))
for firewall in self.firewall_names:
firewall_cmd = []
firewall_cmd.extend([FLAGS.neutron_path,
'security-group-show',
'--format', 'value'])
firewall_cmd.append(firewall)
stdout, _ = vm_util.IssueRetryableCommand(firewall_cmd,
env=firewall_env)
rules = [v for v in stdout.split('\n') if v != ''][2:-1]
for rule in rules:
rule_id = str(json.loads(rule)['id'])
rule_cmd = []
rule_cmd.extend([FLAGS.neutron_path,
'security-group-rule-delete'])
rule_cmd.append(rule_id)
vm_util.IssueRetryableCommand(rule_cmd, env=firewall_env)
firewall_cmd = [FLAGS.neutron_path]
firewall_cmd.extend(['security-group-delete'])
firewall_cmd.append(firewall)
vm_util.IssueRetryableCommand(firewall_cmd, env=firewall_env)
self.firewall_names.remove(firewall)
| 38.477124
| 80
| 0.565993
| 620
| 5,887
| 5.214516
| 0.298387
| 0.040829
| 0.039592
| 0.042066
| 0.339004
| 0.32725
| 0.290752
| 0.272502
| 0.241571
| 0.201361
| 0
| 0.004639
| 0.340921
| 5,887
| 152
| 81
| 38.730263
| 0.828608
| 0.174282
| 0
| 0.34375
| 0
| 0
| 0.08958
| 0.030278
| 0
| 0
| 0
| 0
| 0
| 1
| 0.03125
| false
| 0
| 0.083333
| 0
| 0.15625
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72b5ec574a910346ea5b219b420b4689179e7f53
| 818
|
py
|
Python
|
vendor-local/src/django-piston/tests/test_project/settings.py
|
jlin/inventory
|
c098c98e570c3bf9fadfd811eb75e1213f6ea428
|
[
"BSD-3-Clause"
] | 22
|
2015-01-16T01:36:32.000Z
|
2020-06-08T00:46:18.000Z
|
vendor-local/src/django-piston/tests/test_project/settings.py
|
jlin/inventory
|
c098c98e570c3bf9fadfd811eb75e1213f6ea428
|
[
"BSD-3-Clause"
] | 9
|
2019-03-15T11:39:32.000Z
|
2019-04-30T00:59:50.000Z
|
vendor-local/src/django-piston/tests/test_project/settings.py
|
jlin/inventory
|
c098c98e570c3bf9fadfd811eb75e1213f6ea428
|
[
"BSD-3-Clause"
] | 13
|
2015-01-13T20:56:22.000Z
|
2022-02-23T06:01:17.000Z
|
import os
DEBUG = True
DATABASES = {
'default':
{
'ENGINE': 'django.db.backends.sqlite3',
'NAME': '/tmp/piston.db'
}
}
DATABASE_ENGINE = 'sqlite3'
DATABASE_NAME = '/tmp/piston.db'
INSTALLED_APPS = (
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.admin',
'piston',
'test_project.apps.testapp',
)
TEMPLATE_DIRS = (
os.path.join(os.path.dirname(__file__), 'templates'),
)
SITE_ID = 1
ROOT_URLCONF = 'test_project.urls'
MIDDLEWARE_CLASSES = (
'piston.middleware.ConditionalMiddlewareCompatProxy',
'django.contrib.sessions.middleware.SessionMiddleware',
'piston.middleware.CommonMiddlewareCompatProxy',
'django.contrib.auth.middleware.AuthenticationMiddleware',
)
| 24.058824
| 62
| 0.687042
| 81
| 818
| 6.777778
| 0.555556
| 0.165756
| 0.047359
| 0.054645
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004412
| 0.168704
| 818
| 33
| 63
| 24.787879
| 0.802941
| 0
| 0
| 0
| 0
| 0
| 0.545232
| 0.370416
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.032258
| 0
| 0.032258
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72b62ed3f7cc27fe8cfe04f0c5e5ac430e3c0735
| 14,958
|
py
|
Python
|
src/sage/combinat/combinatorial_map.py
|
UCD4IDS/sage
|
43474c96d533fd396fe29fe0782d44dc7f5164f7
|
[
"BSL-1.0"
] | null | null | null |
src/sage/combinat/combinatorial_map.py
|
UCD4IDS/sage
|
43474c96d533fd396fe29fe0782d44dc7f5164f7
|
[
"BSL-1.0"
] | null | null | null |
src/sage/combinat/combinatorial_map.py
|
UCD4IDS/sage
|
43474c96d533fd396fe29fe0782d44dc7f5164f7
|
[
"BSL-1.0"
] | null | null | null |
"""
Combinatorial maps
This module provides a decorator that can be used to add semantic to a
Python method by marking it as implementing a *combinatorial map*,
that is a map between two :class:`enumerated sets <EnumeratedSets>`::
sage: from sage.combinat.combinatorial_map import combinatorial_map
sage: class MyPermutation(object):
....: @combinatorial_map()
....: def reverse(self):
....: '''
....: Reverse the permutation
....: '''
....: # ... code ...
By default, this decorator is a no-op: it returns the decorated method
as is::
sage: MyPermutation.reverse
<function MyPermutation.reverse at ...>
See :func:`combinatorial_map_wrapper` for the various options this
decorator can take.
Projects built on top of Sage are welcome to customize locally this
hook to instrument the Sage code and exploit this semantic
information. Typically, the decorator could be used to populate a
database of maps. For a real-life application, see the project
`FindStat <http://findstat.org/>`. As a basic example, a variant of
the decorator is provided as :func:`combinatorial_map_wrapper`; it
wraps the decorated method, so that one can later use
:func:`combinatorial_maps_in_class` to query an object, or class
thereof, for all the combinatorial maps that apply to it.
.. NOTE::
Since decorators are evaluated upon loading Python modules,
customizing :obj:`combinatorial map` needs to be done before the
modules using it are loaded. In the examples below, where we
illustrate the customized ``combinatorial_map`` decorator on the
:mod:`sage.combinat.permutation` module, we resort to force a
reload of this module after dynamically changing
``sage.combinat.combinatorial_map.combinatorial_map``. This is
good enough for those doctests, but remains fragile.
For real use cases, it is probably best to just edit this source
file statically (see below).
"""
# ****************************************************************************
# Copyright (C) 2011 Christian Stump <christian.stump at gmail.com>
#
# Distributed under the terms of the GNU General Public License (GPL)
# https://www.gnu.org/licenses/
# ****************************************************************************
def combinatorial_map_trivial(f=None, order=None, name=None):
r"""
Combinatorial map decorator
See :ref:`sage.combinat.combinatorial_map` for a description of
this decorator and its purpose. This default implementation does
nothing.
INPUT:
- ``f`` -- (default: ``None``, if combinatorial_map is used as a decorator) a function
- ``name`` -- (default: ``None``) the name for nicer outputs on combinatorial maps
- ``order`` -- (default: ``None``) the order of the combinatorial map, if it is known. Is not used, but might be helpful later
OUTPUT:
- ``f`` unchanged
EXAMPLES::
sage: from sage.combinat.combinatorial_map import combinatorial_map_trivial as combinatorial_map
sage: class MyPermutation(object):
....: @combinatorial_map
....: def reverse(self):
....: '''
....: Reverse the permutation
....: '''
....: # ... code ...
....: @combinatorial_map(name='descent set of permutation')
....: def descent_set(self):
....: '''
....: The descent set of the permutation
....: '''
....: # ... code ...
sage: MyPermutation.reverse
<function MyPermutation.reverse at ...>
sage: MyPermutation.descent_set
<function MyPermutation.descent_set at ...>
"""
if f is None:
return lambda f: f
else:
return f
def combinatorial_map_wrapper(f=None, order=None, name=None):
r"""
Combinatorial map decorator (basic example).
See :ref:`sage.combinat.combinatorial_map` for a description of
the ``combinatorial_map`` decorator and its purpose. This
implementation, together with :func:`combinatorial_maps_in_class`
illustrates how to use this decorator as a hook to instrument the
Sage code.
INPUT:
- ``f`` -- (default: ``None``, if combinatorial_map is used as a decorator) a function
- ``name`` -- (default: ``None``) the name for nicer outputs on combinatorial maps
- ``order`` -- (default: ``None``) the order of the combinatorial map, if it is known. Is not used, but might be helpful later
OUTPUT:
- A combinatorial map. This is an instance of the :class:`CombinatorialMap`.
EXAMPLES:
We define a class illustrating the use of this implementation of
the :obj:`combinatorial_map` decorator with its various arguments::
sage: from sage.combinat.combinatorial_map import combinatorial_map_wrapper as combinatorial_map
sage: class MyPermutation(object):
....: @combinatorial_map()
....: def reverse(self):
....: '''
....: Reverse the permutation
....: '''
....: pass
....: @combinatorial_map(order=2)
....: def inverse(self):
....: '''
....: The inverse of the permutation
....: '''
....: pass
....: @combinatorial_map(name='descent set of permutation')
....: def descent_set(self):
....: '''
....: The descent set of the permutation
....: '''
....: pass
....: def major_index(self):
....: '''
....: The major index of the permutation
....: '''
....: pass
sage: MyPermutation.reverse
Combinatorial map: reverse
sage: MyPermutation.descent_set
Combinatorial map: descent set of permutation
sage: MyPermutation.inverse
Combinatorial map: inverse
One can now determine all the combinatorial maps associated with a
given object as follows::
sage: from sage.combinat.combinatorial_map import combinatorial_maps_in_class
sage: X = combinatorial_maps_in_class(MyPermutation); X # random
[Combinatorial map: reverse,
Combinatorial map: descent set of permutation,
Combinatorial map: inverse]
The method ``major_index`` defined about is not a combinatorial map::
sage: MyPermutation.major_index
<function MyPermutation.major_index at ...>
But one can define a function that turns ``major_index`` into a combinatorial map::
sage: def major_index(p):
....: return p.major_index()
sage: major_index
<function major_index at ...>
sage: combinatorial_map(major_index)
Combinatorial map: major_index
"""
if f is None:
return lambda f: CombinatorialMap(f, order=order, name=name)
else:
return CombinatorialMap(f, order=order, name=name)
##############################################################################
# Edit here to customize the combinatorial_map hook
##############################################################################
combinatorial_map = combinatorial_map_trivial
# combinatorial_map = combinatorial_map_wrapper
class CombinatorialMap(object):
r"""
This is a wrapper class for methods that are *combinatorial maps*.
For further details and doctests, see
:ref:`sage.combinat.combinatorial_map` and
:func:`combinatorial_map_wrapper`.
"""
def __init__(self, f, order=None, name=None):
"""
Constructor for combinatorial maps.
EXAMPLES::
sage: from sage.combinat.combinatorial_map import combinatorial_map_wrapper as combinatorial_map
sage: def f(x):
....: "doc of f"
....: return x
sage: x = combinatorial_map(f); x
Combinatorial map: f
sage: x.__doc__
'doc of f'
sage: x.__name__
'f'
sage: x.__module__
'__main__'
"""
import types
if not isinstance(f, types.FunctionType):
raise ValueError("Only plain functions are supported")
self._f = f
self._order = order
self._name = name
if hasattr(f, "__doc__"):
self.__doc__ = f.__doc__
if hasattr(f, "__name__"):
self.__name__ = f.__name__
else:
self.__name__ = "..."
if hasattr(f, "__module__"):
self.__module__ = f.__module__
def __repr__(self):
"""
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: p = Permutation([1,3,2,4])
sage: p.left_tableau.__repr__()
'Combinatorial map: Robinson-Schensted insertion tableau'
"""
return "Combinatorial map: %s" % self.name()
def _sage_src_lines_(self):
r"""
Return the source code location for the wrapped function.
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: p = Permutation([1,3,2,4])
sage: cm = p.left_tableau; cm
Combinatorial map: Robinson-Schensted insertion tableau
sage: (src, lines) = cm._sage_src_lines_()
sage: src[0]
" @combinatorial_map(name='Robinson-Schensted insertion tableau')\n"
sage: lines # random
2653
"""
from sage.misc.sageinspect import sage_getsourcelines
return sage_getsourcelines(self._f)
def __get__(self, inst, cls=None):
"""
Bounds the method of self to the given instance.
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: p = Permutation([1,3,2,4])
sage: p.left_tableau #indirect doctest
Combinatorial map: Robinson-Schensted insertion tableau
"""
self._inst = inst
return self
def __call__(self, *args, **kwds):
"""
Calls the combinatorial map.
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: p = Permutation([1,3,2,4])
sage: cm = type(p).left_tableau; cm
Combinatorial map: Robinson-Schensted insertion tableau
sage: cm(p)
[[1, 2, 4], [3]]
sage: cm(Permutation([4,3,2,1]))
[[1], [2], [3], [4]]
"""
if self._inst is not None:
return self._f(self._inst, *args, **kwds)
else:
return self._f(*args, **kwds)
def unbounded_map(self):
r"""
Return the unbounded version of ``self``.
You can use this method to return a function which takes as input
an element in the domain of the combinatorial map.
See the example below.
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: from sage.combinat.permutation import Permutation
sage: pi = Permutation([1,3,2])
sage: f = pi.reverse
sage: F = f.unbounded_map()
sage: F(pi)
[2, 3, 1]
"""
return self._f
def order(self):
"""
Returns the order of ``self``, or ``None`` if the order is not known.
EXAMPLES::
sage: from sage.combinat.combinatorial_map import combinatorial_map
sage: class CombinatorialClass:
....: @combinatorial_map(order=2)
....: def to_self_1(): pass
....: @combinatorial_map()
....: def to_self_2(): pass
sage: CombinatorialClass.to_self_1.order()
2
sage: CombinatorialClass.to_self_2.order() is None
True
"""
return self._order
def name(self):
"""
Returns the name of a combinatorial map.
This is used for the string representation of ``self``.
EXAMPLES::
sage: from sage.combinat.combinatorial_map import combinatorial_map
sage: class CombinatorialClass:
....: @combinatorial_map(name='map1')
....: def to_self_1(): pass
....: @combinatorial_map()
....: def to_self_2(): pass
sage: CombinatorialClass.to_self_1.name()
'map1'
sage: CombinatorialClass.to_self_2.name()
'to_self_2'
"""
if self._name is not None:
return self._name
else:
return self._f.__name__
def combinatorial_maps_in_class(cls):
"""
Return the combinatorial maps of the class as a list of combinatorial maps.
For further details and doctests, see
:ref:`sage.combinat.combinatorial_map` and
:func:`combinatorial_map_wrapper`.
EXAMPLES::
sage: sage.combinat.combinatorial_map.combinatorial_map = sage.combinat.combinatorial_map.combinatorial_map_wrapper
sage: from importlib import reload
sage: _ = reload(sage.combinat.permutation)
sage: from sage.combinat.combinatorial_map import combinatorial_maps_in_class
sage: p = Permutation([1,3,2,4])
sage: cmaps = combinatorial_maps_in_class(p)
sage: cmaps # random
[Combinatorial map: Robinson-Schensted insertion tableau,
Combinatorial map: Robinson-Schensted recording tableau,
Combinatorial map: Robinson-Schensted tableau shape,
Combinatorial map: complement,
Combinatorial map: descent composition,
Combinatorial map: inverse, ...]
sage: p.left_tableau in cmaps
True
sage: p.right_tableau in cmaps
True
sage: p.complement in cmaps
True
"""
result = set()
for method in dir(cls):
entry = getattr(cls, method)
if isinstance(entry, CombinatorialMap):
result.add(entry)
return list(result)
| 36.043373
| 130
| 0.595935
| 1,659
| 14,958
| 5.212779
| 0.17179
| 0.197965
| 0.072271
| 0.080944
| 0.509135
| 0.461147
| 0.410384
| 0.392808
| 0.390495
| 0.390495
| 0
| 0.00572
| 0.28707
| 14,958
| 414
| 131
| 36.130435
| 0.805233
| 0.759794
| 0
| 0.114754
| 0
| 0
| 0.040826
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.180328
| false
| 0
| 0.032787
| 0
| 0.459016
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72b71a7472c7bdcc100bc857b45e0a2173cf0beb
| 5,048
|
py
|
Python
|
tests/cppproj/xdressrc.py
|
xdress/xdress
|
eb7f0a02b3edf617d401939ede7f0d713a88917f
|
[
"BSD-2-Clause-FreeBSD"
] | 88
|
2015-01-04T14:49:05.000Z
|
2021-03-25T15:32:41.000Z
|
tests/cppproj/xdressrc.py
|
scopatz/xdress
|
2d95c900e849f924644756d421b1f4da4624e6c9
|
[
"BSD-2-Clause-FreeBSD"
] | 26
|
2015-02-03T19:09:11.000Z
|
2022-03-24T00:15:55.000Z
|
tests/cppproj/xdressrc.py
|
scopatz/xdress
|
2d95c900e849f924644756d421b1f4da4624e6c9
|
[
"BSD-2-Clause-FreeBSD"
] | 25
|
2015-01-27T18:25:15.000Z
|
2022-03-24T00:10:18.000Z
|
import os
from xdress.utils import apiname
package = 'cppproj'
packagedir = 'cppproj'
includes = ['src']
plugins = ('xdress.autoall', 'xdress.pep8names', 'xdress.cythongen',
'xdress.stlwrap', )
extra_types = 'cppproj_extra_types' # non-default value
dtypes = [
('map', 'str', 'int'),
('set', 'int'),
'float32',
('vector', 'int32'),
'ThreeNums',
]
stlcontainers = [
('pair', 'int', ('vector', 'int')),
('pair', 'int', 'str'),
('pair', 'int', 'int'),
('pair', 'int', 'SomeCrazyPairValue'),
('pair', 'ThreeNums', 'int'),
('vector', 'float64'),
('vector', 'str'),
('vector', 'int32'),
('vector', 'complex'),
('vector', ('vector', 'float64')),
('set', 'int'),
('set', 'str'),
('set', 'uint'),
('set', 'char'),
('set', 'ThreeNums'),
('map', 'str', 'str'),
('map', 'str', 'int'),
('map', 'int', 'str'),
('map', 'str', 'uint'),
('map', 'uint', 'str'),
('map', 'uint', 'uint'),
('map', 'str', 'float'),
('map', 'ThreeNums', 'float'),
('map', 'int', 'int'),
('map', 'int', 'bool'),
('map', 'int', 'char'),
('map', 'int', 'float'),
('map', 'uint', 'float'),
('map', 'int', 'complex'),
('map', ('pair', 'int', 'int'), 'float'),
('map', 'int', ('set', 'int')),
('map', 'int', ('set', 'str')),
('map', 'int', ('set', 'uint')),
('map', 'int', ('set', 'char')),
('map', 'int', ('vector', 'str')),
('map', 'int', ('vector', 'int')),
('map', 'int', ('vector', 'uint')),
('map', 'int', ('vector', 'char')),
('map', 'int', ('vector', 'bool')),
('map', 'int', ('vector', 'float')),
('map', 'int', ('vector', ('vector', 'float64'))),
('map', 'int', ('map', 'int', 'bool')),
('map', 'int', ('map', 'int', 'char')),
('map', 'int', ('map', 'int', 'float')),
('map', 'int', ('map', 'int', ('vector', 'bool'))),
('map', 'int', ('map', 'int', ('vector', 'char'))),
('map', 'int', ('map', 'int', ('vector', 'float'))),
('map', 'int', ('vector', ('set', 'int'))),
]
dtypes_module = 'dt'
stlcontainers_module = 'stlc'
_fromsrcdir = lambda x: os.path.join('src', x)
_inbasics = {'srcfiles': _fromsrcdir('basics.[ch]*'),
'incfiles': 'basics.hpp', # trick to get around cython generating *.h
'language': 'c++',
}
_indiscovery = {'srcfiles': _fromsrcdir('discovery*'),
'incfiles': 'discovery.h',
'language': 'c++',
}
variables = [
apiname('PersonID', tarbase='pybasics', **_inbasics),
apiname('*', **_indiscovery),
]
functions = [
apiname('voided', **_inbasics),
apiname('pairs_be_crazy', tarbase='pybasics', **_inbasics),
apiname('call_with_void_fp_struct', **_inbasics),
{'srcname': 'func0',
'tarname': 'a_better_name',
'incfiles': 'basics.h',
'srcfiles': _fromsrcdir('basics.[ch]*')},
apiname('func1', **_inbasics),
apiname('func2', **_inbasics),
apiname('func3', **_inbasics),
apiname('func4', tarbase='pybasics', **_inbasics),
apiname('setfunc', **_inbasics),
apiname(('findmin', 'int32', 'float32',), **_inbasics),
apiname(('findmin', 'float64', 'float32',), **_inbasics),
{'srcname': ('findmin', 'int', 'int',),
'incfiles': 'basics.h',
'tarname': ('regmin', 'int', 'int',),
'srcfiles': _fromsrcdir('basics.[ch]*')},
{'srcname': ('findmin', 'bool', 'bool',),
'tarname': 'sillyBoolMin',
'incfiles': 'basics.h',
'srcfiles': _fromsrcdir('basics.[ch]*')},
apiname(('lessthan', 'int32', 3,), **_inbasics),
apiname('call_threenums_op_from_c', tarbase='pybasics', **_inbasics),
apiname('*', **_indiscovery),
]
classes = [
#apiname('struct0', 'basics', 'pybasics', 'My_Struct_0'), FIXME This needs more work
apiname('Union0', **_inbasics),
apiname('VoidFPStruct', **_inbasics),
apiname('A', **_inbasics),
apiname('B', **_inbasics),
apiname('C', **_inbasics),
apiname('SomeCrazyPairValue', tarbase='pybasics', **_inbasics),
# apiname('SomeCrazyPairValue', **_inbasics),
apiname(('TClass1', 'int32'), **_inbasics),
apiname(('TClass1', 'float64'), **_inbasics),
{'srcname': ('TClass1', 'float32'),
'tarname': 'TC1Floater',
'incfiles': 'basics.h',
'srcfiles': _fromsrcdir('basics.[ch]*')},
apiname(('TClass0', 'int32'), **_inbasics),
apiname(('TClass0', 'float64'), **_inbasics),
{'srcname': ('TClass0', 'bool'),
'tarname': ('TC0Bool', 'bool'),
'incfiles': 'basics.h',
'srcfiles': _fromsrcdir('basics.[ch]*')},
apiname('Untemplated', **_inbasics),
apiname('ThreeNums', tarbase='pybasics', **_inbasics),
apiname('*', **_indiscovery),
apiname(('TClass0', 'float32'), **_inbasics),
apiname(('TClass2', 'float32'), **_inbasics),
apiname('NoDefault', **_inbasics),
apiname('NoDefaultChild', **_inbasics),
apiname(('EnumArg', 'JOAN'), tarbase='pybasics', **_inbasics),
]
del os
del apiname
| 33.210526
| 89
| 0.522187
| 469
| 5,048
| 5.486141
| 0.251599
| 0.069957
| 0.051302
| 0.027983
| 0.204042
| 0.141469
| 0.097163
| 0.074621
| 0
| 0
| 0
| 0.013896
| 0.201664
| 5,048
| 151
| 90
| 33.430464
| 0.624566
| 0.037044
| 0
| 0.153285
| 0
| 0
| 0.340062
| 0.009887
| 0
| 0
| 0
| 0.006623
| 0
| 1
| 0
| false
| 0
| 0.014599
| 0
| 0.014599
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72b766456e6162990d0a72bfeab659fdbe69bb40
| 1,259
|
py
|
Python
|
routines/server.py
|
henryshunt/c-aws
|
6e15bb18c2243f11a129b01298cb31749033f8d4
|
[
"MIT"
] | null | null | null |
routines/server.py
|
henryshunt/c-aws
|
6e15bb18c2243f11a129b01298cb31749033f8d4
|
[
"MIT"
] | null | null | null |
routines/server.py
|
henryshunt/c-aws
|
6e15bb18c2243f11a129b01298cb31749033f8d4
|
[
"MIT"
] | null | null | null |
import os
import subprocess
import routines.config as config
import routines.helpers as helpers
def get_static_info():
""" Outputs data concerning the computer in the C-AWS station
"""
startup_time = None
data_drive_space = None
camera_drive_space = None
# Get system startup time
try:
startup_time = (subprocess
.check_output(["uptime", "-s"]).decode().rstrip())
except: pass
# Get data and camera drive space
if config.load() == True:
if os.path.isdir(config.data_directory):
free_space = helpers.remaining_space(config.data_directory)
if free_space != None:
data_drive_space = round(free_space, 2)
if (config.camera_directory != None and os.path.isdir(
config.camera_directory) and os.path.ismount(
config.camera_directory)):
free_space = helpers.remaining_space(config.camera_directory)
if free_space != None:
camera_drive_space = round(free_space, 2)
print(str(helpers.none_to_null(startup_time)) + "\n"
+ str(helpers.none_to_null(data_drive_space)) + "\n"
+ str(helpers.none_to_null(camera_drive_space)))
| 33.131579
| 74
| 0.629071
| 155
| 1,259
| 4.864516
| 0.341935
| 0.092838
| 0.084881
| 0.06366
| 0.372679
| 0.241379
| 0.119363
| 0
| 0
| 0
| 0
| 0.002198
| 0.277204
| 1,259
| 38
| 75
| 33.131579
| 0.826374
| 0.094519
| 0
| 0.076923
| 0
| 0
| 0.010949
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038462
| false
| 0.038462
| 0.153846
| 0
| 0.192308
| 0.038462
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72b93e836d4145542223f40d01a9abd8ec9065ef
| 654
|
py
|
Python
|
DQM/DTMonitorModule/python/dtChamberEfficiencyHI_cfi.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
DQM/DTMonitorModule/python/dtChamberEfficiencyHI_cfi.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
DQM/DTMonitorModule/python/dtChamberEfficiencyHI_cfi.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
import FWCore.ParameterSet.Config as cms
from RecoMuon.TrackingTools.MuonServiceProxy_cff import MuonServiceProxy
dtEfficiencyMonitor = cms.EDAnalyzer("DTChamberEfficiency",
MuonServiceProxy,
debug = cms.untracked.bool(True),
TrackCollection = cms.InputTag("standAloneMuons"),
theMaxChi2 = cms.double(1000.),
theNSigma = cms.double(3.),
theMinNrec = cms.double(5.),
dt4DSegments = cms.InputTag("dt4DSegments"),
theRPCRecHits = cms.InputTag("dummy"),
thegemRecHits = cms.InputTag("dummy"),
cscSegments = cms.InputTag("dummy"),
RPCLayers = cms.bool(False),
NavigationType = cms.string("Standard")
)
| 32.7
| 73
| 0.717125
| 63
| 654
| 7.428571
| 0.603175
| 0.117521
| 0.102564
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016334
| 0.157492
| 654
| 19
| 74
| 34.421053
| 0.833031
| 0
| 0
| 0
| 0
| 0
| 0.105666
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.125
| 0
| 0.125
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ba794e4ffc0f8c96701df9930a1aeef6a247aa
| 3,075
|
py
|
Python
|
test.py
|
jasonivey/scripts
|
09f9702e5ce62abbb7699aae16b45b33711fe856
|
[
"MIT"
] | null | null | null |
test.py
|
jasonivey/scripts
|
09f9702e5ce62abbb7699aae16b45b33711fe856
|
[
"MIT"
] | null | null | null |
test.py
|
jasonivey/scripts
|
09f9702e5ce62abbb7699aae16b45b33711fe856
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# vim:softtabstop=4:ts=4:sw=4:expandtab:tw=120
from ansimarkup import AnsiMarkup, parse
import csv
import datetime
import operator
import os
from pathlib import Path
import re
import sys
import traceback
_VERBOSE = False
user_tags = {
'error' : parse('<bold><red>'),
'name' : parse('<bold><cyan>'),
'value' : parse('<bold><white>'),
}
am = AnsiMarkup(tags=user_tags)
def _assert_msg(msg):
return am.ansistring(f'<error>{msg}</error>')
def _print_name_value(name, max_name_len, value, prefix=None, postfix=None):
prefix = prefix if prefix is not None else ''
postfix = postfix if postfix is not None else ''
lh = am.ansistring(f'<name>{name}</name>')
rh = am.ansistring(f'<value>{value}</value>')
print(f'{prefix}{lh:{max_name_len + lh.delta}} {rh}{postfix}')
def _get_name_value_compact(name, max_name_len, value, prefix=None, postfix=None):
prefix = prefix if prefix is not None else ''
postfix = postfix if postfix is not None else ''
return am.ansistring(f'{prefix}<name>{name}</name> <value>{value}</value>{postfix}')
def _get_timezone_info():
return datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo
def _convert_date_time(dt):
return f'{dt:%d-%b-%Y %I:%M:%S%p %Z}'.replace('AM', 'am').replace('PM', 'pm')
def _parse_datetime(dt_str):
dt = datetime.datetime.strptime(dt_str, '%m/%d/%Y %I:%M %p') # Example '11/08/2011 03:00 PM'
tz = _get_timezone_info()
return dt.replace(tzinfo=tz)
def _parse_datetime_row(row):
return _parse_datetime(' '.join(row[2:4]))
def _parse_appointment_row(row, index):
assert len(row) >= 4, _assert_msg(f'row {index} does not have 4 or more columns as required')
appt_time = _parse_datetime(' '.join(row[2:4]))
appt_type = row[0].title()
doctor = row[1].title()
return appt_time, appt_type, doctor
def parse_doctor_appointments(file_name):
path = Path(os.path.expandvars(file_name))
with path.open(newline='', encoding='utf-8') as handle:
reader = csv.reader(handle)
sorted_rows = sorted(reader, key=lambda x: _parse_datetime_row(x))
for index, row in enumerate(sorted_rows):
yield _parse_appointment_row(row, index)
def get_doctors_appointments():
MAX_WIDTH = len('Appointment:')
file_name = '$HOME/Downloads/crump-visits.csv'
for appt_time, appt_type, doctor in parse_doctor_appointments(file_name):
s = _get_name_value_compact('Appointment:', None, _convert_date_time(appt_time), postfix=', ')
s += _get_name_value_compact('Type:', None, appt_type, postfix=', ')
print(s + _get_name_value_compact('Doctor:', None, doctor))
def main(args):
try:
get_doctors_appointments()
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_traceback, file=sys.stdout)
return 1
return 0
if __name__ == '__main__':
sys.exit(main(sys.argv))
| 35.755814
| 103
| 0.666016
| 442
| 3,075
| 4.400452
| 0.316742
| 0.027764
| 0.026735
| 0.026735
| 0.275578
| 0.162468
| 0.112082
| 0.112082
| 0.112082
| 0.112082
| 0
| 0.012058
| 0.190894
| 3,075
| 85
| 104
| 36.176471
| 0.769695
| 0.03122
| 0
| 0.058824
| 0
| 0
| 0.143895
| 0.047388
| 0
| 0
| 0
| 0
| 0.029412
| 1
| 0.161765
| false
| 0
| 0.132353
| 0.058824
| 0.426471
| 0.058824
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ba9085ddab97bd04e95141330b44b2e8f9e59c
| 625
|
py
|
Python
|
RDyn-master/rdyn/test/rdyn_test.py
|
nogrady/dynamo
|
4a94453c810cb6cd0eb976c6db9e379cfb2e1f3b
|
[
"MIT"
] | 12
|
2020-02-05T10:24:54.000Z
|
2022-02-24T02:26:00.000Z
|
RDyn-master/rdyn/test/rdyn_test.py
|
nogrady/dynamo
|
4a94453c810cb6cd0eb976c6db9e379cfb2e1f3b
|
[
"MIT"
] | 4
|
2020-12-03T04:24:24.000Z
|
2021-09-18T13:14:50.000Z
|
RDyn-master/rdyn/test/rdyn_test.py
|
nogrady/dynamo
|
4a94453c810cb6cd0eb976c6db9e379cfb2e1f3b
|
[
"MIT"
] | 6
|
2019-07-30T12:55:44.000Z
|
2021-09-05T06:26:18.000Z
|
import unittest
import shutil
from rdyn.alg.RDyn_v2 import RDynV2
class RDynTestCase(unittest.TestCase):
def test_rdyn_simplified(self):
print("1")
rdb = RDynV2(size=500, iterations=100)
rdb.execute(simplified=True)
print("2")
rdb = RDynV2(size=500, iterations=100, max_evts=2)
rdb.execute(simplified=True)
print("3")
rdb = RDynV2(size=500, iterations=100, new_node=0.1, del_node=0.1, max_evts=2, paction=0.8)
rdb.execute(simplified=False)
print("Done")
shutil.rmtree("results")
if __name__ == '__main__':
unittest.main()
| 24.038462
| 99
| 0.6384
| 83
| 625
| 4.626506
| 0.493976
| 0.070313
| 0.101563
| 0.125
| 0.377604
| 0.226563
| 0
| 0
| 0
| 0
| 0
| 0.070833
| 0.232
| 625
| 25
| 100
| 25
| 0.729167
| 0
| 0
| 0.111111
| 0
| 0
| 0.0352
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.055556
| false
| 0
| 0.166667
| 0
| 0.277778
| 0.222222
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72bb20592ab5dcb9752888e9d174ab8e1560ff6a
| 1,436
|
py
|
Python
|
recognition/datasets/build.py
|
Jung-Jun-Uk/UNPG
|
a6f9c1731a68fc035eb8fe8198f5a5c643825a5b
|
[
"Apache-2.0"
] | 7
|
2022-03-15T13:09:05.000Z
|
2022-03-31T04:11:19.000Z
|
recognition/datasets/build.py
|
Jung-Jun-Uk/UNPG
|
a6f9c1731a68fc035eb8fe8198f5a5c643825a5b
|
[
"Apache-2.0"
] | 1
|
2022-03-31T02:03:05.000Z
|
2022-03-31T11:18:02.000Z
|
recognition/datasets/build.py
|
Jung-Jun-Uk/UNPG
|
a6f9c1731a68fc035eb8fe8198f5a5c643825a5b
|
[
"Apache-2.0"
] | null | null | null |
import os
from .kface import KFace
from .ms1m import MS1M
from .bin_datasets import BIN
from .ijb import IJB
def build_datasets(data_cfg, batch_size, cuda, workers, mode, rank=-1):
assert mode in ['train', 'test']
cfg = data_cfg[mode]
if cfg['dataset'] == 'kface':
dataset = KFace(cfg['data_path'], cfg['test_idx_txt'], cfg['acs'], cfg['lux'], cfg['eps'], cfg['pose'],
cfg['img_size'], batch_size, cuda, workers, mode=mode)
elif cfg['dataset'] == 'ms1m':
dataset = MS1M(cfg['data_path'], cfg['preprocessed_file'], cfg['img_size'], cfg['min_img'],
batch_size, cuda, workers, mode=mode, rank=rank)
elif cfg['dataset'] == 'bin':
root, file_names = cfg['root'], cfg['file_names']
if isinstance(file_names, str):
data_path = os.path.join(root, file_names)
dataset = BIN(data_path, cfg['img_size'], batch_size, cuda, workers)
elif isinstance(file_names, list):
data_path = [os.path.join(root, f) for f in file_names]
dataset = [BIN(dp, cfg['img_size'], batch_size, cuda, workers) for dp in data_path]
elif cfg['dataset'] in ['ijbb', 'ijbc']:
dataset = IJB(cfg['root'], cfg['inf_list'], cfg['img_size'], batch_size, cuda, workers)
return dataset
| 46.322581
| 112
| 0.559192
| 186
| 1,436
| 4.145161
| 0.268817
| 0.070039
| 0.101167
| 0.155642
| 0.290532
| 0.259403
| 0.155642
| 0
| 0
| 0
| 0
| 0.004931
| 0.293872
| 1,436
| 30
| 113
| 47.866667
| 0.755424
| 0
| 0
| 0
| 0
| 0
| 0.132312
| 0
| 0
| 0
| 0
| 0
| 0.04
| 1
| 0.04
| false
| 0
| 0.2
| 0
| 0.28
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72bb21a725c716541be205c2b5b7874c878a779b
| 1,316
|
py
|
Python
|
django_cd/notifications.py
|
ppinard/django-cd
|
1bc9304466ace12867df3b18a8ef7f204b9744b4
|
[
"MIT"
] | 1
|
2021-12-22T15:18:17.000Z
|
2021-12-22T15:18:17.000Z
|
django_cd/notifications.py
|
ppinard/django-cd
|
1bc9304466ace12867df3b18a8ef7f204b9744b4
|
[
"MIT"
] | null | null | null |
django_cd/notifications.py
|
ppinard/django-cd
|
1bc9304466ace12867df3b18a8ef7f204b9744b4
|
[
"MIT"
] | null | null | null |
""""""
# Standard library modules.
import abc
# Third party modules.
from django.core.mail import send_mail
from django.template import Engine, Context
# Local modules.
from .models import RunState
# Globals and constants variables.
class Notification(metaclass=abc.ABCMeta):
@classmethod
def notify(self, jobrun):
raise NotImplementedError
class EmailNotification(Notification):
def __init__(self, recipients, on_success=False, on_failure=True):
self.recipients = tuple(recipients)
self.on_success = on_success
self.on_failure = on_failure
def __str__(self):
return "email"
def notify(self, jobrun):
if (jobrun.state in [RunState.ERROR, RunState.FAILED] and self.on_failure) or (
jobrun.state == RunState.SUCCESS and self.on_success
):
engine = Engine.get_default()
template = engine.get_template("django_cd/jobrun_report.html")
context = Context({"jobrun": jobrun})
html_message = template.render(context)
send_mail(
subject=f"Job report - {jobrun.name} - {jobrun.state}",
message="",
from_email=None,
recipient_list=self.recipients,
html_message=html_message,
)
| 28
| 87
| 0.637538
| 144
| 1,316
| 5.638889
| 0.451389
| 0.044335
| 0.03202
| 0.046798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.268997
| 1,316
| 46
| 88
| 28.608696
| 0.844075
| 0.071429
| 0
| 0.066667
| 0
| 0
| 0.067713
| 0.023121
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0
| 0.133333
| 0.033333
| 0.366667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72bde1ffa295f39cff6155beef6e3b3159a43bd3
| 536
|
py
|
Python
|
30_days_of_code_10.py
|
sercangul/HackerRank
|
e6d7056babe03baafee8d7f1cacdca7c28b72ded
|
[
"Apache-2.0"
] | null | null | null |
30_days_of_code_10.py
|
sercangul/HackerRank
|
e6d7056babe03baafee8d7f1cacdca7c28b72ded
|
[
"Apache-2.0"
] | null | null | null |
30_days_of_code_10.py
|
sercangul/HackerRank
|
e6d7056babe03baafee8d7f1cacdca7c28b72ded
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 3 19:02:33 2019
@author: sercangul
"""
def maxConsecutiveOnes(x):
# Initialize result
count = 0
# Count the number of iterations to
# reach x = 0.
while (x!=0):
# This operation reduces length
# of every sequence of 1s by one.
x = (x & (x << 1))
count=count+1
return count
if __name__ == '__main__':
n = int(input())
result = maxConsecutiveOnes(n)
print(result)
| 18.482759
| 42
| 0.544776
| 68
| 536
| 4.176471
| 0.735294
| 0.014085
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053073
| 0.33209
| 536
| 29
| 43
| 18.482759
| 0.740223
| 0.429104
| 0
| 0
| 0
| 0
| 0.027397
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.2
| 0.1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72be55bfd5b76b9d59e5532d60fbf00e392fbde4
| 6,744
|
py
|
Python
|
artap/algorithm_cmaes.py
|
artap-framework/artap
|
7e4b01abbe5ca0fce9fa87a1a307ebd11ace36b4
|
[
"MIT"
] | 5
|
2021-06-13T17:04:37.000Z
|
2022-03-04T17:16:06.000Z
|
artap/algorithm_cmaes.py
|
artap-framework/artap
|
7e4b01abbe5ca0fce9fa87a1a307ebd11ace36b4
|
[
"MIT"
] | null | null | null |
artap/algorithm_cmaes.py
|
artap-framework/artap
|
7e4b01abbe5ca0fce9fa87a1a307ebd11ace36b4
|
[
"MIT"
] | 8
|
2021-03-11T18:23:47.000Z
|
2022-02-22T11:13:23.000Z
|
import numpy as np
from .problem import Problem
from .algorithm_genetic import GeneralEvolutionaryAlgorithm
from .individual import Individual
from .operators import CustomGenerator, nondominated_truncate, RandomGenerator, UniformGenerator
import time
class CMA_ES(GeneralEvolutionaryAlgorithm):
"""
Implementation of CMA_ES, Covariance Matrix Adaptation Evolutionary strategy (CMA_ES).
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [1] is one of the most effective approaches
for black-box optimization, in which objective functions cannot be specified explicitly in general.
CMA-ES outperformed over 100 black-box optimization approaches for a variety of benchmark problems [2].
The CMA-ES algorithm selects solutions from a multivariate gaussian distribution. Following the evaluation of
all solutions, the solutions are sorted by evaluation values, and the distribution parameters
(i.e., the mean vector and the covariance matrix) are updated depending on the ranking of evaluation values.
[1] Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies.
Evol. Comput., 9(2):159–195, June 2001.
DOI: http://dx.doi.org/10.1162/106365601750190398.
[2] Nikolaus Hansen. The CMA Evolution Strategy: A Comparing Review, pages 75–102. Springer Berlin Heidelberg,
Berlin, Heidelberg, 2006.
DOI: https://doi.org/10.1007/3-540-32494-1_4.
"""
def __init__(self, problem: Problem, name="Covariance Matrix Adaptation Evolutionary Strategy"):
super().__init__(problem, name)
# Population Size
self.n_samples = self.options['max_population_size']
# Number of generation
self.t = self.options['max_population_number']
self.individual_features['velocity'] = dict()
self.individual_features['best_cost'] = dict()
self.individual_features['best_vector'] = dict()
self.individual_features['dominate'] = []
self.individual_features['crowding_distance'] = 0
self.individual_features['domination_counter'] = 0
# Add front_number feature
self.individual_features['front_number'] = 0
self.dim_theta = len(self.problem.parameters)
# Elite ratio percentage
self.top_p = 30
# Range of values
self.min_val = 0
self.max_val = 1
# Number of Runs
self.runs = 1
self.theta_mean = np.random.uniform(self.min_val, self.max_val, self.dim_theta)
# self.individuals = []
theta_std = np.random.uniform(self.max_val - 1, self.max_val, self.dim_theta)
self.theta_cov = np.diag(theta_std)
self.generator = CustomGenerator(self.problem.parameters, self.individual_features)
# self.fit_gaussian()
def fit_gaussian(self):
"""
generates individuals from a multivariate gaussian distribution
:param
:return population: list of individuals
"""
theta = np.random.multivariate_normal(self.theta_mean, self.theta_cov, self.options['max_population_size'])
individuals = np.clip(theta, self.min_val, self.max_val)
self.generator.init(individuals)
individuals = self.generator.generate()
return individuals
def take_elite(self, candidates):
"""
Based on the fitness, it will take top individuals
:param candidates
:return elite: list of top individuals
"""
n_top = int((self.n_samples * self.top_p) / 100)
elite = candidates[:n_top]
return elite
def compute_new_mean(self, e_candidates):
"""
Update distribution parameters. Here, the mean vector will be updated depending on the ranking of
evaluation values.
:param e_candidates
:return new_means vector
"""
new_means = np.mean(e_candidates, axis=0)
return new_means
def compute_new_cov(self, e_candidates):
"""
Update distribution parameters. Here, the covariance matrix will be updated depending on the ranking of
evaluation values
:param e_candidates
:return new_covariance matrix
"""
e_candidates = np.array(e_candidates)
I = np.identity(self.dim_theta)
cov = np.zeros((self.dim_theta, self.dim_theta))
for i in range(self.dim_theta):
for j in range(self.dim_theta):
cov[i, j] = np.sum(
((e_candidates[:, i] - self.theta_mean[i]) * (e_candidates[:, j] - self.theta_mean[j])), axis=0)
return 1 / e_candidates.shape[0] * cov + I * 1e-3
def run(self):
mean_fitness = []
best_fitness = []
worst_fitness = []
fitness = []
individuals = self.fit_gaussian()
for individual in individuals:
# append to problem
self.problem.individuals.append(individual)
# add to population
individual.population_id = 0
self.problem.data_store.sync_individual(individual)
self.evaluate(individuals)
start = time.time()
self.problem.logger.info("CMA_ES: {}/{}".format(self.options['max_population_number'],
self.options['max_population_size']))
for it in range(self.options['max_population_number']):
lists = []
for individual in individuals:
# fitness.append(individual.costs)
lists.append(individual.costs)
lists = np.array(lists)
mean_fitness.append(np.mean(lists))
best_fitness.append(np.min(lists))
worst_fitness.append(np.max(lists))
fitness.append(lists)
elite = self.take_elite(individuals)
e_candidates = [i.vector for i in elite]
self.theta_cov = self.compute_new_cov(e_candidates)
self.theta_mean = self.compute_new_mean(e_candidates)
individuals = self.fit_gaussian()
# individuals = nondominated_truncate(new_individuals, self.options['max_population_size'])
self.evaluate(individuals)
for individual in individuals:
# add to population
individual.population_id = it + 1
# append to problem
self.problem.individuals.append(individual)
# sync to datastore
self.problem.data_store.sync_individual(individual)
t = time.time() - start
self.problem.logger.info("CMA_ES: elapsed time: {} s".format(t))
# sync changed individual informations
self.problem.data_store.sync_all()
| 40.626506
| 116
| 0.647539
| 798
| 6,744
| 5.319549
| 0.264411
| 0.033687
| 0.041461
| 0.039576
| 0.282686
| 0.182332
| 0.136631
| 0.095642
| 0.036278
| 0.036278
| 0
| 0.017936
| 0.264235
| 6,744
| 165
| 117
| 40.872727
| 0.837162
| 0.307088
| 0
| 0.129412
| 0
| 0
| 0.066048
| 0.01425
| 0
| 0
| 0
| 0
| 0
| 1
| 0.070588
| false
| 0
| 0.070588
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72bf6f79bc537479ff8af423d399ec3e3244b8ce
| 4,988
|
py
|
Python
|
apns_proxy_client/core.py
|
hagino3000/apns-proxy-client-py
|
b5ce34be940a8f8a990dc369e293408380d0c359
|
[
"BSD-2-Clause"
] | null | null | null |
apns_proxy_client/core.py
|
hagino3000/apns-proxy-client-py
|
b5ce34be940a8f8a990dc369e293408380d0c359
|
[
"BSD-2-Clause"
] | null | null | null |
apns_proxy_client/core.py
|
hagino3000/apns-proxy-client-py
|
b5ce34be940a8f8a990dc369e293408380d0c359
|
[
"BSD-2-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
APNS Proxy Serverのクライアント
"""
import time
import zmq
import simplejson as json
READ_TIMEOUT = 1500 # msec
FLUSH_TIMEOUT = 5000 # msec
COMMAND_ASK_ADDRESS = b'\1'
COMMAND_SEND = b'\2'
COMMAND_FEEDBACK = b'\3'
DEVICE_TOKEN_LENGTH = 64
JSON_ALERT_KEY_SET = set(['body', 'action_loc_key', 'loc_key', 'loc_args', 'launch_image'])
class APNSProxyClient(object):
def __init__(self, host, port, application_id):
"""
ZMQコンテキストとソケットの初期化
"""
if host is None or not isinstance(host, str):
raise ValueError("host must be string")
if port is None or not isinstance(port, int):
raise ValueError("host must be int type")
self.host = host
self.port = port
self.context = zmq.Context()
self.context.setsockopt(zmq.LINGER, FLUSH_TIMEOUT)
self.communicator = self.context.socket(zmq.REQ)
self.publisher = self.context.socket(zmq.PUSH)
self.connected = False
if not isinstance(application_id, str):
raise ValueError("application_id must be string type")
self.application_id = application_id
def __enter__(self):
self.connect()
def connect(self):
"""リモートサーバーへ接続"""
if self.connected is False:
self.communicator.connect(self.build_address(self.port))
push_port = self.get_push_port()
self.publisher.connect(self.build_address(push_port))
self.connected = True
def build_address(self, port):
return "tcp://%s:%s" % (self.host, port)
def get_push_port(self):
"""
PUSH-PULL接続用のポートを取得する
"""
self.communicator.send(COMMAND_ASK_ADDRESS)
poller = zmq.Poller()
poller.register(self.communicator, zmq.POLLIN)
if poller.poll(READ_TIMEOUT):
return self.communicator.recv()
else:
self.close()
raise IOError("Cannot connect to APNs Proxy Server. Timeout!!")
def send(self, token, alert, sound='default', badge=None, content_available=False,
custom=None, expiry=None, priority=None, test=False):
"""
デバイストークンの送信
"""
self._check_token(token)
self._check_alert(alert)
self._check_custom(custom)
self.publisher.send(self._serialize(
COMMAND_SEND, token, alert, sound, badge, content_available, custom,
expiry, priority, test
))
def get_feedback(self):
data = {
'appid': self.application_id,
}
command = COMMAND_FEEDBACK + json.dumps(data, ensure_ascii=True)
self.communicator.send(command)
return json.loads(self.communicator.recv())
@staticmethod
def _check_token(token):
if len(token) != DEVICE_TOKEN_LENGTH:
raise ValueError('Invalid token length %s' % token)
@staticmethod
def _check_alert(alert):
if (alert is None or isinstance(alert, basestring)):
return
elif isinstance(alert, dict):
if len(set(alert.keys()) - JSON_ALERT_KEY_SET) > 0:
raise ValueError('JSON Alert allows only'
'body, action_loc_key, loc_key, loc_args, launch_image')
else:
raise ValueError('alert must be string, unicode or dict type')
@staticmethod
def _check_custom(custom):
if custom is None or isinstance(custom, dict):
return
raise ValueError('custom must be dict type')
def _serialize(self, command, token, alert, sound, badge, content_available, custom,
expiry, priority, test):
"""
送信データのフォーマット
"""
aps = {}
if alert is not None:
aps['alert'] = alert
if sound is not None:
aps['sound'] = sound
if badge is not None:
aps['badge'] = badge
if content_available is True:
aps['content_available'] = True
if custom is not None:
aps['custom'] = custom
data = {
'appid': self.application_id,
'token': token,
'aps': aps,
'test': test
}
if expiry is not None:
data['expiry'] = expiry
if priority is not None:
data['priority'] = priority
return command + json.dumps(data, ensure_ascii=True)
def __exit__(self, exc_type, exc_value, traceback):
if exc_type:
self._close()
return False
self.close()
def close(self):
start_time = time.time()
self._close()
end_time = time.time()
if (end_time - start_time) > (FLUSH_TIMEOUT - 20)/1000.0:
raise IOError('Timeout close operation. Some messages may not reached to server.')
return True
def _close(self):
self.publisher.close()
self.communicator.close()
self.context.term()
| 30.414634
| 94
| 0.589014
| 573
| 4,988
| 4.965096
| 0.246073
| 0.044991
| 0.018981
| 0.016872
| 0.140598
| 0.089982
| 0.070299
| 0.070299
| 0.070299
| 0.070299
| 0
| 0.006369
| 0.307538
| 4,988
| 163
| 95
| 30.601227
| 0.817313
| 0.027065
| 0
| 0.125
| 0
| 0
| 0.103666
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.116667
| false
| 0
| 0.025
| 0.008333
| 0.216667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72c1ef9e3306b06082ecfe37e40b05472ed66d4a
| 1,047
|
py
|
Python
|
dictionary.py
|
WilliamHackspeare/profanity-percentage
|
4aab708620b7543a2a5cb30c9cee8404dcc836cb
|
[
"MIT"
] | null | null | null |
dictionary.py
|
WilliamHackspeare/profanity-percentage
|
4aab708620b7543a2a5cb30c9cee8404dcc836cb
|
[
"MIT"
] | null | null | null |
dictionary.py
|
WilliamHackspeare/profanity-percentage
|
4aab708620b7543a2a5cb30c9cee8404dcc836cb
|
[
"MIT"
] | null | null | null |
#Import the json library to parse JSON file to Python
import json
#Import list of punctuation characters from the string library
from string import punctuation as p
#This method checks if the given word is a profanity
def is_profanity(word):
#Open the JSON file
words_file = open('data.json')
#Parse the JSON file as a dictionary and extract the values
bad_words = json.load(words_file).values()
#Check and return if the word is a bad work
return word in bad_words
#This method calculates the degree of profanity for a list of strings
def calculate_profanity(sentence):
#Initialise the count of bad words
count_bad = 0
#Initialise the total count of words
count = 0
#Loop through the list of words
for word in sentence:
#Check if the word, stripped of any leading or trailing punctuations or spaces, is a bad word and update count
if is_profanity(word.strip(p+" ")):
count_bad += 1
count += 1
#Calculate the degree of the list
deg = (count_bad/count)*100
#Return the degree
return deg
| 26.846154
| 114
| 0.73639
| 173
| 1,047
| 4.398844
| 0.364162
| 0.027595
| 0.018397
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008475
| 0.211079
| 1,047
| 39
| 115
| 26.846154
| 0.912833
| 0.578797
| 0
| 0
| 0
| 0
| 0.02331
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0
| 0.133333
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72c22519e149895de228608442ca88e10bbdc5d3
| 1,613
|
py
|
Python
|
setup.py
|
cyfrmedia/cerridwen
|
6ac9193d41d7c6fdea0abab5e5f207132844fb4e
|
[
"MIT"
] | 25
|
2015-01-20T13:13:51.000Z
|
2021-11-05T12:52:13.000Z
|
setup.py
|
cyfrmedia/cerridwen
|
6ac9193d41d7c6fdea0abab5e5f207132844fb4e
|
[
"MIT"
] | 2
|
2018-11-11T21:02:10.000Z
|
2020-04-10T09:18:52.000Z
|
setup.py
|
cyfrmedia/cerridwen
|
6ac9193d41d7c6fdea0abab5e5f207132844fb4e
|
[
"MIT"
] | 14
|
2015-01-26T10:20:28.000Z
|
2021-10-31T13:05:24.000Z
|
from setuptools import setup
import os
here = os.path.abspath(os.path.dirname(__file__))
README = open(os.path.join(here, 'README.rst')).read()
#NEWS = open(os.path.join(here, 'NEWS.txt')).read()
rootdir = os.path.dirname(os.path.abspath(__file__))
exec(open(rootdir + '/cerridwen/version.py').read())
version = __VERSION__
setup(name='cerridwen',
version=version,
description='Accurate solar system data for everyone',
long_description=README,
author='Leslie P. Polzer',
author_email='polzer@fastmail.com',
url='http://cerridwen.bluemagician.vc/',
license='MIT',
classifiers=[
# Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers
"Development Status :: 4 - Beta"
, "Environment :: Console"
, "Intended Audience :: Science/Research"
, "Intended Audience :: Developers"
, "License :: OSI Approved :: MIT License"
, "Operating System :: OS Independent"
, "Programming Language :: Python :: 3"
, "Topic :: Scientific/Engineering :: Astronomy"
, "Topic :: Other/Nonlisted Topic"
, "Topic :: Software Development :: Libraries :: Python Modules"
, "Topic :: Utilities"
],
maintainer='Leslie P. Polzer',
maintainer_email='polzer@fastmail.com',
packages=['cerridwen'],
requires=['pyswisseph', 'numpy', 'astropy(>=0.4)'],
extras_require={'Flask':['flask']},
entry_points={
'console_scripts':
['cerridwen = cerridwen.cli:main',
'cerridwen-server = cerridwen.api_server:main [Flask]']
})
| 35.844444
| 81
| 0.629882
| 170
| 1,613
| 5.858824
| 0.570588
| 0.036145
| 0.026104
| 0.028112
| 0.036145
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003953
| 0.215747
| 1,613
| 44
| 82
| 36.659091
| 0.783399
| 0.075635
| 0
| 0
| 0
| 0
| 0.476478
| 0.045699
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.052632
| 0
| 0.052632
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72c2286c61223e879e49fc3a51d82e195787e502
| 4,768
|
py
|
Python
|
pajbot/apiwrappers/authentication/access_token.py
|
JoachimFlottorp/pajbot
|
4fb88c403dedb20d95be80e38da72be1ed064901
|
[
"MIT"
] | 128
|
2015-12-28T01:02:30.000Z
|
2019-05-24T21:20:50.000Z
|
pajbot/apiwrappers/authentication/access_token.py
|
JoachimFlottorp/pajbot
|
4fb88c403dedb20d95be80e38da72be1ed064901
|
[
"MIT"
] | 277
|
2015-05-03T18:48:57.000Z
|
2019-05-23T17:41:28.000Z
|
pajbot/apiwrappers/authentication/access_token.py
|
JoachimFlottorp/pajbot
|
4fb88c403dedb20d95be80e38da72be1ed064901
|
[
"MIT"
] | 96
|
2015-08-07T18:49:50.000Z
|
2019-05-20T19:49:27.000Z
|
import datetime
from abc import ABC, abstractmethod
import pajbot
class AccessToken(ABC):
SHOULD_REFRESH_THRESHOLD = 0.9
"""Fraction between 0 and 1 indicating what fraction/percentage of the specified full validity period
should actually be utilized. E.g. if this is set to 0.9, the implementation will refresh the token
once at least 90% of the full validity period (expires_in) is over."""
def __init__(self, access_token, created_at, expires_in, token_type, refresh_token, scope):
self.access_token = access_token
self.created_at = created_at
# can both be None
self.expires_in = expires_in
if self.expires_in is not None:
self.expires_at = self.created_at + self.expires_in
else:
self.expires_at = None
self.token_type = token_type
# can be None
self.refresh_token = refresh_token
# always a list, can be empty list
self.scope = scope
@abstractmethod
def can_refresh(self):
pass
def should_refresh(self):
"""Returns True if less than 10% of the token's lifetime remains, False otherwise"""
if not self.can_refresh():
return False
# intended lifetime of the token
if self.expires_at is not None:
expires_after = self.expires_at - self.created_at
else:
# this is a token that never expires
# because we don't want any issues, refresh it anyways
expires_after = datetime.timedelta(hours=1)
# how much time has passed since token creation
token_age = pajbot.utils.now() - self.created_at
# maximum token age before token should be refreshed (90% of the total token lifetime)
max_token_age = expires_after * self.SHOULD_REFRESH_THRESHOLD
# expired?
return token_age >= max_token_age
def jsonify(self):
"""serialize for storage"""
if self.expires_in is None:
expires_in_milliseconds = None
else:
expires_in_milliseconds = self.expires_in.total_seconds() * 1000
return {
"access_token": self.access_token,
"created_at": self.created_at.timestamp() * 1000,
"expires_in": expires_in_milliseconds,
"token_type": self.token_type,
"refresh_token": self.refresh_token,
"scope": self.scope,
}
@classmethod
def from_json(cls, json_data):
"""deserialize json produced by jsonify()"""
if json_data["expires_in"] is None:
expires_in = None
else:
expires_in = datetime.timedelta(milliseconds=json_data["expires_in"])
return cls(
access_token=json_data["access_token"],
created_at=pajbot.utils.datetime_from_utc_milliseconds(json_data["created_at"]),
expires_in=expires_in,
token_type=json_data["token_type"],
refresh_token=json_data["refresh_token"],
scope=json_data["scope"],
)
@classmethod
def from_api_response(cls, response):
"""Construct new object from twitch response json data"""
# expires_in is only missing for old Client-IDs to which twitch will respond with
# infinitely-lived tokens (the "expires_in" field is absent in that case).
expires_in_seconds = response.get("expires_in", None)
if expires_in_seconds is None:
expires_in = None
else:
expires_in = datetime.timedelta(seconds=expires_in_seconds)
return cls(
access_token=response["access_token"],
created_at=pajbot.utils.now(),
expires_in=expires_in,
token_type=response["token_type"],
refresh_token=response.get("refresh_token", None),
scope=response.get("scope", []),
)
@abstractmethod
def refresh(self, api):
pass
class UserAccessToken(AccessToken):
def can_refresh(self):
return self.refresh_token is not None
def refresh(self, api):
if not self.can_refresh():
raise ValueError("This user access token cannot be refreshed, it has no refresh token")
return api.refresh_user_access_token(self.refresh_token)
@staticmethod
def from_implicit_auth_flow_token(access_token):
return UserAccessToken(
access_token=access_token,
created_at=None,
expires_in=None,
token_type="bearer",
refresh_token=None,
scope=[],
)
class AppAccessToken(AccessToken):
def can_refresh(self):
return True
def refresh(self, api):
return api.get_app_access_token(self.scope)
| 32.216216
| 105
| 0.635067
| 592
| 4,768
| 4.890203
| 0.261824
| 0.090155
| 0.018998
| 0.034542
| 0.173748
| 0.12677
| 0.033851
| 0.033851
| 0.033851
| 0.033851
| 0
| 0.006173
| 0.286493
| 4,768
| 147
| 106
| 32.435374
| 0.844797
| 0.139681
| 0
| 0.260417
| 0
| 0
| 0.066719
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| false
| 0.020833
| 0.03125
| 0.041667
| 0.302083
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72c2e94771b614f6c939030fdbb56bca1d8a8d06
| 1,965
|
py
|
Python
|
scan_predict.py
|
ychu196/chicago_scan
|
ed5f32a9f27fd5b9350cb3232a2631c3aaa60744
|
[
"Apache-2.0"
] | null | null | null |
scan_predict.py
|
ychu196/chicago_scan
|
ed5f32a9f27fd5b9350cb3232a2631c3aaa60744
|
[
"Apache-2.0"
] | null | null | null |
scan_predict.py
|
ychu196/chicago_scan
|
ed5f32a9f27fd5b9350cb3232a2631c3aaa60744
|
[
"Apache-2.0"
] | null | null | null |
# Image classification using AWS Sagemaker and Linear Learner
# Program set up and import libraries
import numpy as np
import pandas as pd
import os
from sagemaker import get_execution_role
role = get_execution_role()
bucket = 'chi-hackathon-skin-images'
# Import Data
import boto3
from sagemaker import get_execution_role
role = get_execution_role()
bucket='chi-hackathon-skin-images'
data_key = 'ISIC_0000000.json' # need a way to go through entire library
data_location = 's3://{}/{}'.format(bucket, data_key)
metadata_set = pd.read_json(data_location)
image_set = np.asarray(data_location)
# TBD - transform json data to array
# TBD - transform image data to dataframe
train_set = zip(image_set, metadata_set)
# Split Data into Train and Validate
import random
random.seed(9001)
split = np.random.rand(len(df)) < 0.8
valid_set = train_set[split]
train_set = train_set[~split]
# Train Model
import boto
import sagemaker
data_location = 's3://{}/linearlearner_highlevel_example/data'.format(bucket)
output_location = 's3://{}/linearlearner_highlevel_example/output'.format(bucket)
print('training data will be uploaded to: {}'.format(data_location))
print('training artifacts will be uploaded to: {}'.format(output_location))
sess = sagemaker.Session()
linear = sagemaker.estimator.Estimator(container, role, train_instance_count=1, rain_instance_type='ml.c4.xlarge',
output_path=output_location, sagemaker_session=sess)
linear.set_hyperparameters(feature_dim=784, predictor_type='binary_classifier', mini_batch_size=200)
linear.fit({'train': train_set})
# Deploy Model
linear_predictor = linear.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')
# Validate
from sagemaker.predictor import csv_serializer, json_deserializer
linear_predictor.content_type = 'text/csv'
linear_predictor.serializer = csv_serializer
linear_predictor.deserializer = json_deserializer
result = linear_predictor.predict(train_set[0][30:31])
print(result)
| 30.703125
| 114
| 0.795929
| 279
| 1,965
| 5.390681
| 0.412186
| 0.031915
| 0.042553
| 0.029255
| 0.219415
| 0.110372
| 0.110372
| 0.110372
| 0.110372
| 0.110372
| 0
| 0.018151
| 0.102799
| 1,965
| 63
| 115
| 31.190476
| 0.83494
| 0.148092
| 0
| 0.157895
| 0
| 0
| 0.180397
| 0.084185
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.263158
| 0
| 0.263158
| 0.078947
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 0
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| 0
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| 0
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| 0
| 0
| 0
| 0
|
1
| 0
|
72c448e2ac75cf97f325c368c89cf5c864f7ebd6
| 34,227
|
py
|
Python
|
gerber/am_statements.py
|
FixturFab/pcb-tools
|
7b8d1c6ccd9c242c162ede47557bb816233cf66f
|
[
"Apache-2.0"
] | null | null | null |
gerber/am_statements.py
|
FixturFab/pcb-tools
|
7b8d1c6ccd9c242c162ede47557bb816233cf66f
|
[
"Apache-2.0"
] | null | null | null |
gerber/am_statements.py
|
FixturFab/pcb-tools
|
7b8d1c6ccd9c242c162ede47557bb816233cf66f
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# copyright 2015 Hamilton Kibbe <ham@hamiltonkib.be> and Paulo Henrique Silva
# <ph.silva@gmail.com>
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from math import asin
import math
from .primitives import *
from .utils import validate_coordinates, inch, metric, rotate_point
# TODO: Add support for aperture macro variables
__all__ = ['AMPrimitive', 'AMCommentPrimitive', 'AMCirclePrimitive',
'AMVectorLinePrimitive', 'AMOutlinePrimitive', 'AMPolygonPrimitive',
'AMMoirePrimitive', 'AMThermalPrimitive', 'AMCenterLinePrimitive',
'AMLowerLeftLinePrimitive', 'AMUnsupportPrimitive']
class AMPrimitive(object):
""" Aperture Macro Primitive Base Class
Parameters
----------
code : int
primitive shape code
exposure : str
on or off Primitives with exposure on create a slid part of
the macro aperture, and primitives with exposure off erase the
solid part created previously in the aperture macro definition.
.. note::
The erasing effect is limited to the aperture definition in
which it occurs.
Returns
-------
primitive : :class: `gerber.am_statements.AMPrimitive`
Raises
------
TypeError, ValueError
"""
def __init__(self, code, exposure=None):
VALID_CODES = (0, 1, 2, 4, 5, 6, 7, 20, 21, 22, 9999)
if not isinstance(code, int):
raise TypeError('Aperture Macro Primitive code must be an integer')
elif code not in VALID_CODES:
raise ValueError('Invalid Code. Valid codes are %s.' %
', '.join(map(str, VALID_CODES)))
if exposure is not None and exposure.lower() not in ('on', 'off'):
raise ValueError('Exposure must be either on or off')
self.code = code
self.exposure = exposure.lower() if exposure is not None else None
def to_inch(self):
raise NotImplementedError('Subclass must implement `to-inch`')
def to_metric(self):
raise NotImplementedError('Subclass must implement `to-metric`')
@property
def _level_polarity(self):
if self.exposure == 'off':
return 'clear'
return 'dark'
def to_primitive(self, units):
""" Return a Primitive instance based on the specified macro params.
"""
print('Rendering {}s is not supported yet.'.format(str(self.__class__)))
def __eq__(self, other):
return self.__dict__ == other.__dict__
class AMCommentPrimitive(AMPrimitive):
""" Aperture Macro Comment primitive. Code 0
The comment primitive has no image meaning. It is used to include human-
readable comments into the AM command.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.1:** Comment, primitive code 0
Parameters
----------
code : int
Aperture Macro primitive code. 0 Indicates an AMCommentPrimitive
comment : str
The comment as a string.
Returns
-------
CommentPrimitive : :class:`gerbers.am_statements.AMCommentPrimitive`
An Initialized AMCommentPrimitive
Raises
------
ValueError
"""
@classmethod
def from_gerber(cls, primitive):
primitive = primitive.strip()
code = int(primitive[0])
comment = primitive[1:]
return cls(code, comment)
def __init__(self, code, comment):
if code != 0:
raise ValueError('Not a valid Aperture Macro Comment statement')
super(AMCommentPrimitive, self).__init__(code)
self.comment = comment.strip(' *')
def to_inch(self):
pass
def to_metric(self):
pass
def to_gerber(self, settings=None):
return '0 %s *' % self.comment
def to_primitive(self, units):
"""
Returns None - has not primitive representation
"""
return None
def __str__(self):
return '<Aperture Macro Comment: %s>' % self.comment
class AMCirclePrimitive(AMPrimitive):
""" Aperture macro Circle primitive. Code 1
A circle primitive is defined by its center point and diameter.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.2:** Circle, primitive code 1
Parameters
----------
code : int
Circle Primitive code. Must be 1
exposure : string
'on' or 'off'
diameter : float
Circle diameter
position : tuple (<float>, <float>)
Position of the circle relative to the macro origin
Returns
-------
CirclePrimitive : :class:`gerbers.am_statements.AMCirclePrimitive`
An initialized AMCirclePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(',')
code = int(modifiers[0])
exposure = 'on' if float(modifiers[1]) == 1 else 'off'
diameter = float(modifiers[2])
position = (float(modifiers[3]), float(modifiers[4]))
return cls(code, exposure, diameter, position)
@classmethod
def from_primitive(cls, primitive):
return cls(1, 'on', primitive.diameter, primitive.position)
def __init__(self, code, exposure, diameter, position):
validate_coordinates(position)
if code != 1:
raise ValueError('CirclePrimitive code is 1')
super(AMCirclePrimitive, self).__init__(code, exposure)
self.diameter = diameter
self.position = position
def to_inch(self):
self.diameter = inch(self.diameter)
self.position = tuple([inch(x) for x in self.position])
def to_metric(self):
self.diameter = metric(self.diameter)
self.position = tuple([metric(x) for x in self.position])
def to_gerber(self, settings=None):
data = dict(code=self.code,
exposure='1' if self.exposure == 'on' else 0,
diameter=self.diameter,
x=self.position[0],
y=self.position[1])
return '{code},{exposure},{diameter},{x},{y}*'.format(**data)
def to_primitive(self, units):
return Circle((self.position), self.diameter, units=units, level_polarity=self._level_polarity)
class AMVectorLinePrimitive(AMPrimitive):
""" Aperture Macro Vector Line primitive. Code 2 or 20.
A vector line is a rectangle defined by its line width, start, and end
points. The line ends are rectangular.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.3:** Vector Line, primitive code 2 or 20.
Parameters
----------
code : int
Vector Line Primitive code. Must be either 2 or 20.
exposure : string
'on' or 'off'
width : float
Line width
start : tuple (<float>, <float>)
coordinate of line start point
end : tuple (<float>, <float>)
coordinate of line end point
rotation : float
Line rotation about the origin.
Returns
-------
LinePrimitive : :class:`gerbers.am_statements.AMVectorLinePrimitive`
An initialized AMVectorLinePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_primitive(cls, primitive):
return cls(2, 'on', primitive.aperture.width, primitive.start, primitive.end, 0)
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(',')
code = int(modifiers[0])
exposure = 'on' if float(modifiers[1]) == 1 else 'off'
width = float(modifiers[2])
start = (float(modifiers[3]), float(modifiers[4]))
end = (float(modifiers[5]), float(modifiers[6]))
rotation = float(modifiers[7])
return cls(code, exposure, width, start, end, rotation)
def __init__(self, code, exposure, width, start, end, rotation):
validate_coordinates(start)
validate_coordinates(end)
if code not in (2, 20):
raise ValueError('VectorLinePrimitive codes are 2 or 20')
super(AMVectorLinePrimitive, self).__init__(code, exposure)
self.width = width
self.start = start
self.end = end
self.rotation = rotation
def to_inch(self):
self.width = inch(self.width)
self.start = tuple([inch(x) for x in self.start])
self.end = tuple([inch(x) for x in self.end])
def to_metric(self):
self.width = metric(self.width)
self.start = tuple([metric(x) for x in self.start])
self.end = tuple([metric(x) for x in self.end])
def to_gerber(self, settings=None):
fmtstr = '{code},{exp},{width},{startx},{starty},{endx},{endy},{rotation}*'
data = dict(code=self.code,
exp=1 if self.exposure == 'on' else 0,
width=self.width,
startx=self.start[0],
starty=self.start[1],
endx=self.end[0],
endy=self.end[1],
rotation=self.rotation)
return fmtstr.format(**data)
def to_primitive(self, units):
"""
Convert this to a primitive. We use the Outline to represent this (instead of Line)
because the behaviour of the end caps is different for aperture macros compared to Lines
when rotated.
"""
# Use a line to generate our vertices easily
line = Line(self.start, self.end, Rectangle(None, self.width, self.width))
vertices = line.vertices
aperture = Circle((0, 0), 0)
lines = []
prev_point = rotate_point(vertices[-1], self.rotation, (0, 0))
for point in vertices:
cur_point = rotate_point(point, self.rotation, (0, 0))
lines.append(Line(prev_point, cur_point, aperture))
return Outline(lines, units=units, level_polarity=self._level_polarity)
class AMOutlinePrimitive(AMPrimitive):
""" Aperture Macro Outline primitive. Code 4.
An outline primitive is an area enclosed by an n-point polygon defined by
its start point and n subsequent points. The outline must be closed, i.e.
the last point must be equal to the start point. Self intersecting
outlines are not allowed.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.6:** Outline, primitive code 4.
Parameters
----------
code : int
OutlinePrimitive code. Must be 6.
exposure : string
'on' or 'off'
start_point : tuple (<float>, <float>)
coordinate of outline start point
points : list of tuples (<float>, <float>)
coordinates of subsequent points
rotation : float
outline rotation about the origin.
Returns
-------
OutlinePrimitive : :class:`gerber.am_statements.AMOutlineinePrimitive`
An initialized AMOutlinePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_primitive(cls, primitive):
start_point = (round(primitive.primitives[0].start[0], 6), round(primitive.primitives[0].start[1], 6))
points = []
for prim in primitive.primitives:
points.append((round(prim.end[0], 6), round(prim.end[1], 6)))
rotation = 0.0
return cls(4, 'on', start_point, points, rotation)
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
exposure = "on" if float(modifiers[1]) == 1 else "off"
n = int(float(modifiers[2]))
start_point = (float(modifiers[3]), float(modifiers[4]))
points = []
for i in range(n):
points.append((float(modifiers[5 + i * 2]),
float(modifiers[5 + i * 2 + 1])))
rotation = float(modifiers[-1])
return cls(code, exposure, start_point, points, rotation)
def __init__(self, code, exposure, start_point, points, rotation):
""" Initialize AMOutlinePrimitive
"""
validate_coordinates(start_point)
for point in points:
validate_coordinates(point)
if code != 4:
raise ValueError('OutlinePrimitive code is 4')
super(AMOutlinePrimitive, self).__init__(code, exposure)
self.start_point = start_point
if points[-1] != start_point:
raise ValueError('OutlinePrimitive must be closed')
self.points = points
self.rotation = rotation
def to_inch(self):
self.start_point = tuple([inch(x) for x in self.start_point])
self.points = tuple([(inch(x), inch(y)) for x, y in self.points])
def to_metric(self):
self.start_point = tuple([metric(x) for x in self.start_point])
self.points = tuple([(metric(x), metric(y)) for x, y in self.points])
def to_gerber(self, settings=None):
data = dict(
code=self.code,
exposure="1" if self.exposure == "on" else "0",
n_points=len(self.points),
start_point="%.6g,%.6g" % self.start_point,
points=",\n".join(["%.6g,%.6g" % point for point in self.points]),
rotation=str(self.rotation)
)
return "{code},{exposure},{n_points},{start_point},{points},{rotation}*".format(**data)
def to_primitive(self, units):
"""
Convert this to a drawable primitive. This uses the Outline instead of Line
primitive to handle differences in end caps when rotated.
"""
lines = []
prev_point = rotate_point(self.start_point, self.rotation)
for point in self.points:
cur_point = rotate_point(point, self.rotation)
lines.append(Line(prev_point, cur_point, Circle((0,0), 0)))
prev_point = cur_point
if lines[0].start != lines[-1].end:
raise ValueError('Outline must be closed')
return Outline(lines, units=units, level_polarity=self._level_polarity)
class AMPolygonPrimitive(AMPrimitive):
""" Aperture Macro Polygon primitive. Code 5.
A polygon primitive is a regular polygon defined by the number of
vertices, the center point, and the diameter of the circumscribed circle.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.8:** Polygon, primitive code 5.
Parameters
----------
code : int
PolygonPrimitive code. Must be 5.
exposure : string
'on' or 'off'
vertices : int, 3 <= vertices <= 12
Number of vertices
position : tuple (<float>, <float>)
X and Y coordinates of polygon center
diameter : float
diameter of circumscribed circle.
rotation : float
polygon rotation about the origin.
Returns
-------
PolygonPrimitive : :class:`gerbers.am_statements.AMPolygonPrimitive`
An initialized AMPolygonPrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_primitive(cls, primitive):
return cls(5, 'on', primitive.sides, primitive.position, primitive.diameter, primitive.rotation)
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
exposure = "on" if float(modifiers[1]) == 1 else "off"
vertices = int(float(modifiers[2]))
position = (float(modifiers[3]), float(modifiers[4]))
try:
diameter = float(modifiers[5])
except:
diameter = 0
rotation = float(modifiers[6])
return cls(code, exposure, vertices, position, diameter, rotation)
def __init__(self, code, exposure, vertices, position, diameter, rotation):
""" Initialize AMPolygonPrimitive
"""
if code != 5:
raise ValueError('PolygonPrimitive code is 5')
super(AMPolygonPrimitive, self).__init__(code, exposure)
if vertices < 3 or vertices > 12:
raise ValueError('Number of vertices must be between 3 and 12')
self.vertices = vertices
validate_coordinates(position)
self.position = position
self.diameter = diameter
self.rotation = rotation
def to_inch(self):
self.position = tuple([inch(x) for x in self.position])
self.diameter = inch(self.diameter)
def to_metric(self):
self.position = tuple([metric(x) for x in self.position])
self.diameter = metric(self.diameter)
def to_gerber(self, settings=None):
data = dict(
code=self.code,
exposure="1" if self.exposure == "on" else "0",
vertices=self.vertices,
position="%.4g,%.4g" % self.position,
diameter='%.4g' % self.diameter,
rotation=str(self.rotation)
)
fmt = "{code},{exposure},{vertices},{position},{diameter},{rotation}*"
return fmt.format(**data)
def to_primitive(self, units):
return Polygon(self.position, self.vertices, self.diameter / 2.0, 0, rotation=math.radians(self.rotation), units=units, level_polarity=self._level_polarity)
class AMMoirePrimitive(AMPrimitive):
""" Aperture Macro Moire primitive. Code 6.
The moire primitive is a cross hair centered on concentric rings (annuli).
Exposure is always on.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.9:** Moire, primitive code 6.
Parameters
----------
code : int
Moire Primitive code. Must be 6.
position : tuple (<float>, <float>)
X and Y coordinates of moire center
diameter : float
outer diameter of outer ring.
ring_thickness : float
thickness of concentric rings.
gap : float
gap between concentric rings.
max_rings : float
maximum number of rings
crosshair_thickness : float
thickness of crosshairs
crosshair_length : float
length of crosshairs
rotation : float
moire rotation about the origin.
Returns
-------
MoirePrimitive : :class:`gerbers.am_statements.AMMoirePrimitive`
An initialized AMMoirePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
position = (float(modifiers[1]), float(modifiers[2]))
diameter = float(modifiers[3])
ring_thickness = float(modifiers[4])
gap = float(modifiers[5])
max_rings = int(float(modifiers[6]))
crosshair_thickness = float(modifiers[7])
crosshair_length = float(modifiers[8])
rotation = float(modifiers[9])
return cls(code, position, diameter, ring_thickness, gap, max_rings, crosshair_thickness, crosshair_length, rotation)
def __init__(self, code, position, diameter, ring_thickness, gap, max_rings, crosshair_thickness, crosshair_length, rotation):
""" Initialize AMoirePrimitive
"""
if code != 6:
raise ValueError('MoirePrimitive code is 6')
super(AMMoirePrimitive, self).__init__(code, 'on')
validate_coordinates(position)
self.position = position
self.diameter = diameter
self.ring_thickness = ring_thickness
self.gap = gap
self.max_rings = max_rings
self.crosshair_thickness = crosshair_thickness
self.crosshair_length = crosshair_length
self.rotation = rotation
def to_inch(self):
self.position = tuple([inch(x) for x in self.position])
self.diameter = inch(self.diameter)
self.ring_thickness = inch(self.ring_thickness)
self.gap = inch(self.gap)
self.crosshair_thickness = inch(self.crosshair_thickness)
self.crosshair_length = inch(self.crosshair_length)
def to_metric(self):
self.position = tuple([metric(x) for x in self.position])
self.diameter = metric(self.diameter)
self.ring_thickness = metric(self.ring_thickness)
self.gap = metric(self.gap)
self.crosshair_thickness = metric(self.crosshair_thickness)
self.crosshair_length = metric(self.crosshair_length)
def to_gerber(self, settings=None):
data = dict(
code=self.code,
position="%.4g,%.4g" % self.position,
diameter=self.diameter,
ring_thickness=self.ring_thickness,
gap=self.gap,
max_rings=self.max_rings,
crosshair_thickness=self.crosshair_thickness,
crosshair_length=self.crosshair_length,
rotation=self.rotation
)
fmt = "{code},{position},{diameter},{ring_thickness},{gap},{max_rings},{crosshair_thickness},{crosshair_length},{rotation}*"
return fmt.format(**data)
def to_primitive(self, units):
#raise NotImplementedError()
return None
class AMThermalPrimitive(AMPrimitive):
""" Aperture Macro Thermal primitive. Code 7.
The thermal primitive is a ring (annulus) interrupted by four gaps.
Exposure is always on.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.10:** Thermal, primitive code 7.
Parameters
----------
code : int
Thermal Primitive code. Must be 7.
position : tuple (<float>, <float>)
X and Y coordinates of thermal center
outer_diameter : float
outer diameter of thermal.
inner_diameter : float
inner diameter of thermal.
gap : float
gap thickness
rotation : float
thermal rotation about the origin.
Returns
-------
ThermalPrimitive : :class:`gerbers.am_statements.AMThermalPrimitive`
An initialized AMThermalPrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
position = (float(modifiers[1]), float(modifiers[2]))
outer_diameter = float(modifiers[3])
inner_diameter = float(modifiers[4])
gap = float(modifiers[5])
rotation = float(modifiers[6])
return cls(code, position, outer_diameter, inner_diameter, gap, rotation)
def __init__(self, code, position, outer_diameter, inner_diameter, gap, rotation):
if code != 7:
raise ValueError('ThermalPrimitive code is 7')
super(AMThermalPrimitive, self).__init__(code, 'on')
validate_coordinates(position)
self.position = position
self.outer_diameter = outer_diameter
self.inner_diameter = inner_diameter
self.gap = gap
self.rotation = rotation
def to_inch(self):
self.position = tuple([inch(x) for x in self.position])
self.outer_diameter = inch(self.outer_diameter)
self.inner_diameter = inch(self.inner_diameter)
self.gap = inch(self.gap)
def to_metric(self):
self.position = tuple([metric(x) for x in self.position])
self.outer_diameter = metric(self.outer_diameter)
self.inner_diameter = metric(self.inner_diameter)
self.gap = metric(self.gap)
def to_gerber(self, settings=None):
data = dict(
code=self.code,
position="%.4g,%.4g" % self.position,
outer_diameter=self.outer_diameter,
inner_diameter=self.inner_diameter,
gap=self.gap,
rotation=self.rotation
)
fmt = "{code},{position},{outer_diameter},{inner_diameter},{gap},{rotation}*"
return fmt.format(**data)
def _approximate_arc_cw(self, start_angle, end_angle, radius, center):
"""
Get an arc as a series of points
Parameters
----------
start_angle : The start angle in radians
end_angle : The end angle in radians
radius`: Radius of the arc
center : The center point of the arc (x, y) tuple
Returns
-------
array of point tuples
"""
# The total sweep
sweep_angle = end_angle - start_angle
num_steps = 10
angle_step = sweep_angle / num_steps
radius = radius
center = center
points = []
for i in range(num_steps + 1):
current_angle = start_angle + (angle_step * i)
nextx = (center[0] + math.cos(current_angle) * radius)
nexty = (center[1] + math.sin(current_angle) * radius)
points.append((nextx, nexty))
return points
def to_primitive(self, units):
# We start with calculating the top right section, then duplicate it
inner_radius = self.inner_diameter / 2.0
outer_radius = self.outer_diameter / 2.0
# Calculate the start angle relative to the horizontal axis
inner_offset_angle = asin(self.gap / 2.0 / inner_radius)
outer_offset_angle = asin(self.gap / 2.0 / outer_radius)
rotation_rad = math.radians(self.rotation)
inner_start_angle = inner_offset_angle + rotation_rad
inner_end_angle = math.pi / 2 - inner_offset_angle + rotation_rad
outer_start_angle = outer_offset_angle + rotation_rad
outer_end_angle = math.pi / 2 - outer_offset_angle + rotation_rad
outlines = []
aperture = Circle((0, 0), 0)
points = (self._approximate_arc_cw(inner_start_angle, inner_end_angle, inner_radius, self.position)
+ list(reversed(self._approximate_arc_cw(outer_start_angle, outer_end_angle, outer_radius, self.position))))
# Add in the last point since outlines should be closed
points.append(points[0])
# There are four outlines at rotated sections
for rotation in [0, 90.0, 180.0, 270.0]:
lines = []
prev_point = rotate_point(points[0], rotation, self.position)
for point in points[1:]:
cur_point = rotate_point(point, rotation, self.position)
lines.append(Line(prev_point, cur_point, aperture))
prev_point = cur_point
outlines.append(Outline(lines, units=units, level_polarity=self._level_polarity))
return outlines
class AMCenterLinePrimitive(AMPrimitive):
""" Aperture Macro Center Line primitive. Code 21.
The center line primitive is a rectangle defined by its width, height, and center point.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.4:** Center Line, primitive code 21.
Parameters
----------
code : int
Center Line Primitive code. Must be 21.
exposure : str
'on' or 'off'
width : float
Width of rectangle
height : float
Height of rectangle
center : tuple (<float>, <float>)
X and Y coordinates of line center
rotation : float
rectangle rotation about its center.
Returns
-------
CenterLinePrimitive : :class:`gerbers.am_statements.AMCenterLinePrimitive`
An initialized AMCenterLinePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_primitive(cls, primitive):
width = primitive.width
height = primitive.height
center = primitive.position
rotation = math.degrees(primitive.rotation)
return cls(21, 'on', width, height, center, rotation)
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
exposure = 'on' if float(modifiers[1]) == 1 else 'off'
width = float(modifiers[2])
height = float(modifiers[3])
center = (float(modifiers[4]), float(modifiers[5]))
rotation = float(modifiers[6])
return cls(code, exposure, width, height, center, rotation)
def __init__(self, code, exposure, width, height, center, rotation):
if code != 21:
raise ValueError('CenterLinePrimitive code is 21')
super(AMCenterLinePrimitive, self).__init__(code, exposure)
self.width = width
self.height = height
validate_coordinates(center)
self.center = center
self.rotation = rotation
def to_inch(self):
self.center = tuple([inch(x) for x in self.center])
self.width = inch(self.width)
self.height = inch(self.height)
def to_metric(self):
self.center = tuple([metric(x) for x in self.center])
self.width = metric(self.width)
self.height = metric(self.height)
def to_gerber(self, settings=None):
data = dict(
code=self.code,
exposure = '1' if self.exposure == 'on' else '0',
width = self.width,
height = self.height,
center="%.4g,%.4g" % self.center,
rotation=self.rotation
)
fmt = "{code},{exposure},{width},{height},{center},{rotation}*"
return fmt.format(**data)
def to_primitive(self, units):
x = self.center[0]
y = self.center[1]
half_width = self.width / 2.0
half_height = self.height / 2.0
points = []
points.append((x - half_width, y + half_height))
points.append((x - half_width, y - half_height))
points.append((x + half_width, y - half_height))
points.append((x + half_width, y + half_height))
aperture = Circle((0, 0), 0)
lines = []
prev_point = rotate_point(points[3], self.rotation, self.center)
for point in points:
cur_point = rotate_point(point, self.rotation, self.center)
lines.append(Line(prev_point, cur_point, aperture))
return Outline(lines, units=units, level_polarity=self._level_polarity)
class AMLowerLeftLinePrimitive(AMPrimitive):
""" Aperture Macro Lower Left Line primitive. Code 22.
The lower left line primitive is a rectangle defined by its width, height, and the lower left point.
.. seealso::
`The Gerber File Format Specification <http://www.ucamco.com/files/downloads/file/81/the_gerber_file_format_specification.pdf>`_
**Section 4.12.3.5:** Lower Left Line, primitive code 22.
Parameters
----------
code : int
Center Line Primitive code. Must be 22.
exposure : str
'on' or 'off'
width : float
Width of rectangle
height : float
Height of rectangle
lower_left : tuple (<float>, <float>)
X and Y coordinates of lower left corner
rotation : float
rectangle rotation about its origin.
Returns
-------
LowerLeftLinePrimitive : :class:`gerbers.am_statements.AMLowerLeftLinePrimitive`
An initialized AMLowerLeftLinePrimitive
Raises
------
ValueError, TypeError
"""
@classmethod
def from_gerber(cls, primitive):
modifiers = primitive.strip(' *').split(",")
code = int(modifiers[0])
exposure = 'on' if float(modifiers[1]) == 1 else 'off'
width = float(modifiers[2])
height = float(modifiers[3])
lower_left = (float(modifiers[4]), float(modifiers[5]))
rotation = float(modifiers[6])
return cls(code, exposure, width, height, lower_left, rotation)
def __init__(self, code, exposure, width, height, lower_left, rotation):
if code != 22:
raise ValueError('LowerLeftLinePrimitive code is 22')
super (AMLowerLeftLinePrimitive, self).__init__(code, exposure)
self.width = width
self.height = height
validate_coordinates(lower_left)
self.lower_left = lower_left
self.rotation = rotation
def to_inch(self):
self.lower_left = tuple([inch(x) for x in self.lower_left])
self.width = inch(self.width)
self.height = inch(self.height)
def to_metric(self):
self.lower_left = tuple([metric(x) for x in self.lower_left])
self.width = metric(self.width)
self.height = metric(self.height)
def to_gerber(self, settings=None):
data = dict(
code=self.code,
exposure = '1' if self.exposure == 'on' else '0',
width = self.width,
height = self.height,
lower_left="%.4g,%.4g" % self.lower_left,
rotation=self.rotation
)
fmt = "{code},{exposure},{width},{height},{lower_left},{rotation}*"
return fmt.format(**data)
class AMUnsupportPrimitive(AMPrimitive):
@classmethod
def from_gerber(cls, primitive):
return cls(primitive)
def __init__(self, primitive):
super(AMUnsupportPrimitive, self).__init__(9999)
self.primitive = primitive
def to_inch(self):
pass
def to_metric(self):
pass
def to_gerber(self, settings=None):
return self.primitive
| 32.690544
| 164
| 0.624302
| 4,023
| 34,227
| 5.195377
| 0.097688
| 0.034161
| 0.011196
| 0.016363
| 0.507488
| 0.4384
| 0.387206
| 0.363667
| 0.313956
| 0.287259
| 0
| 0.01399
| 0.266953
| 34,227
| 1,046
| 165
| 32.721797
| 0.819051
| 0.293891
| 0
| 0.458
| 0
| 0
| 0.066719
| 0.026819
| 0
| 0
| 0
| 0.000956
| 0
| 1
| 0.142
| false
| 0.008
| 0.008
| 0.022
| 0.248
| 0.002
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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|
1
| 0
|
72c7d7e3f8694c5c646ef95b15742cc54526c455
| 4,174
|
py
|
Python
|
networks/adabins/utils.py
|
EvoCargo/mono_depth
|
3a77291a7fc8f3eaad5f93aa17e2b60c9339a0b1
|
[
"MIT"
] | null | null | null |
networks/adabins/utils.py
|
EvoCargo/mono_depth
|
3a77291a7fc8f3eaad5f93aa17e2b60c9339a0b1
|
[
"MIT"
] | 1
|
2021-06-09T12:56:47.000Z
|
2021-06-11T10:49:06.000Z
|
networks/adabins/utils.py
|
EvoCargo/mono_depth
|
3a77291a7fc8f3eaad5f93aa17e2b60c9339a0b1
|
[
"MIT"
] | null | null | null |
import base64
import math
import re
from io import BytesIO
import matplotlib.cm
import numpy as np
import torch
import torch.nn
from PIL import Image
# Compute edge magnitudes
from scipy import ndimage
class RunningAverage:
def __init__(self):
self.avg = 0
self.count = 0
def append(self, value):
self.avg = (value + self.count * self.avg) / (self.count + 1)
self.count += 1
def get_value(self):
return self.avg
def denormalize(x, device='cpu'):
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
return x * std + mean
class RunningAverageDict:
def __init__(self):
self._dict = None
def update(self, new_dict):
if self._dict is None:
self._dict = dict()
for key, _ in new_dict.items():
self._dict[key] = RunningAverage()
for key, value in new_dict.items():
self._dict[key].append(value)
def get_value(self):
return {key: value.get_value() for key, value in self._dict.items()}
def colorize(value, vmin=10, vmax=1000, cmap='magma_r'):
value = value.cpu().numpy()[0, :, :]
invalid_mask = value == -1
# normalize
vmin = value.min() if vmin is None else vmin
vmax = value.max() if vmax is None else vmax
if vmin != vmax:
value = (value - vmin) / (vmax - vmin) # vmin..vmax
else:
# Avoid 0-division
value = value * 0.0
# squeeze last dim if it exists
# value = value.squeeze(axis=0)
cmapper = matplotlib.cm.get_cmap(cmap)
value = cmapper(value, bytes=True) # (nxmx4)
value[invalid_mask] = 255
img = value[:, :, :3]
# return img.transpose((2, 0, 1))
return img
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
a1 = (thresh < 1.25).mean()
a2 = (thresh < 1.25 ** 2).mean()
a3 = (thresh < 1.25 ** 3).mean()
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred) ** 2) / gt)
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
err = np.log(pred) - np.log(gt)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean()
return dict(
a1=a1,
a2=a2,
a3=a3,
abs_rel=abs_rel,
rmse=rmse,
log_10=log_10,
rmse_log=rmse_log,
silog=silog,
sq_rel=sq_rel,
)
# Demo Utilities
def b64_to_pil(b64string):
image_data = re.sub('^data:image/.+;base64,', '', b64string)
# image = Image.open(cStringIO.StringIO(image_data))
return Image.open(BytesIO(base64.b64decode(image_data)))
def edges(d):
dx = ndimage.sobel(d, 0) # horizontal derivative
dy = ndimage.sobel(d, 1) # vertical derivative
return np.abs(dx) + np.abs(dy)
class PointCloudHelper:
def __init__(self, width=640, height=480):
self.xx, self.yy = self.worldCoords(width, height)
def worldCoords(self, width=640, height=480):
hfov_degrees, vfov_degrees = 57, 43
hFov = math.radians(hfov_degrees)
vFov = math.radians(vfov_degrees)
cx, cy = width / 2, height / 2
fx = width / (2 * math.tan(hFov / 2))
fy = height / (2 * math.tan(vFov / 2))
xx, yy = np.tile(range(width), height), np.repeat(range(height), width)
xx = (xx - cx) / fx
yy = (yy - cy) / fy
return xx, yy
def depth_to_points(self, depth):
depth[edges(depth) > 0.3] = np.nan # Hide depth edges
length = depth.shape[0] * depth.shape[1]
# depth[edges(depth) > 0.3] = 1e6 # Hide depth edges
z = depth.reshape(length)
return np.dstack((self.xx * z, self.yy * z, z)).reshape((length, 3))
| 28.394558
| 80
| 0.565884
| 594
| 4,174
| 3.880471
| 0.281145
| 0.020824
| 0.014317
| 0.013015
| 0.086768
| 0.035575
| 0.035575
| 0
| 0
| 0
| 0
| 0.047716
| 0.292046
| 4,174
| 146
| 81
| 28.589041
| 0.732318
| 0.081696
| 0
| 0.039216
| 0
| 0
| 0.008717
| 0.005993
| 0
| 0
| 0
| 0
| 0
| 1
| 0.147059
| false
| 0
| 0.098039
| 0.029412
| 0.372549
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72c85d886bda8e81edae28edb917d772be6187cc
| 8,439
|
py
|
Python
|
gdsfactory/types.py
|
simbilod/gdsfactory
|
4d76db32674c3edb4d16260e3177ee29ef9ce11d
|
[
"MIT"
] | null | null | null |
gdsfactory/types.py
|
simbilod/gdsfactory
|
4d76db32674c3edb4d16260e3177ee29ef9ce11d
|
[
"MIT"
] | null | null | null |
gdsfactory/types.py
|
simbilod/gdsfactory
|
4d76db32674c3edb4d16260e3177ee29ef9ce11d
|
[
"MIT"
] | null | null | null |
"""In programming, a factory is a function that returns an object.
Functions are easy to understand because they have clear inputs and outputs.
Most gdsfactory functions take some inputs and return a Component object.
Some of these inputs parameters are also functions.
- Component: Object with.
- name.
- references: to other components (x, y, rotation).
- polygons in different layers.
- ports dict.
- Route: dataclass with 3 attributes.
- references: list of references (straights, bends and tapers).
- ports: dict(input=PortIn, output=PortOut).
- length: how long is this route?
Factories:
- ComponentFactory: function that returns a Component.
- RouteFactory: function that returns a Route.
Specs:
- ComponentSpec: Component, ComponentFactory or dict(component=mzi, settings=dict(delta_length=20)).
- LayerSpec: (3, 0), 3 (asumes 0 as datatype) or string.
"""
import json
import pathlib
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from omegaconf import OmegaConf
from phidl.device_layout import Label as LabelPhidl
from phidl.device_layout import Path
from pydantic import BaseModel, Extra
from typing_extensions import Literal
from gdsfactory.component import Component, ComponentReference
from gdsfactory.cross_section import CrossSection
from gdsfactory.port import Port
Anchor = Literal[
"ce",
"cw",
"nc",
"ne",
"nw",
"sc",
"se",
"sw",
"center",
"cc",
]
Axis = Literal["x", "y"]
NSEW = Literal["N", "S", "E", "W"]
class Label(LabelPhidl):
@classmethod
def __get_validators__(cls):
yield cls.validate
@classmethod
def validate(cls, v):
"""check with pydantic Label valid type"""
assert isinstance(v, LabelPhidl), f"TypeError, Got {type(v)}, expecting Label"
return v
Float2 = Tuple[float, float]
Float3 = Tuple[float, float, float]
Floats = Tuple[float, ...]
Strs = Tuple[str, ...]
Int2 = Tuple[int, int]
Int3 = Tuple[int, int, int]
Ints = Tuple[int, ...]
Layer = Tuple[int, int]
Layers = Tuple[Layer, ...]
LayerSpec = NewType("LayerSpec", Union[Layer, int, str, None])
LayerSpecs = Tuple[LayerSpec, ...]
ComponentFactory = Callable[..., Component]
ComponentFactoryDict = Dict[str, ComponentFactory]
PathFactory = Callable[..., Path]
PathType = Union[str, pathlib.Path]
PathTypes = Tuple[PathType, ...]
ComponentOrPath = Union[PathType, Component]
ComponentOrReference = Union[Component, ComponentReference]
NameToFunctionDict = Dict[str, ComponentFactory]
Number = Union[float, int]
Coordinate = Tuple[float, float]
Coordinates = Tuple[Coordinate, ...]
ComponentOrPath = Union[Component, PathType]
CrossSectionFactory = Callable[..., CrossSection]
CrossSectionOrFactory = Union[CrossSection, Callable[..., CrossSection]]
PortSymmetries = Dict[str, Dict[str, List[str]]]
PortsDict = Dict[str, Port]
PortsList = Dict[str, Port]
ComponentSpec = NewType(
"ComponentSpec", Union[str, ComponentFactory, Component, Dict[str, Any]]
)
ComponentSpecOrList = Union[ComponentSpec, List[ComponentSpec]]
CellSpec = Union[str, ComponentFactory, Dict[str, Any]]
ComponentSpecDict = Dict[str, ComponentSpec]
CrossSectionSpec = NewType(
"CrossSectionSpec", Union[str, CrossSectionFactory, CrossSection, Dict[str, Any]]
)
MultiCrossSectionAngleSpec = List[Tuple[CrossSectionSpec, Tuple[int, ...]]]
class Route(BaseModel):
references: List[ComponentReference]
labels: Optional[List[Label]] = None
ports: Tuple[Port, Port]
length: float
class Config:
extra = Extra.forbid
class Routes(BaseModel):
references: List[ComponentReference]
lengths: List[float]
ports: Optional[List[Port]] = None
bend_radius: Optional[List[float]] = None
class Config:
extra = Extra.forbid
class ComponentModel(BaseModel):
component: Union[str, Dict[str, Any]]
settings: Optional[Dict[str, Any]]
class Config:
extra = Extra.forbid
class PlacementModel(BaseModel):
x: Union[str, float] = 0
y: Union[str, float] = 0
xmin: Optional[Union[str, float]] = None
ymin: Optional[Union[str, float]] = None
xmax: Optional[Union[str, float]] = None
ymax: Optional[Union[str, float]] = None
dx: float = 0
dy: float = 0
port: Optional[Union[str, Anchor]] = None
rotation: int = 0
mirror: bool = False
class Config:
extra = Extra.forbid
class RouteModel(BaseModel):
links: Dict[str, str]
settings: Optional[Dict[str, Any]] = None
routing_strategy: Optional[str] = None
class Config:
extra = Extra.forbid
class NetlistModel(BaseModel):
"""Netlist defined component.
Attributes:
instances: dict of instances (name, settings, component).
placements: dict of placements.
connections: dict of connections.
routes: dict of routes.
name: component name.
info: information (polarization, wavelength ...).
settings: input variables.
pdk: pdk module name.
ports: exposed component ports.
"""
instances: Dict[str, ComponentModel]
placements: Optional[Dict[str, PlacementModel]] = None
connections: Optional[List[Dict[str, str]]] = None
routes: Optional[Dict[str, RouteModel]] = None
name: Optional[str] = None
info: Optional[Dict[str, Any]] = None
settings: Optional[Dict[str, Any]] = None
pdk: Optional[str] = None
ports: Optional[Dict[str, str]] = None
class Config:
extra = Extra.forbid
# factory: Dict[str, ComponentFactory] = {}
# def add_instance(self, name: str, component: str, **settings) -> None:
# assert component in self.factory.keys()
# component_model = ComponentModel(component=component, settings=settings)
# self.instances[name] = component_model
# def add_route(self, port1: Port, port2: Port, **settings) -> None:
# self.routes = component_model
RouteFactory = Callable[..., Route]
class TypedArray(np.ndarray):
"""based on https://github.com/samuelcolvin/pydantic/issues/380"""
@classmethod
def __get_validators__(cls):
yield cls.validate_type
@classmethod
def validate_type(cls, val):
return np.array(val, dtype=cls.inner_type)
class ArrayMeta(type):
def __getitem__(self, t):
return type("Array", (TypedArray,), {"inner_type": t})
class Array(np.ndarray, metaclass=ArrayMeta):
pass
__all__ = (
"ComponentFactory",
"ComponentFactoryDict",
"ComponentSpec",
"ComponentOrPath",
"ComponentOrReference",
"Coordinate",
"Coordinates",
"CrossSectionFactory",
"CrossSectionOrFactory",
"MultiCrossSectionAngleSpec",
"Float2",
"Float3",
"Floats",
"Int2",
"Int3",
"Ints",
"Layer",
"Layers",
"NameToFunctionDict",
"Number",
"PathType",
"PathTypes",
"Route",
"RouteFactory",
"Routes",
"Strs",
)
def write_schema(model: BaseModel = NetlistModel) -> None:
s = model.schema_json()
d = OmegaConf.create(s)
dirpath = pathlib.Path(__file__).parent / "schemas"
f1 = dirpath / "netlist.yaml"
f1.write_text(OmegaConf.to_yaml(d))
f2 = dirpath / "netlist.json"
f2.write_text(json.dumps(OmegaConf.to_container(d)))
if __name__ == "__main__":
write_schema()
import jsonschema
import yaml
from gdsfactory.config import CONFIG
schema_path = CONFIG["schema_netlist"]
schema_dict = json.loads(schema_path.read_text())
yaml_text = """
name: mzi
pdk: ubcpdk
settings:
dy: -90
info:
polarization: te
wavelength: 1.55
description: mzi for ubcpdk
instances:
yr:
component: y_splitter
yl:
component: y_splitter
placements:
yr:
rotation: 180
x: 100
y: 0
routes:
route_top:
links:
yl,opt2: yr,opt3
settings:
cross_section: strip
route_bot:
links:
yl,opt3: yr,opt2
routing_strategy: get_bundle_from_steps
settings:
steps: [dx: 30, dy: '${settings.dy}', dx: 20]
cross_section: strip
ports:
o1: yl,opt1
o2: yr,opt1
"""
yaml_dict = yaml.safe_load(yaml_text)
jsonschema.validate(yaml_dict, schema_dict)
# from gdsfactory.components import factory
# c = NetlistModel(factory=factory)
# c.add_instance("mmi1", "mmi1x2", length=13.3)
| 25.041543
| 100
| 0.670103
| 958
| 8,439
| 5.824635
| 0.298539
| 0.027599
| 0.014337
| 0.022581
| 0.100179
| 0.063978
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| 0.016487
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| 8,439
| 336
| 101
| 25.116071
| 0.829851
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| 0.004608
| 1
| 0.02765
| false
| 0.004608
| 0.069124
| 0.009217
| 0.336406
| 0
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| null | 0
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|
1
| 0
|
72caabf05592563e94088a4e1c8a8ae64828efbb
| 3,253
|
py
|
Python
|
5 - FC layers retraining/4 - FC weights to C++ code/weights_pck_to_cpp_unrolled_loop.py
|
brouwa/CNNs-on-FPSPs
|
71bcc2335e6d71ad21ba66e04a651d4db218356d
|
[
"MIT"
] | 1
|
2021-02-23T21:53:30.000Z
|
2021-02-23T21:53:30.000Z
|
5 - FC layers retraining/4 - FC weights to C++ code/weights_pck_to_cpp_unrolled_loop.py
|
brouwa/CNNs-on-FPSPs
|
71bcc2335e6d71ad21ba66e04a651d4db218356d
|
[
"MIT"
] | 1
|
2020-11-13T19:08:27.000Z
|
2020-11-13T19:08:27.000Z
|
5 - FC layers retraining/4 - FC weights to C++ code/weights_pck_to_cpp_unrolled_loop.py
|
brouwa/CNNs-on-FPSPs
|
71bcc2335e6d71ad21ba66e04a651d4db218356d
|
[
"MIT"
] | 1
|
2021-03-04T10:17:01.000Z
|
2021-03-04T10:17:01.000Z
|
import pickle
import numpy as np
INPUT_FILENAME = 'NP_WEIGHTS.pck'
PRECISION = 100
# Open weights
fc1_k, fc1_b, fc2_k, fc2_b = pickle.load(
open(INPUT_FILENAME, 'rb'))
# Round them
fc1_k, fc1_b, fc2_k, fc2_b = fc1_k*PRECISION//1, fc1_b*PRECISION//1, fc2_k*PRECISION//1, fc2_b*PRECISION*PRECISION//1
fc1_k, fc1_b, fc2_k, fc2_b = fc1_k.astype(np.int), fc1_b.astype(np.int), fc2_k.astype(np.int), fc2_b.astype(np.int)
"""
0: GENERATE C++ ARRAYS, TO BE USED IN A STANDARD LOOP
"""
OUTPUT_FILENAME = 'fc_weights_arrays.cpp'
def to_cpp_1_dim(array):
txt = '{\t'
for coeff in array[:-1]:
txt += str(coeff) + ',\t'
txt += str(array[-1]) + '}'
return txt
def to_cpp_2_dims(array):
txt = '{'
for line in array[:-1]:
txt += to_cpp_1_dim(line) + ',\n'
txt += to_cpp_1_dim(array[-1]) + '}'
return txt
# Generate .cpp text
out = 'int fc1_k[' + str(fc1_k.shape[0]) + '][' + str(fc1_k.shape[1]) + '] = '
out += to_cpp_2_dims(fc1_k) + ';\n\n'
out += 'int fc1_b[' + str(fc1_b.shape[0]) + '] = '
out += to_cpp_1_dim(fc1_b) + ';\n\n'
out += 'int fc2_k[' + str(fc2_k.shape[0]) + '][' + str(fc2_k.shape[1]) + '] = '
out += to_cpp_2_dims(fc2_k) + ';\n\n'
out += 'int fc2_b[' + str(fc2_b.shape[0]) + '] = '
out += to_cpp_1_dim(fc2_b) + ';\n\n'
# Output it
with open(OUTPUT_FILENAME, 'w+', encoding='utf-8') as f:
f.write(out)
"""
1: GENERATE C++ LOOP, USING THE ABOVE ARRAY
"""
OUTPUT_FILENAME = 'fc_loop_unrolled.cpp'
def to_cpp_function(k, b, function_name, in_dim, out_dim):
"""
Generates C++ code for computing a fully connected layer of int values,
applying weights k and bias b, with hardcoded values in the source code.
The function is names after function_name.
"""
out = ""
out += "inline void "+function_name+"(int in["+str(in_dim)+"], int out["+str(out_dim)+"]){\n"
for j in range(out_dim):
out += "\tout["+str(j)+"] = \n"
for i in range(in_dim):
out += "\t\tin["+str(i)+"]*("+k+"["+str(i)+"]["+str(j)+"]) +\n"
out += "\t\t("+b+"["+str(j)+"]);\n"
out += "}\n\n"
return out
## Generate .cpp text
out = ""
# First layer
out += to_cpp_function('fc1_k', 'fc1_b', 'fc_1', 27, 50)
# Second layer
out += to_cpp_function('fc2_k', 'fc2_b', 'fc_2', 50, 10)
# Output it
with open(OUTPUT_FILENAME, 'w+', encoding='utf-8') as f:
f.write(out)
"""
3: GENERATE C++ LOOP, WITH HARDCODED WEIGHTS
"""
OUTPUT_FILENAME = 'fc_loop_unrolled_hardcoded_weights.cpp'
def to_cpp_function(k, b, function_name):
"""
Generates C++ code for computing a fully connected layer of int values,
applying weights k and bias b, with hardcoded values in the source code.
The function is names after function_name.
"""
out = ""
(in_dim, out_dim) = k.shape
out += "inline void "+function_name+"(int in["+str(in_dim)+"], int out["+str(out_dim)+"]){\n"
for j in range(out_dim):
out += "\tout["+str(j)+"] = \n"
for i in range(in_dim):
out += "\t\tin["+str(i)+"]*("+str(k[i][j])+") +\n"
out += "\t\t("+str(b[j])+");\n"
out += "}\n\n"
return out
## Generate .cpp text
out = ""
# First layer
out += to_cpp_function(fc1_k, fc1_b, 'fc_1')
# Second layer
out += to_cpp_function(fc2_k, fc2_b, 'fc_2')
# Output it
with open(OUTPUT_FILENAME, 'w+', encoding='utf-8') as f:
f.write(out)
| 27.567797
| 117
| 0.619428
| 574
| 3,253
| 3.30662
| 0.167247
| 0.036881
| 0.03372
| 0.021075
| 0.660695
| 0.584299
| 0.584299
| 0.584299
| 0.534773
| 0.5
| 0
| 0.031622
| 0.173686
| 3,253
| 117
| 118
| 27.803419
| 0.674479
| 0.165078
| 0
| 0.375
| 0
| 0
| 0.161085
| 0.023525
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0625
| false
| 0
| 0.03125
| 0
| 0.15625
| 0
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| 0
| 0
| null | 0
| 0
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| 0
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| null | 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cb0ad23b1774315b100a3169e33454e096362a
| 346
|
py
|
Python
|
python/Canny_EdgeDetection.py
|
yubaoliu/Computer-Vision
|
2fe4d3e1db0a65ef8c9def5f84d5e494bec3faa9
|
[
"BSD-3-Clause"
] | null | null | null |
python/Canny_EdgeDetection.py
|
yubaoliu/Computer-Vision
|
2fe4d3e1db0a65ef8c9def5f84d5e494bec3faa9
|
[
"BSD-3-Clause"
] | null | null | null |
python/Canny_EdgeDetection.py
|
yubaoliu/Computer-Vision
|
2fe4d3e1db0a65ef8c9def5f84d5e494bec3faa9
|
[
"BSD-3-Clause"
] | null | null | null |
import cv2
import numpy as np
import random
img = cv2.imread('../../Assets/Images/flower-white.jpeg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('img', img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgG = cv2.GaussianBlur(gray, (3, 3), 0)
dst = cv2.Canny(img, 50, 50)
cv2.imshow('dst', dst)
cv2.waitKey(0)
| 17.3
| 60
| 0.687861
| 57
| 346
| 4.157895
| 0.54386
| 0.050633
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070234
| 0.135838
| 346
| 20
| 61
| 17.3
| 0.722408
| 0
| 0
| 0
| 0
| 0
| 0.124277
| 0.106936
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.230769
| 0
| 0.230769
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cb9ae8cd277faadce0f3f6be82d9d90c087279
| 7,767
|
py
|
Python
|
avod/core/trainer_stride.py
|
Guoxs/DODT
|
f354cda6ef08465018fdeec1a8b4be4002e6a71f
|
[
"MIT"
] | 1
|
2021-09-01T00:34:17.000Z
|
2021-09-01T00:34:17.000Z
|
avod/core/trainer_stride.py
|
Guoxs/DODT
|
f354cda6ef08465018fdeec1a8b4be4002e6a71f
|
[
"MIT"
] | null | null | null |
avod/core/trainer_stride.py
|
Guoxs/DODT
|
f354cda6ef08465018fdeec1a8b4be4002e6a71f
|
[
"MIT"
] | null | null | null |
"""Detection model trainer.
This file provides a generic training method to train a
DetectionModel.
"""
import datetime
import os
import tensorflow as tf
import time
from avod.builders import optimizer_builder
from avod.core import trainer_utils
from avod.core import summary_utils
slim = tf.contrib.slim
def train(model, train_config):
"""Training function for detection models.
Args:
model: The detection model object.
train_config: a train_*pb2 protobuf.
training i.e. loading RPN weights onto AVOD model.
"""
model = model
train_config = train_config
# Get model configurations
model_config = model.model_config
# Create a variable tensor to hold the global step
global_step_tensor = tf.Variable(
0, trainable=False, name='global_step')
#############################
# Get training configurations
#############################
max_iterations = train_config.max_iterations
summary_interval = train_config.summary_interval
checkpoint_interval = train_config.checkpoint_interval
max_checkpoints = train_config.max_checkpoints_to_keep
paths_config = model_config.paths_config
logdir = paths_config.logdir
if not os.path.exists(logdir):
os.makedirs(logdir)
checkpoint_dir = paths_config.checkpoint_dir
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_path = checkpoint_dir + '/' + \
model_config.checkpoint_name
pretrained_checkpoint_dir = checkpoint_dir + '/../../' + \
'pyramid_cars_with_aug_dt_5_tracking_corr_pretrained/checkpoints'
global_summaries = set([])
# The model should return a dictionary of predictions
prediction_dict = model.build()
summary_histograms = train_config.summary_histograms
summary_img_images = train_config.summary_img_images
summary_bev_images = train_config.summary_bev_images
# get variables to train
if not train_config.use_pretrained_model:
variable_to_train = None
else:
trainable_variables = tf.trainable_variables()
variable_to_train = trainable_variables[68:72] + \
trainable_variables[96:]
##############################
# Setup loss
##############################
losses_dict, total_loss = model.loss(prediction_dict)
# Optimizer
training_optimizer = optimizer_builder.build(
train_config.optimizer,
global_summaries,
global_step_tensor)
# Create the train op
with tf.variable_scope('train_op'):
train_op = slim.learning.create_train_op(
total_loss,
training_optimizer,
variables_to_train=variable_to_train,
clip_gradient_norm=1.0,
global_step=global_step_tensor)
# Add the result of the train_op to the summary
tf.summary.scalar("training_loss", train_op)
# Add maximum memory usage summary op
# This op can only be run on device with gpu
# so it's skipped on travis
is_travis = 'TRAVIS' in os.environ
if not is_travis:
# tf.summary.scalar('bytes_in_use',
# tf.contrib.memory_stats.BytesInUse())
tf.summary.scalar('max_bytes',
tf.contrib.memory_stats.MaxBytesInUse())
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summary_merged = summary_utils.summaries_to_keep(
summaries,
global_summaries,
histograms=summary_histograms,
input_imgs=summary_img_images,
input_bevs=summary_bev_images
)
allow_gpu_mem_growth = train_config.allow_gpu_mem_growth
if allow_gpu_mem_growth:
# GPU memory config
config = tf.ConfigProto()
config.gpu_options.allow_growth = allow_gpu_mem_growth
sess = tf.Session(config=config)
else:
sess = tf.Session()
# Create unique folder name using datetime for summary writer
datetime_str = str(datetime.datetime.now())
logdir = logdir + '/train'
train_writer = tf.summary.FileWriter(logdir + '/' + datetime_str,
sess.graph)
# Save checkpoints regularly.
saver = tf.train.Saver(max_to_keep=max_checkpoints, pad_step_number=True)
# Create init op
# if train_config.use_pretrained_model:
# init = tf.initialize_variables(variable_to_train)
# else:
# init = tf.global_variables_initializer()
init = tf.global_variables_initializer()
# Continue from last saved checkpoint
if not train_config.overwrite_checkpoints:
trainer_utils.load_checkpoints(checkpoint_dir,saver)
if len(saver.last_checkpoints) > 0:
checkpoint_to_restore = saver.last_checkpoints[-1]
saver.restore(sess, checkpoint_to_restore)
else:
sess.run(init)
# load pretrained model
if train_config.use_pretrained_model:
variable_to_restore = tf.trainable_variables()
variable_to_restore = variable_to_restore[:68] + \
variable_to_restore[72:96]
variable_to_restore = {var.op.name: var for var in variable_to_restore}
saver2 = tf.train.Saver(var_list=variable_to_restore)
print('Loading pretrained model...')
trainer_utils.load_checkpoints(pretrained_checkpoint_dir, saver2)
checkpoint_to_restore = saver2.last_checkpoints[11]
saver2.restore(sess, checkpoint_to_restore)
else:
sess.run(init)
# load pretrained model
if train_config.use_pretrained_model:
variable_to_restore = tf.trainable_variables()
variable_to_restore = variable_to_restore[:68] + \
variable_to_restore[72:96]
variable_to_restore = {var.op.name: var for var in variable_to_restore}
saver2 = tf.train.Saver(var_list=variable_to_restore)
print('Loading pretrained model...')
trainer_utils.load_checkpoints(pretrained_checkpoint_dir, saver2)
checkpoint_to_restore = saver2.last_checkpoints[11]
saver2.restore(sess, checkpoint_to_restore)
# Read the global step if restored
global_step = tf.train.global_step(sess, global_step_tensor)
print('Starting from step {} / {}'.format(
global_step, max_iterations))
# Main Training Loop
last_time = time.time()
for step in range(global_step, max_iterations + 1):
# Save checkpoint
if step % checkpoint_interval == 0:
global_step = tf.train.global_step(sess,
global_step_tensor)
saver.save(sess,
save_path=checkpoint_path,
global_step=global_step)
print('Step {} / {}, Checkpoint saved to {}-{:08d}'.format(
step, max_iterations,
checkpoint_path, global_step))
feed_dict = model.create_feed_dict()
# Write summaries and train op
if step % summary_interval == 0:
current_time = time.time()
time_elapsed = current_time - last_time
last_time = current_time
train_op_loss, summary_out = sess.run(
[train_op, summary_merged], feed_dict=feed_dict)
print('Step {}, Total Loss {:0.3f}, Time Elapsed {:0.3f} s'.format(
step, train_op_loss, time_elapsed))
train_writer.add_summary(summary_out, step)
else:
# Run the train op only
sess.run(train_op, feed_dict)
# Close the summary writers
train_writer.close()
| 35.62844
| 87
| 0.643234
| 904
| 7,767
| 5.225664
| 0.21792
| 0.038103
| 0.050381
| 0.020322
| 0.255292
| 0.217189
| 0.210627
| 0.202371
| 0.202371
| 0.202371
| 0
| 0.0081
| 0.26883
| 7,767
| 218
| 88
| 35.62844
| 0.823737
| 0.15862
| 0
| 0.231343
| 0
| 0
| 0.04705
| 0.009913
| 0
| 0
| 0
| 0
| 0
| 1
| 0.007463
| false
| 0
| 0.052239
| 0
| 0.059701
| 0.037313
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cbf3d35b93e0b877b0f490045834b6cee53f3c
| 1,237
|
py
|
Python
|
rest_framework_hmac/hmac_key/models.py
|
nickc92/django-rest-framework-hmac
|
c32e37cf00ef0c13957a6e02eb0a7fabac3d1ac1
|
[
"BSD-2-Clause"
] | null | null | null |
rest_framework_hmac/hmac_key/models.py
|
nickc92/django-rest-framework-hmac
|
c32e37cf00ef0c13957a6e02eb0a7fabac3d1ac1
|
[
"BSD-2-Clause"
] | null | null | null |
rest_framework_hmac/hmac_key/models.py
|
nickc92/django-rest-framework-hmac
|
c32e37cf00ef0c13957a6e02eb0a7fabac3d1ac1
|
[
"BSD-2-Clause"
] | null | null | null |
import binascii
import os
from django.conf import settings
from django.db import models
from django.utils.translation import ugettext_lazy as _
class HMACKey(models.Model):
"""
The default HMACKey model that can auto generate a
key/secret for HMAC Auth via a signal
"""
def generate_key():
"""
Returns a 40 character hex string based on binary random data
"""
return binascii.hexlify(os.urandom(20)).decode()
key = models.CharField(
_("Key"), primary_key=True, max_length=40, default=generate_key)
secret = models.CharField(
_("Secret"), max_length=40, default=generate_key)
user = models.OneToOneField(
settings.AUTH_USER_MODEL, related_name='hmac_key',
on_delete=models.CASCADE, verbose_name=_("User")
)
nonce = models.BigIntegerField(default=1)
created = models.DateTimeField(_("Created"), auto_now_add=True)
class Meta:
# Only create a DB table for this Model if this app is registered
abstract = 'rest_framework_hmac.hmac_key' \
not in settings.INSTALLED_APPS
verbose_name = _("HMACKey")
verbose_name_plural = _("HMACKey")
def __str__(self):
return self.key
| 30.925
| 73
| 0.673403
| 157
| 1,237
| 5.095541
| 0.541401
| 0.0375
| 0.0275
| 0.045
| 0.0725
| 0.0725
| 0
| 0
| 0
| 0
| 0
| 0.009504
| 0.234438
| 1,237
| 39
| 74
| 31.717949
| 0.835269
| 0.173808
| 0
| 0
| 0
| 0
| 0.071502
| 0.028601
| 0
| 0
| 0
| 0
| 0
| 1
| 0.08
| false
| 0
| 0.2
| 0.04
| 0.64
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cc196deafcaa7796f8d6ee97d9294d3efde7f3
| 6,222
|
py
|
Python
|
test/conftest.py
|
Geoiv/river
|
d013985145c09f263172b054819e811689002ae9
|
[
"MIT"
] | null | null | null |
test/conftest.py
|
Geoiv/river
|
d013985145c09f263172b054819e811689002ae9
|
[
"MIT"
] | 2
|
2021-02-10T22:44:36.000Z
|
2021-04-09T22:36:41.000Z
|
test/conftest.py
|
Geoiv/river
|
d013985145c09f263172b054819e811689002ae9
|
[
"MIT"
] | 1
|
2021-08-24T21:55:34.000Z
|
2021-08-24T21:55:34.000Z
|
import os
from tempfile import NamedTemporaryFile
import boto3
from moto import mock_s3
import pandas as pd
import pandavro as pdx
import pickle
import pytest
@pytest.fixture(autouse=True, scope='session')
def aws_credentials():
"""
Sets AWS credentials to invalid values. Applied to all test functions and
scoped to the entire testing session, so there's no chance of interfering
with production buckets.
"""
os.environ['AWS_ACCESS_KEY_ID'] = 'testing'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'testing'
os.environ['AWS_SECURITY_TOKEN'] = 'testing'
os.environ['AWS_SESSION_TOKEN'] = 'testing'
@pytest.fixture
def test_bucket():
"""Universal bucket name for use throughout testing"""
return 'test_bucket'
@pytest.fixture
def test_keys():
"""List of keys to be used for populating a bucket with empty objects"""
return sorted([
'test_key_0.csv',
'folder0/test_key_1.pq',
'folder1/test_key_2.pkl',
'folder1/subfolder0/test_key_3.pkl',
'folder2/'
])
@pytest.fixture
def test_df_keys():
"""List of keys to be used for populating a bucket with DataFrames"""
return {
'avro': ['df.avro'],
'csv': ['df.csv'],
'csv.gz': ['df.csv.gz'],
'csv.zip': ['df.csv.zip'],
'csv.bz2': ['df.csv.bz2'],
'csv.xz': ['df.csv.xz'],
'psv': ['df.psv'],
'psv.gz': ['df.psv.gz'],
'psv.zip': ['df.psv.zip'],
'psv.bz2': ['df.psv.bz2'],
'psv.xz': ['df.psv.xz'],
'feather': ['df.feather'],
'json': ['df.json'],
'pkl': ['df.pkl', 'df.pickle'],
'pq': ['df.pq', 'df.parquet']
}
@pytest.fixture
def test_df():
"""
Universal dataframe for use throughout testing. Multiple data types
used to test for proper encoding/decoding.
"""
return pd.DataFrame({
'intcol': [1, 2, 3],
'strcol': ['four', 'five', 'six'],
'floatcol': [7.0, 8.5, 9.0]
})
@pytest.fixture
def mock_s3_client():
"""Mocks all s3 connections in any test or fixture that includes it"""
with mock_s3():
yield
@pytest.fixture
def setup_bucket_w_contents(mock_s3_client, test_bucket, test_keys):
"""
Sets up a bucket with objects containing the empty string, based off
keys in 'test_keys'
"""
s3 = boto3.client('s3')
s3.create_bucket(Bucket=test_bucket)
for key in test_keys:
s3.put_object(Bucket=test_bucket, Key=key, Body='')
yield
@pytest.fixture
def setup_bucket_wo_contents(mock_s3_client, test_bucket):
"""Sets up a bucket with no contents."""
s3 = boto3.client('s3')
s3.create_bucket(Bucket=test_bucket)
yield
@pytest.fixture
def setup_bucket_w_dfs(mock_s3_client, test_bucket, test_df, test_df_keys):
"""
Sets up a bucket populated with dataframes that contain the data as
defined in 'test_df', at the keys and storage formats defined in
'test_df_keys'
"""
s3 = boto3.client('s3')
s3.create_bucket(Bucket=test_bucket)
for key in test_df_keys['avro']:
with NamedTemporaryFile() as tmpfile:
pdx.to_avro(tmpfile, test_df)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['csv']:
with NamedTemporaryFile() as tmpfile:
test_df.to_csv(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['csv.gz']:
with NamedTemporaryFile(suffix='.csv.gz') as tmpfile:
test_df.to_csv(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['csv.zip']:
with NamedTemporaryFile(suffix='.csv.zip') as tmpfile:
test_df.to_csv(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['csv.bz2']:
with NamedTemporaryFile(suffix='.csv.bz2') as tmpfile:
test_df.to_csv(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['csv.xz']:
with NamedTemporaryFile(suffix='.csv.xz') as tmpfile:
test_df.to_csv(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['psv']:
with NamedTemporaryFile() as tmpfile:
test_df.to_csv(tmpfile.name, index=False, sep='|')
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['psv.gz']:
with NamedTemporaryFile(suffix='.psv.gz') as tmpfile:
test_df.to_csv(tmpfile.name, index=False, sep='|')
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['psv.zip']:
with NamedTemporaryFile(suffix='.psv.zip') as tmpfile:
test_df.to_csv(tmpfile.name, index=False, sep='|')
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['psv.bz2']:
with NamedTemporaryFile(suffix='.psv.bz2') as tmpfile:
test_df.to_csv(tmpfile.name, index=False, sep='|')
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['psv.xz']:
with NamedTemporaryFile(suffix='.psv.xz') as tmpfile:
test_df.to_csv(tmpfile.name, index=False, sep='|')
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['feather']:
with NamedTemporaryFile() as tmpfile:
test_df.to_feather(tmpfile.name)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['json']:
with NamedTemporaryFile() as tmpfile:
test_df.to_json(tmpfile.name)
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['pkl']:
with NamedTemporaryFile() as tmpfile:
pickle.dump(test_df, tmpfile, protocol=pickle.HIGHEST_PROTOCOL)
tmpfile.flush()
s3.upload_file(tmpfile.name, test_bucket, key)
for key in test_df_keys['pq']:
with NamedTemporaryFile() as tmpfile:
test_df.to_parquet(tmpfile.name, index=False)
s3.upload_file(tmpfile.name, test_bucket, key)
yield
| 31.907692
| 77
| 0.635969
| 868
| 6,222
| 4.381336
| 0.172811
| 0.056797
| 0.047331
| 0.050486
| 0.531948
| 0.505916
| 0.472259
| 0.424139
| 0.424139
| 0.424139
| 0
| 0.013031
| 0.235294
| 6,222
| 194
| 78
| 32.072165
| 0.786255
| 0.128576
| 0
| 0.373134
| 0
| 0
| 0.116152
| 0.018261
| 0
| 0
| 0
| 0
| 0
| 1
| 0.067164
| false
| 0
| 0.059701
| 0
| 0.156716
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cfd36241688b520b69fa546395cf4b9423fe79
| 779
|
py
|
Python
|
code/contours_sorting_by_area.py
|
Asadullah-Dal17/contours-detection-advance
|
45522492363cc01cb8c66b18790b1859c4efe44d
|
[
"MIT"
] | 1
|
2021-12-12T12:17:11.000Z
|
2021-12-12T12:17:11.000Z
|
code/contours_sorting_by_area.py
|
Asadullah-Dal17/contours-detection-advance
|
45522492363cc01cb8c66b18790b1859c4efe44d
|
[
"MIT"
] | null | null | null |
code/contours_sorting_by_area.py
|
Asadullah-Dal17/contours-detection-advance
|
45522492363cc01cb8c66b18790b1859c4efe44d
|
[
"MIT"
] | null | null | null |
import cv2 as cv
import numpy as np
def areaFinder(contours):
areas = []
for c in contours:
a =cv.contourArea(c)
areas.append(a)
return areas
def sortedContoursByArea(img, larger_to_smaller=True):
edges_img = cv.Canny(img, 100, 150)
contours , h = cv.findContours(edges_img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
sorted_contours = sorted(contours, key=cv.contourArea, reverse=larger_to_smaller)
return sorted_contours
img = cv.imread('./Images/sample-image.png')
sorted_contours = sortedContoursByArea(img, larger_to_smaller=True)
# print(areaFinder(contours))
print(areaFinder(sorted_contours))
for c in sorted_contours:
cv.drawContours(img, c, -1, 244, 3)
cv.imshow('img', img)
cv.waitKey(0)
cv.destroyAllWindows()
| 35.409091
| 88
| 0.724005
| 109
| 779
| 5.027523
| 0.46789
| 0.153285
| 0.082117
| 0.113139
| 0.153285
| 0.153285
| 0
| 0
| 0
| 0
| 0
| 0.019939
| 0.16303
| 779
| 22
| 89
| 35.409091
| 0.820552
| 0.03466
| 0
| 0
| 0
| 0
| 0.037284
| 0.033289
| 0
| 0
| 0
| 0
| 0
| 1
| 0.095238
| false
| 0
| 0.095238
| 0
| 0.285714
| 0.047619
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72cfd74acaa21b51c8cdcd979a394eceb3c1b59d
| 1,813
|
py
|
Python
|
matchzoo/metrics/precision.py
|
ChrisRBXiong/MatchZoo-py
|
8883d0933a62610d71fec0215dce643630e03b1c
|
[
"Apache-2.0"
] | 468
|
2019-07-03T02:43:52.000Z
|
2022-03-30T05:51:03.000Z
|
matchzoo/metrics/precision.py
|
ChrisRBXiong/MatchZoo-py
|
8883d0933a62610d71fec0215dce643630e03b1c
|
[
"Apache-2.0"
] | 126
|
2019-07-04T15:51:57.000Z
|
2021-07-31T13:14:40.000Z
|
matchzoo/metrics/precision.py
|
ChrisRBXiong/MatchZoo-py
|
8883d0933a62610d71fec0215dce643630e03b1c
|
[
"Apache-2.0"
] | 117
|
2019-07-04T11:31:08.000Z
|
2022-03-18T12:21:32.000Z
|
"""Precision for ranking."""
import numpy as np
from matchzoo.engine.base_metric import (
BaseMetric, sort_and_couple, RankingMetric
)
class Precision(RankingMetric):
"""Precision metric."""
ALIAS = 'precision'
def __init__(self, k: int = 1, threshold: float = 0.):
"""
:class:`PrecisionMetric` constructor.
:param k: Number of results to consider.
:param threshold: the label threshold of relevance degree.
"""
self._k = k
self._threshold = threshold
def __repr__(self) -> str:
""":return: Formated string representation of the metric."""
return f"{self.ALIAS}@{self._k}({self._threshold})"
def __call__(self, y_true: np.array, y_pred: np.array) -> float:
"""
Calculate precision@k.
Example:
>>> y_true = [0, 0, 0, 1]
>>> y_pred = [0.2, 0.4, 0.3, 0.1]
>>> Precision(k=1)(y_true, y_pred)
0.0
>>> Precision(k=2)(y_true, y_pred)
0.0
>>> Precision(k=4)(y_true, y_pred)
0.25
>>> Precision(k=5)(y_true, y_pred)
0.2
:param y_true: The ground true label of each document.
:param y_pred: The predicted scores of each document.
:return: Precision @ k
:raises: ValueError: len(r) must be >= k.
"""
if self._k <= 0:
raise ValueError(f"k must be greater than 0."
f"{self._k} received.")
coupled_pair = sort_and_couple(y_true, y_pred)
precision = 0.0
for idx, (label, score) in enumerate(coupled_pair):
if idx >= self._k:
break
if label > self._threshold:
precision += 1.
return precision / self._k
| 30.216667
| 68
| 0.539989
| 226
| 1,813
| 4.137168
| 0.362832
| 0.042781
| 0.032086
| 0.053476
| 0.070588
| 0.047059
| 0.047059
| 0.047059
| 0
| 0
| 0
| 0.0266
| 0.336459
| 1,813
| 59
| 69
| 30.728814
| 0.750623
| 0.385549
| 0
| 0
| 0
| 0
| 0.10262
| 0.04476
| 0
| 0
| 0
| 0
| 0
| 1
| 0.130435
| false
| 0
| 0.086957
| 0
| 0.391304
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72d0cee317d77c14ae420378473b099449564673
| 4,168
|
py
|
Python
|
src/main/py/ltprg/config/seq.py
|
forkunited/ltprg
|
4e40d3571d229023df0f845c68643024e04bc202
|
[
"MIT"
] | 11
|
2017-08-03T15:42:19.000Z
|
2021-02-04T12:43:35.000Z
|
src/main/py/ltprg/config/seq.py
|
forkunited/ltprg
|
4e40d3571d229023df0f845c68643024e04bc202
|
[
"MIT"
] | null | null | null |
src/main/py/ltprg/config/seq.py
|
forkunited/ltprg
|
4e40d3571d229023df0f845c68643024e04bc202
|
[
"MIT"
] | 1
|
2021-02-04T12:43:37.000Z
|
2021-02-04T12:43:37.000Z
|
from mung.torch_ext.eval import Loss
from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput
from ltprg.model.seq import VariableLengthNLLLoss
# Expects config of the form:
# {
# data_parameter : {
# seq : [SEQUENCE PARAMETER NAME]
# input : [INPUT PARAMETER NAME]
# }
# name : [ID FOR MODEL]
# arch_type : [SequenceModelNoInput|SequenceModelInputToHidden]
# dropout : [DROPOUT]
# rnn_layers : [RNN_LAYERS]
# rnn_size : [SIZE OF RNN HIDDEN LAYER]
# embedding_size : [EMBEDDING_SIZE]
# rnn_type : [RNN TYPE]
# (SequenceModelAttendedInput) attn_type : [EMBEDDING|OUTPUT]
# (SequenceModelInputToHidden) conv_input : [INDICATOR OF WHETHER OR NOT TO CONVOLVE THE INPUT]
# (SequenceModelInputToHidden|SequenceModelAttendedInput) conv_kernel : [KERNEL SIZE FOR CONVOLUTION]
# (SequenceModelInputToHidden|SequenceModelAttendedInput) conv_stride : [STRIDE LENGTH FOR CONVOLUTION]
# }
def load_seq_model(config, D, gpu=False):
data_parameter = DataParameter.make(**config["data_parameter"])
seq_field = data_parameter["seq"]
utterance_size = D[seq_field].get_matrix(0).get_feature_set().get_token_count()
dropout = float(config["dropout"])
rnn_layers = int(config["rnn_layers"])
rnn_size = int(config["rnn_size"])
embedding_size = int(config["embedding_size"])
rnn_type = config["rnn_type"]
if config["arch_type"] == "SequenceModelNoInput":
model = SequenceModelNoInput(config["name"], utterance_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type)
elif config["arch_type"] == "SequenceModelAttendedInput":
input_field = data_parameter["input"]
input_size = D[input_field].get_feature_set().get_token_count()
conv_kernel = int(config["conv_kernel"])
conv_stride = int(config["conv_stride"])
attn_type = "EMBEDDING"
if "attn_type" in config:
attn_type = config["attn_type"]
model = SequenceModelAttendedInput(config["name"], utterance_size, input_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type, \
conv_kernel=conv_kernel, conv_stride=conv_stride, attn_type=attn_type)
else:
input_field = data_parameter["input"]
input_size = D[input_field].get_feature_set().get_token_count()
conv_input = False
conv_kernel = 1
conv_stride = 1
if "conv_input" in config:
conv_input = bool(int(config["conv_input"]))
conv_kernel = int(config["conv_kernel"])
conv_stride = int(config["conv_stride"])
model = SequenceModelInputToHidden(config["name"], utterance_size, input_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type, \
conv_input=conv_input, conv_kernel=conv_kernel, conv_stride=conv_stride)
return data_parameter, model
# Expects config of the form:
# {
# data_parameter : {
# seq : [SEQUENCE PARAMETER NAME]
# input : [INPUT PARAMETER NAME]
# },
# evaluations : [
# name : [NAME FOR EVALUATION]
# type : (VariableLengthNLLLoss)
# data : [NAME OF DATA SUBSET]
# (Optional) data_size : [SIZE OF RANDOM SUBET OF DATA TO TAKE]
# ]
# }
def load_evaluations(config, D, gpu=False):
data_parameter = DataParameter.make(**config["data_parameter"])
evaluations = []
loss_criterion = VariableLengthNLLLoss(norm_dim=True)
if gpu:
loss_criterion = loss_criterion.cuda()
for eval_config in config["evaluations"]:
data = D[eval_config["data"]]
if "data_size" in eval_config:
data = data.get_random_subset(int(eval_config["data_size"]))
if eval_config["type"] == "VariableLengthNLLLoss":
loss = Loss(eval_config["name"], data, data_parameter, loss_criterion, norm_dim=True)
evaluations.append(loss)
else:
raise ValueError("Invalid seq evaluation type in config (" + str(eval_config["type"]))
return evaluations
| 43.416667
| 120
| 0.676823
| 476
| 4,168
| 5.661765
| 0.189076
| 0.053061
| 0.031169
| 0.029685
| 0.35436
| 0.337291
| 0.327644
| 0.327644
| 0.299443
| 0.299443
| 0
| 0.000917
| 0.215451
| 4,168
| 95
| 121
| 43.873684
| 0.823242
| 0.25048
| 0
| 0.25
| 0
| 0
| 0.12012
| 0.015682
| 0
| 0
| 0
| 0
| 0
| 1
| 0.035714
| false
| 0
| 0.053571
| 0
| 0.125
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72d67501443e4ca7891e84e39882fcf4f2a78705
| 1,623
|
py
|
Python
|
scripts/game.py
|
davidnegrazis/PyPlayText-Workshop
|
70156b73c1d2ab52daaef839b72450e331ff1e80
|
[
"MIT"
] | null | null | null |
scripts/game.py
|
davidnegrazis/PyPlayText-Workshop
|
70156b73c1d2ab52daaef839b72450e331ff1e80
|
[
"MIT"
] | null | null | null |
scripts/game.py
|
davidnegrazis/PyPlayText-Workshop
|
70156b73c1d2ab52daaef839b72450e331ff1e80
|
[
"MIT"
] | null | null | null |
from sys import exit
# ------------------------------------------------------------------------------
global dev_name
global game_title
dev_name = "" # enter your name in the quotes!
game_title = "" # enter the game title in the quotes!
# ------------------------------------------------------------------------------
# ---------- initial values ----------
# these are used to define the starting values of your game variables
init_health = 100
init_mana = 200
init_boss_health = 50
# ---------- game variables ----------
# these will be used during the game
health = 0
mana = 0
boss_health = 0
# ---------- some useful functions ----------
# initialize game variables
def init():
global health
global mana
health = init_health
mana = init_mana
# game over
def game_over(msg):
print(msg)
print("Play again? (y / n)")
while (True):
choice = input("> ")
if (choice == "y"):
start()
break
elif (choice == "n"):
exit(0)
else:
print("Options: y / n")
# ---------- room definitions ----------
# here is where you'll create the flow of the game!
# room 0: where the game starts
def room_0():
global health
print("This is the first stage of the game. Create a custom description and get coding!")
print("Current health: " + str(health))
choice = input("> ");
if "end" in choice:
game_over("The game is over")
def start():
start_msg = "Now playing " + game_title + " by " + dev_name
print(start_msg)
init()
room_0()
# ---------- game start ----------
start()
| 22.541667
| 93
| 0.51756
| 194
| 1,623
| 4.231959
| 0.42268
| 0.051157
| 0.026797
| 0
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| 0
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| 0
| 0.012195
| 0.242144
| 1,623
| 71
| 94
| 22.859155
| 0.655285
| 0.390018
| 0
| 0.097561
| 0
| 0
| 0.174538
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.097561
| false
| 0
| 0.02439
| 0
| 0.121951
| 0.146341
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72d812548fd737a5e6c2dd14c16ac5901a2c0669
| 1,018
|
py
|
Python
|
src/elections_address_files/commands/zip_files.py
|
gregbunce/assign_vista_pcts_to_sgid_addrpnts
|
c1a3210e68c8c1e94c0b68547d0c26697de77ff7
|
[
"MIT"
] | null | null | null |
src/elections_address_files/commands/zip_files.py
|
gregbunce/assign_vista_pcts_to_sgid_addrpnts
|
c1a3210e68c8c1e94c0b68547d0c26697de77ff7
|
[
"MIT"
] | 1
|
2021-09-01T20:10:29.000Z
|
2021-09-01T20:10:29.000Z
|
src/elections_address_files/commands/zip_files.py
|
gregbunce/assign_vista_pcts_to_sgid_addrpnts
|
c1a3210e68c8c1e94c0b68547d0c26697de77ff7
|
[
"MIT"
] | null | null | null |
import os, zipfile
# Zip files.
def zipfiles(directory):
# File extension to zip.
#ext = ('.gdb', '.csv')
ext = ('.gdb')
# Iterate over all files and check for desired extentions for zipping.
for file in os.listdir(directory):
if file.endswith(ext):
#: Zip it.
input_fgdb_name = file.rsplit( ".", 1)[0]
output_zipped_fgdb_name = "/" + input_fgdb_name + "_gdb.zip"
full_path_to_fgdb = directory + "/" + file
print(" Zipping " + str(full_path_to_fgdb))
outFile = f'{full_path_to_fgdb[0:-4]}_gdb.zip'
gdbName = os.path.basename(full_path_to_fgdb)
with zipfile.ZipFile(outFile,mode='w',compression=zipfile.ZIP_DEFLATED,allowZip64=True) as myzip:
for f in os.listdir(full_path_to_fgdb):
if f[-5:] != '.lock':
myzip.write(os.path.join(full_path_to_fgdb,f),gdbName+'\\'+os.path.basename(f))
else:
continue
| 33.933333
| 109
| 0.570727
| 131
| 1,018
| 4.221374
| 0.458015
| 0.086799
| 0.108499
| 0.151899
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009818
| 0.299607
| 1,018
| 29
| 110
| 35.103448
| 0.765778
| 0.130648
| 0
| 0
| 0
| 0
| 0.076223
| 0.037543
| 0
| 0
| 0
| 0
| 0
| 1
| 0.058824
| false
| 0
| 0.058824
| 0
| 0.117647
| 0.058824
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72dca5ec94eec75c7728a1bea9a137060f5e6849
| 5,097
|
py
|
Python
|
mars/services/web/tests/test_core.py
|
yuyiming/mars
|
5e6990d1ea022444dd646c56697e596ef5d7e747
|
[
"Apache-2.0"
] | 1
|
2022-02-24T08:39:26.000Z
|
2022-02-24T08:39:26.000Z
|
mars/services/web/tests/test_core.py
|
yuyiming/mars
|
5e6990d1ea022444dd646c56697e596ef5d7e747
|
[
"Apache-2.0"
] | null | null | null |
mars/services/web/tests/test_core.py
|
yuyiming/mars
|
5e6990d1ea022444dd646c56697e596ef5d7e747
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import os
import sys
import pytest
from tornado import httpclient
from .... import oscar as mo
from ....utils import get_next_port
from .. import WebActor, web_api, MarsServiceWebAPIHandler, MarsWebAPIClientMixin
from ..api.web import MarsApiEntryHandler
class TestAPIHandler(MarsServiceWebAPIHandler):
__test__ = False
_root_pattern = "/api/test/(?P<test_id>[^/]+)"
@web_api("", method="get")
def get_method_root(self, test_id):
self.write(f"get_root_value_{test_id}")
@web_api("", method="post")
def post_method_root(self, test_id):
self.write(f"post_root_value_{test_id}")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get")
def get_method_sub_patt(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get", arg_filter={"action": "a1"})
async def get_method_sub_patt_match_arg1(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}_action1")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get", arg_filter={"action": "a2"})
async def get_method_sub_patt_match_arg2(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}_action2")
@web_api("subtest_error", method="get")
def get_with_error(self, test_id):
raise ValueError
@web_api("subtest_delay", method="get")
async def get_with_timeout(self, test_id):
await asyncio.sleep(100)
raise ValueError(test_id)
@pytest.fixture
async def actor_pool():
start_method = (
os.environ.get("POOL_START_METHOD", "forkserver")
if sys.platform != "win32"
else None
)
pool = await mo.create_actor_pool(
"127.0.0.1", n_process=0, subprocess_start_method=start_method
)
async with pool:
web_config = {
"host": "127.0.0.1",
"port": get_next_port(),
"web_handlers": {
"/api": MarsApiEntryHandler,
TestAPIHandler.get_root_pattern(): TestAPIHandler,
},
"extra_discovery_modules": ["mars.services.web.tests.extra_handler"],
}
await mo.create_actor(WebActor, web_config, address=pool.external_address)
yield pool, web_config["port"]
class SimpleWebClient(MarsWebAPIClientMixin):
async def fetch(self, path, method="GET", **kwargs):
return await self._request_url(method, path, **kwargs)
@pytest.mark.asyncio
async def test_web_api(actor_pool):
_pool, web_port = actor_pool
recorded_urls = []
def url_recorder(request):
recorded_urls.append(request.url)
return request
client = SimpleWebClient()
client.request_rewriter = url_recorder
res = await client.fetch(f"http://localhost:{web_port}/")
assert res.body.decode()
res = await client.fetch(f"http://localhost:{web_port}/api")
assert res.body.decode()
res = await client.fetch(f"http://localhost:{web_port}/api/test/test_id")
assert res.body.decode() == "get_root_value_test_id"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id", method="POST", data=b""
)
assert res.body.decode() == "post_root_value_test_id"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid?action=a1"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid_action1"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid?action=a2"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid_action2"
with pytest.raises(httpclient.HTTPError) as excinfo:
await client.fetch(f"http://localhost:{web_port}/api/test/test_id/non_exist")
assert excinfo.value.code == 404
with pytest.raises(ValueError):
await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest_error"
)
with pytest.raises(TimeoutError):
await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest_delay",
request_timeout=0.5,
)
res = await client.fetch(f"http://localhost:{web_port}/api/extra_test")
assert "Test" in res.body.decode()
assert len(recorded_urls) > 0
| 33.754967
| 88
| 0.679419
| 707
| 5,097
| 4.649222
| 0.261669
| 0.049285
| 0.043505
| 0.056891
| 0.404624
| 0.378765
| 0.368421
| 0.350776
| 0.332522
| 0.310009
| 0
| 0.011157
| 0.191093
| 5,097
| 150
| 89
| 33.98
| 0.786078
| 0.111634
| 0
| 0.07619
| 0
| 0
| 0.267612
| 0.10988
| 0
| 0
| 0
| 0
| 0.095238
| 1
| 0.047619
| false
| 0
| 0.085714
| 0
| 0.190476
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72dd8b3d7047f515b38a96161e263e0136b29c7e
| 3,419
|
py
|
Python
|
test/test.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
test/test.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
test/test.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
"""
例子为MNIST，对手写图片进行分类。
神经网络hello world。
"""
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# 封装网络用到的API
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x,
W,
strides= [1, 1, 1, 1],
padding= 'SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,
ksize= [1, 2, 2, 1],
strides= [1, 2, 2, 1],
padding='SAME')
"""
MNIST进阶
"""
sess = tf.InteractiveSession()
# [batch_size, 784]
x = tf.placeholder('float', shape=[None, 784])
y_ = tf.placeholder('float', shape=[None, 10])
"""
第一层卷积
"""
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
# [batch_size, 28, 28, 1]
x_image = tf.reshape(x, [-1, 28, 28, 1])
# [batch_size, 28, 28, 32]
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
# [batch_size, 14, 14, 32]
h_pool1 = max_pool_2x2(h_conv1)
"""
第二层卷积
"""
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
# [batch_size, 14, 14, 64]
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
# [batch_size, 7, 7, 64]
h_pool2 = max_pool_2x2(h_conv2)
"""
全连接层
"""
w_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
# [batch_size, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# [batch_size, 1024]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
"""
dropout
"""
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
"""
输出层
"""
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
# [batch_size, 10]
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_sum = tf.reduce_sum(y_conv[0])
# 计算损失和添加优化器
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 评估模型
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 初始化
sess.run(tf.initialize_all_variables())
for i in range(1):
batch = mnist.train.next_batch(50)
# train_accuracy = accuracy.eval(feed_dict={x:batch[0],
# y_: batch[1],
# keep_prob: 1.0})
# print("step %d, training accuracy %g" % (i, train_accuracy))
y_conv_re = y_conv.eval(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 1.0})
# print(y_conv_re.shape)
print(y_conv_re)
y_sum_re = y_sum.eval(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 1.0})
print(y_sum_re)
train_step.run(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 0.5})
print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0}))
| 24.956204
| 74
| 0.563908
| 495
| 3,419
| 3.652525
| 0.262626
| 0.044801
| 0.024889
| 0.028761
| 0.181416
| 0.106748
| 0.106748
| 0.08573
| 0.08573
| 0.08573
| 0
| 0.071076
| 0.288096
| 3,419
| 136
| 75
| 25.139706
| 0.671734
| 0.157356
| 0
| 0.142857
| 0
| 0
| 0.019473
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.063492
| false
| 0
| 0.047619
| 0.031746
| 0.174603
| 0.047619
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72df31fd0b80ac5547d308a5a1ccd1a315222eb8
| 7,607
|
py
|
Python
|
Camvid/CamVid_utlis.py
|
Water2style/FCN-pytorch-CanRun
|
b2994f98930580cd2c32f58d19f94becb68a3ccb
|
[
"MIT"
] | 7
|
2019-03-17T15:58:44.000Z
|
2022-01-28T20:06:38.000Z
|
Camvid/CamVid_utlis.py
|
cenchaojun/FCN-pytorch-CanRun
|
364d42590c592bed77a760b0a567ccffe93f59bb
|
[
"MIT"
] | null | null | null |
Camvid/CamVid_utlis.py
|
cenchaojun/FCN-pytorch-CanRun
|
364d42590c592bed77a760b0a567ccffe93f59bb
|
[
"MIT"
] | 1
|
2019-11-04T06:42:05.000Z
|
2019-11-04T06:42:05.000Z
|
# -*- coding: utf-8 -*-
from __future__ import print_function
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import scipy.misc
import random
import os
import imageio
#############################
# global variables #
#############################
root_dir = "/home/water/DATA/camvid-master"
data_dir = os.path.join(root_dir, "701_StillsRaw_full") # train data
label_dir = os.path.join(root_dir, "LabeledApproved_full") # train label
label_colors_file = os.path.join(root_dir, "label_colors.txt") # color to label
val_label_file = os.path.join(root_dir, "val.csv") # validation file
train_label_file = os.path.join(root_dir, "train.csv") # train file
# create dir for label index
label_idx_dir = os.path.join(root_dir, "Labeled_idx")
if not os.path.exists(label_idx_dir):
os.makedirs(label_idx_dir)
label2color = {}
color2label = {}
label2index = {}
index2label = {}
def divide_train_val(val_rate=0.1, shuffle=True, random_seed=None):
data_list = os.listdir(data_dir) #返回这个目录里，所有内容，‘图1’‘，图2’......
data_len = len(data_list) #702个图片 #注意这里是训练集
val_len = int(data_len * val_rate) #训练集700张，分10%的数量给验证集
if random_seed: #设置随机种子
random.seed(random_seed) #看看后面哪里用
if shuffle:
#sample(seq, n) 从序列seq中选择n个随机且独立的元素
data_idx = random.sample(range(data_len), data_len)
# data_idx 是从0到702 随机排序的数组
else:
data_idx = list(range(data_len)) #这个就是从0到702 依次排序
val_idx = [data_list[i] for i in data_idx[:val_len]] # 前70个，图片名 List
train_idx = [data_list[i] for i in data_idx[val_len:]] # 71到702个
# !创建 create val.csv
# "w"打开一个文件只用于写入。如果该文件已存在则打开文件，
# 并从开头开始编辑，即原有内容会被删除。
# 如果该文件不存在，创建新文件。
v = open(val_label_file, "w")
v.write("img,label\n") #write() 方法用于向文件中写入指定字符串
for idx, name in enumerate(val_idx):
if 'png' not in name: ##跳过损坏文件
continue
img_name = os.path.join(data_dir, name)
lab_name = os.path.join(label_idx_dir, name)
lab_name = lab_name.split(".")[0] + "_L.png.npy"
v.write("{},{}\n".format(img_name, lab_name))
#最后生成了一个.csv文件，位于根目录
## 装的信息是： 2列，一列是验证集，70张 生图路径+名字，第二列是验证集对应的：标签图+名字+.npy
#png.npy ：后面parse_label函数，就是在标签图路径里 生成 标签图+名字+.npy 文件！！！
# create train.csv 所以这2个.csv文件，这里存放的是信息 ，是： 生图信息和标签图+npy信息
t = open(train_label_file, "w")
t.write("img,label\n")
for idx, name in enumerate(train_idx):
if 'png' not in name:
continue
img_name = os.path.join(data_dir, name)
lab_name = os.path.join(label_idx_dir, name)
lab_name = lab_name.split(".")[0] + "_L.png.npy"
t.write("{},{}\n".format(img_name, lab_name))
#parse：分析 分析标签
def parse_label():
# change label to class index
#“r”：以只读方式打开文件。文件的指针将会放在文件的开头。这是默认模式。
#label_colors.txt :！！装的是颜色和对应标签 64 128 64\tAnimal 颜色\t类别
# 只读，读好了之后 #不igore 就会bug
f = open(label_colors_file, "r").read().split("\n")[:-1] # ignore the last empty line
for idx, line in enumerate(f):
label = line.split()[-1] #提取所有label形成一个字符串 #动物，人，墙..
color = tuple([int(x) for x in line.split()[:-1]]) #形成一个元组 对应动物，人，墙..
#的颜色，比如动物的颜色是红色 ：[128,0,0]....
print(label, color)
#d[key] = value
#设置d[key]的值为value，如果该key不存在，则为新增
#label2color[label] = color 运行后：
#就形成了1个字典： 以label做key，以color做value的新字典
#包含内容：{'Animal': (64, 128, 64), 'Archway': (192, 0, 128).....}
#后面有精彩用法....
label2color[label] = color
color2label[color] = label #{颜色：标签}
label2index[label] = idx # {标签：idx} {'Animal': 0, 'Archway': 1...}
index2label[idx] = label # {idx:标签}
#下面是作者自己标注的：
# rgb = np.zeros((255, 255, 3), dtype=np.uint8)
# rgb[..., 0] = color[0]
# rgb[..., 1] = color[1]
# rgb[..., 2] = color[2]
# imshow(rgb, title=label)
#enumerate ：迭代器，0号，内容0;1号，内容1
for idx, name in enumerate(os.listdir(label_dir)): #os.listdir(label_dir) 是标签集里所有图片
#idx就是从0开始的序号 name是图片名 #os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表，这个列表以字母顺序。
filename = os.path.join(label_idx_dir, name) # labeled_idx/所有图片名
if os.path.exists(filename + '.npy'): #检查是否有图片名.png.npy，当前应该是没有的
print("Skip %s" % (name)) #有了就跳过这个图 npy是numpy文件
continue
print("Parse %s" % (name)) ## 打出：Parse 图片名（不包含路径）
img = os.path.join(label_dir, name) ## img是路径，LabeledApproved_full/所有图片名
## 区分一下 和 filename之间的用法和关联？
img = imageio.imread(img) #用numpy(npy)格式打开一个图
height, weight, _ = img.shape # numpy存储图片格式（高，宽，3通道）
#Tensor是（3，高，宽）
#在大for循环里，对每一张图执行下面操作 img是上面读取的一个npy格式的图哈
idx_mat = np.zeros((height, weight)) #720*960
for h in range(height):
for w in range(weight): #前面也有个color啊，不同作用域功能不同
color = tuple(img[h, w]) # tuple(序列)，把序列转为元组
#这里应该是把img[h,w]这个！像素点！（128,64,64）
# 抓出来弄成了一个元组，又因为遍历
#所以color是一个有 height*weight个元素的tuple
#color包含着这个图片里，所有的颜色
try: #try，except： 异常检测，try里顺序执行，如果，去执行except
#tuple类型的color在这里作为key，输出相应的value，也就是label值，dict的存储是一一对应的
#所以 出来的label是和输入的color 一一对应
label = color2label[color] # 给彩图像素点，返回像素点的label，就像是上面那图里只有猫和北京，返回：cat space
index = label2index[label] # 给label返回类型代表的号码，给cat sapce，返回1,5
idx_mat[h, w] = index #构成了一个由颜色到标签到标签序号处理后的图，一个点一个点送？
except:
print("error: img:%s, h:%d, w:%d" % (name, h, w))
idx_mat = idx_mat.astype(np.uint8) #转换数据类型
np.save(filename, idx_mat) #numpy.save(file, arr, allow_pickle=True, fix_imports=True)
#把当前（因为这个for里是逐像素点处理一张图）这个图的信息(numpy)存起来
print("Finish %s" % (name))
#跳出for，这个位置就是处理好了所有的图，生成了702个 png.npy图
#生成的这个是一个numpy图，每个图上，是标记好的序号
#就像 一个张图里是 建筑和空白，建筑位置上显示：4，4 = buildings标签 = buildings颜色[128,0,0]
# test some pixels' label ～～～～～～～～～～～～～～～～～～～～～～～～～～`
#img = os.path.join(label_dir, os.listdir(label_dir)[0]) #img数据：img[height,weight,rgb]
#img = imageio.imread(img)
#test_cases = [(555, 405), (0, 0), (380, 645), (577, 943)] # img[555,405]:此图此点的！位置信息！
#test_ans = ['Car', 'Building', 'Truck_Bus', 'Car'] #这个是肉眼去看哈，看上面的位置，对应的是啥label
#for idx, t in enumerate(test_cases):
#color = img[t] #相当于访问 img上的4个点的位置信息，输出的是这4个点对应的像素值(img是labeled，就那32个规整的颜色)
#assert color2label[tuple(color)] == test_ans[idx] ##检查一下对不对
#上面是作者乱标的，所以报错，我在jupyter通过肉眼看图并且调试，就对了哈！！
'''debug function'''
def imshow(img, title=None):
try:
img = mpimg.imread(img) #mpimg： matplotlib.image 输入的img是个地址哈，不是啥处理后的numpy数组
imgplot = plt.imshow(img)
except:
plt.imshow(img, interpolation='nearest')
if title is not None:
plt.title(title)
plt.show()
if __name__ == '__main__':
print("it starts working")
divide_train_val(random_seed=1)
parse_label()
print("process finished")
| 40.897849
| 102
| 0.574471
| 927
| 7,607
| 4.581446
| 0.389428
| 0.021191
| 0.03061
| 0.019779
| 0.151401
| 0.136567
| 0.08971
| 0.059336
| 0.059336
| 0.059336
| 0
| 0.029423
| 0.285132
| 7,607
| 186
| 103
| 40.897849
| 0.751563
| 0.384909
| 0
| 0.154639
| 0
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| 0.06257
| 0.00668
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030928
| false
| 0
| 0.082474
| 0
| 0.113402
| 0.082474
| 0
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| 0
| null | 0
| 0
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| 0
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| 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72dfdaa4454ede71b658a424efe5fbeaae467461
| 804
|
py
|
Python
|
stream-reasoner/ws_client.py
|
patrik999/AdaptiveStreamReasoningMonitoring
|
7bbfa1a394e0127e0c4ea670a632be216c83faea
|
[
"Apache-2.0"
] | 1
|
2021-04-23T11:37:01.000Z
|
2021-04-23T11:37:01.000Z
|
stream-reasoner/ws_client.py
|
patrik999/AdaptiveStreamReasoningMonitoring
|
7bbfa1a394e0127e0c4ea670a632be216c83faea
|
[
"Apache-2.0"
] | null | null | null |
stream-reasoner/ws_client.py
|
patrik999/AdaptiveStreamReasoningMonitoring
|
7bbfa1a394e0127e0c4ea670a632be216c83faea
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
import websocket
import time
try:
import thread
except ImportError:
import _thread as thread
runs = 100
def on_message(ws, message):
print(message)
def on_error(ws, error):
print(error)
def on_close(ws):
print("### closed ###")
def on_open(ws):
def run(*args):
for i in range(runs):
time.sleep(5)
ws.send("Ping")
time.sleep(1)
ws.close()
print("thread terminating...")
thread.start_new_thread(run, ())
if __name__ == "__main__":
websocket.enableTrace(True)
url = "ws://localhost:8082"
ws = websocket.WebSocketApp(url, on_message = on_message, on_error = on_error, on_close = on_close)
ws.on_open = on_open
ws.run_forever()
| 18.697674
| 104
| 0.589552
| 104
| 804
| 4.326923
| 0.451923
| 0.044444
| 0.04
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015679
| 0.28607
| 804
| 42
| 105
| 19.142857
| 0.768293
| 0.024876
| 0
| 0
| 0
| 0
| 0.089069
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.178571
| false
| 0
| 0.178571
| 0
| 0.357143
| 0.142857
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e01bf0a4210399b76b4de5d871a56ed311bc12
| 3,915
|
py
|
Python
|
whole_cell_patch/filterDialog.py
|
11uc/whole_cell_patch
|
84e11bbb904b363a6bb5af878d46e23d789c5be0
|
[
"MIT"
] | 2
|
2021-08-03T13:05:55.000Z
|
2021-08-25T15:03:24.000Z
|
whole_cell_patch/filterDialog.py
|
11uc/whole_cell_patch
|
84e11bbb904b363a6bb5af878d46e23d789c5be0
|
[
"MIT"
] | null | null | null |
whole_cell_patch/filterDialog.py
|
11uc/whole_cell_patch
|
84e11bbb904b363a6bb5af878d46e23d789c5be0
|
[
"MIT"
] | null | null | null |
# Dialogs for setting filter parameters.
from PyQt5.QtWidgets import QLabel, QGridLayout, QPushButton, \
QLineEdit, QVBoxLayout, QHBoxLayout, QDialog, QComboBox, QWidget
from PyQt5.QtCore import pyqtSignal
class FilterDialog(QDialog):
'''
Dialog for choosing filter types.
'''
def __init__(self, default, parent = None):
'''
Build ui and set up parameter setting
Parameters
----------
default: list
List of filters, which are dictionaries with names under
key "name" and parameter elements.
parent: QWidget, optional
Parent widget.
Attributes
----------
fnames: dictionary
Names of filters, two nested dictionaries to specify two
properties about the type of filters.
'''
self.defaultFilters = default
super().__init__(parent)
self.filterCb = QComboBox(self) # Filter type
self.bandCb = QComboBox(self) # Band type
self.fnames = {}
count = 0
for f in default:
names = f["name"].split(',')
if names[0] not in self.fnames:
self.fnames[names[0]] = {}
self.filterCb.addItem(names[0])
if len(names) > 1:
if names[1] not in self.fnames[names[0]]:
self.fnames[names[0]][names[1]] = count
else:
self.fnames[names[0]][''] = count
count += 1
okBtn = QPushButton("OK", self)
cancelBtn = QPushButton("Cancel", self)
okBtn.clicked.connect(self.accept)
cancelBtn.clicked.connect(self.reject)
self.filterCb.currentTextChanged.connect(self.updateBand)
topVB = QVBoxLayout(self)
topVB.addWidget(self.filterCb)
topVB.addWidget(self.bandCb)
topVB.addWidget(okBtn)
topVB.addWidget(cancelBtn)
def updateBand(self, name):
'''
Update list of band in the band combobox.
Parameters
----------
name: str
Name of filter type.
'''
self.bandCb.clear()
self.bandCb.addItems(list(self.fnames[name].keys()))
def exec_(self):
'''
Override QDialog exec_ function. Alter return code to -1 for rejection
and integer number for chosen filter's id.
'''
ret = super().exec_()
if ret:
return self.fnames[self.filterCb.currentText()][
self.bandCb.currentText()]
else:
return -1
class FilterParamDialog(QDialog):
'''
Dialog for setting filter parameters.
'''
def __init__(self, parent = None):
'''
Build ui and set up connections.
Parameters
----------
parent: QWidget, optional
Parent widget.
Attributes
----------
form: dictionary
Parameter names as keys and corresponding QLineEdit object
as values.
formWd: QWidget
Container for displaying the parameter setting form.
'''
super().__init__(parent)
self.form = {}
okBtn = QPushButton("OK", self)
cancelBtn = QPushButton("Cancel", self)
topVB = QVBoxLayout(self)
self.formVB = QVBoxLayout()
self.formWd = None
btnHB = QHBoxLayout()
btnHB.addWidget(okBtn)
btnHB.addWidget(cancelBtn)
cancelBtn.clicked.connect(self.reject)
okBtn.clicked.connect(self.accept)
topVB.addLayout(self.formVB)
topVB.addLayout(btnHB)
def makeForm(self, filt):
'''
Build parameters setting grid layout for filter filt.
Parameters
----------
filt: dictionary
Filter information, parameters are in string format.
'''
# clear the previous form widget
if self.formWd != None:
self.formVB.removeWidget(self.formWd)
self.form = {}
self.formWd.setParent(None)
del self.formWd
self.formWd = None
self.formWd = QWidget()
formGrid = QGridLayout(self.formWd)
row = 0
for k, v in filt.items():
if k != "name":
self.form[k] = QLineEdit(v, self.formWd)
formGrid.addWidget(QLabel(k, self.formWd), row, 0)
formGrid.addWidget(self.form[k], row, 1)
row = row + 1
self.formVB.addWidget(self.formWd)
def getForm(self):
'''
Get the parameters filled in the QLineEdit objects.
Returns
-------
filt: dictionary
Filter information, without name.
'''
filt = {}
for k, v in self.form.items():
filt[k] = v.text()
return filt
| 24.778481
| 72
| 0.678927
| 490
| 3,915
| 5.385714
| 0.297959
| 0.041682
| 0.022736
| 0.024252
| 0.153088
| 0.090944
| 0.058355
| 0.039409
| 0
| 0
| 0
| 0.006001
| 0.191315
| 3,915
| 157
| 73
| 24.936306
| 0.827543
| 0.347126
| 0
| 0.225
| 0
| 0
| 0.010081
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.075
| false
| 0
| 0.025
| 0
| 0.1625
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e01bffe818f26ef544964b5648f4372f9a04d4
| 813
|
py
|
Python
|
projects/controllable_dialogue/tasks/agents.py
|
zl930216/ParlAI
|
abf0ad6d1779af0f8ce0b5aed00d2bab71416684
|
[
"MIT"
] | 41
|
2019-06-07T17:36:10.000Z
|
2021-11-16T06:26:16.000Z
|
projects/controllable_dialogue/tasks/agents.py
|
zl930216/ParlAI
|
abf0ad6d1779af0f8ce0b5aed00d2bab71416684
|
[
"MIT"
] | 316
|
2021-03-19T14:53:31.000Z
|
2022-03-27T03:36:51.000Z
|
projects/controllable_dialogue/tasks/agents.py
|
zl930216/ParlAI
|
abf0ad6d1779af0f8ce0b5aed00d2bab71416684
|
[
"MIT"
] | 11
|
2019-06-06T01:19:08.000Z
|
2020-07-23T07:34:56.000Z
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
from .build import build, make_path
from parlai.utils.misc import warn_once
from parlai.core.teachers import ParlAIDialogTeacher
def _path(opt):
build(opt)
datatype = opt['datatype'].split(':')[0]
if datatype == 'test':
warn_once("WARNING: Test set not included. Setting datatype to valid.")
datatype = 'valid'
return make_path(opt, datatype + '.txt')
class DefaultTeacher(ParlAIDialogTeacher):
def __init__(self, opt, shared=None):
opt = copy.deepcopy(opt)
opt['parlaidialogteacher_datafile'] = _path(opt)
super().__init__(opt, shared)
| 30.111111
| 79
| 0.702337
| 108
| 813
| 5.148148
| 0.611111
| 0.03777
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003044
| 0.191882
| 813
| 26
| 80
| 31.269231
| 0.843227
| 0.233702
| 0
| 0
| 0
| 0
| 0.174475
| 0.045234
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| false
| 0
| 0.25
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e12151f37d1939bde729526720c6ed8432a926
| 4,345
|
py
|
Python
|
Roche.py
|
murbanec/Roche2D
|
a4d7e85e893fd6f18c12b682c2c8ca33b2b549a6
|
[
"MIT"
] | null | null | null |
Roche.py
|
murbanec/Roche2D
|
a4d7e85e893fd6f18c12b682c2c8ca33b2b549a6
|
[
"MIT"
] | null | null | null |
Roche.py
|
murbanec/Roche2D
|
a4d7e85e893fd6f18c12b682c2c8ca33b2b549a6
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 14 10:37:04 2021
@author: martin urbanec
"""
#calculates trajectory of small mass positioned close to L4 Lagrange point
#creates gif as output
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
DistanceJ = 778570000000. # m JUPITER FROM SUN
G = 6.67259*10**-11
Jupiter_mass = 1.8982*10**27 # kg
Sun_mass = 1.989*10**30 # kg
M1=Sun_mass
M2=Jupiter_mass
a=DistanceJ
Ang_vel=math.sqrt(G*(M1+M2)/(a**3)) #FROM KEPLER LAW
P=2.*math.pi/Ang_vel #Period
#center of mass is located at [0,0] massive object (Sun) is located at -r1, secondary object (Jupiter) is located at +r2
r2=M1*a/(M1+M2)
r1=M2*a/(M1+M2)
# Calculations are done in corotating frame
# s1, s2 are distances from sources of gravity (Sun, Jupiter)
def pot(x,y):
r=math.sqrt(x*x + y*y)
if x==0:
if y>0:
theta=math.pi/2.
if y<0:
theta=math.pi/2.
if x>0:
theta=math.atan(abs(y)/x)
else:
theta=math.pi-math.atan(abs(y)/x)
s1=math.sqrt(r1*r1 + r*r + 2.*r1*r*math.cos(theta))
s2=math.sqrt(r2*r2 + r*r - 2.*r2*r*math.cos(theta))
result = -G*(M1/s1 + M2/s2) -1.*Ang_vel*Ang_vel*r*r/2.
return result
#Force per unit mass (acceleration) in x direction
# ax = \partial pot(x,y) / \partial x - 2 \Omega \times v
# in our case \Omega=(0,0,\Omega) and v=(vx,vy,0)
# second term is corresponding to Coriolis force
def ax(x,y,vx,vy):
dx=a/1000.
# result=-(pot(x+dx,y) -pot(x-dx,y))/(2.*dx) + 2.* Ang_vel*vy
result=-(-pot(x+2.*dx,y) + 8.*pot(x+dx,y) - 8.*pot(x-dx,y) + pot(x-2.*dx,y))/(12.*dx) + 2.* Ang_vel*vy
return result
def ay(x,y,vx,vy):
dy=a/1000.
# result=-( pot(x,y+dy)-pot(x,y-dy))/(dy*2.) - 2.* Ang_vel*vx
result=-(-pot(x,y+2.*dy) + 8.*pot(x,y+dy) - 8.*pot(x,y-dy) + pot(x,y-2*dy))/(dy*12.) - 2.* Ang_vel*vx
return result
pot2=np.vectorize(pot)
#TRAJECTORY OF ASTEROID CLOSE STARTING CLOSE TO L4 in rest with respect to the rotating frame
x0=a/2.-r1
y0=math.sqrt(3)*a/2.
x0=1.005*x0
y0=1.005*y0
vx0=0.
vy0=0.
steps=300000
#initialize arrays
x= np.linspace(0, 10, steps)
y= np.linspace(0, 10, steps)
vx=np.linspace(0, 10, steps)
vy=np.linspace(0, 10, steps)
t= np.linspace(0, 10, steps)
x[0]=x0
vx[0]=vx0
y[0]=y0
vy[0]=vy0
t[0]=0.
i=0
timescale = math.sqrt((a*a)**1.5 / G/(M1+M2))
dt=timescale/1000.
#using 4th order Runge-Kutta to solve the a_x= d v_x/ dt
# dt is constant set to timescale/1000
for i in range (1,steps):
t[i]=(t[i-1]+dt)
Kx1=dt*ax(x[i-1],y[i-1],vx[i-1],vy[i-1])
Kx2=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx1/2.,vy[i-1])
Kx3=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx2/2.,vy[i-1])
Kx4=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx3,vy[i-1])
vx[i]=vx[i-1] + Kx1/6. + Kx2/3. + Kx3/3. + Kx4/6.
Ky1=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1])
Ky2=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky1/2.)
Ky3=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky2/2.)
Ky4=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky3)
vy[i]=vy[i-1] + Ky1/6. + Ky2/3. + Ky3/3. + Ky4/6.
x[i]=x[i-1] + (vx[i-1]+vx[i])*dt/2. #taking the average of velocities
y[i]=y[i-1] + (vy[i-1]+vy[i])*dt/2.
dt=timescale/1000.
#LAGRANGE POINTS
#L3, L1 and L2 points are lying on x-axis (left to right) for small values of alpha=M2/(M1+M2) the positions can are given analytically (to first order in alpha)
alpha=M2/(M1+M2)
L1X=a*(1.-(alpha/3.)**(1./3.))
L1Y=0.
P1=pot(L1X,L1Y)
L2X=a*(1.+(alpha/3.)**(1./3.))
L2Y=0.
P2=pot(L2X,L2Y)
L3X=-a*(1. + 5.*alpha/12)
L3Y=0.
P3=pot(L3X,L3Y)
L4X=a/2.-r1
L4Y=math.sqrt(3)*a/2.
P4=pot2(L4X,L4Y)
P0=pot(x0,y0)
steps=301
xx= np.arange(-2*a, 2.*a,a/steps)
yy= np.arange(-1.5*a, 1.5*a,a/steps)
X, Y = np.meshgrid(xx, yy)
Z1=pot2(X,Y)
fig, ax = plt.subplots()
ax.set_aspect('equal','box')
ln1, = plt.plot([],[], 'k+')
ln2, = plt.plot([], [], 'm*')
XXX,YYY=[],[]
def init():
ax.set_xlim(-1.25,1.25)
ax.set_ylim(-1.25,1.25)
ax.contour(X/a, Y/a, Z1,levels=[P1,P2,P3,P0],colors=('r', 'green', 'blue', 'm'))
def update(i):
ln1.set_data(x[1000*i]/a, y[1000*i]/a)
zed= np.arange(60)
ani = FuncAnimation(fig, update, np.arange(300), init_func=init)
plt.show()
writer = PillowWriter(fps=25)
ani.save("Animation.gif", writer=writer)
| 22.630208
| 161
| 0.595397
| 924
| 4,345
| 2.779221
| 0.257576
| 0.030374
| 0.017134
| 0.021417
| 0.190421
| 0.101636
| 0.083723
| 0.066978
| 0.053738
| 0.053738
| 0
| 0.104114
| 0.177675
| 4,345
| 191
| 162
| 22.748691
| 0.61461
| 0.272957
| 0
| 0.06422
| 0
| 0
| 0.011513
| 0
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| 0
| 0
| 0
| 0
| 1
| 0.045872
| false
| 0
| 0.036697
| 0
| 0.110092
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| null | 0
| 0
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| 0
|
1
| 0
|
72e1bd59d28fcd4bceaa6c1453fe80d65e9ccc96
| 5,078
|
py
|
Python
|
youtube_dl/extractor/azubu.py
|
LyleH/youtube-dl
|
7564b09ef5c09454908f78cb91c3bd2d6daacac5
|
[
"Unlicense"
] | null | null | null |
youtube_dl/extractor/azubu.py
|
LyleH/youtube-dl
|
7564b09ef5c09454908f78cb91c3bd2d6daacac5
|
[
"Unlicense"
] | null | null | null |
youtube_dl/extractor/azubu.py
|
LyleH/youtube-dl
|
7564b09ef5c09454908f78cb91c3bd2d6daacac5
|
[
"Unlicense"
] | null | null | null |
from __future__ import unicode_literals
import json
from .common import InfoExtractor
from ..utils import (
ExtractorError,
float_or_none,
sanitized_Request,
)
class AzubuIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?azubu\.tv/[^/]+#!/play/(?P<id>\d+)'
_TESTS = [
{
'url': 'http://www.azubu.tv/GSL#!/play/15575/2014-hot6-cup-last-big-match-ro8-day-1',
'md5': 'a88b42fcf844f29ad6035054bd9ecaf4',
'info_dict': {
'id': '15575',
'ext': 'mp4',
'title': '2014 HOT6 CUP LAST BIG MATCH Ro8 Day 1',
'description': 'md5:d06bdea27b8cc4388a90ad35b5c66c01',
'thumbnail': 're:^https?://.*\.jpe?g',
'timestamp': 1417523507.334,
'upload_date': '20141202',
'duration': 9988.7,
'uploader': 'GSL',
'uploader_id': 414310,
'view_count': int,
},
},
{
'url': 'http://www.azubu.tv/FnaticTV#!/play/9344/-fnatic-at-worlds-2014:-toyz---%22i-love-rekkles,-he-has-amazing-mechanics%22-',
'md5': 'b72a871fe1d9f70bd7673769cdb3b925',
'info_dict': {
'id': '9344',
'ext': 'mp4',
'title': 'Fnatic at Worlds 2014: Toyz - "I love Rekkles, he has amazing mechanics"',
'description': 'md5:4a649737b5f6c8b5c5be543e88dc62af',
'thumbnail': 're:^https?://.*\.jpe?g',
'timestamp': 1410530893.320,
'upload_date': '20140912',
'duration': 172.385,
'uploader': 'FnaticTV',
'uploader_id': 272749,
'view_count': int,
},
'skip': 'Channel offline',
},
]
def _real_extract(self, url):
video_id = self._match_id(url)
data = self._download_json(
'http://www.azubu.tv/api/video/%s' % video_id, video_id)['data']
title = data['title'].strip()
description = data.get('description')
thumbnail = data.get('thumbnail')
view_count = data.get('view_count')
user = data.get('user', {})
uploader = user.get('username')
uploader_id = user.get('id')
stream_params = json.loads(data['stream_params'])
timestamp = float_or_none(stream_params.get('creationDate'), 1000)
duration = float_or_none(stream_params.get('length'), 1000)
renditions = stream_params.get('renditions') or []
video = stream_params.get('FLVFullLength') or stream_params.get('videoFullLength')
if video:
renditions.append(video)
if not renditions and not user.get('channel', {}).get('is_live', True):
raise ExtractorError('%s said: channel is offline.' % self.IE_NAME, expected=True)
formats = [{
'url': fmt['url'],
'width': fmt['frameWidth'],
'height': fmt['frameHeight'],
'vbr': float_or_none(fmt['encodingRate'], 1000),
'filesize': fmt['size'],
'vcodec': fmt['videoCodec'],
'container': fmt['videoContainer'],
} for fmt in renditions if fmt['url']]
self._sort_formats(formats)
return {
'id': video_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'timestamp': timestamp,
'duration': duration,
'uploader': uploader,
'uploader_id': uploader_id,
'view_count': view_count,
'formats': formats,
}
class AzubuLiveIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?azubu\.tv/(?P<id>[^/]+)$'
_TEST = {
'url': 'http://www.azubu.tv/MarsTVMDLen',
'only_matching': True,
}
def _real_extract(self, url):
user = self._match_id(url)
info = self._download_json(
'http://api.azubu.tv/public/modules/last-video/{0}/info'.format(user),
user)['data']
if info['type'] != 'STREAM':
raise ExtractorError('{0} is not streaming live'.format(user), expected=True)
req = sanitized_Request(
'https://edge-elb.api.brightcove.com/playback/v1/accounts/3361910549001/videos/ref:' + info['reference_id'])
req.add_header('Accept', 'application/json;pk=BCpkADawqM1gvI0oGWg8dxQHlgT8HkdE2LnAlWAZkOlznO39bSZX726u4JqnDsK3MDXcO01JxXK2tZtJbgQChxgaFzEVdHRjaDoxaOu8hHOO8NYhwdxw9BzvgkvLUlpbDNUuDoc4E4wxDToV')
bc_info = self._download_json(req, user)
m3u8_url = next(source['src'] for source in bc_info['sources'] if source['container'] == 'M2TS')
formats = self._extract_m3u8_formats(m3u8_url, user, ext='mp4')
self._sort_formats(formats)
return {
'id': info['id'],
'title': self._live_title(info['title']),
'uploader_id': user,
'formats': formats,
'is_live': True,
'thumbnail': bc_info['poster'],
}
| 36.797101
| 200
| 0.551398
| 505
| 5,078
| 5.382178
| 0.362376
| 0.018028
| 0.022075
| 0.020603
| 0.185798
| 0.135394
| 0.049301
| 0.049301
| 0.022075
| 0
| 0
| 0.06883
| 0.29618
| 5,078
| 137
| 201
| 37.065693
| 0.691662
| 0
| 0
| 0.136752
| 0
| 0.025641
| 0.337141
| 0.086648
| 0
| 0
| 0
| 0
| 0
| 1
| 0.017094
| false
| 0
| 0.034188
| 0
| 0.119658
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e2f4f20411bdef4f641e8d7563731afc8c78a7
| 8,157
|
py
|
Python
|
conda_build/main_develop.py
|
dan-blanchard/conda-build
|
2db31bb2c48d2459e16df80172967d906f43b355
|
[
"BSD-3-Clause"
] | null | null | null |
conda_build/main_develop.py
|
dan-blanchard/conda-build
|
2db31bb2c48d2459e16df80172967d906f43b355
|
[
"BSD-3-Clause"
] | null | null | null |
conda_build/main_develop.py
|
dan-blanchard/conda-build
|
2db31bb2c48d2459e16df80172967d906f43b355
|
[
"BSD-3-Clause"
] | null | null | null |
# (c) Continuum Analytics, Inc. / http://continuum.io
# All Rights Reserved
#
# conda is distributed under the terms of the BSD 3-clause license.
# Consult LICENSE.txt or http://opensource.org/licenses/BSD-3-Clause.
from __future__ import absolute_import, division, print_function
import sys
from os.path import join, isdir, abspath, expanduser, exists
import shutil
from conda.cli.common import add_parser_prefix, get_prefix
from conda.cli.conda_argparse import ArgumentParser
from conda_build.main_build import args_func
from conda_build.post import mk_relative_osx
from conda_build.utils import _check_call, rec_glob
from conda.install import linked
def main():
p = ArgumentParser(
description="""
Install a Python package in 'development mode'.
This works by creating a conda.pth file in site-packages."""
# TODO: Use setup.py to determine any entry-points to install.
)
p.add_argument(
'source',
action="store",
metavar='PATH',
nargs='+',
help="Path to the source directory."
)
p.add_argument('-npf', '--no-pth-file',
action='store_true',
help=("Relink compiled extension dependencies against "
"libraries found in current conda env. "
"Do not add source to conda.pth."))
p.add_argument('-b', '--build_ext',
action='store_true',
help=("Build extensions inplace, invoking: "
"python setup.py build_ext --inplace; "
"add to conda.pth; relink runtime libraries to "
"environment's lib/."))
p.add_argument('-c', '--clean',
action='store_true',
help=("Invoke clean on setup.py: "
"python setup.py clean "
"use with build_ext to clean before building."))
p.add_argument('-u', '--uninstall',
action='store_true',
help=("Removes package if installed in 'development mode' "
"by deleting path from conda.pth file. Ignore other "
"options - just uninstall and exit"))
add_parser_prefix(p)
p.set_defaults(func=execute)
args = p.parse_args()
args_func(args, p)
def relink_sharedobjects(pkg_path, build_prefix):
'''
invokes functions in post module to relink to libraries in conda env
:param pkg_path: look for shared objects to relink in pkg_path
:param build_prefix: path to conda environment which contains lib/. to find
runtime libraries.
.. note:: develop mode builds the extensions in place and makes a link to
package in site-packages/. The build_prefix points to conda environment
since runtime libraries should be loaded from environment's lib/. first
'''
# find binaries in package dir and make them relocatable
bin_files = rec_glob(pkg_path, ['.so'])
for b_file in bin_files:
if sys.platform == 'darwin':
mk_relative_osx(b_file, build_prefix)
else:
print("Nothing to do on Linux or Windows.")
def write_to_conda_pth(sp_dir, pkg_path):
'''
Append pkg_path to conda.pth in site-packages directory for current
environment. Only add path if it doens't already exist.
:param sp_dir: path to site-packages/. directory
:param pkg_path: the package path to append to site-packes/. dir.
'''
c_file = join(sp_dir, 'conda.pth')
with open(c_file, 'a') as f:
with open(c_file, 'r') as cf:
# make sure file exists, before we try to read from it hence nested
# in append with block
# expect conda.pth to be small so read it all in at once
pkgs_in_dev_mode = cf.readlines()
# only append pkg_path if it doesn't already exist in conda.pth
if pkg_path + '\n' in pkgs_in_dev_mode:
print("path exits, skipping " + pkg_path)
else:
f.write(pkg_path + '\n')
print("added " + pkg_path)
def get_site_pkg(prefix, py_ver):
'''
Given the path to conda environment, find the site-packages directory
:param prefix: path to conda environment. Look here for current
environment's site-packages
:returns: absolute path to site-packages directory
'''
# get site-packages directory
stdlib_dir = join(prefix, 'Lib' if sys.platform == 'win32' else
'lib/python%s' % py_ver)
sp_dir = join(stdlib_dir, 'site-packages')
return sp_dir
def get_setup_py(path_):
''' Return full path to setup.py or exit if not found '''
# build path points to source dir, builds are placed in the
setup_py = join(path_, 'setup.py')
if not exists(setup_py):
sys.exit("No setup.py found in {0}. Exiting.".format(path_))
return setup_py
def clean(setup_py):
'''
This invokes:
$ python setup.py clean
:param setup_py: path to setup.py
'''
# first call setup.py clean
cmd = ['python', setup_py, 'clean']
_check_call(cmd)
print("Completed: " + " ".join(cmd))
print("===============================================")
def build_ext(setup_py):
'''
Define a develop function - similar to build function
todo: need to test on win32 and linux
It invokes:
$ python setup.py build_ext --inplace
:param setup_py: path to setup.py
'''
# next call setup.py develop
cmd = ['python', setup_py, 'build_ext', '--inplace']
_check_call(cmd)
print("Completed: " + " ".join(cmd))
print("===============================================")
def uninstall(sp_dir, pkg_path):
'''
Look for pkg_path in conda.pth file in site-packages directory and remove
it. If pkg_path is not found in conda.pth, it means package is not
installed in 'development mode' via conda develop.
:param sp_dir: path to site-packages/. directory
:param pkg_path: the package path to be uninstalled.
'''
o_c_pth = join(sp_dir, 'conda.pth')
n_c_pth = join(sp_dir, 'conda.pth.temp')
found = False
with open(n_c_pth, 'w') as new_c:
with open(o_c_pth, 'r') as orig_c:
for line in orig_c:
if line != pkg_path + '\n':
new_c.write(line)
else:
print("uninstalled: " + pkg_path)
found = True
if not found:
print("conda.pth does not contain path: " + pkg_path)
print("package not installed via conda develop")
shutil.move(n_c_pth, o_c_pth)
def execute(args, parser):
prefix = get_prefix(args)
if not isdir(prefix):
sys.exit("""\
Error: environment does not exist: %s
#
# Use 'conda create' to create the environment first.
#""" % prefix)
for package in linked(prefix):
name, ver, _ = package .rsplit('-', 2)
if name == 'python':
py_ver = ver[:3] # x.y
break
else:
raise RuntimeError("python is not installed in %s" % prefix)
# current environment's site-packages directory
sp_dir = get_site_pkg(prefix, py_ver)
for path in args.source:
pkg_path = abspath(expanduser(path))
if args.uninstall:
# uninstall then exit - does not do any other operations
uninstall(sp_dir, pkg_path)
sys.exit(0)
if args.clean or args.build_ext:
setup_py = get_setup_py(pkg_path)
if args.clean:
clean(setup_py)
if not args.build_ext:
sys.exit(0)
# build extensions before adding to conda.pth
if args.build_ext:
build_ext(setup_py)
if not args.no_pth_file:
write_to_conda_pth(sp_dir, pkg_path)
# go through the source looking for compiled extensions and make sure
# they use the conda environment for loading libraries at runtime
relink_sharedobjects(pkg_path, prefix)
print("completed operation for: " + pkg_path)
if __name__ == '__main__':
main()
| 32.891129
| 79
| 0.60868
| 1,102
| 8,157
| 4.35118
| 0.244102
| 0.039416
| 0.035037
| 0.01585
| 0.161001
| 0.111783
| 0.074661
| 0.055474
| 0.044213
| 0.027112
| 0
| 0.001891
| 0.286993
| 8,157
| 247
| 80
| 33.024292
| 0.822558
| 0.301581
| 0
| 0.117647
| 0
| 0
| 0.239803
| 0.017116
| 0
| 0
| 0
| 0.008097
| 0
| 1
| 0.066176
| false
| 0
| 0.073529
| 0
| 0.154412
| 0.088235
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e3326bd28b6a407fd1315276c6cbaaa56add9a
| 494
|
py
|
Python
|
benchmarking/experiments/sanity_check.py
|
ltgoslo/norBERT
|
d75d5c12d9b7f9cc11c65757f2228b7e6070b69b
|
[
"CC0-1.0"
] | 19
|
2021-01-18T13:51:08.000Z
|
2022-03-05T07:32:26.000Z
|
benchmarking/experiments/sanity_check.py
|
ltgoslo/norBERT
|
d75d5c12d9b7f9cc11c65757f2228b7e6070b69b
|
[
"CC0-1.0"
] | 2
|
2021-02-05T16:09:44.000Z
|
2021-06-16T18:56:47.000Z
|
benchmarking/experiments/sanity_check.py
|
ltgoslo/norBERT
|
d75d5c12d9b7f9cc11c65757f2228b7e6070b69b
|
[
"CC0-1.0"
] | 1
|
2021-04-29T20:26:55.000Z
|
2021-04-29T20:26:55.000Z
|
#!/bin/env python3
from transformers import TFBertForTokenClassification
from data_preparation.data_preparation_pos import MBERTTokenizer as MBERT_Tokenizer_pos
import sys
if __name__ == "__main__":
if len(sys.argv) > 1:
modelname = sys.argv[1]
else:
modelname = "ltgoslo/norbert"
model = TFBertForTokenClassification.from_pretrained(modelname, from_pt=True)
tokenizer = MBERT_Tokenizer_pos.from_pretrained(modelname, do_lower_case=False)
print(tokenizer)
| 32.933333
| 87
| 0.769231
| 58
| 494
| 6.206897
| 0.586207
| 0.177778
| 0.094444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007177
| 0.153846
| 494
| 14
| 88
| 35.285714
| 0.854067
| 0.034413
| 0
| 0
| 0
| 0
| 0.048319
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.272727
| 0
| 0.272727
| 0.090909
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e3f9ddf2ff488e4523f7cf3d57f420ea39a9f3
| 6,992
|
py
|
Python
|
mlmodels/model_tch/nbeats/model.py
|
gitter-badger/mlmodels
|
f08cc9b6ec202d4ad25ecdda2f44487da387569d
|
[
"MIT"
] | 1
|
2019-12-10T06:38:08.000Z
|
2019-12-10T06:38:08.000Z
|
mlmodels/model_tch/nbeats/model.py
|
whitetiger1002/mlmodels
|
f70f1da7434e8855eed50adc67b49cc169f2ea24
|
[
"MIT"
] | null | null | null |
mlmodels/model_tch/nbeats/model.py
|
whitetiger1002/mlmodels
|
f70f1da7434e8855eed50adc67b49cc169f2ea24
|
[
"MIT"
] | null | null | null |
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def seasonality_model(thetas, t, device):
p = thetas.size()[-1]
assert p < 10, 'thetas_dim is too big.'
p1, p2 = (p // 2, p // 2) if p % 2 == 0 else (p // 2, p // 2 + 1)
s1 = torch.tensor([np.cos(2 * np.pi * i * t) for i in range(p1)]).float() # H/2-1
s2 = torch.tensor([np.sin(2 * np.pi * i * t) for i in range(p2)]).float()
S = torch.cat([s1, s2])
return thetas.mm(S.to(device))
def trend_model(thetas, t, device):
p = thetas.size()[-1]
assert p <= 4, 'thetas_dim is too big.'
T = torch.tensor([t ** i for i in range(p)]).float()
return thetas.mm(T.to(device))
def linspace(backcast_length, forecast_length):
lin_space = np.linspace(-backcast_length, forecast_length, backcast_length + forecast_length)
b_ls = lin_space[:backcast_length]
f_ls = lin_space[backcast_length:]
return b_ls, f_ls
class Block(nn.Module):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5, share_thetas=False):
super(Block, self).__init__()
self.units = units
self.thetas_dim = thetas_dim
self.backcast_length = backcast_length
self.forecast_length = forecast_length
self.share_thetas = share_thetas
self.fc1 = nn.Linear(backcast_length, units)
self.fc2 = nn.Linear(units, units)
self.fc3 = nn.Linear(units, units)
self.fc4 = nn.Linear(units, units)
self.device = device
self.backcast_linspace, self.forecast_linspace = linspace(backcast_length, forecast_length)
if share_thetas:
self.theta_f_fc = self.theta_b_fc = nn.Linear(units, thetas_dim)
else:
self.theta_b_fc = nn.Linear(units, thetas_dim)
self.theta_f_fc = nn.Linear(units, thetas_dim)
def forward(self, x):
x = F.relu(self.fc1(x.to(self.device)))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
return x
def __str__(self):
block_type = type(self).__name__
return f'{block_type}(units={self.units}, thetas_dim={self.thetas_dim}, ' \
f'backcast_length={self.backcast_length}, forecast_length={self.forecast_length}, ' \
f'share_thetas={self.share_thetas}) at @{id(self)}'
class SeasonalityBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(SeasonalityBlock, self).__init__(units, thetas_dim, device, backcast_length,
forecast_length, share_thetas=True)
def forward(self, x):
x = super(SeasonalityBlock, self).forward(x)
backcast = seasonality_model(self.theta_b_fc(x), self.backcast_linspace, self.device)
forecast = seasonality_model(self.theta_f_fc(x), self.forecast_linspace, self.device)
return backcast, forecast
class TrendBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(TrendBlock, self).__init__(units, thetas_dim, device, backcast_length,
forecast_length, share_thetas=True)
def forward(self, x):
x = super(TrendBlock, self).forward(x)
backcast = trend_model(self.theta_b_fc(x), self.backcast_linspace, self.device)
forecast = trend_model(self.theta_f_fc(x), self.forecast_linspace, self.device)
return backcast, forecast
class GenericBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(GenericBlock, self).__init__(units, thetas_dim, device, backcast_length, forecast_length)
self.backcast_fc = nn.Linear(thetas_dim, backcast_length)
self.forecast_fc = nn.Linear(thetas_dim, forecast_length)
def forward(self, x):
# no constraint for generic arch.
x = super(GenericBlock, self).forward(x)
theta_b = F.relu(self.theta_b_fc(x))
theta_f = F.relu(self.theta_f_fc(x))
backcast = self.backcast_fc(theta_b) # generic. 3.3.
forecast = self.forecast_fc(theta_f) # generic. 3.3.
return backcast, forecast
class NBeatsNet(nn.Module):
SEASONALITY_BLOCK = 'seasonality'
TREND_BLOCK = 'trend'
GENERIC_BLOCK = 'generic'
def __init__(self,
device,
stack_types=[TREND_BLOCK, SEASONALITY_BLOCK],
nb_blocks_per_stack=3,
forecast_length=5,
backcast_length=10,
thetas_dims=[4, 8],
share_weights_in_stack=False,
hidden_layer_units=256, ):
super(NBeatsNet, self).__init__()
self.forecast_length = forecast_length
self.backcast_length = backcast_length
self.hidden_layer_units = hidden_layer_units
self.nb_blocks_per_stack = nb_blocks_per_stack
self.share_weights_in_stack = share_weights_in_stack
self.stack_types = stack_types
self.stacks = []
self.thetas_dim = thetas_dims
self.parameters = []
self.device = device
print(f'| N-Beats')
for stack_id in range(len(self.stack_types)):
self.stacks.append(self.create_stack(stack_id))
self.parameters = nn.ParameterList(self.parameters)
self.to(self.device)
def create_stack(self, stack_id):
stack_type = self.stack_types[stack_id]
print(f'| -- Stack {stack_type.title()} (#{stack_id}) (share_weights_in_stack={self.share_weights_in_stack})')
blocks = []
for block_id in range(self.nb_blocks_per_stack):
block_init = NBeatsNet.select_block(stack_type)
if self.share_weights_in_stack and block_id != 0:
block = blocks[-1] # pick up the last one to make the
else:
block = block_init(self.hidden_layer_units, self.thetas_dim[stack_id],
self.device, self.backcast_length, self.forecast_length)
self.parameters.extend(block.parameters())
print(f' | -- {block}')
blocks.append(block)
return blocks
@staticmethod
def select_block(block_type):
if block_type == NBeatsNet.SEASONALITY_BLOCK:
return SeasonalityBlock
elif block_type == NBeatsNet.TREND_BLOCK:
return TrendBlock
else:
return GenericBlock
def forward(self, backcast):
forecast = torch.zeros(size=(backcast.size()[0], self.forecast_length,)) # maybe batch size here.
for stack_id in range(len(self.stacks)):
for block_id in range(len(self.stacks[stack_id])):
b, f = self.stacks[stack_id][block_id](backcast)
backcast = backcast.to(self.device) - b
forecast = forecast.to(self.device) + f
return backcast, forecast
| 39.502825
| 119
| 0.635441
| 923
| 6,992
| 4.547129
| 0.138678
| 0.076721
| 0.036693
| 0.053371
| 0.433167
| 0.297832
| 0.239695
| 0.228258
| 0.228258
| 0.203479
| 0
| 0.011523
| 0.255292
| 6,992
| 176
| 120
| 39.727273
| 0.794507
| 0.017305
| 0
| 0.169014
| 0
| 0
| 0.05609
| 0.032925
| 0
| 0
| 0
| 0
| 0.014085
| 1
| 0.112676
| false
| 0
| 0.028169
| 0
| 0.288732
| 0.021127
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e5fa5123e3b4ee554b59dbd26a061b553bcda4
| 2,916
|
py
|
Python
|
BACKPROPAGATION/Backprop.py
|
chaya-v/AI-ML-Lab-Programs
|
cb2e91cf62376f5f95395e89357fa0bef730deed
|
[
"MIT"
] | 2
|
2022-01-03T07:28:21.000Z
|
2022-01-23T06:49:47.000Z
|
BACKPROPAGATION/Backprop.py
|
chaya-v/AI-ML-Lab-Programs
|
cb2e91cf62376f5f95395e89357fa0bef730deed
|
[
"MIT"
] | null | null | null |
BACKPROPAGATION/Backprop.py
|
chaya-v/AI-ML-Lab-Programs
|
cb2e91cf62376f5f95395e89357fa0bef730deed
|
[
"MIT"
] | 1
|
2022-01-03T07:28:22.000Z
|
2022-01-03T07:28:22.000Z
|
from math import exp
from random import seed
from random import random
def initialize_network(n_inputs, n_hidden, n_outputs):
network = list()
hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
network.append(hidden_layer)
output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]
network.append(output_layer)
return network
def activate(weights, inputs):
activation = weights[-1]
for i in range(len(weights)-1):
activation += weights[i] * inputs[i]
return activation
def transfer(activation):
return 1.0 / (1.0 + exp(-activation))
def forward_propagate(network, row):
inputs = row
for layer in network:
new_inputs = []
for neuron in layer:
activation = activate(neuron['weights'], inputs)
neuron['output'] = transfer(activation)
new_inputs.append(neuron['output'])
inputs = new_inputs
return inputs
def transfer_derivative(output):
return output * (1.0 - output)
def backward_propagate_error(network, expected):
for i in reversed(range(len(network))):
layer = network[i]
errors = list()
if i != len(network)-1:
for j in range(len(layer)):
error = 0.0
for neuron in network[i + 1]:
error += (neuron['weights'][j] * neuron['delta'])
errors.append(error)
else:
for j in range(len(layer)):
neuron = layer[j]
errors.append(expected[j] - neuron['output'])
for j in range(len(layer)):
neuron = layer[j]
neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])
def update_weights(network, row, l_rate):
for i in range(len(network)):
inputs = row[:-1]
if i != 0:
inputs = [neuron['output'] for neuron in network[i - 1]]
for neuron in network[i]:
for j in range(len(inputs)):
neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j]
neuron['weights'][-1] += l_rate * neuron['delta']
def train_network(network, train, l_rate, n_epoch, n_outputs):
for epoch in range(n_epoch):
sum_error = 0
for row in train:
outputs = forward_propagate(network, row)
expected = [0 for i in range(n_outputs)]
expected[row[-1]] = 1
sum_error += sum([(expected[i]-outputs[i])**2 for i in range(len(expected))])
backward_propagate_error(network, expected)
update_weights(network, row, l_rate)
print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))
seed(1)
dataset = [[2.7810836,2.550537003,0],
[1.465489372,2.362125076,0],
[3.396561688,4.400293529,0],
[1.38807019,1.850220317,0],
[3.06407232,3.005305973,0],
[7.627531214,2.759262235,1],
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]
n_inputs = len(dataset[0]) - 1
n_outputs = len(set([row[-1] for row in dataset]))
network = initialize_network(n_inputs, 2, n_outputs)
train_network(network, dataset, 0.5, 30, n_outputs)
for layer in network:
print(layer)
| 29.16
| 95
| 0.682785
| 448
| 2,916
| 4.341518
| 0.1875
| 0.046787
| 0.027764
| 0.045244
| 0.231877
| 0.154756
| 0.062725
| 0.062725
| 0.031877
| 0
| 0
| 0.097511
| 0.159465
| 2,916
| 100
| 96
| 29.16
| 0.696042
| 0
| 0
| 0.084337
| 0
| 0
| 0.042852
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.096386
| false
| 0
| 0.036145
| 0.024096
| 0.192771
| 0.024096
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e64a1d83c3d728c1a241962b109b3208e3da0f
| 1,993
|
py
|
Python
|
tests/multi_design_test.py
|
benoitc/hypercouch
|
23055c26529a7f2198288b249b45d05b796e78bf
|
[
"MIT"
] | 3
|
2016-05-08T23:45:29.000Z
|
2020-01-21T11:12:46.000Z
|
tests/multi_design_test.py
|
benoitc/hypercouch
|
23055c26529a7f2198288b249b45d05b796e78bf
|
[
"MIT"
] | null | null | null |
tests/multi_design_test.py
|
benoitc/hypercouch
|
23055c26529a7f2198288b249b45d05b796e78bf
|
[
"MIT"
] | null | null | null |
"""\
Copyright (c) 2009 Paul J. Davis <paul.joseph.davis@gmail.com>
This file is part of hypercouch which is released uner the MIT license.
"""
import time
import unittest
import couchdb
COUCHURI = "http://127.0.0.1:5984/"
TESTDB = "hyper_tests"
class MultiDesignTest(unittest.TestCase):
def setUp(self):
self.srv = couchdb.Server(COUCHURI)
if TESTDB in self.srv:
del self.srv[TESTDB]
self.db = self.srv.create(TESTDB)
self.db["_design/test1"] = {
"ft_index": """\
function(doc) {
if(doc.body) index(doc.body);
if(doc.foo != undefined) property("foo", doc.foo);
}
"""
}
self.db["_design/test2"] = {
"ft_index": """\
function(doc) {
if(doc.bar) property("bar", doc.bar)
}
"""
}
self._wait()
def tearDown(self):
del self.srv[TESTDB]
def _query(self, **kwargs):
resp, data = self.db.resource.get("_fti", **kwargs)
return data
def _wait(self, expect=0, retries=10):
data = self._query(q="*.**")
while retries > 0 and len(data["rows"]) != expect:
retries -= 1
time.sleep(0.2)
data = self._query(q="*.**")
if retries < 1:
raise RuntimeError("Failed to find expected index state.")
def test_attr(self):
docs = [{"_id": str(i), "body": "This is document %d" % i, "foo": i, "bar": str(i*i)} for i in range(10)]
self.db.update(docs)
self._wait(expect=10)
data = self._query(q="*.**", foo="NUMEQ 3", bar="NUMEQ 9")
self.assertEqual(data["total_rows"], 1)
self.assertEqual(data["rows"][0]["id"], "3")
data = self._query(q="*.**")
self.assertEqual(len(data["rows"]), 10)
for row in data["rows"]:
self.assertEqual(int(row["foo"]) ** 2, int(row["bar"]))
| 32.145161
| 113
| 0.515805
| 247
| 1,993
| 4.093117
| 0.421053
| 0.034619
| 0.051434
| 0.055391
| 0.077151
| 0.0455
| 0
| 0
| 0
| 0
| 0
| 0.026393
| 0.315605
| 1,993
| 61
| 114
| 32.672131
| 0.714809
| 0.067235
| 0
| 0.215686
| 0
| 0
| 0.280151
| 0
| 0
| 0
| 0
| 0
| 0.078431
| 1
| 0.098039
| false
| 0
| 0.058824
| 0
| 0.196078
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e6e5211adcbf36c0973a390acaf06195e58f6f
| 6,739
|
py
|
Python
|
python/dgl/nn/pytorch/sparse_emb.py
|
wcyjames/dgl
|
00a668ac6898971aa154a8a3fe851010034fd6bf
|
[
"Apache-2.0"
] | null | null | null |
python/dgl/nn/pytorch/sparse_emb.py
|
wcyjames/dgl
|
00a668ac6898971aa154a8a3fe851010034fd6bf
|
[
"Apache-2.0"
] | null | null | null |
python/dgl/nn/pytorch/sparse_emb.py
|
wcyjames/dgl
|
00a668ac6898971aa154a8a3fe851010034fd6bf
|
[
"Apache-2.0"
] | 1
|
2021-08-16T08:33:31.000Z
|
2021-08-16T08:33:31.000Z
|
"""Torch NodeEmbedding."""
from datetime import timedelta
import torch as th
from ...backend import pytorch as F
from ...utils import get_shared_mem_array, create_shared_mem_array
_STORE = None
class NodeEmbedding: # NodeEmbedding
'''Class for storing node embeddings.
The class is optimized for training large-scale node embeddings. It updates the embedding in
a sparse way and can scale to graphs with millions of nodes. It also supports partitioning
to multiple GPUs (on a single machine) for more acceleration. It does not support partitioning
across machines.
Currently, DGL provides two optimizers that work with this NodeEmbedding
class: ``SparseAdagrad`` and ``SparseAdam``.
The implementation is based on torch.distributed package. It depends on the pytorch
default distributed process group to collect multi-process information and uses
``torch.distributed.TCPStore`` to share meta-data information across multiple gpu processes.
It use the local address of '127.0.0.1:12346' to initialize the TCPStore.
Parameters
----------
num_embeddings : int
The number of embeddings. Currently, the number of embeddings has to be the same as
the number of nodes.
embedding_dim : int
The dimension size of embeddings.
name : str
The name of the embeddings. The name should uniquely identify the embeddings in the system.
init_func : callable, optional
The function to create the initial data. If the init function is not provided,
the values of the embeddings are initialized to zero.
Examples
--------
Before launching multiple gpu processes
>>> def initializer(emb):
th.nn.init.xavier_uniform_(emb)
return emb
In each training process
>>> emb = dgl.nn.NodeEmbedding(g.number_of_nodes(), 10, 'emb', init_func=initializer)
>>> optimizer = dgl.optim.SparseAdam([emb], lr=0.001)
>>> for blocks in dataloader:
... ...
... feats = emb(nids, gpu_0)
... loss = F.sum(feats + 1, 0)
... loss.backward()
... optimizer.step()
'''
def __init__(self, num_embeddings, embedding_dim, name,
init_func=None):
global _STORE
# Check whether it is multi-gpu training or not.
if th.distributed.is_initialized():
rank = th.distributed.get_rank()
world_size = th.distributed.get_world_size()
else:
rank = -1
world_size = 0
self._rank = rank
self._world_size = world_size
host_name = '127.0.0.1'
port = 12346
if rank <= 0:
emb = create_shared_mem_array(name, (num_embeddings, embedding_dim), th.float32)
if init_func is not None:
emb = init_func(emb)
if rank == 0:
if world_size > 1:
# for multi-gpu training, setup a TCPStore for
# embeding status synchronization across GPU processes
if _STORE is None:
_STORE = th.distributed.TCPStore(
host_name, port, world_size, True, timedelta(seconds=30))
for _ in range(1, world_size):
# send embs
_STORE.set(name, name)
elif rank > 0:
# receive
if _STORE is None:
_STORE = th.distributed.TCPStore(
host_name, port, world_size, False, timedelta(seconds=30))
_STORE.wait([name])
emb = get_shared_mem_array(name, (num_embeddings, embedding_dim), th.float32)
self._store = _STORE
self._tensor = emb
self._num_embeddings = num_embeddings
self._embedding_dim = embedding_dim
self._name = name
self._optm_state = None # track optimizer state
self._trace = [] # track minibatch
def __call__(self, node_ids, device=th.device('cpu')):
"""
node_ids : th.tensor
Index of the embeddings to collect.
device : th.device
Target device to put the collected embeddings.
"""
emb = self._tensor[node_ids].to(device)
if F.is_recording():
emb = F.attach_grad(emb)
self._trace.append((node_ids.to(device, non_blocking=True), emb))
return emb
@property
def store(self):
"""Return torch.distributed.TCPStore for
meta data sharing across processes.
Returns
-------
torch.distributed.TCPStore
KVStore used for meta data sharing.
"""
return self._store
@property
def rank(self):
"""Return rank of current process.
Returns
-------
int
The rank of current process.
"""
return self._rank
@property
def world_size(self):
"""Return world size of the pytorch distributed training env.
Returns
-------
int
The world size of the pytorch distributed training env.
"""
return self._world_size
@property
def name(self):
"""Return the name of NodeEmbedding.
Returns
-------
str
The name of NodeEmbedding.
"""
return self._name
@property
def num_embeddings(self):
"""Return the number of embeddings.
Returns
-------
int
The number of embeddings.
"""
return self._num_embeddings
def set_optm_state(self, state):
"""Store the optimizer related state tensor.
Parameters
----------
state : tuple of torch.Tensor
Optimizer related state.
"""
self._optm_state = state
@property
def optm_state(self):
"""Return the optimizer related state tensor.
Returns
-------
tuple of torch.Tensor
The optimizer related state.
"""
return self._optm_state
@property
def trace(self):
"""Return a trace of the indices of embeddings
used in the training step(s).
Returns
-------
[torch.Tensor]
The indices of embeddings used in the training step(s).
"""
return self._trace
def reset_trace(self):
"""Clean up the trace of the indices of embeddings
used in the training step(s).
"""
self._trace = []
@property
def emb_tensor(self):
"""Return the tensor storing the node embeddings
Returns
-------
torch.Tensor
The tensor storing the node embeddings
"""
return self._tensor
| 30.355856
| 99
| 0.588811
| 787
| 6,739
| 4.905972
| 0.265565
| 0.030303
| 0.014245
| 0.021756
| 0.16317
| 0.135198
| 0.118104
| 0.118104
| 0.09583
| 0.09583
| 0
| 0.010137
| 0.326606
| 6,739
| 221
| 100
| 30.493213
| 0.840679
| 0.485384
| 0
| 0.181818
| 0
| 0
| 0.004289
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.155844
| false
| 0
| 0.051948
| 0
| 0.337662
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72e73a6f2f22fa84ad441b95a06268e872edfef4
| 2,815
|
py
|
Python
|
tests/sentry/web/frontend/test_create_team.py
|
seukjung/sentry-custom
|
c5f6bb2019aef3caff7f3e2b619f7a70f2b9b963
|
[
"BSD-3-Clause"
] | 20
|
2016-10-01T04:29:24.000Z
|
2020-10-09T07:23:34.000Z
|
tests/sentry/web/frontend/test_create_team.py
|
fotinakis/sentry
|
c5cfa5c5e47475bf5ef41e702548c2dfc7bb8a7c
|
[
"BSD-3-Clause"
] | 8
|
2019-12-28T23:49:55.000Z
|
2022-03-02T04:34:18.000Z
|
tests/sentry/web/frontend/test_create_team.py
|
fotinakis/sentry
|
c5cfa5c5e47475bf5ef41e702548c2dfc7bb8a7c
|
[
"BSD-3-Clause"
] | 7
|
2016-10-27T05:12:45.000Z
|
2021-05-01T14:29:53.000Z
|
from __future__ import absolute_import
from django.core.urlresolvers import reverse
from sentry.models import OrganizationMember, OrganizationMemberTeam, Team
from sentry.testutils import TestCase, PermissionTestCase
class CreateTeamPermissionTest(PermissionTestCase):
def setUp(self):
super(CreateTeamPermissionTest, self).setUp()
self.path = reverse('sentry-create-team', args=[self.organization.slug])
def test_teamless_admin_can_load(self):
self.assert_teamless_admin_can_access(self.path)
def test_team_admin_can_load(self):
self.assert_team_admin_can_access(self.path)
def test_member_cannot_load(self):
self.assert_member_cannot_access(self.path)
def test_owner_can_load(self):
self.assert_owner_can_access(self.path)
class CreateTeamTest(TestCase):
def test_renders_with_context(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
self.login_as(self.user)
resp = self.client.get(path)
assert resp.status_code == 200
self.assertTemplateUsed(resp, 'sentry/create-team.html')
assert resp.context['organization'] == organization
assert resp.context['form']
def test_submission(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
self.login_as(self.user)
resp = self.client.post(path, {
'name': 'bar',
})
assert resp.status_code == 302, resp.context['form'].errors
team = Team.objects.get(organization=organization, name='bar')
member = OrganizationMember.objects.get(
user=self.user,
organization=organization,
)
assert OrganizationMemberTeam.objects.filter(
organizationmember=member,
team=team,
is_active=True,
).exists()
redirect_uri = reverse('sentry-create-project', args=[organization.slug])
assert resp['Location'] == 'http://testserver%s?team=%s' % (
redirect_uri, team.slug)
def test_admin_can_create_team(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
admin = self.create_user('admin@example.com')
self.create_member(
organization=organization,
user=admin,
role='admin',
teams=[],
)
self.login_as(admin)
resp = self.client.post(path, {
'name': 'bar',
})
assert resp.status_code == 302, resp.context['form'].errors
assert Team.objects.filter(
organization=organization,
name='bar',
).exists()
| 32.356322
| 81
| 0.649023
| 304
| 2,815
| 5.828947
| 0.25
| 0.027652
| 0.053612
| 0.051919
| 0.358916
| 0.335214
| 0.288375
| 0.255643
| 0.255643
| 0.255643
| 0
| 0.004212
| 0.240853
| 2,815
| 86
| 82
| 32.732558
| 0.824988
| 0
| 0
| 0.318182
| 0
| 0
| 0.077087
| 0.015631
| 0
| 0
| 0
| 0
| 0.19697
| 1
| 0.121212
| false
| 0
| 0.060606
| 0
| 0.212121
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ee24f7120a48a59768912e69a446c1ca036274
| 10,706
|
py
|
Python
|
pyxdsm/tests/test_xdsm.py
|
yqliaohk/pyXDSM
|
3bcfab710543d6624ba0698093c6522bc94601e8
|
[
"Apache-2.0"
] | null | null | null |
pyxdsm/tests/test_xdsm.py
|
yqliaohk/pyXDSM
|
3bcfab710543d6624ba0698093c6522bc94601e8
|
[
"Apache-2.0"
] | null | null | null |
pyxdsm/tests/test_xdsm.py
|
yqliaohk/pyXDSM
|
3bcfab710543d6624ba0698093c6522bc94601e8
|
[
"Apache-2.0"
] | null | null | null |
import unittest
import os
from pyxdsm.XDSM import XDSM, __file__
from numpy.distutils.exec_command import find_executable
def filter_lines(lns):
# Empty lines are excluded.
# Leading and trailing whitespaces are removed
# Comments are removed.
return [ln.strip() for ln in lns if ln.strip() and not ln.strip().startswith('%')]
class TestXDSM(unittest.TestCase):
def setUp(self):
import os
import tempfile
self.startdir = os.getcwd()
self.tempdir = tempfile.mkdtemp(prefix='testdir-')
os.chdir(self.tempdir)
def tearDown(self):
import os
import shutil
os.chdir(self.startdir)
try:
shutil.rmtree(self.tempdir)
except OSError:
pass
def test_examples(self):
'''
This test just builds the three examples, and assert that the output files exist.
Unlike the other tests, this one requires LaTeX to be available.
'''
os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../examples'))
filenames = ['kitchen_sink', 'mdf']
for f in filenames:
os.system('python {}.py'.format(f))
self.assertTrue(os.path.isfile(f + '.tikz'))
self.assertTrue(os.path.isfile(f + '.tex'))
# look for the pdflatex executable
pdflatex = find_executable('pdflatex') is not None
# if no pdflatex, then do not assert that the pdf was compiled
self.assertTrue(not pdflatex or os.path.isfile(f + '.pdf'))
os.system('python mat_eqn.py')
self.assertTrue(os.path.isfile('mat_eqn_example.pdf'))
# change back to previous directory
os.chdir(self.tempdir)
def test_connect(self):
x = XDSM(use_sfmath=False)
x.add_system('D1', 'Function', 'D_1', label_width=2)
x.add_system('D2', 'Function', 'D_2', stack=False)
try:
x.connect('D1', 'D2', r'\mathcal{R}(y_1)', 'foobar')
except ValueError as err:
self.assertEquals(str(err), 'label_width argument must be an integer')
else:
self.fail('Expected ValueError')
def test_options(self):
filename = 'xdsm_test_options'
spec_dir = filename + '_specs'
# Change `use_sfmath` to False to use computer modern
x = XDSM(use_sfmath=False)
x.add_system('opt', 'Optimization', r'\text{Optimizer}')
x.add_system('solver', 'MDA', r'\text{Newton}')
x.add_system('D1', 'Function', 'D_1', label_width=2)
x.add_system('D2', 'Function', 'D_2', stack=False)
x.add_system('F', 'Function', 'F', faded=True)
x.add_system('G', 'Function', 'G', spec_name="G_spec")
x.connect('opt', 'D1', 'x, z')
x.connect('opt', 'D2', 'z')
x.connect('opt', 'F', 'x, z')
x.connect('solver', 'D1', 'y_2')
x.connect('solver', 'D2', 'y_1')
x.connect('D1', 'solver', r'\mathcal{R}(y_1)')
x.connect('solver', 'F', 'y_1, y_2')
x.connect('D2', 'solver', r'\mathcal{R}(y_2)')
x.connect('solver', 'G', 'y_1, y_2')
x.connect('F', 'opt', 'f')
x.connect('G', 'opt', 'g')
x.add_output('opt', 'x^*, z^*', side='right')
x.add_output('D1', 'y_1^*', side='left', stack=True)
x.add_output('D2', 'y_2^*', side='left')
x.add_output('F', 'f^*', side='left')
x.add_output('G', 'g^*')
x.write(filename)
x.write_sys_specs(spec_dir)
# Test if files where created
self.assertTrue(os.path.isfile(filename + '.tikz'))
self.assertTrue(os.path.isfile(filename + '.tex'))
self.assertTrue(os.path.isdir(spec_dir))
self.assertTrue(os.path.isfile(os.path.join(spec_dir, 'F.json')))
self.assertTrue(os.path.isfile(os.path.join(spec_dir, 'G_spec.json')))
def test_stacked_system(self):
x = XDSM()
x.add_system('test', 'Optimization', r'\text{test}', stack=True)
file_name = "stacked_test"
x.write(file_name)
tikz_file = file_name + '.tikz'
with open(tikz_file, "r") as f:
tikz = f.read()
self.assertIn(r"\node [Optimization,stack]", tikz)
def test_tikz_content(self):
# Check if TiKZ file was created.
# Compare the content of the sample below and the newly created TiKZ file.
sample_txt = r"""
%%% Preamble Requirements %%%
% \usepackage{geometry}
% \usepackage{amsfonts}
% \usepackage{amsmath}
% \usepackage{amssymb}
% \usepackage{tikz}
% Optional packages such as sfmath set through python interface
% \usepackage{sfmath}
% \usetikzlibrary{arrows,chains,positioning,scopes,shapes.geometric,shapes.misc,shadows}
%%% End Preamble Requirements %%%
\input{"path/to/diagram_styles"}
\begin{tikzpicture}
\matrix[MatrixSetup]{
%Row 0
\node [DataIO] (left_output_opt) {$x^*, z^*$};&
\node [Optimization] (opt) {$\text{Optimizer}$};&
&
\node [DataInter] (opt-D1) {$x, z$};&
\node [DataInter] (opt-D2) {$z$};&
\node [DataInter] (opt-F) {$x, z$};&
\\
%Row 1
&
&
\node [MDA] (solver) {$\text{Newton}$};&
\node [DataInter] (solver-D1) {$y_2$};&
\node [DataInter] (solver-D2) {$y_1$};&
\node [DataInter] (solver-F) {$y_1, y_2$};&
\node [DataInter] (solver-G) {$y_1, y_2$};\\
%Row 2
\node [DataIO] (left_output_D1) {$y_1^*$};&
&
\node [DataInter] (D1-solver) {$\mathcal{R}(y_1)$};&
\node [Function] (D1) {$D_1$};&
&
&
\\
%Row 3
\node [DataIO] (left_output_D2) {$y_2^*$};&
&
\node [DataInter] (D2-solver) {$\mathcal{R}(y_2)$};&
&
\node [Function] (D2) {$D_2$};&
&
\\
%Row 4
\node [DataIO] (left_output_F) {$f^*$};&
\node [DataInter] (F-opt) {$f$};&
&
&
&
\node [Function] (F) {$F$};&
\\
%Row 5
\node [DataIO] (left_output_G) {$g^*$};&
\node [DataInter] (G-opt) {$g$};&
&
&
&
&
\node [Function] (G) {$G$};\\
%Row 6
&
&
&
&
&
&
\\
};
% XDSM process chains
\begin{pgfonlayer}{data}
\path
% Horizontal edges
(opt) edge [DataLine] (opt-D1)
(opt) edge [DataLine] (opt-D2)
(opt) edge [DataLine] (opt-F)
(solver) edge [DataLine] (solver-D1)
(solver) edge [DataLine] (solver-D2)
(D1) edge [DataLine] (D1-solver)
(solver) edge [DataLine] (solver-F)
(D2) edge [DataLine] (D2-solver)
(solver) edge [DataLine] (solver-G)
(F) edge [DataLine] (F-opt)
(G) edge [DataLine] (G-opt)
(opt) edge [DataLine] (left_output_opt)
(D1) edge [DataLine] (left_output_D1)
(D2) edge [DataLine] (left_output_D2)
(F) edge [DataLine] (left_output_F)
(G) edge [DataLine] (left_output_G)
% Vertical edges
(opt-D1) edge [DataLine] (D1)
(opt-D2) edge [DataLine] (D2)
(opt-F) edge [DataLine] (F)
(solver-D1) edge [DataLine] (D1)
(solver-D2) edge [DataLine] (D2)
(D1-solver) edge [DataLine] (solver)
(solver-F) edge [DataLine] (F)
(D2-solver) edge [DataLine] (solver)
(solver-G) edge [DataLine] (G)
(F-opt) edge [DataLine] (opt)
(G-opt) edge [DataLine] (opt);
\end{pgfonlayer}
\end{tikzpicture}"""
filename = 'xdsm_test_tikz'
x = XDSM(use_sfmath=True)
x.add_system('opt', 'Optimization', r'\text{Optimizer}')
x.add_system('solver', 'MDA', r'\text{Newton}')
x.add_system('D1', 'Function', 'D_1')
x.add_system('D2', 'Function', 'D_2')
x.add_system('F', 'Function', 'F')
x.add_system('G', 'Function', 'G')
x.connect('opt', 'D1', 'x, z')
x.connect('opt', 'D2', 'z')
x.connect('opt', 'F', 'x, z')
x.connect('solver', 'D1', 'y_2')
x.connect('solver', 'D2', 'y_1')
x.connect('D1', 'solver', r'\mathcal{R}(y_1)')
x.connect('solver', 'F', 'y_1, y_2')
x.connect('D2', 'solver', r'\mathcal{R}(y_2)')
x.connect('solver', 'G', 'y_1, y_2')
x.connect('F', 'opt', 'f')
x.connect('G', 'opt', 'g')
x.add_output('opt', 'x^*, z^*', side='left')
x.add_output('D1', 'y_1^*', side='left')
x.add_output('D2', 'y_2^*', side='left')
x.add_output('F', 'f^*', side='left')
x.add_output('G', 'g^*', side='left')
x.write(filename)
# Check if file was created
tikz_file = filename + '.tikz'
self.assertTrue(os.path.isfile(tikz_file))
sample_lines = sample_txt.split('\n')
sample_lines = filter_lines(sample_lines)
with open(tikz_file, "r") as f:
new_lines = filter_lines(f.readlines())
sample_no_match = [] # Sample text
new_no_match = [] # New text
for new_line, sample_line in zip(new_lines, sample_lines):
if new_line.startswith(r'\input{'):
continue
if new_line != sample_line: # else everything is okay
# This can be because of the different ordering of lines or because of an error.
sample_no_match.append(new_line)
new_no_match.append(sample_line)
# Sort both sets of suspicious lines
sample_no_match.sort()
new_no_match.sort()
for sample_line, new_line in zip(sample_no_match, new_no_match):
# Now the lines should match, if only the ordering was different
self.assertEqual(new_line, sample_line)
# To be sure, check the length, otherwise a missing last line could get unnoticed because of using zip
self.assertEqual(len(new_lines), len(sample_lines))
if __name__ == "__main__":
unittest.main()
| 33.772871
| 110
| 0.515412
| 1,296
| 10,706
| 4.114198
| 0.204475
| 0.060765
| 0.028132
| 0.033758
| 0.306264
| 0.249437
| 0.213053
| 0.187172
| 0.171793
| 0.171793
| 0
| 0.014456
| 0.32804
| 10,706
| 316
| 111
| 33.879747
| 0.726717
| 0.084252
| 0
| 0.219409
| 0
| 0.004219
| 0.488878
| 0.027781
| 0
| 0
| 0
| 0
| 0.059072
| 1
| 0.033755
| false
| 0.004219
| 0.033755
| 0.004219
| 0.075949
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72f6da1974a7d86bd87801e4461a62cded6e702d
| 1,379
|
py
|
Python
|
to_display.py
|
namib-project/weatherstation-image
|
ae6a11943bfd21135bf0ce5d113865b69c58bbe2
|
[
"MIT"
] | null | null | null |
to_display.py
|
namib-project/weatherstation-image
|
ae6a11943bfd21135bf0ce5d113865b69c58bbe2
|
[
"MIT"
] | null | null | null |
to_display.py
|
namib-project/weatherstation-image
|
ae6a11943bfd21135bf0ce5d113865b69c58bbe2
|
[
"MIT"
] | null | null | null |
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
import sys
import ST7735
# Create ST7735 LCD display class object and set pin numbers and display hardware information.
disp = ST7735.ST7735(
dc=24,
cs=ST7735.BG_SPI_CS_BACK,
rst=25,
port=0,
width=122,
height=160,
rotation=270
)
# Initialize display.
disp.begin()
WIDTH = disp.width
HEIGHT = disp.height
img = Image.new('RGB', (WIDTH, HEIGHT), color=(0, 0, 0))
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf", 12)
# Initialize a secondary text with the empty string
text2 = ""
# Print test-output on the display if n oargument is given
if len(sys.argv) == 1:
text = "Temperature:\nHumidity:\nUV:\nRain:\nLight:"
text2 = "20°C\n50 %\n42\nyes\nOn"
# Print the argument if only one is given
elif len(sys.argv) == 2:
text = sys.argv[1]
# If 2 arguments are given use the second as the secondary text
elif len(sys.argv) == 3:
text = sys.argv[1]
text2 = sys.argv[2]
# For any other number of arguments draw them in one line each
else:
text = ''.join(i + "\n" for i in sys.argv[1:])
# Print both texts, with the secondary one starting with an 100 px offset
draw.text((10, 10), text, font=font, fill=(255, 255, 255))
draw.text((110, 10), text2, font=font, fill=(255, 255, 255))
disp.display(img)
| 26.519231
| 96
| 0.701233
| 229
| 1,379
| 4.213974
| 0.519651
| 0.050777
| 0.033161
| 0.029016
| 0.043523
| 0.043523
| 0
| 0
| 0
| 0
| 0
| 0.076249
| 0.172589
| 1,379
| 51
| 97
| 27.039216
| 0.768624
| 0.329224
| 0
| 0.058824
| 0
| 0
| 0.146288
| 0.115721
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.147059
| 0
| 0.147059
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72f7f3e6d5b462f2c1a23997a28ebc45762b8fc7
| 245
|
py
|
Python
|
Smart User Targeted Advertising/MinorPro/FINALPROJECT/Resources/testInsert.py
|
saransh808/Projects
|
7449ed6b53900ebb16a9084cff389cc50f3c9f6c
|
[
"MIT"
] | null | null | null |
Smart User Targeted Advertising/MinorPro/FINALPROJECT/Resources/testInsert.py
|
saransh808/Projects
|
7449ed6b53900ebb16a9084cff389cc50f3c9f6c
|
[
"MIT"
] | null | null | null |
Smart User Targeted Advertising/MinorPro/FINALPROJECT/Resources/testInsert.py
|
saransh808/Projects
|
7449ed6b53900ebb16a9084cff389cc50f3c9f6c
|
[
"MIT"
] | null | null | null |
import sqlite3
conn=sqlite3.connect('Survey.db')
fo=open('insertcommand.txt')
str=fo.readline()
while str:
str="INSERT INTO data VALUES"+str
conn.execute(str)
#print(str)
str=fo.readline()
conn.commit()
conn.close()
fo.close()
| 16.333333
| 37
| 0.689796
| 36
| 245
| 4.694444
| 0.583333
| 0.059172
| 0.153846
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009524
| 0.142857
| 245
| 14
| 38
| 17.5
| 0.795238
| 0.040816
| 0
| 0.181818
| 0
| 0
| 0.209402
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.090909
| 0
| 0.090909
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72f91b913afb43954a794d5c35602920d06bf7b3
| 11,325
|
py
|
Python
|
tests/test_core.py
|
d066y/detectem
|
648ddff159e17777e41b1dd266a759e9f0774ea8
|
[
"MIT"
] | null | null | null |
tests/test_core.py
|
d066y/detectem
|
648ddff159e17777e41b1dd266a759e9f0774ea8
|
[
"MIT"
] | null | null | null |
tests/test_core.py
|
d066y/detectem
|
648ddff159e17777e41b1dd266a759e9f0774ea8
|
[
"MIT"
] | 1
|
2019-07-28T10:11:01.000Z
|
2019-07-28T10:11:01.000Z
|
import pytest
from detectem.core import Detector, Result, ResultCollection
from detectem.plugin import Plugin, PluginCollection
from detectem.settings import INDICATOR_TYPE, HINT_TYPE, MAIN_ENTRY, GENERIC_TYPE
from detectem.plugins.helpers import meta_generator
class TestDetector():
HAR_ENTRY_1 = {
'request': {
'url': 'http://domain.tld/libA-1.4.2.js'
},
'response': {
'url': 'http://domain.tld/libA-1.4.2.js'
},
}
HAR_NO_URL_REDIRECT = [
{
'request': {'url': 'http://domain.tld/'},
'response': {},
},
{
'request': {'url': 'http://domain.tld/js/script.js'},
'response': {},
}
]
HAR_URL_REDIRECT_PATH = [
{
'request': {'url': 'http://domain.tld/'},
'response': {'headers': [
{'name': 'Location', 'value': '/new/default.html'}
]},
},
{
'request': {'url': 'http://domain.tld/new/default.html'},
'response': {},
}
]
HAR_URL_REDIRECT_ABS = [
{
'request': {'url': 'http://domain.tld/'},
'response': {'headers': [
{'name': 'Location', 'value': 'http://other-domain.tld/'}
]},
},
{
'request': {'url': 'http://other-domain.tld/'},
'response': {},
}
]
URL = 'http://domain.tld/'
FOO_PLUGIN = {
'name': 'foo',
'homepage': 'foo',
'matchers': {
'url': 'foo.*-(?P<version>[0-9\.]+)\.js',
'header': ('FooHeader', 'Foo.* v(?P<version>[0-9\.]+)'),
'body': 'Foo.* v(?P<version>[0-9\.]+)',
'xpath': (meta_generator('foo-min'), '(?P<version>[0-9\.]+)'),
},
'indicators': {
'url': 'foo.*\.js',
'header': ('FooHeader', 'Foo'),
'body': 'Foo',
'xpath': "//meta[@name='generator']",
},
'modular_matchers': {
'url': 'foo-(?P<name>\w+)-.*\.js',
'header': ('FooHeader', 'Foo-(?P<name>\w+)'),
'body': 'Foo-(?P<name>\w+)',
'xpath': (meta_generator('foo-min'), 'foo-(?P<name>\w+)'),
},
}
FOO_RESULTS = [
[{'name': 'foo', 'version': '1.1'}],
[{'name': 'foo'}],
[{'name': 'foo-min', 'version': '1.1'}],
]
MATCHER_SOURCES = [
['matchers'],
['indicators'],
['matchers', 'modular_matchers'],
]
def test_detector_starts_with_empty_results(self):
d = Detector({'har': None, 'softwares': None}, [], None)
assert not d._results.get_results()
@pytest.mark.parametrize("har,index", [
(HAR_NO_URL_REDIRECT, 0),
(HAR_URL_REDIRECT_PATH, 1),
(HAR_URL_REDIRECT_ABS, 1),
])
def test_mark_main_entry(self, har, index):
d = self._create_detector(har, [])
assert d.har[index]['detectem']['type'] == MAIN_ENTRY
def test_convert_inline_script_to_har_entry(self):
script = 'Inline script'
d = Detector({'har': [], 'softwares': [], 'scripts': [script]}, None, self.URL)
e = d.har[0]
assert e['request']['url'] == self.URL
assert e['response']['content']['text'] == script
@pytest.mark.parametrize("scripts,n_entries", [
([], 0),
(['script1', 'script2'], 2),
])
def test_add_inline_scripts_to_har(self, scripts, n_entries):
d = Detector({'har': [], 'softwares': [], 'scripts': scripts}, None, self.URL)
assert len(d.har) == n_entries
def _create_plugin(self, template, sources, matchers):
class TestPlugin(Plugin):
name = template['name']
homepage = template['homepage']
p = TestPlugin()
for s in sources:
g = [{m: template[s][m]} for m in matchers]
setattr(p, s, g)
return p
def _create_detector(self, har, plugins):
pc = PluginCollection()
for p in plugins:
pc.add(p)
return Detector({'har': har, 'softwares': []}, pc, self.URL)
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_headers(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'headers': [
{'name': 'FooHeader', 'value': 'Foo-min v1.1'}
]
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['header'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources', MATCHER_SOURCES)
def test_match_from_headers_ignores_resource_entries(self, sources):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'headers': [],
},
},
{
'request': {'url': 'http://foo.org/lib/foo.js'},
'response': {
'url': 'http://foo.org/lib/foo.js',
'headers': [
{'name': 'FooHeader', 'value': 'Foo-min v1.1'}
]
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['header'])
d = self._create_detector(har, [p])
assert not d.get_results()
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_body(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {'text': 'Main content'},
},
},
{
'request': {'url': 'http://foo.org/lib/foo.js'},
'response': {
'url': 'http://foo.org/lib/foo.js',
'content': {'text': 'Plugin Foo-min v1.1'},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['body'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources', MATCHER_SOURCES)
def test_match_from_body_excludes_main_entry(self, sources):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {'text': 'About Foo-min v1.1'},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['body'])
d = self._create_detector(har, [p])
assert not d.get_results()
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_url(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {'url': self.URL},
},
{
'request': {'url': 'http://foo.org/lib/foo-min-1.1.js'},
'response': {
'url': 'http://foo.org/lib/foo-min-1.1.js',
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['url'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_xpath(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {
'text': '<meta name="generator" content="foo-min 1.1">'
},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['xpath'])
d = self._create_detector(har, [p])
assert d.get_results() == result
def test_get_hints_with_valid_hint(self):
class TestPlugin(Plugin):
name = 'test'
homepage = 'test'
class BlaPlugin(Plugin):
name = 'bla'
hints = ['test']
detector = self._create_detector(None, [TestPlugin()])
hints = detector.get_hints(BlaPlugin())
assert hints
def test_get_hints_with_invalid_hint(self):
class BlaPlugin(Plugin):
name = 'bla'
hints = ['test']
detector = self._create_detector(None, [])
hints = detector.get_hints(BlaPlugin())
assert not hints
class TestResultCollection():
@staticmethod
def _assert_results(detected, results):
c = ResultCollection()
for d in detected:
c.add_result(d)
assert set(c.get_results()) == set(results)
@pytest.mark.parametrize('detected,results', [
(
[Result('pluginA', '1.1'), Result('pluginB', '3.8.7'), Result('pluginC', '4.0')],
[Result('pluginA', '1.1'), Result('pluginB', '3.8.7'), Result('pluginC', '4.0')]
),
(
[Result('pluginA', '1.3'), Result('pluginA', '1.2'), Result('pluginA', '1.1')],
[Result('pluginA', '1.1'), Result('pluginA', '1.2'), Result('pluginA', '1.3')],
),
(
[
Result('pluginA', '1.1'),
Result('pluginC', type=HINT_TYPE),
Result('pluginB', type=INDICATOR_TYPE),
Result('pluginD', type=GENERIC_TYPE),
],
[
Result('pluginA', '1.1'),
Result('pluginB', type=INDICATOR_TYPE),
Result('pluginC', type=HINT_TYPE),
Result('pluginD', type=GENERIC_TYPE),
]
),
])
def test_get_all_detected_plugins(self, detected, results):
self._assert_results(detected, results)
@pytest.mark.parametrize('detected,results', [
(
[Result('pluginA', '1.1'), Result('pluginA', '1.2'), Result('pluginA', '1.1')],
[Result('pluginA', '1.1'), Result('pluginA', '1.2')]
),
(
[
Result('pluginA', '1.1'),
Result('pluginA', type=INDICATOR_TYPE),
Result('pluginA', type=HINT_TYPE),
],
[Result('pluginA', '1.1')]
),
(
[Result('pluginB', type=HINT_TYPE), Result('pluginB', type=HINT_TYPE)],
[Result('pluginB', type=HINT_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=INDICATOR_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=HINT_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=GENERIC_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
])
def test_remove_duplicated_results(self, detected, results):
self._assert_results(detected, results)
| 32.449857
| 93
| 0.47947
| 1,117
| 11,325
| 4.696509
| 0.120859
| 0.047083
| 0.045368
| 0.048037
| 0.62886
| 0.571102
| 0.521159
| 0.516775
| 0.479604
| 0.453679
| 0
| 0.011486
| 0.346578
| 11,325
| 348
| 94
| 32.543103
| 0.697432
| 0
| 0
| 0.357143
| 0
| 0
| 0.189404
| 0.012804
| 0
| 0
| 0
| 0
| 0.055195
| 1
| 0.055195
| false
| 0
| 0.016234
| 0
| 0.123377
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72fa110e2fe65a7ff391593c876372e3cc4ad11c
| 8,317
|
py
|
Python
|
twitter-clone/twitter/views.py
|
Mlitwin98/twitter-clone
|
4fbe754a4693c39ac4e9623f51ca42a7facecd2e
|
[
"MIT"
] | null | null | null |
twitter-clone/twitter/views.py
|
Mlitwin98/twitter-clone
|
4fbe754a4693c39ac4e9623f51ca42a7facecd2e
|
[
"MIT"
] | null | null | null |
twitter-clone/twitter/views.py
|
Mlitwin98/twitter-clone
|
4fbe754a4693c39ac4e9623f51ca42a7facecd2e
|
[
"MIT"
] | null | null | null |
from django.dispatch.dispatcher import receiver
from django.shortcuts import get_object_or_404, redirect, render
from django.contrib.auth.decorators import login_required
from django.http.response import HttpResponse
from django.contrib.auth.models import User
from django.contrib.auth import authenticate, logout as auth_logout, login as auth_login
from django.contrib import messages
from django.db.models import Count
from django.template.loader import render_to_string
from django.http import HttpResponseRedirect, JsonResponse
from twitter.models import Tweet, Follow, Notification, Comment
from twitter.myDecor import check_if_user_logged
from twitter.forms import SignUpForm
# Create your views here.
@check_if_user_logged
def index(request):
return render(request, 'index.html')
@check_if_user_logged
def login(request):
if request.method == 'POST':
if 'login' in request.POST:
mail = request.POST['email']
pwd = request.POST['password']
user = authenticate(request, username=mail, password=pwd)
if user is not None:
auth_login(request, user)
return redirect('home')
else:
messages.error(request, 'Invalid credentials')
return render(request, 'login.html')
elif 'cancel' in request.POST:
return redirect('index')
else:
return render(request, 'login.html')
def logout(reqeuest):
auth_logout(reqeuest)
return redirect('index')
@check_if_user_logged
def register(request):
if request.method == 'POST':
if 'cancel' in request.POST:
return redirect('index')
elif 'register' in request.POST:
form = SignUpForm(request.POST)
if form.is_valid():
user = form.save()
email = form.cleaned_data.get('email')
raw_password = form.cleaned_data.get('password')
user.set_password(raw_password)
user.save()
user = authenticate(request, username=email, password = raw_password)
auth_login(request, user)
return redirect('home')
else:
form = SignUpForm()
messages.error(request, 'Invalid form fill')
return render(request, 'register.html', {'form':form})
else:
form = SignUpForm()
return render(request, 'register.html', {'form':form})
@login_required(redirect_field_name=None)
def home(request):
if request.method == 'POST':
author = request.user
content = request.POST['tweet']
tweet = Tweet(author=author, content=content)
tweet.save()
for follower in request.user.following.all().values_list('following_user_id', flat=True):
Notification.objects.create(sender = request.user, receiver = User.objects.get(id=follower), target = tweet, type = 'L')
return redirect('home')
else:
followedUsers = [request.user]
for followed in request.user.followers.all():
followedUsers.append(User.objects.get(id=followed.user_id_id))
tweets = Tweet.objects.filter(author__in=followedUsers).order_by('-timeStamp')
rec_profiles = User.objects.annotate(count=Count('followers')).order_by('followers').exclude(username=request.user.username).exclude(id__in=request.user.followers.all().values_list('user_id', flat=True))[:5]
return render(request, 'home.html', {'tweets':tweets, 'rec_profiles':rec_profiles})
def profile(request, username):
if request.method == 'POST':
user = User.objects.get(username=username)
user.profile.bio = request.POST['bio']
user.profile.profilePic = request.FILES['pic'] if 'pic' in request.FILES else user.profile.profilePic
user.profile.backgroundPic = request.FILES['banner'] if 'banner' in request.FILES else user.profile.backgroundPic
user.save()
return redirect('profile', username=username)
else:
try:
userProfile = User.objects.get(username=username)
except User.DoesNotExist:
return HttpResponse('User Not Found')
tweets = Tweet.objects.filter(author__exact=userProfile).order_by('-timeStamp')
is_following = False
for follow in request.user.followers.all():
if userProfile.id == follow.user_id_id:
is_following=True
rec_profiles = User.objects.annotate(count=Count('followers')).order_by('followers').exclude(username=request.user.username).exclude(username=username).exclude(id__in=request.user.followers.all().values_list('user_id', flat=True))[:5]
return render(request, 'profile.html', {'userProfile':userProfile, 'tweets':tweets, 'is_following':is_following, 'rec_profiles':rec_profiles})
@login_required(redirect_field_name=None)
def delete_post(request, tweetID):
if request.method == 'POST':
tweet = Tweet.objects.get(id=tweetID)
if tweet.author == request.user:
tweet.delete()
return redirect('profile', username=request.user.username)
else:
return redirect('home')
@login_required(redirect_field_name=None)
def like_post(request):
tweet = get_object_or_404(Tweet, id=request.POST.get('id'))
if tweet.likes.filter(id=request.user.id).exists():
tweet.likes.remove(request.user)
is_liked = False
else:
tweet.likes.add(request.user)
is_liked = True
if(request.user != tweet.author):
Notification.objects.create(sender = request.user, receiver = User.objects.get(username = tweet.author), target = tweet, type = 'L')
context = {
'tweet': tweet,
'is_liked': is_liked,
}
if request.is_ajax():
html = render_to_string('tweet.html', context, request=request)
return JsonResponse({'form':html})
@login_required(redirect_field_name=None)
def change_mode(request):
if request.method == 'POST':
usr = User.objects.get(id=request.user.id)
usr.profile.mode = request.POST['mode']
usr.save()
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
else:
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
@login_required(redirect_field_name=None)
def follow_profile(request):
followed_user = get_object_or_404(User, id=request.POST.get('id'))
if Follow.objects.filter(user_id=followed_user.id, following_user_id = request.user.id).exists():
Follow.objects.filter(user_id=followed_user.id, following_user_id = request.user.id).delete()
is_following = False
else:
Follow.objects.create(user_id=followed_user, following_user_id = request.user)
Notification.objects.create(sender = request.user, receiver = followed_user, target = None, type = 'F')
is_following = True
context = {
'profile':followed_user,
'userProfile':followed_user,
'is_following':is_following
}
if request.is_ajax():
html = render_to_string('follow_button.html', context, request=request)
return JsonResponse({'form':html})
def notifications(request):
notifics = request.user.your_notifications.all()
for notific in notifics:
notific.seen = True
notific.save()
notifics = request.user.your_notifications.all().order_by('-id')[:10]
return render(request, 'notifications.html', {'notifics':notifics})
def tweet_details(request, tweetID):
tweet = Tweet.objects.get(id=tweetID)
comments = tweet.main_tweet.all().order_by('-timeStamp')
return render(request, 'tweet_details.html', {'tweet':tweet, 'comments':comments})
def comment(request, tweetID):
if request.method == 'POST':
author = request.user
content = request.POST['comment']
tweet = Tweet.objects.get(id=tweetID)
Comment.objects.create(author=author, main_tweet=tweet, content=content)
if(request.user != tweet.author):
Notification.objects.create(sender = request.user, receiver = tweet.author, target = tweet, type = 'C')
return redirect(tweet_details, tweetID=tweetID)
else:
return redirect(home)
#Notification on post comment
| 39.046948
| 242
| 0.666106
| 990
| 8,317
| 5.464646
| 0.157576
| 0.058965
| 0.031608
| 0.024584
| 0.438078
| 0.359704
| 0.292052
| 0.201294
| 0.154713
| 0.154713
| 0
| 0.001993
| 0.215823
| 8,317
| 213
| 243
| 39.046948
| 0.827507
| 0.006132
| 0
| 0.360465
| 0
| 0
| 0.075387
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.075581
| false
| 0.02907
| 0.075581
| 0.005814
| 0.296512
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72fc770cdae73372ef5eddce8deb799fc40b9990
| 3,078
|
py
|
Python
|
tests/kbcr/smart/test_smart.py
|
alex4321/ctp
|
22a6a55442a648e5f7d8c10f90708a7340360720
|
[
"MIT"
] | null | null | null |
tests/kbcr/smart/test_smart.py
|
alex4321/ctp
|
22a6a55442a648e5f7d8c10f90708a7340360720
|
[
"MIT"
] | null | null | null |
tests/kbcr/smart/test_smart.py
|
alex4321/ctp
|
22a6a55442a648e5f7d8c10f90708a7340360720
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import numpy as np
import torch
from torch import nn
from kbcr.kernels import GaussianKernel
from kbcr.smart import NeuralKB
import pytest
@pytest.mark.light
def test_smart_v1():
embedding_size = 50
rs = np.random.RandomState(0)
for _ in range(32):
with torch.no_grad():
triples = [
('a', 'p', 'b'),
('c', 'q', 'd'),
('e', 'q', 'f'),
('g', 'q', 'h'),
('i', 'q', 'l'),
('m', 'q', 'n'),
('o', 'q', 'p'),
('q', 'q', 'r'),
('s', 'q', 't'),
('u', 'q', 'v')
]
entity_lst = sorted({s for (s, _, _) in triples} | {o for (_, _, o) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities, embedding_size * 2, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates, embedding_size * 2, sparse=True)
fact_rel = torch.LongTensor(np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
kernel=kernel, facts=facts)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 2
xp_np[1] = 1
xo_np[1] = 3
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
print('xp_emb', xp_emb.shape)
res_sp, res_po = model.forward(xp_emb, xs_emb, xo_emb)
inf = model.score(xp_emb, xs_emb, xo_emb)
assert inf[0] > 0.9
assert inf[1] > 0.9
scores_sp, emb_sp = res_sp
scores_po, emb_po = res_po
print(scores_sp.shape, emb_sp.shape)
print(scores_po.shape, emb_po.shape)
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
print('AAA', inf)
print('BBB', scores_sp)
if __name__ == '__main__':
pytest.main([__file__])
# test_smart_v1()
| 30.475248
| 108
| 0.525016
| 394
| 3,078
| 3.819797
| 0.266497
| 0.03588
| 0.025914
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| 0.190033
| 0.102326
| 0.082392
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| 3,078
| 100
| 109
| 30.78
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| 1
| 0.014493
| false
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| 0
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| 0
|
1
| 0
|
72fca82d10a22b6f1dadf793abb5d2d66ab69ad0
| 254
|
py
|
Python
|
test.py
|
eseJiHeaLim/find_child
|
29596529ccf39241492b092b01baf03b76d0eb3a
|
[
"MIT"
] | null | null | null |
test.py
|
eseJiHeaLim/find_child
|
29596529ccf39241492b092b01baf03b76d0eb3a
|
[
"MIT"
] | null | null | null |
test.py
|
eseJiHeaLim/find_child
|
29596529ccf39241492b092b01baf03b76d0eb3a
|
[
"MIT"
] | null | null | null |
import tkinter
window=tkinter.Tk()
window.title("YUN DAE HEE")
window.geometry("640x400+100+100")
window.resizable(True, True)
image=tkinter.PhotoImage(file="opencv_frame_0.png")
label=tkinter.Label(window, image=image)
label.pack()
window.mainloop()
| 19.538462
| 51
| 0.775591
| 37
| 254
| 5.27027
| 0.621622
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| 0.066929
| 254
| 13
| 52
| 19.538462
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| 0
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| 0
| false
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| null | 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72fd5b11bfca65c6e323b75581cbff1627fbd28f
| 1,547
|
py
|
Python
|
ievv_opensource/utils/ievv_colorize.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | null | null | null |
ievv_opensource/utils/ievv_colorize.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | 37
|
2015-10-26T09:14:12.000Z
|
2022-02-10T10:35:33.000Z
|
ievv_opensource/utils/ievv_colorize.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | 1
|
2015-11-06T07:56:34.000Z
|
2015-11-06T07:56:34.000Z
|
from django.conf import settings
from termcolor import colored
#: Red color constant for :func:`.ievv_colorize`.
COLOR_RED = 'red'
#: Blue color constant for :func:`.ievv_colorize`.
COLOR_BLUE = 'blue'
#: Yellow color constant for :func:`.ievv_colorize`.
COLOR_YELLOW = 'yellow'
#: Grey color constant for :func:`.ievv_colorize`.
COLOR_GREY = 'grey'
#: Green color constant for :func:`.ievv_colorize`.
COLOR_GREEN = 'green'
def colorize(text, color, bold=False):
"""
Colorize a string for stdout/stderr.
Colors are only applied if :setting:`IEVV_COLORIZE_USE_COLORS` is
``True`` or not defined (so it defaults to ``True``).
Examples:
Print blue text::
from ievv_opensource.utils import ievv_colorize
print(ievv_colorize('Test', color=ievv_colorize.COLOR_BLUE))
Print bold red text::
print(ievv_colorize('Test', color=ievv_colorize.COLOR_RED, bold=True))
Args:
text: The text (string) to colorize.
color: The color to use.
Should be one of:
- :obj:`.COLOR_RED`
- :obj:`.COLOR_BLUE`
- :obj:`.COLOR_YELLOW`
- :obj:`.COLOR_GREY`
- :obj:`.COLOR_GREEN`
- ``None`` (no color)
bold: Set this to ``True`` to use bold font.
"""
if getattr(settings, 'IEVV_COLORIZE_USE_COLORS', True) and color:
attrs = []
if bold:
attrs.append('bold')
return colored(text, color=color, attrs=attrs)
else:
return text
| 25.783333
| 82
| 0.614092
| 195
| 1,547
| 4.723077
| 0.333333
| 0.156352
| 0.129207
| 0.108578
| 0.294245
| 0.294245
| 0.294245
| 0.093377
| 0
| 0
| 0
| 0
| 0.266322
| 1,547
| 59
| 83
| 26.220339
| 0.811454
| 0.648352
| 0
| 0
| 0
| 0
| 0.111607
| 0.053571
| 0
| 0
| 0
| 0
| 0
| 1
| 0.066667
| false
| 0
| 0.133333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ff28fd3335697c188eb234e3558bfd46b20d35
| 12,438
|
py
|
Python
|
RSICompute.py
|
bluefin1986/tinyspark
|
0b086d3af5316062c2f3aaa7d4492341ed5c71c2
|
[
"MIT"
] | 3
|
2020-04-14T14:08:11.000Z
|
2021-01-27T00:36:23.000Z
|
RSICompute.py
|
bluefin1986/tinyspark
|
0b086d3af5316062c2f3aaa7d4492341ed5c71c2
|
[
"MIT"
] | null | null | null |
RSICompute.py
|
bluefin1986/tinyspark
|
0b086d3af5316062c2f3aaa7d4492341ed5c71c2
|
[
"MIT"
] | 5
|
2020-02-15T09:54:13.000Z
|
2021-08-19T17:31:57.000Z
|
# coding: utf-8
# In[1]:
import baostock as bs
import pandas as pd
import numpy as np
import talib as ta
import matplotlib.pyplot as plt
import KlineService
import BaoStockUtil
import math
import datetime
from scipy import integrate
from RSI import DayRSI,WeekRSI,MonthRSI,SixtyMinRSI
from concurrent.futures import ThreadPoolExecutor, as_completed
from Stock import Stock
import dbutil
from IPython.core.debugger import set_trace
#算积分用的节点数
INTEGRATE_CALC_RANGE = 4
RSI_OVER_BUY = 80
RSI_OVER_SELL = 20
RSI_OVER_BUY_12 = 75
RSI_OVER_SELL_12 = 25
RSI_OVER_BUY_24 = 70
RSI_OVER_SELL_24 = 30
RSI_MIDDLE = 50
#日线超卖区域积分阈值
RSI_INTE_OVERSELL_THRESHOLD_DAY = 50
# In[3]:
def findLatestRSIDate(period):
mydb = dbutil.connectDB()
collection = mydb[chooseRSICollection(period)]
cursor = collection.find().sort("date",-1).limit(1)
df = pd.DataFrame(list(cursor))
if df.empty:
return "1970-01-01"
return df["date"][0]
def clearRSI(period):
mydb = dbutil.connectDB()
collection = mydb[chooseRSICollection(period)]
collection.delete_many({})
indexes = collection.index_information()
if "code_1_date_1" in indexes.keys():
collection.drop_index( "code_1_date_1" )
def createIndex(period):
mydb = dbutil.connectDB()
collection = mydb[chooseRSICollection(period)]
collection.create_index( [("code", 1), ("date",1)])
def integrateValues(valuesArray):
return integrate.trapz(valuesArray, x=None, dx=1.0, axis=-1)
##
# 从数据库读指定日期RSI数据
#
#
def readRSI(period, stockCode, startDate, endDate):
mydb = dbutil.connectDB()
collection = mydb[chooseRSICollection(period)]
if type(startDate) == str:
startDate = datetime.datetime.strptime(startDate + "T00:00:00.000Z", "%Y-%m-%dT%H:%M:%S.000Z")
endDate = datetime.datetime.strptime(endDate + "T23:59:59.000Z", "%Y-%m-%dT%H:%M:%S.000Z")
cursor = collection.find({"code":stockCode,"date":{"$gte":startDate,"$lte":endDate}})
df = pd.DataFrame(list(cursor))
return df
##
# 写RSI数据库
#
#
def writeRSIToDB(period, stockCode, stockName, rsi_df):
dataList = []
for index,rsi in rsi_df.iterrows():
rsiDate = rsi['date']
if period == "day":
rsiObj = DayRSI(stockCode, stockName)
elif period == "week":
rsiObj = WeekRSI(stockCode, stockName)
elif period == "month":
rsiObj = MonthRSI(stockCode, stockName)
elif period == "5m":
rsiObj = FiveMinRSI(stockCode, stockName)
elif period == "15m":
rsiObj = FiftyMinRSI(stockCode, stockName)
elif period == "30m":
rsiObj = ThirtyMinRSI(stockCode, stockName)
elif period == "60m":
rsiObj = SixtyMinRSI(stockCode, stockName)
rsiObj.date = rsiDate
rsiObj.rsi_6 = rsi['rsi_6']
rsiObj.rsi_12 = rsi['rsi_12']
rsiObj.rsi_24 = rsi['rsi_24']
rsiObj.overBuy = rsi['overBuyFlag']
rsiObj.overSell = rsi['overSellFlag']
dataList.append(rsiObj.__dict__)
mydb = dbutil.connectDB()
collection = mydb[chooseRSICollection(period)]
if len(dataList) > 0:
collection.insert_many(dataList)
else:
raise RuntimeError("RSI数据为空")
def computeStockRSI(period, stockCode, stockName, startDate, endDate):
try:
# compute1 = datetime.datetime.now().timestamp()
df = KlineService.readStockKline(stockCode, period, startDate, endDate)
# compute2 = datetime.datetime.now().timestamp()
# print("read stockLine:", compute2 - compute1)
if df.empty:
return False
if period == "day":
# 剔除日线停盘数据
df = df[df['tradeStatus'] == '1']
rsi_df = computeRSI(df)
# compute3 = datetime.datetime.now().timestamp()
# print("compute rsi:", compute3 - compute2)
writeRSIToDB(period, stockCode, stockName, rsi_df)
# compute4 = datetime.datetime.now().timestamp()
# print("write to db:", compute4 - compute3)
return True
except BaseException as e:
print ("download " + stockCode + " error:" + str(e))
return False
##
# 选择不同的Kline Collection
#
def chooseRSICollection(period):
periodRSICollection = {
"day" : "RSI_Day",
"week" : "RSI_Week",
"month" : "RSI_Month",
"5m" : "RSI_5m",
"15m" : "RSI_15m",
"30m" : "RSI_30m",
"60m" : "RSI_60m"
}
return periodRSICollection.get(period)
def computeRSI(klineDataFrame):
rsi_12days = ta.RSI(klineDataFrame['closePrice'],timeperiod=12)
rsi_6days = ta.RSI(klineDataFrame['closePrice'],timeperiod=6)
rsi_24days = ta.RSI(klineDataFrame['closePrice'],timeperiod=24)
rsiFrame = pd.DataFrame(klineDataFrame, columns=["date"])
rsiFrame['rsi_6'] = rsi_6days
rsiFrame['rsi_12'] = rsi_12days
rsiFrame['rsi_24'] = rsi_24days
##添加参考线位置
rsiFrame['overBuy'] = RSI_OVER_BUY
rsiFrame['overSell'] = RSI_OVER_SELL
rsiFrame['middle'] = RSI_MIDDLE
# RSI超卖和超买
rsi_buy_position = rsiFrame['rsi_12'] > RSI_OVER_BUY_12
rsi_sell_position = rsiFrame['rsi_12'] < RSI_OVER_SELL_12
rsiFrame.loc[rsi_buy_position[(rsi_buy_position == True) & (rsi_buy_position.shift() == False)].index, 'overBuyFlag'] = 'Yes'
rsiFrame.loc[rsi_sell_position[(rsi_sell_position == True) & (rsi_sell_position.shift() == False)].index, 'overSellFlag'] = 'Yes'
return rsiFrame
##
# 计算自起始日期起的RSI
#
#
def computeAllRSIDataOfPeriod(period, startDate):
# currtime = datetime.datetime.now().timestamp()
print("begin clear RSI period:", period)
clearRSI(period)
print("cleared RSI period:", period)
# time1 = datetime.datetime.now().timestamp()
# print("clear finished:",time1 - currtime)
stockDict = KlineService.allStocks()
# time2 = datetime.datetime.now().timestamp()
# print("read stocks finished:",time2 - time1)
endDate = str(datetime.date.today())
jobStart = datetime.datetime.now().timestamp()
processCount = 0
failCount = 0
jobTotal = len(stockDict)
'''
#起线程池来跑，单线程太慢了, 事实证明慢个鬼
executor = ThreadPoolExecutor(max_workers=1)
funcVars = []
for key,stock in stockDict.items():
#指数没有分钟线，调过指数的RSI分钟线计算
if period.endswith("m") and (key.startswith("sh.000") or key.startswith("sz.399")):
continue
funcVars.append([period, key, stock["name"], startDate, endDate])
all_task = [executor.submit(computeStockRSI, funcVar[0], funcVar[1], funcVar[2], funcVar[3], funcVar[4])
for funcVar in funcVars]
for future in as_completed(all_task):
processCount = processCount + 1
if not future.result():
failCount = failCount + 1
if processCount % 100 == 0 and processCount > 0:
print ("rsi process:", processCount, " of ", jobTotal ," failed:", failCount)
'''
for key,stock in stockDict.items():
processCount = processCount + 1
#指数没有分钟线，调过指数的RSI分钟线计算
if period.endswith("m") and (key.startswith("sh.000") or key.startswith("sz.399")):
continue
result = computeStockRSI(period, key, stock["name"], startDate, endDate)
if not result:
failCount = failCount + 1
if processCount % 100 == 0 and processCount > 0:
print ("rsi process:", processCount, " of ", jobTotal ," failed:", failCount)
jobFinished = datetime.datetime.now().timestamp()
createIndex(period)
print("write all stock RSI to db finished, cost:", jobFinished - jobStart)
return True
##
# 计算指定日期的RSI积分
#
#
def computeAllRSIDataIntegrate(period, specifiedDateStr, includeST):
BaoStockUtil.customLogin()
specifiedDate = datetime.datetime.strptime(specifiedDateStr, "%Y-%m-%d")
today = datetime.date.today()
#如果把时间设成未来，自动调成今天
if specifiedDate > datetime.datetime.today():
specifiedDate = datetime.date.today()
#避免跨年问题，直接从去年开始取
startDate = specifiedDate - datetime.timedelta(days = 365)
#取交易日列表，用作倒推周期使用
rs = bs.query_trade_dates(start_date=datetime.datetime.strftime(startDate, "%Y-%m-%d"), end_date = specifiedDate)
BaoStockUtil.customLogout()
if rs.error_code != '0':
raise RuntimeError("交易日api调用失败了:" + rs.error_code)
tradeDates = []
while (rs.error_code == '0') & rs.next():
row = rs.get_row_data()
if row[1] == "1":
tradeDates.append(row[0])
if len(tradeDates) == 0:
raise RuntimeError("取不到最新的交易日")
#若期望计算的日期比库里RSI最新日期还晚，数据不全待补齐
rsiLatestDate = findLatestRSIDate(period)
rsiLatestDateStr = datetime.datetime.strftime(rsiLatestDate, "%Y-%m-%d")
if rsiLatestDate < specifiedDate:
raise RuntimeError(specifiedDateStr + " 的 " + period + " RSI的数据不存在，待补齐数据")
#找到指定日期以及rsi存量数据最近日期在交易日周期的序号
specifiedDateIndex = tradeDates.index(specifiedDateStr)
if specifiedDateIndex == -1:
raise RuntimeError(specifiedDateStr + " 可能不是交易日")
daysBefore = computeRSIDataStartTradeDateRange(period, specifiedDateStr)
startDateIndex = specifiedDateIndex - daysBefore
#起始日期index负数，说明rsi数据不够
if startDateIndex < 0:
raise RuntimeError(period + " rsi数据不够")
startDateStr = tradeDates[startDateIndex]
print("compute rsi tradeDates from ", startDateStr, "to", specifiedDateStr)
processCount = 0
failCount = 0
startDateIndex = -1
dictStocks = KlineService.allStocks()
klineDataFrame = KlineService.readAllStockKline(period, specifiedDateStr, specifiedDateStr)
klineDataFrame = klineDataFrame.set_index("code")
klineDict = klineDataFrame.to_dict('index')
jobTotal = len(dictStocks)
rsiValueArrs = []
for i in range(0, 6):
rsiValueArrs.append([])
for key,stock in dictStocks.items():
processCount = processCount + 1
#指数没有分钟线，跳过指数的RSI分钟线计算
if period.endswith("m") and stock.stockType != 1:
continue
#如果不计算ST，跳过
if not includeST and stock["isST"]:
continue
#退市股就不要算了
if "退" in stock["name"]:
continue
#科创板不达门槛没法买，不看
if key.startswith("sh.68"):
continue
try:
rsiDF = readRSI(period, key, startDateStr, specifiedDateStr)
rsiCount = len(rsiDF)
if rsiCount < INTEGRATE_CALC_RANGE:
raise RuntimeError("积分计算节点不够")
rsiValueArrs[0].append(key)
rsiValueArrs[1].append(stock["name"])
rsiValueArrs[2].append(klineDict[key]["closePrice"])
#取最近的数据用于计算积分
rsiValueArrs[3].append(rsiDF["rsi_6"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount])
rsiValueArrs[4].append(rsiDF["rsi_12"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount])
rsiValueArrs[5].append(rsiDF["rsi_24"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount])
except BaseException as e:
failCount = failCount + 1
print ("compute rsi integrate " + key + " error:" + str(e))
if processCount % 100 == 0 and processCount > 0:
print ("compute rsi integrate process:", processCount, " of ", jobTotal ," failed:", failCount)
rsi6Arr = np.array(rsiValueArrs[3]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi6InteArr = integrateValues(rsi6Arr)
rsi12Arr = np.array(rsiValueArrs[4]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi12InteArr = integrateValues(rsi12Arr)
rsi24Arr = np.array(rsiValueArrs[5]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi24InteArr = integrateValues(rsi24Arr)
rsiInteDF = pd.DataFrame()
rsiInteDF["code"] = rsiValueArrs[0]
rsiInteDF["name"] = rsiValueArrs[1]
rsiInteDF["closePrice"] = rsiValueArrs[2]
rsiInteDF["rsi_inte_6"] = rsi6InteArr
rsiInteDF["rsi_inte_12"] = rsi12InteArr
rsiInteDF["rsi_inte_24"] = rsi24InteArr
return rsiInteDF
#算出计算本周期下指定数据需要的起始交易日
#每个交易日一共4小时，所以取4小时为一天，而不是24小时
#每个计算周期一共至少需要4个节点，分钟线RSI统一除以4*60=240分钟算出所需计算数据天数，最少为一天
#日线不用除分钟
## TODO 周线没想好怎么算，更别说月线了。
def computeRSIDataStartTradeDateRange(period, specifiedDate):
daysBefore = 0
if period.endswith("m"):
daysBefore = math.ceil(INTEGRATE_CALC_RANGE * (int(period.replace("m", "")) + 1) / (60 * 4))
elif period == "day":
daysBefore = INTEGRATE_CALC_RANGE
else:
raise RuntimeError("周期有误")
return daysBefore
| 34.359116
| 133
| 0.651069
| 1,325
| 12,438
| 6.008302
| 0.258113
| 0.030147
| 0.02261
| 0.031654
| 0.224846
| 0.163422
| 0.099862
| 0.099862
| 0.068082
| 0.049491
| 0
| 0.027968
| 0.226725
| 12,438
| 361
| 134
| 34.454294
| 0.79975
| 0.087474
| 0
| 0.171548
| 0
| 0
| 0.082378
| 0.004205
| 0
| 0
| 0
| 0.00277
| 0
| 1
| 0.050209
| false
| 0
| 0.062762
| 0.004184
| 0.16318
| 0.033473
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ff4801f405ee21c99ddd54c7ec445e3fe9a25d
| 1,558
|
py
|
Python
|
osnoise/conf/base.py
|
abousselmi/OSNoise
|
f0e4baa51921f672179c014beb89555958c7ddca
|
[
"Apache-2.0"
] | 4
|
2017-11-17T13:19:32.000Z
|
2020-05-29T05:10:58.000Z
|
osnoise/conf/base.py
|
abousselmi/osnoise
|
f0e4baa51921f672179c014beb89555958c7ddca
|
[
"Apache-2.0"
] | null | null | null |
osnoise/conf/base.py
|
abousselmi/osnoise
|
f0e4baa51921f672179c014beb89555958c7ddca
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2016 Orange
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from oslo_config import cfg
base_options = [
cfg.StrOpt(
'log_file_name',
default='osnoise.log',
help='Osnoise file name.'),
cfg.StrOpt(
'log_dir',
default='/var/log/osnoise/',
help='Osnoise log directory.'),
cfg.StrOpt(
'log_level',
default='info',
help='Log level.'),
cfg.StrOpt(
'log_file',
default='/var/log/osnoise/osnoise.log',
help='Log file'),
cfg.IntOpt(
'log_maxBytes',
default=1000000,
min=1000,
help='Log level.'),
cfg.IntOpt(
'log_backupCount',
default=5,
min=1,
help='Log level.'),
cfg.BoolOpt('log_config_append',
default=False,
deprecated_group='DEFAULT',
help='To append logs to existent log file or not.'),
]
def register_opts(conf):
conf.register_opts(base_options)
def list_opts():
return {'DEFAULT' : base_options}
| 26.40678
| 74
| 0.625802
| 200
| 1,558
| 4.79
| 0.505
| 0.06263
| 0.050104
| 0.046973
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018341
| 0.265083
| 1,558
| 59
| 75
| 26.40678
| 0.818341
| 0.348524
| 0
| 0.243243
| 0
| 0
| 0.275449
| 0.027944
| 0
| 0
| 0
| 0
| 0
| 1
| 0.054054
| false
| 0
| 0.027027
| 0.027027
| 0.108108
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
72ff9b4fe0f33f7f62e39cedf2e3740b3be6be6c
| 9,360
|
py
|
Python
|
Cogs/HelpCommand.py
|
gudtldn/DiscordStockBot
|
d1b06e49738092ccf3c5d5a26b35fd321a3bd0f2
|
[
"MIT"
] | 1
|
2022-03-12T13:43:36.000Z
|
2022-03-12T13:43:36.000Z
|
Cogs/HelpCommand.py
|
gudtldn/DiscordStockBot
|
d1b06e49738092ccf3c5d5a26b35fd321a3bd0f2
|
[
"MIT"
] | 1
|
2022-03-12T04:53:08.000Z
|
2022-03-12T13:41:15.000Z
|
Cogs/HelpCommand.py
|
gudtldn/DiscordStockBot
|
d1b06e49738092ccf3c5d5a26b35fd321a3bd0f2
|
[
"MIT"
] | null | null | null |
#도움말
import discord
from discord.ext import commands
from discord.ext.commands import Context
from define import *
class HelpCommand_Context(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.command(name="도움말", aliases=["명령어", "?"])
@CommandExecutionTime
async def _HelpCommand(self, ctx: Context, command: str=None):
logger.info(f"[{type(ctx)}] {ctx.author.name}: {ctx.invoked_with} {command}")
if ctx.guild is None:
logger.info("Guild is None")
return
if command is not None:
if command.startswith("."):
command = command.replace(".", "", 1)
if command is None:
embed = discord.Embed(title="도움말", description="[] <-- 필수 입력항목 | <> <-- 선택 입력항목", color=RandomEmbedColor())
embed.add_field(name=".사용자등록", value="데이터 베이스에 사용자를 등록합니다.", inline=False)
embed.add_field(name=".자산정보", value="현재 자신의 자산정보를 확인합니다.", inline=False)
embed.add_field(name=".주가", value="현재 주가를 검색합니다.", inline=False)
embed.add_field(name=".매수", value="입력한 기업의 주식을 매수합니다.", inline=False)
embed.add_field(name=".매도", value="입력한 기업의 주식을 매도합니다.", inline=False)
embed.add_field(name=".지원금", value="1만원 ~ 10만원 사이의 돈을 랜덤으로 지급합니다.", inline=False)
embed.add_field(name=".초기화", value="자신의 자산정보를 초기화 합니다.", inline=False)
embed.add_field(name=".탈퇴", value="이 봇에 저장되어있는 사용자의 정보를 삭제합니다.", inline=False)
embed.add_field(name=".개인설정", value="개인설정을 확인 또는 수정합니다.", inline=False)
embed.add_field(name=".단축어설정", value="단축어목록을 확인하거나, 추가 또는 제거합니다.", inline=False)
embed.add_field(name=".관심종목", value="관심종목에 추가된 주식의 가격을 확인하거나, 추가 또는 제거합니다.", inline=False)
embed.set_footer(text="명령어를 자세히 보려면 「.도움말 <명령어 이름>」 을 써 주세요.")
await ctx.reply(embed=embed)
return
elif command in ("도움말", "명령어", "?"):
command_list = ["도움말", "명령어", "?"]
command_list.remove(command)
embed = discord.Embed(title="도움말", description="등록되어있는 명령어들을 출력합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("사용자등록", "등록"):
command_list = ["사용자등록", "등록"]
command_list.remove(command)
embed = discord.Embed(title="사용자등록", description="데이터 베이스에 사용자를 등록합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("자산정보", "자산조회"):
command_list = ["자산정보", "자산조회"]
command_list.remove(command)
embed = discord.Embed(title="자산정보", description="자신의 자산정보를 확인합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".자산정보 <@유저>", value="@유저의 자산정보를 확인합니다.", inline=False)
embed.add_field(name=".자산정보 <랭킹 | 순위>", value="이 서버에 있는 유저의 자산랭킹을 나열합니다.", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("주가", "시세"):
command_list = ["주가", "시세"]
command_list.remove(command)
embed = discord.Embed(title="주가", description="입력한 기업의 현재 주가를 확인합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".주가 [기업이름 | 기업번호]", value="기업이름 또는 기업번호로 검색합니다.", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("매수", "구매", "주식구매", "주식매수"):
command_list = ["매수", "구매", "주식구매", "주식매수"]
command_list.remove(command)
embed = discord.Embed(title="매수", description="입력한 기업의 주식을 매수합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".매수 [기업이름 | 기업번호] [매수 할 주식 개수]", value="입력한 기업의 주식을, 주식 개수만큼 매수합니다.", inline=False)
embed.add_field(name=".매수 [기업이름 | 기업번호] [풀매수 | 모두]", value="입력한 기업의 주식을 최대까지 매수합니다.", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("매도", "판매", "주식판매", "주식매도"):
command_list = ["매도", "판매", "주식판매", "주식매도"]
command_list.remove(command)
embed = discord.Embed(title="매도", description="입력한 기업의 주식을 매도합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".매도 [기업이름 | 기업번호] [매도 할 주식 개수]", value="입력한 기업의 주식을, 주식 개수만큼 매도합니다.", inline=False)
embed.add_field(name=".매도 [기업이름 | 기업번호] [반매도]", value="입력한 기업의 주식의 절반을 매도합니다.", inline=False)
embed.add_field(name=".매도 [기업이름 | 기업번호] [풀매도 | 모두]", value="입력한 기업의 주식을 모두 매도합니다.", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("지원금", "돈받기"):
command_list = ["지원금", "돈받기"]
command_list.remove(command)
embed = discord.Embed(title="지원금", description="1만원 ~ 10만원 사이의 돈을 랜덤으로 지급합니다. (쿨타임: 4시간)", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
await ctx.reply(embed=embed)
return
elif command == "초기화":
embed = discord.Embed(title="초기화", description="「초기화확인」를 입력해 자신의 자산정보를 초기화 합니다.", color=RandomEmbedColor())
embed.add_field(name=".초기화 [확인문구]", value="확인문구에는 「초기화확인」를 입력해 주세요.")
await ctx.reply(embed=embed)
return
elif command in ("탈퇴", "회원탈퇴"):
command_list = ["탈퇴", "회원탈퇴"]
command_list.remove(command)
embed = discord.Embed(title="탈퇴", description="「탈퇴확인」를 입력해 데이터 베이스에서 자신의 자산정보를 삭제합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".탈퇴 [확인문구]", value="확인문구에는 「탈퇴확인」를 입력해 주세요.")
await ctx.reply(embed=embed)
return
elif command in ("개인설정", "설정"):
command_list = ["개인설정", "설정"]
command_list.remove(command)
embed = discord.Embed(title="개인설정", description="개인설정을 확인 또는 수정합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".개인설정 설정정보", value="설정할 수 있는 목록을 확인합니다.", inline=False)
embed.add_field(name=".개인설정 자산정보 [true | false]", value="자산정보 공개여부를 설정합니다.", inline=False)
embed.add_field(name=".개인설정 지원금표시 [true | false]", value="지원금으로 얻은 돈 표시여부를 설정합니다.", inline=False)
embed.add_field(name=".개인설정 차트표시 [true | false]", value="`주가` 명령어에 차트를 표시합니다.", inline=False)
embed.add_field(name=".개인설정 쿨타임표시 [true | false]", value="`지원금` 명령어에 쿨타임을 바로 표시합니다.", inline=False)
embed.add_field(name=".개인설정 어제대비가격 [true | false]", value="`자산정보` 명령어에 현재 주가 대신, 어제 대비 가격을 표시합니다.", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("단축어설정", "단축어"):
command_list = ["단축어설정", "단축어"]
command_list.remove(command)
embed = discord.Embed(title="단축어설정", description="단축어목록을 확인하거나, 추가 또는 제거합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".단축어설정 목록", value="자신의 단축어 목록을 확인합니다.", inline=False)
embed.add_field(name=".단축어설정 추가 -이름 [기업이름] -번호 [기업번호]", value="단축어 목록에 단축어를 새로 추가합니다.\n\
사용 예: `.단축어 추가 -이름 삼전 -번호 005930`", inline=False)
embed.add_field(name=".단축어설정 추가 -번호 [기업번호]", value="단축어 목록에 단축어를 새로 추가합니다.(이름은 기업이름으로 설정됩니다)\n\
사용 예: `.단축어 추가 -번호 005930`", inline=False)
embed.add_field(name=".단축어설정 제거 -이름 [기업이름]", value="단축어 목록에 있는 단축어를 제거합니다.\n\
사용 예: `.단축어 제거 -이름 삼전`", inline=False)
await ctx.reply(embed=embed)
return
elif command in ("관심종목", "관심"):
command_list = ["관심종목", "관심"]
command_list.remove(command)
embed = discord.Embed(title="관심종목", description="관심종목에 추가된 주식의 가격을 확인하거나, 추가 또는 제거합니다.", color=RandomEmbedColor())
embed.add_field(name="다른이름", value=f"{', '.join(command_list)}", inline=False)
embed.add_field(name=".관심종목 주가", value="관심종목에 추가된 주식의 주가를 나열합니다.", inline=False)
embed.add_field(name=".관심종목 추가", value="관심종목에 주식을 추가합니다.", inline=False)
embed.add_field(name=".관심종목 제거", value="관심종목에서 주식을 제거합니다.", inline=False)
await ctx.reply(embed=embed)
return
else:
await ctx.reply("알 수 없는 명령어 입니다.")
return
def setup(bot: commands.Bot):
bot.add_cog(HelpCommand_Context(bot))
| 52.58427
| 128
| 0.570833
| 1,171
| 9,360
| 4.487617
| 0.181042
| 0.068506
| 0.111323
| 0.145576
| 0.669648
| 0.656708
| 0.606089
| 0.532445
| 0.351855
| 0.315699
| 0
| 0.002947
| 0.274893
| 9,360
| 178
| 129
| 52.58427
| 0.771328
| 0.000321
| 0
| 0.344828
| 0
| 0
| 0.232767
| 0.024687
| 0
| 0
| 0
| 0
| 0
| 1
| 0.013793
| false
| 0
| 0.027586
| 0
| 0.151724
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f400616765ba783e10a8ef7b8571b9c9e51facfb
| 778
|
py
|
Python
|
test/test_model/cprofile_test.py
|
SupermeLC/PyNeval
|
2cccfb1af7d97857454e9cbc3515ba75e5d8d4b0
|
[
"BSD-3-Clause"
] | 12
|
2020-07-18T16:55:23.000Z
|
2022-03-14T12:26:08.000Z
|
test/test_model/cprofile_test.py
|
SupermeLC/PyNeval
|
2cccfb1af7d97857454e9cbc3515ba75e5d8d4b0
|
[
"BSD-3-Clause"
] | 5
|
2021-05-31T22:08:51.000Z
|
2021-08-31T15:42:44.000Z
|
test/test_model/cprofile_test.py
|
SupermeLC/PyNeval
|
2cccfb1af7d97857454e9cbc3515ba75e5d8d4b0
|
[
"BSD-3-Clause"
] | 2
|
2021-09-24T03:02:27.000Z
|
2021-11-09T06:21:00.000Z
|
import cProfile
import pstats
import os
# 性能分析装饰器定义
def do_cprofile(filename):
"""
Decorator for function profiling.
"""
def wrapper(func):
def profiled_func(*args, **kwargs):
# Flag for do profiling or not.
DO_PROF = False
if DO_PROF:
profile = cProfile.Profile()
profile.enable()
result = func(*args, **kwargs)
profile.disable()
# Sort stat by internal time.
sortby = "tottime"
ps = pstats.Stats(profile).sort_stats(sortby)
ps.dump_stats(filename)
else:
result = func(*args, **kwargs)
return result
return profiled_func
return wrapper
| 27.785714
| 61
| 0.521851
| 76
| 778
| 5.25
| 0.513158
| 0.06015
| 0.105263
| 0.100251
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.394602
| 778
| 28
| 62
| 27.785714
| 0.847134
| 0.131105
| 0
| 0.1
| 0
| 0
| 0.010622
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15
| false
| 0
| 0.15
| 0
| 0.45
| 0
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| 0
| null | 0
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| 0
| 0
| 0
| 0
|
1
| 0
|
f40395409149c4799e946dbfb2cb47f22353b013
| 4,531
|
py
|
Python
|
vega/security/run_dask.py
|
zjzh/vega
|
aa6e7b8c69024262fc483ee06113b4d1bd5156d8
|
[
"Apache-2.0"
] | null | null | null |
vega/security/run_dask.py
|
zjzh/vega
|
aa6e7b8c69024262fc483ee06113b4d1bd5156d8
|
[
"Apache-2.0"
] | null | null | null |
vega/security/run_dask.py
|
zjzh/vega
|
aa6e7b8c69024262fc483ee06113b4d1bd5156d8
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. 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.
"""Run dask scheduler and worker."""
import os
import subprocess
import shutil
import logging
import socket
import random
from distributed import Client
from distributed.security import Security
from .conf import get_config
from .verify_cert import verify_cert
sec_cfg = get_config('server')
def get_client_security(address):
"""Get client."""
address = address.replace("tcp", "tls")
if not verify_cert(sec_cfg.ca_cert, sec_cfg.client_cert_dask):
logging.error(f"The cert {sec_cfg.ca_cert} and {sec_cfg.client_cert_dask} are invalid, please check.")
sec = Security(tls_ca_file=sec_cfg.ca_cert,
tls_client_cert=sec_cfg.client_cert_dask,
tls_client_key=sec_cfg.client_secret_key_dask,
require_encryption=True)
return Client(address, security=sec)
def get_address_security(master_host, master_port):
"""Get address."""
return "tls://{}:{}".format(master_host, master_port)
def run_scheduler_security(ip, port, tmp_file):
"""Run scheduler."""
if not verify_cert(sec_cfg.ca_cert, sec_cfg.server_cert_dask):
logging.error(f"The cert {sec_cfg.ca_cert} and {sec_cfg.server_cert_dask} are invalid, please check.")
return subprocess.Popen(
[
"dask-scheduler",
"--no-dashboard",
"--no-show",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.server_cert_dask}",
f"--tls-key={sec_cfg.server_secret_key_dask}",
f"--host={ip}",
"--protocol=tls",
f"--port={port}",
f"--scheduler-file={tmp_file}",
f"--local-directory={os.path.dirname(tmp_file)}",
],
env=os.environ
)
def _available_port(min_port, max_port) -> int:
_sock = socket.socket()
while True:
port = random.randint(min_port, max_port)
try:
_sock.bind(('', port))
_sock.close()
return port
except Exception:
logging.debug('Failed to get available port, continue.')
continue
return None
def run_local_worker_security(slave_ip, address, local_dir):
"""Run dask-worker on local node."""
address = address.replace("tcp", "tls")
nanny_port = _available_port(30000, 30999)
worker_port = _available_port(29000, 29999)
pid = subprocess.Popen(
[
"dask-worker",
address,
'--nthreads=1',
'--nprocs=1',
'--memory-limit=0',
f"--local-directory={local_dir}",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.client_cert_dask}",
f"--tls-key={sec_cfg.client_secret_key_dask}",
"--no-dashboard",
f"--host={slave_ip}",
"--protocol=tls",
f"--nanny-port={nanny_port}",
f"--worker-port={worker_port}",
],
env=os.environ
)
return pid
def run_remote_worker_security(slave_ip, address, local_dir):
"""Run dask-worker on remote node."""
address = address.replace("tcp", "tls")
nanny_port = _available_port(30000, 30999)
worker_port = _available_port(29000, 29999)
pid = subprocess.Popen(
[
"ssh",
slave_ip,
shutil.which("dask-worker"),
address,
'--nthreads=1',
'--nprocs=1',
'--memory-limit=0',
f"--local-directory={local_dir}",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.client_cert_dask}",
f"--tls-key={sec_cfg.client_secret_key_dask}",
"--no-dashboard",
f"--host={slave_ip}",
"--protocol=tls",
f"--nanny-port={nanny_port}",
f"--worker-port={worker_port}",
],
env=os.environ
)
return pid
| 32.364286
| 110
| 0.604282
| 580
| 4,531
| 4.508621
| 0.27931
| 0.048184
| 0.042065
| 0.036711
| 0.469981
| 0.455067
| 0.416826
| 0.390057
| 0.390057
| 0.390057
| 0
| 0.016462
| 0.262635
| 4,531
| 139
| 111
| 32.597122
| 0.766238
| 0.165306
| 0
| 0.446602
| 0
| 0
| 0.286784
| 0.164794
| 0
| 0
| 0
| 0
| 0
| 1
| 0.058252
| false
| 0
| 0.097087
| 0
| 0.223301
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f404334cff69f2f2935c67baf019e6df2ad2e301
| 12,512
|
py
|
Python
|
MISSGANvsStarGAN/core/solver.py
|
NoaBrazilay/DeepLearningProject
|
5c44d21069de1fc5fa2687c4121286670be3d773
|
[
"MIT"
] | 2
|
2021-09-03T11:44:31.000Z
|
2021-09-22T11:51:47.000Z
|
MISSGANvsStarGAN/core/solver.py
|
NoaBrazilay/MISSGAN
|
5c44d21069de1fc5fa2687c4121286670be3d773
|
[
"MIT"
] | null | null | null |
MISSGANvsStarGAN/core/solver.py
|
NoaBrazilay/MISSGAN
|
5c44d21069de1fc5fa2687c4121286670be3d773
|
[
"MIT"
] | 1
|
2020-10-20T08:06:50.000Z
|
2020-10-20T08:06:50.000Z
|
"""
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""
import os
from os.path import join as ospj
import time
import datetime
from munch import Munch
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.model import build_model
from core.checkpoint import CheckpointIO
from core.data_loader import InputFetcher
import core.utils as utils
from metrics.eval import calculate_metrics
class Solver(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.nets, self.nets_ema, self.vgg, self.VggExtract = build_model(args)
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.L1Loss = nn.L1Loss()
# below setattrs are to make networks be children of Solver, e.g., for self.to(self.device)
for name, module in self.nets.items():
utils.print_network(module, name)
setattr(self, name, module)
for name, module in self.nets_ema.items():
setattr(self, name + '_ema', module)
if args.mode == 'train':
self.optims = Munch()
for net in self.nets.keys():
if net == 'fan':
continue
self.optims[net] = torch.optim.Adam(
params=self.nets[net].parameters(),
lr=args.f_lr if net == 'mapping_network' else args.lr,
betas=[args.beta1, args.beta2],
weight_decay=args.weight_decay)
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '100000_nets.ckpt'), **self.nets),
CheckpointIO(ospj(args.checkpoint_dir, '100000_nets_ema.ckpt'), **self.nets_ema),
CheckpointIO(ospj(args.checkpoint_dir, '100000_optims.ckpt'), **self.optims)]
else:
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '100000_nets_ema.ckpt'), **self.nets_ema)]
self.to(self.device)
for name, network in self.named_children():
# Do not initialize the FAN parameters
if ('ema' not in name) and ('fan' not in name):
print('Initializing %s...' % name)
network.apply(utils.he_init)
def _save_checkpoint(self, step):
for ckptio in self.ckptios:
ckptio.save(step)
def _load_checkpoint(self, step):
for ckptio in self.ckptios:
ckptio.load(step)
def _reset_grad(self):
for optim in self.optims.values():
optim.zero_grad()
def train(self, loaders):
args = self.args
nets = self.nets
nets_ema = self.nets_ema
optims = self.optims
# fetch random validation images for debugging
fetcher = InputFetcher(loaders.src, loaders.ref, args.latent_dim, 'train')
fetcher_val = InputFetcher(loaders.val, None, args.latent_dim, 'val')
inputs_val = next(fetcher_val)
# resume training if necessary
if args.resume_iter > 0:
self._load_checkpoint(args.resume_iter)
# remember the initial value of ds weight
initial_lambda_ds = args.lambda_ds
print('Start training...')
start_time = time.time()
for i in range(args.resume_iter, args.total_iters):
# fetch images and labels
inputs = next(fetcher)
x_real, y_org = inputs.x_src, inputs.y_src
x_ref, x_ref2, y_trg = inputs.x_ref, inputs.x_ref2, inputs.y_ref
z_trg, z_trg2 = inputs.z_trg, inputs.z_trg2
masks = nets.fan.get_heatmap(x_real) if args.w_hpf > 0 else None
# train the discriminator
d_loss, d_losses_latent = compute_d_loss(
nets, args, x_real, y_org, y_trg, z_trg=z_trg, masks=masks)
self._reset_grad()
d_loss.backward()
optims.discriminator.step()
d_loss, d_losses_ref = compute_d_loss(
nets, args, x_real, y_org, y_trg, x_ref=x_ref, masks=masks)
self._reset_grad()
d_loss.backward()
optims.discriminator.step()
# train the generator
g_loss, g_losses_latent = compute_g_loss(
nets, args, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], masks=masks,VggExtract=self.VggExtract, IN = self.instancenorm, L1Loss=self.L1Loss)
self._reset_grad()
g_loss.backward()
optims.generator.step()
optims.mapping_network.step()
optims.style_encoder.step()
g_loss, g_losses_ref = compute_g_loss(
nets, args, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], masks=masks, VggExtract=self.VggExtract, IN = self.instancenorm, L1Loss=self.L1Loss)
self._reset_grad()
g_loss.backward()
optims.generator.step()
# compute moving average of network parameters
moving_average(nets.generator, nets_ema.generator, beta=0.999)
moving_average(nets.mapping_network, nets_ema.mapping_network, beta=0.999)
moving_average(nets.style_encoder, nets_ema.style_encoder, beta=0.999)
# decay weight for diversity sensitive loss
if args.lambda_ds > 0:
args.lambda_ds -= (initial_lambda_ds / args.ds_iter)
# print out log info
if (i+1) % args.print_every == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))[:-7]
log = "Elapsed time [%s], Iteration [%i/%i], " % (elapsed, i+1, args.total_iters)
all_losses = dict()
for loss, prefix in zip([d_losses_latent, d_losses_ref, g_losses_latent, g_losses_ref],
['D/latent_', 'D/ref_', 'G/latent_', 'G/ref_']):
for key, value in loss.items():
all_losses[prefix + key] = value
all_losses['G/lambda_ds'] = args.lambda_ds
log += ' '.join(['%s: [%.4f]' % (key, value) for key, value in all_losses.items()])
print(log)
# generate images for debugging
if (i+1) % args.sample_every == 0:
os.makedirs(args.sample_dir, exist_ok=True)
utils.debug_image(nets_ema, args, inputs=inputs_val, step=i+1)
# save model checkpoints
if (i+1) % args.save_every == 0:
self._save_checkpoint(step=i+1)
# compute FID and LPIPS if necessary
if (i+1) % args.eval_every == 0:
calculate_metrics(nets_ema, args, i+1, mode='latent')
calculate_metrics(nets_ema, args, i+1, mode='reference')
@torch.no_grad()
def sample(self, loaders):
args = self.args
nets_ema = self.nets_ema
os.makedirs(args.result_dir, exist_ok=True)
self._load_checkpoint(args.resume_iter)
src = next(InputFetcher(loaders.src, None, args.latent_dim, 'test'))
ref = next(InputFetcher(loaders.ref, None, args.latent_dim, 'test'))
fname = ospj(args.result_dir, 'reference.jpg')
print('Working on {}...'.format(fname))
utils.translate_using_reference(nets_ema, args, src.x, ref.x, ref.y, fname)
# fname = ospj(args.result_dir, 'video_ref.mp4')
# print('Working on {}...'.format(fname))
# utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname)
@torch.no_grad()
def evaluate(self):
args = self.args
nets_ema = self.nets_ema
resume_iter = args.resume_iter
self._load_checkpoint(args.resume_iter)
calculate_metrics(nets_ema, args, step=resume_iter, mode='latent')
calculate_metrics(nets_ema, args, step=resume_iter, mode='reference')
def compute_d_loss(nets, args, x_real, y_org, y_trg, z_trg=None, x_ref=None, masks=None):
assert (z_trg is None) != (x_ref is None)
# with real images
x_real.requires_grad_()
out = nets.discriminator(x_real, y_org)
loss_real = adv_loss(out, 1)
loss_reg = r1_reg(out, x_real)
# with fake images
with torch.no_grad():
if z_trg is not None:
s_trg = nets.mapping_network(z_trg, y_trg)
else: # x_ref is not None
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake,_ = nets.generator(x_real, s_trg, masks=masks)
out = nets.discriminator(x_fake, y_trg)
loss_fake = adv_loss(out, 0)
loss = loss_real + loss_fake + args.lambda_reg * loss_reg
return loss, Munch(real=loss_real.item(),
fake=loss_fake.item(),
reg=loss_reg.item())
def compute_g_loss(nets, args, x_real, y_org, y_trg, z_trgs=None, x_refs=None, masks=None, VggExtract= None, IN= None, L1Loss=None):
assert (z_trgs is None) != (x_refs is None)
if z_trgs is not None:
z_trg, z_trg2 = z_trgs
if x_refs is not None:
x_ref, x_ref2 = x_refs
# adversarial loss
if z_trgs is not None:
s_trg = nets.mapping_network(z_trg, y_trg)
else:
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake, content_latent_real = nets.generator(x_real, s_trg, masks=masks)
out = nets.discriminator(x_fake, y_trg)
loss_adv = adv_loss(out, 1)
# style reconstruction loss
s_pred = nets.style_encoder(x_fake, y_trg)
loss_sty = torch.mean(torch.abs(s_pred - s_trg))
# diversity sensitive loss
if z_trgs is not None:
s_trg2 = nets.mapping_network(z_trg2, y_trg)
else:
s_trg2 = nets.style_encoder(x_ref2, y_trg)
x_fake2, content_latent_real2 = nets.generator(x_real, s_trg2, masks=masks)
x_fake2 = x_fake2.detach()
loss_ds = torch.mean(torch.abs(x_fake - x_fake2))
# cycle-consistency loss
masks = nets.fan.get_heatmap(x_fake) if args.w_hpf > 0 else None
s_org = nets.style_encoder(x_real, y_org)
x_rec, content_latent_reco = nets.generator(x_fake, s_org, masks=masks)
loss_cyc = torch.mean(torch.abs(x_rec - x_real))
loss_vgg = compute_vgg_loss(x_fake, x_real, VggExtract, IN, L1Loss) if args.vgg_w > 0 else 0
loss_sacl = utils.abs_criterion(content_latent_real, content_latent_reco) if args.loss_sacl > 0 else 0 # Loss style aware content loss
loss_sacl2 = utils.abs_criterion(content_latent_real2, content_latent_reco) if args.loss_sacl > 0 else 0 # Loss style aware content loss
loss = loss_adv + args.lambda_sty * loss_sty \
- args.lambda_ds * loss_ds + args.lambda_cyc * loss_cyc + args.lambda_vgg * loss_vgg + args.lambda_loss_sacl * loss_sacl+ args.lambda_loss_sacl * loss_sacl2
return loss, Munch(adv=loss_adv.item(),
sty=loss_sty.item(),
ds=loss_ds.item(),
cyc=loss_cyc.item())
def moving_average(model, model_test, beta=0.999):
for param, param_test in zip(model.parameters(), model_test.parameters()):
param_test.data = torch.lerp(param.data, param_test.data, beta)
def adv_loss(logits, target):
assert target in [1, 0]
targets = torch.full_like(logits, fill_value=target)
loss = F.binary_cross_entropy_with_logits(logits, targets)
return loss
def compute_vgg_loss(img, target, VggExtract, IN, L1Loss):
# img_vgg = utils.vgg_preprocess(img)
# target_vgg = utils.vgg_preprocess(target)
# img_fea = vgg(img_vgg)
# target_fea = vgg(target_vgg)
img_fea_dict = VggExtract(img)
target_fea_dict = VggExtract(target)
# loss = torch.mean((img_fea_dict['relu3_3'] - target_fea_dict['relu3_3']) ** 2)
# loss = torch.mean(torch.abs(img_fea_dict['relu3_3'] - target_fea_dict['relu3_3']))
loss = L1Loss(img_fea_dict['relu2_2'] , target_fea_dict['relu2_2'])
return loss
def r1_reg(d_out, x_in):
# zero-centered gradient penalty for real images
batch_size = x_in.size(0)
grad_dout = torch.autograd.grad(
outputs=d_out.sum(), inputs=x_in,
create_graph=True, retain_graph=True, only_inputs=True
)[0]
grad_dout2 = grad_dout.pow(2)
assert(grad_dout2.size() == x_in.size())
reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0)
return reg
| 40.623377
| 164
| 0.629955
| 1,783
| 12,512
| 4.176108
| 0.174986
| 0.020682
| 0.007252
| 0.010878
| 0.326887
| 0.284448
| 0.22549
| 0.218775
| 0.197556
| 0.164384
| 0
| 0.016447
| 0.261349
| 12,512
| 308
| 165
| 40.623377
| 0.789223
| 0.120844
| 0
| 0.185185
| 0
| 0
| 0.029924
| 0
| 0
| 0
| 0
| 0
| 0.018519
| 1
| 0.060185
| false
| 0
| 0.060185
| 0
| 0.148148
| 0.027778
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f404f9b14c52ee5b292f41c316a483d68139b651
| 2,261
|
py
|
Python
|
guessing_game.py
|
JoviCastillo/TH-Project-1-guessing-game-
|
efa5c7080b1a484b20655ddb01873dc3edefc415
|
[
"BSD-2-Clause"
] | null | null | null |
guessing_game.py
|
JoviCastillo/TH-Project-1-guessing-game-
|
efa5c7080b1a484b20655ddb01873dc3edefc415
|
[
"BSD-2-Clause"
] | null | null | null |
guessing_game.py
|
JoviCastillo/TH-Project-1-guessing-game-
|
efa5c7080b1a484b20655ddb01873dc3edefc415
|
[
"BSD-2-Clause"
] | null | null | null |
import random
highscore = []
def not_in_range(guess_it):
"""This is to check that the numbers inputted by the user are in range,
and will let the user know. If the numbers are in range then it passes.
"""
if guess_it < 1:
print('I am not thinking of negative numbers!')
elif guess_it > 10:
print('That number is way bigger than 10!')
else:
pass
def new_game(tries):
"""After the user has guessed the number correctly, the game
will ask the player if they would like to play again. Yes will start
the game again. No will exit the game. Highscore will be displayed
by the lowest amount of tries recorded.
"""
play_again = input('Would you like to play again? (Yes/No) ')
if play_again.upper() == 'YES':
highscore.append(tries)
highscore.sort
print('The highscore is {}.'.format(highscore[0]))
start_game()
elif play_again.upper() == 'NO':
exit()
else:
play_again = input('Please let me know by typing yes or no: ')
def start_game(): # title screen of the game
"""This is the start of the game which include the title screen and
is the main function that runs all the other functions as well.
"""
print('-' * 40)
print('Welcome to the Number Guessing Game!!!')
print('-' * 40)
print('I am thinking of a number between 1-10.')
random_number = random.randint(1, 10)
tries = 0
while True:
try:
guess_it = int(input('Can you guess it?: '))
except ValueError:
print('I said number, not gibberish!')
else:
while guess_it != random_number:
not_in_range(guess_it)
tries += 1
if guess_it > random_number:
print('That is too high!')
elif guess_it < random_number:
print('That is too low')
break
else:
print('You guessed it right! Your number was {}.'.format(random_number))
print('It took you {} tries.'.format(tries))
break
new_game(tries)
if __name__ == '__main__':
# Kick off the program by calling the start_game function.
start_game()
| 32.3
| 88
| 0.590889
| 313
| 2,261
| 4.15655
| 0.370607
| 0.048424
| 0.029977
| 0.043812
| 0.104535
| 0.05073
| 0.05073
| 0.05073
| 0
| 0
| 0
| 0.011643
| 0.316232
| 2,261
| 69
| 89
| 32.768116
| 0.829884
| 0.259177
| 0
| 0.208333
| 0
| 0
| 0.249538
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0625
| false
| 0.020833
| 0.020833
| 0
| 0.083333
| 0.25
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f407eb6974ae23f62280d5ff068afc9b35ea9eeb
| 984
|
py
|
Python
|
cli.py
|
palazzem/elmo-server
|
b2e02d600a431dc1db31090f0d8dd09a8d586373
|
[
"BSD-3-Clause"
] | null | null | null |
cli.py
|
palazzem/elmo-server
|
b2e02d600a431dc1db31090f0d8dd09a8d586373
|
[
"BSD-3-Clause"
] | 8
|
2019-05-20T19:26:01.000Z
|
2019-05-26T13:02:45.000Z
|
cli.py
|
palazzem/elmo-server
|
b2e02d600a431dc1db31090f0d8dd09a8d586373
|
[
"BSD-3-Clause"
] | null | null | null |
import click
APP_YAML_TEMPLATE = """runtime: python37
env_variables:
ELMO_BASE_URL: '{BASE_URL}'
ELMO_VENDOR: '{VENDOR}'
handlers:
- url: /.*
script: auto
secure: always
redirect_http_response_code: 301
"""
@click.command()
@click.argument("base_url")
@click.argument("vendor")
def generate_app_yaml(base_url, vendor):
"""Use APP_YAML_TEMPLATE to generate app.yaml for AppEngine deployments.
Args:
base_url: defines ELMO_BASE_URL env variable in AppEngine config.
vendor: defines ELMO_VENDOR env variable in AppEngine config.
Returns:
Writes `app.yaml` file in the current folder.
"""
print("Writing the following deployment config to disk:")
app_yaml = APP_YAML_TEMPLATE.format(BASE_URL=base_url, VENDOR=vendor)
print(app_yaml)
with open("app.yaml", "w") as f:
f.write(app_yaml)
print("Done! You can deploy the service with `gcloud app deploy`")
if __name__ == "__main__":
generate_app_yaml()
| 25.230769
| 76
| 0.704268
| 136
| 984
| 4.823529
| 0.470588
| 0.117378
| 0.068598
| 0.042683
| 0.085366
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006258
| 0.188008
| 984
| 38
| 77
| 25.894737
| 0.814768
| 0.275407
| 0
| 0
| 0
| 0
| 0.458944
| 0.041056
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043478
| false
| 0
| 0.043478
| 0
| 0.086957
| 0.130435
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f408a9fe238e011fdbd51d60d3da477f1a193548
| 1,713
|
py
|
Python
|
prepareDataSet.py
|
Dakewe-DS1000/LapRSNet
|
47e630acd3f0523ee5ac698566ff45e645681b23
|
[
"Apache-2.0"
] | 6
|
2019-11-14T12:12:43.000Z
|
2021-07-10T13:05:14.000Z
|
prepareDataSet.py
|
Dakewe-DS1000/LapRSNet
|
47e630acd3f0523ee5ac698566ff45e645681b23
|
[
"Apache-2.0"
] | null | null | null |
prepareDataSet.py
|
Dakewe-DS1000/LapRSNet
|
47e630acd3f0523ee5ac698566ff45e645681b23
|
[
"Apache-2.0"
] | 1
|
2021-05-18T06:41:11.000Z
|
2021-05-18T06:41:11.000Z
|
# Prepare my dataset for Digital Pathology
import os
import math
import cv2
import pdb
rootFolder = "F:\DataBase\LymphnodePathology"
trainFolder = rootFolder + "\\trainDataSet"
testFolder = rootFolder + "\\testDataSet"
srcTrainFilePath = trainFolder + "\\20X\\"
dstTrainFilePath = trainFolder + "\\5X\\"
srcTestFilePath = testFolder + "\\20X\\"
dstTestFilePath = testFolder + "\\5X\\"
factor = 4
if __name__ == '__main__':
srcTrainFileNameList = os.listdir(srcTrainFilePath)
srcTestFileNameList = os.listdir(srcTestFilePath)
for srcTrainFileName in srcTrainFileNameList:
srcTrainImage = cv2.imread(srcTrainFilePath + srcTrainFileName)
imgHeight, imgWidth, _ = srcTrainImage.shape
newWidth = int(imgWidth / factor)
newHeight = int(imgHeight / factor)
newSize = (newWidth, newHeight)
dstTrainImage = cv2.resize(srcTrainImage, newSize, interpolation=cv2.INTER_AREA)
print("Train File Name : %s, (%d, %d) => (%d, %d)" %(srcTrainFileName, imgWidth, imgHeight, newSize[0], newSize[1]))
cv2.imwrite(dstTrainFilePath + srcTrainFileName, dstTrainImage)
for srcTestFileName in srcTestFileNameList:
srcTestImage = cv2.imread(srcTestFilePath + srcTestFileName)
imgHeight, imgWidth, _ = srcTestImage.shape
newWidth = int(imgWidth / factor)
newHeight = int(imgHeight / factor)
newSize = (newWidth, newHeight)
dstTestImage = cv2.resize(srcTestImage, newSize, interpolation=cv2.INTER_AREA)
print("Test File Name : %s, (%d, %d) => (%d, %d)" %(srcTestFileName, imgWidth, imgHeight, newSize[0], newSize[1]))
cv2.imwrite(dstTestFilePath + srcTestFileName, dstTestImage)
| 34.959184
| 124
| 0.694688
| 157
| 1,713
| 7.503185
| 0.388535
| 0.010187
| 0.010187
| 0.040747
| 0.295416
| 0.295416
| 0.232598
| 0.210526
| 0.137521
| 0.137521
| 0
| 0.01444
| 0.191477
| 1,713
| 48
| 125
| 35.6875
| 0.836101
| 0.023351
| 0
| 0.181818
| 0
| 0
| 0.104129
| 0.017953
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.121212
| 0
| 0.121212
| 0.060606
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f40be0fa2a141ea92705b94cef65862a1f2be235
| 2,619
|
py
|
Python
|
junn-predict/junn_predict/common/logging.py
|
modsim/junn
|
a40423b98c6a3739dd0b2ba02d546a5db91f9215
|
[
"BSD-2-Clause"
] | null | null | null |
junn-predict/junn_predict/common/logging.py
|
modsim/junn
|
a40423b98c6a3739dd0b2ba02d546a5db91f9215
|
[
"BSD-2-Clause"
] | null | null | null |
junn-predict/junn_predict/common/logging.py
|
modsim/junn
|
a40423b98c6a3739dd0b2ba02d546a5db91f9215
|
[
"BSD-2-Clause"
] | null | null | null |
"""Logging helpers."""
import logging
import sys
import colorlog
import tqdm
class TqdmLoggingHandler(logging.StreamHandler):
"""TqdmLoggingHandler, outputs log messages to the console compatible with tqdm."""
def emit(self, record): # noqa: D102
message = self.format(record)
tqdm.tqdm.write(message)
class DelayedFileLog(logging.StreamHandler):
"""DelayedFileLog will cache messages till it can write them to a specified file."""
def __init__(self): # noqa: D107
super().__init__()
self.file_name = None
self.buffer = []
def emit(self, record): # noqa: D102
if self.file_name is None:
message = self.format(record)
self.buffer.append(message)
else:
super().emit(record)
def setFilename(self, file_name, mode='a'):
"""
Set the filename to write the log messages to.
:param file_name: File name to use.
:param mode: File open mode, by default 'a'.
:return: None
"""
self.file_name = file_name
stream = open(file_name, mode)
for old_message in self.buffer:
stream.write(old_message + self.terminator)
self.setStream(stream)
def setup_logging(level):
"""
Set the logging up to the specified level.
:param level: Log level
:return: None
"""
name_to_log_level = get_name_to_log_level_dict()
if level in name_to_log_level:
level = name_to_log_level[level]
tqdm_log_handler = TqdmLoggingHandler()
log_format = (
"%(asctime)-15s.%(msecs)03d %(process)d %(levelname)s %(name)s %(message)s"
)
log_datefmt = '%Y-%m-%d %H:%M:%S'
tqdm_log_handler.setFormatter(
colorlog.TTYColoredFormatter(
fmt='%(log_color)s' + log_format, datefmt=log_datefmt, stream=sys.stdout
)
)
buffer = DelayedFileLog()
log_handlers = [tqdm_log_handler, buffer]
# noinspection PyArgumentList
logging.basicConfig(
level=level, format=log_format, datefmt=log_datefmt, handlers=log_handlers
)
def get_name_to_log_level_dict():
"""
Return a dict with a mapping of log levels.
:return: The dict
"""
# noinspection PyProtectedMember
name_to_log_level = logging._nameToLevel.copy()
return name_to_log_level
def get_log_levels():
"""
Return supported log levels.
:return: List of log levels
"""
log_levels = [
k for k, v in sorted(get_name_to_log_level_dict().items(), key=lambda ab: ab[1])
]
log_levels.remove('NOTSET')
return log_levels
| 25.930693
| 88
| 0.640321
| 331
| 2,619
| 4.858006
| 0.323263
| 0.033582
| 0.044776
| 0.069652
| 0.126244
| 0.070274
| 0
| 0
| 0
| 0
| 0
| 0.007179
| 0.255441
| 2,619
| 100
| 89
| 26.19
| 0.817436
| 0.233677
| 0
| 0.075472
| 0
| 0.018868
| 0.058542
| 0.013837
| 0
| 0
| 0
| 0
| 0
| 1
| 0.132075
| false
| 0
| 0.075472
| 0
| 0.283019
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f40cae84710b69af5184821f31d2608460ea3b50
| 2,284
|
py
|
Python
|
subpartcode/ultrasonic_basic_code.py
|
LesterYHZ/Automated-Bridge-Inspection-Robot-Project
|
c3f4e12f9b60a8a6b041bf2b6d0461a1bb39c726
|
[
"MIT"
] | 1
|
2020-04-15T01:17:06.000Z
|
2020-04-15T01:17:06.000Z
|
subpartcode/ultrasonic_basic_code.py
|
LesterYHZ/Automated-Bridge-Inspection-Robot-Project
|
c3f4e12f9b60a8a6b041bf2b6d0461a1bb39c726
|
[
"MIT"
] | null | null | null |
subpartcode/ultrasonic_basic_code.py
|
LesterYHZ/Automated-Bridge-Inspection-Robot-Project
|
c3f4e12f9b60a8a6b041bf2b6d0461a1bb39c726
|
[
"MIT"
] | 1
|
2020-04-13T16:45:06.000Z
|
2020-04-13T16:45:06.000Z
|
#Basic Ultrasonic sensor (HC-SR04) code
import RPi.GPIO as GPIO #GPIO RPI library
import time # makes sure Pi waits between steps
GPIO.setmode(GPIO.BCM) #sets GPIO pin numbering
#GPIO.setmode(GPIO.BOARD)
#Remove warnings
GPIO.setwarnings(False)
#Create loop variable
#loop = 1
#BCM
TRIG = 23 #output pin - triggers the sensor
ECHO = 24 #input pin - reads the return signal from the sensor
#BOARD
#TRIG=16
#ECHO=18
#Looping not necessary
#Print a message to let the user know that distance measurement is in progress
print ("Distance Measurement In Progress")
#Set two GPIO ports as inputs/outputs
GPIO.setup(TRIG,GPIO.OUT)
GPIO.setup(ECHO,GPIO.IN)
#while loop == 1: #Looping forever
while True: #Looping forever
#Ensure the trigger pin is set low
GPIO.output(TRIG, False)
#Give the sensor a second to settle
print ("Waiting for Sensor to Settle")
#time.sleep(2)
time.sleep(1)
#Create trigger pulse
GPIO.output(TRIG,True)
#Set trigger pin high for 10uS
time.sleep(0.00001)
#Set it low again
GPIO.output(TRIG,False)
#Record the last low timestamp for ECHO (just before the return signal is received and the pin goes high)
while GPIO.input(ECHO)==0:
pulse_start = time.time()
#Once a signal is received, the value changes from low to high, and the signal will remain high for the duration of the echo pulse
while GPIO.input(ECHO)==1:
pulse_end = time.time()
#speed=distance/time
#speed of sound at sea level = 343m/s
#34300 = distance/(time/2)
#17150 = distance/time
#17150*time = distance
#Calculating...
pulse_duration = pulse_end - pulse_start
distance_cm = pulse_duration*17150
#distance_cm = pulse_duration*0.034/2;
distance_cm = round(distance_cm,2)
distance_inch = distance_cm/2.54 #2.54 cm in 1 inch
#distance_inch = pulse_duration*0.0133/2
distance_inch = round(distance_inch,2)
distance_feet = distance_inch/12
distance_feet = round(distance_feet,2)
#Print distance
#print ("Distance:",distance_cm,"cm")
#print ("Distance:",distance_inch,"in")
print ("Distance:",distance_feet,"ft")
#Delay
time.sleep(2)
#Clean GPIO pins to ensure all inputs/outputs are reset
GPIO.cleanup()
| 26.252874
| 134
| 0.700088
| 348
| 2,284
| 4.525862
| 0.37931
| 0.038095
| 0.026667
| 0.024127
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.039713
| 0.206217
| 2,284
| 86
| 135
| 26.55814
| 0.829013
| 0.533275
| 0
| 0.066667
| 0
| 0
| 0.069472
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.066667
| 0
| 0.066667
| 0.1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f40ff4bc5a583d0c231681fd8bba22b2aa827939
| 3,481
|
py
|
Python
|
6_refin_widgets.py
|
jiaxinjiang2919/Refinance-Calculator
|
f4bb0c536b88692ef90f504fdb2d9bed85588b7c
|
[
"Apache-2.0"
] | 14
|
2019-05-01T05:03:20.000Z
|
2022-01-08T03:18:05.000Z
|
6_refin_widgets.py
|
jiaxinjiang2919/Refinance-Calculator
|
f4bb0c536b88692ef90f504fdb2d9bed85588b7c
|
[
"Apache-2.0"
] | null | null | null |
6_refin_widgets.py
|
jiaxinjiang2919/Refinance-Calculator
|
f4bb0c536b88692ef90f504fdb2d9bed85588b7c
|
[
"Apache-2.0"
] | 8
|
2019-05-19T11:24:28.000Z
|
2022-02-16T20:19:30.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 24 15:02:37 2019
@author: Matt Macarty
"""
from tkinter import *
import numpy as np
class LoanCalculator:
def __init__(self):
window = Tk()
window.title("Loan Calculator")
Label(window, text="Loan Amount").grid(row=1, column=1, sticky=W)
Label(window, text="Interest rate").grid(row=2, column=1, sticky=W)
Label(window, text="Term (years)").grid(row=3, column=1, sticky=W)
Label(window, text=None).grid(row=4,column=1) # space between inputs and outputs
Label(window, text="Payment:").grid(row=5, column=1, sticky=W)
Label(window, text="Total Payments:").grid(row=6, column=1, sticky=W)
# variables to store loan inputs
self.pv = StringVar()
self.interest_rate = StringVar()
self.term = StringVar()
# varianbles for loan outputs
self.pmt = StringVar()
self.total = StringVar()
# text boxes to hold inputs and outputs
Entry(window, textvariable = self.pv,
justify=RIGHT).grid(row=1,column=2, padx=(0,5))
Entry(window, textvariable = self.interest_rate,
justify=RIGHT).grid(row=2,column=2, padx=(0,5))
Entry(window, textvariable = self.term,
justify=RIGHT).grid(row=3,column=2, padx=(0,5))
Label(window, textvariable = self.pmt,
font="Helvetica 12 bold",
justify=RIGHT).grid(row=5,column=2,sticky= E )
Label(window, textvariable = self.total,
font="Helvetica 12 bold",
justify=RIGHT).grid(row=6,column=2, sticky= E)
Button(window, text="Calculate Payment", command=self.calcPayment).grid(row=7,column=2, padx= (60,5), pady=5)
# Refinance variables
self.old_pmt = StringVar()
self.time_left = StringVar()
self.refi_cost = StringVar()
# Refinance widgets
Label(window, text="Current Payment").grid(row=8,column=1)
Label(window, text="Time Left").grid(row=9,column=1)
Label(window, text="Cost of Refi").grid(row=10,column=1)
Entry(window, textvariable=self.old_pmt, justify=RIGHT).grid(row=8,column=2, padx=(0,5))
Entry(window, textvariable=self.time_left, justify=RIGHT).grid(row=9,column=2, padx=(0,5))
Entry(window, textvariable=self.refi_cost, justify=RIGHT).grid(row=10,column=2, padx=(0,5))
# Refi output variables
self.monthly_savings = StringVar()
self.payback = StringVar()
self.overall_savings = StringVar()
Label(window, text="Payback Months:").grid(row=11,column=1)
Label(window, text="Monthly Savings:").grid(row=12,column=1)
Label(window, text="Overall Savings:").grid(row=13,column=1)
Button(window, text="Evaluate Refi", command=self.evalRefi).grid(row=14,column=2, padx= (100,5), pady=5)
window.mainloop()
def calcPayment(self):
pv = float(self.pv.get())
rate = float(self.interest_rate.get())
term = int(self.term.get())
pmt = np.pmt(rate / 1200, term * 12, -pv,0)
total = pmt * term * 12
self.pmt.set("$" + format(pmt, "5,.2f"))
self.total.set("$" + format(total, "8,.2f"))
def evalRefi():
pass
LoanCalculator()
| 36.642105
| 118
| 0.578857
| 447
| 3,481
| 4.474273
| 0.263982
| 0.077
| 0.09
| 0.076
| 0.233
| 0.176
| 0.176
| 0.118
| 0.08
| 0
| 0
| 0.040048
| 0.275496
| 3,481
| 94
| 119
| 37.031915
| 0.752974
| 0.077851
| 0
| 0.034483
| 0
| 0
| 0.075137
| 0
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| 0
| 0
| 1
| 0.051724
| false
| 0.017241
| 0.034483
| 0
| 0.103448
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
|
1
| 0
|
f4117b390cbdb79866a23c18436a60de53454ed6
| 19,224
|
py
|
Python
|
ndctl.py
|
davelarsen58/pmemtool
|
a7acb0991cbcd683f761d4b108d018d7d2d10aeb
|
[
"MIT"
] | 3
|
2021-12-17T04:26:30.000Z
|
2022-03-30T06:32:21.000Z
|
ndctl.py
|
davelarsen58/pmemtool
|
a7acb0991cbcd683f761d4b108d018d7d2d10aeb
|
[
"MIT"
] | 9
|
2021-12-21T17:14:58.000Z
|
2022-02-12T00:45:11.000Z
|
ndctl.py
|
davelarsen58/pmemtool
|
a7acb0991cbcd683f761d4b108d018d7d2d10aeb
|
[
"MIT"
] | 1
|
2022-01-18T23:26:02.000Z
|
2022-01-18T23:26:02.000Z
|
#!/usr/bin/python3
#
# PMTOOL NDCTL Python Module
# Copyright (C) David P Larsen
# Released under MIT License
import os
import json
from common import message, get_linenumber, pretty_print
from common import V0, V1, V2, V3, V4, V5, D0, D1, D2, D3, D4, D5
import common as c
import time
DEFAULT_FSTAB_FILE = "/etc/fstab"
DEFAULT_NDCTL_FILE = "/tmp/ndctl_list_NDRH.txt"
DEBUG = 0
VERBOSE = c.VERBOSE
tmp_dir = '/tmp'
timers = []
# If working in a test sandbox, change paths
# to start with path to sandbox
#
if not os.getenv('SANDBOX'):
SANDBOX = ''
else:
SANDBOX = os.environ['SANDBOX']
print('Enabling Sandbox at:', SANDBOX)
# FSTAB = SANDBOX + '/etc/fstab'
DEVDIR = SANDBOX + '/dev'
DEV_UUID = DEVDIR + '/disk/by-uuid/'
NDCTL_FILE = SANDBOX + "/tmp/ndctl_list_NDRH.txt"
ndctl = {}
# ---------------------------------------------------------------------
def clean_up():
'''clean up all tmp files associated with this mdule'''
name = 'clean_up()'
tic = time.perf_counter()
status = False
file_name = '/tmp/ndctl*.txt'
status = c.clean_up(file_name)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return status
def get_nmem_dev_list(node):
''' returns list of nmems['nmem0' 'nmem1' 'nmem2' 'nmem3' 'nmem4' 'nmem5']
ndctl list -D -U 0
{
"dev":"nmem2",
"id":"8089-a2-1836-00002716",
"handle":33,
"phys_id":42,
"flag_failed_flush":true,
"flag_smart_event":true,
"security":"disabled"
}
'''
name = 'get_nmem_dev_list()'
tic = time.perf_counter()
file_name = '/tmp/ndctl_list_-D_-U_node' + str(node) + '.txt'
cmd = "/usr/bin/ndctl list -D -U " + str(node) + " > " + file_name
if not os.path.exists(file_name):
os.system(cmd)
tmp = {}
my_list = []
with open(file_name, 'r') as f:
tmp = json.load(f)
for t in range(len(tmp)):
my_list.append(tmp[0]['dev'])
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return my_list
# ---------------------------------------------------------------------
def get_region_dev_list(node):
''' returns list of regions devices, ie: "region0"
ndctl list -U 0
[
{
"dev":"region0",
"size":1623497637888,
"available_size":0,
"max_available_extent":0,
"type":"pmem",
"iset_id":-7155516910447809332,
"persistence_domain":"memory_controller"
}
]
'''
name = 'get_region_dev_list()'
tic = time.perf_counter()
file_name = '/tmp/ndctl_list_-R_-U_node' + str(node) + '.txt'
cmd = "/usr/bin/ndctl list -R -U " + str(node) + " > " + file_name
if not os.path.exists(file_name):
os.system(cmd)
#
tmp = {}
with open(file_name, 'r') as f:
tmp = json.load(f)
my_list = []
for t in range(len(tmp)):
my_list.append(tmp[0]['dev'])
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return my_list
# ---------------------------------------------------------------------
def get_ns_dev(node):
''' returns list of namespace names, ie: "namespace0.0"
ndctl list -U 0
[
{
"dev":"namespace0.0",
"mode":"fsdax",
"map":"dev",
"size":1598128390144,
"uuid":"115ff8e8-bd52-47b8-a678-9b200902d864",
"sector_size":512,
"align":2097152,
"blockdev":"pmem0"
}
]
'''
name = 'get_ns_dev()'
tic = time.perf_counter()
file_name = '/tmp/ndctl_list_-N_-U' + str(node) + '.txt'
cmd = "/usr/bin/ndctl list -N -U " + str(node) + " > " + file_name
os.system(cmd)
#
tmp = {}
with open(file_name, 'r') as f:
tmp = json.load(f)
#
my_list = []
for t in range(len(tmp)):
my_list.append(tmp[0]['dev'])
#
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return my_list
# ---------------------------------------------------------------------
def get_ns_block_dev(node):
''' returns list of ns blockdevs, ie: "pmem0"
ndctl list -U 0
[
{
"dev":"namespace0.0",
"mode":"fsdax",
"map":"dev",
"size":1598128390144,
"uuid":"115ff8e8-bd52-47b8-a678-9b200902d864",
"sector_size":512,
"align":2097152,
"blockdev":"pmem0"
}
]
'''
name = 'get_ns_block_dev()'
tic = time.perf_counter()
file_name = '/tmp/ndctl_list_-N_-U' + str(node) + '.txt'
cmd = "/usr/bin/ndctl list -N -U " + str(node) + " > " + file_name
os.system(cmd)
#
tmp = {}
with open(file_name, 'r') as f:
tmp = json.load(f)
#
my_list = []
for t in range(len(tmp)):
my_list.append(tmp[0]['blockdev'])
#
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return my_list
# ---------------------------------------------------------------------
def dump(file_name = NDCTL_FILE):
"""
dump the config to a file to parse
"""
name = 'dump()'
tic = time.perf_counter()
# message("Function:", __name__, "File:", file_name )
# if VERBOSE: print(' Querying ndctl data:', file_name, end="...")
# ndctl list -NDRH
cmd = "/usr/bin/ndctl list -NDRH > " + file_name
os.system(cmd)
# if VERBOSE: print('Done')
def parse(file_name = NDCTL_FILE):
"""
parse ndctl dump file into dict: ndctl
"""
name = 'parse()'
tic = time.perf_counter()
global ndctl
# if DEBUG: print("DEBUG: Function:", __name__, "File:", file_name )
# if VERBOSE: print(' Parsing ndctl data:', file_name, end="...")
with open(file_name, 'r') as f:
ndctl = json.load(f)
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", ndctl)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return ndctl
# - +- +- +- +- +- +- +- +- +- +- +- +- +- +- +- +- +- +- +- +
# Accessor Functions
#
def get_region_dimm_list(region):
"""
returns list of pmem dimms assocaited with pmem region
"""
name = 'get_region_dimm_list()'
tic = time.perf_counter()
global ndctl
dimm_list = []
# if DEBUG: print("DEBUG: Function:", __name__, "Region:", region )
# if VERBOSE: print(' getting:', __name__, end="...")
for r in range(len(ndctl['regions'])):
# if this region matches arg, get DIMM mappings
if ndctl['regions'][r]['dev'] == region:
for d in range(len(ndctl['regions'][r]['mappings'])):
if DEBUG: print(' ndctl[regions][r]mappings', ndctl['regions'][r]['mappings'][d]['dimm'])
dimm_list.append(ndctl['regions'][r]['mappings'][d]['dimm'])
continue
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, region, "DIMMS", dimm_list)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return dimm_list
def get_region_list():
"""
Region List
returns list of all pmem regions
"""
name = 'get_region_list()'
tic = time.perf_counter()
global ndctl
region_list = []
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
for r in range(len(ndctl['regions'])):
region_list.append(ndctl['regions'][r]['dev'])
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", region_list)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return region_list
def get_region_ns_device_list(region):
"""
Region Namespace Device List
returns list of all pmem namespaces names associated w/ pmem region
"""
name = 'get_region_ns_device_list()'
tic = time.perf_counter()
ns_list = []
# if DEBUG: print("DEBUG: Function:", __name__, "Region:", region )
# if VERBOSE: print(' getting:', __name__, end="...")
for r in range(len(ndctl['regions'])):
# if this region matches arg, get DIMM mappings
if ndctl['regions'][r]['dev'] == region:
for d in range(len(ndctl['regions'][r]['namespaces'])):
if DEBUG: print(' ndctl[regions][r]mappings', ndctl['regions'][r]['mappings'][d]['dimm'])
ns_list.append(ndctl['regions'][r]['namespaces'][d]['blockdev'])
continue
# if VERBOSE: print('Done')
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return ns_list
def get_region_ns_name_list(region):
"""
Region Namespace List
returns list of all pmem namespaces names associated w/ pmem region
"""
name = 'get_region_ns_name_list()'
tic = time.perf_counter()
ns_list = []
# if DEBUG: print("DEBUG: Function:", __name__, "Region:", region )
# if VERBOSE: print(' getting:', __name__, end="...")
for r in range(len(ndctl['regions'])):
# if this region matches arg, get DIMM mappings
if ndctl['regions'][r]['dev'] == region:
for d in range(len(ndctl['regions'][r]['namespaces'])):
if DEBUG: print(' ndctl[regions][r]mappings', ndctl['regions'][r]['mappings'][d]['dimm'])
ns_list.append(ndctl['regions'][r]['namespaces'][d]['dev'])
continue
# if VERBOSE: print('Done')
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return ns_list
def get_dimm_status(dimm):
"""
DIMM List
returns status of given dimm
"""
name = 'get_dimm_status()'
tic = time.perf_counter()
# dimm_list = []
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
for d in range(len(ndctl['dimms'])):
if DEBUG: print(ndctl['dimms'][d]['dev'], ndctl['dimms'][d]['health']['health_state'])
if ndctl['dimms'][d]['dev'] == dimm:
status = ndctl['dimms'][d]['health']['health_state']
break
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", dimmList)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return status
def get_dimm_list():
"""
DIMM List
returns list of all pmem devices in system
"""
name = 'get_dimm_list()'
tic = time.perf_counter()
dimm_list = []
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
for d in range(len(ndctl['dimms'])):
dimm_list.append(ndctl['dimms'][d]['dev'])
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", dimmList)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return dimm_list
def get_region_by_dimm(dimm):
"""
Get Region by DIMM
returns region associated with PMEM device
"""
name = 'get_region_by_dimm()'
tic = time.perf_counter()
region = "regionX"
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
# loop through regions, get dimmList for each, check if match
for r in range(len(ndctl['regions'])):
region = ndctl['regions'][r]['dev']
dimmList = get_region_dimm_list(region)
# print("get_region_by_dimm.r", r, region, dimmList )
if dimm in dimmList: break
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", region)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return region
def get_ns_name_list_by_dimm(dimm):
"""
Get PMEM Namespace name by DIMM
returns list of pmem namespaces associated with name
"""
name = 'get_ns_name_list_by_dimm()'
tic = time.perf_counter()
nsNameList = []
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
# loop through regions, get dimmList for each, check if match
for r in range(len(ndctl['regions'])):
region = ndctl['regions'][r]['dev']
dimmList = get_region_dimm_list(region)
# we should have a region to lookup namespaces
nsNameList = get_region_ns_name_list(region)
if dimm in dimmList: break
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", nsNameList)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return nsNameList
def get_ns_device_list_by_dimm(dimm):
"""
Get Namespace Devices by DIMM
returns pmem namespace device for given DIMM
"""
name = 'get_ns_device_list_by_dimm()'
tic = time.perf_counter()
ns_device_list = []
dimm_list = []
# if DEBUG: print("DEBUG: Function:", __name__ )
# if VERBOSE: print(' getting:', __name__, end="...")
# loop through regions, get dimmList for each, check if match
for r in range(len(ndctl['regions'])):
region = ndctl['regions'][r]['dev']
dimm_list = get_region_dimm_list(region)
# we should have a region to lookup namespaces
ns_device_list = get_region_ns_device_list(region)
if dimm in dimm_list: break
# if VERBOSE: print('Done')
# if DEBUG: print("Debug:", __name__, ":", ns_device_list)
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
return ns_device_list
def list_dimm_table():
name = 'list_dimm_table()'
tic = time.perf_counter()
print()
print("Optane Persistent Memory DIMM Status")
print()
print("%-7s %-21s %-6s %-6s %-6s %-6s" % ("Linux", "DIMM", "DIMM", "DIMM", "Cntrl", "Remaining") )
print("%-7s %-21s %-6s %-6s %-6s %-6s" % ("Device", "UID", "Health", "Temp", "Temp", "Life") )
print("%-7s %-21s %-6s %-6s %-6s %-6s" % ("-------", "--------------------", "------", "------", "------", "----") )
for x in range(len(ndctl['dimms'])):
print("%-7s %-21s %6s %-6s %-6s %-6s" % (
ndctl['dimms'][x]['dev'], \
ndctl['dimms'][x]['id'], \
ndctl['dimms'][x]['health']['health_state'], \
ndctl['dimms'][x]['health']['temperature_celsius'], \
ndctl['dimms'][x]['health']['controller_temperature_celsius'], \
ndctl['dimms'][x]['health']['spares_percentage'] \
))
def module_test():
name = 'module_test()'
tic = time.perf_counter()
import sys
import os
global VERBOSE
global DEBUG
VERBOSE = 0
DEBUG = 0
# Dicts
ndctl = {}
# Lists
regionList = []
dimmList = []
nsList = []
nsDeviceList = []
nsNameList = []
region = "region1"
dimm = "nmem0"
print("Module: ndctl.py: Testing Functions")
dump()
ndctl = parse()
# OK
dimmList = get_dimm_list()
print(" MAIN:get_dimm_list:dimmList:", dimmList)
# OK
regionList = get_region_list()
print(" MAIN:get_region_list:regionList:", regionList)
# OK
dimmList = get_region_dimm_list(region)
print(" MAIN:get_region_dimm_list:dimmList", " Region:", region, "DIMM's", dimmList)
# OK
region = "region0"
nsList = get_region_ns_name_list(region)
print(" MAIN:get_region_ns_name_list:nsList", " Region:", region, "NS", nsList)
# OK
region = "region1"
nsList = get_region_ns_device_list(region)
print(" MAIN:get_region_ns_device_list:nsList", " Region:", region, "NS", nsList)
dimm = "nmem1"
region = get_region_by_dimm(dimm)
print(" MAIN:get_region_by_dimm:region", " DIMM:", dimm, "Region:", region)
nsDeviceList = get_ns_device_list_by_dimm(dimm)
print(" MAIN:get_ns_device_list_by_dimm:nsDeviceList", nsDeviceList)
nsNameList = get_ns_name_list_by_dimm(dimm)
print(" MAIN:get_ns_name_list_by_dimm:nsNameList", nsNameList)
dimm = "nmem8"
dimmStatus = get_dimm_status(dimm)
print(" MAIN:get_dimm_status:dimmStatus", dimm, dimmStatus)
print(" MAIN:listDimmsFull")
list_dimm_table()
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
def print_timers(t = timers):
'''
------------ndctl function timers---------------------
Function Elapsed Start End
-------------------- --------- ----------- ------------
show_socket() 0.5140 941291.4208 941291.9348
parse_socket() 0.0004 941291.9348 941291.9352
show_dimm() 2.0074 941291.9352 941293.9426
parse_dimm() 0.0068 941293.9426 941293.9494
show_region() 3.8237 941293.9494 941297.7731
parse_region() 0.0006 941297.7732 941297.7737
show_dimm() 2.5911 941297.7781 941300.3692
parse_dimm() 0.0051 941300.3692 941300.3743
get_dimms() 2.5962 941297.7781 941300.3744
list_dimms() 0.0004 941300.3744 941300.3748
'''
print('------------Start ndctl function timers---------------')
print('%30s %8s %11s %11s' % ('Function', 'Elapsed', 'Start', 'End') )
print('%30s %8s %11s %11s' % ('------------------------------', '---------', '-----------', '------------') )
first = t[0]['tic']
last = t[len(t) -1]['toc']
for i in t:
print('%30s %9.4f %11.4f %11.4f' % (i['name'], i['elapsed'], i['tic'], i['toc']) )
print('%30s %9.4f %11.4f %11.4f' % ('NDCTL Overall', last - first, first, last) )
print()
print('------------End ndctl function timers-----------------')
def main():
name = 'main()'
tic = time.perf_counter()
print("This module is not intended to run standalone")
print("import this module into your script to use or use")
print("Persistent Memory Tool, pmt")
module_test()
toc = time.perf_counter()
delta_t = toc - tic
td = {'name': name, "elapsed": delta_t, 'tic': tic, 'toc': toc}
timers.append(td)
print_timers()
if __name__ == "__main__":
main()
| 27.700288
| 120
| 0.5567
| 2,408
| 19,224
| 4.240864
| 0.126246
| 0.028202
| 0.052879
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1
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f41456d2af09359f55da03d5a94e013a18221147
| 3,935
|
py
|
Python
|
core/swift3.1.1Action/swift3runner.py
|
ianpartridge/incubator-openwhisk-runtime-swift
|
5aacba1435f46b13cbb0a70874afb4b53c1a78bc
|
[
"Apache-2.0"
] | 2
|
2017-08-18T23:02:29.000Z
|
2018-01-20T22:44:33.000Z
|
core/swift3.1.1Action/swift3runner.py
|
ianpartridge/incubator-openwhisk-runtime-swift
|
5aacba1435f46b13cbb0a70874afb4b53c1a78bc
|
[
"Apache-2.0"
] | 4
|
2017-02-03T17:01:33.000Z
|
2017-03-27T01:29:56.000Z
|
core/swift3.1.1Action/swift3runner.py
|
ianpartridge/incubator-openwhisk-runtime-swift
|
5aacba1435f46b13cbb0a70874afb4b53c1a78bc
|
[
"Apache-2.0"
] | 4
|
2019-10-08T13:43:47.000Z
|
2021-11-10T15:36:35.000Z
|
"""Python proxy to run Swift action.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
"""
import os
import glob
import sys
import subprocess
import codecs
import json
sys.path.append('../actionProxy')
from actionproxy import ActionRunner, main, setRunner # noqa
SRC_EPILOGUE_FILE = '/swift3Action/epilogue.swift'
DEST_SCRIPT_FILE = '/swift3Action/spm-build/main.swift'
DEST_SCRIPT_DIR = '/swift3Action/spm-build'
DEST_BIN_FILE = '/swift3Action/spm-build/.build/release/Action'
BUILD_PROCESS = ['./swiftbuildandlink.sh']
class Swift3Runner(ActionRunner):
def __init__(self):
ActionRunner.__init__(self, DEST_SCRIPT_FILE, DEST_BIN_FILE)
# remove pre-existing binary before receiving a new binary
def preinit(self):
try:
os.remove(self.binary)
except: pass
def epilogue(self, init_message):
# skip if executable already exists (was unzipped)
if os.path.isfile(self.binary):
return
if 'main' in init_message:
main_function = init_message['main']
else:
main_function = 'main'
# make sure there is a main.swift file
open(DEST_SCRIPT_FILE, 'a').close()
with codecs.open(DEST_SCRIPT_FILE, 'a', 'utf-8') as fp:
os.chdir(DEST_SCRIPT_DIR)
for file in glob.glob("*.swift"):
if file not in ["Package.swift", "main.swift", "_WhiskJSONUtils.swift", "_Whisk.swift"]:
with codecs.open(file, 'r', 'utf-8') as f:
fp.write(f.read())
with codecs.open(SRC_EPILOGUE_FILE, 'r', 'utf-8') as ep:
fp.write(ep.read())
fp.write('_run_main(mainFunction: %s)\n' % main_function)
def build(self, init_message):
# short circuit the build, if there already exists a binary
# from the zip file
if os.path.isfile(self.binary):
# file may not have executable permission, set it
os.chmod(self.binary, 0o555)
return
p = subprocess.Popen(
BUILD_PROCESS,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=DEST_SCRIPT_DIR)
# run the process and wait until it completes.
# stdout/stderr will not be None because we passed PIPEs to Popen
(o, e) = p.communicate()
# stdout/stderr may be either text or bytes, depending on Python
# version, so if bytes, decode to text. Note that in Python 2
# a string will match both types; so also skip decoding in that case
if isinstance(o, bytes) and not isinstance(o, str):
o = o.decode('utf-8')
if isinstance(e, bytes) and not isinstance(e, str):
e = e.decode('utf-8')
if o:
sys.stdout.write(o)
sys.stdout.flush()
if e:
sys.stderr.write(e)
sys.stderr.flush()
def env(self, message):
env = ActionRunner.env(self, message)
args = message.get('value', {}) if message else {}
env['WHISK_INPUT'] = json.dumps(args)
return env
if __name__ == '__main__':
setRunner(Swift3Runner())
main()
| 34.217391
| 104
| 0.636595
| 524
| 3,935
| 4.681298
| 0.389313
| 0.028536
| 0.022829
| 0.013045
| 0.044028
| 0.019568
| 0
| 0
| 0
| 0
| 0
| 0.006928
| 0.266328
| 3,935
| 114
| 105
| 34.517544
| 0.842743
| 0.357814
| 0
| 0.061538
| 0
| 0
| 0.128737
| 0.078119
| 0
| 0
| 0
| 0
| 0
| 1
| 0.076923
| false
| 0.015385
| 0.107692
| 0
| 0.246154
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f416d0a9f34ba173050cd0b0ffffe6b5fee17622
| 6,006
|
py
|
Python
|
yandex_market_language/models/promo.py
|
stefanitsky/yandex_market_language
|
e17595b556fc55e183cf366227b2739c5c6178dc
|
[
"MIT"
] | 7
|
2020-03-28T22:35:52.000Z
|
2021-09-16T10:50:10.000Z
|
yandex_market_language/models/promo.py
|
stefanitsky/yandex_market_language
|
e17595b556fc55e183cf366227b2739c5c6178dc
|
[
"MIT"
] | 192
|
2020-03-29T12:38:53.000Z
|
2021-09-01T14:12:07.000Z
|
yandex_market_language/models/promo.py
|
stefanitsky/yandex_market_language
|
e17595b556fc55e183cf366227b2739c5c6178dc
|
[
"MIT"
] | 6
|
2020-06-05T09:07:02.000Z
|
2021-11-28T14:37:58.000Z
|
import typing as t
from yandex_market_language import models
from yandex_market_language.models.abstract import XMLElement, XMLSubElement
class Promo(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
MAPPING = {
"start-date": "start_date",
"end-date": "end_date",
"description": "description",
"url": "url",
}
__slots__ = [
'promo_id',
'promo_type',
'purchase',
'promo_gifts',
'start_date',
'end_date',
'description',
'url'
]
def __init__(
self,
promo_id: str,
promo_type: str,
purchase: "Purchase",
promo_gifts: t.List["PromoGift"],
start_date=None,
end_date=None,
description=None,
url=None,
):
self.promo_id = promo_id
self.promo_type = promo_type
self.start_date = start_date
self.end_date = end_date
self.description = description
self.url = url
self.purchase = purchase
self.promo_gifts = promo_gifts
def create_dict(self, **kwargs) -> dict:
return dict(
promo_id=self.promo_id,
promo_type=self.promo_type,
start_date=self.start_date,
end_date=self.end_date,
description=self.description,
url=self.url,
purchase=self.purchase.to_dict(),
promo_gifts=[pg.to_dict() for pg in self.promo_gifts],
)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {"id": self.promo_id, "type": self.promo_type}
promo_el = XMLElement("promo", attribs)
for tag, attr in self.MAPPING.items():
v = getattr(self, attr)
if v:
el = XMLSubElement(promo_el, tag)
el.text = v
# Add purchase el
self.purchase.to_xml(promo_el)
# Add promo gifts
promo_gifts_el = XMLSubElement(promo_el, "promo-gifts")
for pg in self.promo_gifts:
pg.to_xml(promo_gifts_el)
return promo_el
@classmethod
def from_xml(cls, promo_el: XMLElement) -> "Promo":
kwargs = dict(
promo_id=promo_el.attrib.get("id"),
promo_type=promo_el.attrib.get("type"),
promo_gifts=[]
)
for el in promo_el:
if el.tag in cls.MAPPING:
kwargs[cls.MAPPING[el.tag]] = el.text
elif el.tag == "purchase":
kwargs["purchase"] = Purchase.from_xml(el)
elif el.tag == "promo-gifts":
for pg_el in el:
kwargs["promo_gifts"].append(PromoGift.from_xml(pg_el))
return Promo(**kwargs)
class Purchase(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'products',
'required_quantity'
]
def __init__(self, products: t.List["Product"], required_quantity="1"):
self.required_quantity = required_quantity
self.products = products
def create_dict(self, **kwargs) -> dict:
return dict(
required_quantity=self.required_quantity,
products=[p.to_dict() for p in self.products]
)
def create_xml(self, **kwargs) -> XMLElement:
purchase_el = XMLElement("purchase")
# Add required quantity el
required_quantity_el = XMLSubElement(purchase_el, "required-quantity")
required_quantity_el.text = self.required_quantity
# Add products el
for p in self.products:
p.to_xml(purchase_el)
return purchase_el
@staticmethod
def from_xml(purchase_el: XMLElement) -> "Purchase":
kwargs = {"products": []}
for el in purchase_el:
if el.tag == "required-quantity":
kwargs["required_quantity"] = el.text
elif el.tag == "product":
kwargs["products"].append(Product.from_xml(el))
return Purchase(**kwargs)
class Product(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'offer_id',
'category_id'
]
def __init__(self, offer_id: str = None, category_id: str = None):
self.offer_id = offer_id
self.category_id = category_id
def create_dict(self, **kwargs) -> dict:
return dict(
offer_id=self.offer_id,
category_id=self.category_id,
)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {}
if self.offer_id:
attribs["offer-id"] = self.offer_id
if self.category_id:
attribs["category-id"] = self.category_id
return XMLElement("product", attribs)
@staticmethod
def from_xml(product_el: XMLElement) -> "Product":
return Product(
offer_id=product_el.attrib.get("offer-id"),
category_id=product_el.attrib.get("category-id")
)
class PromoGift(models.AbstractModel):
"""
Docs:
https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'offer_id',
'gift_id'
]
def __init__(self, offer_id: str = None, gift_id: str = None):
self.offer_id = offer_id
self.gift_id = gift_id
def create_dict(self, **kwargs) -> dict:
return dict(offer_id=self.offer_id, gift_id=self.gift_id)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {}
if self.offer_id:
attribs["offer-id"] = self.offer_id
elif self.gift_id:
attribs["gift-id"] = self.gift_id
return XMLElement("promo-gift", attribs)
@staticmethod
def from_xml(el: XMLElement) -> "PromoGift":
return PromoGift(
offer_id=el.attrib.get("offer-id"),
gift_id=el.attrib.get("gift-id")
)
| 27.934884
| 78
| 0.580087
| 693
| 6,006
| 4.784993
| 0.108225
| 0.046442
| 0.033173
| 0.033776
| 0.354946
| 0.27503
| 0.252714
| 0.240953
| 0.202352
| 0.183655
| 0
| 0.000239
| 0.304529
| 6,006
| 214
| 79
| 28.065421
| 0.793632
| 0.059441
| 0
| 0.178344
| 0
| 0
| 0.085827
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.101911
| false
| 0
| 0.019108
| 0.038217
| 0.254777
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f4173149ff496f494a4326e1f0ac4dc7014b0225
| 3,834
|
py
|
Python
|
src/testCmd.py
|
skogsbaer/check-assignments
|
cda8208c10644eecfe0bb988bee61098485aa6c4
|
[
"BSD-3-Clause"
] | null | null | null |
src/testCmd.py
|
skogsbaer/check-assignments
|
cda8208c10644eecfe0bb988bee61098485aa6c4
|
[
"BSD-3-Clause"
] | null | null | null |
src/testCmd.py
|
skogsbaer/check-assignments
|
cda8208c10644eecfe0bb988bee61098485aa6c4
|
[
"BSD-3-Clause"
] | 1
|
2021-03-26T14:00:14.000Z
|
2021-03-26T14:00:14.000Z
|
import shell
from dataclasses import dataclass
from utils import *
from ownLogging import *
from typing import *
from ansi import *
import re
import os
import testHaskell
import testPython
import testJava
@dataclass
class TestArgs:
dirs: List[str]
assignments: List[str] # take all if empty
interactive: bool
startAt: str
INSPECT_COMMAND = 'inspect'
RERUN_COMMAND = 'rerun'
CONTINUE_COMMAND = 'continue'
HELP_COMMAND = 'help'
def readCommand(cfg, args, studentDir, assignment):
f = assignment.getMainFile(studentDir)
commands = [('h', HELP_COMMAND, 'Print this help message')]
if f:
commands.append( ('i', INSPECT_COMMAND, f'Inspect file {f}') )
commands.append( ('r', RERUN_COMMAND, f'Re-run tests') )
commands.append( ('c', CONTINUE_COMMAND, f'Continue with next assignment/student') )
def printHelp():
for char, cmd, help in commands:
print(f' {char}: {help}')
shortcutHelp = [x[0] for x in commands]
while True:
try:
c = input(f'What to do next? {"/".join(shortcutHelp)} ')
except EOFError:
raise KeyboardInterrupt()
for chars, cmd, help in commands:
if c in chars:
if cmd == HELP_COMMAND:
printHelp()
else:
return cmd
break
else:
print(f'Unknown command {c}.')
printHelp()
def inspectFile(cfg, args, studentDir, assignment):
f = assignment.getMainFile(studentDir)
editor = cfg.editor()
os.system(f"{editor} '{f}'")
TEST_DICT = {
'python': testPython.runPythonTests,
'java': testJava.runJavaTests,
'haskell': testHaskell.runHaskellTests
}
def prettyStudent(cfg, studentDir):
try:
(name, matrikel) = parseSubmissionDir(cfg, studentDir)
return f'{name} ({matrikel})'
except ValueError:
x = shell.basename(studentDir)
if not x:
x = studentDir
x = stripLeadingSlash(x)
x = stripTrailingSlash(x)
return x
def runTestsForAssignment(cfg, args, studentDir, assignment):
print(blue(f'Checking assignment {assignment.id} for student {prettyStudent(cfg, studentDir)}'))
k = assignment.kind
if k in TEST_DICT:
fun = TEST_DICT[k]
fun(cfg, args, studentDir, assignment)
else:
abort(f"Don't know how to run tests for assignment kind {k}")
def interactiveLoop(cfg, args, studentDir, a):
runTestsForAssignment(cfg, args, studentDir, a)
if args.interactive:
while True:
print()
print(blue(f'Just checked assignment {a.id} for student {prettyStudent(cfg, studentDir)}'))
cmd = readCommand(cfg, args, studentDir, a)
if cmd == INSPECT_COMMAND:
inspectFile(cfg, args, studentDir, a)
elif cmd == RERUN_COMMAND:
runTestsForAssignment(cfg, args, studentDir, a)
elif cmd == CONTINUE_COMMAND:
return
def runTests(cfg, args):
dirs = args.dirs
if not dirs:
dirs = collectSubmissionDirs(cfg)
dirs = sorted(dirs)
if args.startAt:
l = dirs
dirs = []
for x in l:
if shell.basename(x) >= args.startAt:
dirs.append(x)
else:
print(f'Skipping {x} as requested')
for d in dirs:
assignments = cfg.assignments
if args.assignments:
assignments = []
for a in cfg.assignments:
if a.id in args.assignments:
assignments.append(a)
if not assignments:
print(f'No assignments found or selected!')
for i, a in enumerate(assignments):
interactiveLoop(cfg, args, d, a)
if i > 0:
print()
| 30.919355
| 103
| 0.594158
| 429
| 3,834
| 5.275058
| 0.30303
| 0.034026
| 0.067609
| 0.03977
| 0.144057
| 0.107821
| 0.052143
| 0.052143
| 0
| 0
| 0
| 0.000751
| 0.305164
| 3,834
| 123
| 104
| 31.170732
| 0.848724
| 0.004434
| 0
| 0.140351
| 0
| 0
| 0.133159
| 0.006291
| 0
| 0
| 0
| 0
| 0
| 1
| 0.061404
| false
| 0
| 0.096491
| 0
| 0.236842
| 0.096491
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f4189a148892e47a3efe2ef760b39a4a07630dfd
| 14,098
|
py
|
Python
|
kipoi_containers/singularityhelper.py
|
kipoi/kipoi-containers
|
5978cf1563dcc1072170f28a0a956cc28aa3c406
|
[
"MIT"
] | null | null | null |
kipoi_containers/singularityhelper.py
|
kipoi/kipoi-containers
|
5978cf1563dcc1072170f28a0a956cc28aa3c406
|
[
"MIT"
] | 11
|
2021-11-30T19:30:50.000Z
|
2022-03-29T17:06:15.000Z
|
kipoi_containers/singularityhelper.py
|
kipoi/kipoi-containers
|
5978cf1563dcc1072170f28a0a956cc28aa3c406
|
[
"MIT"
] | null | null | null |
from collections import Counter
from datetime import datetime
import os
import requests
from subprocess import Popen, PIPE
from pathlib import Path
import json
from typing import Dict, Union, TYPE_CHECKING
from kipoi_utils.external.torchvision.dataset_utils import download_url
if TYPE_CHECKING:
import zenodoclient
ZENODO_BASE = "https://zenodo.org"
ZENODO_DEPOSITION = f"{ZENODO_BASE}/api/deposit/depositions"
PathType = Union[str, Path]
def cleanup(singularity_file_path: PathType) -> None:
"""
Deletes the singularity image that was created by build_singularity_image
"""
if isinstance(singularity_file_path, str):
singularity_file_path = Path(singularity_file_path)
if singularity_file_path.exists():
singularity_file_path.unlink()
def build_singularity_image(
name_of_docker_image: str,
singularity_image_name: str,
singularity_image_folder: PathType,
) -> PathType:
"""
This function builds a singularity image from a dockerhub image
using singularity pull. The resulting .sif is stored in <singularity_image_folder> and
the filepath is returned.
"""
if isinstance(singularity_image_folder, Path):
singularity_image_folder = str(singularity_image_folder)
pull_cmd = [
"singularity",
"pull",
"--name",
f"{singularity_image_folder}/{singularity_image_name}",
"--force",
f"docker://{name_of_docker_image}",
]
print(f"Building {singularity_image_name} - {' '.join(pull_cmd)}")
process = Popen(pull_cmd, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
if process.returncode != 0:
print(stderr)
print(stdout)
raise ValueError(
f"Singularity image {singularity_image_name} can not be built"
)
singularity_image_path = (
f"{singularity_image_folder}/{singularity_image_name}"
)
return singularity_image_path
def test_singularity_image(
singularity_image_folder: PathType, singularity_image_name: str, model: str
) -> None:
"""Tests a singularity image residing in singularity_image_folder
with kipoi test <model> --source=kipoi
Raises:
ValueError: Raise valueerror if the test is not successful"""
print(
f"Testing {model} with {singularity_image_folder}/{singularity_image_name}"
)
if model == "Basenji":
test_cmd = [
"kipoi",
"test",
f"{model}",
"--source=kipoi",
"--batch_size=2",
]
else:
test_cmd = ["kipoi", "test", f"{model}", "--source=kipoi"]
if isinstance(singularity_image_folder, str):
singularity_image_folder = Path(singularity_image_folder)
if isinstance(singularity_image_name, str):
singularity_image_name = Path(singularity_image_name)
exec_cmd = [
"singularity",
"exec",
f"{singularity_image_folder}/{singularity_image_name}",
]
exec_cmd.extend(test_cmd)
process = Popen(exec_cmd, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
if process.returncode != 0:
print(stdout)
print(stderr)
raise ValueError(
f"Singularity image {singularity_image_name} for {model} did not pass relevant tests"
)
def create_new_deposition(
zenodo_client: "zenodoclient.Client", deposition_id: str
) -> str:
"""Creates a new version of an existing depsosition on zenodo and returns the
corresponding id"""
status_code, response = zenodo_client.post_content(
f"{ZENODO_DEPOSITION}/{deposition_id}/actions/newversion"
)
return response["links"]["latest_draft"].split("/")[-1]
def get_deposit(
zenodo_client: "zenodoclient.Client", deposition_id: str
) -> Dict:
"""Returns the response body of a get request for an existing deposition"""
response = zenodo_client.get_content(
f"{ZENODO_DEPOSITION}/{deposition_id}"
)
return response
def upload_file(
zenodo_client: "zenodoclient.Client",
url: str,
singularity_image_folder: PathType,
filename: str,
) -> None:
"""Upload singularity_image_folder/filename to a url"""
path = Path(singularity_image_folder) / Path(filename)
zenodo_client.put_content(url, data=path)
def upload_metadata(
zenodo_client: "zenodoclient.Client",
url: str,
model_group: str = "",
shared_env: str = "",
) -> None:
"""Upload metadata for a model group to a given url"""
if not model_group and not shared_env:
raise ValueError(
"You need to provide atlease a shared env name or a model group name"
)
if model_group:
data = {
"metadata": {
"title": f"{model_group} singularity container",
"upload_type": "physicalobject",
"description": "This is a singularity container for models "
f"under https://kipoi.org/models/{model_group}/",
"creators": [
{"name": "Haimasree, Bhattacharya", "affiliation": "EMBL"}
],
"publication_date": datetime.today().strftime("%Y-%m-%d"),
"license": "MIT",
}
}
elif shared_env:
if "shared" in shared_env:
data = {
"metadata": {
"title": f"{shared_env} singularity container",
"upload_type": "physicalobject",
"description": "Singularity container with conda environment "
f"https://github.com/kipoi/kipoi-containers/blob/main/envfiles/{shared_env}.yml",
"creators": [
{
"name": "Haimasree, Bhattacharya",
"affiliation": "EMBL",
}
],
"publication_date": datetime.today().strftime("%Y-%m-%d"),
"license": "MIT",
}
}
elif shared_env == "mmsplice":
data = {
"metadata": {
"title": "MMSplice singularity container except mtsplice",
"upload_type": "physicalobject",
"description": "Singularity container for MMSplice models except mtsplice "
"under http://kipoi.org/models/MMSplice/",
"creators": [
{
"name": "Haimasree, Bhattacharya",
"affiliation": "EMBL",
}
],
"publication_date": datetime.today().strftime("%Y-%m-%d"),
"license": "MIT",
}
}
else:
raise ValueError(
"Available options are - mmsplice, sharedpy3keras2tf1, sharedpy3keras2tf2, sharedpy3keras1.2"
)
zenodo_client.put_content(url, data=data)
def push_deposition(
zenodo_client: "zenodoclient.Client", deposition_id: str
) -> Dict:
"""Pushes a deposition to zenodo. An additional get request is made to the newy pushed
deposition and a response body is returned"""
status_code, response = zenodo_client.post_content(
f"{ZENODO_DEPOSITION}/{deposition_id}/actions/publish"
)
response = get_deposit(zenodo_client, deposition_id)
return response
def update_existing_singularity_container(
zenodo_client: "zenodoclient.Client",
singularity_dict: Dict,
singularity_image_folder: PathType,
model_group: str,
file_to_upload: str = "",
push: bool = True,
) -> None:
"""This function creates a new draft version of an existing image's zenodo entry with updated
metadata and file after deleting the old file. If push is True, the draft version is finalized
and the url, name and md5 fields are updated and the new deposition id and file id is added to
singularity dict which contains information about the existing image. Otherwise, only
the new deposotion id and file id is added to the dictionary. This modified dictionary is
returned"""
# Create a new version of an existing deposition
deposition_id = singularity_dict["url"].split("/")[4]
new_deposition_id = create_new_deposition(zenodo_client, deposition_id)
response = get_deposit(zenodo_client, new_deposition_id)
bucket_url = response["links"]["bucket"]
filename = (
file_to_upload if file_to_upload else f"{singularity_dict['name']}.sif"
)
file_id = ""
for fileobj in response["files"]:
if fileobj["filename"] == filename:
file_id = fileobj["id"] # Assuming only 1 version is added
# Delete existing file from this new version
if file_id:
zenodo_client.delete_content(
f"{ZENODO_DEPOSITION}/{new_deposition_id}/files/{file_id}"
)
# Add a new file to this new version
upload_file(
zenodo_client,
f"{bucket_url}/{filename}",
singularity_image_folder,
filename,
)
url = f"{ZENODO_DEPOSITION}/{new_deposition_id}"
if (
"shared" in singularity_dict["name"]
or singularity_dict["name"] == "kipoi-docker_mmsplice-slim"
):
shared_env_name = (
singularity_dict["name"]
.replace("kipoi-docker_", "")
.replace("-slim", "")
)
upload_metadata(zenodo_client, url, shared_env=shared_env_name)
else:
upload_metadata(zenodo_client, url, model_group=model_group)
# publish the newly created revision
if push:
response = push_deposition(zenodo_client, new_deposition_id)
record_id = response["metadata"]["prereserve_doi"]["recid"]
file_id, file_name, file_md5 = "", "", ""
for fileobj in response["files"]:
if fileobj["filename"] == filename:
file_id = fileobj["id"] # Assuming only 1 version is added
file_name = fileobj["filename"].replace(".sif", "")
file_md5 = fileobj["checksum"]
return {
"new_deposition_id": new_deposition_id,
"file_id": file_id,
"url": f"{ZENODO_BASE}/record/{record_id}/files/{filename}?download=1",
"name": file_name,
"md5": file_md5,
}
else:
return singularity_dict | {
"new_deposition_id": new_deposition_id,
"file_id": "",
}
def push_new_singularity_image(
zenodo_client: "zenodoclient.Client",
singularity_image_folder: PathType,
singularity_dict: Dict,
model_group: str,
file_to_upload: str = "",
path: str = "",
push: bool = True,
) -> None:
"""This function creates a draft version of a new zenodo entry with the
metadata and singularity image. If push is True, the draft version is finalized
and the url, name and md5 fields are updated and the new deposition id and file id is added to
singularity dict which contains empty strings as url and md5. Otherwise, only
the new deposotion id and file id is added to the dictionary. This modified dictionary is
returned"""
status_code, response = zenodo_client.post_content(f"{ZENODO_DEPOSITION}")
deposition_id = response["id"]
bucket_url = response["links"]["bucket"]
filename = (
file_to_upload if file_to_upload else f"{singularity_dict['name']}.sif"
)
upload_file(
zenodo_client,
f"{bucket_url}/{filename}",
singularity_image_folder,
filename,
)
url = f"{ZENODO_DEPOSITION}/{deposition_id}"
if "shared" in singularity_dict["name"]:
shared_env_name = (
singularity_dict["name"]
.replace("kipoi-docker_", "")
.replace("-slim", "")
)
upload_metadata(zenodo_client, url, shared_env=shared_env_name)
else:
upload_metadata(zenodo_client, url, model_group=model_group)
if push:
push_deposition(zenodo_client, deposition_id)
response = get_deposit(zenodo_client, deposition_id)
record_id = response["metadata"]["prereserve_doi"]["recid"]
return {
"new_deposition_id": deposition_id,
"file_id": response["files"][0]["id"],
"url": f"{ZENODO_BASE}/record/{record_id}/files/{filename}?download=1",
"name": response["files"][0]["filename"].replace(".sif", ""),
"md5": response["files"][0]["checksum"],
}
else:
return singularity_dict | {
"new_deposition_id": deposition_id,
"file_id": "",
}
def get_singularity_image(
singularity_image_folder: PathType,
singularity_image_dict: Dict,
model_or_model_group: str,
) -> PathType:
"""This function downloads the singularity image corresponding to the given model or
model group from zenodo to singularity_image_folder and returns the name of the image"""
if (
model_or_model_group in singularity_image_dict
): # Special case for MMSPlice/mtsplice, APARENT/veff
image_name = (
f"{singularity_image_dict[model_or_model_group]['name']}.sif"
)
image_url = f"{singularity_image_dict[model_or_model_group]['url']}"
image_md5 = f"{singularity_image_dict[model_or_model_group]['md5']}"
else:
model_group = model_or_model_group.split("/")[0]
image_name = f"{singularity_image_dict[model_group]['name']}.sif"
image_url = f"{singularity_image_dict[model_group]['url']}"
image_md5 = f"{singularity_image_dict[model_group]['md5']}"
if isinstance(singularity_image_folder, str):
singularity_image_folder = Path(singularity_image_folder)
if isinstance(image_name, str):
image_name = Path(image_name)
if not (singularity_image_folder / image_name).exists():
download_url(
url=image_url,
root=singularity_image_folder,
filename=image_name,
md5=image_md5,
)
return image_name
| 36.148718
| 109
| 0.62633
| 1,589
| 14,098
| 5.319698
| 0.155444
| 0.117355
| 0.072874
| 0.024843
| 0.57175
| 0.535195
| 0.469301
| 0.415829
| 0.338578
| 0.316574
| 0
| 0.003498
| 0.269967
| 14,098
| 389
| 110
| 36.241645
| 0.81782
| 0.152007
| 0
| 0.453968
| 0
| 0.003175
| 0.251758
| 0.098805
| 0
| 0
| 0
| 0
| 0
| 1
| 0.034921
| false
| 0.003175
| 0.031746
| 0
| 0.095238
| 0.019048
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f418aa86180868641545c7ca6a350482c74458ed
| 1,152
|
py
|
Python
|
policy/_cache.py
|
garenchan/policy
|
fbd056c0474e62252d1fe986fe029cacde6845d8
|
[
"Apache-2.0"
] | 5
|
2018-10-17T21:06:07.000Z
|
2021-12-31T01:33:09.000Z
|
policy/_cache.py
|
garenchan/policy
|
fbd056c0474e62252d1fe986fe029cacde6845d8
|
[
"Apache-2.0"
] | 1
|
2018-09-07T09:00:41.000Z
|
2018-09-07T11:06:14.000Z
|
policy/_cache.py
|
garenchan/policy
|
fbd056c0474e62252d1fe986fe029cacde6845d8
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
policy._cache
~~~~~~~~~~~~~~~
Cache for policy file.
"""
import os
import logging
LOG = logging.getLogger(__name__)
# Global file cache
CACHE = {}
def read_file(filename: str, force_reload=False):
"""Read a file if it has been modified.
:param filename: File name which want to be read from.
:param force_reload: Whether to reload the file.
:returns: A tuple with a boolean specifying if the data is fresh or not.
"""
if force_reload:
_delete_cached_file(filename)
reloaded = False
mtime = os.path.getmtime(filename)
cache_info = CACHE.setdefault(filename, {})
if not cache_info or mtime > cache_info.get('mtime', 0):
LOG.debug('Reloading cached file %s', filename)
with open(filename) as fp:
cache_info['data'] = fp.read()
cache_info['mtime'] = mtime
reloaded = True
return reloaded, cache_info['data']
def _delete_cached_file(filename: str):
"""Delete cached file if present.
:param filename: Filename to delete
"""
try:
del CACHE[filename]
except KeyError:
pass
| 21.735849
| 76
| 0.631944
| 150
| 1,152
| 4.713333
| 0.46
| 0.076379
| 0.067893
| 0.067893
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002326
| 0.253472
| 1,152
| 52
| 77
| 22.153846
| 0.819767
| 0.327257
| 0
| 0
| 0
| 0
| 0.058577
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0.045455
| 0.090909
| 0
| 0.227273
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f419167b819e5ee174fbe6b84ca88ef1f496b659
| 10,858
|
py
|
Python
|
contrib/opencensus-ext-django/opencensus/ext/django/middleware.py
|
samn/opencensus-python
|
d8709f141b67f7f5ba011c440b8ba8fb9cbc419a
|
[
"Apache-2.0"
] | null | null | null |
contrib/opencensus-ext-django/opencensus/ext/django/middleware.py
|
samn/opencensus-python
|
d8709f141b67f7f5ba011c440b8ba8fb9cbc419a
|
[
"Apache-2.0"
] | null | null | null |
contrib/opencensus-ext-django/opencensus/ext/django/middleware.py
|
samn/opencensus-python
|
d8709f141b67f7f5ba011c440b8ba8fb9cbc419a
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017, OpenCensus Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Django middleware helper to capture and trace a request."""
import logging
from opencensus.ext.django.config import (settings, convert_to_import)
from opencensus.trace import attributes_helper
from opencensus.trace import execution_context
from opencensus.trace import span as span_module
from opencensus.trace import tracer as tracer_module
from opencensus.trace import utils
from opencensus.trace.samplers import probability
try:
from django.utils.deprecation import MiddlewareMixin
except ImportError: # pragma: NO COVER
MiddlewareMixin = object
HTTP_METHOD = attributes_helper.COMMON_ATTRIBUTES['HTTP_METHOD']
HTTP_URL = attributes_helper.COMMON_ATTRIBUTES['HTTP_URL']
HTTP_STATUS_CODE = attributes_helper.COMMON_ATTRIBUTES['HTTP_STATUS_CODE']
REQUEST_THREAD_LOCAL_KEY = 'django_request'
SPAN_THREAD_LOCAL_KEY = 'django_span'
BLACKLIST_PATHS = 'BLACKLIST_PATHS'
GCP_EXPORTER_PROJECT = 'GCP_EXPORTER_PROJECT'
SAMPLING_RATE = 'SAMPLING_RATE'
TRANSPORT = 'TRANSPORT'
SERVICE_NAME = 'SERVICE_NAME'
ZIPKIN_EXPORTER_SERVICE_NAME = 'ZIPKIN_EXPORTER_SERVICE_NAME'
ZIPKIN_EXPORTER_HOST_NAME = 'ZIPKIN_EXPORTER_HOST_NAME'
ZIPKIN_EXPORTER_PORT = 'ZIPKIN_EXPORTER_PORT'
ZIPKIN_EXPORTER_PROTOCOL = 'ZIPKIN_EXPORTER_PROTOCOL'
JAEGER_EXPORTER_HOST_NAME = 'JAEGER_EXPORTER_HOST_NAME'
JAEGER_EXPORTER_PORT = 'JAEGER_EXPORTER_PORT'
JAEGER_EXPORTER_AGENT_HOST_NAME = 'JAEGER_EXPORTER_AGENT_HOST_NAME'
JAEGER_EXPORTER_AGENT_PORT = 'JAEGER_EXPORTER_AGENT_PORT'
JAEGER_EXPORTER_SERVICE_NAME = 'JAEGER_EXPORTER_SERVICE_NAME'
OCAGENT_TRACE_EXPORTER_ENDPOINT = 'OCAGENT_TRACE_EXPORTER_ENDPOINT'
BLACKLIST_HOSTNAMES = 'BLACKLIST_HOSTNAMES'
log = logging.getLogger(__name__)
class _DjangoMetaWrapper(object):
"""
Wrapper class which takes HTTP header name and retrieve the value from
Django request.META
"""
def __init__(self, meta=None):
self.meta = meta or _get_django_request().META
def get(self, key):
return self.meta.get('HTTP_' + key.upper().replace('-', '_'))
def _get_django_request():
"""Get Django request from thread local.
:rtype: str
:returns: Django request.
"""
return execution_context.get_opencensus_attr(REQUEST_THREAD_LOCAL_KEY)
def _get_django_span():
"""Get Django span from thread local.
:rtype: str
:returns: Django request.
"""
return execution_context.get_opencensus_attr(SPAN_THREAD_LOCAL_KEY)
def _get_current_tracer():
"""Get the current request tracer."""
return execution_context.get_opencensus_tracer()
def _set_django_attributes(span, request):
"""Set the django related attributes."""
django_user = getattr(request, 'user', None)
if django_user is None:
return
user_id = django_user.pk
try:
user_name = django_user.get_username()
except AttributeError:
# AnonymousUser in some older versions of Django doesn't implement
# get_username
return
# User id is the django autofield for User model as the primary key
if user_id is not None:
span.add_attribute('django.user.id', str(user_id))
if user_name is not None:
span.add_attribute('django.user.name', str(user_name))
class OpencensusMiddleware(MiddlewareMixin):
"""Saves the request in thread local"""
def __init__(self, get_response=None):
# One-time configuration and initialization.
self.get_response = get_response
self._sampler = settings.SAMPLER
self._exporter = settings.EXPORTER
self._propagator = settings.PROPAGATOR
self._blacklist_paths = settings.params.get(BLACKLIST_PATHS)
# Initialize the sampler
if self._sampler.__name__ == 'ProbabilitySampler':
_rate = settings.params.get(
SAMPLING_RATE, probability.DEFAULT_SAMPLING_RATE)
self.sampler = self._sampler(_rate)
else:
self.sampler = self._sampler()
# Initialize the exporter
transport = convert_to_import(settings.params.get(TRANSPORT))
if self._exporter.__name__ == 'GoogleCloudExporter':
_project_id = settings.params.get(GCP_EXPORTER_PROJECT, None)
self.exporter = self._exporter(
project_id=_project_id,
transport=transport)
elif self._exporter.__name__ == 'ZipkinExporter':
_service_name = self._get_service_name(settings.params)
_zipkin_host_name = settings.params.get(
ZIPKIN_EXPORTER_HOST_NAME, 'localhost')
_zipkin_port = settings.params.get(
ZIPKIN_EXPORTER_PORT, 9411)
_zipkin_protocol = settings.params.get(
ZIPKIN_EXPORTER_PROTOCOL, 'http')
self.exporter = self._exporter(
service_name=_service_name,
host_name=_zipkin_host_name,
port=_zipkin_port,
protocol=_zipkin_protocol,
transport=transport)
elif self._exporter.__name__ == 'TraceExporter':
_service_name = self._get_service_name(settings.params)
_endpoint = settings.params.get(
OCAGENT_TRACE_EXPORTER_ENDPOINT, None)
self.exporter = self._exporter(
service_name=_service_name,
endpoint=_endpoint,
transport=transport)
elif self._exporter.__name__ == 'JaegerExporter':
_service_name = settings.params.get(
JAEGER_EXPORTER_SERVICE_NAME,
self._get_service_name(settings.params))
_jaeger_host_name = settings.params.get(
JAEGER_EXPORTER_HOST_NAME, None)
_jaeger_port = settings.params.get(
JAEGER_EXPORTER_PORT, None)
_jaeger_agent_host_name = settings.params.get(
JAEGER_EXPORTER_AGENT_HOST_NAME, 'localhost')
_jaeger_agent_port = settings.params.get(
JAEGER_EXPORTER_AGENT_PORT, 6831)
self.exporter = self._exporter(
service_name=_service_name,
host_name=_jaeger_host_name,
port=_jaeger_port,
agent_host_name=_jaeger_agent_host_name,
agent_port=_jaeger_agent_port,
transport=transport)
else:
self.exporter = self._exporter(transport=transport)
self.blacklist_hostnames = settings.params.get(
BLACKLIST_HOSTNAMES, None)
# Initialize the propagator
self.propagator = self._propagator()
def process_request(self, request):
"""Called on each request, before Django decides which view to execute.
:type request: :class:`~django.http.request.HttpRequest`
:param request: Django http request.
"""
# Do not trace if the url is blacklisted
if utils.disable_tracing_url(request.path, self._blacklist_paths):
return
# Add the request to thread local
execution_context.set_opencensus_attr(
REQUEST_THREAD_LOCAL_KEY,
request)
execution_context.set_opencensus_attr(
'blacklist_hostnames',
self.blacklist_hostnames)
try:
# Start tracing this request
span_context = self.propagator.from_headers(
_DjangoMetaWrapper(_get_django_request().META))
# Reload the tracer with the new span context
tracer = tracer_module.Tracer(
span_context=span_context,
sampler=self.sampler,
exporter=self.exporter,
propagator=self.propagator)
# Span name is being set at process_view
span = tracer.start_span()
span.span_kind = span_module.SpanKind.SERVER
tracer.add_attribute_to_current_span(
attribute_key=HTTP_METHOD,
attribute_value=request.method)
tracer.add_attribute_to_current_span(
attribute_key=HTTP_URL,
attribute_value=str(request.path))
# Add the span to thread local
# in some cases (exceptions, timeouts) currentspan in
# response event will be one of a child spans.
# let's keep reference to 'django' span and
# use it in response event
execution_context.set_opencensus_attr(
SPAN_THREAD_LOCAL_KEY,
span)
except Exception: # pragma: NO COVER
log.error('Failed to trace request', exc_info=True)
def process_view(self, request, view_func, *args, **kwargs):
"""Process view is executed before the view function, here we get the
function name add set it as the span name.
"""
# Do not trace if the url is blacklisted
if utils.disable_tracing_url(request.path, self._blacklist_paths):
return
try:
# Get the current span and set the span name to the current
# function name of the request.
tracer = _get_current_tracer()
span = tracer.current_span()
span.name = utils.get_func_name(view_func)
except Exception: # pragma: NO COVER
log.error('Failed to trace request', exc_info=True)
def process_response(self, request, response):
# Do not trace if the url is blacklisted
if utils.disable_tracing_url(request.path, self._blacklist_paths):
return response
try:
span = _get_django_span()
span.add_attribute(
attribute_key=HTTP_STATUS_CODE,
attribute_value=str(response.status_code))
_set_django_attributes(span, request)
tracer = _get_current_tracer()
tracer.end_span()
tracer.finish()
except Exception: # pragma: NO COVER
log.error('Failed to trace request', exc_info=True)
finally:
return response
def _get_service_name(self, params):
_service_name = params.get(
SERVICE_NAME, None)
if _service_name is None:
_service_name = params.get(
ZIPKIN_EXPORTER_SERVICE_NAME, 'my_service')
return _service_name
| 36.682432
| 79
| 0.667618
| 1,254
| 10,858
| 5.454545
| 0.191388
| 0.041813
| 0.034795
| 0.018275
| 0.351462
| 0.264035
| 0.200585
| 0.182749
| 0.125292
| 0.11155
| 0
| 0.001995
| 0.261374
| 10,858
| 295
| 80
| 36.80678
| 0.850873
| 0.193314
| 0
| 0.237838
| 0
| 0
| 0.075052
| 0.025367
| 0
| 0
| 0
| 0
| 0
| 1
| 0.059459
| false
| 0
| 0.059459
| 0.005405
| 0.189189
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f419f2c87349548809cd06192323167246871ccd
| 1,322
|
py
|
Python
|
codeblockCar/codingPage/tests.py
|
ICT2x01-p2-4/ICT2x01-p2-4
|
6249c0a807354b33db80f367344fe14cb5512840
|
[
"MIT"
] | null | null | null |
codeblockCar/codingPage/tests.py
|
ICT2x01-p2-4/ICT2x01-p2-4
|
6249c0a807354b33db80f367344fe14cb5512840
|
[
"MIT"
] | 24
|
2021-09-29T02:46:17.000Z
|
2021-11-06T13:32:11.000Z
|
codeblockCar/codingPage/tests.py
|
ICT2x01-p2-4/Codeblock-car
|
6249c0a807354b33db80f367344fe14cb5512840
|
[
"MIT"
] | null | null | null |
from typing import Reversible
from django.test import TestCase, Client
from challenge.models import Challenge
from codingPage.models import Command, Log
from django.core.exceptions import ValidationError
from django.urls import reverse
class CodingPageTest(TestCase):
def setUp(self) -> None:
self.client = Client(HTTP_USER_AGENT='Mozilla/5.0')
self.challenge = Challenge.objects.create(name='abc', map='0,0,0,0,0,0,0,0,0', size=3, difficulty='Easy')
self.command = Command.objects.create(action='Dodo', code=1)
self.log = Log.objects.create(data='123', challenge = self.challenge)
return super().setUp()
def test_validation(self):
"""Test if validation works for creating new command"""
Command.objects.create(action='asd', code=5)
self.assertRaises(ValidationError)
def test_check_code(self):
"""Test if code checkers dont upload to database if log false is given"""
response = self.client.post(
reverse('ajax_view'),
data = {
'code': '1\n2\n3\n',
'log': False,
'challenge_id': 1
},
HTTP_X_REQUESTED_WITH='XMLHttpRequest',
url = '/codingPage/test_code'
)
self.assertEqual(response, '123')
| 38.882353
| 113
| 0.630862
| 162
| 1,322
| 5.080247
| 0.481481
| 0.019441
| 0.025516
| 0.029162
| 0.09113
| 0.010936
| 0.010936
| 0.010936
| 0
| 0
| 0
| 0.024267
| 0.251891
| 1,322
| 33
| 114
| 40.060606
| 0.807887
| 0.088502
| 0
| 0
| 0
| 0
| 0.100503
| 0.017588
| 0
| 0
| 0
| 0
| 0.071429
| 1
| 0.107143
| false
| 0
| 0.214286
| 0
| 0.392857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f41b63806a18c6ea9b6ee2484bb3111d3bc16034
| 33,899
|
py
|
Python
|
app/main/views/templates.py
|
cds-snc/notification-admin
|
d4056798bf889ad29893667bbb67ead2f8e466e4
|
[
"MIT"
] | 16
|
2019-11-05T21:35:49.000Z
|
2022-01-12T15:00:32.000Z
|
app/main/views/templates.py
|
cds-snc/notification-admin
|
d4056798bf889ad29893667bbb67ead2f8e466e4
|
[
"MIT"
] | 509
|
2019-07-11T22:03:19.000Z
|
2022-03-30T15:19:26.000Z
|
app/main/views/templates.py
|
cds-snc/notification-admin
|
d4056798bf889ad29893667bbb67ead2f8e466e4
|
[
"MIT"
] | 8
|
2020-02-21T20:19:29.000Z
|
2022-03-31T14:17:02.000Z
|
from datetime import datetime, timedelta
from string import ascii_uppercase
from dateutil.parser import parse
from flask import abort, flash, jsonify, redirect, render_template, request, url_for
from flask_babel import _
from flask_babel import lazy_gettext as _l
from flask_login import current_user
from markupsafe import Markup
from notifications_python_client.errors import HTTPError
from notifications_utils.formatters import nl2br
from notifications_utils.recipients import first_column_headings
from app import (
current_service,
service_api_client,
template_api_prefill_client,
template_folder_api_client,
template_statistics_client,
)
from app.main import main
from app.main.forms import (
CreateTemplateForm,
EmailTemplateForm,
LetterTemplateForm,
LetterTemplatePostageForm,
SearchByNameForm,
SetTemplateSenderForm,
SMSTemplateForm,
TemplateAndFoldersSelectionForm,
TemplateFolderForm,
)
from app.main.views.send import get_example_csv_rows, get_sender_details
from app.models.service import Service
from app.models.template_list import TemplateList, TemplateLists
from app.template_previews import TemplatePreview, get_page_count_for_letter
from app.utils import (
email_or_sms_not_enabled,
get_template,
should_skip_template_page,
user_has_permissions,
user_is_platform_admin,
)
form_objects = {
"email": EmailTemplateForm,
"sms": SMSTemplateForm,
"letter": LetterTemplateForm,
}
def get_email_preview_template(template, template_id, service_id):
email_preview_template = get_template(
template,
current_service,
letter_preview_url=url_for(
".view_letter_template_preview",
service_id=service_id,
template_id=template_id,
filetype="png",
),
show_recipient=True,
page_count=get_page_count_for_letter(template),
)
return email_preview_template
@main.route("/services/<service_id>/templates/<uuid:template_id>")
@user_has_permissions()
def view_template(service_id, template_id):
template = current_service.get_template(template_id)
template_folder = current_service.get_template_folder(template["folder"])
user_has_template_permission = current_user.has_template_folder_permission(template_folder)
if should_skip_template_page(template["template_type"]):
return redirect(url_for(".send_one_off", service_id=service_id, template_id=template_id))
return render_template(
"views/templates/template.html",
template=get_email_preview_template(template, template_id, service_id),
template_postage=template["postage"],
user_has_template_permission=user_has_template_permission,
)
@main.route("/services/<service_id>/start-tour/<uuid:template_id>")
@user_has_permissions("view_activity")
def start_tour(service_id, template_id):
template = current_service.get_template(template_id)
if template["template_type"] != "email":
abort(404)
return render_template(
"views/templates/start-tour.html",
template=get_template(
template,
current_service,
show_recipient=True,
),
help="1",
)
@main.route("/services/<service_id>/templates", methods=["GET", "POST"])
@main.route(
"/services/<service_id>/templates/folders/<template_folder_id>",
methods=["GET", "POST"],
)
@main.route("/services/<service_id>/templates/<template_type>", methods=["GET", "POST"])
@main.route(
"/services/<service_id>/templates/<template_type>/folders/<template_folder_id>",
methods=["GET", "POST"],
)
@user_has_permissions()
def choose_template(service_id, template_type="all", template_folder_id=None):
template_folder = current_service.get_template_folder(template_folder_id)
user_has_template_folder_permission = current_user.has_template_folder_permission(template_folder)
template_list = TemplateList(current_service, template_type, template_folder_id, current_user)
templates_and_folders_form = TemplateAndFoldersSelectionForm(
all_template_folders=current_service.get_user_template_folders(current_user),
template_list=template_list,
template_type=template_type,
allow_adding_letter_template=current_service.has_permission("letter"),
allow_adding_copy_of_template=(current_service.all_templates or len(current_user.service_ids) > 1),
)
option_hints = {template_folder_id: "current folder"}
if request.method == "POST" and templates_and_folders_form.validate_on_submit():
if not current_user.has_permissions("manage_templates"):
abort(403)
try:
return process_folder_management_form(templates_and_folders_form, template_folder_id)
except HTTPError as e:
flash(e.message)
if "templates_and_folders" in templates_and_folders_form.errors:
flash(_("Select at least one template or folder"))
initial_state = request.args.get("initial_state")
if request.method == "GET" and initial_state:
templates_and_folders_form.op = initial_state
sending_view = request.args.get("view") == "sending"
return render_template(
"views/templates/choose.html",
current_template_folder_id=template_folder_id,
template_folder_path=current_service.get_template_folder_path(template_folder_id),
template_list=template_list,
show_search_box=current_service.count_of_templates_and_folders > 7,
show_template_nav=(current_service.has_multiple_template_types and (len(current_service.all_templates) > 2)),
sending_view=sending_view,
template_nav_items=get_template_nav_items(template_folder_id, sending_view),
template_type=template_type,
search_form=SearchByNameForm(),
templates_and_folders_form=templates_and_folders_form,
move_to_children=templates_and_folders_form.move_to.children(),
user_has_template_folder_permission=user_has_template_folder_permission,
option_hints=option_hints,
)
def process_folder_management_form(form, current_folder_id):
current_service.get_template_folder_with_user_permission_or_403(current_folder_id, current_user)
new_folder_id = None
if form.is_add_folder_op:
new_folder_id = template_folder_api_client.create_template_folder(
current_service.id, name=form.get_folder_name(), parent_id=current_folder_id
)
if form.is_move_op:
# if we've just made a folder, we also want to move there
move_to_id = new_folder_id or form.move_to.data
current_service.move_to_folder(ids_to_move=form.templates_and_folders.data, move_to=move_to_id)
return redirect(request.url)
def get_template_nav_label(value):
return {
"all": _l("All"),
"sms": _l("Text message"),
"email": _l("Email"),
"letter": _l("Letter"),
}[value]
def get_template_nav_items(template_folder_id, sending_view):
return [
(
get_template_nav_label(key),
key,
url_for(
".choose_template",
service_id=current_service.id,
template_type=key,
template_folder_id=template_folder_id,
view="sending" if sending_view else None,
),
"",
)
for key in ["all"] + current_service.available_template_types
]
@main.route("/services/<service_id>/templates/<template_id>.<filetype>")
@user_has_permissions()
def view_letter_template_preview(service_id, template_id, filetype):
if filetype not in ("pdf", "png"):
abort(404)
db_template = current_service.get_template(template_id)
return TemplatePreview.from_database_object(db_template, filetype, page=request.args.get("page"))
@main.route("/templates/letter-preview-image/<filename>")
@user_is_platform_admin
def letter_branding_preview_image(filename):
template = {
"subject": "An example letter",
"content": (
"Lorem Ipsum is simply dummy text of the printing and typesetting "
"industry.\n\nLorem Ipsum has been the industry’s standard dummy "
"text ever since the 1500s, when an unknown printer took a galley "
"of type and scrambled it to make a type specimen book.\n\n"
"# History\n\nIt has survived not only\n\n"
"* five centuries\n"
"* but also the leap into electronic typesetting\n\n"
"It was popularised in the 1960s with the release of Letraset "
"sheets containing Lorem Ipsum passages, and more recently with "
"desktop publishing software like Aldus PageMaker including "
"versions of Lorem Ipsum.\n\n"
"The point of using Lorem Ipsum is that it has a more-or-less "
"normal distribution of letters, as opposed to using ‘Content "
"here, content here’, making it look like readable English."
),
}
filename = None if filename == "no-branding" else filename
return TemplatePreview.from_example_template(template, filename)
def _view_template_version(service_id, template_id, version, letters_as_pdf=False):
return dict(
template=get_template(
current_service.get_template(template_id, version=version),
current_service,
letter_preview_url=url_for(
".view_template_version_preview",
service_id=service_id,
template_id=template_id,
version=version,
filetype="png",
)
if not letters_as_pdf
else None,
)
)
@main.route("/services/<service_id>/templates/<template_id>/version/<int:version>")
@user_has_permissions()
def view_template_version(service_id, template_id, version):
return render_template(
"views/templates/template_history.html",
**_view_template_version(service_id=service_id, template_id=template_id, version=version),
)
@main.route("/services/<service_id>/templates/<template_id>/version/<int:version>.<filetype>")
@user_has_permissions()
def view_template_version_preview(service_id, template_id, version, filetype):
db_template = current_service.get_template(template_id, version=version)
return TemplatePreview.from_database_object(db_template, filetype)
def _add_template_by_type(template_type, template_folder_id):
if template_type == "copy-existing":
return redirect(
url_for(
".choose_template_to_copy",
service_id=current_service.id,
)
)
if template_type == "letter":
blank_letter = service_api_client.create_service_template(
"New letter template",
"letter",
"Body",
current_service.id,
"Main heading",
"normal",
template_folder_id,
)
return redirect(
url_for(
".view_template",
service_id=current_service.id,
template_id=blank_letter["data"]["id"],
)
)
if email_or_sms_not_enabled(template_type, current_service.permissions):
return redirect(
url_for(
".action_blocked",
service_id=current_service.id,
notification_type=template_type,
return_to="add_new_template",
template_id="0",
)
)
else:
return redirect(
url_for(
".add_service_template",
service_id=current_service.id,
template_type=template_type,
template_folder_id=template_folder_id,
)
)
@main.route("/services/<service_id>/templates/create", methods=["GET", "POST"])
@main.route("/services/<service_id>/templates/folders/<template_folder_id>/create", methods=["GET", "POST"])
@main.route("/services/<service_id>/templates/<template_type>/create", methods=["GET", "POST"])
@main.route("/services/<service_id>/templates/<template_type>/folders/<template_folder_id>/create", methods=["GET", "POST"])
@user_has_permissions("manage_templates")
def create_template(service_id, template_type="all", template_folder_id=None):
form = CreateTemplateForm()
if request.method == "POST" and form.validate_on_submit():
try:
return _add_template_by_type(
form.what_type.data,
template_folder_id,
)
except HTTPError as e:
flash(e.message)
return render_template(
"views/templates/create.html",
service_id=service_id,
template_folder_id=template_folder_id,
template_type=template_type,
form=form,
disabled_options={},
option_hints={},
)
@main.route("/services/<service_id>/templates/copy")
@main.route("/services/<service_id>/templates/all/copy")
@main.route("/services/<service_id>/templates/email/copy")
@main.route("/services/<service_id>/templates/sms/copy")
@main.route("/services/<service_id>/templates/copy/from-folder/<uuid:from_folder>")
@main.route("/services/<service_id>/templates/copy/from-service/<uuid:from_service>")
@main.route("/services/<service_id>/templates/copy/from-service/<uuid:from_service>/from-folder/<uuid:from_folder>")
@main.route("/services/<service_id>/templates/all/folders/<uuid:from_folder>/copy")
@user_has_permissions("manage_templates")
def choose_template_to_copy(
service_id,
from_service=None,
from_folder=None,
):
if from_folder and from_service is None:
from_service = service_id
if from_service:
current_user.belongs_to_service_or_403(from_service)
service = Service(service_api_client.get_service(from_service)["data"])
return render_template(
"views/templates/copy.html",
services_templates_and_folders=TemplateList(service, template_folder_id=from_folder, user=current_user),
template_folder_path=service.get_template_folder_path(from_folder),
from_service=service,
search_form=SearchByNameForm(),
)
else:
return render_template(
"views/templates/copy.html",
services_templates_and_folders=TemplateLists(current_user),
search_form=SearchByNameForm(),
)
@main.route("/services/<service_id>/templates/copy/<uuid:template_id>", methods=["GET", "POST"])
@user_has_permissions("manage_templates")
def copy_template(service_id, template_id):
from_service = request.args.get("from_service")
current_user.belongs_to_service_or_403(from_service)
template = service_api_client.get_service_template(from_service, str(template_id))["data"]
template_folder = template_folder_api_client.get_template_folder(from_service, template["folder"])
if not current_user.has_template_folder_permission(template_folder):
abort(403)
if request.method == "POST":
return add_service_template(
service_id,
template["template_type"],
template_folder_id=template_folder.get("id"),
)
template["template_content"] = template["content"]
template["name"] = _get_template_copy_name(template, current_service.all_templates)
form = form_objects[template["template_type"]](**template)
return render_template(
f"views/edit-{template['template_type']}-template.html",
form=form,
template=template,
heading=_l("Copy email template") if template["template_type"] == "email" else _l("Copy text message template"),
service_id=service_id,
services=current_user.service_ids,
)
def _get_template_copy_name(template, existing_templates):
template_names = [existing["name"] for existing in existing_templates]
for index in reversed(range(1, 10)):
if "{} (copy {})".format(template["name"], index) in template_names:
return "{} (copy {})".format(template["name"], index + 1)
if "{} (copy)".format(template["name"]) in template_names:
return "{} (copy 2)".format(template["name"])
return "{} (copy)".format(template["name"])
@main.route("/services/<service_id>/templates/action-blocked/<notification_type>/<return_to>/<template_id>")
@user_has_permissions("manage_templates")
def action_blocked(service_id, notification_type, return_to, template_id):
if notification_type == "sms":
notification_type = "text messages"
elif notification_type == "email":
notification_type = "emails"
return render_template(
"views/templates/action_blocked.html",
service_id=service_id,
notification_type=notification_type,
return_to=return_to,
template_id=template_id,
)
@main.route(
"/services/<service_id>/templates/folders/<template_folder_id>/manage",
methods=["GET", "POST"],
)
@user_has_permissions("manage_templates")
def manage_template_folder(service_id, template_folder_id):
template_folder = current_service.get_template_folder_with_user_permission_or_403(template_folder_id, current_user)
form = TemplateFolderForm(
name=template_folder["name"],
users_with_permission=template_folder.get("users_with_permission", None),
all_service_users=[user for user in current_service.active_users if user.id != current_user.id],
)
if form.validate_on_submit():
if current_user.has_permissions("manage_service") and form.users_with_permission.all_service_users:
users_with_permission = form.users_with_permission.data + [current_user.id]
else:
users_with_permission = None
template_folder_api_client.update_template_folder(
current_service.id,
template_folder_id,
name=form.name.data,
users_with_permission=users_with_permission,
)
return redirect(
url_for(
".choose_template",
service_id=service_id,
template_folder_id=template_folder_id,
)
)
return render_template(
"views/templates/manage-template-folder.html",
form=form,
template_folder_path=current_service.get_template_folder_path(template_folder_id),
current_service_id=current_service.id,
template_folder_id=template_folder_id,
template_type="all",
)
@main.route(
"/services/<service_id>/templates/folders/<template_folder_id>/delete",
methods=["GET", "POST"],
)
@user_has_permissions("manage_templates")
def delete_template_folder(service_id, template_folder_id):
template_folder = current_service.get_template_folder_with_user_permission_or_403(template_folder_id, current_user)
if len(current_service.get_template_folders_and_templates(template_type="all", template_folder_id=template_folder_id)) > 0:
flash(_l("You must empty this folder before you can delete it"), "info")
return redirect(
url_for(
".choose_template",
service_id=service_id,
template_type="all",
template_folder_id=template_folder_id,
)
)
if request.method == "POST":
try:
template_folder_api_client.delete_template_folder(current_service.id, template_folder_id)
return redirect(
url_for(
".choose_template",
service_id=service_id,
template_folder_id=template_folder["parent_id"],
)
)
except HTTPError as e:
msg = _l("Folder is not empty")
if e.status_code == 400 and msg in e.message:
flash(_("You must empty this folder before you can delete it"), "info")
return redirect(
url_for(
".choose_template",
service_id=service_id,
template_type="all",
template_folder_id=template_folder_id,
)
)
else:
abort(500, e)
else:
flash(
"{} ‘{}’ {}".format(
_l("Are you sure you want to delete the"),
template_folder["name"],
_l("folder?"),
),
"delete",
)
return manage_template_folder(service_id, template_folder_id)
@main.route("/services/templates/<template_id>/get-data", methods=["POST"])
def get_template_data(template_id):
data = template_api_prefill_client.get_template(template_id)
return jsonify({"result": data})
@main.route("/services/<service_id>/templates/add-<template_type>", methods=["GET", "POST"])
@main.route(
"/services/<service_id>/templates/folders/<template_folder_id>/add-<template_type>",
methods=["GET", "POST"],
)
@user_has_permissions("manage_templates")
def add_service_template(service_id, template_type, template_folder_id=None):
if template_type not in ["sms", "email", "letter"]:
abort(404)
if not current_service.has_permission("letter") and template_type == "letter":
abort(403)
form = form_objects[template_type]()
if form.validate_on_submit():
if form.process_type.data != "normal":
abort_403_if_not_admin_user()
try:
new_template = service_api_client.create_service_template(
form.name.data,
template_type,
form.template_content.data,
service_id,
form.subject.data if hasattr(form, "subject") else None,
form.process_type.data,
template_folder_id,
)
except HTTPError as e:
if (
e.status_code == 400
and "content" in e.message
and any(["character count greater than" in x for x in e.message["content"]])
):
form.template_content.errors.extend(e.message["content"])
else:
raise e
else:
flash(_("'{}' template saved").format(form.name.data), "default_with_tick")
return redirect(
url_for(
".view_template",
service_id=service_id,
template_id=new_template["data"]["id"],
)
)
if email_or_sms_not_enabled(template_type, current_service.permissions):
return redirect(
url_for(
".action_blocked",
service_id=service_id,
notification_type=template_type,
template_folder_id=template_folder_id,
return_to="templates",
template_id="0",
)
)
else:
return render_template(
f"views/edit-{template_type}-template.html",
form=form,
template_type=template_type,
template_folder_id=template_folder_id,
service_id=service_id,
heading=_l("New email template") if template_type == "email" else _l("New text message template"),
)
def abort_403_if_not_admin_user():
if not current_user.platform_admin:
abort(403)
@main.route("/services/<service_id>/templates/<template_id>/edit", methods=["GET", "POST"])
@user_has_permissions("manage_templates")
def edit_service_template(service_id, template_id):
template = current_service.get_template_with_user_permission_or_403(template_id, current_user)
template["template_content"] = template["content"]
form = form_objects[template["template_type"]](**template)
if form.validate_on_submit():
if form.process_type.data != template["process_type"]:
abort_403_if_not_admin_user()
subject = form.subject.data if hasattr(form, "subject") else None
new_template_data = {
"name": form.name.data,
"content": form.template_content.data,
"subject": subject,
"template_type": template["template_type"],
"id": template["id"],
"process_type": form.process_type.data,
"reply_to_text": template["reply_to_text"],
}
new_template = get_template(new_template_data, current_service)
template_change = get_template(template, current_service).compare_to(new_template)
if template_change.placeholders_added and not request.form.get("confirm"):
example_column_headings = first_column_headings[new_template.template_type] + list(new_template.placeholders)
return render_template(
"views/templates/breaking-change.html",
template_change=template_change,
new_template=new_template,
column_headings=list(ascii_uppercase[: len(example_column_headings)]),
example_rows=[
example_column_headings,
get_example_csv_rows(new_template),
get_example_csv_rows(new_template),
],
form=form,
)
try:
service_api_client.update_service_template(
template_id,
form.name.data,
template["template_type"],
form.template_content.data,
service_id,
subject,
form.process_type.data,
)
except HTTPError as e:
if e.status_code == 400:
if "content" in e.message and any(["character count greater than" in x for x in e.message["content"]]):
form.template_content.errors.extend(e.message["content"])
else:
raise e
else:
raise e
else:
flash(_("'{}' template saved").format(form.name.data), "default_with_tick")
return redirect(url_for(".view_template", service_id=service_id, template_id=template_id))
if email_or_sms_not_enabled(template["template_type"], current_service.permissions):
return redirect(
url_for(
".action_blocked",
service_id=service_id,
notification_type=template["template_type"],
return_to="view_template",
template_id=template_id,
)
)
else:
return render_template(
f"views/edit-{template['template_type']}-template.html",
form=form,
template=template,
heading=_l("Edit email template") if template["template_type"] == "email" else _l("Edit text message template"),
)
@main.route("/services/<service_id>/templates/<template_id>/delete", methods=["GET", "POST"])
@user_has_permissions("manage_templates")
def delete_service_template(service_id, template_id):
template = current_service.get_template_with_user_permission_or_403(template_id, current_user)
if request.method == "POST":
service_api_client.delete_service_template(service_id, template_id)
return redirect(
url_for(
".choose_template",
service_id=service_id,
template_folder_id=template["folder"],
)
)
try:
last_used_notification = template_statistics_client.get_template_statistics_for_template(service_id, template["id"])
last_used_text = ""
if not last_used_notification:
last_used_text = _l("more than seven days")
else:
last_used_date = parse(last_used_notification["created_at"]).replace(tzinfo=None)
last_used_text = get_human_readable_delta(last_used_date, datetime.utcnow())
message = "{} {} {}".format(_l("This template was last used"), last_used_text, _l("ago."))
except HTTPError as e:
if e.status_code == 404:
message = None
else:
raise e
flash(
[
"{} ‘{}’?".format(_l("Are you sure you want to delete"), template["name"]),
message,
],
"delete",
)
return render_template(
"views/templates/template.html",
template=get_email_preview_template(template, template["id"], service_id),
user_has_template_permission=True,
)
@main.route("/services/<service_id>/templates/<template_id>/redact", methods=["GET"])
@user_has_permissions("manage_templates")
def confirm_redact_template(service_id, template_id):
template = current_service.get_template_with_user_permission_or_403(template_id, current_user)
return render_template(
"views/templates/template.html",
template=get_email_preview_template(template, template["id"], service_id),
user_has_template_permission=True,
show_redaction_message=True,
)
@main.route("/services/<service_id>/templates/<template_id>/redact", methods=["POST"])
@user_has_permissions("manage_templates")
def redact_template(service_id, template_id):
service_api_client.redact_service_template(service_id, template_id)
flash(
_("Personalised content will be hidden for messages sent with this template"),
"default_with_tick",
)
return redirect(
url_for(
".view_template",
service_id=service_id,
template_id=template_id,
)
)
@main.route("/services/<service_id>/templates/<template_id>/versions")
@user_has_permissions("view_activity")
def view_template_versions(service_id, template_id):
return render_template(
"views/templates/choose_history.html",
versions=[
get_template(
template,
current_service,
letter_preview_url=url_for(
".view_template_version_preview",
service_id=service_id,
template_id=template_id,
version=template["version"],
filetype="png",
),
)
for template in service_api_client.get_service_template_versions(service_id, template_id)["data"]
],
)
@main.route(
"/services/<service_id>/templates/<template_id>/set-template-sender",
methods=["GET", "POST"],
)
@user_has_permissions("manage_templates")
def set_template_sender(service_id, template_id):
template = current_service.get_template_with_user_permission_or_403(template_id, current_user)
sender_details = get_template_sender_form_dict(service_id, template)
no_senders = sender_details.get("no_senders", False)
form = SetTemplateSenderForm(
sender=sender_details["current_choice"],
sender_choices=sender_details["value_and_label"],
)
option_hints = {sender_details["default_sender"]: "(Default)"}
if form.validate_on_submit():
service_api_client.update_service_template_sender(
service_id,
template_id,
form.sender.data if form.sender.data else None,
)
return redirect(url_for(".view_template", service_id=service_id, template_id=template_id))
return render_template(
"views/templates/set-template-sender.html",
form=form,
template_id=template_id,
no_senders=no_senders,
option_hints=option_hints,
)
@main.route(
"/services/<service_id>/templates/<template_id>/edit-postage",
methods=["GET", "POST"],
)
@user_has_permissions("manage_templates")
def edit_template_postage(service_id, template_id):
template = current_service.get_template_with_user_permission_or_403(template_id, current_user)
if template["template_type"] != "letter":
abort(404)
form = LetterTemplatePostageForm(**template)
if form.validate_on_submit():
postage = form.postage.data
service_api_client.update_service_template_postage(service_id, template_id, postage)
return redirect(url_for(".view_template", service_id=service_id, template_id=template_id))
return render_template(
"views/templates/edit-template-postage.html",
form=form,
service_id=service_id,
template_id=template_id,
template_postage=template["postage"],
)
def get_template_sender_form_dict(service_id, template):
context = {
"email": {"field_name": "email_address"},
"letter": {"field_name": "contact_block"},
"sms": {"field_name": "sms_sender"},
}[template["template_type"]]
sender_format = context["field_name"]
service_senders = get_sender_details(service_id, template["template_type"])
context["default_sender"] = next((x["id"] for x in service_senders if x["is_default"]), "Not set")
if not service_senders:
context["no_senders"] = True
context["value_and_label"] = [(sender["id"], Markup(nl2br(sender[sender_format]))) for sender in service_senders]
context["value_and_label"].insert(0, ("", "Blank")) # Add blank option to start of list
context["current_choice"] = template["service_letter_contact"] if template["service_letter_contact"] else ""
return context
def get_human_readable_delta(from_time, until_time):
delta = until_time - from_time
if delta < timedelta(seconds=60):
return "under a minute"
elif delta < timedelta(hours=1):
minutes = int(delta.seconds / 60)
return "{} minute{}".format(minutes, "" if minutes == 1 else "s")
elif delta < timedelta(days=1):
hours = int(delta.seconds / 3600)
return "{} hour{}".format(hours, "" if hours == 1 else "s")
else:
days = delta.days
return "{} day{}".format(days, "" if days == 1 else "s")
| 36.927015
| 127
| 0.661081
| 3,925
| 33,899
| 5.355159
| 0.094522
| 0.056948
| 0.041867
| 0.038822
| 0.586565
| 0.514915
| 0.438127
| 0.400447
| 0.349018
| 0.28498
| 0
| 0.004404
| 0.236467
| 33,899
| 917
| 128
| 36.967285
| 0.807673
| 0.002625
| 0
| 0.397419
| 0
| 0.00129
| 0.187032
| 0.088325
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04129
| false
| 0.00129
| 0.024516
| 0.006452
| 0.138065
| 0.002581
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f41c237f71cc3272ed38dd3e63b60d183d0e2aa0
| 7,999
|
py
|
Python
|
linearRegression_gradientDescent/linearRegression_gradientDescent.py
|
MarcelloVendruscolo/DeepLearningForImageAnalysis
|
0f57d63510d0f7b2729d214b3729a21a663794b5
|
[
"MIT"
] | null | null | null |
linearRegression_gradientDescent/linearRegression_gradientDescent.py
|
MarcelloVendruscolo/DeepLearningForImageAnalysis
|
0f57d63510d0f7b2729d214b3729a21a663794b5
|
[
"MIT"
] | null | null | null |
linearRegression_gradientDescent/linearRegression_gradientDescent.py
|
MarcelloVendruscolo/DeepLearningForImageAnalysis
|
0f57d63510d0f7b2729d214b3729a21a663794b5
|
[
"MIT"
] | null | null | null |
import numpy as np
from load_auto import load_auto
import matplotlib.pyplot as plt
import math
def initialize_parameters(observation_dimension):
# observation_dimension: number of features taken into consideration of the input
# returns weights as a vector and offset as a scalar
weights = np.zeros((observation_dimension, 1))
offset_b = 0
return weights, offset_b
def model_forward(train_dataset, weights, offset_b):
# train_dataset: input data points
# weights and offset_b: model parameters
# returns the output predictions as a vector corresponding to each input data point
number_observations = np.size(train_dataset, axis = 1)
predictions = np.zeros((1, number_observations))
for observation in range(0, number_observations):
with np.errstate(over='raise', invalid='raise'):
try:
predictions[0, observation] = weights.T @ train_dataset[:, observation] + offset_b
except:
predictions[0, observation] = np.inf
return predictions
def compute_cost(predictions, train_labels):
# predictions: computed output values
# train_labels: true output values (ground truth)
# returns the cost function value
number_observations = np.size(predictions, axis = 1)
sum = 0
with np.errstate(over='raise', invalid='raise'):
try:
for observation in range(0, number_observations):
sum += (train_labels[observation, 0] - predictions[0, observation])**2
except:
return np.inf
return sum/number_observations
def model_backward(observation_dimension, train_dataset, predictions, train_labels):
# observation_dimension: number of features taken into consideration of the input
# train_dataset: input data points
# predictions: computed output values
# train_labels: true output values (ground truth)
# returns the gradient of the cost function with respect to all parameters
number_observations = np.size(train_dataset, axis = 1)
sum_weights = np.zeros((observation_dimension, 1))
sum_offset = 0
for observation in range(0, number_observations):
diff = predictions[0, observation] - train_labels[observation, 0]
with np.errstate(over='raise', invalid='raise'):
try:
sum_weights += train_dataset[:, observation].reshape(observation_dimension,-1) * diff
sum_offset += diff
except:
return np.full(sum_weights.shape, np.inf), np.inf
gradient_weights = sum_weights * (2/number_observations)
gradient_offset = sum_offset * (2/number_observations)
return gradient_weights, gradient_offset
def update_parameters(weights, offset_b, gradient_weights, gradient_offset, learning_rate):
# weights and offset_b: parameters computed (or initialised) in this iteration
# gradient_weights and gradient_offset: gradients of the cost function
# learning_rate: step size
# returns the updated parameters for the next iteration
updated_weights = weights - (learning_rate * gradient_weights)
updated_offset = offset_b - (learning_rate * gradient_offset)
return updated_weights, updated_offset
def predict(train_dataset, weights, offset_b):
return model_forward(train_dataset, weights, offset_b)
def train_linear_model(train_dataset, train_labels, number_iterations, learning_rate):
# train_dataset: input data points
# train_labels: true output values (ground truth)
# number_iterations and learning_rate: user-defined hyperparameters
# returns the model parameters and cost function values as a vector
cost = []
observation_dimension = np.size(train_dataset, axis = 0)
weights, offset_b = initialize_parameters(observation_dimension)
while number_iterations > 0:
predictions = predict(train_dataset, weights, offset_b)
cost.append(compute_cost(predictions, train_labels))
gradient_weights, gradient_offset = model_backward(observation_dimension, train_dataset, predictions, train_labels)
weights, offset_b = update_parameters(weights, offset_b, gradient_weights, gradient_offset, learning_rate)
number_iterations -= 1
return weights, offset_b, cost
def plotting_cost_iteration(learning_rates, cost_consolidated):
for counter in range(0, cost_consolidated.shape[0]):
plt.plot(np.arange(start=1, stop = (cost_consolidated.shape[1] + 1), step= 1), cost_consolidated[counter,:], label=r'$\alpha = $' + str(learning_rates[counter]))
plt.xlabel('Iteration')
plt.ylabel('Cost')
plt.title('Cost per Iteration')
plt.ylim(0,720)
plt.legend()
plt.show()
def plotting_horsepower_mpg(train_dataset, train_labels, weights, offset_b):
plt.scatter(train_dataset[0,:], train_labels[:,0], label='Data points')
plt.plot(train_dataset[0,:], np.array(train_dataset[0,:]*weights + offset_b).reshape(train_labels.shape),'r-', label='Linear Regression')
plt.xlabel('(normalised) Horsepower')
plt.ylabel('MPG')
plt.title('MPG vs (normalised) Horsepower')
plt.legend()
plt.show()
PATH_DATASET = '/Users/marcellovendruscolo/Documents/vscode-workspace/DeepLearningForImageAnalysis/linearRegression_gradientDescent/Auto.csv'
train_dataset, train_labels = load_auto(PATH_DATASET)
train_dataset = np.array(train_dataset)
non_normalised_dataset = np.array(np.transpose(train_dataset))
non_normalised_horsepower = non_normalised_dataset[2,:].reshape(1,-1)
train_labels = np.array(train_labels)
mean = np.mean(train_dataset, axis=0)
sd = np.std(train_dataset, axis=0)
for col in range(0, train_dataset.shape[1]):
train_dataset[:,col] = (train_dataset[:,col] - mean[col])/sd[col]
normalised_dataset = np.transpose(train_dataset)
horsepower_dataset = normalised_dataset[2,:].reshape(1,-1)
# Exercise 1.4.1 and Exercise 1.4.2:
# learning_rate = 0.1
# number_iterations = 1000
# print('\nChoice of input dataset: (i) Only horsepower feature.')
# weights, offset_b, cost_function_value = train_linear_model(horsepower_dataset, train_labels, number_iterations, learning_rate)
# print('Number of iterations: ' +str(number_iterations))
# print('Learning rate: ' +str(learning_rate))
# print('Cost function value: ' +str(cost_function_value[len(cost_function_value) - 1]))
# print('Weights: ' +str(weights))
# print('Offset: ' +str(offset_b))
# print('\nChoice of input dataset: (ii) All features except name.')
# weights, offset_b, cost_function_value = train_linear_model(normalised_dataset, train_labels, number_iterations, learning_rate)
# print('Number of iterations: ' +str(number_iterations))
# print('Learning rate: ' +str(learning_rate))
# print('Cost function value: ' +str(cost_function_value[len(cost_function_value) - 1]))
# print('Weights: ' +str(weights))
# print('Offset: ' +str(offset_b) + '\n')
# Exercise 1.4.3:
# learning_rates = [1, 1e-1, 1e-2, 1e-3, 1e-4]
# number_iterations = 1000
# cost_consolidated = np.ndarray(shape=(len(learning_rates), number_iterations))
# for counter in range(0, len(learning_rates)):
# weights, offset_b, cost_consolidated[counter,:] = train_linear_model(normalised_dataset, train_labels, number_iterations, learning_rates[counter])
# plotting_cost_iteration(learning_rates, cost_consolidated)
# Exercise 1.4.4:
# learning_rate = [1, 1e-1, 1e-2, 1e-3, 1e-4]
# number_iterations = 1000
# cost_consolidated = np.ndarray(shape=(len(learning_rate), number_iterations))
# for counter in range(0, len(learning_rate)):
# weights, offset_b, cost_consolidated[counter,:] = train_linear_model(non_normalised_dataset, train_labels, number_iterations, learning_rate[counter])
# plotting_cost_iteration(learning_rate, cost_consolidated)
# Exercise 1.4.5:
# learning_rate = 0.1
# number_iterations = 1000
# weights, offset_b, cost_function_value = train_linear_model(horsepower_dataset, train_labels, number_iterations, learning_rate)
# plotting_horsepower_mpg(horsepower_dataset, train_labels, weights, offset_b)
| 48.478788
| 169
| 0.739717
| 1,042
| 7,999
| 5.446257
| 0.154511
| 0.061322
| 0.044405
| 0.022203
| 0.515066
| 0.456564
| 0.411806
| 0.332863
| 0.30185
| 0.227313
| 0
| 0.015704
| 0.156145
| 7,999
| 165
| 170
| 48.478788
| 0.825037
| 0.3998
| 0
| 0.202247
| 0
| 0
| 0.059444
| 0.026138
| 0
| 0
| 0
| 0
| 0
| 1
| 0.101124
| false
| 0
| 0.044944
| 0.011236
| 0.247191
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
1
| 0
|
f41df8a9a5f75d57ee4443306eca56bc32c0d2b4
| 3,426
|
py
|
Python
|
unit_tests/test_hr_calculations.py
|
mdholbrook/heart_rate_sentinel_server
|
927b59ad6d2078bd6e3491014fdebbc610d25e63
|
[
"MIT"
] | null | null | null |
unit_tests/test_hr_calculations.py
|
mdholbrook/heart_rate_sentinel_server
|
927b59ad6d2078bd6e3491014fdebbc610d25e63
|
[
"MIT"
] | null | null | null |
unit_tests/test_hr_calculations.py
|
mdholbrook/heart_rate_sentinel_server
|
927b59ad6d2078bd6e3491014fdebbc610d25e63
|
[
"MIT"
] | null | null | null |
import pytest
from functions.hr_calculations import *
@pytest.mark.parametrize("candidate, database, expected", [
('jack', [{'patient_id': 'jump'}, {'patient_id': 'jack'}], 1),
('jungle', [{'patient_id': 'jungle'}, {'patient_id': 'jack'}], 0),
('bo', [{'patient_id': 'james'}, {'patient_id': 'boo'},
{'patient_id': 'bo'}], 2)])
def test_find_id_ind(candidate, database, expected):
# Run the test
assert find_id_ind(candidate, database) == expected
@pytest.mark.parametrize("candidate, database, expected", [
('jump', [{'patient_id': 'jump', 'heart_rate': [50, 60, 70]},
{'patient_id': 'jack', 'heart_rate': [120, 112, 131]}],
[50, 60, 70]),
('jack', [{'patient_id': 'jump', 'heart_rate': [50, 60, 70]},
{'patient_id': 'jack', 'heart_rate': [120, 112, 131]}],
[120, 112, 131])
])
def test_get_heart_rates(candidate, database, expected):
# Run the test
assert get_heart_rates(candidate, database) == expected
@pytest.mark.parametrize("candidate, expected", [
([50, 60, 70], 60),
([50, 55, 56], 53.66),
([0, 50, 100], 50),
])
def test_average_heart_rate(candidate, expected):
# Run the test
assert pytest.approx(average_heart_rate(candidate), 1e-2) == expected
@pytest.mark.parametrize("candidate", [
'2018-03-09 11:00:36.372339',
'2017-10-19 15:11:36.167854',
])
def test_get_date_as_numeric(candidate):
# Run the test
result = pytest.approx(get_date_as_numeric(candidate), rel=1e-6)
# Generate expected result
expression = "%Y-%m-%d %H:%M:%S.%f"
expected = datetime.strptime(candidate, expression).timestamp()
assert result == expected
@pytest.mark.parametrize("candidate, expected", [
('Mark', ['2018-03-09 11:00:36.372339', '2017-10-19 15:11:36.167854']),
('Matt', ['2018-03-10 11:00:32.372339', '2017-10-19 35:11:36.167854'])
])
def test_get_times(candidate, expected):
database = [{'patient_id': 'Mark', 'time':
['2018-03-09 11:00:36.372339', '2017-10-19 15:11:36.167854']},
{'patient_id': 'Matt', 'time':
['2018-03-10 11:00:32.372339', '2017-10-19 35:11:36.167854']}]
# Run the test
assert get_times(candidate, database) == expected
@pytest.mark.parametrize("ref_time, times, hr, expected", [
('2018-03-09 11:00:36.372339',
['2018-03-09 11:00:34.372339',
'2018-03-09 11:00:35.372339',
'2018-03-09 11:00:36.872339'], [0, 0, 0], [0]),
('2018-03-09 11:00:36.372339',
['2018-03-09 11:00:35.372339',
'2018-03-09 11:00:36.372359',
'2018-03-09 11:00:37.372339'], [0, 0, 0], [0, 0])])
def test_hr_after_time(ref_time, times, hr, expected):
# Run the test
assert hr_after_time(ref_time, times, hr) == expected
@pytest.mark.parametrize("times, ref_time, expected", [
([0, 1, 2, 3, 4], 3, 4),
([0, 1, 2, 3, 4], 2.5, 3),
([0, 1, 2, 3, 4], 1.5, 2)
])
def test_find_index_larger(ref_time, times, expected):
# Run the test
assert find_index_larger(times, ref_time) == expected
@pytest.mark.parametrize("times, ref_time, expected", [
([0, 1, 2, 3, 4], 3, True),
([0, 1, 2, 3, 4], 4, True),
([0, 1, 2, 3, 4], 4.5, False),
([0, 1, 2, 3, 4], 0, True)
])
def test_check_recent_timestamps(ref_time, times, expected):
# Run the test
assert check_recent_timestamps(times, ref_time) == expected
| 32.018692
| 78
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|
1
| 0
|
f41e1e3571049d96370122828fa85b57484158ca
| 2,492
|
py
|
Python
|
selfdrive/boardd/tests/test_boardd_api.py
|
919bot/Tessa
|
9b48ff9020e8fb6992fc78271f2720fd19e01093
|
[
"MIT"
] | 114
|
2020-02-24T14:18:01.000Z
|
2022-03-19T03:42:00.000Z
|
selfdrive/boardd/tests/test_boardd_api.py
|
919bot/Tessa
|
9b48ff9020e8fb6992fc78271f2720fd19e01093
|
[
"MIT"
] | 15
|
2020-02-25T03:37:44.000Z
|
2021-09-08T01:51:15.000Z
|
selfdrive/boardd/tests/test_boardd_api.py
|
919bot/Tessa
|
9b48ff9020e8fb6992fc78271f2720fd19e01093
|
[
"MIT"
] | 73
|
2018-12-03T19:34:42.000Z
|
2020-07-27T05:10:23.000Z
|
import random
import numpy as np
import selfdrive.boardd.tests.boardd_old as boardd_old
import selfdrive.boardd.boardd as boardd
from common.realtime import sec_since_boot
from cereal import log
import unittest
def generate_random_can_data_list():
can_list = []
cnt = random.randint(1, 64)
for j in range(cnt):
can_data = np.random.bytes(random.randint(1, 8))
can_list.append([random.randint(0, 128), random.randint(0, 128), can_data, random.randint(0, 128)])
return can_list, cnt
class TestBoarddApiMethods(unittest.TestCase):
def test_correctness(self):
for i in range(1000):
can_list, _ = generate_random_can_data_list()
# Sendcan
# Old API
m_old = boardd_old.can_list_to_can_capnp(can_list, 'sendcan').to_bytes()
# new API
m = boardd.can_list_to_can_capnp(can_list, 'sendcan')
ev_old = log.Event.from_bytes(m_old)
ev = log.Event.from_bytes(m)
self.assertEqual(ev_old.which(), ev.which())
self.assertEqual(len(ev.sendcan), len(ev_old.sendcan))
for i in range(len(ev.sendcan)):
attrs = ['address', 'busTime', 'dat', 'src']
for attr in attrs:
self.assertEqual(getattr(ev.sendcan[i], attr, 'new'), getattr(ev_old.sendcan[i], attr, 'old'))
# Can
m_old = boardd_old.can_list_to_can_capnp(can_list, 'can').to_bytes()
# new API
m = boardd.can_list_to_can_capnp(can_list, 'can')
ev_old = log.Event.from_bytes(m_old)
ev = log.Event.from_bytes(m)
self.assertEqual(ev_old.which(), ev.which())
self.assertEqual(len(ev.can), len(ev_old.can))
for i in range(len(ev.can)):
attrs = ['address', 'busTime', 'dat', 'src']
for attr in attrs:
self.assertEqual(getattr(ev.can[i], attr, 'new'), getattr(ev_old.can[i], attr, 'old'))
def test_performance(self):
can_list, cnt = generate_random_can_data_list()
recursions = 1000
n1 = sec_since_boot()
for i in range(recursions):
boardd_old.can_list_to_can_capnp(can_list, 'sendcan').to_bytes()
n2 = sec_since_boot()
elapsed_old = n2 - n1
# print('Old API, elapsed time: {} secs'.format(elapsed_old))
n1 = sec_since_boot()
for i in range(recursions):
boardd.can_list_to_can_capnp(can_list)
n2 = sec_since_boot()
elapsed_new = n2 - n1
# print('New API, elapsed time: {} secs'.format(elapsed_new))
self.assertTrue(elapsed_new < elapsed_old / 2)
if __name__ == '__main__':
unittest.main()
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| 104
| 0.668539
| 378
| 2,492
| 4.142857
| 0.203704
| 0.07599
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| 0.57599
| 0.501277
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| 0.415709
| 0.392082
| 0.392082
| 0
| 0.017051
| 0.199839
| 2,492
| 77
| 105
| 32.363636
| 0.768305
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| 0
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| 0.037355
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| 0
| 0.12963
| 1
| 0.055556
| false
| 0
| 0.12963
| 0
| 0.222222
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| null | 0
| 0
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| 0
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| 0
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| 0
| 0
|
1
| 0
|
f41f9b1b5316c6d5a7a52a8e3e8227d25b183272
| 2,037
|
py
|
Python
|
py_types/static/parse.py
|
zekna/py-types
|
ec39da1277986f0ea44830dfb0da9d906deb13e1
|
[
"MIT"
] | 5
|
2015-06-18T20:04:56.000Z
|
2016-03-15T15:32:44.000Z
|
py_types/static/parse.py
|
sarlianna/py-types
|
ec39da1277986f0ea44830dfb0da9d906deb13e1
|
[
"MIT"
] | 1
|
2016-01-19T01:39:54.000Z
|
2016-01-27T19:17:31.000Z
|
py_types/static/parse.py
|
zekna/py-types
|
ec39da1277986f0ea44830dfb0da9d906deb13e1
|
[
"MIT"
] | null | null | null |
import ast
import inspect
import sys
import argparse
from ..runtime.asserts import typecheck
@typecheck
def pretty_print_defs(defs: list) -> None:
for d in defs:
print("Function definition for {}".format(d["name"]))
print("Arguments:")
for arg in d["args"]:
arg_type = "untyped"
if arg[2]:
arg_type = arg[2].id
print("\t{} : type {}".format(arg[1], arg_type))
if len(d["args"]) == 0:
print("\tNo arguments.")
return_type = None
if d["return"]:
return_type = d["return"].id
print("Return type: {}".format(return_type))
print("")
@typecheck
def parse(filename: str) -> list:
"""Parses and does basic analysis of functions declared at the top level of a file."""
with open(filename, "r") as file_to_parse:
a = file_to_parse.read()
file_ast = ast.parse(a)
# initial pass -- get all function definitions, their names, args, and annotations
@typecheck
def get_name_annotations(block) -> dict:
if not isinstance(block, ast.FunctionDef):
return
return_annotation = block.returns
arg_annotations = []
for i, arg in enumerate(block.args.args):
arg_annotations.append((i, arg.arg, arg.annotation))
fn_name = block.name
annotations = {
"name": fn_name,
"return": return_annotation,
"args": arg_annotations
}
return annotations
definitions = [get_name_annotations(block) for block in file_ast.body]
pretty_print_defs(definitions)
# second pass -- find all expressions, double check origins of any arguments passed to any function in definitions
def depth_first_traversal(ast_tree, filter_type, results: list) -> ast.Module:
pass
return definitions
if __name__ == "__main__":
parse("static/example_parse_me.py")
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| 122
| 0.587138
| 242
| 2,037
| 4.772727
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1
| 0
|