content
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1.04M
| input_ids
sequencelengths 1
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| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import unittest
from Aggregation import Aggregation
if __name__ == '__main__':
unittest.main()
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# coding: utf-8
from PIL import Image, ImageDraw
import numpy as np
lena = Image.open("lena.bmp")
mask1 = np.array([[0 , 1 , 0],
[1 ,-4 , 1],
[0 , 1 , 0]])
mask2 = (1/3) * np.array([[1, 1, 1],
[1,-8, 1],
[1, 1, 1]])
minimum_mask = (1/3) * np.array([[ 2,-1, 2],
[-1,-4,-1],
[ 2,-1, 2]])
'''
gaussian_mask = np.array([[ 0, 0, 0, -1, -1, -2, -1, -1, 0, 0, 0],
[ 0, 0, -2, -4, -8, -9, -8, -4, -2, 0, 0],
[ 0,-2, -7,-15,-22,-23,-22,-15, -7,-2, 0],
[-1,-4,-15,-24,-14, -1,-14,-24,-15,-4,-1],
[-1,-8,-22,-14, 52,103, 52,-14,-22,-8,-1],
[-2,-9,-23, -1,103,179,103, -1,-23,-9,-2],
[-1,-8,-22,-14, 52,103, 52,-14,-22,-8,-1],
[-1,-4,-15,-24,-14, -1,-14,-24,-15,-4,-1],
[ 0,-2, -7,-15,-22,-23,-22,-15, -7,-2, 0],
[ 0, 0, -2, -4, -8, -9, -8, -4, -2, 0, 0],
[ 0, 0, 0, -1, -1, -2, -1, -1, 0, 0, 0]])
diff_gaussian = np.array([[ -1. -3. -4. -6. -7. -8. -7. -6. -4. -3. -1.]
[ -3. -5. -8. -11. -13. -13. -13. -11. -8. -5. -3.]
[ -4. -8. -12. -16. -17. -17. -17. -16. -12. -8. -4.]
[ -6. -11. -16. -16. 0. 15. 0. -16. -16. -11. -6.]
[ -7. -13. -17. 0. 85. 160. 85. 0. -17. -13. -7.]
[ -8. -13. -17. 15. 160. 283. 160. 15. -17. -13. -8.]
[ -7. -13. -17. 0. 85. 160. 85. 0. -17. -13. -7.]
[ -6. -11. -16. -16. 0. 15. 0. -16. -16. -11. -6.]
[ -4. -8. -12. -16. -17. -17. -17. -16. -12. -8. -4.]
[ -3. -5. -8. -11. -13. -13. -13. -11. -8. -5. -3.]
[ -1. -3. -4. -6. -7. -8. -7. -6. -4. -3. -1.]])
'''
gaussian_mask = np.zeros((11,11))
sigma = 1.4
for i in range(-5,6,1):
for j in range(-5,6,1):
tmp = -175 * (((i * i) + (j * j) - 2 * sigma * sigma) * np.exp(((i * i) + (j * j)) / (-2 * sigma * sigma)) / (sigma * sigma * sigma * sigma))
if tmp > 0:
gaussian_mask[i+5,j+5] = int (tmp + 0.5)
else:
gaussian_mask[i+5,j+5] = int (tmp - 0.5)
diff_gaussian = np.zeros((11,11))
sigma1 = 1
sigma2 = 3
for i in range(-5,6,1):
for j in range(-5,6,1):
tmp1 = ( 1/(2 * np.pi * sigma1 ** 2) ) * np.exp( (-0.5) * ((i**2) + (j**2)) / ((sigma1)**2 ) )
tmp2 = ( 1/(2 * np.pi * sigma2 ** 2) ) * np.exp( (-0.5) * ((i**2) + (j**2)) / ((sigma2)**2 ) )
diff_gaussian[i+5,j+5] = int ( (tmp1 - tmp2) * 2000.0 + 0.5 )
Laplace_Mask1 = Laplace(lena,18,mask1)
Laplace_Mask2 = Laplace(lena,18,mask2)
Minimum_variance_Laplace = Laplace(lena,20,minimum_mask)
Laplace_Mask1.save("Laplace_Mask1.bmp")
Laplace_Mask2.save("Laplace_Mask2.bmp")
Minimum_variance_Laplace.save("Minimum_variance_Laplace.bmp")
Laplace_of_Gaussuan = Gaussian(lena,5000,gaussian_mask)
Laplace_of_Gaussuan.save("Laplace_of_Gaussuan.bmp")
Difference_of_Gaussian = Gaussian(lena,70000,diff_gaussian)
Difference_of_Gaussian.save("Difference_of_Gaussian.bmp")
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1945,
62,
1659,
62,
35389,
31562,
13,
65,
3149,
4943,
628
] | 1.543789 | 2,238 |
from Engine.network import Net
| [
6738,
7117,
13,
27349,
1330,
3433,
628,
628
] | 4.25 | 8 |
"""
State.py
========
Perform actions of the rocket and manage state.
`hooks` is a dictionary mapping a hook string to a
list of functions to thread when the hook occurs.
"""
import datetime
from os import system
from threading import Thread
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] | 3.828125 | 64 |
import sys
if __name__ == '__main__':
if len(sys.argv) < 5:
print("Error: too few arguments")
print("Usage: python3 %s <input-filename> <output-filename> <sample-size> <threshold>" % (sys.argv[0]))
sys.exit()
input_filename = sys.argv[1]
output_filename = sys.argv[2]
sample_size = int(sys.argv[3])
threshold = int(sys.argv[4])
with open(input_filename, 'r', encoding='utf-8') as file:
data = list(map(int, file.readlines()))
frame_start = 0
frame_end = 0
frame_start_is_set = False
data_size = len(data)
total = 0
for i in range(sample_size):
total += data[i]
if (total >= threshold):
frame_start = 0
frame_start_is_set = True
for i in range(data_size - sample_size):
total -= data[i]
total += data[i + sample_size]
if (frame_start_is_set == False and total >= threshold):
frame_start = i + 1
frame_start_is_set = True
elif (frame_start_is_set == True and total < threshold):
frame_end = i + sample_size
break
with open(output_filename, 'w', encoding='utf-8') as file:
file.write('%d\n%d' % (frame_start, frame_end))
print('red light interval (frame_id):')
print(' start: %d' % (frame_start))
print(' end: %d' % (frame_end))
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12,
23,
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25,
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220,
220,
220,
220,
220,
220,
220,
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13,
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4,
67,
6,
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62,
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11,
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62,
437,
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628,
220,
220,
220,
3601,
10786,
445,
1657,
16654,
357,
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62,
312,
2599,
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198,
220,
220,
220,
3601,
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220,
923,
25,
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67,
6,
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357,
14535,
62,
9688,
4008,
198,
220,
220,
220,
3601,
10786,
220,
886,
25,
4064,
67,
6,
4064,
357,
14535,
62,
437,
4008,
198
] | 2.223856 | 612 |
import sys
import os
sys.path.append(os.path.abspath('../'))
from unittest import TestCase
from komand_recorded_future.connection.connection import Connection
from komand_recorded_future.actions.lookup_vulnerability import LookupVulnerability
import json
import logging
| [
11748,
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] | 3.578947 | 76 |
"""
demo06_vc.py 验证曲线
"""
import numpy as np
import sklearn.preprocessing as sp
import sklearn.ensemble as se
import sklearn.model_selection as ms
import matplotlib.pyplot as mp
# 读取文件
data = np.loadtxt('../ml_data/car.txt',
delimiter=',', dtype='U20',
converters={0:f, 1:f, 2:f, 3:f, 4:f, 5:f, 6:f})
# 整理训练集的输入与输出
data = data.T
train_x, train_y = [], []
encoders = []
for col in range(len(data)):
lbe = sp.LabelEncoder()
if col < len(data)-1: # 不是最后一列
train_x.append(lbe.fit_transform(data[col]))
else:
train_y = lbe.fit_transform(data[col])
encoders.append(lbe) #保存每列的标签编码器
train_x = np.array(train_x).T
print(train_x)
# 交叉验证 训练模型
model = se.RandomForestClassifier(max_depth=9,
n_estimators=140, random_state=7)
# # 使用validation curve选择最优超参数
# train_scores, test_scores = \
# ms.validation_curve(model, train_x,
# train_y, 'max_depth',
# np.arange(5, 15), cv=5)
# # 画图显示超参数取值与模型性能之间的关系
# x = np.arange(5, 15)
# y = test_scores.mean(axis=1)
# mp.figure('max_depth', facecolor='lightgray')
# mp.title('max_depth', fontsize=20)
# mp.xlabel('max_depth', fontsize=14)
# mp.ylabel('F1 Score', fontsize=14)
# mp.tick_params(labelsize=10)
# mp.grid(linestyle=':')
# mp.plot(x, y, 'o-', c='dodgerblue', label='Training')
# mp.xticks(x)
# mp.legend()
# mp.show()
model.fit(train_x, train_y)
# 模型测试
data = [
['high', 'med', '5more', '4', 'big', 'low', 'unacc'],
['high', 'high', '4', '4', 'med', 'med', 'acc'],
['low', 'low', '2', '4', 'small', 'high', 'good'],
['low', 'med', '3', '4', 'med', 'high', 'vgood']]
# 在训练时需要把所有的LabelEncoder保存下来，
# 在测试时，对测试数据的每一列使用相同的编码器进行编码，
# 然后进行预测，得出预测结果
data = np.array(data).T
test_x, test_y = [], []
for col in range(len(data)):
encoder = encoders[col]
if col<len(data)-1:
test_x.append(encoder.transform(data[col]))
else:
test_y = encoder.transform(data[col])
test_x = np.array(test_x).T
pred_test_y = model.predict(test_x)
print(encoders[-1].inverse_transform(pred_test_y))
print(encoders[-1].inverse_transform(test_y))
| [
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] | 1.754156 | 1,143 |
import functools
import numpy as np
import settings
from imagenetmod.interface import imagenet
from cifar10 import cifar10_input
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] | 2.8 | 50 |
from flask import Flask
from flask_apscheduler import APScheduler
from trivia.config import configs
scheduler: APScheduler = None
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] | 3.410256 | 39 |
t = int(input("Input: "))
for i in range(t) :
ans = ''
for j in range(i+1) :
ans += str(cal(i,j)) + " "
###print(f"({i},{j} )",end=" ")
print(ans) | [
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] | 1.930233 | 86 |
"""
Functions common to both neural networks
"""
import math
import numpy as np
import data_util
import test_util
import feed_forward_nn
import elman_rnn
from nn_globals import NetHyperparams
from nn_globals import OUTPUT_DIM, NUM_VARS
def stochastic_gradient_descent(network_setup, train_data, model, \
verbose=2, verbose_n=1):
""" Performs stochastic gradient descent
network_setup: info on loss function, hyperparams, and RNN vs FFNN
train_data: dataset on which to train (in parsed form)
model: neural net current model
verbose: 0 to not print; 1 to print every inner iteration;
2 to print every verbose_n iterations
verbose_n: used for verbose=2 printing
"""
# Compute average levels
loss_func, loss_func_grad, possible_update, hyper = network_setup
temp = []
for elem in zip(*train_data)[0]:
if elem != None:
temp.append(elem)
temp_np = np.array(temp)
output_dim = model['b2'].shape[0]
data_len = float(len(temp))
divisor_vector = np.zeros((output_dim, 1))
divisor_vector.fill(data_len)
average_levels = np.sum(temp_np, axis=0)[: output_dim] / divisor_vector
# Main loop for SGD
num_updates = 0 # used for step size
train_inputs = [j[0] for j in train_data]
train_outputs = [j[1] for j in train_data]
for t in xrange(hyper.num_iterations):
for input_data, correct_output_data in train_data:
if input_data == None:
model['h'] = [np.zeros((hyper.hidden_dim, 1))]
continue
num_updates += 1
eta = hyper.step_scale / (math.sqrt(num_updates))
for param in loss_func_grad:
grad = loss_func_grad[param](input_data, \
correct_output_data, model, average_levels, hyper)
model[param] -= eta * grad
possible_update(model, input_data)
if verbose == 1:
print "Iteration ", t, ": W1 = ", model['W1'], " ; b1 = ", \
model['b1'], " ; W2 = ", model['W2'], " ; b2 = ", model['b2']
if verbose == 1 or verbose == 2 and t % verbose_n == 0:
print "-------------------------------------------------------"
print "ITERATION ", t, " COMPLETE"
current_loss = loss_func(train_inputs, train_outputs, model, \
average_levels, possible_update, hyper)
print "Current Loss: ", current_loss
final_loss = loss_func(train_inputs, train_outputs, model, \
average_levels, possible_update, hyper)
return (model, final_loss)
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] | 2.297835 | 1,155 |
"""
Author: OMKAR PATHAK
Created On: 17th August 2017
"""
from collections import Counter
import inspect
def is_anagram(word, _list):
"""ANAGRAM
An anagram is direct word switch or word play,
the result of rearranging the letters of a word
or phrase to produce a new word or phrase, using
all the original letters exactly once we are taking
a word and a list. We return the anagrams of that
word from the given list and return the list of
anagrams else return empty list.
:param word: word
:param _list: list of words
:return: anagrams
"""
word = word.lower()
anagrams = []
for words in _list:
if word != words.lower():
if Counter(word) == Counter(words.lower()):
anagrams.append(words)
return anagrams
def get_code():
"""
returns the code for the is_anagram function
:return: source code
"""
return inspect.getsource(is_anagram)
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1441,
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6713,
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198
] | 2.784257 | 343 |
#!/usr/bin/env python3
import argparse
import re
import subprocess
import sys
from pathlib import Path
from typing import List
from list_test_packages import create_test_packages_list
from test_packages_support import get_platform_list_path, get_platform_packages_dir_path
def process_command_line(argv: List[str]) -> argparse.Namespace:
"""Process command line invocation arguments and switches.
Args:
argv: list of arguments, or `None` from ``sys.argv[1:]``.
Returns:
argparse.Namespace: named attributes of arguments and switches
"""
# script_name = argv[0]
argv = argv[1:]
# initialize the parser object:
parser = argparse.ArgumentParser(
description="Check and update as needed the pipx tests package cache "
"for use with the pipx tests local pypiserver."
)
# specifying nargs= puts outputs of parser in list (even if nargs=1)
# required arguments
parser.add_argument(
"package_list_dir",
help="Directory where platform- and python-specific package lists are found for pipx tests.",
)
parser.add_argument(
"pipx_package_cache_dir",
help="Directory to store the packages distribution files.",
)
# switches/options:
parser.add_argument(
"-c",
"--check-only",
action="store_true",
help="Only check to see if needed packages are in PACKAGES_DIR, do not "
"download or delete files.",
)
args = parser.parse_args(argv)
return args
if __name__ == "__main__":
try:
status = main(sys.argv)
except KeyboardInterrupt:
print("Stopped by Keyboard Interrupt", file=sys.stderr)
status = 130
sys.exit(status)
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] | 2.739336 | 633 |
import numpy as np
import contextlib
from collections import deque
from spirl.utils.general_utils import listdict2dictlist, AttrDict, ParamDict, obj2np
from spirl.modules.variational_inference import MultivariateGaussian
from spirl.rl.utils.reward_fcns import sparse_threshold
class Sampler:
"""Collects rollouts from the environment using the given agent."""
def init(self, is_train):
"""Starts a new rollout. Render indicates whether output should contain image."""
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
self._episode_reset()
def sample_batch(self, batch_size, is_train=True, global_step=None):
"""Samples an experience batch of the required size."""
experience_batch = []
step = 0
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while step < batch_size:
# perform one rollout step
agent_output = self.sample_action(self._obs)
if agent_output.action is None:
self._episode_reset(global_step)
continue
agent_output = self._postprocess_agent_output(agent_output)
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
experience_batch.append(AttrDict(
observation=self._obs,
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs,
))
# update stored observation
self._obs = obs
step += 1; self._episode_step += 1; self._episode_reward += reward
# reset if episode ends
if done or self._episode_step >= self._max_episode_len:
if not done: # force done to be True for timeout
experience_batch[-1].done = True
self._episode_reset(global_step)
return listdict2dictlist(experience_batch), step
def sample_episode(self, is_train, render=False):
"""Samples one episode from the environment."""
self.init(is_train)
episode, done = [], False
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while not done and self._episode_step < self._max_episode_len:
# perform one rollout step
agent_output = self.sample_action(self._obs)
if agent_output.action is None:
break
agent_output = self._postprocess_agent_output(agent_output)
if render:
render_obs = self._env.render()
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
episode.append(AttrDict(
observation=self._obs,
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs,
info=obj2np(info),
))
if render:
episode[-1].update(AttrDict(image=render_obs))
# update stored observation
self._obs = obs
self._episode_step += 1
episode[-1].done = True # make sure episode is marked as done at final time step
return listdict2dictlist(episode)
def _episode_reset(self, global_step=None):
"""Resets sampler at the end of an episode."""
if global_step is not None and self._logger is not None: # logger is none in non-master threads
self._logger.log_scalar_dict(self.get_episode_info(),
prefix='train' if self._agent._is_train else 'val',
step=global_step)
self._episode_step, self._episode_reward = 0, 0.
self._obs = self._postprocess_obs(self._reset_env())
self._agent.reset()
def _postprocess_obs(self, obs):
"""Optionally post-process observation."""
return obs
def _postprocess_agent_output(self, agent_output):
"""Optionally post-process / store agent output."""
return agent_output
class HierarchicalSampler(Sampler):
"""Collects experience batches by rolling out a hierarchical agent. Aggregates low-level batches into HL batch."""
def sample_batch(self, batch_size, is_train=True, global_step=None, store_ll=True):
"""Samples the required number of high-level transitions. Number of LL transitions can be higher."""
hl_experience_batch, ll_experience_batch = [], []
env_steps, hl_step = 0, 0
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while hl_step < batch_size or len(ll_experience_batch) <= 1:
# perform one rollout step
agent_output = self.sample_action(self._obs)
agent_output = self._postprocess_agent_output(agent_output)
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
# update last step's 'observation_next' with HL action
if store_ll:
if ll_experience_batch:
ll_experience_batch[-1].observation_next = \
self._agent.make_ll_obs(ll_experience_batch[-1].observation_next, agent_output.hl_action)
# store current step in ll_experience_batch
ll_experience_batch.append(AttrDict(
observation=self._agent.make_ll_obs(self._obs, agent_output.hl_action),
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs, # this will get updated in the next step
))
# store HL experience batch if this was HL action or episode is done
if agent_output.is_hl_step or (done or self._episode_step >= self._max_episode_len-1):
if self.last_hl_obs is not None and self.last_hl_action is not None:
hl_experience_batch.append(AttrDict(
observation=self.last_hl_obs,
reward=self.reward_since_last_hl,
done=done,
action=self.last_hl_action,
observation_next=obs,
))
hl_step += 1
if hl_step % 1000 == 0:
print("Sample step {}".format(hl_step))
self.last_hl_obs = self._obs
self.last_hl_action = agent_output.hl_action
self.reward_since_last_hl = 0
# update stored observation
self._obs = obs
env_steps += 1; self._episode_step += 1; self._episode_reward += reward
self.reward_since_last_hl += reward
# reset if episode ends
if done or self._episode_step >= self._max_episode_len:
if not done: # force done to be True for timeout
ll_experience_batch[-1].done = True
if hl_experience_batch: # can potentially be empty
hl_experience_batch[-1].done = True
self._episode_reset(global_step)
return AttrDict(
hl_batch=listdict2dictlist(hl_experience_batch),
ll_batch=listdict2dictlist(ll_experience_batch[:-1]), # last element does not have updated obs_next!
), env_steps
class ImageAugmentedSampler(Sampler):
"""Appends image rendering to raw observation."""
class MultiImageAugmentedSampler(Sampler):
"""Appends multiple past images to current observation."""
class ACImageAugmentedSampler(ImageAugmentedSampler):
"""Adds no-op renders to make sure agent-centric camera reaches agent."""
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] | 1.918405 | 4,939 |
import time
from datetime import datetime, timedelta
import random
import re
import logging
from config import SLACK_TOKEN
import pandas as pd
import slack
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from pymongo import MongoClient
from sqlalchemy import create_engine
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
import pickle
import os
# defining path to read files for airflow
AIRFLOW_HOME = os.getenv('AIRFLOW_HOME')
# Creating connections
CLIENT = MongoClient("mongodb") # Mongodb
DB = CLIENT.mongodb
DATABASE_UP = False
# PostGres connections and table
PG = create_engine('postgres://postgres:1234@postgresdb:5432/tweets')
PG.execute('''CREATE TABLE IF NOT EXISTS kung_tweets (
id BIGSERIAL,
username VARCHAR(128),
text VARCHAR(2048),
date_created TIMESTAMPTZ,
followers INTEGER,
friends INTEGER,
negative NUMERIC,
positive NUMERIC,
neuteral NUMERIC
);
''')
client = slack.WebClient(token=SLACK_TOKEN) # Slac
# Loading models:
# Sentiment analysis
s = SentimentIntensityAnalyzer()
# Sarcasm classifier
tv = pickle.load(open(AIRFLOW_HOME + '/dags/vectorizer.sav', 'rb'))
lsvc = pickle.load(open(AIRFLOW_HOME + '/dags/sarcasm_model.sav', 'rb'))
# Creating python callables
def extract():
"""extract a random tweet from MongoDB"""
tweets = list(DB.kung_tweets.find())
if tweets:
t = random.choice(tweets)
logging.critical("random tweet: " + t['text'])
return t
def transform(**context):
"""
Transform tweets
Input: Tweets in JSON format from the extract function
Cleans the text, analyzes the sentiment and extracts metadata
return: A list with all metadata of the tweet
"""
extract_connection = context['task_instance']
tweet = extract_connection.xcom_pull(task_ids="extract")
# getting the full text from the tweets
if 'retweeted_status'in tweet and 'extended_tweet' in tweet['retweeted_status']:
text = tweet['retweeted_status']['extended_tweet']["full_text"]
elif 'retweeted_status'in tweet and 'text' in tweet['retweeted_status']:
text = tweet['retweeted_status']["text"]
elif "extended_tweet" in tweet:
text = tweet['extended_tweet']["full_text"]
else:
text = tweet['text']
text = re.sub(r"'", ' ', text)
text = re.sub(r'@\S+|https?://\S+', '', text) # removing @user and links
username = tweet['user']['screen_name']
date = str(pd.to_datetime(tweet["created_at"]))
followers = tweet['user']['followers_count']
friends = tweet['user']['friends_count']
sentiment = s.polarity_scores(text)
neg = sentiment["neg"]
pos = sentiment["pos"]
neu = sentiment["neu"]
logging.critical("sentiment" + str(sentiment))
results = [username, text, date, followers, friends, neg, pos, neu]
return results
def load(**context):
"""Loads metadata to PostGreSQL server"""
exctract_connection = context["task_instance"]
results = exctract_connection.xcom_pull(task_ids='transform')
PG.execute(f"""INSERT INTO kung_tweets (username, text, date_created, followers, friends, negative, positive, neuteral)
VALUES ('''{results[0]}''', '''{results[1]}''', '{results[2]}', {results[3]}, {results[4]}, {results[5]}, {results[6]}, {results[7]});""")
logging.critical(f"{results[1]} written to PostGres")
def predict_sarcasm(**context):
"""Classify the sarcasm of the tweet"""
exctract_connection = context["task_instance"]
results = exctract_connection.xcom_pull(task_ids='transform')
text = results[1]
text = tv.transform([text]).toarray()
sarcasm = lsvc.predict(text)
return sarcasm
def slackbot(**context):
"""Posts a message on the Slack channel if the tweet was clasified as sarcastic"""
exctract_connection = context["task_instance"]
results = exctract_connection.xcom_pull(task_ids='transform')
sarcasm = exctract_connection.xcom_pull(task_ids='predict_sarcasm')
prev_tweet = ''
if sarcasm == 1:
tweet_result = results[1] # text
if tweet_result != prev_tweet:
prev_tweet = tweet_result
response = client.chat_postMessage(
channel='#kung_flu', text=f"Here is a sarcastic tweet about coronavirus: {tweet_result}")
# delay for one minute
time.sleep(60)
# define default arguments
default_args = {
'owner': 'Amirali',
'start_date': datetime(2020, 4, 1),
# 'end_date':
'email': ['[email protected]'],
'email_on_failure': False,
'email_on_retry': False,
"retries": 1,
"retry_delay": timedelta(minutes=1)
}
# instantiate a DAG
dag = DAG('etl', description='', catchup=False,
schedule_interval=timedelta(minutes=1), default_args=default_args)
# define task
t1 = PythonOperator(task_id='extract', python_callable=extract, dag=dag)
t2 = PythonOperator(task_id='transform', provide_context=True,
python_callable=transform, dag=dag)
t3 = PythonOperator(task_id='load', provide_context=True,
python_callable=load, dag=dag)
t4 = PythonOperator(task_id='predict_sarcasm', provide_context=True,
python_callable=predict_sarcasm, dag=dag)
t5 = PythonOperator(task_id='slackbot', provide_context=True,
python_callable=slackbot, dag=dag)
# setup dependencies
t1 >> t2 >> t3
t2 >> t4 >> t5
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220,
220,
220,
220,
220,
640,
13,
42832,
7,
1899,
8,
201,
198,
201,
198,
201,
198,
2,
8160,
4277,
7159,
201,
198,
12286,
62,
22046,
796,
1391,
201,
198,
220,
220,
220,
705,
18403,
10354,
705,
5840,
343,
7344,
3256,
201,
198,
220,
220,
220,
705,
9688,
62,
4475,
10354,
4818,
8079,
7,
42334,
11,
604,
11,
352,
828,
201,
198,
220,
220,
220,
1303,
705,
437,
62,
4475,
10354,
201,
198,
220,
220,
220,
705,
12888,
10354,
37250,
321,
343,
7344,
13,
88,
1031,
10989,
31,
40774,
13,
785,
6,
4357,
201,
198,
220,
220,
220,
705,
12888,
62,
261,
62,
32165,
495,
10354,
10352,
11,
201,
198,
220,
220,
220,
705,
12888,
62,
261,
62,
1186,
563,
10354,
10352,
11,
201,
198,
220,
220,
220,
366,
1186,
1678,
1298,
352,
11,
201,
198,
220,
220,
220,
366,
1186,
563,
62,
40850,
1298,
28805,
12514,
7,
1084,
1769,
28,
16,
8,
201,
198,
92,
201,
198,
201,
198,
2,
9113,
9386,
257,
360,
4760,
201,
198,
67,
363,
796,
360,
4760,
10786,
316,
75,
3256,
6764,
11639,
3256,
4929,
929,
28,
25101,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7269,
62,
3849,
2100,
28,
16514,
276,
12514,
7,
1084,
1769,
28,
16,
828,
4277,
62,
22046,
28,
12286,
62,
22046,
8,
201,
198,
201,
198,
2,
8160,
4876,
201,
198,
83,
16,
796,
11361,
18843,
1352,
7,
35943,
62,
312,
11639,
2302,
974,
3256,
21015,
62,
13345,
540,
28,
2302,
974,
11,
48924,
28,
67,
363,
8,
201,
198,
83,
17,
796,
11361,
18843,
1352,
7,
35943,
62,
312,
11639,
35636,
3256,
2148,
62,
22866,
28,
17821,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21015,
62,
13345,
540,
28,
35636,
11,
48924,
28,
67,
363,
8,
201,
198,
83,
18,
796,
11361,
18843,
1352,
7,
35943,
62,
312,
11639,
2220,
3256,
2148,
62,
22866,
28,
17821,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21015,
62,
13345,
540,
28,
2220,
11,
48924,
28,
67,
363,
8,
201,
198,
83,
19,
796,
11361,
18843,
1352,
7,
35943,
62,
312,
11639,
79,
17407,
62,
82,
5605,
8597,
3256,
2148,
62,
22866,
28,
17821,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21015,
62,
13345,
540,
28,
79,
17407,
62,
82,
5605,
8597,
11,
48924,
28,
67,
363,
8,
201,
198,
83,
20,
796,
11361,
18843,
1352,
7,
35943,
62,
312,
11639,
6649,
441,
13645,
3256,
2148,
62,
22866,
28,
17821,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21015,
62,
13345,
540,
28,
6649,
441,
13645,
11,
48924,
28,
67,
363,
8,
201,
198,
201,
198,
201,
198,
2,
9058,
20086,
201,
198,
83,
16,
9609,
256,
17,
9609,
256,
18,
201,
198,
83,
17,
9609,
256,
19,
9609,
256,
20,
201,
198
] | 2.491868 | 2,275 |
#looping over the generated range of numbers
total = 0
for i in range(1, 101):
total = total + i
print(total) | [
2,
5439,
15816,
625,
262,
7560,
2837,
286,
3146,
198,
23350,
796,
657,
198,
1640,
1312,
287,
2837,
7,
16,
11,
8949,
2599,
198,
220,
220,
220,
2472,
796,
2472,
1343,
1312,
198,
198,
4798,
7,
23350,
8
] | 3 | 38 |
# [Super SloMo]
##High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
from comet_ml import Experiment, ExistingExperiment
import argparse
import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import model
import dataloader_c as dataloader
from math import log10
import datetime
import numpy as np
import warnings
from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM # pip install pytorch-msssim
from PIL import Image
import torchvision.utils as vutils
from utils import init_net, upload_images
warnings.simplefilter("ignore", UserWarning)
# from tensorboardX import SummaryWriter
# For parsing commandline arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset_root",
type=str,
required=True,
help="path to dataset folder containing train-test-validation folders",
)
parser.add_argument(
"--checkpoint_dir",
type=str,
required=True,
help="path to folder for saving checkpoints",
)
parser.add_argument(
"--checkpoint", type=str, help="path of checkpoint for pretrained model"
)
parser.add_argument(
"--train_continue", action="store_true", help="resuming from checkpoint."
)
parser.add_argument(
"-it",
"--init_type",
default="",
type=str,
help="the name of an initialization method: normal | xavier | kaiming | orthogonal",
)
parser.add_argument(
"--epochs", type=int, default=200, help="number of epochs to train. Default: 200."
)
parser.add_argument(
"-tbs",
"--train_batch_size",
type=int,
default=384,
help="batch size for training. Default: 6.",
)
parser.add_argument(
"-nw", "--num_workers", default=4, type=int, help="number of CPU you get"
)
parser.add_argument(
"-vbs",
"--validation_batch_size",
type=int,
default=384,
help="batch size for validation. Default: 10.",
)
parser.add_argument(
"-ilr",
"--init_learning_rate",
type=float,
default=0.0001,
help="set initial learning rate. Default: 0.0001.",
)
parser.add_argument(
"--milestones",
type=list,
default=[100, 150],
help="Set to epoch values where you want to decrease learning rate by a factor of 0.1. Default: [100, 150]",
)
parser.add_argument(
"--progress_iter",
type=int,
default=100,
help="frequency of reporting progress and validation. N: after every N iterations. Default: 100.",
)
parser.add_argument(
"--logimagefreq", type=int, default=1, help="frequency of logging image.",
)
parser.add_argument(
"--checkpoint_epoch",
type=int,
default=5,
help="checkpoint saving frequency. N: after every N epochs. Each checkpoint is roughly of size 151 MB.Default: 5.",
)
parser.add_argument(
"-wp", "--workspace", default="tianyu-z", type=str, help="comet-ml workspace"
)
parser.add_argument(
"-dh", "--data_h", default=128, type=int, help="H of the data shape"
)
parser.add_argument(
"-dw", "--data_w", default=128, type=int, help="W of the data shape"
)
parser.add_argument(
"-pn",
"--projectname",
default="super-slomo",
type=str,
help="comet-ml project name",
)
parser.add_argument(
"--nocomet", action="store_true", help="not using comet_ml logging."
)
parser.add_argument(
"--cometid", type=str, default="", help="the comet id to resume exps",
)
parser.add_argument(
"-rs",
"--randomseed",
type=int,
default=2021,
help="batch size for validation. Default: 10.",
)
args = parser.parse_args()
random_seed = args.randomseed
np.random.seed(random_seed)
torch.manual_seed(random_seed)
if torch.cuda.device_count() > 1:
torch.cuda.manual_seed_all(random_seed)
else:
torch.cuda.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
##[TensorboardX](https://github.com/lanpa/tensorboardX)
### For visualizing loss and interpolated frames
# writer = SummaryWriter("log")
###Initialize flow computation and arbitrary-time flow interpolation CNNs.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
flowComp = model.UNet(6, 4)
flowComp.to(device)
if args.init_type != "":
init_net(flowComp, args.init_type)
print(args.init_type + " initializing flowComp done")
ArbTimeFlowIntrp = model.UNet(20, 5)
ArbTimeFlowIntrp.to(device)
if args.init_type != "":
init_net(ArbTimeFlowIntrp, args.init_type)
print(args.init_type + " initializing ArbTimeFlowIntrp done")
### Initialization
if args.train_continue:
if not args.nocomet and args.cometid != "":
comet_exp = ExistingExperiment(previous_experiment=args.cometid)
elif not args.nocomet and args.cometid == "":
comet_exp = Experiment(workspace=args.workspace, project_name=args.projectname)
else:
comet_exp = None
dict1 = torch.load(args.checkpoint)
ArbTimeFlowIntrp.load_state_dict(dict1["state_dictAT"])
flowComp.load_state_dict(dict1["state_dictFC"])
print("Pretrained model loaded!")
else:
# start logging info in comet-ml
if not args.nocomet:
comet_exp = Experiment(workspace=args.workspace, project_name=args.projectname)
# comet_exp.log_parameters(flatten_opts(args))
else:
comet_exp = None
dict1 = {"loss": [], "valLoss": [], "valPSNR": [], "valSSIM": [], "epoch": -1}
###Initialze backward warpers for train and validation datasets
trainFlowBackWarp = model.backWarp(128, 128, device)
trainFlowBackWarp = trainFlowBackWarp.to(device)
validationFlowBackWarp = model.backWarp(128, 128, device)
validationFlowBackWarp = validationFlowBackWarp.to(device)
###Load Datasets
# Channel wise mean calculated on adobe240-fps training dataset
mean = [0.5, 0.5, 0.5]
std = [1, 1, 1]
normalize = transforms.Normalize(mean=mean, std=std)
transform = transforms.Compose([transforms.ToTensor(), normalize])
trainset = dataloader.SuperSloMo(
root=args.dataset_root + "/train", transform=transform, train=True
)
trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=args.train_batch_size,
num_workers=args.num_workers,
shuffle=True,
)
validationset = dataloader.SuperSloMo(
root=args.dataset_root + "/validation",
transform=transform,
randomCropSize=(128, 128),
train=False,
)
validationloader = torch.utils.data.DataLoader(
validationset,
batch_size=args.validation_batch_size,
num_workers=args.num_workers,
shuffle=False,
)
print(trainset, validationset)
###Create transform to display image from tensor
negmean = [x * -1 for x in mean]
revNormalize = transforms.Normalize(mean=negmean, std=std)
TP = transforms.Compose([revNormalize, transforms.ToPILImage()])
###Utils
###Loss and Optimizer
L1_lossFn = nn.L1Loss()
MSE_LossFn = nn.MSELoss()
params = list(ArbTimeFlowIntrp.parameters()) + list(flowComp.parameters())
optimizer = optim.Adam(params, lr=args.init_learning_rate)
# scheduler to decrease learning rate by a factor of 10 at milestones.
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, milestones=args.milestones, gamma=0.1
)
###Initializing VGG16 model for perceptual loss
vgg16 = torchvision.models.vgg16(pretrained=True)
vgg16_conv_4_3 = nn.Sequential(*list(vgg16.children())[0][:22])
vgg16_conv_4_3.to(device)
if args.init_type != "":
init_net(vgg16_conv_4_3, args.init_type)
for param in vgg16_conv_4_3.parameters():
param.requires_grad = False
### Validation function
#
### Training
import time
best_psnr = -1
best_ssim = -1
best_valloss = 9999
start = time.time()
cLoss = dict1["loss"]
valLoss = dict1["valLoss"]
valPSNR = dict1["valPSNR"]
valSSIM = dict1["valSSIM"]
checkpoint_counter = int((dict1["epoch"] + 1) / args.checkpoint_epoch)
### Main training loop
for epoch in range(dict1["epoch"] + 1, args.epochs):
print("Epoch: ", epoch)
# Append and reset
cLoss.append([])
valLoss.append([])
valPSNR.append([])
valSSIM.append([])
iLoss = 0
if epoch > dict1["epoch"] + 1:
# Increment scheduler count
scheduler.step()
# if epoch == dict1["epoch"] + 1:
# # test if validate works
# validate(epoch, True)
for trainIndex, (trainData, trainFrameIndex) in enumerate(trainloader, 0):
## Getting the input and the target from the training set
frame0, frameT, frame1 = trainData
I0 = frame0.to(device)
I1 = frame1.to(device)
IFrame = frameT.to(device)
optimizer.zero_grad()
# Calculate flow between reference frames I0 and I1
flowOut = flowComp(torch.cat((I0, I1), dim=1))
# Extracting flows between I0 and I1 - F_0_1 and F_1_0
F_0_1 = flowOut[:, :2, :, :]
F_1_0 = flowOut[:, 2:, :, :]
fCoeff = model.getFlowCoeff(trainFrameIndex, device)
# Calculate intermediate flows
F_t_0 = fCoeff[0] * F_0_1 + fCoeff[1] * F_1_0
F_t_1 = fCoeff[2] * F_0_1 + fCoeff[3] * F_1_0
# Get intermediate frames from the intermediate flows
g_I0_F_t_0 = trainFlowBackWarp(I0, F_t_0)
g_I1_F_t_1 = trainFlowBackWarp(I1, F_t_1)
# Calculate optical flow residuals and visibility maps
intrpOut = ArbTimeFlowIntrp(
torch.cat(
(I0, I1, F_0_1, F_1_0, F_t_1, F_t_0, g_I1_F_t_1, g_I0_F_t_0), dim=1
)
)
# Extract optical flow residuals and visibility maps
F_t_0_f = intrpOut[:, :2, :, :] + F_t_0
F_t_1_f = intrpOut[:, 2:4, :, :] + F_t_1
V_t_0 = torch.sigmoid(intrpOut[:, 4:5, :, :])
V_t_1 = 1 - V_t_0
# Get intermediate frames from the intermediate flows
g_I0_F_t_0_f = trainFlowBackWarp(I0, F_t_0_f)
g_I1_F_t_1_f = trainFlowBackWarp(I1, F_t_1_f)
wCoeff = model.getWarpCoeff(trainFrameIndex, device)
# Calculate final intermediate frame
Ft_p = (wCoeff[0] * V_t_0 * g_I0_F_t_0_f + wCoeff[1] * V_t_1 * g_I1_F_t_1_f) / (
wCoeff[0] * V_t_0 + wCoeff[1] * V_t_1
)
# Loss
recnLoss = L1_lossFn(Ft_p, IFrame)
prcpLoss = MSE_LossFn(vgg16_conv_4_3(Ft_p), vgg16_conv_4_3(IFrame))
warpLoss = (
L1_lossFn(g_I0_F_t_0, IFrame)
+ L1_lossFn(g_I1_F_t_1, IFrame)
+ L1_lossFn(trainFlowBackWarp(I0, F_1_0), I1)
+ L1_lossFn(trainFlowBackWarp(I1, F_0_1), I0)
)
loss_smooth_1_0 = torch.mean(
torch.abs(F_1_0[:, :, :, :-1] - F_1_0[:, :, :, 1:])
) + torch.mean(torch.abs(F_1_0[:, :, :-1, :] - F_1_0[:, :, 1:, :]))
loss_smooth_0_1 = torch.mean(
torch.abs(F_0_1[:, :, :, :-1] - F_0_1[:, :, :, 1:])
) + torch.mean(torch.abs(F_0_1[:, :, :-1, :] - F_0_1[:, :, 1:, :]))
loss_smooth = loss_smooth_1_0 + loss_smooth_0_1
# Total Loss - Coefficients 204 and 102 are used instead of 0.8 and 0.4
# since the loss in paper is calculated for input pixels in range 0-255
# and the input to our network is in range 0-1
loss = 204 * recnLoss + 102 * warpLoss + 0.005 * prcpLoss + loss_smooth
# Backpropagate
loss.backward()
optimizer.step()
iLoss += loss.item()
# Validation and progress every `args.progress_iter` iterations
# if (trainIndex % args.progress_iter) == args.progress_iter - 1:
end = time.time()
psnr, ssim_val, vLoss, valImg = validate(epoch, logimage=True)
valPSNR[epoch].append(psnr)
valSSIM[epoch].append(ssim_val)
valLoss[epoch].append(vLoss)
# Tensorboard
itr = int(trainIndex + epoch * (len(trainloader)))
# writer.add_scalars(
# "Loss",
# {"trainLoss": iLoss / args.progress_iter, "validationLoss": vLoss},
# itr,
# )
# writer.add_scalar("PSNR", psnr, itr)
# writer.add_image("Validation", valImg, itr)
comet_exp.log_metrics(
{"trainLoss": iLoss / args.progress_iter, "validationLoss": vLoss},
step=itr,
epoch=epoch,
)
comet_exp.log_metric("PSNR", psnr, step=itr, epoch=epoch)
comet_exp.log_metric("SSIM", ssim_val, step=itr, epoch=epoch)
# valImage = torch.movedim(valImg, 0, -1)
# print(type(valImage))
# print(valImage.shape)
# print(valImage.max())
# print(valImage.min())
# comet_exp.log_image(
# valImage,
# name="iter: " + str(iter) + ";epoch: " + str(epoch),
# image_format="jpg",
# step=itr,
# )
#####
endVal = time.time()
print(
" Loss: %0.6f Iterations: %4d/%4d TrainExecTime: %0.1f ValLoss:%0.6f ValPSNR: %0.4f ValSSIM: %0.4f ValEvalTime: %0.2f LearningRate: %f"
% (
iLoss / args.progress_iter,
trainIndex,
len(trainloader),
end - start,
vLoss,
psnr,
ssim_val,
endVal - end,
get_lr(optimizer),
)
)
cLoss[epoch].append(iLoss / args.progress_iter)
iLoss = 0
start = time.time()
# Create checkpoint after every `args.checkpoint_epoch` epochs
if (epoch % args.checkpoint_epoch) == args.checkpoint_epoch - 1:
dict1 = {
"Detail": "End to end Super SloMo.",
"epoch": epoch,
"timestamp": datetime.datetime.now(),
"trainBatchSz": args.train_batch_size,
"validationBatchSz": args.validation_batch_size,
"learningRate": get_lr(optimizer),
"loss": cLoss,
"valLoss": valLoss,
"valPSNR": valPSNR,
"valSSIM": valSSIM,
"state_dictFC": flowComp.state_dict(),
"state_dictAT": ArbTimeFlowIntrp.state_dict(),
}
torch.save(
dict1,
args.checkpoint_dir + "/SuperSloMo" + str(checkpoint_counter) + ".ckpt",
)
checkpoint_counter += 1
if psnr > best_psnr:
best_psnr = psnr
dict1 = {
"Detail": "End to end Super SloMo.",
"epoch": epoch,
"timestamp": datetime.datetime.now(),
"trainBatchSz": args.train_batch_size,
"validationBatchSz": args.validation_batch_size,
"learningRate": get_lr(optimizer),
"loss": cLoss,
"valLoss": valLoss,
"valPSNR": valPSNR,
"valSSIM": valSSIM,
"state_dictFC": flowComp.state_dict(),
"state_dictAT": ArbTimeFlowIntrp.state_dict(),
}
torch.save(
dict1, args.checkpoint_dir + "/SuperSloMo" + "bestpsnr_epoch" + ".ckpt",
)
print("New Best PSNR found and saved at " + str(epoch))
if vLoss < best_valloss:
best_valloss = vLoss
dict1 = {
"Detail": "End to end Super SloMo.",
"epoch": epoch,
"timestamp": datetime.datetime.now(),
"trainBatchSz": args.train_batch_size,
"validationBatchSz": args.validation_batch_size,
"learningRate": get_lr(optimizer),
"loss": cLoss,
"valLoss": valLoss,
"valPSNR": valPSNR,
"valSSIM": valSSIM,
"state_dictFC": flowComp.state_dict(),
"state_dictAT": ArbTimeFlowIntrp.state_dict(),
}
torch.save(
dict1, args.checkpoint_dir + "/SuperSloMo" + "bestvalloss_epoch" + ".ckpt",
)
print("New Best valloss found and saved at " + str(epoch))
if ssim_val > best_ssim:
best_ssim = ssim_val
dict1 = {
"Detail": "End to end Super SloMo.",
"epoch": epoch,
"timestamp": datetime.datetime.now(),
"trainBatchSz": args.train_batch_size,
"validationBatchSz": args.validation_batch_size,
"learningRate": get_lr(optimizer),
"loss": cLoss,
"valLoss": valLoss,
"valPSNR": valPSNR,
"valSSIM": valSSIM,
"state_dictFC": flowComp.state_dict(),
"state_dictAT": ArbTimeFlowIntrp.state_dict(),
}
torch.save(
dict1, args.checkpoint_dir + "/SuperSloMo" + "bestssim_epoch" + ".ckpt",
)
print("New Best SSIM found and saved at " + str(epoch))
| [
2,
685,
12442,
3454,
78,
16632,
60,
198,
2235,
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] | 2.202523 | 7,372 |
# -*- coding:utf-8 -*-
# @author xupingmao
# @since 2022/04/10 15:06:25
# @modified 2022/04/10 15:10:04
# @filename case2.py
input_text = """
def foo(bar):
return bar * 5
"""
output = [
"nl", "nl",
"def", "def",
"name", "foo",
"(", "(",
"name", "bar",
")", ")",
":", ":",
"nl", "nl",
"indent", 4,
"return", "return",
"name", "bar",
"*", "*",
"number", 5,
"nl", "nl",
"dedent", 4,
]
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] | 1.922747 | 233 |
import matplotlib as mpl
mpl.use("Agg")
from .utils import CollisionDataset, AutoencoderDataset, train, test, plot_curves
from . import nn_classes as nn | [
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import uuid
import hashlib
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponse
from .models import Survey, Question, Answers
from .forms import SurveyForm
from .results import Results
# Create your views here.
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import torch
from torchvision import datasets
from torch.utils.data import DataLoader, RandomSampler
import torchvision.transforms as transforms
from tqdm import tqdm
from .dataset import data_loaders, axi_loader
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import MySQLdb
from urllib import parse
class PySQL:
"""
For making Mariadb / Mysql db queries
"""
FILTER_COMMANDS = {
"$eq":" = %s ",
"$in":" IN (%s) ",
"$nin":" NOT IN (%s) ",
"$neq":" != %s ",
"$lt":" < %s ",
"$lte":" <= %s ",
"$gt":" > %s ",
"$gte":" >= %s ",
"$contains":" LIKE %s ",#like %var%
"$ncontains":" NOT LIKE %s ",#
"$null":" IS NULL ", #if 1 else "IS NOT NULL" if 0
"$sw":" LIKE %s ",#starts with . like %var
"$ew":" LIKE %s "# endswith like var%
}
def execute(self,sql,params=None,many=None,dict_cursor=True):
#runs the db query . can also be used to run raw queries directly
""" by default returns cursor object """
if dict_cursor:
self.cursor = self._mysqldb_connection.cursor(MySQLdb.cursors.DictCursor)
else:
self.cursor = self._mysqldb_connection.cursor()
if many:
self.cursor.executemany(sql,params)
else:
self.cursor.execute(sql,params)
return self.cursor
#PySQL specific method begin from here
def __make_table_column(self,column,table_name=None):
"""Example
Input: => Output:
users.id => users.id
name => users.name
"""
if '.' in column:
return column
return "{}.{}".format(table_name,column) if table_name else "{}.{}".format(self.table_name,column)
def fields(self,columns):
#sets columns to select
""" Example: ['id','name']
"""
self.columns = columns
return self
def filter(self,filter_data):
"""
Filters Requests
#example full including or
{ "name":{"$contains":"mosoti"},
"age":{"$lte":30},
"msisdn":"2541234567",
"$or":[{ "name":{"$contains":"mogaka"}},
{"age":31}
], #this evaluates to => .. OR name like '%mogaka%' OR age=31
"$xor":[{ "name":{"$contains":"mogaka"}},
{"age":31}
] # this evalautes to =>... AND ( name like '%mogaka%' OR age=31 )
}
"""
#reset vals /parameters so that we begin here
if filter_data:
filter_q_l = self.__filter_query(filter_data)
filters_qls = ''.join(filter_q_l).strip()
if filters_qls.startswith("AND"):
filters_qls = filters_qls[3:]
elif filters_qls.startswith("OR"):
filters_qls = filters_qls[2:]
self.__set_where(filters_qls)
return self
def __get_order_by_text(self,val):
""" Receives string e.g -id or name """
if val.startswith('-'):
return "{} DESC".format(self.__make_table_column(val[1:]))
else:
return "{} ASC".format(self.__make_table_column(val))
def order_by(self,order_by_fields):
"""Expects list of fields e.g ['-id','name'] where - is DESC"""
order_by_sql = ','.join([self.__get_order_by_text(v) for v in order_by_fields])
if self.order_by_sql:
self.order_by_sql = self.order_by_sql + ' , ' + order_by_sql
else:
self.order_by_sql = " ORDER BY " + order_by_sql
return self
def group_by(self,group_by_fields):
""" Expects fields in list ['id','name'] ... """
group_by_sql = ','.join([self.__make_table_column(v) for v in group_by_fields])
if self.group_by_sql:
self.group_by_sql = self.group_by_sql + group_by_sql
else:
self.group_by_sql = " GROUP BY " + group_by_sql
return self
def __make_join(self,join_type,table_name,condition_data,related_fields):
""" makes join sql based on type of join and tables """
on_sql = []
for k,v in condition_data.items():
on_sql.append("{} = {} ".format(self.__make_table_column(k),self.__make_table_column(v,table_name)))
on_sql_str = ' ON {} ' .format(' AND '.join(on_sql))
join_type_sql = '{} {} '.format(join_type,table_name)
self.join_sql = self.join_sql + join_type_sql + on_sql_str
#append the columns to select based on related fields
if related_fields:
self.columns.extend([self.__make_table_column(c,table_name) for c in related_fields])
def inner_join(self,table_name,condition,related_fields=None):
""" e.g Orders,{"id":"customer_id"}, ['quantity']
This will result to :
.... Orders.quantity, .... INNER JOIN Orders ON Customers.id = Orders.customer_id
"""
self.__make_join('INNER JOIN',table_name,condition,related_fields)
return self
def right_join(self,table_name,condition,related_fields=None):
""" e.g Orders,{"id":"customer_id"}, ['quantity']
This will result to :
.... Orders.quantity, .... RIGHT JOIN Orders ON Customers.id = Orders.customer_id
"""
self.__make_join('RIGHT JOIN',table_name,condition,related_fields)
return self
def left_join(self,table_name,condition,related_fields=None):
""" e.g Orders,{"id":"customer_id"}, ['quantity']
This will result to :
.... Orders.quantity, .... LEFT JOIN Orders ON Customers.id = Orders.customer_id
"""
self.__make_join('LEFT JOIN',table_name,condition,related_fields)
return self
def update(self,new_data,limit=None):
""" set this new data as new details
Returns cursor object
"""
col_set = ','.join([" {} = %s ".format(k) for k,v in new_data.items()])
filter_params = self.query_params
self.query_params = []
update_params = [v for k,v in new_data.items()]
update_params.extend(filter_params) #we start with update thn filter
self.__build_query_params(update_params)
self.sql = "UPDATE {} SET {} ".format(self.table_name,col_set)
self.__make_sql(self.where_sql)
self.__limit(limit)
print(self.query_params)
print (self.sql)
return self.execute(self.sql,self.query_params)
def delete(self,limit=None):
""" Delete with given limit """
self.sql = "DELETE FROM {} ".format(self.table_name)
self.__make_sql(self.where_sql)
self.__limit(limit)
print (self.sql)
return self.execute(self.sql,self.query_params)
def insert(self,data):
"""
Creates records to db table . Expects a dict of key abd values pair
"""
columns = []
params = []
for k,v in data.items():
columns.append(k)
params.append(v)
column_placeholders = ','.join(["%s" for v in columns])
columns = ','.join([v for v in columns])
self.query_params = params
self.sql = "INSERT INTO {}({}) VALUES({})".format(self.table_name,columns,column_placeholders)
print (self.sql)
print (self.query_params)
return self.execute(self.sql,self.query_params).lastrowid
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] | 2.090039 | 3,554 |
import yaml
import sys
def meta_report(meta_data_loc="Experiment_Data.yml"):
"""
Reads meta data from file called "Experiment_Data.yml" and adds a report
Parameters
----------
meta_data_loc : str
the path to file that contains experiment meta data
Returns
-------
config_data : none
"""
try:
with open(meta_data_loc, "r") as stream:
meta_data = yaml.safe_load(stream)
report_message = report_writer(meta_data)
f = open("Experiment_Info.txt","w" )
f.write(report_message)
f.close()
except FileNotFoundError:
sys.exit("File containing meta data, {}, not found. Exiting...".format(meta_data_loc))
def report_writer(md):
"""
Reads meta data into function and makes txt message report.
----------
md : dict
Contains meta data from experiment file
Returns
-------
message : string
The text output for the report
"""
s_name = md["sample_meta_data"]["sample_name"]
s_date = md["sample_meta_data"]["sample_date"]
s_surface = md["sample_meta_data"]["sample_surface_area"]
imp_mode = md["experiment_meta_data"]["impedance_mode"]
meas_volt = md["experiment_meta_data"]["measurement_voltage"]
vs = md["experiment_meta_data"]["vs"]
pert_v = md["experiment_meta_data"]["pertubation_voltage"]
sf = md["experiment_meta_data"]["starting_frequency"]
ef = md["experiment_meta_data"]["ending_frequency"]
ppi = md["experiment_meta_data"]["points_per_interval"]
ig = md["experiment_meta_data"]["interval_group"]
spacing = md["experiment_meta_data"]["spacing"]
intro_line = "Report for "+str(s_name)+" experiment conducted on "+str(s_date)+".\n\n"
imp_line = "A "+str(imp_mode)+" measurement was made with a "+str(pert_v)+"mV pertubation voltage at "+str(meas_volt)+"V vs. "+str(vs)+".\n\n"
range_line = "Experiment conducted from "+str(sf)+"Hz to "+str(ef)+"Hz with "+str(ppi)+ " points "+str(ig)+" using "+str(spacing)+" spacing.\n\n"
surface_line = "Sample has a surface area of "+str(s_surface)+"cm^2."
message = intro_line+imp_line+range_line+surface_line
return message | [
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#
# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Copyright (C) 2018-2020 UAVCAN Development Team <uavcan.org>
# This software is distributed under the terms of the MIT License.
#
"""Language-specific support in nunavut.
This package contains modules that provide specific support for generating
source for various languages using templates.
"""
import abc
import functools
import importlib
import logging
import pathlib
import types
import typing
import pydsdl
from ..dependencies import Dependencies, DependencyBuilder
from .._utilities import YesNoDefault, iter_package_resources
from ._config import LanguageConfig, VersionReader
logger = logging.getLogger(__name__)
class LanguageLoader:
"""
Factory class that loads language meta-data and concrete :class:`nunavut.lang.Language` objects.
:param additional_config_files: A list of paths to additional configuration files to load as configuration.
These will override any values found in the :file:`nunavut.lang.properties.yaml` file and files
appearing later in this list will override value found in earlier entries.
.. invisible-code-block: python
from nunavut.lang import LanguageLoader
subject = LanguageLoader()
c_reserved_identifiers = subject.config.get_config_value('nunavut.lang.c','reserved_identifiers')
assert len(c_reserved_identifiers) > 0
"""
@classmethod
@classmethod
@property
def config(self) -> LanguageConfig:
"""
Meta-data about all languages merged from the Nunavut internal defaults and any additional
configuration files provided to this class's constructor.
"""
if self._config is None:
self._config = self._load_config(*self._additional_config_files)
return self._config
def load_language(
self,
language_name: str,
omit_serialization_support: bool,
language_options: typing.Optional[typing.Mapping[str, typing.Any]] = None,
) -> "Language":
"""
:param str language_name: The name of the language used by the :mod:`nunavut.lang` module.
:param LanguageConfig config: The parser to load language properties into.
:param bool omit_serialization_support: The value to set for the :func:`omit_serialization_support` property
for this language.
:param typing.Optional[typing.Mapping[str, typing.Any]] language_options: Opaque arguments passed through to the
target :class:`nunavut.lang.Language` object.
:return: A new object that extends :class:`nunavut.lang.Language`.
:rtype: nunavut.lang.Language
.. invisible-code-block: python
from nunavut.lang import LanguageLoader
.. code-block:: python
lang_c = LanguageLoader().load_language('c', True)
assert lang_c.name == 'c'
.. invisible-code-block: python
# let's go ahead and load the rest of our known, internally supported languages just to raise
# test failures right here at the wellspring.
lang_cpp = LanguageLoader().load_language('py', True)
assert lang_cpp.name == 'py'
lang_js = LanguageLoader().load_language('js', True)
assert lang_js.name == 'js'
"""
ln_module = self.load_language_module(language_name)
try:
language_type = typing.cast(typing.Type["Language"], getattr(ln_module, "Language"))
except AttributeError:
logging.debug(
"Unable to find a Language object in nunavut.lang.{}. Using a Generic language object".format(
language_name
)
)
language_type = _GenericLanguage
if language_type == Language:
# the language module just imported the base class so let's go ahead and use _GenericLanguage
language_type = _GenericLanguage
return language_type(ln_module, self.config, omit_serialization_support, language_options)
class Language(metaclass=abc.ABCMeta):
"""
Facilities for generating source code for a specific language. Concrete Language classes must be implemented
by the language support package below lang and should be instantiated using
:class:`nunavut.lang.LanguageLoader`.
:param str language_name: The name of the language used by the :mod:`nunavut.lang` module.
:param LanguageConfig config: The parser to load language properties into.
:param bool omit_serialization_support: The value to set for the :func:`omit_serialization_support` property
for this language.
:param typing.Optional[typing.Mapping[str, typing.Any]] language_options: Opaque arguments passed through to the
target :class:`nunavut.lang.Language` object.
.. invisible-code-block: python
from nunavut.lang import Language, _GenericLanguage
from unittest.mock import MagicMock
mock_config = MagicMock()
mock_module = MagicMock()
mock_module.__name__ = 'foo'
try:
my_lang = _GenericLanguage(mock_module, mock_config, True)
# module must be within 'nunavut'
assert False
except RuntimeError:
pass
mock_module.__name__ = 'nunavut.foo'
try:
my_lang = _GenericLanguage(mock_module, mock_config, True)
# module must be within 'nunavut.lang'
assert False
except RuntimeError:
pass
mock_module.__name__ = 'not.nunavut.foo'
try:
my_lang = _GenericLanguage(mock_module, mock_config, True)
# module must be within 'nunavut.lang'
assert False
except RuntimeError:
pass
mock_module.__name__ = 'nunavut.lang.foo'
my_lang = _GenericLanguage(mock_module, mock_config, True)
assert my_lang.name == 'foo'
"""
@classmethod
def default_filter_id_for_target(cls, instance: typing.Any) -> str:
"""
The default transformation of any object into a string.
:param any instance: Any object or data that either has a name property or can be converted to a string.
:return: Either ``str(instance.name)`` if the instance has a name property or just ``str(instance)``
"""
if hasattr(instance, "name"):
return str(instance.name)
else:
return str(instance)
def __getattr__(self, name: str) -> typing.Any:
"""
Any attribute access to a Language object will return the regular properties and
any globals defined for the language. Because of this do not extend properties
on this object in a way that will clash with the globals it defines (e.g. typename_
or valuetoken_ should not be used as the start of attribute names).
"""
try:
return self.get_globals()[name]
except KeyError as e:
raise AttributeError(e)
def get_support_module(self) -> typing.Tuple[str, typing.Tuple[int, int, int], typing.Optional["types.ModuleType"]]:
"""
Returns the module object for the language support files.
:return: A tuple of module name, x.y.z module version, and the module object itself.
.. invisible-code-block: python
from nunavut.lang import Language, _GenericLanguage
from unittest.mock import MagicMock
mock_config = MagicMock()
mock_module = MagicMock()
mock_module.__name__ = 'nunavut.lang.cpp'
my_lang = _GenericLanguage(mock_module, mock_config, True)
my_lang._section = "nunavut.lang.cpp"
module_name, support_version, _ = my_lang.get_support_module()
assert module_name == "nunavut.lang.cpp.support"
assert support_version[0] == 1
"""
module_name = "{}.support".format(self._section)
try:
module = importlib.import_module(module_name)
version_tuple = VersionReader.read_version(module)
return (module_name, version_tuple, module)
except (ImportError, ValueError):
# No serialization support for this language
logger.info("No serialization support for selected target. Cannot retrieve module.")
return (module_name, (0, 0, 0), None)
@functools.lru_cache()
@abc.abstractmethod
def get_includes(self, dep_types: Dependencies) -> typing.List[str]:
"""
Get a list of include paths that are specific to this language and the options set for it.
:param Dependencies dep_types: A description of the dependencies includes are needed for.
:return: A list of include file paths. The list may be empty if no includes were needed.
"""
pass
def filter_id(self, instance: typing.Any, id_type: str = "any") -> str:
"""
Produces a valid identifier in the language for a given object. The encoding may not be reversible.
:param any instance: Any object or data that either has a name property or can be converted
to a string.
:param str id_type: A type of identifier. This is different for each language. For example, for C this
value can be 'typedef', 'macro', 'function', or 'enum'.
Use 'any' to apply stropping rules for all identifier types to the instance.
:return: A token that is a valid identifier in the language, is not a reserved keyword, and is transformed
in a deterministic manner based on the provided instance.
"""
return self.default_filter_id_for_target(instance)
def filter_short_reference_name(
self, t: pydsdl.CompositeType, stropping: YesNoDefault = YesNoDefault.DEFAULT, id_type: str = "any"
) -> str:
"""
Provides a string that is a shorted version of the full reference name omitting any namespace parts of the type.
:param pydsdl.CompositeType t: The DSDL type to get the reference name for.
:param YesNoDefault stropping: If DEFAULT then the stropping value configured for the target language is used
else this overrides that value.
:param str id_type: A type of identifier. This is different for each language. For example, for C this
value can be 'typedef', 'macro', 'function', or 'enum'.
Use 'any' to apply stropping rules for all identifier types to the instance.
"""
short_name = "{short}_{major}_{minor}".format(short=t.short_name, major=t.version.major, minor=t.version.minor)
if YesNoDefault.test_truth(stropping, self.enable_stropping):
return self.filter_id(short_name, id_type)
else:
return short_name
def get_config_value(self, key: str, default_value: typing.Optional[str] = None) -> str:
"""
Get an optional language property from the language configuration.
:param str key: The config value to retrieve.
:param default_value: The value to return if the key was not in the configuration. If provided
this method will not raise.
:type default_value: typing.Optional[str]
:return: Either the value from the config or the default_value if provided.
:rtype: str
:raises: KeyError if the section or the key in the section does not exist and a default_value was not provided.
"""
return self._config.get_config_value(self._section, key, default_value)
def get_config_value_as_bool(self, key: str, default_value: bool = False) -> bool:
"""
Get an optional language property from the language configuration returning a boolean.
:param str key: The config value to retrieve.
:param bool default_value: The value to use if no value existed.
:return: The config value as either True or False.
:rtype: bool
"""
return self._config.get_config_value_as_bool(self._section, key, default_value)
def get_config_value_as_dict(
self, key: str, default_value: typing.Optional[typing.Dict] = None
) -> typing.Dict[str, typing.Any]:
"""
Get a language property parsing it as a map with string keys.
.. invisible-code-block: python
from nunavut.lang import LanguageConfig, LanguageLoader, Language, _GenericLanguage
config = LanguageConfig()
config.add_section('nunavut.lang.c')
config.set('nunavut.lang.c', 'foo', {'one': 1})
lang_c = _GenericLanguage(LanguageLoader.load_language_module('c'), config, True)
assert lang_c.get_config_value_as_dict('foo')['one'] == 1
assert lang_c.get_config_value_as_dict('bar', {'one': 2})['one'] == 2
:param str key: The config value to retrieve.
:param default_value: The value to return if the key was not in the configuration. If provided this method
will not raise a KeyError nor a TypeError.
:type default_value: typing.Optional[typing.Mapping[str, typing.Any]]
:return: Either the value from the config or the default_value if provided.
:rtype: typing.Mapping[str, typing.Any]
:raises: KeyError if the key does not exist and a default_value was not provided.
:raises: TypeError if the value exists but is not a dict and a default_value was not provided.
"""
return self._config.get_config_value_as_dict(self._section, key, default_value)
def get_config_value_as_list(
self, key: str, default_value: typing.Optional[typing.List] = None
) -> typing.List[typing.Any]:
"""
Get a language property parsing it as a map with string keys.
:param str key: The config value to retrieve.
:param default_value: The value to return if the key was not in the configuration. If provided this method
will not raise a KeyError nor a TypeError.
:type default_value: typing.Optional[typing.List[typing.Any]]
:return: Either the value from the config or the default_value if provided.
:rtype: typing.List[typing.Any]
:raises: KeyError if the key does not exist and a default_value was not provided.
:raises: TypeError if the value exists but is not a dict and a default_value was not provided.
"""
return self._config.get_config_value_as_list(self._section, key, default_value)
@property
def extension(self) -> str:
"""
The extension to use for files generated in this language.
"""
return self._config.get_config_value(self._section, "extension", "get")
@property
def namespace_output_stem(self) -> typing.Optional[str]:
"""
The name of a namespace file for this language.
"""
try:
return self._config.get_config_value(self._section, "namespace_file_stem")
except KeyError:
return None
@property
def name(self) -> str:
"""
The name of the language used by the :mod:`nunavut.lang` module.
"""
return self._language_name
@property
def support_namespace(self) -> typing.List[str]:
"""
The hierarchical namespace used by the support software. The property
is a dot separated string when specified in configuration. This
property returns that value split into namespace components with the
first identifier being the first index in the array, etc.
.. invisible-code-block: python
from nunavut.lang import Language
config = {
'nunavut.lang.cpp':
{
'support_namespace': 'foo.bar'
}
}
lctx = configurable_language_context_factory(config, 'cpp')
lang_cpp = lctx.get_target_language()
assert len(lang_cpp.support_namespace) == 2
assert lang_cpp.support_namespace[0] == 'foo'
assert lang_cpp.support_namespace[1] == 'bar'
"""
namespace_str = self._config.get_config_value(self._section, "support_namespace", default_value="")
return namespace_str.split(".")
@property
def enable_stropping(self) -> bool:
"""
Whether or not to strop identifiers for this language.
"""
return self._config.get_config_value_as_bool(self._section, "enable_stropping")
@property
def has_standard_namespace_files(self) -> bool:
"""
Whether or not the language defines special namespace files as part of
its core standard (e.g. python's __init__).
"""
return self._config.get_config_value_as_bool(self._section, "has_standard_namespace_files")
@property
def stable_support(self) -> bool:
"""
Whether support for this language is designated 'stable', and not experimental.
"""
return self._config.get_config_value_as_bool(self._section, "stable_support")
@property
def omit_serialization_support(self) -> bool:
"""
If True then generators should not include serialization routines, types,
or support libraries for this language.
"""
return self._omit_serialization_support
@property
def support_files(self) -> typing.Generator[pathlib.Path, None, None]:
"""
Iterates over non-templated supporting files embedded within the Nunavut distribution.
.. invisible-code-block: python
from nunavut.lang import Language, _GenericLanguage
from unittest.mock import MagicMock
mock_config = MagicMock()
mock_module = MagicMock()
mock_module.__name__ = 'nunavut.lang.foo'
my_lang = _GenericLanguage(mock_module, mock_config, True)
my_lang._section = "nunavut.lang.not_a_language_really_not_a_language"
for support_file in my_lang.support_files:
# if the module doesn't exist it shouldn't have any support files.
assert False
"""
_, _, module = self.get_support_module()
if module is not None:
# All language support modules must provide a list_support_files method
# to allow the copy generator access to the packaged support files.
list_support_files = getattr(
module, "list_support_files"
) # type: typing.Callable[[], typing.Generator[pathlib.Path, None, None]]
return list_support_files()
else:
# No serialization support for this language
return list_support_files()
def get_option(
self, option_key: str, default_value: typing.Union[typing.Mapping[str, typing.Any], str, None] = None
) -> typing.Union[typing.Mapping[str, typing.Any], str, None]:
"""
Get a language option for this language.
.. invisible-code-block: python
config = {
'nunavut.lang.cpp':
{
'options': {'target_endianness': 'little'}
}
}
lctx = configurable_language_context_factory(config, 'cpp')
lang_cpp = lctx.get_target_language()
.. code-block:: python
# Values can come from defaults...
assert lang_cpp.get_option('target_endianness') == 'little'
# ... or can come from a sane default.
assert lang_cpp.get_option('foobar', 'sane_default') == 'sane_default'
:return: Either the value provided to the :class:`Language` instance, the value from properties.yaml,
or the :code:`default_value`.
"""
try:
return self._language_options[option_key] # type: ignore
except KeyError:
return default_value
def get_templates_package_name(self) -> str:
"""
The name of the nunavut python package containing filters, types, and configuration
for this language.
"""
return self._section
def get_named_types(self) -> typing.Mapping[str, str]:
"""
Get a map of named types to the type name to emit for this language.
"""
return self._config.get_config_value_as_dict(self._section, "named_types", default_value={})
def get_named_values(self) -> typing.Mapping[str, str]:
"""
Get a map of named values to the token to emit for this language.
"""
return self._config.get_config_value_as_dict(self._section, "named_values", default_value={})
def get_globals(self) -> typing.Mapping[str, typing.Any]:
"""
Get all values for this language that should be available in a global context.
:return: A mapping of global names to global values.
"""
if self._globals is None:
globals_map = dict() # type: typing.Dict[str, typing.Any]
for key, value in self.get_named_types().items():
globals_map["typename_{}".format(key)] = value
for key, value in self.get_named_values().items():
globals_map["valuetoken_{}".format(key)] = value
self._globals = globals_map
return self._globals
def get_options(self) -> typing.Mapping[str, typing.Any]:
"""
Get all language options for this Language.
:return: A mapping of option names to option values.
"""
return self._language_options
class _GenericLanguage(Language):
"""
Language type used when the language support within Nunavut does not define a language-specific
subclass.
Do not use this. Use :class:`nunavut.lang.LanguageLoader` which will create the proper object type
based on inspection of the Nunavut internals.
"""
class LanguageContext:
"""
Context object containing the current target language (if any) and used to access
:class:`Language` objects.
:param str target_language: If provided a :class:`Language` object will be created to hold
the target language set for this context. If None then there is
no target language.
:param str extension: The extension to use for generated file types or None to use a default
based on the target_language.
:param str namespace_output_stem: The filename stem to give to Namespace output files if
emitted or None to use a default based on a target_language.
:param additional_config_files: A list of paths to additional files to load as configuration.
These will override any values found in the :file:`nunavut.lang.properties.yaml` file and files
appearing later in this list will override value found in earlier entries.
:type additional_config_files: typing.List[pathlib.Path]
:param bool omit_serialization_support_for_target: If True then generators should not include
serialization routines, types, or support libraries for the target language.
:param typing.Optional[typing.Mapping[str, typing.Any]] language_options: Opaque arguments passed through to the
target :class:`nunavut.lang.Language` object.
:param bool include_experimental_languages: If True, expose languages with experimental (non-stable) support.
:raises ValueError: If extension is None and no target language was provided.
:raises KeyError: If the target language is not known.
"""
def get_language(self, key_or_module_name: str) -> Language:
"""
Get a :class:`Language` object for a given language identifier.
:param str key_or_module_name: Either one of the Nunavut mnemonics for a supported language or
the ``__name__`` of one of the ``nunavut.lang.[language]`` python modules.
:return: A :class:`Language` object cached by this context.
:rtype: Language
"""
if key_or_module_name is None or len(key_or_module_name) == 0:
raise ValueError("key argument is required.")
key = key_or_module_name[13:] if key_or_module_name.startswith("nunavut.lang.") else key_or_module_name
return self.get_supported_languages()[key]
def get_supported_language_names(self) -> typing.Iterable[str]:
"""Get a list of target languages supported by Nunavut.
:return: An iterable of strings which are languages with special
support within Nunavut templates.
"""
return [s[13:] for s in self._config.sections() if s.startswith("nunavut.lang.")]
def get_output_extension(self) -> str:
"""
Gets the output extension to use regardless of a target language being available or not.
:return: A file extension name with a leading dot.
"""
if self._extension is not None:
return self._extension
elif self._target_language is not None:
return self._target_language.extension
else:
raise RuntimeError(
"No extension was provided and no target language was set. Cannot determine the extension to use."
)
def get_default_namespace_output_stem(self) -> typing.Optional[str]:
"""
The filename stem to give to Namespace output files if emitted or None if there was none
specified and there is no target language.
:return: A file name stem or None
"""
return self._namespace_output_stem
def get_target_language(self) -> typing.Optional[Language]:
"""
Returns the target language configured on this object or None
if no target language was specified.
"""
return self._target_language
def filter_id_for_target(self, instance: typing.Any, id_type: str = "any") -> str:
"""
A filter that will transform a given string or pydsdl identifier into a valid identifier in the target language.
A default transformation is applied if no target language is set.
:param any instance: Any object or data that either has a name property or can be converted to a string.
:param str id_type: A type of identifier. This is different for each language.
Use 'any' to apply stropping rules for all identifier types to the instance.
:return: A token that is a valid identifier in the target language, is not a reserved keyword, and is
transformed in a deterministic manner based on the provided instance.
"""
if self._target_language is not None:
return self._target_language.filter_id(instance, id_type)
else:
return Language.default_filter_id_for_target(instance)
def get_supported_languages(self) -> typing.Dict[str, Language]:
"""
Returns a collection of available language support objects.
"""
return self._languages
@property
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13,
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1,
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1267,
4613,
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25,
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220,
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37227,
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43,
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9660,
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25,
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5550,
38865,
788,
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1988,
17839,
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318,
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2073,
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327,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
460,
307,
705,
774,
9124,
891,
3256,
705,
20285,
305,
3256,
705,
8818,
3256,
393,
705,
44709,
4458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5765,
705,
1092,
6,
284,
4174,
9660,
2105,
3173,
329,
477,
27421,
3858,
284,
262,
4554,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1790,
62,
3672,
796,
45144,
19509,
92,
23330,
22478,
92,
23330,
1084,
273,
92,
1911,
18982,
7,
19509,
28,
83,
13,
19509,
62,
3672,
11,
1688,
28,
83,
13,
9641,
13,
22478,
11,
4159,
28,
83,
13,
9641,
13,
1084,
273,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3363,
2949,
19463,
13,
9288,
62,
35310,
7,
20661,
2105,
11,
2116,
13,
21633,
62,
20661,
2105,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
24455,
62,
312,
7,
19509,
62,
3672,
11,
4686,
62,
4906,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1790,
62,
3672,
628,
220,
220,
220,
825,
651,
62,
11250,
62,
8367,
7,
944,
11,
1994,
25,
965,
11,
4277,
62,
8367,
25,
19720,
13,
30719,
58,
2536,
60,
796,
6045,
8,
4613,
965,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
281,
11902,
3303,
3119,
422,
262,
3303,
8398,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
1994,
25,
383,
4566,
1988,
284,
19818,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
4277,
62,
8367,
25,
383,
1988,
284,
1441,
611,
262,
1994,
373,
407,
287,
262,
8398,
13,
1002,
2810,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
428,
2446,
481,
407,
5298,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
4277,
62,
8367,
25,
19720,
13,
30719,
58,
2536,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
15467,
262,
1988,
422,
262,
4566,
393,
262,
4277,
62,
8367,
611,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
25,
7383,
12331,
611,
262,
2665,
393,
262,
1994,
287,
262,
2665,
857,
407,
2152,
290,
257,
4277,
62,
8367,
373,
407,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
7,
944,
13557,
5458,
11,
1994,
11,
4277,
62,
8367,
8,
628,
220,
220,
220,
825,
651,
62,
11250,
62,
8367,
62,
292,
62,
30388,
7,
944,
11,
1994,
25,
965,
11,
4277,
62,
8367,
25,
20512,
796,
10352,
8,
4613,
20512,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
281,
11902,
3303,
3119,
422,
262,
3303,
8398,
8024,
257,
25131,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
1994,
25,
383,
4566,
1988,
284,
19818,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
20512,
4277,
62,
8367,
25,
383,
1988,
284,
779,
611,
645,
1988,
11196,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
383,
4566,
1988,
355,
2035,
6407,
393,
10352,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
30388,
7,
944,
13557,
5458,
11,
1994,
11,
4277,
62,
8367,
8,
628,
220,
220,
220,
825,
651,
62,
11250,
62,
8367,
62,
292,
62,
11600,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
11,
1994,
25,
965,
11,
4277,
62,
8367,
25,
19720,
13,
30719,
58,
774,
13886,
13,
35,
713,
60,
796,
6045,
198,
220,
220,
220,
1267,
4613,
19720,
13,
35,
713,
58,
2536,
11,
19720,
13,
7149,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
257,
3303,
3119,
32096,
340,
355,
257,
3975,
351,
4731,
8251,
13,
628,
220,
220,
220,
220,
220,
220,
220,
11485,
14836,
12,
8189,
12,
9967,
25,
21015,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
48157,
615,
315,
13,
17204,
1330,
15417,
16934,
11,
15417,
17401,
11,
15417,
11,
4808,
46189,
32065,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
796,
15417,
16934,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
13,
2860,
62,
5458,
10786,
77,
403,
615,
315,
13,
17204,
13,
66,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
13,
2617,
10786,
77,
403,
615,
315,
13,
17204,
13,
66,
3256,
705,
21943,
3256,
1391,
6,
505,
10354,
352,
30072,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42392,
62,
66,
796,
4808,
46189,
32065,
7,
32065,
17401,
13,
2220,
62,
16129,
62,
21412,
10786,
66,
33809,
4566,
11,
6407,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
42392,
62,
66,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
11600,
10786,
21943,
11537,
17816,
505,
20520,
6624,
352,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
42392,
62,
66,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
11600,
10786,
5657,
3256,
1391,
6,
505,
10354,
362,
30072,
17816,
505,
20520,
6624,
362,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
1994,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4566,
1988,
284,
19818,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
4277,
62,
8367,
25,
220,
220,
220,
220,
383,
1988,
284,
1441,
611,
262,
1994,
373,
407,
287,
262,
8398,
13,
1002,
2810,
428,
2446,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
481,
407,
5298,
257,
7383,
12331,
4249,
257,
5994,
12331,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
4277,
62,
8367,
25,
19720,
13,
30719,
58,
774,
13886,
13,
44,
5912,
58,
2536,
11,
19720,
13,
7149,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15467,
262,
1988,
422,
262,
4566,
393,
262,
4277,
62,
8367,
611,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
19720,
13,
44,
5912,
58,
2536,
11,
19720,
13,
7149,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
25,
7383,
12331,
611,
262,
1994,
857,
407,
2152,
290,
257,
4277,
62,
8367,
373,
407,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
25,
5994,
12331,
611,
262,
1988,
7160,
475,
318,
407,
257,
8633,
290,
257,
4277,
62,
8367,
373,
407,
2810,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
11600,
7,
944,
13557,
5458,
11,
1994,
11,
4277,
62,
8367,
8,
628,
220,
220,
220,
825,
651,
62,
11250,
62,
8367,
62,
292,
62,
4868,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
11,
1994,
25,
965,
11,
4277,
62,
8367,
25,
19720,
13,
30719,
58,
774,
13886,
13,
8053,
60,
796,
6045,
198,
220,
220,
220,
1267,
4613,
19720,
13,
8053,
58,
774,
13886,
13,
7149,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
257,
3303,
3119,
32096,
340,
355,
257,
3975,
351,
4731,
8251,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
1994,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
4566,
1988,
284,
19818,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
4277,
62,
8367,
25,
220,
220,
220,
220,
383,
1988,
284,
1441,
611,
262,
1994,
373,
407,
287,
262,
8398,
13,
1002,
2810,
428,
2446,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
481,
407,
5298,
257,
7383,
12331,
4249,
257,
5994,
12331,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
4277,
62,
8367,
25,
220,
220,
220,
220,
220,
19720,
13,
30719,
58,
774,
13886,
13,
8053,
58,
774,
13886,
13,
7149,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15467,
262,
1988,
422,
262,
4566,
393,
262,
4277,
62,
8367,
611,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19720,
13,
8053,
58,
774,
13886,
13,
7149,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7383,
12331,
611,
262,
1994,
857,
407,
2152,
290,
257,
4277,
62,
8367,
373,
407,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5994,
12331,
611,
262,
1988,
7160,
475,
318,
407,
257,
8633,
290,
257,
4277,
62,
8367,
373,
407,
2810,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
4868,
7,
944,
13557,
5458,
11,
1994,
11,
4277,
62,
8367,
8,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
7552,
7,
944,
8,
4613,
965,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
383,
7552,
284,
779,
329,
3696,
7560,
287,
428,
3303,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
7,
944,
13557,
5458,
11,
366,
2302,
3004,
1600,
366,
1136,
4943,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
25745,
62,
22915,
62,
927,
7,
944,
8,
4613,
19720,
13,
30719,
58,
2536,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
383,
1438,
286,
257,
25745,
2393,
329,
428,
3303,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
7,
944,
13557,
5458,
11,
366,
14933,
10223,
62,
7753,
62,
927,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2644,
393,
460,
1282,
422,
257,
33241,
4277,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
42392,
62,
20322,
13,
1136,
62,
18076,
10786,
6513,
30973,
3256,
705,
82,
1531,
62,
12286,
11537,
6624,
705,
82,
1531,
62,
12286,
6,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
15467,
262,
1988,
2810,
284,
262,
1058,
4871,
25,
63,
32065,
63,
4554,
11,
262,
1988,
422,
6608,
13,
88,
43695,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
262,
1058,
8189,
25,
63,
12286,
62,
8367,
44646,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
16129,
62,
25811,
58,
18076,
62,
2539,
60,
220,
1303,
2099,
25,
8856,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
4277,
62,
8367,
628,
220,
220,
220,
825,
651,
62,
11498,
17041,
62,
26495,
62,
3672,
7,
944,
8,
4613,
965,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
383,
1438,
286,
262,
48157,
615,
315,
21015,
5301,
7268,
16628,
11,
3858,
11,
290,
8398,
198,
220,
220,
220,
220,
220,
220,
220,
329,
428,
3303,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
5458,
628,
220,
220,
220,
825,
651,
62,
13190,
62,
19199,
7,
944,
8,
4613,
19720,
13,
44,
5912,
58,
2536,
11,
965,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
257,
3975,
286,
3706,
3858,
284,
262,
2099,
1438,
284,
27588,
329,
428,
3303,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
11600,
7,
944,
13557,
5458,
11,
366,
13190,
62,
19199,
1600,
4277,
62,
8367,
34758,
30072,
628,
220,
220,
220,
825,
651,
62,
13190,
62,
27160,
7,
944,
8,
4613,
19720,
13,
44,
5912,
58,
2536,
11,
965,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
257,
3975,
286,
3706,
3815,
284,
262,
11241,
284,
27588,
329,
428,
3303,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
11250,
13,
1136,
62,
11250,
62,
8367,
62,
292,
62,
11600,
7,
944,
13557,
5458,
11,
366,
13190,
62,
27160,
1600,
4277,
62,
8367,
34758,
30072,
628,
220,
220,
220,
825,
651,
62,
4743,
672,
874,
7,
944,
8,
4613,
19720,
13,
44,
5912,
58,
2536,
11,
19720,
13,
7149,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
477,
3815,
329,
428,
3303,
326,
815,
307,
1695,
287,
257,
3298,
4732,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
317,
16855,
286,
3298,
3891,
284,
3298,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
4743,
672,
874,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15095,
874,
62,
8899,
796,
8633,
3419,
220,
1303,
2099,
25,
19720,
13,
35,
713,
58,
2536,
11,
19720,
13,
7149,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
2116,
13,
1136,
62,
13190,
62,
19199,
22446,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15095,
874,
62,
8899,
14692,
774,
3617,
480,
23330,
92,
1911,
18982,
7,
2539,
15437,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
2116,
13,
1136,
62,
13190,
62,
27160,
22446,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15095,
874,
62,
8899,
14692,
2100,
84,
316,
4233,
23330,
92,
1911,
18982,
7,
2539,
15437,
796,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
4743,
672,
874,
796,
15095,
874,
62,
8899,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
4743,
672,
874,
628,
220,
220,
220,
825,
651,
62,
25811,
7,
944,
8,
4613,
19720,
13,
44,
5912,
58,
2536,
11,
19720,
13,
7149,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
477,
3303,
3689,
329,
428,
15417,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
317,
16855,
286,
3038,
3891,
284,
3038,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
16129,
62,
25811,
628,
198,
4871,
4808,
46189,
32065,
7,
32065,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
15417,
2099,
973,
618,
262,
3303,
1104,
1626,
39342,
615,
315,
857,
407,
8160,
257,
3303,
12,
11423,
198,
220,
220,
220,
47611,
13,
628,
220,
220,
220,
2141,
407,
779,
428,
13,
5765,
1058,
4871,
25,
63,
77,
403,
615,
315,
13,
17204,
13,
32065,
17401,
63,
543,
481,
2251,
262,
1774,
2134,
2099,
198,
220,
220,
220,
1912,
319,
15210,
286,
262,
39342,
615,
315,
1788,
874,
13,
198,
220,
220,
220,
37227,
628,
198,
4871,
15417,
21947,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
30532,
2134,
7268,
262,
1459,
2496,
3303,
357,
361,
597,
8,
290,
973,
284,
1895,
198,
220,
220,
220,
1058,
4871,
25,
63,
32065,
63,
5563,
13,
628,
220,
220,
220,
1058,
17143,
965,
2496,
62,
16129,
25,
1002,
2810,
257,
1058,
4871,
25,
63,
32065,
63,
2134,
481,
307,
2727,
284,
1745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
2496,
3303,
900,
329,
428,
4732,
13,
1002,
6045,
788,
612,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
645,
2496,
3303,
13,
198,
220,
220,
220,
1058,
17143,
965,
7552,
25,
383,
7552,
284,
779,
329,
7560,
2393,
3858,
393,
6045,
284,
779,
257,
4277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1912,
319,
262,
2496,
62,
16129,
13,
198,
220,
220,
220,
1058,
17143,
965,
25745,
62,
22915,
62,
927,
25,
383,
29472,
10717,
284,
1577,
284,
28531,
10223,
5072,
3696,
611,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31234,
393,
6045,
284,
779,
257,
4277,
1912,
319,
257,
2496,
62,
16129,
13,
198,
220,
220,
220,
1058,
17143,
3224,
62,
11250,
62,
16624,
25,
317,
1351,
286,
13532,
284,
3224,
3696,
284,
3440,
355,
8398,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2312,
481,
20957,
597,
3815,
1043,
287,
262,
1058,
7753,
25,
63,
77,
403,
615,
315,
13,
17204,
13,
48310,
13,
88,
43695,
63,
2393,
290,
3696,
198,
220,
220,
220,
220,
220,
220,
220,
12655,
1568,
287,
428,
1351,
481,
20957,
1988,
1043,
287,
2961,
12784,
13,
198,
220,
220,
220,
1058,
4906,
3224,
62,
11250,
62,
16624,
25,
19720,
13,
8053,
58,
6978,
8019,
13,
15235,
60,
198,
220,
220,
220,
1058,
17143,
20512,
42848,
62,
46911,
1634,
62,
11284,
62,
1640,
62,
16793,
25,
1002,
6407,
788,
27298,
815,
407,
2291,
198,
220,
220,
220,
220,
220,
220,
220,
11389,
1634,
31878,
11,
3858,
11,
393,
1104,
12782,
329,
262,
2496,
3303,
13,
198,
220,
220,
220,
1058,
17143,
19720,
13,
30719,
58,
774,
13886,
13,
44,
5912,
58,
2536,
11,
19720,
13,
7149,
11907,
3303,
62,
25811,
25,
8670,
18251,
7159,
3804,
832,
284,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2496,
1058,
4871,
25,
63,
77,
403,
615,
315,
13,
17204,
13,
32065,
63,
2134,
13,
198,
220,
220,
220,
1058,
17143,
20512,
2291,
62,
23100,
9134,
62,
75,
33213,
25,
1002,
6407,
11,
15651,
8950,
351,
11992,
357,
13159,
12,
31284,
8,
1104,
13,
198,
220,
220,
220,
1058,
430,
2696,
11052,
12331,
25,
1002,
7552,
318,
6045,
290,
645,
2496,
3303,
373,
2810,
13,
198,
220,
220,
220,
1058,
430,
2696,
7383,
12331,
25,
1002,
262,
2496,
3303,
318,
407,
1900,
13,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
825,
651,
62,
16129,
7,
944,
11,
1994,
62,
273,
62,
21412,
62,
3672,
25,
965,
8,
4613,
15417,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
257,
1058,
4871,
25,
63,
32065,
63,
2134,
329,
257,
1813,
3303,
27421,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
1994,
62,
273,
62,
21412,
62,
3672,
25,
15467,
530,
286,
262,
39342,
615,
315,
285,
77,
7966,
873,
329,
257,
4855,
3303,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
7559,
834,
3672,
834,
15506,
286,
530,
286,
262,
7559,
77,
403,
615,
315,
13,
17204,
3693,
16129,
60,
15506,
21015,
13103,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
317,
1058,
4871,
25,
63,
32065,
63,
2134,
39986,
416,
428,
4732,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
15417,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
62,
273,
62,
21412,
62,
3672,
318,
6045,
393,
18896,
7,
2539,
62,
273,
62,
21412,
62,
3672,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
2539,
4578,
318,
2672,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
1994,
62,
273,
62,
21412,
62,
3672,
58,
1485,
47715,
611,
1994,
62,
273,
62,
21412,
62,
3672,
13,
9688,
2032,
342,
7203,
77,
403,
615,
315,
13,
17204,
19570,
2073,
1994,
62,
273,
62,
21412,
62,
3672,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
1136,
62,
15999,
62,
75,
33213,
3419,
58,
2539,
60,
628,
220,
220,
220,
825,
651,
62,
15999,
62,
16129,
62,
14933,
7,
944,
8,
4613,
19720,
13,
29993,
540,
58,
2536,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
257,
1351,
286,
2496,
8950,
4855,
416,
39342,
615,
315,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
1052,
11629,
540,
286,
13042,
543,
389,
8950,
351,
2041,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1104,
1626,
39342,
615,
315,
24019,
13,
198,
220,
220,
220,
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] | 2.523831 | 10,973 |
#!/usr/bin/env python3
import os
import argparse
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import numpy
from ROOT import TFile, TH1D, TH2D, TCanvas
from ROOT import gROOT, gPad, gStyle
import ROOT
from etnet.dataset import EtrackDataset
from etnet.neuralnet import EtrackNet
if __name__ == "__main__":
parser = usage()
args = parser.parse_args()
for file in args.file:
main(file, args)
# main()
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] | 2.733696 | 184 |
import tensorflow as tf
from tensorflow.keras.layers import Layer, InputSpec
from tensorflow.keras import initializers, regularizers
import tensorflow.keras.backend as K
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] | 3.4 | 50 |
# -*- coding: utf-8 -*-
#
import enum
import json
from typing import Any, Callable, Dict, List, Optional, Union
from pydantic import Json
from .abc import Model
[v.update_forward_refs() for v in globals().values() if hasattr(v, "update_forward_refs")]
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] | 2.232416 | 327 |
import json
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] | 2 | 10 |
from math import sqrt
import string
############################################################################################
# Important: Read over the information in the "Appendix: Utility Functions" in the Project #
# Project Specification in order to better understand how to use the functions below. #
############################################################################################
###############################
# Submitting design questions #
###############################
passphrase = '814716d640bad70cbb9c76c72f2810e06f588a1bc1039d2510acab2d'
def check_passphrase(p):
"""
You do not need to understand this code. This will only
be used to ensure you have entered the correct passphrase.
"""
import hashlib
return hashlib.sha224(p.encode('utf-8')).hexdigest()
#################
# Reading files #
#################
def close(file):
"""Closes the file object passed in. """
file.close()
def readable(file):
"""Return True if this file can be read from. """
return file.readable()
def readline(file):
"""
Return the first unread line from this file,
or the empty string if all lines are read.
"""
return file.readline()
def readlines(file):
"""
Return all unread lines in a list.
"""
return file.readlines()
############################
# String utility functions #
############################
def lower(s):
"""Return a copy of string s with all letters converted to lowercase."""
return s.lower()
def split(s, sep=None):
"""
Returns a list of words contained in s, which are
sequences of characters separated by a string sep.
By default, this splits on whitespace (spaces, tabs, etc.)
but by defining a different sep, you can split on arbitrary
characters.
"""
return s.split(sep)
def strip(s, chars=None):
"""
Return a version of s with characters in chars removed
from the start and end.
By default, removes whitespace characters.
"""
return s.strip(chars)
#########################################
# Functions relating to keyboard layout #
#########################################
KEY_LAYOUT = [["q", "w", "e", "r", "t", "y", "u", "i", "o", "p"],
["a", "s", "d", "f", "g", "h", "j", "k", "l"],
["z", "x", "c", "v", "b", "n", "m"]]
def distance(p1, p2):
"""Return the Euclidean distance between two points
The Euclidean distance between two points, (x1, y1) and (x2, y2)
is the square root of (x1 - x2) ** 2 + (y1 - y2) ** 2
>>> distance((0, 1), (1, 1))
1
>>> distance((1, 1), (1, 1))
0
>>> round(distance((4, 0), (0, 4)), 3)
5.657
"""
return sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
def get_key_distances():
"""Return a new dictionary mapping key pairs to distances.
Each key of the dictionary is a tuple of two
letters as strings, and each value is the euclidean distance
between the two letters on a standard QWERTY keyboard normalized
such that the greatest distance is 2.0
The scaling is constant, so a pair of keys that are twice
as far have a distance value that is twice as great
>>> distances = get_key_distances()
>>> distances["a", "a"]
0.0
>>> distances["a", "d"] # 2.0 / 9
2.0
>>> distances["d", "a"]
2.0
"""
key_distance = {}
for i in range(len(KEY_LAYOUT)):
for j in range(len(KEY_LAYOUT[i])):
compute_pairwise_distances(i, j, key_distance)
max_value = max(key_distance.values())
return {key : value * 2 / max_value for key, value in key_distance.items()}
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] | 3.11786 | 1,103 |
import os
from lambda_handler import handle
# Use an event structure that follows GuardDuty Schema 2.0 to simulate a ticketing system integration.
EVENT_FIXTURE = {
"detail-type": "GuardDuty Finding",
"source": "aws.guardduty",
"detail": {
"schemaVersion": "2.0",
"accountId": "874153891031",
"region": "us-west-2",
"partition": "aws",
"id": "b2b7236dda0e4eff8aa17738d9ad18c5",
"type": "Custom:UserReport/WebsiteDefacement",
"resource": {
"resourceType": "FargateContainer",
"instanceDetails": {
"tags": [
{"Key": "app", "Value": "fluffykittenwww"},
{"Key": "risk", "Value": "maximum"},
],
},
},
"severity": 10,
"createdAt": "2019-11-07T17:13:53.948Z",
"updatedAt": "2019-11-09T17:41:09.360Z",
"title": "A user has reported a defacement of fluffykitten securities main www site.",
"description": "All images on the site have been replaced with dogs instead of fluffy cats.",
"detail": {},
},
}
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] | 2.137736 | 530 |
from setuptools import setup
setup(name='pyfooty',
version='0.0',
description='Parse the fbref website with python',
url='https://github.com/rmatsum836/pyfooty',
author='Ray Matsumoto',
author_email='[email protected]',
license='MIT',
packages=['pyfooty'],
zip_safe=False)
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"""This module wraps the fortran backend."""
import numpy as np
import compflow_fort_from_Ma as fort_from_Ma
import compflow_fort_der_from_Ma as fort_der_from_Ma
import compflow_fort_to_Ma as fort_to_Ma
def _restore_shape(func, args):
"""Call a function and restore output to same shape as first argument."""
shape = np.shape(args[0])
if shape == ():
return(func(*args)[0])
elif len(shape) == 1:
return(func(*args))
else:
return(func(*args).reshape(shape, order='F'))
# Functions from Ma
def To_T_from_Ma(Ma, ga):
r"""Stagnation temperature ratio as function of Mach number.
.. math::
\frac{T_0}{T} = 1 + \frac{\gamma - 1}{2} \Ma^2
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
To_T : array
Stagnation temperature ratio, :math:`T_0/T`.
"""
return _restore_shape(fort_from_Ma.to_t, (Ma,ga))
def Po_P_from_Ma(Ma,ga):
r"""Stagnation pressure ratio as function of Mach number.
.. math::
\frac{p_0}{p} = \left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^\tfrac{\gamma}{\gamma - 1}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Po_P : array
Stagnation pressure ratio, :math:`p_0/p`.
"""
return _restore_shape(fort_from_Ma.po_p, (Ma,ga))
def rhoo_rho_from_Ma(Ma,ga):
r"""Stagnation density ratio as function of Mach number.
.. math::
\frac{\rho_0}{\rho} = \left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^\tfrac{1}{\gamma - 1}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
rhoo_rho : array
Stagnation density ratio, :math:`\rho_0/\rho`.
"""
return _restore_shape(fort_from_Ma.rhoo_rho, (Ma,ga))
def V_cpTo_from_Ma(Ma,ga):
r"""Normalised velocity as function of Mach number.
.. math::
\frac{V}{\sqrt{c_p T_0}} = \sqrt{\gamma -1}\, \Ma
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)^{-\tfrac{1}{2}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
V_cpTo : array
Normalised velocity, :math:`V/\sqrt{c_p T_0}`.
"""
return _restore_shape(fort_from_Ma.v_cpto, (Ma,ga))
def mcpTo_APo_from_Ma(Ma,ga):
r"""Normalised mass flow as function of Mach number.
.. math::
\frac{\dot{m}\sqrt{c_p T_0}}{A p_0} =
\frac{\gamma}{\sqrt{\gamma -1}}\, \Ma
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^{-\tfrac{1}{2}\tfrac{\gamma + 1}{\gamma - 1}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
mcpTo_APo : array
Normalised mass flow, :math:`{\dot{m}\sqrt{c_p T_0}}/{A p_0}`.
"""
return _restore_shape(fort_from_Ma.mcpto_apo, (Ma,ga))
def mcpTo_AP_from_Ma(Ma,ga):
r"""Static normalised mass flow as function of Mach number.
.. math::
\frac{\dot{m}\sqrt{c_p T_0}}{A p} =
\frac{\gamma}{\sqrt{\gamma -1}}\, \Ma
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^{\tfrac{1}{2}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
mcpTo_AP : array
Static pressure variant of normalised mass flow, :math:`{\dot{m}\sqrt{c_p T_0}}/{A p}`.
"""
return _restore_shape(fort_from_Ma.mcpto_ap, (Ma,ga))
def A_Acrit_from_Ma(Ma,ga):
r"""Ratio of area to choking area as function of Mach number.
.. math::
\frac{A}{A_*} = \frac{1}{\Ma}\left[\frac{2}{\gamma +1}
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)\right]
^{\tfrac{1}{2}\tfrac{\gamma + 1}{\gamma - 1}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
A_Acrit : array
Ratio of area to choking area, :math:`A/A_*`.
"""
return _restore_shape(fort_from_Ma.a_acrit, (Ma,ga))
def Mash_from_Ma(Ma,ga):
r"""Post-shock Mach number as function of Mach number.
.. math::
\Ma_\mathrm{sh} = \left(\frac{1 + \frac{\gamma - 1}{2} \Ma^2}
{\gamma \Ma^2 - \frac{\gamma - 1}{2}} \right)^{\tfrac{1}{2}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Mash : array
Post-shock Mach number, :math:`\Ma_\mathrm{sh}`.
"""
return _restore_shape(fort_from_Ma.mash, (Ma,ga))
def Posh_Po_from_Ma(Ma,ga):
r"""Shock stagnation pressure ratio as function of Mach number.
.. math::
\frac{p_{0\mathrm{sh}}}{p_0} = \left(
\frac{\frac{\gamma + 1}{2}\Ma^2}{1 + \frac{\gamma - 1}{2} \Ma^2}
\right)^{\tfrac{\gamma}{\gamma-1}}
\left(
\frac{2 \gamma }{\gamma + 1} \Ma^2 - \frac{\gamma - 1}{\gamma + 1}
\right)^{\tfrac{-1}{\gamma -1}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Posh_Po : array
Shock stagnation pressure ratio, :math:`p_{0\mathrm{sh}}/p_0`.
"""
return _restore_shape(fort_from_Ma.posh_po, (Ma,ga))
# Inversions to Ma
def Ma_from_To_T(To_T, ga):
r"""Mach number as function of stagnation temperature ratio.
The inverse of :func:`compflow.To_T_from_Ma`, which permits a direct
analytical solution.
.. math::
\Ma = \sqrt{\frac{2}{\gamma - 1} \left[\frac{T_0}{T} - 1\right]}
Returns `NaN` if input data is not physically possible, where
:math:`{T_0}/{T}<1`.
Parameters
----------
To_T : array
Stagnation temperature ratio, :math:`T_0/T`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.to_t, (To_T, ga))
def Ma_from_Po_P(Po_P, ga):
r"""Mach number as function of stagnation pressure ratio.
The inverse of :func:`compflow.Po_P_from_Ma`, which permits a direct
analytical solution.
.. math::
\Ma = \sqrt{ \frac{2}{\gamma - 1}\left[\left(\frac{p_0}{p}\right)
^\tfrac{\gamma - 1}{\gamma} - 1\right]}
Returns `NaN` if input data is not physically possible, where
:math:`{p_0}/{p}<1`.
Parameters
----------
Po_P : array
Stagnation pressure ratio, :math:`p_0/p`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.po_p, (Po_P, ga))
def Ma_from_rhoo_rho(rhoo_rho, ga):
r"""Mach number as function of stagnation density ratio.
The inverse of :func:`compflow.rhoo_rho_from_Ma`, which permits a direct
analytical solution.
.. math::
\Ma = \sqrt{ \frac{2}{\gamma - 1}\left[\left(\frac{\rho_0}{\rho}\right)
^{\gamma - 1} - 1\right]}
Returns `NaN` if input data is not physically possible, where
:math:`{\rho_0}/{\rho}<1`.
Parameters
----------
rhoo_rho : array
Stagnation density ratio, :math:`\rho_0/\rho`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.rhoo_rho, (rhoo_rho, ga))
def Ma_from_V_cpTo(V_cpTo, ga):
r"""Mach number as function of normalised velocity.
Inverse of :func:`compflow.V_cpTo_from_Ma`, which permits a direct
analytical solution.
.. math::
\Ma = \sqrt{\frac{1}{\gamma-1}\left[
\frac{\left(\frac{V}{\sqrt{c_p T_0}}\right)^2}
{1 - \frac{1}{2}\left(\frac{V}{\sqrt{c_p T_0}}\right)^2}
\right]}
Returns `NaN` if input data is not physically possible, where
:math:`V/\sqrt{c_pT_0} < 0` or :math:`V/\sqrt{c_pT_0} > \sqrt{2}`.
Parameters
----------
V_cpTo : array
Normalised velocity, :math:`V/\sqrt{c_p T_0}`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.v_cpto, (V_cpTo, ga))
def Ma_from_mcpTo_APo(mcpTo_APo, ga, sup=False):
r"""Mach number as function of normalised mass flow.
The inverse of :func:`compflow.mcpTo_APo_from_Ma`, which at a given value
of :math:`{\dot{m}\sqrt{c_p T_0}}/{A p_0}` must be solved iteratively for
:math:`\Ma` using Newton's method.
.. math::
\frac{\dot{m}\sqrt{c_p T_0}}{A p_0} =
\frac{\gamma}{\sqrt{\gamma -1}}\, \Ma
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^{-\tfrac{1}{2}\tfrac{\gamma + 1}{\gamma - 1}}
For each :math:`{\dot{m}\sqrt{c_p T_0}}/{A p_0}`, there are two possible
values of :math:`\Ma`. Return the subsonic solution with :math:`\Ma\le 1`
by default; the supersonic solution with :math:`\Ma>`` is retrived by
setting the parameter `sup=True`.
Returns `NaN` if input data is not physically possible, where
:math:`{\dot{m}\sqrt{c_p T_0}}/{A p_0} < 0`. The normalised mass flow
reaches a maximum at the sonic velocity :math:`\Ma=1`. Input data above the
maximum value correspond to choking --- also return `NaN` in this case.
Parameters
----------
mcpTo_APo : array
Normalised mass flow, :math:`{\dot{m}\sqrt{c_p T_0}}/{A p_0}`.
ga : float
Ratio of specific heats, :math:`\gamma`.
sup : bool, default False
If true, return the supersonic solution, otherwise the subsonic
solution.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.mcpto_apo, (mcpTo_APo, ga, sup))
def Ma_from_mcpTo_AP(mcpTo_AP, ga):
r"""Mach number as function of static normalised mass flow.
The inverse of :func:`compflow.mcpTo_AP_from_Ma`, which at a given value of
:math:`{\dot{m}\sqrt{c_p T_0}}/{A p}` must be solved iteratively for
:math:`\Ma` using Newton's method.
.. math::
\frac{\dot{m}\sqrt{c_p T_0}}{A p} =
\frac{\gamma}{\sqrt{\gamma -1}}\, \Ma
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^{\tfrac{1}{2}}
Returns `NaN` if input data is not physically possible, where
:math:`{\dot{m}\sqrt{c_p T_0}}/{A p} < 0`.
Parameters
----------
mcpTo_AP : array
Static normalised mass flow, :math:`{\dot{m}\sqrt{c_p T_0}}/{A p}`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.mcpto_ap, (mcpTo_AP, ga))
def Ma_from_A_Acrit(A_Acrit, ga):
r"""Mach number as function of area to choking area ratio.
The inverse of :func:`compflow.A_Acrit_from_Ma`, which at a given value of
:math:`A/A_*` must be solved iteratively for :math:`\Ma` using Newton's
method.
.. math::
\frac{A}{A_*} = \frac{1}{\Ma}\left[
\frac{2}{\gamma +1} \left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
\right]^{\tfrac{1}{2}\tfrac{\gamma + 1}{\gamma - 1}}
Returns `NaN` if input data is not physically possible, where :math:`A/A_*
< 1`.
Parameters
----------
A_Acrit : array
Ratio of area to choking area, :math:`A/A_*`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.a_acrit, (A_Acrit, ga))
def Ma_from_Mash(Mash, ga):
r"""Mach number as function of post-shock Mach number.
The inverse of :func:`compflow.Mash_from_Ma`, which at a given value of
:math:`\Ma_\mathrm{sh}` must be solved iteratively for :math:`\Ma` using
Newton's method.
.. math::
\Ma_\mathrm{sh} = \left(
\frac{1 + \frac{\gamma - 1}{2} \Ma^2}
{\gamma \Ma^2 - \frac{\gamma - 1}{2}}
\right)^{\tfrac{1}{2}}
Returns `NaN` if input data is not physically possible, where
:math:`\Ma_\mathrm{sh}>1` or :math:`\Ma_\mathrm{sh}<0`.
Parameters
----------
Mash : array
Post-shock Mach number, :math:`\Ma_\mathrm{sh}`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.mash, (Mash, ga))
def Ma_from_Posh_Po(Posh_Po, ga):
r"""Mach number as function of shock stagnation pressure ratio.
The inverse of :func:`compflow.Posh_Po_from_Ma`, which at a given value of
:math:`p_{0\mathrm{sh}}/p_0` must be solved iteratively for :math:`\Ma`
using Newton's method.
.. math::
\frac{p_{0\mathrm{sh}}}{p_0} =
\left(\frac{\frac{\gamma+1}{2}\Ma^2}{1+\frac{\gamma-1}{2}\Ma^2}\right)
^{\tfrac{\gamma}{\gamma-1}}
\left(\frac{2\gamma}{\gamma+1}\Ma^2-\frac{\gamma-1}{\gamma+1}\right)
^{\tfrac{-1}{\gamma -1}}
Returns `NaN` if input data is not physically possible, where
:math:`p_{0\mathrm{sh}}/p_0 > 1` or :math:`p_{0\mathrm{sh}}/p_0 < 0`.
Parameters
----------
Posh_Po : array
Shock stagnation pressure ratio, :math:`p_{0\mathrm{sh}}/p_0`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
Ma : array
Mach number, :math:`\Ma`.
"""
return _restore_shape(fort_to_Ma.posh_po, (Posh_Po, ga))
# Derivatives from Ma
def der_To_T_from_Ma(Ma, ga):
r"""Derivative of stagnation temperature ratio by Mach number.
The derivative of :func:`compflow.To_T_from_Ma` with respect to Mach
number.
.. math::
\frac{\D}{\D\Ma}\left(\frac{T_0}{T}\right) = \left(\gamma - 1\right)\Ma
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_To_T : array
Derivative of stagnation temperature ratio,
:math:`\DMa(T_0/T)`.
"""
return _restore_shape(fort_der_from_Ma.to_t, (Ma,ga))
def der_Po_P_from_Ma(Ma, ga):
r"""Derivative of stagnation pressure ratio by Mach number.
The derivative of :func:`compflow.Po_P_from_Ma` with respect to Mach
number.
.. math::
\DMa\left(\frac{p_0}{p}\right) =
\gamma \Ma \left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^\tfrac{1}{\gamma - 1}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_Po_P : array
Derivative of stagnation pressure ratio, :math:`\DMa(p_0/p)`.
"""
return _restore_shape(fort_der_from_Ma.po_p, (Ma,ga))
def der_rhoo_rho_from_Ma(Ma,ga):
r"""Derivative of stagnation density ratio by Mach number.
The derivative of :func:`compflow.rhoo_rho_from_Ma` with respect to Mach
number.
.. math::
\DMa\left(\frac{\rho_0}{\rho}\right) =
\Ma\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^\tfrac{-1}{\gamma - 1}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_rhoo_rho : array
Derivative of stagnation density ratio, :math:`\DMa(\rho_0/\rho)`.
"""
return _restore_shape(fort_der_from_Ma.rhoo_rho, (Ma,ga))
def der_V_cpTo_from_Ma(Ma, ga):
r"""Derivative of normalised velocity by Mach number.
The derivative of :func:`compflow.V_cpTo_from_Ma` with respect to Mach
number.
.. math::
\DMa\left(\frac{V}{\sqrt{c_p T_0}}\right) = {\sqrt{\gamma -1}}
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)^{-\tfrac{3}{2}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_V_cpTo : array
Derivative of normalised velocity, :math:`\DMa(V/\sqrt{c_p T_0})`.
"""
return _restore_shape(fort_der_from_Ma.v_cpto, (Ma,ga))
def der_mcpTo_APo_from_Ma(Ma, ga):
r"""Derivative of normalised mass flow by Mach number.
The derivative of :func:`compflow.mcpTo_APo_from_Ma` with respect to Mach
number.
.. math::
\DMa\left(\frac{\dot{m}\sqrt{c_p T_0}}{A p_0} \right)=
\frac{\gamma}{\sqrt{\gamma -1}}
\left(1 - \frac{\frac{\gamma + 1}{2} \Ma^2}
{1 + \frac{\gamma - 1}{2} \Ma^2 } \right)
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)
^{-\tfrac{1}{2}\tfrac{\gamma + 1}{\gamma - 1}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_mcpTo_APo : array
Derivative of normalised mass flow,
:math:`\DMa({\dot{m}\sqrt{c_pT_0}}/{Ap_0})`.
"""
return _restore_shape(fort_der_from_Ma.mcpto_apo, (Ma,ga))
def der_mcpTo_AP_from_Ma(Ma, ga):
r"""Derivative of static normalised mass flow by Mach number.
The derivative of :func:`compflow.mcpTo_AP_from_Ma` with respect to Mach
number.
.. math::
\DMa\left(\frac{\dot{m}\sqrt{c_p T_0}}{A p} \right)=
\frac{\gamma}{\sqrt{\gamma -1}}
\Big(1 + (\gamma - 1) \Ma^2 \Big)
\left(1 + \frac{\gamma - 1}{2} \Ma^2 \right)^{-\tfrac{1}{2}}
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_mcpTo_AP : array
Derivative of static pressure variant of normalised mass flow, :math:`\DMa({\dot{m}\sqrt{c_p T_0}}/{A p})`.
"""
return _restore_shape(fort_der_from_Ma.mcpto_ap, (Ma,ga))
def der_A_Acrit_from_Ma(Ma, ga):
r"""Derivative of choking area ratio by Mach number.
The derivative of :func:`compflow.A_Acrit_from_Ma` with respect to Mach
number.
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_A_Acrit : array
Derivative of ratio of area to choking area, :math:`\DMa(A/A_*)`.
"""
return _restore_shape(fort_der_from_Ma.a_acrit, (Ma,ga))
def der_Mash_from_Ma(Ma, ga):
r"""Derivative of post-shock Mach number by Mach number.
The derivative of :func:`compflow.Mash_from_Ma` with respect to Mach
number.
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_Mash : array
Derivative of post-shock Mach number, :math:`\DMa(\Ma_\mathrm{sh})`.
"""
return _restore_shape(fort_der_from_Ma.mash, (Ma,ga))
def der_Posh_Po_from_Ma(Ma, ga):
r"""Derivative of shock pressure ratio by Mach number.
The derivative of :func:`compflow.Posh_Po_from_Ma` with respect to Mach
number.
Parameters
----------
Ma : array
Mach number, :math:`\Ma`.
ga : float
Ratio of specific heats, :math:`\gamma`.
Returns
-------
der_Posh_Po : array
Derivative of shock stagnation pressure ratio,
:math:`\DMa(p_{0\mathrm{sh}}/p_0)`.
"""
return _restore_shape(fort_der_from_Ma.posh_po, (Ma,ga))
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198,
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25,
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] | 2.173956 | 9,100 |
import asyncio
import datetime
import os
from .. import ALIVE_NAME
from ..cmdhelp import CmdHelp
from ..utils import admin_cmd, edit_or_reply, sudo_cmd
from . import *
DEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else "eviral User"
eviral = borg.uid
eviral_IMG = os.environ.get(
"PING_PIC", "https://te.legra.ph/file/a59da36828333262c9848.jpg"
)
start = datetime.datetime.now()
end = datetime.datetime.now()
ms = (end - start).microseconds / 1000
@bot.on(admin_cmd(pattern=f"hbping$", outgoing=True))
@bot.on(sudo_cmd(pattern='hbping$', allow_sudo=True))
@bot.on(admin_cmd(pattern="ping$", outgoing=True))
@bot.on(sudo_cmd(pattern="ping$", allow_sudo=True))
CmdHelp("ping").add_command(
"ping", None, "Shows you the ping speed of server"
).add_command(
"hbping", None, "Shows you the ping speed of server with an animation"
).add_type(
"Official"
).add()
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] | 2.616071 | 336 |
import glob
import json
from copy import deepcopy
import numpy as np
from astropy.table import Table
from astropy.time import Time
from cos_monitoring import dark
from cos_monitoring.dark import solar
import pdb
solar.get_solar_data("./")
data = Table.read('solar_flux.txt', format='ascii')
all_info = []
for item in glob.glob("new/*_corrtag_c.fits") + glob.glob('out/*_corrtag_?.fits'):
print(item)
for item in dark.pull_orbital_info(item, step=700):
try:
darkrate = item['dark']
except KeyError:
continue
t = Time(item['date'], format='decimalyear').mjd
index = np.argmin(abs(data['col1'] - t))
item['fsol'] = float(data['col2'][index])
print(item)
#if darkrate < 2e-6:
# continue
all_info.append(deepcopy(item))
with open('./orbital_info.json', 'w') as out:
json.dump(all_info, out, sort_keys=False, indent=4, separators=(',', ': '))
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3256,
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] | 2.314149 | 417 |
from matplotlib import rc
import matplotlib.pyplot as plt
import os
import seaborn as sns
from particle.plotting import (
plot_averaged_convergence_from_clusters,
plot_averaged_avg_vel,
plot_avg_vel,
)
sns.set(style="white", context="talk")
search_parameters = {
# "particle_count": 480,
# "G": "Smooth",
# "scaling": "Local",
"phi": "Bump",
# # "initial_dist_x": "one_cluster",
"initial_dist_v": "pos_const_near_0",
"T_end": 2000.0,
# "dt": 0.01,
# "D": 1.0,
# "option": "numba",
}
if os.name == "nt":
rc("text", usetex=True) # I only have TeX on Windows :(
os.chdir("D:/InteractingParticleSystems/noisysystem_temp")
elif os.name == "posix":
os.chdir("/Volumes/Extreme SSD/InteractingParticleSystems/noisysystem_temp")
yaml_path = "Experiments/positive_phi_no_of_clusters_high_noise_bump"
fn = "_switch_"
logged = False
include_traj = True
fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
ax2 = plot_avg_vel(ax2, search_parameters, logx=logged, exp_yaml=yaml_path)
ax2 = plot_averaged_avg_vel(ax2, search_parameters, logx=logged, exp_yaml=yaml_path)
ax1 = plot_averaged_convergence_from_clusters(
ax1, search_parameters, yaml_path, logx=logged
)
ax1.plot([0, search_parameters["T_end"]], [7.5, 7.5], "k--", alpha=0.2)
ax2.set(xlabel="Time", ylabel=r"$M^N(t) $")
# ax2.legend()
# fig.savefig(f"img/PositivePhiClusters{fn}logged.jpg", dpi=300)
# plt.tight_layout()
plt.subplots_adjust(left=0.07, right=0.97, bottom=0.15, top=0.9, wspace=0.23)
plt.show()
# logged = False
# fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(14, 5), sharex=True)
# ax2 = plot_avg_vel(ax2, search_parameters, logx=logged, exp_yaml=yaml_path)
# ax2 = plot_averaged_avg_vel(ax2, search_parameters, logx=logged, exp_yaml=yaml_path)
#
# ax1 = plot_averaged_convergence_from_clusters(
# ax1, search_parameters, yaml_path, logx=logged
# )
#
# ax1.plot([0, search_parameters["T_end"]], [7.5, 7.5], "k--", alpha=0.2)
# ax2.set(xlabel="Time", ylabel=r"$M^N(t) $")
# # ax2.legend()
# fig.savefig(f"img/PositivePhiClusters{fn}linear.jpg", dpi=300)
# # plt.tight_layout()
# plt.subplots_adjust(left=0.07, right=0.97, bottom=0.15, top=0.9, wspace=0.23)
# plt.show()
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] | 2.168616 | 1,026 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
import os
import typing
import pandas as pd
from . import internals
def build_hierarchy(
data: pd.DataFrame,
get_parent=os.path.dirname,
root: str = "",
max_iter: int = 100,
col_name: typing.Optional[str] = None,
) -> pd.DataFrame:
"""Build a hierarchy from a data set and a get_parent relationship.
The output frame adds 2 columns in front: id and parent. Both are numerical
where the parent id identifies the id of the parent as returned by the
get_parent function.
The id of the root element is set to 0 and the parent is set to np.nan.
Args:
data: data containing the leaves of the tree.
get_parent: function returning the parent of an element.
root: expected root of the hierarchy.
max_iter: maximum number of iterations.
col_name: name of the column to use as input (default to column 0).
Returns:
pandas.DataFrame with the columns id, parent and col_name.
The parent value identifies the id of the parent in the hierarchy where
the id 0 is the root. The columns other than col_name are discarded.
"""
assert data.ndim == 2, "DataFrame-like object expected"
if not col_name:
col_name = data.columns[0]
parent = get_parent.__name__
df = data[[col_name]]
frames = []
seen = {root}
root_actually_seen = False
count = 0
for _ in range(max_iter):
df.loc[:, parent] = df[col_name].apply(get_parent)
if root in df[parent].values:
root_actually_seen = True
df["id"] = range(count, count + len(df))
count += len(df)
frames.append(df)
df = (
df.loc[~df[parent].isin(seen), [parent]]
.drop_duplicates()
.rename(columns={parent: col_name})
)
seen.update(df[col_name])
if len(df) == 0:
frames.append(
pd.DataFrame(data={"id": [count], col_name: [root], parent: [None]})
)
break
if not root_actually_seen:
msg = f"cannot find root {root} in input frame"
internals.log.error(msg)
raise ValueError(msg)
df = pd.concat(frames, sort=False).drop_duplicates()
df["id"] = len(df) - df["id"] - 1
assert col_name is not None
y_name = col_name + "_y"
merged = pd.merge(df, df, left_on=col_name, right_on=parent, how="right")[
[y_name, "id_y", "id_x"]
].rename(columns={y_name: col_name, "id_y": "id", "id_x": "parent"})
return (
merged[["id", "parent", col_name]].sort_values(by="id").reset_index(drop=True)
)
def _vis_generic(
df: pd.DataFrame,
size_column: str,
color_column: str,
colorscheme: str,
height: int = 300,
width: int = 400,
) -> dict:
"""Factors common parts of vis_xxx functions.
Internal. See vis_hot_spots or vis_ages for documentation.
"""
if len(df) <= 0:
raise ValueError("dataframe is empty")
if size_column not in df.columns:
raise ValueError(f"{size_column} not found in columns")
if color_column not in df.columns:
raise ValueError(f"{color_column} not found in columns")
hierarchy = build_hierarchy(df[["path"]], root="")
hierarchy = (
pd.merge(hierarchy, df, left_on="path", right_on="path", how="left")
.rename(columns={size_column: "size", color_column: "intensity"})
.sort_values(by="id")
)
hierarchy.loc[:, ["size", "intensity"]] = hierarchy[["size", "intensity"]].fillna(0)
json_values = hierarchy.to_json(orient="records")
signal = (
"datum.path + "
f"(datum.intensity ? ', ' + datum.intensity + ' {color_column}' : '') + "
f"(datum.size ? ', ' + datum.size + ' {size_column}' : '')"
)
desc: typing.Dict[str, typing.Any] = {
"$schema": "https://vega.github.io/schema/vega/v4.json",
"width": width,
"height": height,
"padding": 5,
"autosize": "none",
"data": [
{
"name": "tree",
# 'values': ...,
"transform": [
{"type": "stratify", "key": "id", "parentKey": "parent"},
{
"type": "pack",
"field": "size",
"sort": {"field": "value", "order": "descending"},
"size": [{"signal": "width"}, {"signal": "height"}],
},
],
}
],
"scales": [
{
"name": "color",
"type": "linear",
"domain": {"data": "tree", "field": "intensity"},
"range": {"scheme": colorscheme},
"domainMin": 0,
}
],
"marks": [
{
"type": "symbol",
"from": {"data": "tree"},
"encode": {
"enter": {
"shape": {"value": "circle"},
"fill": {"scale": "color", "field": "intensity"},
"tooltip": {"signal": signal},
},
"update": {
"x": {"field": "x"},
"y": {"field": "y"},
"size": {"signal": "4 * datum.r * datum.r"},
"stroke": {"value": "white"},
"strokeWidth": {"value": 0.5},
},
"hover": {
"stroke": {"value": "black"},
"strokeWidth": {"value": 2},
},
},
}
],
}
desc["data"][0]["values"] = json.loads(json_values)
return desc
def vis_hot_spots(
df: pd.DataFrame,
height: int = 300,
width: int = 400,
size_column: str = "lines",
color_column: str = "changes",
colorscheme: str = "yelloworangered",
) -> dict:
"""Convert get_hot_spots output to a json vega dict.
Args:
df: input data returned by :func:`codemetrics.get_hot_spots`
height: vertical size of the figure.
width: horizontal size of the figure.
size_column: column that drives the size of the circles.
color_column: column that drives the color intensity of the circles.
colorscheme: color scheme. See https://vega.github.io/vega/docs/schemes/
Returns:
Vega description suitable to be use with Altair.
Example::
import codemetrics as cm
from altair.vega.v4 import Vega
hspots = cm.get_hot_spots(loc_df, log_df)
desc = cm.vega.vis_hot_spots(hspots)
Vega(desc) # display the visualization inline in you notebook.
See also:
`Vega circle pack example`_
.. _Vega circle pack example: https://vega.github.io/editor/#/examples/vega/circle-packing
"""
return _vis_generic(
df,
size_column=size_column,
color_column=color_column,
colorscheme=colorscheme,
width=width,
height=height,
)
def vis_ages(
df: pd.DataFrame,
height: int = 300,
width: int = 400,
colorscheme: str = "greenblue",
) -> dict:
"""Convert get_ages output to a json vega dict.
Args:
df: input data returned by :func:`codemetrics.get_ages`
height: vertical size of the figure.
width: horizontal size of the figure.
colorscheme: color scheme. See https://vega.github.io/vega/docs/schemes/
Returns:
Vega description suitable to be use with Altair.
Example::
import codemetrics as cm
from altair.vega.v4 import Vega
ages = cm.get_ages(loc_df, log_df)
desc = cm.vega.vis_ages(ages)
Vega(desc) # display the visualization inline in you notebook.
See also:
`Vega circle pack example`_
.. _Vega circle pack example: https://vega.github.io/editor/#/examples/vega/circle-packing
"""
df["days"] = df["age"].astype("int32")
df = df.rename(columns={"code": "loc"})
return _vis_generic(
df,
size_column="loc",
color_column="days",
colorscheme=colorscheme,
width=width,
height=height,
)
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92,
6,
1058,
10148,
16725,
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220,
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1267,
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25,
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13,
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] | 2.102201 | 3,953 |
from Alarm_dialog_ui import Alarm_Ui_Dialog
from PyQt5 import QtCore, QtWidgets, QtGui
import threading
import sys
if __name__ == '__main__':
app = QtWidgets.QApplication(sys.argv)
form = AlarmDialog("Slave3", "CPU overloading", "CPU usage > 95%", "50%", "2017/11/25 12:00")
form.setWindowTitle('Alarm')
form.show()
sys.exit(app.exec_()) | [
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] | 2.358974 | 156 |
# Copyright 2016 OpenStack Foundation.
#
# 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.
"""Liberty release
Revision ID: 001
Revises: None
Create Date: 2015-09-13 20:46:25.783444
"""
# revision identifiers, used by Alembic.
revision = '001'
down_revision = None
from alembic import op
import sqlalchemy as sa
MYSQL_ENGINE = 'InnoDB'
MYSQL_CHARSET = 'utf8'
| [
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] | 3.333333 | 258 |
from django.db import models
from ckeditor.fields import RichTextField
# Create your models here. | [
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] | 3.769231 | 26 |
import logging
import sys
import traceback
import re
import os
import socket
import jinja2
import json
from shutil import rmtree
from time import sleep
from datetime import datetime
from string import Formatter
from lib.jobstatus import ReverseMap, JobStatus
from mailer import Mailer
from models import DataFile
def print_line(line, event_list, ignore_text=False):
"""
Prints a message to either the console, the event_list, or the current event
Parameters:
line (str): The message to print
event_list (EventList): the event list
ignore_text (bool): should this be printed to the console if in text mode
"""
logging.info(line)
if not ignore_text:
now = datetime.now()
timestr = '{hour}:{min}:{sec}'.format(
hour=now.strftime('%H'),
min=now.strftime('%M'),
sec=now.strftime('%S'))
msg = '{time}: {line}'.format(
time=timestr,
line=line)
print msg
sys.stdout.flush()
def get_climo_output_files(input_path, start_year, end_year):
"""
Return a list of ncclimo climatologies from start_year to end_year
Parameters:
input_path (str): the directory to look in
start_year (int): the first year of climos to add to the list
end_year (int): the last year
Returns:
file_list (list(str)): A list of the climo files in the directory
"""
if not os.path.exists(input_path):
return None
contents = [s for s in os.listdir(input_path) if not os.path.isdir(s)]
pattern = r'_{start:04d}\d\d_{end:04d}\d\d_climo\.nc'.format(
start=start_year,
end=end_year)
return [x for x in contents if re.search(pattern=pattern, string=x)]
def get_ts_output_files(input_path, var_list, start_year, end_year):
"""
Return a list of ncclimo timeseries files from a list of variables, start_year to end_year
Parameters:
input_path (str): the directory to look in
var_list (list): a list of strings of variable names
start_year (int): the first year of climos to add to the list
end_year (int): the last year
Returns:
ts_list (list): A list of the ts files
"""
if not os.path.exists(input_path):
return None
contents = [s for s in os.listdir(input_path) if not os.path.isdir(s)]
ts_list = list()
for var in var_list:
pattern = r'{var}_{start:04d}01_{end:04d}12\.nc'.format(
var=var,
start=start_year,
end=end_year)
for item in contents:
if re.search(pattern, item):
ts_list.append(item)
break
return ts_list
def print_debug(e):
"""
Print an exceptions relavent information
"""
print '1', e.__doc__
print '2', sys.exc_info()
print '3', sys.exc_info()[0]
print '4', sys.exc_info()[1]
print '5', traceback.tb_lineno(sys.exc_info()[2])
_, _, tb = sys.exc_info()
print '6', traceback.print_tb(tb)
def format_debug(e):
"""
Return a string of an exceptions relavent information
"""
_, _, tb = sys.exc_info()
return """
1: {doc}
2: {exec_info}
3: {exec_0}
4: {exec_1}
5: {lineno}
6: {stack}
""".format(
doc=e.__doc__,
exec_info=sys.exc_info(),
exec_0=sys.exc_info()[0],
exec_1=sys.exc_info()[1],
lineno=traceback.tb_lineno(sys.exc_info()[2]),
stack=traceback.print_tb(tb))
def print_message(message, status='error'):
"""
Prints a message with either a green + or a red -
Parameters:
message (str): the message to print
status (str): th"""
if status == 'error':
print colors.FAIL + '[-] ' + colors.ENDC + colors.BOLD + str(message) + colors.ENDC
elif status == 'ok':
print colors.OKGREEN + '[+] ' + colors.ENDC + str(message)
def render(variables, input_path, output_path):
"""
Renders the jinja2 template from the input_path into the output_path
using the variables from variables
"""
tail, head = os.path.split(input_path)
template_path = os.path.abspath(tail)
loader = jinja2.FileSystemLoader(searchpath=template_path)
env = jinja2.Environment(loader=loader)
template = env.get_template(head)
outstr = template.render(variables)
with open(output_path, 'a') as outfile:
outfile.write(outstr)
def create_symlink_dir(src_dir, src_list, dst):
"""
Create a directory, and fill it with symlinks to all the items in src_list
Parameters:
src_dir (str): the path to the source directory
src_list (list): a list of strings of filenames
dst (str): the path to the directory that should hold the symlinks
"""
if not src_list:
return
if not os.path.exists(dst):
os.makedirs(dst)
for src_file in src_list:
if not src_file:
continue
source = os.path.join(src_dir, src_file)
destination = os.path.join(dst, src_file)
if os.path.lexists(destination):
continue
try:
os.symlink(source, destination)
except Exception as e:
msg = format_debug(e)
logging.error(msg)
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] | 2.343539 | 2,221 |
import discord
from discord.ext import commands
from discord.gateway import DiscordWebSocket
import requests, os
import json
import subprocess
import io
import random
import dotenv
import aiomysql
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# -*- coding: UTF-8 -*-
# Copyright 2012-2017 Rumma & Ko Ltd
# License: BSD (see file COPYING for details)
"""Defines the babel field classes (:class:`BabelCharField` and
:class:`BabelTextField`) and the :class:`LanguageField` class.
**Babel fields** are fields which "generate" in the Django model a
series of normal CharFields (or TextFields), one for each
:attr:`lino.core.site.Site.language`.
Example::
class Foo(models.Model):
name = BabelCharField(_("Foo"), max_length=200)
.. autosummary::
"""
from __future__ import unicode_literals
import six
import logging
logger = logging.getLogger(__name__)
from django.conf import settings
from django.db import models
from django.utils.translation import ugettext_lazy as _
from django.utils.text import format_lazy
from lino.core.fields import RichTextField
LANGUAGE_CODE_MAX_LENGTH = 5
def contribute_to_class(field, cls, fieldclass, **kw):
"Used by both :class:`BabelCharField` and :class:`BabelTextField` "
if cls._meta.abstract:
return
kw.update(blank=True)
if "__fake__" in repr(cls):
# Used to test if we're creating a migration, in that case we don't want to add new fields,
# As they're already detected during site startup.
return
for lang in settings.SITE.BABEL_LANGS:
kw.update(verbose_name=format_lazy(u"{}{}",
field.verbose_name, ' (' + lang.django_code + ')'))
newfield = fieldclass(**kw)
#~ newfield._lino_babel_field = True
# used by dbtools.get_data_elems
newfield._lino_babel_field = field.name
newfield._babel_language = lang
cls.add_to_class(six.text_type(field.name + '_' + lang.name), newfield)
# we must convert the field name to six.text_type because lang.name is a newstr, and Django can raise a
# TypeError: unorderable types: str() and <type 'str'>
# when a model contains both Babelfields and fields with plain Py2 str names.
class BabelCharField(models.CharField):
"""Define a variable number of `CharField` database fields, one for
each language of your :attr:`lino.core.site.Site.languages`. See
:ref:`mldbc`.
"""
class BabelTextField(RichTextField):
"""
Define a variable number of clones of the "master" field,
one for each language .
See :ref:`mldbc`.
"""
class LanguageField(models.CharField):
"""A field that lets the user select a language from the available
:attr:`lino.core.site.Site.languages`.
See also :meth:`lino.core.model.Model.get_print_language`.
"""
| [
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198
] | 2.668731 | 969 |
#!/usr/bin/env python3
# Advent of Code 2016 - Day 2, Part Two
import sys
from turtle import Vec2D
keypad = [[None, None, '1', None, None],
[None, '2', '3', '4', None],
[ '5', '6', '7', '8', '9'],
[None, 'A', 'B', 'C', None],
[None, None, 'D', None, None]]
moves = { 'U' : Vec2D(-1, 0),
'D' : Vec2D(+1, 0),
'L' : Vec2D( 0, -1),
'R' : Vec2D( 0, +1)
}
if __name__ == '__main__':
sys.exit(main(sys.argv)) | [
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] | 1.759582 | 287 |
source_suffix = ".txt"
master_doc = "index"
extensions = ["sphinxcontrib.multilatex"]
| [
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#!/usr/bin/env python
import csv, json, operator, os, sys
import xml.etree.ElementTree as ET
composerJsonPath = 'composer.json'
languageXmlPath = 'language.xml'
if not os.path.exists(composerJsonPath):
# File does not exists
print "File " + composerJsonPath + " not found"
sys.exit(1)
if not os.path.exists(languageXmlPath):
# File does not exists
print "File " + languageXmlPath + " not found"
sys.exit(1)
file = open(composerJsonPath, "r")
composerJsonContent = file.read()
file.close()
composerTypeData = json.loads(composerJsonContent)['type']
if composerTypeData != "magento2-language":
# It's not a language package
print "This is not a language package, type: " + composerTypeData
sys.exit(1)
xml = ET.parse(languageXmlPath)
languageCode = xml.getroot().find('code').text
languageCsvPath = languageCode + '.csv'
if not os.path.exists(languageCsvPath):
# File does not exists
print "File " + languageCsvPath + " not found"
sys.exit(1)
cleanData = []
with open(languageCsvPath) as f:
rawData = csv.reader(f, delimiter=',')
for rawRow in rawData:
if len(rawRow) == 0:
# Empty line
continue;
if rawRow[0][:1] == "#":
# Commented line
continue;
if len(rawRow) < 4:
# Wtf is this
print "Incorrect line: \"" + ','.join(rawRow) + "\""
print "At least 4 columns are expected"
sys.exit(1)
if (rawRow[2] not in ["module", "theme"]):
# 3rd column incorrect
print "Incorrect line: \"" + ','.join(rawRow) + "\""
print "3rd column has to be \"module\" or \"theme\", current value: " + rawRow[2]
sys.exit(1)
cleanData.append(rawRow)
f.close()
cleanData = sorted(cleanData, key=operator.itemgetter(2,3,0,1))
with open(languageCsvPath, 'wb') as f:
currentPackage = ""
writer = csv.writer(f, lineterminator='\n')
for row in cleanData:
rowPackage = row[2].capitalize() + " " + row[3]
if rowPackage != currentPackage:
if currentPackage != "":
writer.writerow([])
currentPackage = rowPackage
writer.writerow(["## " + currentPackage + " ##"])
writer.writerow(row)
f.close()
| [
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8,
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13,
16002,
322,
7,
808,
8,
198,
220,
220,
220,
277,
13,
19836,
3419,
198
] | 2.333333 | 978 |
"""This module contains a representation of an action in the game Dvonn."""
from mopy.action import Action
from enum import Enum
| [
37811,
1212,
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] | 3.487179 | 39 |
# Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
#TOFILL
if __name__ == '__main__':
param = [
(23,16,16,),
(56,95,6,),
(30,63,1,),
(51,89,46,),
(21,99,38,),
(69,63,50,),
(12,69,73,),
(44,52,9,),
(99,65,10,),
(46,58,37,)
]
n_success = 0
for i, parameters_set in enumerate(param):
if f_filled(*parameters_set) == f_gold(*parameters_set):
n_success+=1
print("#Results: %i, %i" % (n_success, len(param))) | [
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62,
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17143,
7307,
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198,
220,
220,
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220,
220,
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220,
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220,
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220,
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11,
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1,
4064,
357,
77,
62,
13138,
11,
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7,
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22305
] | 2.114094 | 298 |
from operator import mul
from functools import reduce
print(reduce(mul, reduce(overlap, [prime_factors(x) for x in range(1, 21)])))
| [
6738,
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22305,
198
] | 2.955556 | 45 |
#!/usr/bin/env python
# ./mkimage.sh --tag m1 m1.py
from __future__ import absolute_import, division, print_function
import os
import shutil
import subprocess
import sys
files=[
'/usr/bin/bash',
'/usr/bin/ls',
]
os.chdir(sys.argv[1])
os.umask(0o22)
for d in (
'etc',
'tmp',
'var/tmp',
'usr/bin',
'usr/lib',
'usr/lib64',
'usr/sbin',
):
os.makedirs(d, mode=0o777)
for s in ('bin', 'lib', 'lib64', 'sbin'):
os.symlink('usr/' + s, s)
libs=set()
for p in files:
_copy(p)
try:
for l in subprocess.check_output(['ldd', p]).split():
if l.startswith('/'):
libs.add(l)
except Exception as e:
print(e)
for l in libs:
_copy(l)
| [
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7,
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220,
220,
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355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
68,
8,
198,
198,
1640,
300,
287,
9195,
82,
25,
198,
220,
220,
220,
4808,
30073,
7,
75,
8,
198
] | 1.975477 | 367 |
import pandas as pd
df_wine = pd.read_csv('../data/wine.data', header=None)
df_wine.columns = [
'Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash',
'Magnesium', 'TOtal phenols', 'Flavanoids', 'Nonflavanoid phenols',
'Proanthocyanins', 'Color intensity', 'Hue',
'OD280/OD315 of diluted wines', 'Proline'
]
df_wine = df_wine[df_wine['Class label'] != 1]
y = df_wine['Class label'].values
X = df_wine[['Alcohol', 'Hue']].values
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
le = LabelEncoder()
y = le.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.40, random_state=1
)
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(criterion="entropy", max_depth=None, random_state=1)
bag = BaggingClassifier(
base_estimator=tree, n_estimators=500, max_samples=1.0, max_features=1.0,
bootstrap=True, bootstrap_features=False, n_jobs=1, random_state=1
)
from sklearn.metrics import accuracy_score
tree = tree.fit(X_train, y_train)
y_train_pred = tree.predict(X_train)
y_test_pred = tree.predict(X_test)
tree_train = accuracy_score(y_train, y_train_pred)
tree_test = accuracy_score(y_test, y_test_pred)
print('Decision tree train/test accuracies %.3f/%.3f' % (tree_train, tree_test))
bag = bag.fit(X_train, y_train)
y_train_pred = bag.predict(X_train)
y_test_pred = bag.predict(X_test)
bag_train = accuracy_score(y_train, y_train_pred)
bag_test = accuracy_score(y_test, y_test_pred)
print('Bagging train/test accuracies %.3f/%.3f' % (bag_train, bag_test))
import numpy as np
x_min = X_train[:, 0].min() - 1
x_max = X_train[:, 0].max() + 1
y_min = X_train[:, 1].min() - 1
y_max = X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
import matplotlib.pyplot as plt
f, axarr = plt.subplots(
nrows=1, ncols=2, sharex='col', sharey='row', figsize=(8,3)
)
for idx, clf, tt in zip([0, 1], [tree, bag], ['Decision Tree', 'Bagging']):
clf.fit(X_train, y_train)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[idx].contourf(xx, yy, Z, alpha=0.3)
axarr[idx].scatter(
X_train[y_train==0, 0], X_train[y_train==0, 1], c='blue', marker='^'
)
axarr[idx].scatter(
X_train[y_train==1, 0], X_train[y_train==1, 1], c='red', marker='o'
)
axarr[idx].set_title(tt)
axarr[0].set_ylabel('Alcohol', fontsize=12)
plt.text(
10.2, -0.7, s='Hue', ha='center', va='center', fontsize=12
)
plt.show() | [
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] | 2.324132 | 1,123 |
from django.shortcuts import render
# Create your views here.
from rest_framework.generics import CreateAPIView
from scraping.serializers import EmployeeSerializer
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 18 11:15:09 2019
@author: panyongyi
"""
#!/usr/bin/env python3
# Suggestion: Do not import the ev3dev.ev3 module in this file
import json
import paho.mqtt.client as mqtt
import uuid
import time
from planet import Planet, Direction
class Communication:
"""
Class to hold the MQTT client
Feel free to add functions, change the constructor and the example send_message() to satisfy your requirements and thereby solve the task according to the specifications
"""
def __init__(self, mqtt_client):
""" Initializes communication module, connect to server, subscribe, etc. """
# THESE TWO VARIABLES MUST NOT BE CHANGED
self.client = mqtt_client
self.client.on_message = self.on_message
# ADD YOUR VARIABLES HERE
# Basic configuration of MQTT
# Wichtig?
self.client.on_message = self.on_message_excepthandler
self.client.username_pw_set('118', password='LQR2AabmwY') # Your group credentials
self.client.connect('mothership.inf.tu-dresden.de', port=8883)
self.client.subscribe('explorer/118', qos=1) # Subscribe to topic explorer/xxx
# self.send_ready()
# Start listening to incoming messages
self.client.loop_start()
#self.timer()
self.planet = Planet()
#Parameter:
self.data = None
self.topic = "explorer/118"
self.planet_Chan = None
self.aktX = None
self.aktY = None
self.direc = None
# this is a helper method that catches errors and prints them
# it is necessary because on_message is called by paho-mqtt in a different thread and exceptions
# are not handled in that thread
#
# you don't need to change this method at all
# THIS FUNCTIONS SIGNATURE MUST NOT BE CHANGED
def on_message(self, client, data, message):
""" Handles the callback if any message arrived """
print('Got message with topic "{}":'.format(message.topic))
data = json.loads(message.payload.decode('utf-8'))
print(json.dumps(data, indent=2))
print('\n')
self.data = data
self.typ_Entsch()
#self.timer()
#Timer: jede 2 Sekunden warten:
client = mqtt.Client(client_id=str(uuid.uuid4()), # client_id has to be unique among ALL users
clean_session=False,
protocol=mqtt.MQTTv31)
com = Communication(client)
com.send_ready()
com.timer()
#com.send_test()
#com.timer()
com.pruefDaten()
com.timer()
com.pruefDaten2()
com.timer()
com.pruefDaten3()
com.timer()
com.pruefDaten4()
com.timer()
com.pruefDaten5()
com.timer()
node = {(17,38):[(Direction.NORTH, -2), (Direction.WEST, -2), (Direction.EAST, -1)]}
com.pathSelect(node)
com.timer()
'''
t0 = time.time()
while (time.time()-t0) < 2:
pass
print("neue Message kommt!")
com.pruefDaten()
while (time.time()-t0) < 2:
pass
print("neue Message kommt!")
'''
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# Generated by Django 3.2.7 on 2021-09-09 18:23
from django.db import migrations, models
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] | 2.84375 | 32 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
if __name__ == "__main__":
unittest.main()
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import copy
import json
import os
from pathlib import Path
import pystache
from PyQt5 import QtCore, QtGui, QtWidgets
from gear_builder_gui.config_dialog import config_dialog
from gear_builder_gui.input_dialog import input_dialog
class Manifest:
"""
A class to manage the manifest of a gear
TODO: Include the flywheel gear toolkit manifest class
"""
def __init__(self, main_window):
"""
Initialize manifest.
Args:
main_window (GearBuilderGUI): The instantiated main window.
"""
self.main_window = main_window
self.ui = main_window.ui
# Initialize "input" Section
self.ui.cmbo_inputs.currentIndexChanged.connect(self.update_tooltip)
self.ui.btn_input_add.clicked.connect(self.add_input)
self.ui.btn_input_edit.clicked.connect(self.edit_input)
self.ui.btn_input_delete.clicked.connect(self.delete_input)
# Initialize "config" Section
self.ui.cmbo_config.currentIndexChanged.connect(self.update_tooltip)
self.ui.btn_config_add.clicked.connect(self.add_config)
self.ui.btn_config_edit.clicked.connect(self.edit_config)
self.ui.btn_config_delete.clicked.connect(self.delete_config)
# Disable edit/delete buttons on default:
self.ui.btn_input_edit.setEnabled(False)
self.ui.btn_input_delete.setEnabled(False)
self.ui.btn_config_edit.setEnabled(False)
self.ui.btn_config_delete.setEnabled(False)
# Save/load functionality
self.ui.btn_load_manifest.clicked.connect(self.load_manifest_from_file)
self.ui.btn_save_manifest.clicked.connect(self.save_manifest)
# connect to docker "maintainer" and validators
self.ui.txt_maintainer.textChanged.connect(self.update_maintainers)
self.init_validators()
# initialize a manifest object
self.manifest = main_window.gear_def["manifest"]
def update_maintainers(self):
"""
Coordinate the maintainer text across Dockerfile and manifest.
"""
if self.ui.txt_maintainer.text() is not self.ui.txt_maintainer_2.text():
self.ui.txt_maintainer_2.setText(self.ui.txt_maintainer.text())
# Add functionality to the input add/edit/deleted buttons
def add_input(self):
"""
Add input to the input combo box through input dialog
"""
dialog = input_dialog()
name, data = dialog.get_data()
if name is not None:
self.ui.cmbo_inputs.addItem(name, userData=data)
self.ui.btn_input_edit.setEnabled(True)
self.ui.btn_input_delete.setEnabled(True)
def edit_input(self):
"""
Edit input through input dialog
"""
obj = self.ui.cmbo_inputs
name = obj.currentText()
data = obj.currentData()
dialog = input_dialog()
name_upd, data = dialog.get_data(cbo_val=(name, data))
if name_upd is not None:
i = obj.findText(name)
obj.setItemText(i, name_upd)
obj.setItemData(i, data)
def delete_input(self):
"""
Delete selected input object from the input combo
"""
i = self.ui.cmbo_inputs.currentIndex()
self.ui.cmbo_inputs.removeItem(i)
if self.ui.cmbo_inputs.count() == 0:
self.ui.btn_input_edit.setEnabled(False)
self.ui.btn_input_delete.setEnabled(False)
def update_tooltip(self):
"""
Update tooltip of config/input combo box item.
"""
sender = self.main_window.sender()
if "inputs" in sender.objectName():
cbo_obj = self.ui.cmbo_inputs
elif "config" in sender.objectName():
cbo_obj = self.ui.cmbo_config
else:
cbo_obj = None
if cbo_obj:
cbo_name = cbo_obj.currentText()
cbo_data = cbo_obj.currentData()
tool_tip_text = ""
for k, v in cbo_data.items():
tool_tip_text += k + ": " + str(v) + "\n"
cbo_obj.setToolTip(tool_tip_text)
def add_config(self):
"""
Add a config object to the config combo box through the config dialog.
"""
dialog = config_dialog()
name, data = dialog.get_data()
if name is not None:
self.ui.cmbo_config.addItem(name, userData=data)
self.ui.btn_config_edit.setEnabled(True)
self.ui.btn_config_delete.setEnabled(True)
def edit_config(self):
"""
Edit selected config object through the config dialog.
"""
obj = self.ui.cmbo_config
name = obj.currentText()
data = obj.currentData()
dialog = config_dialog()
name_upd, data = dialog.get_data(cbo_val=(name, data))
if name_upd is not None:
i = obj.findText(name)
obj.setItemText(i, name_upd)
obj.setItemData(i, data)
def delete_config(self):
"""
Delete selected config object from the config combo
"""
i = self.ui.cmbo_config.currentIndex()
self.ui.cmbo_config.removeItem(i)
if self.ui.cmbo_config.count() == 0:
self.ui.btn_config_edit.setEnabled(False)
self.ui.btn_config_delete.setEnabled(False)
def _update_manifest_from_form(self):
"""
Update the manifest dictionary from the contents of the manifest tab.
This will perserve any items in the self.manifest dictionary not referenced by
the form.
TODO: I want to implement a _update_form_from_manifest function.
"""
manifest = {}
# Required keys all manifests have
keys = [
"name",
"label",
"description",
"author",
"maintainer",
"license",
"url",
"source",
"cite",
"version",
]
for key in keys:
text_obj = eval("self.ui.txt_" + key)
text_type = type(eval("self.ui.txt_" + key))
if text_type == QtWidgets.QPlainTextEdit:
text_value = text_obj.toPlainText()
elif text_type == QtWidgets.QComboBox:
text_value = text_obj.currentText()
else:
text_value = text_obj.text()
manifest[key] = text_value
# Build Custom section
custom = {}
custom["docker-image"] = (
"flywheel/" + manifest["name"] + ":" + manifest["version"]
)
gear_builder = {}
# gear category on radio button
if self.ui.rdo_analysis.isChecked():
gear_builder["category"] = "analysis"
else:
gear_builder["category"] = "converter"
gear_builder["image"] = custom["docker-image"]
custom["gear-builder"] = gear_builder
# if "suite"
if self.ui.chk_flywheel.isChecked():
flywheel = {}
flywheel["suite"] = self.ui.txt_suite.text()
custom["flywheel"] = flywheel
manifest["custom"] = custom
# Build inputs section
# Each input item consists of the text (key) of a combo box and
# specifically constructed data (a dictionary).
inputs = {}
cbo_obj = self.ui.cmbo_inputs
for i in range(cbo_obj.count()):
inputs[cbo_obj.itemText(i)] = cbo_obj.itemData(i)
manifest["inputs"] = inputs
# Build config section
# Each config item consists of the text (key) of a combo box and
# specifically constructed data (a dictionary).
config = {}
cbo_obj = self.ui.cmbo_config
for i in range(cbo_obj.count()):
config[cbo_obj.itemText(i)] = cbo_obj.itemData(i)
manifest["config"] = config
# The command
manifest["command"] = "/flywheel/v0/run.py"
# Using an "update" here instead of a total replace preserves items that may
# have been loaded.
self.manifest.update(manifest)
def load_manifest_from_file(self):
"""
Load manifest from file.
"""
manifest_file = QtWidgets.QFileDialog.getOpenFileName(
self.main_window, "Select manifest.json to load.", filter="manifest.json"
)
# TODO: Should I warn about replacement of all manifest values?
if len(manifest_file[0]) > 0:
with open(manifest_file[0], "r") as manifest_raw:
manifest = json.load(manifest_raw)
self.manifest.clear()
self.manifest.update(manifest)
self._update_form_from_manifest()
def _update_form_from_manifest(self):
"""
Update form values from stored manifest.
"""
# NOTE: This would be a good place to warn if the loaded manifest was invalid
# Required manifest keys:
keys = [
"name",
"label",
"description",
"author",
"maintainer",
"license",
"url",
"source",
"cite",
"version",
]
try:
for key in keys:
text_obj = eval("self.ui.txt_" + key)
text_type = type(eval("self.ui.txt_" + key))
if self.manifest.get(key):
text_value = self.manifest[key]
else:
text_value = ""
if text_type == QtWidgets.QPlainTextEdit:
text_value = text_obj.setPlainText(text_value)
elif text_type == QtWidgets.QComboBox:
index = text_obj.findText(text_value)
if index:
text_obj.setCurrentIndex(index)
else:
text_obj.setText(text_value)
# load custom fields
custom = self.manifest["custom"]
self.ui.rdo_analysis.setChecked(
custom["gear-builder"]["category"] == "analysis"
)
if custom.get("flywheel"):
self.ui.chk_flywheel.setChecked(True)
self.ui.txt_suite.setText(custom["flywheel"]["suite"])
# load inputs section
inputs = self.manifest["inputs"]
cbo_obj = self.ui.cmbo_inputs
cbo_obj.clear()
for name, data in inputs.items():
cbo_obj.addItem(name, userData=data)
if cbo_obj.count() > 0:
self.ui.btn_input_edit.setEnabled(True)
self.ui.btn_input_delete.setEnabled(True)
# load configs section
config = self.manifest["config"]
cbo_obj = self.ui.cmbo_config
cbo_obj.clear()
for name, data in config.items():
cbo_obj.addItem(name, userData=data)
if cbo_obj.count() > 0:
self.ui.btn_config_edit.setEnabled(True)
self.ui.btn_config_delete.setEnabled(True)
except Exception as e:
print(e)
def save_manifest(self):
"""
Select destination to save manifest.json file.
"""
directory = str(
QtWidgets.QFileDialog.getExistingDirectory(
self.main_window, "Select Folder to save manifest.json."
)
)
if len(directory) > 0:
self.save(directory)
def save(self, directory):
"""
Save self.manifest dictionary to manifest.json file in the indicated directory.
Args:
directory (str): Path to directory.
"""
directory = Path(directory)
self._update_manifest_from_form()
json.dump(self.manifest, open(directory / "manifest.json", "w"), indent=2)
def save_draft_readme(self, directory, readme_template=None):
"""
Saves draft of README.md to indicated directory.
Args:
directory (str): Path to directory.
readme_template (str, optional): Path to the mustache template to use.
Defaults to None.
"""
# make a copy to modify for formatting README.md
local_manifest = copy.deepcopy(self.manifest)
directory = Path(directory)
if not readme_template:
source_dir = self.main_window.root_dir / "default_templates"
readme_template = source_dir / "README.md.mu"
renderer = pystache.Renderer()
# Check for non-zero number of inputs
if len(local_manifest["inputs"].keys()) > 0:
local_manifest["has_inputs"] = True
local_manifest["inputs_list"] = []
for inp, val in local_manifest["inputs"].items():
val["name"] = inp
local_manifest["inputs_list"].append(val)
# Check for a non-zero number of configs
if len(local_manifest["config"].keys()) > 0:
local_manifest["has_configs"] = True
local_manifest["config_list"] = []
for conf, val in local_manifest["config"].items():
val["name"] = conf
if "default" in val.keys():
val["default_val"] = {"val": val["default"]}
local_manifest["config_list"].append(val)
template_output = renderer.render_path(
readme_template, {"manifest": local_manifest}
)
with open(directory / "README.md", "w") as fp:
fp.write(template_output)
def _check_description_text_length(self):
"""
Constrains the length of the QPlainTextEdit txt_description member.
The maxLength is initialized below from the manifest schema. The QPlainTextEdit
object does not have an automatic length constraint.
"""
obj = self.ui.txt_description
if len(obj.toPlainText()) > obj.maxLength:
obj.textCursor().deletePreviousChar()
def init_validators(self):
"""
Initializes the field validators to the manifest schema.
TODO: Use a local copy of the manifest schema instead of downloading.
"""
# spec_url = (
# "https://gitlab.com/flywheel-io/public/"
# "gears/-/raw/master/spec/manifest.schema.json"
# )
# request = requests.get(spec_url)
# url = urllib.request.urlopen(spec_url)
with open(
self.main_window.root_dir
/ "gear_builder_gui/resources/manifest.schema.json",
"r",
) as fp:
gear_spec = json.load(fp)
keys = [
"name",
"label",
"description",
"author",
"maintainer",
"license",
"url",
"source",
"cite",
"version",
]
for key in keys:
gear_spec_item = gear_spec["properties"][key]
if key == "license":
text_obj = self.ui.txt_license
text_obj.addItems(gear_spec["properties"]["license"]["enum"])
text_obj.setCurrentIndex(text_obj.__len__() - 1)
else:
text_obj = eval("self.ui.txt_" + key)
text_type = type(eval("self.ui.txt_" + key))
if "maxLength" in gear_spec_item.keys():
text_obj.maxLength = gear_spec_item["maxLength"]
if "pattern" in gear_spec_item.keys():
rx = QtCore.QRegExp(gear_spec_item["pattern"])
val = QtGui.QRegExpValidator(rx, self.main_window)
text_obj.setValidator(val)
elif key == "version":
rx = QtCore.QRegExp(
"^((([0-9]+)\\.([0-9]+)\\.([0-9]+)"
"(?:-_([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?)"
"(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?)$"
)
val = QtGui.QRegExpValidator(rx, self.main_window)
text_obj.setValidator(val)
if text_type == QtWidgets.QPlainTextEdit:
text_obj.textChanged.connect(self._check_description_text_length)
if "description" in gear_spec_item.keys():
text_obj.whatsThis = gear_spec_item["description"]
text_obj.setToolTip(gear_spec_item["description"])
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26801,
13,
14421,
8206,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
7890,
796,
269,
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62,
26801,
13,
14421,
6601,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2891,
62,
22504,
62,
5239,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
479,
11,
410,
287,
269,
2127,
62,
7890,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2891,
62,
22504,
62,
5239,
15853,
479,
1343,
366,
25,
366,
1343,
965,
7,
85,
8,
1343,
37082,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
13,
2617,
25391,
28434,
7,
25981,
62,
22504,
62,
5239,
8,
628,
220,
220,
220,
825,
751,
62,
11250,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3060,
257,
4566,
2134,
284,
262,
4566,
14831,
3091,
832,
262,
4566,
17310,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
17310,
796,
4566,
62,
38969,
519,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1438,
11,
1366,
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17310,
13,
1136,
62,
7890,
3419,
198,
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220,
220,
220,
220,
220,
220,
611,
1438,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
66,
2022,
78,
62,
11250,
13,
2860,
7449,
7,
3672,
11,
2836,
6601,
28,
7890,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
19312,
13,
2617,
20491,
7,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
33678,
13,
2617,
20491,
7,
17821,
8,
628,
220,
220,
220,
825,
4370,
62,
11250,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5312,
6163,
4566,
2134,
832,
262,
4566,
17310,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
796,
2116,
13,
9019,
13,
66,
2022,
78,
62,
11250,
198,
220,
220,
220,
220,
220,
220,
220,
1438,
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26181,
13,
14421,
8206,
3419,
198,
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220,
220,
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1366,
796,
26181,
13,
14421,
6601,
3419,
198,
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220,
220,
220,
220,
220,
220,
17310,
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4566,
62,
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220,
1438,
62,
929,
67,
11,
1366,
796,
17310,
13,
1136,
62,
7890,
7,
66,
2127,
62,
2100,
16193,
3672,
11,
1366,
4008,
198,
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220,
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611,
1438,
62,
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67,
318,
407,
6045,
25,
198,
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220,
220,
220,
220,
220,
220,
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1312,
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26181,
13,
19796,
8206,
7,
3672,
8,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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26181,
13,
2617,
7449,
8206,
7,
72,
11,
1438,
62,
929,
67,
8,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
2617,
7449,
6601,
7,
72,
11,
1366,
8,
628,
220,
220,
220,
825,
12233,
62,
11250,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23520,
6163,
4566,
2134,
422,
262,
4566,
14831,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1312,
796,
2116,
13,
9019,
13,
66,
2022,
78,
62,
11250,
13,
14421,
15732,
3419,
198,
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220,
220,
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220,
220,
2116,
13,
9019,
13,
66,
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78,
62,
11250,
13,
28956,
7449,
7,
72,
8,
198,
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220,
220,
220,
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220,
220,
611,
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13,
9019,
13,
66,
2022,
78,
62,
11250,
13,
9127,
3419,
6624,
657,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
19312,
13,
2617,
20491,
7,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
33678,
13,
2617,
20491,
7,
25101,
8,
628,
220,
220,
220,
825,
4808,
19119,
62,
805,
8409,
62,
6738,
62,
687,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
10133,
262,
10561,
22155,
422,
262,
10154,
286,
262,
10561,
7400,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
481,
2774,
3760,
597,
3709,
287,
262,
2116,
13,
805,
8409,
22155,
407,
20717,
416,
198,
220,
220,
220,
220,
220,
220,
220,
262,
1296,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16926,
46,
25,
314,
765,
284,
3494,
257,
4808,
19119,
62,
687,
62,
6738,
62,
805,
8409,
2163,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
10561,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
20906,
8251,
477,
42190,
423,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3672,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
18242,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11213,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9800,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
76,
2913,
10613,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
43085,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
6371,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10459,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
66,
578,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9641,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
796,
5418,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
4906,
796,
2099,
7,
18206,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2420,
62,
4906,
6624,
33734,
54,
312,
11407,
13,
48,
3646,
391,
8206,
18378,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
8367,
796,
2420,
62,
26801,
13,
1462,
3646,
391,
8206,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2420,
62,
4906,
6624,
33734,
54,
312,
11407,
13,
48,
5377,
2127,
14253,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
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796,
2420,
62,
26801,
13,
14421,
8206,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
8367,
796,
2420,
62,
26801,
13,
5239,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10561,
58,
2539,
60,
796,
2420,
62,
8367,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
10934,
8562,
2665,
198,
220,
220,
220,
220,
220,
220,
220,
2183,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
2183,
14692,
45986,
12,
9060,
8973,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
12254,
22001,
30487,
1343,
10561,
14692,
3672,
8973,
1343,
366,
11097,
1343,
10561,
14692,
9641,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
38272,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7733,
6536,
319,
5243,
4936,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
9019,
13,
4372,
78,
62,
20930,
13,
271,
9787,
276,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
38272,
14692,
22872,
8973,
796,
366,
20930,
1,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
38272,
14692,
22872,
8973,
796,
366,
1102,
332,
353,
1,
628,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
38272,
14692,
9060,
8973,
796,
2183,
14692,
45986,
12,
9060,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
2183,
14692,
31763,
12,
38272,
8973,
796,
7733,
62,
38272,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
366,
2385,
578,
1,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
9019,
13,
354,
74,
62,
12254,
22001,
13,
271,
9787,
276,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6129,
22001,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6129,
22001,
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2385,
578,
8973,
796,
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13,
9019,
13,
14116,
62,
2385,
578,
13,
5239,
3419,
198,
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220,
220,
220,
220,
220,
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220,
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22001,
628,
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220,
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220,
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220,
10561,
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23144,
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628,
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220,
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1303,
10934,
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220,
220,
220,
1303,
5501,
5128,
2378,
10874,
286,
262,
2420,
357,
2539,
8,
286,
257,
14831,
3091,
290,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5734,
12006,
1366,
357,
64,
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737,
198,
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220,
220,
220,
220,
220,
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17311,
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198,
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269,
2127,
62,
26801,
796,
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13,
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13,
66,
2022,
78,
62,
15414,
82,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
66,
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62,
26801,
13,
9127,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17311,
58,
66,
2127,
62,
26801,
13,
9186,
8206,
7,
72,
15437,
796,
269,
2127,
62,
26801,
13,
9186,
6601,
7,
72,
8,
628,
220,
220,
220,
220,
220,
220,
220,
10561,
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15414,
82,
8973,
796,
17311,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
10934,
4566,
2665,
198,
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220,
220,
220,
220,
220,
220,
1303,
5501,
4566,
2378,
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286,
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2420,
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2539,
8,
286,
257,
14831,
3091,
290,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5734,
12006,
1366,
357,
64,
22155,
737,
198,
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220,
220,
220,
220,
220,
220,
4566,
796,
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198,
220,
220,
220,
220,
220,
220,
220,
269,
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62,
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796,
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13,
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13,
66,
2022,
78,
62,
11250,
198,
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220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
66,
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62,
26801,
13,
9127,
3419,
2599,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
58,
66,
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62,
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13,
9186,
8206,
7,
72,
15437,
796,
269,
2127,
62,
26801,
13,
9186,
6601,
7,
72,
8,
628,
220,
220,
220,
220,
220,
220,
220,
10561,
14692,
11250,
8973,
796,
4566,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
383,
3141,
198,
220,
220,
220,
220,
220,
220,
220,
10561,
14692,
21812,
8973,
796,
12813,
12254,
22001,
14,
85,
15,
14,
5143,
13,
9078,
1,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
8554,
281,
366,
19119,
1,
994,
2427,
286,
257,
2472,
6330,
43759,
3709,
326,
743,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
423,
587,
9639,
13,
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2116,
13,
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13,
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7,
805,
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8,
628,
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220,
825,
3440,
62,
805,
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62,
6738,
62,
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7,
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2599,
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220,
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37227,
198,
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422,
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13,
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198,
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220,
10561,
62,
7753,
796,
33734,
54,
312,
11407,
13,
48,
8979,
44204,
13,
1136,
11505,
8979,
5376,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
12417,
62,
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11,
366,
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13,
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284,
3440,
33283,
8106,
2625,
805,
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13,
17752,
1,
198,
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220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
16926,
46,
25,
10358,
314,
9828,
546,
9014,
286,
477,
10561,
3815,
30,
198,
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220,
220,
220,
220,
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18896,
7,
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62,
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7,
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62,
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58,
15,
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366,
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4943,
355,
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62,
1831,
25,
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220,
220,
220,
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220,
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220,
220,
220,
220,
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10561,
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33918,
13,
2220,
7,
805,
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62,
1831,
8,
628,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
805,
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13,
20063,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
805,
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13,
19119,
7,
805,
8409,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
19119,
62,
687,
62,
6738,
62,
805,
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3419,
628,
220,
220,
220,
825,
4808,
19119,
62,
687,
62,
6738,
62,
805,
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7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
10133,
1296,
3815,
422,
8574,
10561,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
24550,
25,
770,
561,
307,
257,
922,
1295,
284,
9828,
611,
262,
9639,
10561,
373,
12515,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
20906,
10561,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3672,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
18242,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11213,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9800,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
76,
2913,
10613,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
43085,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
6371,
1600,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10459,
1600,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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366,
66,
578,
1600,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9641,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
796,
5418,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
4906,
796,
2099,
7,
18206,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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13,
805,
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13,
1136,
7,
2539,
2599,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
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60,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2073,
25,
198,
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220,
220,
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6624,
33734,
54,
312,
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13,
48,
3646,
391,
8206,
18378,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
2420,
62,
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796,
2420,
62,
26801,
13,
2617,
3646,
391,
8206,
7,
5239,
62,
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8,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1288,
361,
2420,
62,
4906,
6624,
33734,
54,
312,
11407,
13,
48,
5377,
2127,
14253,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
796,
2420,
62,
26801,
13,
19796,
8206,
7,
5239,
62,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
6376,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
13,
2617,
11297,
15732,
7,
9630,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
2420,
62,
26801,
13,
2617,
8206,
7,
5239,
62,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3440,
2183,
7032,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2183,
796,
2116,
13,
805,
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14692,
23144,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
4372,
78,
62,
20930,
13,
2617,
9787,
276,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2183,
14692,
31763,
12,
38272,
1,
7131,
1,
22872,
8973,
6624,
366,
20930,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2183,
13,
1136,
7203,
12254,
22001,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
354,
74,
62,
12254,
22001,
13,
2617,
9787,
276,
7,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
14116,
62,
2385,
578,
13,
2617,
8206,
7,
23144,
14692,
12254,
22001,
1,
7131,
1,
2385,
578,
8973,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3440,
17311,
2665,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17311,
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13,
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14692,
15414,
82,
8973,
628,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
796,
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13,
9019,
13,
66,
2022,
78,
62,
15414,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
13,
20063,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
11,
1366,
287,
17311,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
13,
2860,
7449,
7,
3672,
11,
2836,
6601,
28,
7890,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
269,
2127,
62,
26801,
13,
9127,
3419,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
15414,
62,
19312,
13,
2617,
20491,
7,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
15414,
62,
33678,
13,
2617,
20491,
7,
17821,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3440,
4566,
82,
2665,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
796,
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13,
805,
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14692,
11250,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
796,
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13,
9019,
13,
66,
2022,
78,
62,
11250,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
13,
20063,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
11,
1366,
287,
4566,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
2127,
62,
26801,
13,
2860,
7449,
7,
3672,
11,
2836,
6601,
28,
7890,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
269,
2127,
62,
26801,
13,
9127,
3419,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
19312,
13,
2617,
20491,
7,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9019,
13,
46118,
62,
11250,
62,
33678,
13,
2617,
20491,
7,
17821,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2845,
35528,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
68,
8,
628,
220,
220,
220,
825,
3613,
62,
805,
8409,
7,
944,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
9683,
10965,
284,
3613,
10561,
13,
17752,
2393,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
8619,
796,
965,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33734,
54,
312,
11407,
13,
48,
8979,
44204,
13,
1136,
3109,
9665,
43055,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2116,
13,
12417,
62,
17497,
11,
366,
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48107,
284,
3613,
10561,
13,
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526,
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220,
220,
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1267,
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220,
220,
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18896,
7,
34945,
8,
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657,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
21928,
7,
34945,
8,
628,
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220,
220,
825,
3613,
7,
944,
11,
8619,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
12793,
2116,
13,
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62,
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28,
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311,
3080,
4538,
286,
20832,
11682,
13,
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284,
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13,
628,
220,
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2896,
13185,
284,
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13,
628,
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220,
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220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
787,
257,
4866,
284,
13096,
329,
33313,
20832,
11682,
13,
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198,
220,
220,
220,
220,
220,
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220,
1957,
62,
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796,
4866,
13,
22089,
30073,
7,
944,
13,
805,
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8,
198,
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220,
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220,
8619,
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7,
34945,
8,
198,
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220,
220,
611,
407,
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1326,
62,
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25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
2723,
62,
15908,
796,
2116,
13,
12417,
62,
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13,
15763,
62,
15908,
1220,
366,
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62,
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17041,
1,
198,
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220,
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220,
1100,
1326,
62,
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62,
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1220,
366,
15675,
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13,
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13,
30300,
1,
198,
220,
220,
220,
220,
220,
220,
220,
9851,
11882,
796,
12972,
301,
4891,
13,
49,
437,
11882,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
329,
1729,
12,
22570,
1271,
286,
17311,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
12001,
62,
805,
8409,
14692,
15414,
82,
1,
4083,
13083,
28955,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
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14692,
10134,
62,
15414,
82,
8973,
796,
6407,
628,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
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14692,
15414,
82,
62,
4868,
8973,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
287,
79,
11,
1188,
287,
1957,
62,
805,
8409,
14692,
15414,
82,
1,
4083,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1188,
14692,
3672,
8973,
796,
287,
79,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
8409,
14692,
15414,
82,
62,
4868,
1,
4083,
33295,
7,
2100,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
329,
257,
1729,
12,
22570,
1271,
286,
4566,
82,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
12001,
62,
805,
8409,
14692,
11250,
1,
4083,
13083,
28955,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
8409,
14692,
10134,
62,
11250,
82,
8973,
796,
6407,
628,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
8409,
14692,
11250,
62,
4868,
8973,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1013,
11,
1188,
287,
1957,
62,
805,
8409,
14692,
11250,
1,
4083,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1188,
14692,
3672,
8973,
796,
1013,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
366,
12286,
1,
287,
1188,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1188,
14692,
12286,
62,
2100,
8973,
796,
19779,
2100,
1298,
1188,
14692,
12286,
8973,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
805,
8409,
14692,
11250,
62,
4868,
1,
4083,
33295,
7,
2100,
8,
628,
220,
220,
220,
220,
220,
220,
220,
11055,
62,
22915,
796,
9851,
11882,
13,
13287,
62,
6978,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1100,
1326,
62,
28243,
11,
19779,
805,
8409,
1298,
1957,
62,
805,
8409,
92,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
34945,
1220,
366,
15675,
11682,
13,
9132,
1600,
366,
86,
4943,
355,
277,
79,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
79,
13,
13564,
7,
28243,
62,
22915,
8,
628,
220,
220,
220,
825,
4808,
9122,
62,
11213,
62,
5239,
62,
13664,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1482,
2536,
1299,
262,
4129,
286,
262,
1195,
3646,
391,
8206,
18378,
256,
742,
62,
11213,
2888,
13,
628,
220,
220,
220,
220,
220,
220,
220,
383,
3509,
24539,
318,
23224,
2174,
422,
262,
10561,
32815,
13,
383,
1195,
3646,
391,
8206,
18378,
198,
220,
220,
220,
220,
220,
220,
220,
2134,
857,
407,
423,
281,
11353,
4129,
32315,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
796,
2116,
13,
9019,
13,
14116,
62,
11213,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
26801,
13,
1462,
3646,
391,
8206,
28955,
1875,
26181,
13,
9806,
24539,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
5239,
34,
21471,
22446,
33678,
21448,
12441,
3419,
628,
220,
220,
220,
825,
2315,
62,
12102,
2024,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
20768,
4340,
262,
2214,
4938,
2024,
284,
262,
10561,
32815,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16926,
46,
25,
5765,
257,
1957,
4866,
286,
262,
10561,
32815,
2427,
286,
22023,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1020,
62,
6371,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
220,
220,
220,
366,
5450,
1378,
18300,
23912,
13,
785,
14,
12254,
22001,
12,
952,
14,
11377,
30487,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
220,
220,
220,
366,
70,
4127,
16327,
14,
1831,
14,
9866,
14,
16684,
14,
805,
8409,
13,
15952,
2611,
13,
17752,
1,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2581,
796,
7007,
13,
1136,
7,
16684,
62,
6371,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
19016,
796,
2956,
297,
571,
13,
25927,
13,
6371,
9654,
7,
16684,
62,
6371,
8,
198,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
12417,
62,
17497,
13,
15763,
62,
15908,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1220,
366,
31763,
62,
38272,
62,
48317,
14,
37540,
14,
805,
8409,
13,
15952,
2611,
13,
17752,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
81,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
355,
277,
79,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
16684,
796,
33918,
13,
2220,
7,
46428,
8,
628,
220,
220,
220,
220,
220,
220,
220,
8251,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3672,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
18242,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11213,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9800,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
76,
2913,
10613,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
43085,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
6371,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10459,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
66,
578,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
9641,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7733,
62,
16684,
62,
9186,
796,
7733,
62,
16684,
14692,
48310,
1,
7131,
2539,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
6624,
366,
43085,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
796,
2116,
13,
9019,
13,
14116,
62,
43085,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
13,
2860,
23022,
7,
31763,
62,
16684,
14692,
48310,
1,
7131,
1,
43085,
1,
7131,
1,
44709,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
13,
2617,
11297,
15732,
7,
5239,
62,
26801,
13,
834,
11925,
834,
3419,
532,
352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
796,
5418,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
4906,
796,
2099,
7,
18206,
7203,
944,
13,
9019,
13,
14116,
62,
1,
1343,
1994,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
366,
9806,
24539,
1,
287,
7733,
62,
16684,
62,
9186,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
26801,
13,
9806,
24539,
796,
7733,
62,
16684,
62,
9186,
14692,
9806,
24539,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
366,
33279,
1,
287,
7733,
62,
16684,
62,
9186,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
87,
796,
33734,
14055,
13,
48,
8081,
16870,
7,
31763,
62,
16684,
62,
9186,
14692,
33279,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1188,
796,
33734,
8205,
72,
13,
48,
8081,
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] | 2.039502 | 8,025 |
"""Code adapted from Boost, which is:
(C) Copyright John Maddock 2006.
Use, modification and distribution are subject to the
Boost Software License, Version 1.0. (See accompanying file
LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
"""
from numba import njit, vectorize
import numpy as np
from . import settings
from .evalpoly import _devalpoly
P1 = np.array([
-0.000322780120964605683831,
-0.00772758345802133288487,
-0.0509990735146777432841,
-0.338165134459360935041,
0.0834305892146531832907
])
Q1 = np.array([
0.000370900071787748000569,
0.00858571925074406212772,
0.0875222600142252549554,
0.455004033050794024546,
1.0
])
P2 = np.array([
0.00180424538297014223957,
0.0195049001251218801359,
0.0888900368967884466578,
0.191003695796775433986,
0.178114665841120341155,
-0.098090592216281240205
])
Q2 = np.array([
0.337511472483094676155e-5,
0.0113385233577001411017,
0.12385097467900864233,
0.578052804889902404909,
1.42628004845511324508,
1.84759070983002217845,
1.0
])
P3 = np.array([
0.000235839115596880717416,
0.00323962406290842133584,
0.0175679436311802092299,
0.04394818964209516296,
0.0386540375035707201728,
-0.0243500476207698441272
])
Q3 = np.array([
0.00410369723978904575884,
0.0563921837420478160373,
0.325732924782444448493,
0.982403709157920235114,
1.53991494948552447182,
1.0
])
P4 = np.array([
0.113212406648847561139e-4,
0.000250269961544794627958,
0.00212825620914618649141,
0.00840807615555585383007,
0.0137384425896355332126,
0.00295276716530971662634
])
Q4 = np.array([
0.000479411269521714493907,
0.0105982906484876531489,
0.0958492726301061423444,
0.442597659481563127003,
1.04217814166938418171,
1.0
])
P5 = np.array([
-2.8175401114513378771,
-3.22729451764143718517,
-2.5518551727311523996,
-0.687717681153649930619,
-0.212652252872804219852,
0.0175389834052493308818,
0.00628057170626964891937
])
Q5 = np.array([
5.48409182238641741584,
13.5064170191802889145,
22.9367376522880577224,
15.930646027911794143,
11.0567237927800161565,
2.79257750980575282228,
1.0
])
@njit('float64(float64, bool_)', cache=settings.CACHE)
def _erf_erfc(x, invert):
"""Compute erf if invert is False and erfc if invert is True."""
if x < 0:
if not invert:
return -_erf_erfc(-x, False)
elif x < -0.5:
return 2.0 - _erf_erfc(-x, True);
else:
return 1.0 + _erf_erfc(-x, False)
if x < 0.5:
# We're going to calculate erf
if x < 1e-10:
# Single term of the Taylor series
res = 1.128379167095512573896159*x
else:
# - Maximum deviation found: 1.561e-17
# - Expected error term: 1.561e-17
# - Maximum relative change in control points: 1.155e-04
# - Max error found at double precision: 2.961182e-17
Y = np.float32(1.044948577880859375)
xx = x*x
res = x*(Y + _devalpoly(P1, xx)/_devalpoly(Q1, xx))
elif (invert and x < 28) or (not invert and x < 5.8):
# We'll be calculating erfc:
invert = not invert
if x < 1.5:
# Maximum deviation found: 3.702e-17
# Expected error term: 3.702e-17
# Maximum relative change in control points: 2.845e-04
# Max error found at double precision: 4.841816e-17
Y = np.float32(0.405935764312744140625)
res = Y + _devalpoly(P2, x - 0.5)/_devalpoly(Q2, x - 0.5)
res *= np.exp(-x*x)/x
elif x < 2.5:
# Maximum deviation found: 3.909e-18
# Expected error term: 3.909e-18
# Maximum relative change in control points: 9.886e-05
# Max error found at double precision: 6.599585e-18
Y = np.float32(0.50672817230224609375)
res = Y + _devalpoly(P3, x - 1.5)/_devalpoly(Q3, x - 1.5)
res *= np.exp(-x*x)/x
elif x < 4.5:
# Maximum deviation found: 1.512e-17
# Expected error term: 1.512e-17
# Maximum relative change in control points: 2.222e-04
# Max error found at double precision: 2.062515e-17
Y = np.float32(0.5405750274658203125)
res = Y + _devalpoly(P4, x - 3.5)/_devalpoly(Q4, x - 3.5)
res *= np.exp(-x*x)/x
else:
# Maximum deviation found: 2.860e-17
# Expected error term: 2.859e-17
# Maximum relative change in control points: 1.357e-05
# Max error found at double precision: 2.997958e-17
Y = np.float32(0.5579090118408203125)
res = Y + _devalpoly(P5, 1.0/x)/_devalpoly(Q5, 1.0/x)
res *= np.exp(-x*x)/x
else:
# Any value of x larger than 28 will underflow to zero
result = 0.0
invert = not invert
if invert:
res = 1.0 - res
return res
@njit('float64(float64)', cache=settings.CACHE)
@njit('float64(float64)', cache=settings.CACHE)
@vectorize(['float64(float64)'], nopython=True, cache=settings.CACHE)
def erf(x):
"""Error function.
Parameters
----------
x : array-like
Points on the real line
out : ndarray, optional
Output array for the values of `erf` at `x`
Returns
-------
ndarray
Values of `erf` at `x`
"""
return _erf(x)
@vectorize(['float64(float64)'], nopython=True, cache=settings.CACHE)
def erfc(x):
"""Complementary error function.
Parameters
----------
x : array-like
Points on the real line
out : ndarray, optional
Output array for the values of `erfc` at `x`
Returns
-------
ndarray
Values of `erf` at `x`
"""
return _erfc(x)
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# -*- coding: utf-8 -*-
"""A simulation and visualization demonstration.
This module runs a simple demonstration of the capabilities of the adsa
simulations and analysis.
"""
import logging
from pathlib import Path
import csv
from .solver import simulate_droplet_shape
from .analysis import calculate_volume
from .visualisation import plot_drop, plot_drop_3d
from matplotlib import pyplot as plt
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from condor_scheduler import *
from basic_scheduler import *
from helper import *
#scheduler = CondorScheduler()
scheduler = BasicScheduler()
app_flags = {}
trials = ['', '_1', '_2']
trials = ['']
#thds = [4, 8] #, 16, 20, 24, 28, 32]
thds = [8, 16, 32]
thds = [4, 8, 16, 20, 24, 28, 32]
thds = [16]
thds = [4, 8, 16, 24, 32]
num_logger = 4
benchmarks = ['TPCC']
benchmarks = ['YCSB', 'TPCC']
benchmarks = ['YCSB']
algorithms = ['S']
algorithms = ['B', 'P', 'S']
algorithms = ['P', 'B']
algorithms = ['B']
algorithms = ['P']
algorithms = ['P', 'B']
algorithms = ['S', 'P', 'B', 'NO']
types = ['D'] # data logging and command logging
types = ['C'] # data logging and command logging
types = ['D', 'C'] # data logging and command logging
configs = []
for bench in benchmarks:
for alg in algorithms:
if alg == 'NO':
configs += ['%s_%s' % (alg, bench)]
else:
for t in types:
if alg == 'B' and t == 'C': continue
configs += ['%s%s_%s' % (alg, t, bench)]
"""
app_flags = {}
##########################
### test
##########################
# logging
for config in configs:
thd = 16
num_logger = 4
executable = "./rundb_%s" % config
logger = num_logger if config[0] == 'P' else 1
app_flags['LOG_RECOVER'] = 0
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 10000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
app_flags['SYNTH_TABLE_SIZE'] = 1024
app_flags['NUM_WH'] = 1
output_dir = "results/test/%s/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
# recovery
for config in configs:
thd = 16
num_logger = 4
executable = "./rundb_%s" % config
logger = num_logger if config[0] == 'P' else 1
app_flags['LOG_RECOVER'] = 1
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 10000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
app_flags['SYNTH_TABLE_SIZE'] = 1024
app_flags['NUM_WH'] = 1
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000
output_dir = "results/test/%s_rec/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
##########################
##########################
#########################
"""
"""
app_flags = {}
##########################
# Logging Performance
##########################
for trial in trials:
for config in configs:
for thd in thds:
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
if 'TPCC' in config:
app_flags['NUM_WH'] = 16
else : # YCSB
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s%s" % (config, thd, logger, trial)
add_dbms_job(app_flags, executable, output_dir)
"""
"""
app_flags = {}
##########################
# RAMDisk Logging Performance
##########################
for trial in trials:
for config in configs:
for thd in thds:
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
if 'TPCC' in config:
app_flags['NUM_WH'] = 16
else : # YCSB
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 1
output_dir = "results/%s_ramdisk/thd%d_L%s%s" % (config, thd, logger, trial)
add_dbms_job(app_flags, executable, output_dir)
"""
"""
# Batch logging with different epoch length
for trial in trials:
config = 'BD_YCSB'
executable = "./rundb_%s" % config
thd = 4
#for epoch in [10, 20, 40, 80, 160]:
for epoch in [5]:
logger = num_logger
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 400000 if config[1] == 'D' else 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['EPOCH_PERIOD'] = epoch
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s_E%d%s" % (config, thd, logger, epoch, trial)
add_dbms_job(app_flags, executable, output_dir)
"""
"""
##########################
# Generate Log Files
##########################
app_flags = {}
for config in configs:
thd = 16
if 'NO' in config: continue
executable = "./rundb_%s" % config
logger = 1 if config[0] == 'S' else num_logger
if 'TPCC' in config:
app_flags['NUM_WH'] = 16
else : # YCSB
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
app_flags['LOG_RECOVER'] = 0
app_flags['LOG_BUFFER_SIZE'] = 1048576 * 50
app_flags['LOG_CHUNK_SIZE'] = 1048576 * 10
output_dir = "results/%s_gen/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
# recover performance
app_flags = {}
for trial in trials:
app_flags = {}
for bench in benchmarks : #['YCSB', 'TPCC']:
for alg in algorithms:
if alg == 'NO': continue
logger = 1 if alg == 'S' else num_logger
for t in types:
if alg == 'B' and t == 'C': continue
config = '%s%s_%s' % (alg, t, bench)
executable = "./rundb_%s" % config
for thd in thds:
if bench == 'TPCC':
app_flags['NUM_WH'] = 16
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = 4
else:
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = thd
app_flags['THREAD_CNT'] = thd
app_flags['LOG_RECOVER'] = 1
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000000
output_dir = "results/%s_rec/thd%d_L%s%s" % \
(config, thd, logger, trial)
add_dbms_job(app_flags, executable, output_dir)
"""
##############################
# sweep # of warehouse
##############################
app_flags = {}
bench = 'TPCC'
#configs = ['SD', 'SC', 'PD', 'PC', 'BD']
configs = ['NO'] #SD', 'SC', 'PD', 'PC', 'BD']
configs = ['%s_TPCC' % x for x in configs]
for wh in [1, 4, 8, 16, 32]:
#logging performance
for config in configs:
thd = 32
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['NUM_WH'] = wh
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s_wh%d" % (config, thd, logger, wh)
add_dbms_job(app_flags, executable, output_dir)
"""
# recovery performance. generate logs.
for wh in [1, 4, 8, 16, 32]:
app_flags = {}
# generate logs.
for config in configs:
thd = 16
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
if 'TPCC' in config:
app_flags['NUM_WH'] = wh
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s_gen/thd%d_L%s_wh%d" % (config, thd, logger, wh)
add_dbms_job(app_flags, executable, output_dir)
# recover performance
app_flags = {}
for config in configs:
if 'NO' in config: continue
if config[0] == 'B' and config[1] == 'C': continue
logger = 1 if config[0] == 'S' else num_logger
executable = "./rundb_%s" % config
thd = 32
app_flags['NUM_WH'] = wh
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = 4
app_flags['THREAD_CNT'] = thd
app_flags['LOG_RECOVER'] = 1
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000000
output_dir = "results/%s_rec/thd%d_L%s_wh%d" % \
(config, thd, logger, wh)
add_dbms_job(app_flags, executable, output_dir)
"""
"""
##############################
# sweep number of queries in YCSB
##############################
bench = 'YCSB'
configs = ['SD', 'SC', 'PD', 'PC', 'BD']
configs = ['%s_%s' % (x, bench) for x in configs]
app_flags = {}
for queries in [1, 2, 4, 8]:
#logging performance
for config in configs:
thd = 32
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = queries
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s_q%d" % (config, thd, logger, queries)
add_dbms_job(app_flags, executable, output_dir)
# recovery performance. generate logs.
for queries in [1, 2, 4, 8]:
app_flags = {}
# generate logs.
for config in configs:
thd = 16
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = queries
app_flags['READ_PERC'] = 0.5
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s_gen/thd%d_L%s_q%d" % (config, thd, logger, queries)
add_dbms_job(app_flags, executable, output_dir)
# recover performance
app_flags = {}
for config in configs:
if 'NO' in config: continue
if config[0] == 'B' and config[1] == 'C': continue
logger = 1 if config[0] == 'S' else num_logger
executable = "./rundb_%s" % config
thd = 32
app_flags['REQ_PER_QUERY'] = queries
app_flags['READ_PERC'] = 0.5
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = thd
app_flags['THREAD_CNT'] = thd
app_flags['LOG_RECOVER'] = 1
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000000
output_dir = "results/%s_rec/thd%d_L%s_q%d" % \
(config, thd, logger, queries)
add_dbms_job(app_flags, executable, output_dir)
##############################
# sweep contention level
##############################
app_flags = {}
bench = 'YCSB'
configs = ['SD', 'SC', 'PD', 'PC', 'BD']
configs = ['%s_%s' % (x, bench) for x in configs]
for theta in [0, 0.6, 0.8, 0.9]:
#logging performance
for config in configs:
thd = 32
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = theta
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s_z%s" % (config, thd, logger, theta)
add_dbms_job(app_flags, executable, output_dir)
# recovery performance. generate logs.
for theta in [0, 0.6, 0.8, 0.9]:
app_flags = {}
# generate logs.
for config in configs:
thd = 16
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = theta
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s_gen/thd%d_L%s_z%s" % (config, thd, logger, theta)
add_dbms_job(app_flags, executable, output_dir)
# recover performance
app_flags = {}
for config in configs:
if 'NO' in config: continue
if config[0] == 'B' and config[1] == 'C': continue
logger = 1 if config[0] == 'S' else num_logger
executable = "./rundb_%s" % config
thd = 32
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = theta
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = thd
app_flags['THREAD_CNT'] = thd
app_flags['LOG_RECOVER'] = 1
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000000
output_dir = "results/%s_rec/thd%d_L%s_z%s" % (config, thd, logger, theta)
add_dbms_job(app_flags, executable, output_dir)
"""
"""
##################################
# sweep the number of loggers
##################################
app_flags = {}
bench = 'YCSB'
configs = ['PD', 'PC', 'BD']
configs = ['%s_%s' % (x, bench) for x in configs]
for logger in [1, 2]:
#logging performance
for config in configs:
thd = 32
executable = "./rundb_%s" % config
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = 0.6
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
# recovery performance. generate logs.
for num_logger in [1, 2]:
app_flags = {}
# generate logs.
for config in configs:
thd = 16
executable = "./rundb_%s" % config
logger = num_logger
if config[0] == 'S' or config[0] == 'N':
logger = 1
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = 0.6
app_flags['MAX_TXNS_PER_THREAD'] = 1000000
app_flags['THREAD_CNT'] = thd
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_NO_FLUSH'] = 0
output_dir = "results/%s_gen/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
# recover performance
app_flags = {}
for config in configs:
if 'NO' in config: continue
if config[0] == 'B' and config[1] == 'C': continue
logger = 1 if config[0] == 'S' else num_logger
executable = "./rundb_%s" % config
thd = 32
app_flags['REQ_PER_QUERY'] = 2
app_flags['READ_PERC'] = 0.5
app_flags['ZIPF_THETA'] = 0.6
app_flags['LOG_PARALLEL_REC_NUM_POOLS'] = thd
app_flags['THREAD_CNT'] = thd
app_flags['LOG_RECOVER'] = 1
app_flags['NUM_LOGGER'] = logger
app_flags['LOG_PARALLEL_NUM_BUCKETS'] = 10000000
output_dir = "results/%s_rec/thd%d_L%s" % (config, thd, logger)
add_dbms_job(app_flags, executable, output_dir)
"""
scheduler.generateSubmitFile()
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] | 2.252483 | 6,242 |
from construct import *
from construct.lib import *
opaque_with_param = Struct(
'one' / LazyBound(lambda: params_def),
)
_schema = opaque_with_param
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from sympy.external import import_module
matchpy = import_module("matchpy")
from sympy.utilities.decorator import doctest_depends_on
if matchpy:
from matchpy import Pattern, ReplacementRule, CustomConstraint
from sympy.integrals.rubi.utility_function import (Int, Set, With, Module, Scan, MapAnd, FalseQ, ZeroQ, NegativeQ, NonzeroQ, FreeQ, NFreeQ, List, Log, PositiveQ, PositiveIntegerQ, NegativeIntegerQ, IntegerQ, IntegersQ, ComplexNumberQ, PureComplexNumberQ, RealNumericQ, PositiveOrZeroQ, NegativeOrZeroQ, FractionOrNegativeQ, NegQ, Equal, Unequal, IntPart, FracPart, RationalQ, ProductQ, SumQ, NonsumQ, Subst, First, Rest, SqrtNumberQ, SqrtNumberSumQ, LinearQ, Sqrt, ArcCosh, Coefficient, Denominator, Hypergeometric2F1, Not, Simplify, FractionalPart, IntegerPart, AppellF1, EllipticPi, EllipticE, EllipticF, ArcTan, ArcCot, ArcCoth, ArcTanh, ArcSin, ArcSinh, ArcCos, ArcCsc, ArcSec, ArcCsch, ArcSech, Sinh, Tanh, Cosh, Sech, Csch, Coth, LessEqual, Less, Greater, GreaterEqual, FractionQ, IntLinearcQ, Expand, IndependentQ, PowerQ, IntegerPowerQ, PositiveIntegerPowerQ, FractionalPowerQ, AtomQ, ExpQ, LogQ, Head, MemberQ, TrigQ, SinQ, CosQ, TanQ, CotQ, SecQ, CscQ, Sin, Cos, Tan, Cot, Sec, Csc, HyperbolicQ, SinhQ, CoshQ, TanhQ, CothQ, SechQ, CschQ, InverseTrigQ, SinCosQ, SinhCoshQ, LeafCount, Numerator, NumberQ, NumericQ, Length, ListQ, Im, Re, InverseHyperbolicQ, InverseFunctionQ, TrigHyperbolicFreeQ, InverseFunctionFreeQ, RealQ, EqQ, FractionalPowerFreeQ, ComplexFreeQ, PolynomialQ, FactorSquareFree, PowerOfLinearQ, Exponent, QuadraticQ, LinearPairQ, BinomialParts, TrinomialParts, PolyQ, EvenQ, OddQ, PerfectSquareQ, NiceSqrtAuxQ, NiceSqrtQ, Together, PosAux, PosQ, CoefficientList, ReplaceAll, ExpandLinearProduct, GCD, ContentFactor, NumericFactor, NonnumericFactors, MakeAssocList, GensymSubst, KernelSubst, ExpandExpression, Apart, SmartApart, MatchQ, PolynomialQuotientRemainder, FreeFactors, NonfreeFactors, RemoveContentAux, RemoveContent, FreeTerms, NonfreeTerms, ExpandAlgebraicFunction, CollectReciprocals, ExpandCleanup, AlgebraicFunctionQ, Coeff, LeadTerm, RemainingTerms, LeadFactor, RemainingFactors, LeadBase, LeadDegree, Numer, Denom, hypergeom, Expon, MergeMonomials, PolynomialDivide, BinomialQ, TrinomialQ, GeneralizedBinomialQ, GeneralizedTrinomialQ, FactorSquareFreeList, PerfectPowerTest, SquareFreeFactorTest, RationalFunctionQ, RationalFunctionFactors, NonrationalFunctionFactors, Reverse, RationalFunctionExponents, RationalFunctionExpand, ExpandIntegrand, SimplerQ, SimplerSqrtQ, SumSimplerQ, BinomialDegree, TrinomialDegree, CancelCommonFactors, SimplerIntegrandQ, GeneralizedBinomialDegree, GeneralizedBinomialParts, GeneralizedTrinomialDegree, GeneralizedTrinomialParts, MonomialQ, MonomialSumQ, MinimumMonomialExponent, MonomialExponent, LinearMatchQ, PowerOfLinearMatchQ, QuadraticMatchQ, CubicMatchQ, BinomialMatchQ, TrinomialMatchQ, GeneralizedBinomialMatchQ, GeneralizedTrinomialMatchQ, QuotientOfLinearsMatchQ, PolynomialTermQ, PolynomialTerms, NonpolynomialTerms, PseudoBinomialParts, NormalizePseudoBinomial, PseudoBinomialPairQ, PseudoBinomialQ, PolynomialGCD, PolyGCD, AlgebraicFunctionFactors, NonalgebraicFunctionFactors, QuotientOfLinearsP, QuotientOfLinearsParts, QuotientOfLinearsQ, Flatten, Sort, AbsurdNumberQ, AbsurdNumberFactors, NonabsurdNumberFactors, SumSimplerAuxQ, Prepend, Drop, CombineExponents, FactorInteger, FactorAbsurdNumber, SubstForInverseFunction, SubstForFractionalPower, SubstForFractionalPowerOfQuotientOfLinears, FractionalPowerOfQuotientOfLinears, SubstForFractionalPowerQ, SubstForFractionalPowerAuxQ, FractionalPowerOfSquareQ, FractionalPowerSubexpressionQ, Apply, FactorNumericGcd, MergeableFactorQ, MergeFactor, MergeFactors, TrigSimplifyQ, TrigSimplify, TrigSimplifyRecur, Order, FactorOrder, Smallest, OrderedQ, MinimumDegree, PositiveFactors, Sign, NonpositiveFactors, PolynomialInAuxQ, PolynomialInQ, ExponentInAux, ExponentIn, PolynomialInSubstAux, PolynomialInSubst, Distrib, DistributeDegree, FunctionOfPower, DivideDegreesOfFactors, MonomialFactor, FullSimplify, FunctionOfLinearSubst, FunctionOfLinear, NormalizeIntegrand, NormalizeIntegrandAux, NormalizeIntegrandFactor, NormalizeIntegrandFactorBase, NormalizeTogether, NormalizeLeadTermSigns, AbsorbMinusSign, NormalizeSumFactors, SignOfFactor, NormalizePowerOfLinear, SimplifyIntegrand, SimplifyTerm, TogetherSimplify, SmartSimplify, SubstForExpn, ExpandToSum, UnifySum, UnifyTerms, UnifyTerm, CalculusQ, FunctionOfInverseLinear, PureFunctionOfSinhQ, PureFunctionOfTanhQ, PureFunctionOfCoshQ, IntegerQuotientQ, OddQuotientQ, EvenQuotientQ, FindTrigFactor, FunctionOfSinhQ, FunctionOfCoshQ, OddHyperbolicPowerQ, FunctionOfTanhQ, FunctionOfTanhWeight, FunctionOfHyperbolicQ, SmartNumerator, SmartDenominator, SubstForAux, ActivateTrig, ExpandTrig, TrigExpand, SubstForTrig, SubstForHyperbolic, InertTrigFreeQ, LCM, SubstForFractionalPowerOfLinear, FractionalPowerOfLinear, InverseFunctionOfLinear, InertTrigQ, InertReciprocalQ, DeactivateTrig, FixInertTrigFunction, DeactivateTrigAux, PowerOfInertTrigSumQ, PiecewiseLinearQ, KnownTrigIntegrandQ, KnownSineIntegrandQ, KnownTangentIntegrandQ, KnownCotangentIntegrandQ, KnownSecantIntegrandQ, TryPureTanSubst, TryTanhSubst, TryPureTanhSubst, AbsurdNumberGCD, AbsurdNumberGCDList, ExpandTrigExpand, ExpandTrigReduce, ExpandTrigReduceAux, NormalizeTrig, TrigToExp, ExpandTrigToExp, TrigReduce, FunctionOfTrig, AlgebraicTrigFunctionQ, FunctionOfHyperbolic, FunctionOfQ, FunctionOfExpnQ, PureFunctionOfSinQ, PureFunctionOfCosQ, PureFunctionOfTanQ, PureFunctionOfCotQ, FunctionOfCosQ, FunctionOfSinQ, OddTrigPowerQ, FunctionOfTanQ, FunctionOfTanWeight, FunctionOfTrigQ, FunctionOfDensePolynomialsQ, FunctionOfLog, PowerVariableExpn, PowerVariableDegree, PowerVariableSubst, EulerIntegrandQ, FunctionOfSquareRootOfQuadratic, SquareRootOfQuadraticSubst, Divides, EasyDQ, ProductOfLinearPowersQ, Rt, NthRoot, AtomBaseQ, SumBaseQ, NegSumBaseQ, AllNegTermQ, SomeNegTermQ, TrigSquareQ, RtAux, TrigSquare, IntSum, IntTerm, Map2, ConstantFactor, SameQ, ReplacePart, CommonFactors, MostMainFactorPosition, FunctionOfExponentialQ, FunctionOfExponential, FunctionOfExponentialFunction, FunctionOfExponentialFunctionAux, FunctionOfExponentialTest, FunctionOfExponentialTestAux, stdev, rubi_test, If, IntQuadraticQ, IntBinomialQ, RectifyTangent, RectifyCotangent, Inequality, Condition, Simp, SimpHelp, SplitProduct, SplitSum, SubstFor, SubstForAux, FresnelS, FresnelC, Erfc, Erfi, Gamma, FunctionOfTrigOfLinearQ, ElementaryFunctionQ, Complex, UnsameQ, _SimpFixFactor, SimpFixFactor, _FixSimplify, FixSimplify, _SimplifyAntiderivativeSum, SimplifyAntiderivativeSum, _SimplifyAntiderivative, SimplifyAntiderivative, _TrigSimplifyAux, TrigSimplifyAux, Cancel, Part, PolyLog, D, Dist)
from sympy import Integral, S, sqrt
from sympy.integrals.rubi.symbol import WC
from sympy.core.symbol import symbols, Symbol
from sympy.functions import (log, sin, cos, tan, cot, csc, sec, sqrt, erf, exp, log)
from sympy.functions.elementary.hyperbolic import (acosh, asinh, atanh, acoth, acsch, asech, cosh, sinh, tanh, coth, sech, csch)
from sympy.functions.elementary.trigonometric import (atan, acsc, asin, acot, acos, asec)
A_, B_, C_, F_, G_, H_, a_, b_, c_, d_, e_, f_, g_, h_, i_, j_, k_, l_, m_, n_, p_, q_, r_, t_, u_, v_, s_, w_, x_, y_, z_ = [WC(i) for i in 'ABCFGHabcdefghijklmnpqrtuvswxyz']
a1_, a2_, b1_, b2_, c1_, c2_, d1_, d2_, n1_, n2_, e1_, e2_, f1_, f2_, g1_, g2_, n1_, n2_, n3_, Pq_, Pm_, Px_, Qm_, Qr_, Qx_, jn_, mn_, non2_, RFx_, RGx_ = [WC(i) for i in ['a1', 'a2', 'b1', 'b2', 'c1', 'c2', 'd1', 'd2', 'n1', 'n2', 'e1', 'e2', 'f1', 'f2', 'g1', 'g2', 'n1', 'n2', 'n3', 'Pq', 'Pm', 'Px', 'Qm', 'Qr', 'Qx', 'jn', 'mn', 'non2', 'RFx', 'RGx']]
_UseGamma = False
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66,
17,
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705,
67,
16,
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705,
67,
17,
3256,
705,
77,
16,
3256,
705,
77,
17,
3256,
705,
68,
16,
3256,
705,
68,
17,
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69,
16,
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69,
17,
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70,
16,
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70,
17,
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18,
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80,
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47,
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47,
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76,
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48,
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17,
3256,
705,
32754,
87,
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705,
48192,
87,
6,
11907,
628,
220,
220,
220,
4808,
11041,
34777,
2611,
796,
10352,
198
] | 2.782345 | 2,798 |
from tkinter import *
root = Tk()
text1 = Text(width=20,height=1)
label1 = Label(width=20)
but1 = Button(width=5, height=5, bg='#ff0000')
but2 = Button(width=5, height=5, bg='#ff7d00')
but3 = Button(width=5, height=5, bg='#ffff00')
but4 = Button(width=5, height=5, bg='#00ff00')
but5 = Button(width=5, height=5, bg='#007dff')
but6 = Button(width=5, height=5, bg='#0000ff')
but7 = Button(width=5, height=5, bg='#7d00ff')
but1.bind('<Button-1>', color_red)
but2.bind('<Button-1>', color_orange)
but3.bind('<Button-1>', color_yellow)
but4.bind('<Button-1>', color_green)
but5.bind('<Button-1>', color_blue)
but6.bind('<Button-1>', color_indigo)
but7.bind('<Button-1>', color_purple)
text1.pack()
label1.pack()
but1.pack(side='left')
but2.pack(side='left')
but3.pack(side='left')
but4.pack(side='left')
but5.pack(side='left')
but6.pack(side='left')
but7.pack(side='left')
root.mainloop()
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9464,
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4360,
20,
13,
8002,
7,
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11537,
201,
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4360,
21,
13,
8002,
7,
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201,
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4360,
22,
13,
8002,
7,
1589,
11639,
9464,
11537,
201,
198,
201,
198,
15763,
13,
12417,
26268,
3419,
201,
198
] | 2.149533 | 428 |
"""
Copyright Digisim, Computer Architecture team of South China University of Technology,
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.
Author Name: Ruohui Chen
Date: 2021-03-08
File Name: deocder.py
Description:
"""
from pyhcl import *
from src.include.pkg import *
# Helper function for generating CSRs access illegal judgement
# Hardware Performance Monitor
# Hardware Performance Monitor (unprivileged read-only mirror CSRs)
# PULP_XPULP, PULP_CLUSTER, A_EXTENSION, FPU, APU_WOP_CPU not used in our implementation
if __name__ == '__main__':
Emitter.dumpVerilog(Emitter.dump(Emitter.emit(decoder()), "decoder.fir"))
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198,
220,
220,
220,
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13,
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13414,
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7,
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13,
39455,
7,
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13,
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270,
7,
12501,
12342,
3419,
828,
366,
12501,
12342,
13,
69,
343,
48774,
198
] | 3.388235 | 340 |
# -*- coding: utf-8 -*-
"""
Created on Fri May 1 10:16:25 2020
@author: winhl
modify 2022-1-6
"""
import pandas as pd
import json
work_dir = "D:/__E__/支部/积分"
#n0 = "2021s1p9"
n0 = "2021.01-12.xuexi.cn" #2022-1-6
with open(f"{work_dir}/{n0}.json", "r", encoding="utf-8") as f:
fr = f.read()
j1 = json.loads(fr)
l_str = j1.get("data_str")
j2 = json.loads(l_str)
dict_str = j2.get("dataList")["data"]
print(type(dict_str))
# df = pd.read_json(dict_str, orient="records")
df = pd.DataFrame(data=dict_str)
print(df)
df.to_csv(f"{work_dir}/{n0}.csv")
"""
from pandas import json_normalize
import json
data=open("F:/支部/XXQG/202009third_20200930.json",encoding="utf-8").read()
user_dic = json.loads(data)
data_list = user_dic["data"]["list"]
df = json_normalize(data_list)
print(df)
df.to_csv("09.csv")
"""
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67,
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1,
7131,
1,
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201,
198,
7568,
796,
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62,
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7,
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8,
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198,
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7,
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8,
201,
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13,
1462,
62,
40664,
7203,
2931,
13,
40664,
4943,
201,
198,
37811,
201,
198
] | 1.930131 | 458 |
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphConvolution
| [
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] | 3.64 | 25 |
from __future__ import annotations
from argparse import ArgumentError
import functools
from dataclasses import dataclass
from typing import Any, Callable, List, Optional, Type, Union
from .types import Union as FsUnion, FSharpRef, Record
from .util import equal_arrays_with
Constructor = Callable[..., Any]
EnumCase = List[Union[str, int]]
FieldInfo = List[Union[str, "TypeInfo"]]
PropertyInfo = FieldInfo
@dataclass
@dataclass
obj_type: TypeInfo = TypeInfo(fullname="System.Object")
unit_type: TypeInfo = TypeInfo("Microsoft.FSharp.Core.Unit")
char_type: TypeInfo = TypeInfo("System.Char")
string_type: TypeInfo = TypeInfo("System.String")
bool_type: TypeInfo = TypeInfo("System.Boolean")
int8_type: TypeInfo = TypeInfo("System.SByte")
uint8_type: TypeInfo = TypeInfo("System.Byte")
int16_type: TypeInfo = TypeInfo("System.Int16")
uint16_type: TypeInfo = TypeInfo("System.UInt16")
int32_type: TypeInfo = TypeInfo("System.Int32")
uint32_type: TypeInfo = TypeInfo("System.UInt32")
float32_type: TypeInfo = TypeInfo("System.Single")
float64_type: TypeInfo = TypeInfo("System.Double")
decimal_type: TypeInfo = TypeInfo("System.Decimal")
# In .NET this is false for delegates
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] | 3.041975 | 405 |
import numpy as np
import cykdtree as kdtree
from cgal4py import PY_MAJOR_VERSION
def tree(method, pts, left_edge, right_edge, periodic, *args, **kwargs):
r"""Get tree for a given domain decomposition schema.
Args:
method (str): Domain decomposition method. Supported options are:
'kdtree': KDTree based on median position along the dimension
with the greatest domain width. See
:meth:`cgal4py.domain_decomp.kdtree` for details on
accepted keyword arguments.
pts (np.ndarray of float64): (n, m) array of n coordinates in a
m-dimensional domain.
left_edge (np.ndarray of float64): (m,) domain minimum in each
dimension.
right_edge (np.ndarray of float64): (m,) domain maximum in each
dimension.
*args: Variable argument list. Passed to the selected domain
decomposition method.
**kwargs: Variable keyword argument list. Passed to the selected
domain decomposition method.
Returns:
object: Tree of type specified by `method`.
Raises:
ValueError: If `method` is not one of the supported domain
decomposition methods listed above.
"""
# Get leaves
if method.lower() == 'kdtree':
tree = kdtree.PyKDTree(pts, left_edge, right_edge, *args, **kwargs)
else:
raise ValueError("'{}' is not a supported ".format(method) +
"domain decomposition.")
# Return tree
return tree
def process_leaves(leaves, left_edge, right_edge, periodic):
r"""Process leaves ensuring they have the necessary information/methods
for parallel tessellation. Each leaf must have at least the following
attributes:
npts (int): Number of points on the leaf.
left_edge (np.ndarray of float): min of leaf extent in each dimension.
right_edge (np.ndarray of float): max of leaf extent in each dimension.
Args:
leaves (list of leaf objects): Leaves in an arbitrary domain
decomposition.
left_edge (np.ndarray of float64): domain minimum in each dimension.
right_edge (np.ndarray of float64): domain maximum in each dimension.
periodic (bool): True if domain is periodic, False otherwise.
Returns:
list of leaf objects: Leaves process with additional attributes added
if they do not exist and can be added.
Raises:
AttributeError: If a leaf does not have one of the required attributes.
TypeError: If a leaf has a required attribute, but of the wrong type.
ValueError: If a leaf has a required attribute, but of the wrong size.
"""
ndim = len(left_edge)
req_attr = {'npts': [int, np.int32, np.uint32, np.int64, np.uint64],
'left_edge': ([float, np.float32, np.float64], (ndim,)),
'right_edge': ([float, np.float32, np.float64], (ndim,))}
if PY_MAJOR_VERSION < 3:
req_attr['npts'].append(long)
# Check for necessary attributes
for k, v in req_attr.items():
if isinstance(v, tuple):
for i, leaf in enumerate(leaves):
if not hasattr(leaf, k):
raise AttributeError(
"Leaf {} does not have attribute {}.".format(i, k))
lv = getattr(leaf, k)
if not isinstance(lv, np.ndarray):
raise TypeError("Attribute {} ".format(k) +
"of leaf {} ".format(i) +
"is not an array.\n" +
"It is type {}.".format(type(lv)))
if lv.dtype not in v[0]:
raise TypeError("Attribute {} ".format(k) +
"of leaf {} ".format(i) +
"is not an array with dtype " +
"{}.\n".format(v[0]) +
"It is type {}.".format(lv.dtype))
if v[1] is not None and lv.shape != v[1]:
raise ValueError("Attribute {} ".format(k) +
"of leaf {} ".format(i) +
"is not an array with shape " +
"{}.\n".format(v[1]) +
"It is shape {}.".format(lv.shape))
else:
for i, leaf in enumerate(leaves):
if not hasattr(leaf, k):
raise AttributeError("Leaf {} does not ".format(i) +
"have attribute {}.".format(k))
lv = getattr(leaf, k)
if not isinstance(lv, tuple(v)):
raise TypeError("Attribute {} ".format(k) +
"of leaf {} is not ".format(i) +
"of type {}.\n".format(v) +
"It is type {}.".format(type(lv)))
# Set id & ensure leaves are sorted
if getattr(leaves[0], 'id', None) is None:
for i, leaf in enumerate(leaves):
leaf.id = i
else:
leaves = sorted(leaves, key=lambda l: l.id)
# Set number of dimensions
if getattr(leaves[0], 'ndim', None) is None:
for leaf in leaves:
leaf.ndim = ndim
# Set total number of leaves
if getattr(leaves[0], 'num_leaves', None) is None:
num_leaves = len(leaves)
for leaf in leaves:
leaf.num_leaves = num_leaves
# Set starting index
if getattr(leaves[0], 'start_idx', None) is None:
nprev = 0
for leaf in leaves:
leaf.start_idx = nprev
nprev += leaf.npts
# Set stopping index
if getattr(leaves[0], 'stop_idx', None) is None:
for leaf in leaves:
leaf.stop_idx = leaf.start_idx + leaf.npts
# Set domain width
if getattr(leaves[0], 'domain_width', None) is None:
domain_width = right_edge - left_edge
for leaf in leaves:
leaf.domain_width = domain_width
# Determine if leaves are on periodic boundaries
if getattr(leaves[0], 'periodic_left', None) is None:
if periodic:
for leaf in leaves:
leaf.periodic_left = np.isclose(leaf.left_edge, left_edge)
leaf.periodic_right = np.isclose(leaf.right_edge, right_edge)
else:
for leaf in leaves:
leaf.periodic_left = np.zeros(leaf.ndim, 'bool')
leaf.periodic_right = np.zeros(leaf.ndim, 'bool')
# Add neighbors
if getattr(leaves[0], 'left_neighbors', None) is None:
for j, leaf in enumerate(leaves):
leaf.left_neighbors = [[] for _ in range(ndim)]
leaf.right_neighbors = [[] for _ in range(ndim)]
for prev in leaves[:(j+1)]:
match = True
for i in range(ndim):
if leaf.left_edge[i] > prev.right_edge[i]:
if not (leaf.periodic_right[i] and
prev.periodic_left[i]):
match = False
break
if leaf.right_edge[i] < prev.left_edge[i]:
if not (prev.periodic_right[i] and
leaf.periodic_left[i]):
match = False
break
if match:
for i in range(ndim):
if np.isclose(leaf.left_edge[i], prev.right_edge[i]):
leaf.left_neighbors[i].append(prev.id)
prev.right_neighbors[i].append(leaf.id)
elif np.isclose(leaf.right_edge[i], prev.left_edge[i]):
leaf.right_neighbors[i].append(prev.id)
prev.left_neighbors[i].append(leaf.id)
if periodic:
if (leaf.periodic_right[i] and
prev.periodic_left[i]):
leaf.right_neighbors[i].append(prev.id)
prev.left_neighbors[i].append(leaf.id)
if (prev.periodic_right[i] and
leaf.periodic_left[i]):
leaf.left_neighbors[i].append(prev.id)
prev.right_neighbors[i].append(leaf.id)
if getattr(leaves[0], 'neighbors', None) is None:
for leaf in leaves:
neighbors = [leaf.id]
for i in range(ndim):
neighbors += leaf.left_neighbors[i]
neighbors += leaf.right_neighbors[i]
leaf.neighbors = list(set(neighbors))
# Return leaves
return leaves
__all__ = ["tree", "kdtree", "GenericLeaf", "GenericTree", "process_leaves"]
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] | 1.936892 | 4,627 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2016-11-10 17:53
from __future__ import unicode_literals
import django.db.models.deletion
from django.db import migrations, models
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] | 2.73913 | 69 |
import pytest
from pytest import mark
from pytest_lazyfixture import lazy_fixture
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] | 3.458333 | 24 |
#!/usr/bin/env python
######################################################
# GUI to vizualize ROMS input/output files
# Sep 2021
# [email protected]
######################################################
import os
import wx
import datetime as dt
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as Navbar
from matplotlib.backends.backend_wx import NavigationToolbar2Wx
from matplotlib.figure import Figure
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import scipy.io as sp
import netCDF4 as nc
from lib import *
# TO-DO LIST: ====================================================
# - correct bug with date selection: somehow the times re-start
# every 00z
# - need to decide which x-axis to use, lon or lat
# ================================================================
# NICE TIP TO DEBUG THIS PROGRAM: ================================
# - comment out app.MainLoop at the last line of this script
# - ipython --gui=wx
# - run pyromsgui.py
# - trigger the events and check out the objects in the shell
# ================================================================
global currentDirectory
currentDirectory = os.getcwd()
PROJECT_DIR = os.path.abspath(os.path.dirname(__file__))
DEFAULT_VMIN = 0
DEFAULT_VMAX = 1.5
DEFAULT_CMAP = plt.cm.BrBG
DEFAULT_DEPTH_FOR_LAND = -50
class SimpleMPLCanvas(object):
"""docstring for SimpleMPLCanvas"""
def romsTime2string(nctime):
"""
nctime : netCDF4 variable
"""
timeunits = nctime.units
units = timeunits.split(' ')[0]
tstart = dt.datetime.strptime(timeunits.split(' ')[-2], "%Y-%m-%d")
timelist = []
for t in nctime[:]:
if units == 'seconds':
current = tstart + dt.timedelta(seconds=t)
if units == 'days':
current = tstart + dt.timedelta(seconds=t*86400)
timelist.append(current.strftime("%Y-%m-%d %H h"))
return timelist
def load_bitmap(filename, direc=None):
"""
Load a bitmap file from the ./icons subdirectory.
The filename parameter should not
contain any path information as this is determined automatically.
Returns a wx.Bitmap object
copied from matoplotlib resources
"""
if not direc:
basedir = os.path.join(PROJECT_DIR, 'icons')
else:
basedir = os.path.join(PROJECT_DIR, direc)
bmpFilename = os.path.normpath(os.path.join(basedir, filename))
if not os.path.exists(bmpFilename):
raise IOError('Could not find bitmap file "%s"; dying' % bmpFilename)
bmp = wx.Bitmap(bmpFilename)
return bmp
if __name__ == "__main__":
app = App(False)
app.MainLoop()
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] | 2.842916 | 974 |
# Sum of pairwise Hamming Distance
# https://www.interviewbit.com/problems/sum-of-pairwise-hamming-distance/
#
# Hamming distance between two non-negative integers is defined as the number of positions at
# which the corresponding bits are different.
#
# For example,
#
# HammingDistance(2, 7) = 2, as only the first and the third bit differs in the binary
# representation of 2 (010) and 7 (111).
#
# Given an array of N non-negative integers, find the sum of hamming distances of all pairs of
# integers in the array.
#
# Return the answer modulo 1000000007.
#
# Example
#
# Let f(x, y) be the hamming distance defined above.
#
# A=[2, 4, 6]
#
# We return,
# f(2, 2) + f(2, 4) + f(2, 6) +
# f(4, 2) + f(4, 4) + f(4, 6) +
# f(6, 2) + f(6, 4) + f(6, 6) =
#
# 0 + 2 + 1
# 2 + 0 + 1
# 1 + 1 + 0 = 8
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# @param A : tuple of integers
# @return an integer
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
if __name__ == "__main__":
s = Solution()
print(s.hammingDistance((2, 4, 6)))
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] | 2.498911 | 459 |
#!/usr/bin/env python
# coding: utf-8
# In[16]:
#importing required packages
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import BernoulliNB
clf=BernoulliNB()
#loading the data
dataset=pd.read_csv("C:/Users/ASUS/Downloads/train.csv")
dataset.head()
#getting the description of the dataset
dataset.describe()
dataset.describe().sum()
#get some info about the data
dataset.info()
#getting the amount of null values in each column
dataset.isnull().sum()
#dropping the unimportant columns
dataset=dataset.drop('PassengerId', axis=1)
dataset=dataset.drop('Name', axis=1)
dataset=dataset.drop('Ticket', axis=1)
dataset=dataset.drop('Cabin', axis=1)
dataset.head()
#label encoding the categorical values which are of object type
le=preprocessing.LabelEncoder()
dataset['Sex']=le.fit_transform(dataset['Sex'])
dataset['Embarked']=le.fit_transform(dataset['Embarked'])
dataset.head()
"""this functions takes the independent variable column and trains the model after dividing the dataset into x and y
and also spliting the dataset into training and testing data.This also prints the accuracy score and the confusion matrix"""
#Calling the function
navbaiyes('Survived')
# In[ ]:
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14629,
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1572,
11537,
628,
198,
2,
554,
58,
2361,
25,
628,
628,
198
] | 3.029979 | 467 |
#!/usr/bin/env python
import math
var1 = "Hello"
var2 = "Worldpress"
print "var1 = ", var1
print "var2 = ", var2
print "var1[0] = ", var1[0]
print "var2[1:5] = ", var2[1:5]
if ('H' in var1):
print "H is in var1 ", var1
else:
print "H is not in var1 ", var1
if ('H' not in var2):
print "H is not in var2 ", var2
else:
print "H is in var2 ", var2
var3 = var1 + var2;
print "var1 + var2 = ", var3
print "var1 = %s, var2 = %s" % (var1, var2)
| [
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82,
1,
4064,
357,
7785,
16,
11,
1401,
17,
8,
628
] | 2.168224 | 214 |
import random
| [
11748,
4738,
198
] | 4.666667 | 3 |
import markdown2, re, random
from django.shortcuts import render
from django.urls import reverse
from django.http import HttpResponseRedirect
from . import util | [
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] | 3.6 | 45 |
import logging
import multiprocessing
import ctypes
from gunpowder.ext import tensorflow as tf
from gunpowder.freezable import Freezable
logger = logging.getLogger(__name__)
class LocalServer(Freezable):
'''Wrapper around ``tf.train.Server`` to create a local server on-demand.
This class is necessary because tensorflow's GPU support should not be
initialized before forking processes (the CUDA driver needs to be
initialized in each process separately, not in the main process and then
forked). Creating a ``tf.train.Server`` initializes GPU support, however.
With this wrapper, server creating can be delayed until a GPU process
creates a ``tf.Session``::
session = tf.Session(target=LocalServer.get_target())
'''
__target = multiprocessing.Array(ctypes.c_char, b' '*256)
__server = None
@staticmethod
def get_target():
'''Get the target string of this tensorflow server to connect a
``tf.Session()``. This will start the server, if it is not running
already.
'''
with LocalServer.__target.get_lock():
target = LocalServer.__target.value
if target == b' '*256:
logger.info("Creating local tensorflow server")
LocalServer.__server = tf.train.Server.create_local_server()
target = LocalServer.__server.target
if not isinstance(target, bytes):
target = target.encode('ascii')
logger.info("Server running at %s", target)
else:
logger.info("Server already running at %s", target)
LocalServer.__target.value = target
return target
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2496,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2496,
198
] | 2.646512 | 645 |
# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
import itertools
import os
from dataclasses import dataclass
from typing import Dict, List
from pants.backend.go.module import ResolvedGoModule, ResolveGoModuleRequest
from pants.backend.go.pkg import (
ResolvedGoPackage,
ResolveExternalGoModuleToPackagesRequest,
ResolveExternalGoModuleToPackagesResult,
)
from pants.backend.go.target_types import (
GoExternalPackageTarget,
GoModule,
GoModuleSources,
GoPackage,
)
from pants.base.specs import AddressSpecs, MaybeEmptyDescendantAddresses, MaybeEmptySiblingAddresses
from pants.build_graph.address import Address
from pants.core.goals.tailor import (
AllOwnedSources,
PutativeTarget,
PutativeTargets,
PutativeTargetsRequest,
group_by_dir,
)
from pants.engine.fs import PathGlobs, Paths
from pants.engine.internals.selectors import Get, MultiGet
from pants.engine.rules import collect_rules, rule
from pants.engine.target import UnexpandedTargets
from pants.engine.unions import UnionRule
from pants.util.logging import LogLevel
@dataclass(frozen=True)
@rule(level=LogLevel.DEBUG, desc="Determine candidate Go `go_package` targets to create")
@dataclass(frozen=True)
@rule(level=LogLevel.DEBUG, desc="Determine candidate Go `go_module` targets to create")
@dataclass(frozen=True)
@rule(level=LogLevel.DEBUG, desc="Determine candidate Go `go_external_module` targets to create")
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] | 3.162162 | 481 |
import os
import cursos
print(os.getcwd())
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] | 2.588235 | 17 |
# Generated by Django 3.1.3 on 2021-04-20 14:59
from django.db import migrations, models
| [
2,
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] | 2.84375 | 32 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Unit tests for finite_diff library
"""
import unittest
import random
from math import pi
import numpy as np
import finitediff
class TestFiniteDiff(unittest.TestCase):
"""Unit test class for the finitediff library"""
order = 4 # Order of the derivatives
def setUp(self):
"""Initialize a differentiator on a random grid"""
# Randomly pick some x values
numvals = 40
self.x = np.sort(np.array([random.uniform(0.0, 2*pi) for i in range(numvals)]))
# Create the differentiator on these x values
self.diff = finitediff.Derivative(TestFiniteDiff.order)
self.diff.set_x(self.x)
def test_order(self):
"""Make sure we can get the order out correctly"""
self.assertEqual(self.diff.get_order(), TestFiniteDiff.order)
def test_cos(self):
"""Test a cosine function with no boundary conditions"""
ycos = np.cos(self.x)
dycos = self.diff.dydx(ycos)
truevals = -np.sin(self.x)
self.compare_arrays(dycos, truevals)
def test_sin(self):
"""Test a sine function with no boundary conditions"""
ysin = np.sin(self.x)
dysin = self.diff.dydx(ysin)
truevals = np.cos(self.x)
self.compare_arrays(dysin, truevals)
def test_matrix(self):
"""Test a matrix function with no boundary conditions"""
ysin = np.sin(self.x)
ycos = np.cos(self.x)
# pylint: disable=no-member
test = np.array([ysin, ycos]).transpose()
truevals = np.array([ycos, -ysin]).transpose()
# pylint: enable=no-member
dtest = self.diff.dydx(test)
self.assertTrue(np.all(np.abs(dtest - truevals) < 0.01))
def test_sin_odd(self):
"""Test a sine function with odd boundary conditions"""
self.diff.apply_boundary(-1)
ysin = np.sin(self.x)
dysin = self.diff.dydx(ysin)
truevals = np.cos(self.x)
self.compare_arrays(dysin, truevals)
def test_cos_even(self):
"""Test a cosine function with even boundary conditions"""
self.diff.apply_boundary(1)
ycos = np.cos(self.x)
dycos = self.diff.dydx(ycos)
truevals = -np.sin(self.x)
self.compare_arrays(dycos, truevals)
def compare_arrays(self, array1, array2):
"""Helper function to test equality of two arrays"""
for i, _ in enumerate(array1):
self.assertAlmostEqual(array1[i], array2[i], delta=0.01)
def test_conversion1(self):
"""Test converting boundary conditions"""
# pylint: disable=protected-access
oldstencil = self.diff._stencil.copy()
self.diff.set_x(self.x, 1)
newstencil = self.diff._stencil.copy()
self.diff.apply_boundary(0)
# Test converting to no boundary condition
self.assertTrue(np.all(self.diff._stencil == oldstencil))
# Test converting to even boundary condition
newdiff = finitediff.Derivative(TestFiniteDiff.order)
newdiff.set_x(self.x, 1)
self.assertTrue(np.all(newdiff._stencil == newstencil))
# Test converting to odd boundary condition
self.diff.set_x(self.x, -1)
newstencil = self.diff._stencil.copy()
newdiff = finitediff.Derivative(TestFiniteDiff.order)
newdiff.set_x(self.x, -1)
self.assertTrue(np.all(newdiff._stencil == newstencil))
def test_bad(self):
"""Make sure an error is raised appropriately"""
# pylint: disable=protected-access
# Insufficient gridpoints for order
with self.assertRaises(finitediff.DerivativeError):
self.diff.set_x(np.array([0.0, 0.5, 1.0]))
# Something has gone very wrong - stencil and gridpoints are
# out of alignment
with self.assertRaises(finitediff.DerivativeError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test._xvals = np.array([1.0, 2.0, 3.0, 4.0])
test.apply_boundary()
# Various tests for no stencil
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.dydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.leftdydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.rightdydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.position_dydx(np.array([1, 2, 3]), 1)
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.get_xvals()
with self.assertRaises(finitediff.NoStencil):
test = finitediff.Derivative(TestFiniteDiff.order)
test.apply_boundary()
# xvals and yvals are out of alignment
with self.assertRaises(finitediff.DerivativeError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test.dydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.DerivativeError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test.leftdydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.DerivativeError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test.rightdydx(np.array([1, 2, 3]))
with self.assertRaises(finitediff.DerivativeError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test.position_dydx(np.array([1, 2, 3]), 3)
# Position out of bounds
with self.assertRaises(IndexError):
test = finitediff.Derivative(TestFiniteDiff.order)
test.set_x(np.array([1.0, 2, 3, 4, 5, 6]))
test.position_dydx(np.array([1, 2, 3, 4, 5, 6]), 7)
def test_copy(self):
"""Make sure things copy correctly"""
# pylint: disable=protected-access
# Make sure we get references
xref = self.diff.get_xvals(False)
self.x[0] += 1
self.assertTrue(xref[0] == self.x[0])
# Make sure we get copies!
self.diff.set_x(self.x, copy=True)
x = self.diff.get_xvals(True)
xref = self.diff.get_xvals(False)
x[0] += 1
self.assertFalse(xref[0] == x[0]) # get_xvals returned a copy
xref[0] += 1
self.assertFalse(xref[0] == self.x[0]) # set_x stored a copy
def test_positions(self):
"""Test that derivatives at all positions are correct"""
ysin = np.sin(self.x)
dysin = self.diff.dydx(ysin)
for i in range(len(ysin)):
self.assertAlmostEqual(dysin[i], self.diff.position_dydx(ysin, i), delta=1e-12)
self.assertAlmostEqual(dysin[0], self.diff.leftdydx(ysin), delta=1e-12)
self.assertAlmostEqual(dysin[-1], self.diff.rightdydx(ysin), delta=1e-12)
if __name__ == '__main__':
unittest.main()
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555,
715,
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] | 2.13533 | 3,473 |
###########################
#
# #143 Investigating the Torricelli point of a triangle - Project Euler
# https://projecteuler.net/problem=143
#
# Code by Kevin Marciniak
#
###########################
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] | 3.754717 | 53 |
__version__ = '0.1.4'
default_app_config = 'connected_accounts.apps.ConnectedAccountsConfig'
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import socket
from abc import abstractmethod
from threading import Thread
from typing import Optional
from nehushtan.socket.SocketHandleThreadManager import SocketHandlerThreadManager
class NehushtanUDPSocketServer:
"""
Since 0.4.16
"""
def should_terminate(self) -> bool:
"""
If return True, stop the listener.
Override it to implement it with your own logic.
"""
return False
@abstractmethod
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] | 2.923567 | 157 |
# Specify and solve an LP using a Pandas DataFrame
import pandas as pd
if __name__ != "__main__":
from . import LpModel
from . import Result
else:
from LpModel import LpModel
from Result import Result
if __name__ == "__main__":
df = OptModel.read_csv("lpmodel.txt")
#print(df.fillna(""))
lpmodel = OptModel(df)
print(lpmodel)
result = lpmodel.solve()
#print(result.status)
#print("x:\n" , result.x, sep='')
print(result)
print("slack:\n" , result.slack, sep='')
df2 = OptModel.read_str(
"""
name x1 x2 sense rhs
# objective:
obj 3 5 max
# subject to:
#x1lower 1 0 = 2
plant1 1 0 <= 4
plant2 0 2 <= 12
plant3 3 2 <= 18
""")
lpmodel2 = OptModel(df2)
print(lpmodel)
result2 = lpmodel2.solve()
print(result2)
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417,
17,
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13123,
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7,
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17,
8,
198,
220,
220,
3601,
7,
75,
4426,
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8,
198,
220,
220,
1255,
17,
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417,
17,
13,
82,
6442,
3419,
198,
220,
220,
3601,
7,
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8,
198
] | 2.223464 | 358 |
import argparse
import module1 as mod1
import pytest
from hypothesis import given
from hypothesis.strategies import integers
@given(integers())
@given(integers(), integers())
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"""
The script exports the answer table to a CSV file.
Config
------
CFG_NAME : The config name can be Develpment, Staging, Testing
Output
------
The total answer numbers after export, and the CSV file. (aswer_YYYY_MM_DD_HH_mm_ss.csv)
"""
CFG_NAME = "config.config.DevelopmentConfig"
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import csv
from models.model import db
from models.model import Answer
from models.model_operations import location_operations
from models.model_operations import answer_operations
from models.model_operations import user_operations
from config.config import Config
from flask import Flask
from controllers import root
import datetime
# init db
app = Flask(__name__)
app.register_blueprint(root.bp)
app.config.from_object(CFG_NAME)
db.init_app(app)
app.app_context().push()
cvs_file_name = "answer_" + datetime.datetime.today().strftime("%Y_%m_%d_%H_%M_%S") + ".csv"
print("Exporting answers to " + cvs_file_name + "...")
# Get all answers
answer_query = Answer.query.order_by(Answer.user_id)
answers = answer_query.all()
with open(cvs_file_name, "w", newline="") as csvDataFile:
# Write header
csvWriter = csv.writer(csvDataFile, delimiter=",", quotechar='|', quoting=csv.QUOTE_MINIMAL)
csvWriter.writerow(["Userid", "client_id", "location_id", "factory_id", "answer_id", "land_usage", "expansion", "gold_standard_status",
"year_old", "year_new", "bbox_left_top_lat", "bbox_left_top_lng", "bbox_bottom_right_lat", "bbox_bottom_right_lng",
"zoom_level", "timestamp"])
for answer in answers:
# Write each record in answer table
factory_id = location_operations.get_location_by_id(answer.location_id).factory_id
client_id = user_operations.get_user_by_id(answer.user_id).client_id
csvWriter.writerow([answer.user_id, client_id, answer.location_id, factory_id, answer.id, answer.land_usage,answer.expansion,
answer.gold_standard_status, answer.year_old, answer.year_new, answer.bbox_left_top_lat, answer.bbox_left_top_lng, answer.bbox_bottom_right_lat,
answer.bbox_bottom_right_lng, answer.zoom_level, answer.timestamp])
print("{} records reported.".format(len(answers)))
db.session.remove()
db.session.close()
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13,
28956,
3419,
198,
9945,
13,
29891,
13,
19836,
3419,
198
] | 2.633371 | 881 |
import abc
from blacksmith.typing import ServiceName, Url, Version
class AsyncAbstractServiceDiscovery(abc.ABC):
"""Define the Service Discovery interface."""
@abc.abstractmethod
async def get_endpoint(self, service: ServiceName, version: Version) -> Url:
"""Get the endpoint of a service."""
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] | 3.17 | 100 |
#%%
import numpy as np
import pandas as pd
import altair as alt
import anthro.io
# Load the produciton data.
data = pd.read_csv('../processed/Geyer2017_plastic_production.csv')
data['year'] = pd.to_datetime(data['year'], format='%Y')
chart = alt.Chart(data).encode(
x=alt.X(field='year', type='temporal', timeUnit='year', title='year'),
y=alt.Y(field='resin_production_Mt', type='quantitative',
title='plastic resin produced [Mt]'),
tooltip=[alt.Tooltip(field='year', type='temporal', timeUnit='year', title='year'),
alt.Tooltip(field='resin_production_Mt', type='quantitative',
title='produced mass [Mt]')]
).properties(
width="container",
height=300
)
l = chart.mark_line(color='dodgerblue')
p = chart.mark_point(color='dodgerblue', filled=True)
layer = alt.layer(l, p)
layer.save('platic_production.json')
# %%
# %%
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] | 2.1766 | 453 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from fnmatch import fnmatch
from typing import Union, List, Dict, Set
from threading import Timer
import circular
| [
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] | 3.058824 | 51 |
from __future__ import absolute_import
from eduid_userdb.proofing import OidcProofingUserDB, LetterProofingUserDB, LookupMobileProofingUserDB
from eduid_userdb.proofing import EmailProofingUserDB, PhoneProofingUserDB, OrcidProofingUserDB
from eduid_userdb.proofing import EidasProofingUserDB
from eduid_userdb.personal_data import PersonalDataUserDB
from eduid_userdb.security import SecurityUserDB
from celery.utils.log import get_task_logger
logger = get_task_logger(__name__)
def filter_nin(value):
"""
:param value: dict
:return: list
This function will compile a users verified NINs to a list of strings.
"""
result = []
for item in value:
verified = item.get('verified', False)
if verified and type(verified) == bool: # Be sure that it's not something else that evaluates as True in Python
result.append(item['nin'])
return result
class OidcProofingAMPContext(object):
"""
Private data for this AM plugin.
"""
class LetterProofingAMPContext(object):
"""
Private data for this AM plugin.
"""
class LookupMobileProofingAMPContext(object):
"""
Private data for this AM plugin.
"""
class EmailProofingAMPContext(object):
"""
Private data for this AM plugin.
"""
class PhoneProofingAMPContext(object):
"""
Private data for this AM plugin.
"""
class PersonalDataAMPContext(object):
"""
Private data for this AM plugin.
"""
class SecurityAMPContext(object):
"""
Private data for this AM plugin.
"""
class OrcidAMPContext(object):
"""
Private data for this AM plugin.
"""
class EidasAMPContext(object):
"""
Private data for this AM plugin.
"""
def oidc_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: OidcProofingAMPContext
"""
return OidcProofingAMPContext(am_conf['MONGO_URI'])
def letter_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: LetterProofingAMPContext
"""
return LetterProofingAMPContext(am_conf['MONGO_URI'])
def lookup_mobile_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: LetterProofingAMPContext
"""
return LookupMobileProofingAMPContext(am_conf['MONGO_URI'])
def email_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: EmailProofingAMPContext
"""
return EmailProofingAMPContext(am_conf['MONGO_URI'])
def phone_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: PhoneProofingAMPContext
"""
return PhoneProofingAMPContext(am_conf['MONGO_URI'])
def personal_data_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: PersonalDataAMPContext
"""
return PersonalDataAMPContext(am_conf['MONGO_URI'])
def security_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: SecurityAMPContext
"""
return SecurityAMPContext(am_conf['MONGO_URI'])
def orcid_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: OrcidAMPContext
"""
return OrcidAMPContext(am_conf['MONGO_URI'])
def eidas_plugin_init(am_conf):
"""
Create a private context for this plugin.
Whatever is returned by this function will get passed to attribute_fetcher() as
the `context' argument.
:am_conf: Attribute Manager configuration data.
:type am_conf: dict
:rtype: EidasAMPContext
"""
return EidasAMPContext(am_conf['MONGO_URI'])
def attribute_fetcher(context, user_id):
"""
Read a user from the Dashboard private private_db and return an update
dict to let the Attribute Manager update the use in the central
eduid user database.
:param context: Plugin context, see plugin_init above.
:param user_id: Unique identifier
:type context: DashboardAMPContext
:type user_id: ObjectId
:return: update dict
:rtype: dict
"""
attributes = {}
logger.debug('Trying to get user with _id: {} from {}.'.format(user_id, context.private_db))
user = context.private_db.get_user_by_id(user_id)
logger.debug('User: {} found.'.format(user))
user_dict = user.to_dict(old_userdb_format=False)
# white list of valid attributes for security reasons
attributes_set = {}
attributes_unset = {}
for attr in context.WHITELIST_SET_ATTRS:
value = value_filter(attr, user_dict.get(attr, None))
if value:
attributes_set[attr] = value
elif attr in context.WHITELIST_UNSET_ATTRS:
attributes_unset[attr] = value
logger.debug('Will set attributes: {}'.format(attributes_set))
logger.debug('Will remove attributes: {}'.format(attributes_unset))
if attributes_set:
attributes['$set'] = attributes_set
if attributes_unset:
attributes['$unset'] = attributes_unset
return attributes
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62,
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796,
12608,
62,
403,
2617,
628,
220,
220,
220,
1441,
12608,
198
] | 2.9375 | 2,224 |
from kivy.uix.widget import Widget
from kivy.clock import Clock
from runnable import run_on_ui_thread
from kivy.event import EventDispatcher
from jnius import autoclass
from webviewclient import WebviewClient
#Load Native Modules
WebView = autoclass('android.webkit.WebView')
WebViewClient = autoclass('android.webkit.WebViewClient')
activity = autoclass('org.kivy.android.PythonActivity').mActivity
LayoutParams = autoclass('android.view.ViewGroup$LayoutParams')
View = autoclass('android.view.View')
| [
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7680,
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1960,
420,
31172,
10786,
19411,
13,
1177,
13,
7680,
11537,
628,
628
] | 1.906494 | 385 |
from unittest.mock import patch, Mock
from vaas.cluster.models import LogicalCluster, VarnishServer, VclTemplateBlock, VclTemplate, Dc
from vaas.manager.tests.test_views import BaseApiViewPermissionsTest
| [
6738,
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] | 3.21875 | 64 |
"""
android.py
Android views.
"""
import logging
from datetime import datetime
from flask import Blueprint, abort, request, jsonify
from werkzeug.exceptions import BadRequest
from app.decorators import crossdomain, ignore_exception
from app.models.adventure import Adventure
from app.models.point import Point
SFLOAT = ignore_exception(TypeError)(float)
SINT = ignore_exception(TypeError)(int)
SBOOL = ignore_exception(TypeError)(bool)
MOD_ARDUINO = Blueprint('arduino', __name__, url_prefix='/api/v1/adventure/<slug>/arduino')
@MOD_ARDUINO.route('/', methods=['POST'])
@crossdomain('*')
def add_point(slug):
"""Create a new point based on post parameters."""
try:
timestamp = datetime.now()
adventure = Adventure.objects(slug=slug).get()
point = Point(
title='Arduino tracker',
desc=None,
altitude=request.form.get('alt', None),
speed=request.form.get('spd', None),
direction=request.form.get('dir', None),
latitude=SFLOAT(request.form.get('lat', None)),
longitude=SFLOAT(request.form.get('lon', None)),
resource=None,
point_type='tracker',
timestamp=timestamp,
delorme_id=None,
aio_id=None,
hide=bool(request.form.get('hide', None) in ['true', 'True', 'TRUE', '1', 'y', 'yes']),
thumb=None,
photo=None,
video=None,
source='arduino',
battery=SINT(request.form.get('bat', None)),
user=None
)
adventure.points.append(point)
adventure.save()
return jsonify({'status': "ok"})
except ValueError as err:
logging.error(err)
abort(400)
except BadRequest as err:
logging.error(err)
abort(400)
return jsonify({'status': "ok"})
| [
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220,
220,
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28,
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220,
220,
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220,
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6,
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366,
482,
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8,
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220,
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220,
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355,
11454,
25,
198,
220,
220,
220,
220,
220,
220,
220,
18931,
13,
18224,
7,
8056,
8,
198,
220,
220,
220,
220,
220,
220,
220,
15614,
7,
7029,
8,
198,
220,
220,
220,
2845,
7772,
18453,
355,
11454,
25,
198,
220,
220,
220,
220,
220,
220,
220,
18931,
13,
18224,
7,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
15614,
7,
7029,
8,
198,
220,
220,
220,
1441,
33918,
1958,
15090,
6,
13376,
10354,
366,
482,
20662,
8,
198
] | 2.244873 | 829 |
import torch
import torch.nn as nn
import torch.nn.functional as F | [
11748,
28034,
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11748,
28034,
13,
20471,
355,
299,
77,
198,
11748,
28034,
13,
20471,
13,
45124,
355,
376
] | 3.473684 | 19 |
#! /usr/bin/env python
#-----------------------------------------------------------------------
# COPYRIGHT_BEGIN
# Copyright (C) 2016, FixFlyer, LLC.
# All rights reserved.
# COPYRIGHT_END
#-----------------------------------------------------------------------
| [
2,
0,
1220,
14629,
14,
8800,
14,
24330,
21015,
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2,
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198,
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27975,
38162,
9947,
62,
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] | 4.962264 | 53 |
from django import forms
from .models import SignUp
from base.views import GenericModalCreateView
| [
6738,
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198,
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] | 3.777778 | 27 |
from copy import deepcopy
import numpy as np
import scipy.stats as ss
from turbo import Turbo1
from turbo.utils import from_unit_cube, latin_hypercube, to_unit_cube
from bayesmark.abstract_optimizer import AbstractOptimizer
from bayesmark.experiment import experiment_main
from bayesmark.space import JointSpace
if __name__ == "__main__":
experiment_main(TurboOptimizer)
| [
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] | 3.237288 | 118 |
# Copyright (c) 2020 ISP RAS (http://www.ispras.ru)
# Ivannikov Institute for System Programming of the Russian Academy of Sciences
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import setuptools
VERSION = '3.0'
setuptools.setup(
name="klever",
use_scm_version={'fallback_version': get_fallback_version()},
author="ISP RAS",
author_email="[email protected]",
url="http://forge.ispras.ru/projects/klever",
license="LICENSE",
description="Klever is a software verification framework",
long_description=open("README.md", encoding="utf8").read(),
python_requires=">=3.7",
packages=["klever"],
package_data={"klever": package_files("klever")},
entry_points={
"console_scripts": [
"klever-core=klever.core.__main__:main",
"klever-client-controller=klever.scheduler.main:client_controller",
"klever-debug-scheduler=klever.scheduler.main:debug_scheduler",
"klever-native-scheduler=klever.scheduler.main:native_scheduler",
"klever-scheduler-client=klever.scheduler.main:scheduler_client",
"klever-verifiercloud-scheduler=klever.scheduler.main:verifiercloud_scheduler",
"klever-node-check=klever.scheduler.controller.checks.node:main",
"klever-resources-check=klever.scheduler.controller.checks.resources:main",
"klever-schedulers-check=klever.scheduler.controller.checks.schedulers:main",
"klever-build=klever.cli.klever_build:klever_build",
"klever-download-job=klever.cli.cli:download_job",
"klever-download-marks=klever.cli.cli:download_marks",
"klever-download-progress=klever.cli.cli:download_progress",
"klever-download-results=klever.cli.cli:download_results",
"klever-start-preset-solution=klever.cli.cli:start_preset_solution",
"klever-start-solution=klever.cli.cli:start_solution",
"klever-update-preset-mark=klever.cli.cli:update_preset_mark",
"klever-update-job=klever.cli.cli:upload_job",
"klever-deploy-local=klever.deploys.local:main",
"klever-deploy-openstack=klever.deploys.openstack:main",
]
},
install_requires=[
"Django==3.0.6",
"BenchExec==1.18",
"clade==3.2.7",
"psycopg2",
"graphviz",
"celery",
"django_celery_results",
"djangorestframework",
"django-compressor",
"django-mptt",
"gunicorn",
"pika",
"python-slugify",
"pytz",
"jinja2",
"ply",
"pygments",
"requests",
"setuptools_scm",
"sortedcontainers",
"consulate"
],
extras_require={
"strict": open("requirements.txt", encoding="utf8").read().splitlines(),
"docs": ["sphinx", "sphinx_rtd_theme"],
"openstack": [
"python-novaclient",
"python-neutronclient",
"python-glanceclient",
"python-cinderclient",
"keystoneauth1",
"paramiko",
"pycryptodome"
]
},
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: Implementation :: CPython",
"License :: OSI Approved :: Apache Software License",
"Operating System :: POSIX :: Linux",
],
)
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7904,
7020,
1600,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.206098 | 1,771 |
import vanilla
if __name__ == "__main__":
from vanilla.test.testTools import executeVanillaTest
executeVanillaTest(Test)
| [
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] | 3.069767 | 43 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-03-30 20:21
from __future__ import unicode_literals
from django.db import migrations
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] | 2.690909 | 55 |
from collections import OrderedDict
import json
from graphenebase.types import (
Uint8, Int16, Uint16, Uint32, Uint64,
Varint32, Int64, String, Bytes, Void,
Array, PointInTime, Signature, Bool,
Set, Fixed_array, Optional, Static_variant,
Map, Id, VoteId
)
from .objects import GrapheneObject, isArgsThisClass
from .account import PublicKey
from .operationids import operations
from .objects import (
Operation,
Asset,
Memo,
Price,
PriceFeed,
Permission,
AccountOptions,
AssetOptions,
ObjectId
)
default_prefix = "BTS"
def getOperationNameForId(i):
""" Convert an operation id into the corresponding string
"""
for key in operations:
if int(operations[key]) is int(i):
return key
return "Unknown Operation ID %d" % i
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] | 2.720395 | 304 |
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