content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import logging
## Logging Configuration ##
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
ch = logging.StreamHandler() # console handler
ch.setLevel(logging.INFO)
fh = logging.FileHandler('logfile.txt')
fh.setLevel(logging.INFO)
fmtr = logging.Formatter('%(asctime)s | [%(levelname)s] | (%(name)s) | %(message)s')
fh.setFormatter(fmtr)
logger.addHandler(fh)
logger.addHandler(ch) #disable this to stop console output. This better than print statements as you can disable all console output in 1 spot instead of every print statement.
logger.critical(f'testing a critical message from {__name__}') | [
11748,
18931,
198,
198,
2235,
5972,
2667,
28373,
22492,
198,
198,
6404,
1362,
796,
18931,
13,
1136,
11187,
1362,
7,
834,
3672,
834,
8,
198,
6404,
1362,
13,
2617,
4971,
7,
6404,
2667,
13,
10778,
8,
198,
198,
354,
796,
18931,
13,
1212... | 3.024272 | 206 |
#!/bin/python3
import os
if __name__ == "__main__":
f = open(os.environ["OUTPUT_PATH"], "w")
nm = input().split()
n = int(nm[0])
m = int(nm[1])
a = list(map(int, input().rstrip().split()))
b = list(map(int, input().rstrip().split()))
total = getTotalX(a, b)
f.write(str(total) + "\n")
f.close()
| [
2,
48443,
8800,
14,
29412,
18,
201,
198,
201,
198,
11748,
28686,
201,
198,
201,
198,
201,
198,
201,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
201,
198,
220,
220,
220,
277,
796,
1280,
7,
418,
13,
268,
2268,
1469... | 1.941176 | 187 |
import h5py
import numpy as np
import os, pdb
import tensorflow as tf
from rllab.envs.base import EnvSpec
from rllab.envs.normalized_env import normalize as normalize_env
import rllab.misc.logger as logger
from sandbox.rocky.tf.algos.trpo import TRPO
from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.policies.gaussian_gru_policy import GaussianGRUPolicy
from sandbox.rocky.tf.envs.base import TfEnv
from sandbox.rocky.tf.spaces.discrete import Discrete
from hgail.algos.hgail_impl import Level
from hgail.baselines.gaussian_mlp_baseline import GaussianMLPBaseline
from hgail.critic.critic import WassersteinCritic
from hgail.envs.spec_wrapper_env import SpecWrapperEnv
from hgail.envs.vectorized_normalized_env import vectorized_normalized_env
from hgail.misc.datasets import CriticDataset, RecognitionDataset
from hgail.policies.categorical_latent_sampler import CategoricalLatentSampler
from hgail.policies.gaussian_latent_var_gru_policy import GaussianLatentVarGRUPolicy
from hgail.policies.gaussian_latent_var_mlp_policy import GaussianLatentVarMLPPolicy
from hgail.policies.latent_sampler import UniformlyRandomLatentSampler
from hgail.core.models import ObservationActionMLP
from hgail.policies.scheduling import ConstantIntervalScheduler
from hgail.recognition.recognition_model import RecognitionModel
from hgail.samplers.hierarchy_sampler import HierarchySampler
import hgail.misc.utils
from julia_env.julia_env import JuliaEnv
'''
Const
NGSIM_FILENAME_TO_ID = {
'trajdata_i101_trajectories-0750am-0805am.txt': 1,
'trajdata_i101_trajectories-0805am-0820am.txt': 2,
'trajdata_i101_trajectories-0820am-0835am.txt': 3,
'trajdata_i80_trajectories-0400-0415.txt': 4,
'trajdata_i80_trajectories-0500-0515.txt': 5,
'trajdata_i80_trajectories-0515-0530.txt': 6
}'''
NGSIM_FILENAME_TO_ID = {
'trajdata_i101_trajectories-0750am-0805am.txt': 1,
'trajdata_i101-22agents-0750am-0805am.txt' : 1
}
'''
Common
'''
'''
Component build functions
'''
'''
This is about as hacky as it gets, but I want to avoid editing the rllab
source code as much as possible, so it will have to do for now.
Add a reset(self, kwargs**) function to the normalizing environment
https://stackoverflow.com/questions/972/adding-a-method-to-an-existing-object-instance
'''
'''end of hack, back to our regularly scheduled programming'''
# Raunak adding an input argument for multiagent video making
'''
setup
'''
'''
data utilities
'''
| [
198,
11748,
289,
20,
9078,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
28686,
11,
279,
9945,
198,
11748,
11192,
273,
11125,
355,
48700,
198,
198,
6738,
374,
297,
397,
13,
268,
14259,
13,
8692,
1330,
2039,
85,
22882,
198,
6738,
374... | 2.683476 | 932 |
import argparse
import ast
import configparser
import io
import os.path
from typing import Any
from typing import Dict
from typing import Optional
from typing import Sequence
METADATA_KEYS = frozenset((
'name', 'version', 'url', 'download_url', 'project_urls', 'author',
'author_email', 'maintainer', 'maintainer_email', 'classifiers',
'license', 'license_file', 'description', 'long_description',
'long_description_content_type', 'keywords', 'platforms', 'provides',
'requires', 'obsoletes',
))
OPTIONS_AS_SECTIONS = (
'entry_points', 'extras_require', 'package_data', 'exclude_package_data',
)
OPTIONS_KEYS = frozenset((
'zip_safe', 'setup_requires', 'install_requires', 'python_requires',
'use_2to3', 'use_2to3_fixers', 'use_2to3_exclude_fixers',
'convert_2to3_doctests', 'scripts', 'eager_resources', 'dependency_links',
'tests_require', 'include_package_data', 'packages', 'package_dir',
'namespace_packages', 'py_modules', 'data_files',
# need special processing (as sections)
*OPTIONS_AS_SECTIONS,
))
FIND_PACKAGES_ARGS = ('where', 'exclude', 'include')
if __name__ == '__main__':
raise SystemExit(main())
| [
11748,
1822,
29572,
198,
11748,
6468,
198,
11748,
4566,
48610,
198,
11748,
33245,
198,
11748,
28686,
13,
6978,
198,
6738,
19720,
1330,
4377,
198,
6738,
19720,
1330,
360,
713,
198,
6738,
19720,
1330,
32233,
198,
6738,
19720,
1330,
45835,
1... | 2.767606 | 426 |
from .user import User, MultiUserUpload
from .problem import Problem, TestData
from .submit import Submit
| [
6738,
764,
7220,
1330,
11787,
11,
15237,
12982,
41592,
198,
6738,
764,
45573,
1330,
20647,
11,
6208,
6601,
198,
6738,
764,
46002,
1330,
39900,
198
] | 4.24 | 25 |
# pytest tests
import numpy as np
from Advent2019_10 import Day10
| [
2,
12972,
9288,
5254,
198,
11748,
299,
32152,
355,
45941,
198,
198,
6738,
33732,
23344,
62,
940,
1330,
3596,
940,
628,
198
] | 3.136364 | 22 |
import pytest
from tests.test_application import app
def test_hello_resource(client):
"""
Test if it is possible to access to /hello resource
:param client: Test client object
:return:
"""
response = client.get('/hello').get_json()
assert response['hello'] == 'world'
def test_asset_found(client):
"""
Test if Swagger assets are accessible from the new path
:param client: Test client object
:return:
"""
response = client.get('/this_is_a_new/path_for_swagger_internal_documentation/swaggerui/swagger-ui-bundle.js')
assert response.status_code is 200
| [
11748,
12972,
9288,
198,
6738,
5254,
13,
9288,
62,
31438,
1330,
598,
628,
198,
198,
4299,
1332,
62,
31373,
62,
31092,
7,
16366,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6208,
611,
340,
318,
1744,
284,
1895,
284,
1220,
31... | 2.914286 | 210 |
'''
03 - Multiple arguments
In the previous exercise, the square brackets around imag in the documentation showed us that the
imag argument is optional. But Python also uses a different way to tell users about arguments being
optional.
Have a look at the documentation of sorted() by typing help(sorted) in the IPython Shell.
You'll see that sorted() takes three arguments: iterable, key and reverse.
key=None means that if you don't specify the key argument, it will be None. reverse=False means
that if you don't specify the reverse argument, it will be False.
In this exercise, you'll only have to specify iterable and reverse, not key. The first input you
pass to sorted() will be matched to the iterable argument, but what about the second input? To tell
Python you want to specify reverse without changing anything about key, you can use =:
sorted(___, reverse = ___)
Two lists have been created for you on the right. Can you paste them together and sort them in
descending order?
Note: For now, we can understand an iterable as being any collection of objects, e.g. a List.
Instructions:
- Use + to merge the contents of first and second into a new list: full.
- Call sorted() on full and specify the reverse argument to be True. Save the sorted list as
full_sorted.
- Finish off by printing out full_sorted.
'''
# Create lists first and second
first = [11.25, 18.0, 20.0]
second = [10.75, 9.50]
# Paste together first and second: full
full = first + second
# Sort full in descending order: full_sorted
full_sorted = sorted(full, reverse=True)
# Print out full_sorted
print(full_sorted) | [
7061,
6,
198,
3070,
532,
20401,
7159,
198,
198,
818,
262,
2180,
5517,
11,
262,
6616,
28103,
1088,
3590,
287,
262,
10314,
3751,
514,
326,
262,
198,
48466,
4578,
318,
11902,
13,
887,
11361,
635,
3544,
257,
1180,
835,
284,
1560,
2985,
... | 3.797647 | 425 |
"""
Entradas: 3 Valores flotantes que son el valor de diferentes monedas
Chelines autriacos --> float --> x
Dramas griegos --> float --> z
Pesetas --> float --> w
Salidas 4 valores flotantes que es la conversin de las anteriores monedas
Pesetas --> float --> x
Francos franceses --> float --> z
Dolares --> float --> a
Liras italianas --> float --> b
"""
# Entradas
x1 = float(input("Dime los chelines autracos\n"))
z1 = float(input("Dime los dracmas griegos\n"))
w = float(input("Dime las pesetas\n"))
# Caja negra
x = (x1 * 956871)/100
z = z1/22.64572381
a = w/122499
b = (w*100)/9289
# Salidas
print(f"\n{x1} Chelines austracos en pesetas son {x}\n{z1} Dracmas griegos en Francos franceses son {z}\n{w} Pesetas en Dolares son {a}\n{w} Pesetas en Liras italianas son {b}\n") | [
37811,
198,
14539,
6335,
292,
25,
513,
3254,
2850,
781,
313,
39781,
8358,
3367,
1288,
1188,
273,
390,
288,
361,
9100,
274,
937,
276,
292,
220,
198,
198,
38292,
1127,
1960,
380,
330,
418,
14610,
12178,
14610,
2124,
220,
198,
35,
859,
... | 2.451713 | 321 |
import argparse
import os
from glob import glob
import imageio
from tqdm import tqdm
from csbdeep.utils import normalize
from stardist.models import StarDist3D
# could be done more efficiently, see
# https://github.com/hci-unihd/batchlib/blob/master/batchlib/segmentation/stardist_prediction.py
if __name__ == '__main__':
main()
| [
11748,
1822,
29572,
198,
11748,
28686,
198,
6738,
15095,
1330,
15095,
198,
198,
11748,
2939,
952,
198,
6738,
256,
80,
36020,
1330,
256,
80,
36020,
198,
198,
6738,
269,
36299,
22089,
13,
26791,
1330,
3487,
1096,
198,
6738,
336,
446,
396,... | 2.923077 | 117 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from django import forms
from estacionamientos.models import Estacionamiento, Reserva, Pago
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
42625,
14208,
1330,
5107,
198,
6738,
1556,
49443,
321,
1153,
418,
13,
27530,
1330,
10062,
49443,
321,
1153,
78,
1... | 2.483333 | 60 |
"""
Y. Saad, M. Schultz,
GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems,
SIAM J. Sci. and Stat. Comput., 7(3), 856869, 1986,
<https://doi.org/10.1137/0907058>.
Other implementations:
<https://petsc.org/release/docs/manualpages/KSP/KSPGMRES.html>
"""
from __future__ import annotations
from typing import Callable
import numpy as np
import scipy.linalg
from numpy.typing import ArrayLike
from ._helpers import (
Identity,
Info,
LinearOperator,
Product,
assert_correct_shapes,
clip_imag,
get_default_inner,
wrap_inner,
)
from .arnoldi import ArnoldiHouseholder, ArnoldiMGS
from .givens import givens
def multi_matmul(A, b):
"""A @ b for many A, b (i.e., A.shape == (m,n,...), y.shape == (n,...))"""
return np.einsum("ij...,j...->i...", A, b)
def multi_solve_triangular(A, B):
"""This function calls scipy.linalg.solve_triangular for every single A. A
vectorized version would be much better here.
"""
A_shape = A.shape
a = A.reshape(A.shape[0], A.shape[1], -1)
b = B.reshape(B.shape[0], -1)
y = []
for k in range(a.shape[2]):
if np.all(b[:, k] == 0.0):
y.append(np.zeros(b[:, k].shape))
else:
y.append(scipy.linalg.solve_triangular(a[:, :, k], b[:, k]))
y = np.array(y).T.reshape([A_shape[0]] + list(A_shape[2:]))
return y
| [
37811,
198,
56,
13,
10318,
324,
11,
337,
13,
26530,
11,
198,
38,
13599,
1546,
25,
257,
38284,
10926,
29598,
11862,
329,
18120,
14011,
26621,
19482,
14174,
3341,
11,
198,
11584,
2390,
449,
13,
10286,
13,
290,
5133,
13,
22476,
1539,
767... | 2.312292 | 602 |
# Copyright 2020 Northern.tech AS
#
# 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
#
# https://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 pytest
import random
import time
from testutils.api.client import ApiClient
import testutils.api.useradm as useradm
import testutils.api.deviceauth as deviceauth
import testutils.api.tenantadm as tenantadm
import testutils.api.deployments as deployments
from testutils.infra.cli import CliTenantadm, CliUseradm
import testutils.util.crypto
from testutils.common import (
User,
Device,
Tenant,
mongo,
clean_mongo,
create_org,
create_random_authset,
get_device_by_id_data,
change_authset_status,
)
class TestAccountSuspensionEnterprise:
def test_user_cannot_log_in(self, tenants):
tc = ApiClient(tenantadm.URL_INTERNAL)
uc = ApiClient(useradm.URL_MGMT)
for u in tenants[0].users:
r = uc.call("POST", useradm.URL_LOGIN, auth=(u.name, u.pwd))
assert r.status_code == 200
# tenant's users can log in
for u in tenants[0].users:
r = uc.call("POST", useradm.URL_LOGIN, auth=(u.name, u.pwd))
assert r.status_code == 200
assert r.status_code == 200
# suspend tenant
r = tc.call(
"PUT",
tenantadm.URL_INTERNAL_SUSPEND,
tenantadm.req_status("suspended"),
path_params={"tid": tenants[0].id},
)
assert r.status_code == 200
time.sleep(10)
# none of tenant's users can log in
for u in tenants[0].users:
r = uc.call("POST", useradm.URL_LOGIN, auth=(u.name, u.pwd))
assert r.status_code == 401
# but other users still can
for u in tenants[1].users:
r = uc.call("POST", useradm.URL_LOGIN, auth=(u.name, u.pwd))
assert r.status_code == 200
| [
2,
15069,
12131,
8342,
13,
13670,
7054,
198,
2,
198,
2,
220,
220,
220,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
220,
220,
220,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
35... | 2.392675 | 983 |
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import json
import logging
import yaml
import requests
import time
from actions.migrate_job_action import MigrateJobAction
from actions.send_alert_action import SendAlertAction
from actions.reboot_node_action import RebootNodeAction
from actions.uncordon_action import UncordonAction
from datetime import datetime, timedelta, timezone
from rules_abc import Rule
from utils import prometheus_util, k8s_util
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
activity_log = logging.getLogger('activity')
| [
11748,
28686,
11,
25064,
198,
17597,
13,
6978,
13,
33295,
7,
418,
13,
6978,
13,
15908,
3672,
7,
418,
13,
6978,
13,
397,
2777,
776,
7,
834,
7753,
834,
22305,
198,
198,
11748,
33918,
198,
11748,
18931,
198,
11748,
331,
43695,
198,
117... | 3.373626 | 182 |
# Copyright 2019 Nokia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
from ironic_python_agent import hardware
from ironic_python_agent import utils
from oslo_log import log
from oslo_concurrency import processutils
LOG = log.getLogger()
| [
2,
15069,
13130,
26182,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
2,
921,
743,
7330,
257... | 3.724138 | 203 |
from django.core.cache import cache
from datetime import datetime
from museum_site.models.detail import Detail
from museum_site.models.file import File
from museum_site.constants import TERMS_DATE
from museum_site.common import (
DEBUG, EMAIL_ADDRESS, BOOT_TS, CSS_INCLUDES, UPLOAD_CAP, env_from_host,
qs_sans
)
from museum_site.core.detail_identifiers import *
| [
6738,
42625,
14208,
13,
7295,
13,
23870,
1330,
12940,
198,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
198,
6738,
13257,
62,
15654,
13,
27530,
13,
49170,
1330,
42585,
198,
6738,
13257,
62,
15654,
13,
27530,
13,
7753,
1330,
9220,
198,
... | 2.952381 | 126 |
from django.contrib.auth import get_user_model
from django.contrib.staticfiles.templatetags.staticfiles import static
from django.shortcuts import redirect
from misago.conf import settings
UserModel = get_user_model()
| [
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
1330,
651,
62,
7220,
62,
19849,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
12708,
16624,
13,
11498,
489,
265,
316,
3775,
13,
12708,
16624,
1330,
9037,
198,
6738,
42625,
14208,
13,
19509,
... | 3.378788 | 66 |
from __future__ import print_function, unicode_literals, absolute_import, division
from six.moves import range, zip, map, reduce, filter
from keras.layers import Input, Conv2D, Conv3D, Activation, Lambda
from keras.models import Model
from keras.layers.merge import Add, Concatenate
import tensorflow as tf
from keras import backend as K
from .blocks import unet_block, unet_blocks, gaussian_2d
import re
from ..utils import _raise, backend_channels_last
import numpy as np
def custom_unet(input_shape,
last_activation,
n_depth=2,
n_filter_base=16,
kernel_size=(3,3,3),
n_conv_per_depth=2,
activation="relu",
batch_norm=False,
dropout=0.0,
pool_size=(2,2,2),
n_channel_out=1,
residual=False,
prob_out=False,
long_skip=True,
eps_scale=1e-3):
""" TODO """
if last_activation is None:
raise ValueError("last activation has to be given (e.g. 'sigmoid', 'relu')!")
all((s % 2 == 1 for s in kernel_size)) or _raise(ValueError('kernel size should be odd in all dimensions.'))
channel_axis = -1 if backend_channels_last() else 1
n_dim = len(kernel_size)
# TODO: rewrite with conv_block
conv = Conv2D if n_dim == 2 else Conv3D
input = Input(input_shape, name="input")
unet = unet_block(n_depth, n_filter_base, kernel_size, input_planes=input_shape[-1],
activation=activation, dropout=dropout, batch_norm=batch_norm,
n_conv_per_depth=n_conv_per_depth, pool=pool_size, long_skip=long_skip)(input)
final = conv(n_channel_out, (1,)*n_dim, activation='linear')(unet)
if residual:
if not (n_channel_out == input_shape[-1] if backend_channels_last() else n_channel_out == input_shape[0]):
raise ValueError("number of input and output channels must be the same for a residual net.")
final = Add()([final, input])
final = Activation(activation=last_activation)(final)
if prob_out:
scale = conv(n_channel_out, (1,)*n_dim, activation='softplus')(unet)
scale = Lambda(lambda x: x+np.float32(eps_scale))(scale)
final = Concatenate(axis=channel_axis)([final, scale])
return Model(inputs=input, outputs=final)
def uxnet(input_shape,
n_depth=2,
n_filter_base=16,
kernel_size=(3, 3),
n_conv_per_depth=2,
activation="relu",
last_activation='linear',
batch_norm=False,
dropout=0.0,
pool_size=(2, 2),
residual=True,
odd_to_even=False,
shortcut=None,
shared_idx=[],
prob_out=False,
eps_scale=1e-3):
"""
Multi-body U-Net which learns identity by leaving one plane out in each branch
:param input_shape:
:param n_depth:
:param n_filter_base:
:param kernel_size:
:param n_conv_per_depth:
:param activation:
:param last_activation:
:param batch_norm:
:param dropout:
:param pool_size:
:param prob_out:
:param eps_scale:
:return: Model
"""
# TODO: fill params
# TODO: add odd-to-even mode
# Define vars
channel_axis = -1 if backend_channels_last() else 1
n_planes = input_shape[channel_axis]
if n_planes % 2 != 0 and odd_to_even:
raise ValueError('Odd-to-even mode does not support uneven number of planes')
n_dim = len(kernel_size)
conv = Conv2D if n_dim == 2 else Conv3D
# Define functional model
input = Input(shape=input_shape, name='input_main')
# TODO test new implementation and remove old
# Split planes (preserve channel)
input_x = [Lambda(lambda x: x[..., i:i+1], output_shape=(None, None, 1))(input) for i in range(n_planes)]
# We can train either in odd-to-even mode or in LOO mode
if odd_to_even:
# In this mode we stack together odd and even planes, train the net to predict even from odd and vice versa
# input_x_out = [Concatenate(axis=-1)(input_x[j::2]) for j in range(2)]
input_x_out = [Concatenate(axis=-1)(input_x[j::2]) for j in range(1, -1, -1)]
else:
# Concatenate planes back in leave-one-out way
input_x_out = [Concatenate(axis=-1)([plane for i, plane in enumerate(input_x) if i != j]) for j in range(n_planes)]
# if odd_to_even:
# input_x_out = [Lambda(lambda x: x[..., j::2],
# output_shape=(None, None, n_planes // 2),
# name='{}_planes'.format('even' if j == 0 else 'odd'))(input)
# for j in range(1, -1, -1)]
# else:
# # input_x_out = [Lambda(lambda x: x[..., tf.convert_to_tensor([i for i in range(n_planes) if i != j], dtype=tf.int32)],
# # output_shape=(None, None, n_planes-1),
# # name='leave_{}_plane_out'.format(j))(input)
# # for j in range(n_planes)]
#
# input_x_out = [Lambda(lambda x: K.concatenate([x[..., :j], x[..., (j+1):]], axis=-1),
# output_shape=(None, None, n_planes - 1),
# name='leave_{}_plane_out'.format(j))(input)
# for j in range(n_planes)]
# U-Net parameters depend on mode (odd-to-even or LOO)
n_blocks = 2 if odd_to_even else n_planes
input_planes = n_planes // 2 if odd_to_even else n_planes-1
output_planes = n_planes // 2 if odd_to_even else 1
# Create U-Net blocks (by number of planes)
unet_x = unet_blocks(n_blocks=n_blocks, input_planes=input_planes, output_planes=output_planes,
n_depth=n_depth, n_filter_base=n_filter_base, kernel_size=kernel_size,
activation=activation, dropout=dropout, batch_norm=batch_norm,
n_conv_per_depth=n_conv_per_depth, pool=pool_size, shared_idx=shared_idx)
unet_x = [unet(inp_out) for unet, inp_out in zip(unet_x, input_x_out)]
# Version without weight sharing:
# unet_x = [unet_block(n_depth, n_filter_base, kernel_size,
# activation=activation, dropout=dropout, batch_norm=batch_norm,
# n_conv_per_depth=n_conv_per_depth, pool=pool_size,
# prefix='out_{}_'.format(i))(inp_out) for i, inp_out in enumerate(input_x_out)]
# TODO: rewritten for sharing -- remove commented below
# Convolve n_filter_base to 1 as each U-Net predicts a single plane
# unet_x = [conv(1, (1,) * n_dim, activation=activation)(unet) for unet in unet_x]
if residual:
if odd_to_even:
# For residual U-Net sum up output for odd planes with even planes and vice versa
unet_x = [Add()([unet, inp]) for unet, inp in zip(unet_x, input_x[::-1])]
else:
# For residual U-Net sum up output with its neighbor (next for the first plane, previous for the rest
unet_x = [Add()([unet, inp]) for unet, inp in zip(unet_x, [input_x[1]]+input_x[:-1])]
# Concatenate outputs of blocks, should receive (None, None, None, n_planes)
# TODO assert to check shape?
if odd_to_even:
# Split even and odd, assemble them together in the correct order
# TODO tests
unet_even = [Lambda(lambda x: x[..., i:i+1],
output_shape=(None, None, 1),
name='even_{}'.format(i))(unet_x[0]) for i in range(n_planes // 2)]
unet_odd = [Lambda(lambda x: x[..., i:i+1],
output_shape=(None, None, 1),
name='odd_{}'.format(i))(unet_x[1]) for i in range(n_planes // 2)]
unet_x = list(np.array(list(zip(unet_even, unet_odd))).flatten())
unet = Concatenate(axis=-1)(unet_x)
if shortcut is not None:
# We can create a shortcut without long skip connection to prevent noise memorization
if shortcut == 'unet':
shortcut_block = unet_block(long_skip=False, input_planes=n_planes,
n_depth=n_depth, n_filter_base=n_filter_base, kernel_size=kernel_size,
activation=activation, dropout=dropout, batch_norm=batch_norm,
n_conv_per_depth=n_conv_per_depth, pool=pool_size)(input)
shortcut_block = conv(n_planes, (1,) * n_dim, activation='linear', name='shortcut_final_conv')(shortcut_block)
# Or a simple gaussian blur block
elif shortcut == 'gaussian':
shortcut_block = gaussian_2d(n_planes, k=13, s=7)(input)
else:
raise ValueError('Shortcut should be either unet or gaussian')
# TODO add or concatenate?
unet = Add()([unet, shortcut_block])
# unet = Concatenate(axis=-1)([unet, shortcut_unet])
# Final activation layer
final = Activation(activation=last_activation)(unet)
if prob_out:
scale = conv(n_planes, (1,)*n_dim, activation='softplus')(unet)
scale = Lambda(lambda x: x+np.float32(eps_scale))(scale)
final = Concatenate(axis=channel_axis)([final, scale])
return Model(inputs=input, outputs=final)
def common_unet(n_dim=2, n_depth=1, kern_size=3, n_first=16, n_channel_out=1,
residual=True, prob_out=False, long_skip=True, last_activation='linear'):
"""
Construct a common CARE neural net based on U-Net [1]_ and residual learning [2]_
to be used for image restoration/enhancement.
Parameters
----------
n_dim : int
number of image dimensions (2 or 3)
n_depth : int
number of resolution levels of U-Net architecture
kern_size : int
size of convolution filter in all image dimensions
n_first : int
number of convolution filters for first U-Net resolution level (value is doubled after each downsampling operation)
n_channel_out : int
number of channels of the predicted output image
residual : bool
if True, model will internally predict the residual w.r.t. the input (typically better)
requires number of input and output image channels to be equal
prob_out : bool
standard regression (False) or probabilistic prediction (True)
if True, model will predict two values for each input pixel (mean and positive scale value)
last_activation : str
name of activation function for the final output layer
Returns
-------
function
Function to construct the network, which takes as argument the shape of the input image
Example
-------
>>> model = common_unet(2, 1,3,16, 1, True, False)(input_shape)
References
----------
.. [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox, *U-Net: Convolutional Networks for Biomedical Image Segmentation*, MICCAI 2015
.. [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. *Deep Residual Learning for Image Recognition*, CVPR 2016
"""
return _build_this
modelname = re.compile("^(?P<model>resunet|unet)(?P<n_dim>\d)(?P<prob_out>p)?_(?P<n_depth>\d+)_(?P<kern_size>\d+)_(?P<n_first>\d+)(_(?P<n_channel_out>\d+)out)?(_(?P<last_activation>.+)-last)?$")
def common_unet_by_name(model):
r"""Shorthand notation for equivalent use of :func:`common_unet`.
Parameters
----------
model : str
define model to be created via string, which is parsed as a regular expression:
`^(?P<model>resunet|unet)(?P<n_dim>\d)(?P<prob_out>p)?_(?P<n_depth>\d+)_(?P<kern_size>\d+)_(?P<n_first>\d+)(_(?P<n_channel_out>\d+)out)?(_(?P<last_activation>.+)-last)?$`
Returns
-------
function
Calls :func:`common_unet` with the respective parameters.
Raises
------
ValueError
If argument `model` is not a valid string according to the regular expression.
Example
-------
>>> model = common_unet_by_name('resunet2_1_3_16_1out')(input_shape)
>>> # equivalent to: model = common_unet(2, 1,3,16, 1, True, False)(input_shape)
Todo
----
Backslashes in docstring for regexp not rendered correctly.
"""
m = modelname.fullmatch(model)
if m is None:
raise ValueError("model name '%s' unknown, must follow pattern '%s'" % (model, modelname.pattern))
# from pprint import pprint
# pprint(m.groupdict())
options = {k:int(m.group(k)) for k in ['n_depth','n_first','kern_size']}
options['prob_out'] = m.group('prob_out') is not None
options['residual'] = {'unet': False, 'resunet': True}[m.group('model')]
options['n_dim'] = int(m.group('n_dim'))
options['n_channel_out'] = 1 if m.group('n_channel_out') is None else int(m.group('n_channel_out'))
if m.group('last_activation') is not None:
options['last_activation'] = m.group('last_activation')
return common_unet(**options)
def receptive_field_unet(n_depth, kern_size, pool_size=2, n_dim=2, img_size=1024):
"""Receptive field for U-Net model (pre/post for each dimension)."""
x = np.zeros((1,)+(img_size,)*n_dim+(1,))
mid = tuple([s//2 for s in x.shape[1:-1]])
x[(slice(None),) + mid + (slice(None),)] = 1
model = custom_unet (
x.shape[1:],
n_depth=n_depth, kernel_size=[kern_size]*n_dim, pool_size=[pool_size]*n_dim,
n_filter_base=8, activation='linear', last_activation='linear',
)
y = model.predict(x)[0,...,0]
y0 = model.predict(0*x)[0,...,0]
ind = np.where(np.abs(y-y0)>0)
return [(m-np.min(i), np.max(i)-m) for (m, i) in zip(mid, ind)] | [
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
11,
28000,
1098,
62,
17201,
874,
11,
4112,
62,
11748,
11,
7297,
198,
6738,
2237,
13,
76,
5241,
1330,
2837,
11,
19974,
11,
3975,
11,
4646,
11,
8106,
198,
198,
6738,
41927,
292,
13,
75,
... | 2.255625 | 6,044 |
parameters = {}
genome = {}
genome_stats = {}
genome_test_stats = []
brain = {}
cortical_list = []
cortical_map = {}
intercortical_mapping = []
block_dic = {}
upstream_neurons = {}
memory_list = {}
activity_stats = {}
temp_neuron_list = []
original_genome_id = []
fire_list = []
termination_flag = False
variation_counter_actual = 0
exposure_counter_actual = 0
mnist_training = {}
mnist_testing = {}
top_10_utf_memory_neurons = {}
top_10_utf_neurons = {}
v1_members = []
prunning_candidates = set()
genome_id = ""
event_id = '_'
blueprint = ""
comprehension_queue = ''
working_directory = ''
connectome_path = ''
paths = {}
watchdog_queue = ''
exit_condition = False
fcl_queue = ''
proximity_queue = ''
last_ipu_activity = ''
last_alertness_trigger = ''
influxdb = ''
mongodb = ''
running_in_container = False
hardware = ''
gazebo = False
stimulation_data = {}
hw_controller_path = ''
hw_controller = None
opu_pub = None
router_address = None
burst_timer = 1
# rules = ""
brain_is_running = False
# live_mode_status can have modes of idle, learning, testing, tbd
live_mode_status = 'idle'
fcl_history = {}
brain_run_id = ""
burst_detection_list = {}
burst_count = 0
fire_candidate_list = {}
previous_fcl = {}
future_fcl = {}
labeled_image = []
training_neuron_list_utf = {}
training_neuron_list_img = {}
empty_fcl_counter = 0
neuron_mp_list = []
pain_flag = False
cumulative_neighbor_count = 0
time_neuron_update = ''
time_apply_plasticity_ext = ''
plasticity_time_total = None
plasticity_time_total_p1 = None
plasticity_dict = {}
tester_test_stats = {}
# Flags
flag_ready_to_inject_image = False
| [
17143,
7307,
796,
23884,
198,
5235,
462,
796,
23884,
198,
5235,
462,
62,
34242,
796,
23884,
198,
5235,
462,
62,
9288,
62,
34242,
796,
17635,
198,
27825,
796,
23884,
198,
66,
419,
605,
62,
4868,
796,
17635,
198,
66,
419,
605,
62,
889... | 2.687919 | 596 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import subprocess
from geonamescache import GeonamesCache
gc = GeonamesCache()
toposrc = '../data/states-provinces.json'
for iso2, country in gc.get_countries().items():
iso3 = country['iso3']
topojson = 'mapshaper -i {0} -filter \'"{1}" == adm0_a3\' -filter-fields fips,name -o format=topojson {1}.json'
subprocess.call(topojson.format(toposrc, iso3), shell=True)
subprocess.call('mv *.json ../src/topojson/countries/', shell=True) | [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
11748,
850,
14681,
198,
6738,
4903,
261,
1047,
23870,
1330,
2269,
261,
1047,
30562,
198,
198,
36484,
796,
2269,
261,
10... | 2.507614 | 197 |
"""
This module contains all information for pushing a NordicEvent object into the database.
Functions and Classes
---------------------
"""
import psycopg2
import os
import re
import datetime
from nordb.core import usernameUtilities
from nordb.database import creationInfo
INSERT_COMMANDS = {
1: (
"INSERT INTO "
"nordic_header_main "
"(origin_time, origin_date, location_model, "
"distance_indicator, event_desc_id, epicenter_latitude, "
"epicenter_longitude, depth, depth_control, "
"locating_indicator, epicenter_reporting_agency, "
"stations_used, rms_time_residuals, magnitude_1, "
"type_of_magnitude_1, magnitude_reporting_agency_1, "
"magnitude_2, type_of_magnitude_2, magnitude_reporting_agency_2, "
"magnitude_3, type_of_magnitude_3, magnitude_reporting_agency_3, "
"event_id) "
"VALUES "
"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, "
"%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) "
"RETURNING "
"id;"
),
2: (
"INSERT INTO "
"nordic_header_macroseismic "
"(description, diastrophism_code, tsunami_code, seiche_code, "
"cultural_effects, unusual_effects, maximum_observed_intensity, "
"maximum_intensity_qualifier, intensity_scale, macroseismic_latitude, "
"macroseismic_longitude, macroseismic_magnitude, type_of_magnitude, "
"logarithm_of_radius, logarithm_of_area_1, bordering_intensity_1, "
"logarithm_of_area_2, bordering_intensity_2, quality_rank, "
"reporting_agency, event_id) "
"VALUES "
"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, "
" %s, %s, %s, %s, %s, %s) "
"RETURNING "
"id"
),
3: (
"INSERT INTO "
"nordic_header_comment "
"(h_comment, event_id) "
"VALUES "
"(%s, %s) "
"RETURNING "
"id "
),
5: (
"INSERT INTO "
"nordic_header_error "
"(gap, second_error, epicenter_latitude_error, "
"epicenter_longitude_error, depth_error, "
"magnitude_error, header_id) "
"VALUES "
"(%s, %s, %s, %s, %s, %s, %s)"
"RETURNING "
"id"
),
6: (
"INSERT INTO "
"nordic_header_waveform "
"(waveform_info, event_id) "
"VALUES "
"(%s, %s) "
"RETURNING "
"id "
),
7: (
"INSERT INTO "
"nordic_phase_data "
"(station_code, sp_instrument_type, sp_component, quality_indicator, "
"phase_type, weight, first_motion, observation_time, "
"signal_duration, max_amplitude, max_amplitude_period, back_azimuth, "
"apparent_velocity, signal_to_noise, azimuth_residual, "
"travel_time_residual, location_weight, epicenter_distance, "
"epicenter_to_station_azimuth, event_id) "
"VALUES "
"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, "
"%s, %s, %s, %s, %s, %s, %s, %s, %s) "
"RETURNING "
"id "
),
}
def event2Database(nordic_event, solution_type = "O", nordic_filename = None, f_creation_id = None, e_id = -1, privacy_level='public', db_conn = None):
"""
Function that pushes a NordicEvent object to the database
:param NordicEvent nordic_event: Event that will be pushed to the database
:param int solution_type: event type id
:param str nordic_filename: name of the file from which the nordic is read from
:param int f_creation_id: id of the creation_info entry in the database
:param int e_id: id of the event to which this event will be attached to by event_root. If -1 then this event will not be attached to aything.
:param string privacy_level: privacy level of the event in the database
"""
if db_conn is None:
conn = usernameUtilities.log2nordb()
else:
conn = db_conn
if f_creation_id is None:
creation_id = creationInfo.createCreationInfo(privacy_level, conn)
else:
creation_id = f_creation_id
author_id = None
for header in nordic_event.comment_h:
search = re.search(r'\((\w{3})\)', header.h_comment)
if search is not None:
author_id = search.group(0)[1:-1]
if author_id is None:
author_id = '---'
cur = conn.cursor()
try:
cur.execute("SELECT allow_multiple FROM solution_type WHERE type_id = %s", (solution_type,))
ans = cur.fetchone()
if ans is None:
raise Exception("{0} is not a valid solution_type! Either add the event type to the database or use another solution_type".format(solution_type))
allow_multiple = ans[0]
filename_id = -1
cur.execute("SELECT id FROM nordic_file WHERE file_location = %s", (nordic_filename,))
filenameids = cur.fetchone()
if filenameids is not None:
filename_id = filenameids[0]
root_id = -1
if nordic_event.root_id != -1:
root_id = nordic_event.root_id
if e_id >= 0:
cur.execute("SELECT root_id, solution_type FROM nordic_event WHERE id = %s", (e_id,))
try:
root_id, old_solution_type = cur.fetchone()
except:
raise Exception("Given linking even_id does not exist in the database!")
if e_id == -1 and nordic_event.root_id == -1:
cur.execute("INSERT INTO nordic_event_root DEFAULT VALUES RETURNING id;")
root_id = cur.fetchone()[0]
if filename_id == -1:
cur.execute("INSERT INTO nordic_file (file_location) VALUES (%s) RETURNING id", (nordic_filename,))
filename_id = cur.fetchone()[0]
cur.execute("INSERT INTO " +
"nordic_event " +
"(solution_type, root_id, nordic_file_id, author_id, creation_id) " +
"VALUES " +
"(%s, %s, %s, %s, %s) " +
"RETURNING " +
"id",
(solution_type,
root_id,
filename_id,
author_id,
creation_id)
)
event_id = cur.fetchone()[0]
nordic_event.event_id = event_id
if e_id != -1 and solution_type == old_solution_type and not allow_multiple:
cur.execute("UPDATE nordic_event SET solution_type = 'O' WHERE id = %s", (e_id,))
main_header_id = -1
for main in nordic_event.main_h:
main.event_id = event_id
main.h_id = executeCommand( cur,
INSERT_COMMANDS[1],
main.getAsList(),
True)[0][0]
if main.error_h is not None:
main.error_h.header_id = main.h_id
main.error_h.h_id = executeCommand( cur,
INSERT_COMMANDS[5],
main.error_h.getAsList(),
True)[0][0]
for macro in nordic_event.macro_h:
macro.event_id = event_id
macro.h_id = executeCommand(cur,
INSERT_COMMANDS[2],
macro.getAsList(),
True)[0][0]
for comment in nordic_event.comment_h:
comment.event_id = event_id
comment.h_id = executeCommand( cur,
INSERT_COMMANDS[3],
comment.getAsList(),
True)[0][0]
for waveform in nordic_event.waveform_h:
waveform.event_id = event_id
waveform.h_id = executeCommand( cur,
INSERT_COMMANDS[6],
waveform.getAsList(),
True)[0][0]
for phase_data in nordic_event.data:
phase_data.event_id = event_id
d_id = executeCommand( cur,
INSERT_COMMANDS[7],
phase_data.getAsList(),
True)[0][0]
phase_data.d_id = d_id
conn.commit()
except Exception as e:
raise e
finally:
if f_creation_id is None:
creationInfo.deleteCreationInfoIfUnnecessary(creation_id, db_conn=conn)
if db_conn is None:
conn.close()
def executeCommand(cur, command, vals, returnValue):
"""
Function for for executing a command with values and handling exceptions
:param Psycopg.Cursor cur: cursor object from psycopg2 library
:param str command: the sql command string
:param list vals: list of values for the command
:param bool returnValue: boolean values for if the command returns a value
:returns: Values returned by the query or None if returnValue is False
"""
cur.execute(command, vals)
if returnValue:
return cur.fetchall()
else:
return None
| [
37811,
198,
1212,
8265,
4909,
477,
1321,
329,
7796,
257,
35834,
9237,
2134,
656,
262,
6831,
13,
198,
198,
24629,
2733,
290,
38884,
198,
19351,
12,
198,
37811,
198,
198,
11748,
17331,
22163,
70,
17,
198,
11748,
28686,
198,
11748,
302,
... | 1.710564 | 6,295 |
"""This module contains class definitions for storing media files"""
import webbrowser
| [
37811,
1212,
8265,
4909,
1398,
17336,
329,
23069,
2056,
3696,
37811,
198,
198,
11748,
3992,
40259,
198
] | 5.176471 | 17 |
# workserver.py - simple HTTP server with a do_work / stop_work API
# GET /do_work activates a worker thread which uses CPU
# GET /stop_work signals worker thread to stop
import math
import socket
import threading
import time
from bottle import route, run
hostname = socket.gethostname()
hostport = 9000
keepworking = False # boolean to switch worker thread on or off
# thread which maximizes CPU usage while the keepWorking global is True
# start the worker thread
worker_thread = threading.Thread(target=workerthread, args=())
worker_thread.start()
run(host=hostname, port=hostport)
| [
2,
2499,
18497,
13,
9078,
532,
2829,
14626,
4382,
351,
257,
466,
62,
1818,
1220,
2245,
62,
1818,
7824,
198,
2,
17151,
1220,
4598,
62,
1818,
36206,
257,
8383,
4704,
543,
3544,
9135,
198,
2,
17151,
1220,
11338,
62,
1818,
10425,
8383,
... | 3.691358 | 162 |
from maxdb import DB
def _open(self):
"""Create DB instance and preload default models."""
self._db = DB(self._path)
products = self._db.table(
'Products',
columns={'name': 'str', 'price': 'int'}
)
orders = self._db.table(
'Orders',
columns={'product': 'fk', 'client': 'str', 'destination': 'addr'}
)
try:
products.insert_multiple([
{"name": ("product1", "str"), "price": ("50", "int")},
{"name": ("product2", "str"), "price": ("100", "int")},
{"name": ("product3", "str"), "price": ("200", "int")},
])
except:
pass
try:
orders.insert_multiple([
{
"product": ({'table': 'Products', 'fkid': '1'}, 'fk'),
"client": ("honchar", "str"), "destination": ("Kyiv", "addr")
},
{
"product": ({'table': 'Products', 'fkid': '2'}, 'fk'),
"client": ("honchar2", "str"), "destination": ("Kyiv2", "addr")
},
{
"product": ({'table': 'Products', 'fkid': '3'}, 'fk'),
"client": ("honchar3", "str"), "destination": ("Kyiv3", "addr")
},
])
except:
pass
self.run('help', *())
def _close(self, _):
"""Close DB instance routine."""
self._db.close()
def __enter__(self):
self._open()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self._close(None)
| [
6738,
3509,
9945,
1330,
20137,
628,
628,
220,
220,
220,
825,
4808,
9654,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
16447,
20137,
4554,
290,
662,
2220,
4277,
4981,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220... | 1.823144 | 916 |
import easygui as g
# judge=1
# def judge_null(tmp):
# if tmp.isspace()or len(tmp)==0:
# return judge==0
#
# while 1:
# user_info=g.multenterbox(title='',
# msg='*\t*\t*\t*E-mail',
# fields=['*','*','','*','QQ','*E-mail']
# )
#
# if judge_null(user_info[0])==0:
# g.msgbox(title='',msg='')
# elif judge_null(user_info[1])==0:
# g.msgbox(title='',msg='')
# elif judge_null(user_info[3])==0:
# g.msgbox(title='',msg='')
# elif judge_null(user_info[5])==0:
# g.msgbox(title='',msg='E-mail')
# else:
# g.msgbox(title='',msg='')
# break
#2
title=''
msg=''
field_list=['*','*','','*','QQ','*E-mail']
field_value=[]
field_value = g.multenterbox(msg,title,field_list)
while 1:
if field_value==None:
break
err_msg=''
for i in range(len(field_list)):
option=field_list[i].strip()
if field_value[i].strip()==''and option[0]=='*':
err_msg+='%s\n\n'%(field_list[i])
if err_msg=='':
break
field_value = g.multenterbox(err_msg, title, field_list,field_value)
print(''+str(field_value)) | [
11748,
2562,
48317,
355,
308,
198,
198,
2,
5052,
28,
16,
198,
2,
825,
5052,
62,
8423,
7,
22065,
2599,
198,
2,
220,
220,
220,
220,
611,
45218,
13,
747,
10223,
3419,
273,
18896,
7,
22065,
8,
855,
15,
25,
198,
2,
220,
220,
220,
2... | 1.835913 | 646 |
from cognibench.models import CNBModel
from cognibench.capabilities import ContinuousAction, ContinuousObservation
from cognibench.continuous import ContinuousSpace
from cognibench.models.wrappers import MatlabWrapperMixin
| [
6738,
8866,
571,
24421,
13,
27530,
1330,
31171,
33,
17633,
198,
6738,
8866,
571,
24421,
13,
11128,
5738,
1330,
45012,
12502,
11,
45012,
31310,
13208,
198,
6738,
8866,
571,
24421,
13,
18487,
5623,
1330,
45012,
14106,
198,
6738,
8866,
571,
... | 4.148148 | 54 |
from layout import Layout
| [
6738,
12461,
1330,
47639,
198
] | 5.2 | 5 |
import unittest
from programy.config.file.yaml_file import YamlConfigurationFile
from programy.clients.restful.config import RestConfiguration
from programy.clients.events.console.config import ConsoleConfiguration
| [
11748,
555,
715,
395,
198,
198,
6738,
1430,
88,
13,
11250,
13,
7753,
13,
88,
43695,
62,
7753,
1330,
14063,
75,
38149,
8979,
198,
6738,
1430,
88,
13,
565,
2334,
13,
2118,
913,
13,
11250,
1330,
8324,
38149,
198,
6738,
1430,
88,
13,
... | 3.927273 | 55 |
# Write a recursive function to count the number of nodes in a Tree. (first do your self then see code)
Q # 2:
'''The height of a tree is the maximum number of levels in the tree. So, a tree with just one node has a height of 1. If the root has children which are leaves, the height of the tree is 2.
The height of a TreeNode can be computed recursively using a simple algorithm: The height Of a TreeNode With no children is 1. If it has children the height is: max of height of its two sub-trees + 1.
Write a clean, recursive function for the TreeNode class that calculates the height based on the above statement(first do your self then see code) '''
print(self.val)
if self.left.val > self.val or self.right.val < self.val
return False
| [
2,
19430,
257,
45115,
2163,
284,
954,
262,
1271,
286,
13760,
287,
257,
12200,
13,
357,
11085,
466,
534,
2116,
788,
766,
2438,
8,
198,
197,
198,
197,
198,
1195,
1303,
362,
25,
197,
198,
7061,
6,
464,
6001,
286,
257,
5509,
318,
262,... | 3.673171 | 205 |
#! /usr/bin/env python3
# from datetime import datetime
# from random import choices
# from string import ascii_lowercase
from flask import Flask, request, render_template, Response, send_file
from flaskext.markdown import Markdown
from D47crunch import D47data, pretty_table, make_csv, smart_type
from D47crunch import __version__ as vD47crunch
import zipfile, io, time
from pylab import *
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import base64
from werkzeug.wsgi import FileWrapper
from matplotlib import rcParams
# rcParams['backend'] = 'Agg'
# rcParams['interactive'] = False
rcParams['font.family'] = 'Helvetica'
rcParams['font.sans-serif'] = 'Helvetica'
rcParams['font.size'] = 10
rcParams['mathtext.fontset'] = 'custom'
rcParams['mathtext.rm'] = 'sans'
rcParams['mathtext.bf'] = 'sans:bold'
rcParams['mathtext.it'] = 'sans:italic'
rcParams['mathtext.cal'] = 'sans:italic'
rcParams['mathtext.default'] = 'rm'
rcParams['xtick.major.size'] = 4
rcParams['xtick.major.width'] = 1
rcParams['ytick.major.size'] = 4
rcParams['ytick.major.width'] = 1
rcParams['axes.grid'] = False
rcParams['axes.linewidth'] = 1
rcParams['grid.linewidth'] = .75
rcParams['grid.linestyle'] = '-'
rcParams['grid.alpha'] = .15
rcParams['savefig.dpi'] = 150
__author__ = 'Mathieu Daron'
__contact__ = 'daeron@lsce.ipsl.fr'
__copyright__ = 'Copyright (c) 2020 Mathieu Daron'
__license__ = 'Modified BSD License - https://opensource.org/licenses/BSD-3-Clause'
__date__ = '2020-04-22'
__version__ = '2.1.dev2'
rawdata_input_str = '''UID\tSession\tSample\td45\td46\td47\tNominal_d13C_VPDB\tNominal_d18O_VPDB
A01\tSession01\tETH-1\t5.795017\t11.627668\t16.893512\t2.02\t-2.19
A02\tSession01\tIAEA-C1\t6.219070\t11.491072\t17.277490
A03\tSession01\tETH-2\t-6.058681\t-4.817179\t-11.635064\t-10.17\t-18.69
A04\tSession01\tIAEA-C2\t-3.861839\t4.941839\t0.606117
A05\tSession01\tETH-3\t5.543654\t12.052277\t17.405548\t1.71\t-1.78
A06\tSession01\tMERCK\t-35.929352\t-2.087501\t-39.548484
A07\tSession01\tETH-4\t-6.222218\t-5.194170\t-11.944111
A08\tSession01\tETH-2\t-6.067055\t-4.877104\t-11.699265\t-10.17\t-18.69
A09\tSession01\tMERCK\t-35.930739\t-2.080798\t-39.545632
A10\tSession01\tETH-1\t5.788207\t11.559104\t16.801908\t2.02\t-2.19
A11\tSession01\tETH-4\t-6.217508\t-5.221407\t-11.987503
A12\tSession01\tIAEA-C2\t-3.876921\t4.868892\t0.521845
A13\tSession01\tETH-3\t5.539840\t12.013444\t17.368631\t1.71\t-1.78
A14\tSession01\tIAEA-C1\t6.219046\t11.447846\t17.234280
A15\tSession01\tMERCK\t-35.932060\t-2.088659\t-39.531627
A16\tSession01\tETH-3\t5.516658\t11.978320\t17.295740\t1.71\t-1.78
A17\tSession01\tETH-4\t-6.223370\t-5.253980\t-12.025298
A18\tSession01\tETH-2\t-6.069734\t-4.868368\t-11.688559\t-10.17\t-18.69
A19\tSession01\tIAEA-C1\t6.213642\t11.465109\t17.244547
A20\tSession01\tETH-1\t5.789982\t11.535603\t16.789811\t2.02\t-2.19
A21\tSession01\tETH-4\t-6.205703\t-5.144529\t-11.909160
A22\tSession01\tIAEA-C1\t6.212646\t11.406548\t17.187214
A23\tSession01\tETH-3\t5.531413\t11.976697\t17.332700\t1.71\t-1.78
A24\tSession01\tMERCK\t-35.926347\t-2.124579\t-39.582201
A25\tSession01\tETH-1\t5.786979\t11.527864\t16.775547\t2.02\t-2.19
A26\tSession01\tIAEA-C2\t-3.866505\t4.874630\t0.525332
A27\tSession01\tETH-2\t-6.076302\t-4.922424\t-11.753283\t-10.17\t-18.69
A28\tSession01\tIAEA-C2\t-3.878438\t4.818588\t0.467595
A29\tSession01\tETH-3\t5.546458\t12.133931\t17.501646\t1.71\t-1.78
A30\tSession01\tETH-1\t5.802916\t11.642685\t16.904286\t2.02\t-2.19
A31\tSession01\tETH-2\t-6.069274\t-4.847919\t-11.677722\t-10.17\t-18.69
A32\tSession01\tETH-3\t5.523018\t12.007363\t17.362080\t1.71\t-1.78
A33\tSession01\tETH-1\t5.802333\t11.616032\t16.884255\t2.02\t-2.19
A34\tSession01\tETH-3\t5.537375\t12.000263\t17.350856\t1.71\t-1.78
A35\tSession01\tETH-2\t-6.060713\t-4.893088\t-11.728465\t-10.17\t-18.69
A36\tSession01\tETH-3\t5.532342\t11.990022\t17.342273\t1.71\t-1.78
A37\tSession01\tETH-3\t5.533622\t11.980853\t17.342245\t1.71\t-1.78
A38\tSession01\tIAEA-C2\t-3.867587\t4.893554\t0.540404
A39\tSession01\tIAEA-C1\t6.201760\t11.406628\t17.189625
A40\tSession01\tETH-1\t5.802150\t11.563414\t16.836189\t2.02\t-2.19
A41\tSession01\tETH-2\t-6.068598\t-4.897545\t-11.722343\t-10.17\t-18.69
A42\tSession01\tMERCK\t-35.928359\t-2.098440\t-39.577150
A43\tSession01\tETH-4\t-6.219175\t-5.168031\t-11.936923
A44\tSession01\tIAEA-C2\t-3.871671\t4.871517\t0.518290
B01\tSession02\tETH-1\t5.800180\t11.640916\t16.939044\t2.02\t-2.19
B02\tSession02\tETH-1\t5.799584\t11.631297\t16.917656\t2.02\t-2.19
B03\tSession02\tIAEA-C1\t6.225135\t11.512637\t17.335876
B04\tSession02\tETH-2\t-6.030415\t-4.746444\t-11.525506\t-10.17\t-18.69
B05\tSession02\tIAEA-C2\t-3.837017\t4.992780\t0.675292
B06\tSession02\tETH-3\t5.536997\t12.048918\t17.420228\t1.71\t-1.78
B07\tSession02\tMERCK\t-35.928379\t-2.105615\t-39.594573
B08\tSession02\tETH-4\t-6.218801\t-5.185168\t-11.964407
B09\tSession02\tETH-2\t-6.068197\t-4.840037\t-11.686296\t-10.17\t-18.69
B10\tSession02\tMERCK\t-35.926951\t-2.071047\t-39.546767
B11\tSession02\tETH-1\t5.782634\t11.571818\t16.835185\t2.02\t-2.19
B12\tSession02\tETH-2\t-6.070168\t-4.877700\t-11.703876\t-10.17\t-18.69
B13\tSession02\tETH-4\t-6.214873\t-5.190550\t-11.967040
B14\tSession02\tIAEA-C2\t-3.853550\t4.919425\t0.584634
B15\tSession02\tETH-3\t5.522265\t12.011737\t17.368407\t1.71\t-1.78
B16\tSession02\tIAEA-C1\t6.219374\t11.447014\t17.264258
B17\tSession02\tMERCK\t-35.927733\t-2.103033\t-39.603494
B18\tSession02\tETH-3\t5.527002\t11.984062\t17.332660\t1.71\t-1.78
B19\tSession02\tIAEA-C2\t-3.850358\t4.889230\t0.562794
B20\tSession02\tETH-4\t-6.222398\t-5.263817\t-12.033650
B21\tSession02\tETH-3\t5.525478\t11.970096\t17.340498\t1.71\t-1.78
B22\tSession02\tETH-2\t-6.070129\t-4.941487\t-11.773824\t-10.17\t-18.69
B23\tSession02\tIAEA-C1\t6.217001\t11.434152\t17.232308
B24\tSession02\tETH-1\t5.793421\t11.533191\t16.810838\t2.02\t-2.19
B25\tSession02\tETH-4\t-6.217740\t-5.198048\t-11.977179
B26\tSession02\tIAEA-C1\t6.216912\t11.425200\t17.234224
B27\tSession02\tETH-3\t5.522238\t11.932174\t17.286903\t1.71\t-1.78
B28\tSession02\tMERCK\t-35.914404\t-2.133955\t-39.614612
B29\tSession02\tETH-1\t5.784156\t11.517244\t16.786548\t2.02\t-2.19
B30\tSession02\tIAEA-C2\t-3.852750\t4.884339\t0.551587
B31\tSession02\tETH-2\t-6.068631\t-4.924103\t-11.764507\t-10.17\t-18.69
B32\tSession02\tETH-4\t-6.220238\t-5.231375\t-12.009300
B33\tSession02\tIAEA-C2\t-3.855245\t4.866571\t0.534914
B34\tSession02\tETH-1\t5.788790\t11.544306\t16.809117\t2.02\t-2.19
B35\tSession02\tMERCK\t-35.935017\t-2.173682\t-39.664046
B36\tSession02\tETH-3\t5.518320\t11.955048\t17.300668\t1.71\t-1.78
B37\tSession02\tETH-1\t5.790564\t11.521174\t16.781304\t2.02\t-2.19
B38\tSession02\tETH-4\t-6.218809\t-5.205256\t-11.979998
B39\tSession02\tIAEA-C1\t6.204774\t11.391335\t17.181310
B40\tSession02\tETH-2\t-6.076424\t-4.967973\t-11.815466\t-10.17\t-18.69
C01\tSession03\tETH-3\t5.541868\t12.129615\t17.503738\t1.71\t-1.78
C02\tSession03\tETH-3\t5.534395\t12.034601\t17.391274\t1.71\t-1.78
C03\tSession03\tETH-1\t5.797568\t11.563575\t16.857871\t2.02\t-2.19
C04\tSession03\tETH-3\t5.529415\t11.969512\t17.342673\t1.71\t-1.78
C05\tSession03\tETH-1\t5.794026\t11.526540\t16.806934\t2.02\t-2.19
C06\tSession03\tETH-3\t5.527210\t11.937462\t17.294015\t1.71\t-1.78
C07\tSession03\tIAEA-C1\t6.220521\t11.430197\t17.242458
C08\tSession03\tETH-2\t-6.064061\t-4.900852\t-11.732976\t-10.17\t-18.69
C09\tSession03\tIAEA-C2\t-3.846482\t4.889242\t0.558395
C10\tSession03\tETH-1\t5.789644\t11.520663\t16.795837\t2.02\t-2.19
C11\tSession03\tETH-4\t-6.219385\t-5.258604\t-12.036476
C12\tSession03\tMERCK\t-35.936631\t-2.161769\t-39.693775
C13\tSession03\tETH-2\t-6.076357\t-4.939912\t-11.803553\t-10.17\t-18.69
C14\tSession03\tIAEA-C2\t-3.862518\t4.850015\t0.499777
C15\tSession03\tETH-3\t5.515822\t11.928316\t17.287739\t1.71\t-1.78
C16\tSession03\tETH-4\t-6.216625\t-5.252914\t-12.033781
C17\tSession03\tETH-1\t5.792540\t11.537788\t16.801906\t2.02\t-2.19
C18\tSession03\tIAEA-C1\t6.218853\t11.447394\t17.270859
C19\tSession03\tETH-2\t-6.070107\t-4.944520\t-11.806885\t-10.17\t-18.69
C20\tSession03\tMERCK\t-35.935001\t-2.155577\t-39.675070
C21\tSession03\tETH-3\t5.542309\t12.082338\t17.471951\t1.71\t-1.78
C22\tSession03\tETH-4\t-6.209017\t-5.137393\t-11.920935
C23\tSession03\tETH-1\t5.796781\t11.621197\t16.905496\t2.02\t-2.19
C24\tSession03\tMERCK\t-35.926449\t-2.053921\t-39.576918
C25\tSession03\tETH-2\t-6.057158\t-4.797641\t-11.644824\t-10.17\t-18.69
C26\tSession03\tIAEA-C1\t6.221982\t11.501725\t17.321709
C27\tSession03\tETH-3\t5.535162\t12.023486\t17.396560\t1.71\t-1.78
C28\tSession03\tIAEA-C2\t-3.836934\t4.984196\t0.665651
C29\tSession03\tETH-3\t5.531331\t11.991300\t17.353622\t1.71\t-1.78
C30\tSession03\tIAEA-C2\t-3.844008\t4.926554\t0.601156
C31\tSession03\tETH-2\t-6.063163\t-4.907454\t-11.765065\t-10.17\t-18.69
C32\tSession03\tMERCK\t-35.941566\t-2.163022\t-39.704731
C33\tSession03\tETH-3\t5.523894\t11.992718\t17.363902\t1.71\t-1.78
C34\tSession03\tIAEA-C1\t6.220801\t11.462090\t17.282153
C35\tSession03\tETH-1\t5.794369\t11.563017\t16.845673\t2.02\t-2.19
C36\tSession03\tETH-4\t-6.221257\t-5.272969\t-12.055444
C37\tSession03\tETH-3\t5.517832\t11.957180\t17.312487\t1.71\t-1.78
C38\tSession03\tETH-2\t-6.053330\t-4.909476\t-11.740852\t-10.17\t-18.69
C39\tSession03\tIAEA-C1\t6.217139\t11.440085\t17.244787
C40\tSession03\tETH-1\t5.794091\t11.541948\t16.826158\t2.02\t-2.19
C41\tSession03\tIAEA-C2\t-3.803466\t4.894953\t0.624184
C42\tSession03\tETH-3\t5.513788\t11.933062\t17.286883\t1.71\t-1.78
C43\tSession03\tETH-1\t5.793334\t11.569668\t16.844535\t2.02\t-2.19
C44\tSession03\tETH-2\t-6.064928\t-4.935031\t-11.786336\t-10.17\t-18.69
C45\tSession03\tETH-4\t-6.216796\t-5.300373\t-12.075033
C46\tSession03\tETH-3\t5.521772\t11.933713\t17.283775\t1.71\t-1.78
C47\tSession03\tMERCK\t-35.937762\t-2.181553\t-39.739636
D01\tSession04\tETH-4\t-6.218867\t-5.242334\t-12.032129
D02\tSession04\tIAEA-C1\t6.218458\t11.435622\t17.238776
D03\tSession04\tETH-3\t5.522006\t11.946540\t17.300601\t1.71\t-1.78
D04\tSession04\tMERCK\t-35.931765\t-2.175265\t-39.716152
D05\tSession04\tETH-1\t5.786884\t11.560397\t16.823187\t2.02\t-2.19
D06\tSession04\tIAEA-C2\t-3.846071\t4.861980\t0.534465
D07\tSession04\tETH-2\t-6.072653\t-4.917987\t-11.786215\t-10.17\t-18.69
D08\tSession04\tETH-3\t5.516592\t11.923729\t17.275641\t1.71\t-1.78
D09\tSession04\tETH-1\t5.789889\t11.531354\t16.804221\t2.02\t-2.19
D10\tSession04\tIAEA-C2\t-3.845074\t4.865635\t0.546284
D11\tSession04\tETH-1\t5.795006\t11.507829\t16.772751\t2.02\t-2.19
D12\tSession04\tETH-1\t5.791371\t11.540606\t16.822704\t2.02\t-2.19
D13\tSession04\tETH-2\t-6.074029\t-4.937379\t-11.786614\t-10.17\t-18.69
D14\tSession04\tETH-4\t-6.216977\t-5.273352\t-12.057294
D15\tSession04\tIAEA-C1\t6.214304\t11.412869\t17.227005
D16\tSession04\tETH-2\t-6.071021\t-4.966406\t-11.812116\t-10.17\t-18.69
D17\tSession04\tETH-3\t5.543181\t12.065648\t17.455042\t1.71\t-1.78
D18\tSession04\tETH-1\t5.805793\t11.632212\t16.937561\t2.02\t-2.19
D19\tSession04\tIAEA-C1\t6.230425\t11.518038\t17.342943
D20\tSession04\tETH-2\t-6.049292\t-4.811109\t-11.639895\t-10.17\t-18.69
D21\tSession04\tIAEA-C2\t-3.829436\t4.967992\t0.665451
D22\tSession04\tETH-3\t5.538827\t12.064780\t17.438156\t1.71\t-1.78
D23\tSession04\tMERCK\t-35.935604\t-2.092229\t-39.632228
D24\tSession04\tETH-4\t-6.215430\t-5.166894\t-11.939419
D25\tSession04\tETH-2\t-6.068214\t-4.868420\t-11.716099\t-10.17\t-18.69
D26\tSession04\tMERCK\t-35.918898\t-2.041585\t-39.566777
D27\tSession04\tETH-1\t5.786924\t11.584138\t16.861248\t2.02\t-2.19
D28\tSession04\tETH-2\t-6.062115\t-4.820423\t-11.664703\t-10.17\t-18.69
D29\tSession04\tETH-4\t-6.210819\t-5.160997\t-11.943417
D30\tSession04\tIAEA-C2\t-3.842542\t4.937635\t0.603831
D31\tSession04\tETH-3\t5.527648\t11.985083\t17.353603\t1.71\t-1.78
D32\tSession04\tIAEA-C1\t6.221429\t11.481788\t17.284825
D33\tSession04\tMERCK\t-35.922066\t-2.113682\t-39.642962
D34\tSession04\tETH-3\t5.521955\t11.989323\t17.345179\t1.71\t-1.78
D35\tSession04\tIAEA-C2\t-3.838229\t4.937180\t0.617586
D36\tSession04\tETH-4\t-6.215638\t-5.221584\t-11.999819
D37\tSession04\tETH-2\t-6.067508\t-4.893477\t-11.754488\t-10.17\t-18.69
D38\tSession04\tIAEA-C1\t6.214580\t11.440629\t17.254051'''
app = Flask(__name__)
Markdown(app, extensions = [
'markdown.extensions.tables',
# 'pymdownx.magiclink',
# 'pymdownx.betterem',
'pymdownx.highlight',
'pymdownx.tilde',
'pymdownx.caret',
# 'pymdownx.emoji',
# 'pymdownx.tasklist',
'pymdownx.superfences'
])
default_payload = {
'display_results': False,
'error_msg': '',
'rawdata_input_str': rawdata_input_str,
'o17_R13_VPDB': 0.01118,
'o17_R18_VSMOW': 0.0020052,
'o17_R17_VSMOW': 0.00038475,
'o17_lambda': 0.528,
'd13C_stdz_setting': 'd13C_stdz_setting_2pt',
'd18O_stdz_setting': 'd18O_stdz_setting_2pt',
'wg_setting': 'wg_setting_fromsamples',
# 'wg_setting_fromsample_samplename': 'ETH-3',
# 'wg_setting_fromsample_d13C': 1.71,
# 'wg_setting_fromsample_d18O': -1.78,
'acidfrac_setting': 1.008129,
'rf_input_str': '0.258\tETH-1\n0.256\tETH-2\n0.691\tETH-3',
'stdz_method_setting': 'stdz_method_setting_pooled',
}
def start():
payload = default_payload.copy()
# payload['token'] = datetime.now().strftime('%y%m%d') + ''.join(choices(ascii_lowercase, k=5))
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
def proceed():
payload = dict(request.form)
data = D47data()
if payload['d13C_stdz_setting'] == 'd13C_stdz_setting_2pt':
data.d13C_STANDARDIZATION_METHOD = '2pt'
elif payload['d13C_stdz_setting'] == 'd13C_stdz_setting_1pt':
data.d13C_STANDARDIZATION_METHOD = '1pt'
elif payload['d13C_stdz_setting'] == 'd13C_stdz_setting_none':
data.d13C_STANDARDIZATION_METHOD = 'none'
if payload['d18O_stdz_setting'] == 'd18O_stdz_setting_2pt':
data.d18O_STANDARDIZATION_METHOD = '2pt'
elif payload['d18O_stdz_setting'] == 'd18O_stdz_setting_1pt':
data.d18O_STANDARDIZATION_METHOD = '1pt'
elif payload['d18O_stdz_setting'] == 'd18O_stdz_setting_none':
data.d18O_STANDARDIZATION_METHOD = 'none'
anchors = [l.split('\t') for l in payload['rf_input_str'].splitlines() if '\t' in l]
data.Nominal_D47 = {l[1]: float(l[0]) for l in anchors}
try:
data.R13_VPDB = float(payload['o17_R13_VPDB'])
except:
payload['error_msg'] = 'Check the value of R13_VPDB in oxygen-17 correction settings.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
try:
data.R18_VSMOW = float(payload['o17_R18_VSMOW'])
except:
payload['error_msg'] = 'Check the value of R18_VSMOW in oxygen-17 correction settings.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
try:
data.R17_VSMOW = float(payload['o17_R17_VSMOW'])
except:
payload['error_msg'] = 'Check the value of R17_VSMOW in oxygen-17 correction settings.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
try:
data.lambda_17 = float(payload['o17_lambda'])
except:
payload['error_msg'] = 'Check the value of in oxygen-17 correction settings.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
data.input(payload['rawdata_input_str'])
# try:
# data.input(payload['rawdata_input_str'], '\t')
# except:
# payload['error_msg'] = 'Raw data input failed for some reason.'
# return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
for r in data:
for k in ['UID', 'Sample', 'Session', 'd45', 'd46', 'd47']:
if k not in r or r[k] == '':
payload['error_msg'] = f'Analysis "{r["UID"]}" is missing field "{k}".'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
for k in ['d45', 'd46', 'd47']:
if not isinstance(r[k], (int, float)):
payload['error_msg'] = f'Analysis "{r["UID"]}" should have a valid number for field "{k}".'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
if payload['wg_setting'] == 'wg_setting_fromsamples':
# if payload['wg_setting_fromsample_samplename'] == '':
# payload['error_msg'] = 'Empty sample name in WG settings.'
# return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
#
# wg_setting_fromsample_samplename = payload['wg_setting_fromsample_samplename']
#
# for s in data.sessions:
# if wg_setting_fromsample_samplename not in [r['Sample'] for r in data.sessions[s]['data']]:
# payload['error_msg'] = f'Sample name from WG settings ("{wg_setting_fromsample_samplename}") not found in session "{s}".'
# return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
#
# try:
# wg_setting_fromsample_d13C = float(payload['wg_setting_fromsample_d13C'])
# except:
# payload['error_msg'] = 'Check the 13C value in WG settings.'
# return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
#
# try:
# wg_setting_fromsample_d18O = float(payload['wg_setting_fromsample_d18O'])
# except:
# payload['error_msg'] = 'Check the 18O value in WG settings.'
# return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
try:
acidfrac = float(payload['acidfrac_setting'])
except:
payload['error_msg'] = 'Check the acid fractionation value.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
if acidfrac == 0:
payload['error_msg'] = 'Acid fractionation value should be greater than zero.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
if payload['wg_setting'] == 'wg_setting_fromsamples':
data.Nominal_d13C_VPDB = {}
data.Nominal_d18O_VPDB = {}
for r in data:
if 'Nominal_d13C_VPDB' in r:
if r['Sample'] in data.Nominal_d13C_VPDB:
if data.Nominal_d13C_VPDB[r['Sample']] != r['Nominal_d13C_VPDB']:
payload['error_msg'] = f"Inconsistent <span class='field'>Nominal_d13C_VPDB</span> value for {r['Sample']} (analysis: {r['UID']})."
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
else:
data.Nominal_d13C_VPDB[r['Sample']] = r['Nominal_d13C_VPDB']
if 'Nominal_d18O_VPDB' in r:
if r['Sample'] in data.Nominal_d18O_VPDB:
if data.Nominal_d18O_VPDB[r['Sample']] != r['Nominal_d18O_VPDB']:
payload['error_msg'] = f"Inconsistent <span class='field'>Nominal_d18O_VPDB</span> value for {r['Sample']} (analysis {r['UID']})."
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
else:
data.Nominal_d18O_VPDB[r['Sample']] = r['Nominal_d18O_VPDB']
try:
data.wg(a18_acid = acidfrac)
except:
payload['error_msg'] = 'WG computation failed for some reason.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
if payload['wg_setting'] == 'wg_setting_explicit':
for r in data:
for k in ['d13Cwg_VPDB', 'd18Owg_VSMOW']:
if k not in r:
payload['error_msg'] = f'Analysis "{r["UID"]}" is missing field "{k}".'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
try:
data.crunch()
except:
payload['error_msg'] = 'Crunching step failed for some reason.'
return render_template('main.html', payload = payload, vD47crunch = vD47crunch)
method = {
'stdz_method_setting_pooled': 'pooled',
'stdz_method_setting_indep_sessions': 'indep_sessions',
}[payload['stdz_method_setting']]
data.standardize(
consolidate_tables = False,
consolidate_plots = False,
method = method)
csv = 'Session,a,b,c,va,vb,vc,covab,covac,covbc,Xa,Ya,Xu,Yu'
for session in data.sessions:
s = data.sessions[session]
Ga = [r for r in s['data'] if r['Sample'] in data.anchors]
Gu = [r for r in s['data'] if r['Sample'] in data.unknowns]
csv += f"\n{session},{s['a']},{s['b']},{s['c']},{s['CM'][0,0]},{s['CM'][1,1]},{s['CM'][2,2]},{s['CM'][0,1]},{s['CM'][0,2]},{s['CM'][1,2]},{';'.join([str(r['d47']) for r in Ga])},{';'.join([str(r['D47']) for r in Ga])},{';'.join([str(r['d47']) for r in Gu])},{';'.join([str(r['D47']) for r in Gu])}"
# payload['error_msg'] = 'Foo bar.'
# return str(payload).replace(', ','\n')
payload['display_results'] = True
payload['csv_of_sessions'] = csv
summary = data.summary(save_to_file = False, print_out = False)
tosessions = data.table_of_sessions(save_to_file = False, print_out = False)
payload['summary'] = pretty_table(summary, header = 0)
payload['summary_rows'] = len(payload['summary'].splitlines())+2
payload['summary_cols'] = len(payload['summary'].splitlines()[0])
payload['table_of_sessions'] = pretty_table(tosessions)
payload['table_of_sessions_rows'] = len(payload['table_of_sessions'].splitlines())+1
payload['table_of_sessions_cols'] = len(payload['table_of_sessions'].splitlines()[0])
payload['table_of_sessions_csv'] = make_csv(tosessions)
tosamples = data.table_of_samples(save_to_file = False, print_out = False)
payload['table_of_samples'] = pretty_table(tosamples)
payload['table_of_samples'] = payload['table_of_samples'][:] + 'NB: d18O_VSMOW is the composition of the analyzed CO2.'
payload['table_of_samples_rows'] = len(payload['table_of_samples'].splitlines())
payload['table_of_samples_cols'] = len(payload['table_of_samples'].splitlines()[0])+1
payload['table_of_samples_csv'] = make_csv(tosamples)
toanalyses = data.table_of_analyses(save_to_file = False, print_out = False)
payload['table_of_analyses'] = pretty_table(toanalyses)
payload['table_of_analyses_rows'] = len(payload['table_of_analyses'].splitlines())+1
payload['table_of_analyses_cols'] = len(payload['table_of_analyses'].splitlines()[0])
payload['table_of_analyses_csv'] = make_csv(toanalyses)
covars = "\n\nCOVARIANCE BETWEEN SAMPLE 47 VALUES:\n\n"
txt = [['Sample #1', 'Sample #2', 'Covariance', 'Correlation']]
unknowns = [k for k in data.unknowns]
for k, s1 in enumerate(unknowns):
for s2 in unknowns[k+1:]:
txt += [[
s1,
s2,
f"{data.sample_D47_covar(s1,s2):.4e}",
f"{data.sample_D47_covar(s1,s2)/data.samples[s1]['SE_D47']/data.samples[s2]['SE_D47']:.6f}",
]]
covars += pretty_table(txt, align = '<<>>')
payload['report'] = f"Report generated on {time.asctime()}\nClumpyCrunch v{__version__} using D47crunch v{vD47crunch}"
payload['report'] += "\n\nOXYGEN-17 CORRECTION PARAMETERS:\n" + pretty_table([['R13_VPDB', 'R18_VSMOW', 'R17_VSMOW', 'lambda_17'], [payload['o17_R13_VPDB'], payload['o17_R18_VSMOW'], payload['o17_R17_VSMOW'], payload['o17_lambda']]], align = '<<<<')
if payload['wg_setting'] == 'wg_setting_fromsample':
payload['report'] += f"\n\nWG compositions constrained by sample {wg_setting_fromsample_samplename} with:"
payload['report'] += f"\n 13C_VPDB = {wg_setting_fromsample_d13C}"
payload['report'] += f"\n 18O_VPDB = {wg_setting_fromsample_d18O}"
payload['report'] += f"\n(18O/16O) AFF = {wg_setting_fromsample_acidfrac}\n"
elif payload['wg_setting'] == 'wg_setting_explicit':
payload['report'] += f"\n\nWG compositions specified by user.\n"
payload['report'] += f"\n\nSUMMARY:\n{payload['summary']}"
payload['report'] += f"\n\nSAMPLES:\n{payload['table_of_samples']}\n"
payload['report'] += f"\n\nSESSIONS:\n{payload['table_of_sessions']}"
payload['report'] += f"\n\nANALYSES:\n{payload['table_of_analyses']}"
payload['report'] += covars
txt = payload['csv_of_sessions']
txt = [[x.strip() for x in l.split(',')] for l in txt.splitlines() if l.strip()]
sessions = [{k: smart_type(v) for k,v in zip(txt[0], l)} for l in txt[1:]]
payload['plots'] = []
for s in sessions:
s['Xa'] = [float(x) for x in s['Xa'].split(';')]
s['Ya'] = [float(x) for x in s['Ya'].split(';')]
s['Xu'] = [float(x) for x in s['Xu'].split(';')]
s['Yu'] = [float(x) for x in s['Yu'].split(';')]
for s in sessions:
fig = figure(figsize = (3,3))
subplots_adjust(.2,.15,.95,.9)
plot_session(s)
pngImage = io.BytesIO()
FigureCanvas(fig).print_png(pngImage)
pngImageB64String = "data:image/png;base64,"
pngImageB64String += base64.b64encode(pngImage.getvalue()).decode('utf8')
payload['plots'] += [pngImageB64String]
close(fig)
return(render_template('main.html', payload = payload, vD47crunch = vD47crunch))
# @app.route("/csv/<foo>/<filename>", methods = ['POST'])
# def get_file(foo, filename):
# payload = dict(request.form)
# return Response(
# payload[foo],
# mimetype='text/plain',
# headers={'Content-Disposition': f'attachment;filename="{filename}"'}
# )
| [
2,
0,
1220,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
2,
422,
4818,
8079,
1330,
4818,
8079,
198,
2,
422,
4738,
1330,
7747,
198,
2,
422,
4731,
1330,
355,
979,
72,
62,
21037,
7442,
198,
6738,
42903,
1330,
46947,
11,
2581,
11,... | 1.926733 | 12,666 |
r"""Train a neural network to predict feedback for a program string."""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import random
import numpy as np
from tqdm import tqdm
import torch
import torch.optim as optim
import torch.utils.data as data
import torch.nn.functional as F
from .models import ProgramRNN
from .utils import AverageMeter, save_checkpoint, merge_args_with_dict
from .datasets import load_dataset
from .config import default_hyperparams
from .rubric_utils.load_params import get_label_params, get_max_seq_len
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('dataset', type=str, help='annotated|synthetic')
parser.add_argument('problem_id', type=int, help='1|2|3|4|5|6|7|8')
parser.add_argument('out_dir', type=str, help='where to save outputs')
parser.add_argument('--cuda', action='store_true', default=False,
help='enables CUDA training [default: False]')
args = parser.parse_args()
args.cuda = args.cuda and torch.cuda.is_available()
merge_args_with_dict(args, default_hyperparams)
device = torch.device('cuda' if args.cuda else 'cpu')
args.max_seq_len = get_max_seq_len(args.problem_id)
label_dim, _, _, _, _ = get_label_params(args.problem_id)
# reproducibility
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if not os.path.isdir(args.out_dir):
os.makedirs(args.out_dir)
train_dataset = load_dataset( args.dataset, args.problem_id, 'train', vocab=None,
max_seq_len=args.max_seq_len, min_occ=args.min_occ)
val_dataset = load_dataset( args.dataset, args.problem_id, 'val', vocab=train_dataset.vocab,
max_seq_len=args.max_seq_len, min_occ=args.min_occ)
test_dataset = load_dataset(args.dataset, args.problem_id, 'test', vocab=train_dataset.vocab,
max_seq_len=args.max_seq_len, min_occ=args.min_occ)
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
val_loader = data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False)
test_loader = data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False)
model = ProgramRNN( args.z_dim, label_dim, train_dataset.vocab_size, embedding_dim=args.embedding_dim,
hidden_dim=args.hidden_dim, num_layers=args.num_layers)
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
best_loss = sys.maxint
track_train_loss = np.zeros(args.epochs)
track_val_loss = np.zeros(args.epochs)
track_test_loss = np.zeros(args.epochs)
track_train_acc = np.zeros(args.epochs)
track_val_acc = np.zeros(args.epochs)
track_test_acc = np.zeros(args.epochs)
for epoch in xrange(1, args.epochs + 1):
train_loss, train_acc = train(epoch)
val_loss, val_acc = test(epoch, val_loader, name='Val')
test_loss, test_acc = test(epoch, test_loader, name='Test')
track_train_loss[epoch - 1] = train_loss
track_val_loss[epoch - 1] = val_loss
track_test_loss[epoch - 1] = test_loss
track_train_acc[epoch - 1] = train_acc
track_val_acc[epoch - 1] = val_acc
track_test_acc[epoch - 1] = test_acc
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
save_checkpoint({
'state_dict': model.state_dict(),
'cmd_line_args': args,
'vocab': train_dataset.vocab,
}, is_best, folder=args.out_dir)
np.save(os.path.join(args.out_dir, 'train_loss.npy'), track_train_loss)
np.save(os.path.join(args.out_dir, 'val_loss.npy'), track_val_loss)
np.save(os.path.join(args.out_dir, 'test_loss.npy'), track_test_loss)
np.save(os.path.join(args.out_dir, 'train_acc.npy'), track_train_acc)
np.save(os.path.join(args.out_dir, 'val_acc.npy'), track_val_acc)
np.save(os.path.join(args.out_dir, 'test_acc.npy'), track_test_acc)
| [
81,
37811,
44077,
257,
17019,
3127,
284,
4331,
7538,
329,
257,
1430,
4731,
526,
15931,
198,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
6738,
11593,
37443,
834,
1330,
4112,
62,
1174... | 2.261222 | 1,849 |
# --------------
# Code starts here
class_1 = ['Geoffrey Hinton' , 'Andrew Ng' , 'Sebastian Raschka' , 'Yoshua Bengio']
class_2 = ['Hilary Mason' , 'Carla Gentry' , 'Corinna Cortes']
new_class = class_1 + class_2
print(new_class)
new_class.append('Peter Warden')
print(new_class)
del new_class[5]
print(new_class)
# Code ends here
# --------------
# Code starts here
courses = {'Math': 65 , 'English': 70 , 'History': 80 , 'French': 70 , 'Science': 60}
total = sum(courses.values())
print(total)
percentage = total/500*100
print(percentage)
# Code ends here
# --------------
# Code starts here
mathematics = { 'Geoffrey Hinton' : 78, 'Andrew Ng' : 95, 'Sebastian Raschka' : 65 ,
'Yoshua Benjio' : 50 , 'Hilary Mason' : 70 , 'Corinna Cortes' : 66 , 'Peter Warden' : 75}
max_marks_scored = max(mathematics, key=mathematics.get)
print(max_marks_scored)
topper = max_marks_scored
print(topper)
# Code ends here
# --------------
# Given string
topper = ' andrew ng'
# Code starts here
first_name = topper.split()[0]
print(first_name)
last_name = topper.split()[1]
print(last_name)
full_name = last_name +' '+ first_name
print(full_name)
certificate_name = full_name.upper()
print(certificate_name)
# Code ends here
| [
2,
220,
26171,
198,
2,
6127,
4940,
994,
198,
4871,
62,
16,
796,
37250,
10082,
2364,
4364,
367,
2371,
6,
837,
705,
20508,
34786,
6,
837,
705,
50,
1765,
459,
666,
28513,
354,
4914,
6,
837,
705,
56,
3768,
6413,
14964,
952,
20520,
198... | 2.678261 | 460 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
NAME:
checklog.py
DESCRIPTION:
This script checks the tail of the log file and lists the disk space
CREATED:
Sun Mar 15 22:53:54 2015
VERSION:
1.0
AUTHOR:
Mark Tibbett
AUTHOR_EMAIL:
mtibbett67@gmail.com
URL:
N/A
DOWNLOAD_URL:
N/A
INSTALL_REQUIRES:
[]
PACKAGES:
[]
SCRIPTS:
[]
'''
# Standard library imports
import os
import sys
import subprocess
# Related third party imports
# Local application/library specific imports
# Console colors
W = '\033[0m' # white (normal)
R = '\033[31m' # red
G = '\033[32m' # green
O = '\033[33m' # orange
B = '\033[34m' # blue
P = '\033[35m' # purple
C = '\033[36m' # cyan
GR = '\033[37m' # gray
# Section formats
SEPARATOR = B + '=' * 80 + W
NL = '\n'
# Clear the terminal
os.system('clear')
# Check for root or sudo. Remove if not needed.
UID = os.getuid()
if UID != 0:
print R + ' [!]' + O + ' ERROR:' + G + ' sysupdate' + O + \
' must be run as ' + R + 'root' + W
# print R + ' [!]' + O + ' login as root (' + W + 'su root' + O + ') \
# or try ' + W + 'sudo ./wifite.py' + W
os.execvp('sudo', ['sudo'] + sys.argv)
else:
print NL
print G + 'You are running this script as ' + R + 'root' + W
print NL + SEPARATOR + NL
LOG = ['tail', '/var/log/messages']
DISK = ['df', '-h']
def check(arg1, arg2):
'''Call subprocess to check logs'''
print G + arg1 + W + NL
item = subprocess.check_output(arg2)
#subprocess.call(arg2)
print item + NL + SEPARATOR + NL
check('Runing tail on messages', LOG)
check('Disk usage', DISK)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
7061,
6,
198,
20608,
25,
198,
9122,
6404,
13,
9078,
198,
198,
30910,
40165,
25,
198,
1212,
4226,
8794,
262,
7894... | 2.327143 | 700 |
n = int(input().strip())
items = [
int(A_temp)
for A_temp
in input().strip().split(' ')
]
items_map = {}
result = None
for i, item in enumerate(items):
if item not in items_map:
items_map[item] = [i]
else:
items_map[item].append(i)
for _, item_indexes in items_map.items():
items_indexes_length = len(item_indexes)
if items_indexes_length > 1:
for i in range(items_indexes_length):
for j in range(i + 1, items_indexes_length):
diff = item_indexes[j] - item_indexes[i]
if result is None:
result = diff
elif diff < result:
result = diff
print(result if result else -1)
| [
77,
796,
493,
7,
15414,
22446,
36311,
28955,
198,
198,
23814,
796,
685,
198,
220,
220,
220,
493,
7,
32,
62,
29510,
8,
198,
220,
220,
220,
329,
317,
62,
29510,
198,
220,
220,
220,
287,
5128,
22446,
36311,
22446,
35312,
10786,
705,
... | 2.056657 | 353 |
"""
Sparse Poisson Recovery (SPoRe) module for solving Multiple Measurement Vector
problem with Poisson signals (MMVP) by batch stochastic gradient ascent and
Monte Carlo integration
Authors: Pavan Kota, Daniel LeJeune
Reference:
[1] P. K. Kota, D. LeJeune, R. A. Drezek, and R. G. Baraniuk, "Extreme Compressed
Sensing of Poisson Rates from Multiple Measurements," Mar. 2021.
arXiv ID:
"""
from abc import ABC, abstractmethod
import numpy as np
import time
import pdb
from .mmv_models import FwdModelGroup, SPoReFwdModelGroup
| [
37811,
198,
50,
29572,
7695,
30927,
21007,
357,
4303,
78,
3041,
8,
8265,
329,
18120,
20401,
24291,
434,
20650,
220,
198,
45573,
351,
7695,
30927,
10425,
357,
12038,
8859,
8,
416,
15458,
3995,
354,
3477,
31312,
37137,
290,
220,
198,
9069... | 3.005495 | 182 |
"""Use translation table to translate coding sequence to protein."""
from Bio.Data import CodonTable # type: ignore
from Bio.Seq import Seq # type: ignore
def translate_cds(cds: str, translation_table: str) -> str:
"""Translate coding sequence to protein.
:param cds: str: DNA coding sequence (CDS)
:param translation_table: str: translation table
as defined in Bio.Seq.Seq.CodonTable.ambiguous_generic_by_name
:return: str: Protein sequence
"""
table = CodonTable.ambiguous_dna_by_name[translation_table]
cds = "".join(cds.split()) # clean out whitespace
coding_dna = Seq(cds)
protein = coding_dna.translate(table, cds=True, to_stop=True)
return str(protein)
| [
37811,
11041,
11059,
3084,
284,
15772,
19617,
8379,
284,
7532,
526,
15931,
201,
198,
201,
198,
6738,
16024,
13,
6601,
1330,
18720,
261,
10962,
220,
1303,
2099,
25,
8856,
201,
198,
6738,
16024,
13,
4653,
80,
1330,
1001,
80,
220,
1303,
... | 2.688645 | 273 |
from pycodimd import CodiMD
cmd = CodiMD('https://md.noemis.me')
#cmd.login('user@example.com','CorrectHorseBatteryStaple')
cmd.load_cookies()
print(cmd.history()[-1]['text']) # Print Name of latest Note
| [
6738,
12972,
19815,
320,
67,
1330,
18720,
72,
12740,
198,
198,
28758,
796,
18720,
72,
12740,
10786,
5450,
1378,
9132,
13,
3919,
30561,
13,
1326,
11537,
198,
2,
28758,
13,
38235,
10786,
7220,
31,
20688,
13,
785,
41707,
42779,
39,
7615,
... | 2.710526 | 76 |
import os
print('echoenv...', end=' ')
print('Hello,', os.environ['USER'])
| [
11748,
28686,
198,
4798,
10786,
3055,
6571,
85,
986,
3256,
886,
11639,
705,
8,
198,
4798,
10786,
15496,
11,
3256,
28686,
13,
268,
2268,
17816,
29904,
6,
12962,
198
] | 2.586207 | 29 |
"""
Display command results.
"""
from __future__ import unicode_literals
from contextlib import contextmanager
from argparse import Namespace
from io import BytesIO
from colorama import AnsiToWin32
from chromalog.stream import stream_has_color_support
from chromalog.colorizer import Colorizer
from chromalog.mark.helpers.simple import (
warning,
important,
success,
error,
)
| [
37811,
198,
23114,
3141,
2482,
13,
198,
37811,
198,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
6738,
4732,
8019,
1330,
4732,
37153,
198,
6738,
1822,
29572,
1330,
28531,
10223,
198,
6738,
33245,
1330,
2750,
... | 3.344538 | 119 |
###
# Copyright (c) 2020-2021, The Limnoria Contributors
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions, and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the author of this software nor the name of
# contributors to this software may be used to endorse or promote products
# derived from this software without specific prior written consent.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
###
from supybot import conf, ircutils, ircmsgs, callbacks
from supybot.i18n import PluginInternationalization
_ = PluginInternationalization("Autocomplete")
REQUEST_TAG = "+draft/autocomplete-request"
RESPONSE_TAG = "+draft/autocomplete-response"
def _commonPrefix(L):
"""Takes a list of lists, and returns their longest common prefix."""
assert L
if len(L) == 1:
return L[0]
for n in range(1, max(map(len, L)) + 1):
prefix = L[0][:n]
for item in L[1:]:
if prefix != item[:n]:
return prefix[0:-1]
assert False
def _getAutocompleteResponse(irc, msg, payload):
"""Returns the value of the +draft/autocomplete-response tag for the given
+draft/autocomplete-request payload."""
tokens = callbacks.tokenize(
payload, channel=msg.channel, network=irc.network
)
normalized_payload = " ".join(tokens)
candidate_commands = _getCandidates(irc, normalized_payload)
if len(candidate_commands) == 0:
# No result
return None
elif len(candidate_commands) == 1:
# One result, return it directly
commands = candidate_commands
else:
# Multiple results, return only the longest common prefix + one word
tokenized_candidates = [
callbacks.tokenize(c, channel=msg.channel, network=irc.network)
for c in candidate_commands
]
common_prefix = _commonPrefix(tokenized_candidates)
words_after_prefix = {
candidate[len(common_prefix)] for candidate in tokenized_candidates
}
commands = [
" ".join(common_prefix + [word]) for word in words_after_prefix
]
# strip what the user already typed
assert all(command.startswith(normalized_payload) for command in commands)
normalized_payload_length = len(normalized_payload)
response_items = [
command[normalized_payload_length:] for command in commands
]
return "\t".join(sorted(response_items))
def _getCandidates(irc, normalized_payload):
"""Returns a list of commands starting with the normalized_payload."""
candidates = set()
for cb in irc.callbacks:
cb_commands = cb.listCommands()
# copy them with the plugin name (optional when calling a command)
# at the beginning
plugin_name = cb.canonicalName()
cb_commands += [plugin_name + " " + command for command in cb_commands]
candidates |= {
command
for command in cb_commands
if command.startswith(normalized_payload)
}
return candidates
Class = Autocomplete
# vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79:
| [
21017,
198,
2,
15069,
357,
66,
8,
12131,
12,
1238,
2481,
11,
383,
7576,
77,
7661,
25767,
669,
198,
2,
1439,
2489,
10395,
13,
198,
2,
198,
2,
2297,
396,
3890,
290,
779,
287,
2723,
290,
13934,
5107,
11,
351,
393,
1231,
198,
2,
176... | 2.831911 | 1,523 |
"""
preprocess-twitter.py
python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"
Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu
Translation of Ruby script to create features for GloVe vectors for Twitter data.
http://nlp.stanford.edu/projects/glove/preprocess-twitter.rb
"""
import sys
import regex as re
FLAGS = re.MULTILINE | re.DOTALL
if __name__ == '__main__':
_, text = sys.argv
if text == "test":
text = "I TEST alllll kinds of #hashtags and #HASHTAGS, @mentions and 3000 (http://t.co/dkfjkdf). w/ <3 :) haha!!!!!"
tokens = tokenize(text)
print(tokens)
| [
37811,
198,
3866,
14681,
12,
6956,
13,
9078,
198,
29412,
662,
14681,
12,
6956,
13,
9078,
366,
4366,
4738,
2420,
351,
1303,
17831,
31499,
11,
2488,
434,
507,
290,
2638,
1378,
83,
13,
1073,
14,
74,
28241,
69,
74,
28241,
69,
357,
28751... | 2.897233 | 253 |
#! /usr/bin/env python
# -*- coding:utf8 -*-
#
# pw_classes.py
#
# This file is part of pyplanes, a software distributed under the MIT license.
# For any question, please contact one of the authors cited below.
#
# Copyright (c) 2020
# Olivier Dazel <olivier.dazel@univ-lemans.fr>
# Mathieu Gaborit <gaborit@kth.se>
# Peter Gransson <pege@kth.se>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
import numpy as np
import numpy.linalg as LA
import matplotlib.pyplot as plt
from mediapack import from_yaml
from mediapack import Air, PEM, EqFluidJCA
from pyPLANES.utils.io import initialisation_out_files_plain
from pyPLANES.core.calculus import PwCalculus
from pyPLANES.core.multilayer import MultiLayer
from pyPLANES.pw.pw_layers import FluidLayer
from pyPLANES.pw.pw_interfaces import FluidFluidInterface, RigidBacking
Air = Air()
# def initialise_PW_solver(L, b):
# nb_PW = 0
# dofs = []
# for _layer in L:
# if _layer.medium.MODEL == "fluid":
# dofs.append(nb_PW+np.arange(2))
# nb_PW += 2
# elif _layer.medium.MODEL == "pem":
# dofs.append(nb_PW+np.arange(6))
# nb_PW += 6
# elif _layer.medium.MODEL == "elastic":
# dofs.append(nb_PW+np.arange(4))
# nb_PW += 4
# interface = []
# for i_l, _layer in enumerate(L[:-1]):
# interface.append((L[i_l].medium.MODEL, L[i_l+1].medium.MODEL))
# return nb_PW, interface, dofs
# class Solver_PW(PwCalculus):
# def __init__(self, **kwargs):
# PwCalculus.__init__(self, **kwargs)
# ml = kwargs.get("ml")
# termination = kwargs.get("termination")
# self.layers = []
# for _l in ml:
# if _l[0] == "Air":
# mat = Air
# else:
# mat = from_yaml(_l[0]+".yaml")
# d = _l[1]
# self.layers.append(Layer(mat,d))
# if termination in ["trans", "transmission","Transmission"]:
# self.backing = "Transmission"
# else:
# self.backing = backing.rigid
# self.kx, self.ky, self.k = None, None, None
# self.shift_plot = kwargs.get("shift_pw", 0.)
# self.plot = kwargs.get("plot_results", [False]*6)
# self.result = {}
# self.outfiles_directory = False
# initialisation_out_files_plain(self)
# def write_out_files(self, out):
# self.out_file.write("{:.12e}\t".format(self.current_frequency))
# abs = 1-np.abs(out["R"])**2
# self.out_file.write("{:.12e}\t".format(abs))
# self.out_file.write("\n")
# def interface_fluid_fluid(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K)
# SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K)
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]
# M[ieq, d[iinter+1][0]] = -SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[1, 1]
# M[ieq, d[iinter+1][0]] = -SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_fluid_rigid(self, M, ieq, L, d):
# SV, k_y = fluid_SV(self.kx, self.k, L.medium.K)
# M[ieq, d[0]] = SV[0, 0]*np.exp(-1j*k_y*L.thickness)
# M[ieq, d[1]] = SV[0, 1]
# ieq += 1
# return ieq
# def semi_infinite_medium(self, M, ieq, L, d):
# M[ieq, d[1]] = 1.
# ieq += 1
# return ieq
# def interface_pem_pem(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = PEM_SV(L[iinter].medium, self.kx)
# SV_2, k_y_2 = PEM_SV(L[iinter+1].medium, self.kx)
# for _i in range(6):
# M[ieq, d[iinter+0][0]] = SV_1[_i, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[_i, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[_i, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[_i, 3]
# M[ieq, d[iinter+0][4]] = SV_1[_i, 4]
# M[ieq, d[iinter+0][5]] = SV_1[_i, 5]
# M[ieq, d[iinter+1][0]] = -SV_2[_i, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[_i, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[_i, 2]
# M[ieq, d[iinter+1][3]] = -SV_2[_i, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = -SV_2[_i, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = -SV_2[_i, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_fluid_pem(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K)
# SV_2, k_y_2 = PEM_SV(L[iinter+1].medium,self.kx)
# # print(k_y_2)
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]
# M[ieq, d[iinter+1][0]] = -SV_2[2, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[2, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[2, 2]
# M[ieq, d[iinter+1][3]] = -SV_2[2, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = -SV_2[2, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = -SV_2[2, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[1, 1]
# M[ieq, d[iinter+1][0]] = -SV_2[4, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[4, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[4, 2]
# M[ieq, d[iinter+1][3]] = -SV_2[4, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = -SV_2[4, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = -SV_2[4, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+1][0]] = SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = SV_2[0, 1]
# M[ieq, d[iinter+1][2]] = SV_2[0, 2]
# M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[0, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[0, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+1][0]] = SV_2[3, 0]
# M[ieq, d[iinter+1][1]] = SV_2[3, 1]
# M[ieq, d[iinter+1][2]] = SV_2[3, 2]
# M[ieq, d[iinter+1][3]] = SV_2[3, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[3, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[3, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_elastic_pem(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = elastic_SV(L[iinter].medium,self.kx, self.omega)
# SV_2, k_y_2 = PEM_SV(L[iinter+1].medium,self.kx)
# # print(k_y_2)
# M[ieq, d[iinter+0][0]] = -SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[0, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[0, 3]
# M[ieq, d[iinter+1][0]] = SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = SV_2[0, 1]
# M[ieq, d[iinter+1][2]] = SV_2[0, 2]
# M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[0, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[0, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[1, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[1, 3]
# M[ieq, d[iinter+1][0]] = SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = SV_2[1, 1]
# M[ieq, d[iinter+1][2]] = SV_2[1, 2]
# M[ieq, d[iinter+1][3]] = SV_2[1, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[1, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[1, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[1, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[1, 3]
# M[ieq, d[iinter+1][0]] = SV_2[2, 0]
# M[ieq, d[iinter+1][1]] = SV_2[2, 1]
# M[ieq, d[iinter+1][2]] = SV_2[2, 2]
# M[ieq, d[iinter+1][3]] = SV_2[2, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[2, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[2, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = -SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[2, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[2, 3]
# M[ieq, d[iinter+1][0]] = (SV_2[3, 0]-SV_2[4, 0])
# M[ieq, d[iinter+1][1]] = (SV_2[3, 1]-SV_2[4, 1])
# M[ieq, d[iinter+1][2]] = (SV_2[3, 2]-SV_2[4, 2])
# M[ieq, d[iinter+1][3]] = (SV_2[3, 3]-SV_2[4, 3])*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = (SV_2[3, 4]-SV_2[4, 4])*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = (SV_2[3, 5]-SV_2[4, 5])*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = -SV_1[3, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[3, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[3, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[3, 3]
# M[ieq, d[iinter+1][0]] = SV_2[5, 0]
# M[ieq, d[iinter+1][1]] = SV_2[5, 1]
# M[ieq, d[iinter+1][2]] = SV_2[5, 2]
# M[ieq, d[iinter+1][3]] = SV_2[5, 3]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][4]] = SV_2[5, 4]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][5]] = SV_2[5, 5]*np.exp(-1j*k_y_2[2]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_pem_elastic(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = PEM_SV(L[iinter].medium,self.kx)
# SV_2, k_y_2 = elastic_SV(L[iinter+1].medium,self.kx, self.omega)
# # print(k_y_2)
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[0, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[0, 3]
# M[ieq, d[iinter+0][4]] = SV_1[0, 4]
# M[ieq, d[iinter+0][5]] = SV_1[0, 5]
# M[ieq, d[iinter+1][0]] = -SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[0, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[0, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[0, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[1, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[1, 3]
# M[ieq, d[iinter+0][4]] = SV_1[1, 4]
# M[ieq, d[iinter+0][5]] = SV_1[1, 5]
# M[ieq, d[iinter+1][0]] = -SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[1, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[2, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[2, 3]
# M[ieq, d[iinter+0][4]] = SV_1[2, 4]
# M[ieq, d[iinter+0][5]] = SV_1[2, 5]
# M[ieq, d[iinter+1][0]] = -SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[1, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = (SV_1[3, 0]-SV_1[4, 0])*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = (SV_1[3, 1]-SV_1[4, 1])*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = (SV_1[3, 2]-SV_1[4, 2])*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = (SV_1[3, 3]-SV_1[4, 3])
# M[ieq, d[iinter+0][4]] = (SV_1[3, 4]-SV_1[4, 4])
# M[ieq, d[iinter+0][5]] = (SV_1[3, 5]-SV_1[4, 5])
# M[ieq, d[iinter+1][0]] = -SV_2[2, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[2, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[2, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[2, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[5, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[5, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[5, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[5, 3]
# M[ieq, d[iinter+0][4]] = SV_1[5, 4]
# M[ieq, d[iinter+0][5]] = SV_1[5, 5]
# M[ieq, d[iinter+1][0]] = -SV_2[3, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[3, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[3, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[3, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_elastic_elastic(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = elastic_SV(L[iinter].medium,self.kx, self.omega)
# SV_2, k_y_2 = elastic_SV(L[iinter+1].medium,self.kx, self.omega)
# for _i in range(4):
# M[ieq, d[iinter+0][0]] = SV_1[_i, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[_i, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[_i, 2]
# M[ieq, d[iinter+0][3]] = SV_1[_i, 3]
# M[ieq, d[iinter+1][0]] = -SV_2[_i, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[_i, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[_i, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[_i, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_fluid_elastic(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = fluid_SV(self.kx, self.k, L[iinter].medium.K)
# SV_2, k_y_2 = elastic_SV(L[iinter+1].medium, self.kx, self.omega)
# # Continuity of u_y
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]
# M[ieq, d[iinter+1][0]] = -SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = -SV_2[1, 1]
# M[ieq, d[iinter+1][2]] = -SV_2[1, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = -SV_2[1, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# # sigma_yy = -p
# M[ieq, d[iinter+0][0]] = SV_1[1, 0]*np.exp(-1j*k_y_1*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[1, 1]
# M[ieq, d[iinter+1][0]] = SV_2[2, 0]
# M[ieq, d[iinter+1][1]] = SV_2[2, 1]
# M[ieq, d[iinter+1][2]] = SV_2[2, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = SV_2[2, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# # sigma_xy = 0
# M[ieq, d[iinter+1][0]] = SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = SV_2[0, 1]
# M[ieq, d[iinter+1][2]] = SV_2[0, 2]*np.exp(-1j*k_y_2[0]*L[iinter+1].thickness)
# M[ieq, d[iinter+1][3]] = SV_2[0, 3]*np.exp(-1j*k_y_2[1]*L[iinter+1].thickness)
# ieq += 1
# return ieq
# def interface_pem_fluid(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = PEM_SV(L[iinter].medium, self.kx)
# SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K)
# # print(k_y_2)
# M[ieq, d[iinter+0][0]] = -SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[2, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = -SV_1[2, 3]
# M[ieq, d[iinter+0][4]] = -SV_1[2, 4]
# M[ieq, d[iinter+0][5]] = -SV_1[2, 5]
# M[ieq, d[iinter+1][0]] = SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = -SV_1[4, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[4, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[4, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = -SV_1[4, 3]
# M[ieq, d[iinter+0][4]] = -SV_1[4, 4]
# M[ieq, d[iinter+0][5]] = -SV_1[4, 5]
# M[ieq, d[iinter+1][0]] = SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[0, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[0, 3]
# M[ieq, d[iinter+0][4]] = SV_1[0, 4]
# M[ieq, d[iinter+0][5]] = SV_1[0, 5]
# ieq += 1
# M[ieq, d[iinter+0][0]] = SV_1[3, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[3, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[3, 2]*np.exp(-1j*k_y_1[2]*L[iinter].thickness)
# M[ieq, d[iinter+0][3]] = SV_1[3, 3]
# M[ieq, d[iinter+0][4]] = SV_1[3, 4]
# M[ieq, d[iinter+0][5]] = SV_1[3, 5]
# ieq += 1
# return ieq
# def interface_elastic_fluid(self, ieq, iinter, L, d, M):
# SV_1, k_y_1 = elastic_SV(L[iinter].medium, self.kx, self.omega)
# SV_2, k_y_2 = fluid_SV(self.kx, self.k, L[iinter+1].medium.K)
# # Continuity of u_y
# M[ieq, d[iinter+0][0]] = -SV_1[1, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = -SV_1[1, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = -SV_1[1, 2]
# M[ieq, d[iinter+0][3]] = -SV_1[1, 3]
# M[ieq, d[iinter+1][0]] = SV_2[0, 0]
# M[ieq, d[iinter+1][1]] = SV_2[0, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# # sigma_yy = -p
# M[ieq, d[iinter+0][0]] = SV_1[2, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[2, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[2, 2]
# M[ieq, d[iinter+0][3]] = SV_1[2, 3]
# M[ieq, d[iinter+1][0]] = SV_2[1, 0]
# M[ieq, d[iinter+1][1]] = SV_2[1, 1]*np.exp(-1j*k_y_2*L[iinter+1].thickness)
# ieq += 1
# # sigma_xy = 0
# M[ieq, d[iinter+0][0]] = SV_1[0, 0]*np.exp(-1j*k_y_1[0]*L[iinter].thickness)
# M[ieq, d[iinter+0][1]] = SV_1[0, 1]*np.exp(-1j*k_y_1[1]*L[iinter].thickness)
# M[ieq, d[iinter+0][2]] = SV_1[0, 2]
# M[ieq, d[iinter+0][3]] = SV_1[0, 3]
# ieq += 1
# return ieq
# def interface_elastic_rigid(self, M, ieq, L, d):
# SV, k_y = elastic_SV(L.medium,self.kx, self.omega)
# M[ieq, d[0]] = SV[1, 0]*np.exp(-1j*k_y[0]*L.thickness)
# M[ieq, d[1]] = SV[1, 1]*np.exp(-1j*k_y[1]*L.thickness)
# M[ieq, d[2]] = SV[1, 2]
# M[ieq, d[3]] = SV[1, 3]
# ieq += 1
# M[ieq, d[0]] = SV[3, 0]*np.exp(-1j*k_y[0]*L.thickness)
# M[ieq, d[1]] = SV[3, 1]*np.exp(-1j*k_y[1]*L.thickness)
# M[ieq, d[2]] = SV[3, 2]
# M[ieq, d[3]] = SV[3, 3]
# ieq += 1
# return ieq
# def interface_pem_rigid(self, M, ieq, L, d):
# SV, k_y = PEM_SV(L.medium, self.kx)
# M[ieq, d[0]] = SV[1, 0]*np.exp(-1j*k_y[0]*L.thickness)
# M[ieq, d[1]] = SV[1, 1]*np.exp(-1j*k_y[1]*L.thickness)
# M[ieq, d[2]] = SV[1, 2]*np.exp(-1j*k_y[2]*L.thickness)
# M[ieq, d[3]] = SV[1, 3]
# M[ieq, d[4]] = SV[1, 4]
# M[ieq, d[5]] = SV[1, 5]
# ieq += 1
# M[ieq, d[0]] = SV[2, 0]*np.exp(-1j*k_y[0]*L.thickness)
# M[ieq, d[1]] = SV[2, 1]*np.exp(-1j*k_y[1]*L.thickness)
# M[ieq, d[2]] = SV[2, 2]*np.exp(-1j*k_y[2]*L.thickness)
# M[ieq, d[3]] = SV[2, 3]
# M[ieq, d[4]] = SV[2, 4]
# M[ieq, d[5]] = SV[2, 5]
# ieq += 1
# M[ieq, d[0]] = SV[5, 0]*np.exp(-1j*k_y[0]*L.thickness)
# M[ieq, d[1]] = SV[5, 1]*np.exp(-1j*k_y[1]*L.thickness)
# M[ieq, d[2]] = SV[5, 2]*np.exp(-1j*k_y[2]*L.thickness)
# M[ieq, d[3]] = SV[5, 3]
# M[ieq, d[4]] = SV[5, 4]
# M[ieq, d[5]] = SV[5, 5]
# ieq += 1
# return ieq
# def plot_sol_PW(self, X, dofs):
# x_start = self.shift_plot
# for _l, _layer in enumerate(self.layers):
# x_f = np.linspace(0, _layer.thickness,200)
# x_b = x_f-_layer.thickness
# if _layer.medium.MODEL == "fluid":
# SV, k_y = fluid_SV(self.kx, self.k, _layer.medium.K)
# pr = SV[1, 0]*np.exp(-1j*k_y*x_f)*X[dofs[_l][0]]
# pr += SV[1, 1]*np.exp( 1j*k_y*x_b)*X[dofs[_l][1]]
# ut = SV[0, 0]*np.exp(-1j*k_y*x_f)*X[dofs[_l][0]]
# ut += SV[0, 1]*np.exp( 1j*k_y*x_b)*X[dofs[_l][1]]
# if self.plot[2]:
# plt.figure(2)
# plt.plot(x_start+x_f, np.abs(pr), 'r')
# plt.plot(x_start+x_f, np.imag(pr), 'm')
# plt.title("Pressure")
# # plt.figure(5)
# # plt.plot(x_start+x_f,np.abs(ut),'b')
# # plt.plot(x_start+x_f,np.imag(ut),'k')
# if _layer.medium.MODEL == "pem":
# SV, k_y = PEM_SV(_layer.medium, self.kx)
# ux, uy, pr, ut = 0*1j*x_f, 0*1j*x_f, 0*1j*x_f, 0*1j*x_f
# for i_dim in range(3):
# ux += SV[1, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# ux += SV[1, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]]
# uy += SV[5, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# uy += SV[5, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]]
# pr += SV[4, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# pr += SV[4, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]]
# ut += SV[2, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# ut += SV[2, i_dim+3]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+3]]
# if self.plot[0]:
# plt.figure(0)
# plt.plot(x_start+x_f, np.abs(uy), 'r')
# plt.plot(x_start+x_f, np.imag(uy), 'm')
# plt.title("Solid displacement along x")
# if self.plot[1]:
# plt.figure(1)
# plt.plot(x_start+x_f, np.abs(ux), 'r')
# plt.plot(x_start+x_f, np.imag(ux), 'm')
# plt.title("Solid displacement along y")
# if self.plot[2]:
# plt.figure(2)
# plt.plot(x_start+x_f, np.abs(pr), 'r')
# plt.plot(x_start+x_f, np.imag(pr), 'm')
# plt.title("Pressure")
# if _layer.medium.MODEL == "elastic":
# SV, k_y = elastic_SV(_layer.medium, self.kx, self.omega)
# ux, uy, pr, sig = 0*1j*x_f, 0*1j*x_f, 0*1j*x_f, 0*1j*x_f
# for i_dim in range(2):
# ux += SV[1, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# ux += SV[1, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]]
# uy += SV[3, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# uy += SV[3, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]]
# pr -= SV[2, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# pr -= SV[2, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]]
# sig -= SV[0, i_dim ]*np.exp(-1j*k_y[i_dim]*x_f)*X[dofs[_l][i_dim]]
# sig -= SV[0, i_dim+2]*np.exp( 1j*k_y[i_dim]*x_b)*X[dofs[_l][i_dim+2]]
# if self.plot[0]:
# plt.figure(0)
# plt.plot(x_start+x_f, np.abs(uy), 'r')
# plt.plot(x_start+x_f, np.imag(uy), 'm')
# plt.title("Solid displacement along x")
# if self.plot[1]:
# plt.figure(1)
# plt.plot(x_start+x_f, np.abs(ux), 'r')
# plt.plot(x_start+x_f, np.imag(ux), 'm')
# plt.title("Solid displacement along y")
# # if self.plot[2]:
# # plt.figure(2)
# # plt.plot(x_start+x_f, np.abs(pr), 'r')
# # plt.plot(x_start+x_f, np.imag(pr), 'm')
# # plt.title("Sigma_yy")
# # if self.plot[2]:
# # plt.figure(3)
# # plt.plot(x_start+x_f, np.abs(sig), 'r')
# # plt.plot(x_start+x_f, np.imag(sig), 'm')
# # plt.title("Sigma_xy")
# x_start += _layer.thickness
# def PEM_SV(mat,ky):
# ''' S={0:\hat{\sigma}_{xy}, 1:u_y^s, 2:u_y^t, 3:\hat{\sigma}_{yy}, 4:p, 5:u_x^s}'''
# kx_1 = np.sqrt(mat.delta_1**2-ky**2)
# kx_2 = np.sqrt(mat.delta_2**2-ky**2)
# kx_3 = np.sqrt(mat.delta_3**2-ky**2)
# kx = np.array([kx_1, kx_2, kx_3])
# delta = np.array([mat.delta_1, mat.delta_2, mat.delta_3])
# alpha_1 = -1j*mat.A_hat*mat.delta_1**2-1j*2*mat.N*kx[0]**2
# alpha_2 = -1j*mat.A_hat*mat.delta_2**2-1j*2*mat.N*kx[1]**2
# alpha_3 = -2*1j*mat.N*kx[2]*ky
# SV = np.zeros((6,6), dtype=complex)
# SV[0:6, 0] = np.array([-2*1j*mat.N*kx[0]*ky, kx[0], mat.mu_1*kx[0], alpha_1, 1j*delta[0]**2*mat.K_eq_til*mat.mu_1, ky])
# SV[0:6, 3] = np.array([ 2*1j*mat.N*kx[0]*ky,-kx[0],-mat.mu_1*kx[0], alpha_1, 1j*delta[0]**2*mat.K_eq_til*mat.mu_1, ky])
# SV[0:6, 1] = np.array([-2*1j*mat.N*kx[1]*ky, kx[1], mat.mu_2*kx[1],alpha_2, 1j*delta[1]**2*mat.K_eq_til*mat.mu_2, ky])
# SV[0:6, 4] = np.array([ 2*1j*mat.N*kx[1]*ky,-kx[1],-mat.mu_2*kx[1],alpha_2, 1j*delta[1]**2*mat.K_eq_til*mat.mu_2, ky])
# SV[0:6, 2] = np.array([1j*mat.N*(kx[2]**2-ky**2), ky, mat.mu_3*ky, alpha_3, 0., -kx[2]])
# SV[0:6, 5] = np.array([1j*mat.N*(kx[2]**2-ky**2), ky, mat.mu_3*ky, -alpha_3, 0., kx[2]])
# return SV, kx
# def elastic_SV(mat,ky, omega):
# ''' S={0:\sigma_{xy}, 1: u_y, 2 \sigma_{yy}, 3 u_x}'''
# P_mat = mat.lambda_ + 2.*mat.mu
# delta_p = omega*np.sqrt(mat.rho/P_mat)
# delta_s = omega*np.sqrt(mat.rho/mat.mu)
# kx_p = np.sqrt(delta_p**2-ky**2)
# kx_s = np.sqrt(delta_s**2-ky**2)
# kx = np.array([kx_p, kx_s])
# alpha_p = -1j*mat.lambda_*delta_p**2 - 2j*mat.mu*kx[0]**2
# alpha_s = 2j*mat.mu*kx[1]*ky
# SV = np.zeros((4, 4), dtype=np.complex)
# SV[0:4, 0] = np.array([-2.*1j*mat.mu*kx[0]*ky, kx[0], alpha_p, ky])
# SV[0:4, 2] = np.array([ 2.*1j*mat.mu*kx[0]*ky, -kx[0], alpha_p, ky])
# SV[0:4, 1] = np.array([1j*mat.mu*(kx[1]**2-ky**2), ky,-alpha_s, -kx[1]])
# SV[0:4, 3] = np.array([1j*mat.mu*(kx[1]**2-ky**2), ky, alpha_s, kx[1]])
# return SV, kx
# def fluid_SV(kx, k, K):
# ''' S={0:u_y , 1:p}'''
# ky = np.sqrt(k**2-kx**2)
# SV = np.zeros((2, 2), dtype=complex)
# SV[0, 0:2] = np.array([ky/(1j*K*k**2), -ky/(1j*K*k**2)])
# SV[1, 0:2] = np.array([1, 1])
# return SV, ky
# def resolution_PW_imposed_displacement(S, p):
# # print("k={}".format(p.k))
# Layers = S.layers.copy()
# n, interfaces, dofs = initialise_PW_solver(Layers, S.backing)
# M = np.zeros((n, n), dtype=complex)
# i_eq = 0
# # Loop on the layers
# for i_inter, _inter in enumerate(interfaces):
# if _inter[0] == "fluid":
# if _inter[1] == "fluid":
# i_eq = interface_fluid_fluid(i_eq, i_inter, Layers, dofs, M, p)
# if _inter[1] == "pem":
# i_eq = interface_fluid_pem(i_eq, i_inter, Layers, dofs, M, p)
# elif _inter[0] == "pem":
# if _inter[1] == "fluid":
# i_eq = interface_pem_fluid(i_eq, i_inter, Layers, dofs, M, p)
# if _inter[1] == "pem":
# i_eq = interface_pem_pem(i_eq, i_inter, Layers, dofs, M, p)
# if S.backing == backing.rigid:
# if Layers[-1].medium.MODEL == "fluid":
# i_eq = interface_fluid_rigid(M, i_eq, Layers[-1], dofs[-1], p)
# elif Layers[-1].medium.MODEL == "pem":
# i_eq = interface_pem_rigid(M, i_eq, Layers[-1], dofs[-1], p)
# if Layers[0].medium.MODEL == "fluid":
# F = np.zeros(n, dtype=complex)
# SV, k_y = fluid_SV(p.kx, p.k, Layers[0].medium.K)
# M[i_eq, dofs[0][0]] = SV[0, 0]
# M[i_eq, dofs[0][1]] = SV[0, 1]*np.exp(-1j*k_y*Layers[0].thickness)
# F[i_eq] = 1.
# elif Layers[0].medium.MODEL == "pem":
# SV, k_y = PEM_SV(Layers[0].medium, p.kx)
# M[i_eq, dofs[0][0]] = SV[2, 0]
# M[i_eq, dofs[0][1]] = SV[2, 1]
# M[i_eq, dofs[0][2]] = SV[2, 2]
# M[i_eq, dofs[0][3]] = SV[2, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness)
# M[i_eq, dofs[0][4]] = SV[2, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness)
# M[i_eq, dofs[0][5]] = SV[2, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness)
# F = np.zeros(n, dtype=complex)
# F[i_eq] = 1.
# i_eq +=1
# M[i_eq, dofs[0][0]] = SV[0, 0]
# M[i_eq, dofs[0][1]] = SV[0, 1]
# M[i_eq, dofs[0][2]] = SV[0, 2]
# M[i_eq, dofs[0][3]] = SV[0, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness)
# M[i_eq, dofs[0][4]] = SV[0, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness)
# M[i_eq, dofs[0][5]] = SV[0, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness)
# i_eq += 1
# M[i_eq, dofs[0][0]] = SV[3, 0]
# M[i_eq, dofs[0][1]] = SV[3, 1]
# M[i_eq, dofs[0][2]] = SV[3, 2]
# M[i_eq, dofs[0][3]] = SV[3, 3]*np.exp(-1j*k_y[0]*Layers[0].thickness)
# M[i_eq, dofs[0][4]] = SV[3, 4]*np.exp(-1j*k_y[1]*Layers[0].thickness)
# M[i_eq, dofs[0][5]] = SV[3, 5]*np.exp(-1j*k_y[2]*Layers[0].thickness)
# X = LA.solve(M, F)
# # print("|R pyPLANES_PW| = {}".format(np.abs(X[0])))
# print("R pyPLANES_PW = {}".format(X[0]))
# plot_sol_PW(S, X, dofs, p)
| [
2,
0,
1220,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
40477,
23,
532,
9,
12,
198,
2,
198,
2,
279,
86,
62,
37724,
13,
9078,
198,
2,
198,
2,
770,
2393,
318,
636,
286,
12972,
22587,
11,
257,
3788,
9387,
... | 1.510267 | 21,671 |
from __future__ import absolute_import, division, print_function
import sys
import time
from cctbx.array_family import flex
from scitbx.math import superpose
from mmtbx.conformation_dependent_library import mcl_sf4_coordination
from six.moves import range
from mmtbx.conformation_dependent_library import metal_coordination_library
def superpose_ideal_ligand_on_poor_ligand(ideal_hierarchy,
poor_hierarchy,
):
"""Function superpose an ideal ligand onto the mangled ligand from a
ligand fitting procedure
Args:
ideal_hierarchy (pdb_hierarchy): Ideal ligand
poor_hierarchy (pdb_hierarchy): Poor ligand with correct c.o.m. and same
atom names in order. Could become more sophisticated.
"""
sites_moving = flex.vec3_double()
sites_fixed = flex.vec3_double()
for atom1, atom2 in zip(ideal_hierarchy.atoms(), poor_hierarchy.atoms()):
assert atom1.name==atom2.name, '%s!=%s' % (atom1.quote(),atom2.quote())
sites_moving.append(atom1.xyz)
sites_fixed.append(atom2.xyz)
lsq_fit = superpose.least_squares_fit(
reference_sites = sites_fixed,
other_sites = sites_moving)
sites_new = ideal_hierarchy.atoms().extract_xyz()
sites_new = lsq_fit.r.elems * sites_new + lsq_fit.t.elems
# rmsd = sites_fixed.rms_difference(lsq_fit.other_sites_best_fit())
ideal_hierarchy.atoms().set_xyz(sites_new)
return ideal_hierarchy
if __name__=="__main__":
from iotbx import pdb
ideal_inp=pdb.pdb_input(sys.argv[1])
ideal_hierarchy = ideal_inp.construct_hierarchy()
poor_inp=pdb.pdb_input(sys.argv[2])
poor_hierarchy = poor_inp.construct_hierarchy()
ideal_hierarchy = superpose_ideal_ligand_on_poor_ligand(ideal_hierarchy, poor_hierarchy)
ideal_hierarchy.write_pdb_file('new.pdb')
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
11,
7297,
11,
3601,
62,
8818,
198,
11748,
25064,
198,
11748,
640,
198,
198,
6738,
269,
310,
65,
87,
13,
18747,
62,
17989,
1330,
7059,
198,
6738,
629,
270,
65,
87,
13,
11018,
1330,
2208... | 2.39295 | 766 |
from contextlib import contextmanager
import random
import shutil
import stat
import tempfile
import unittest
from os.path import join
from conda import install
from conda.install import (PaddingError, binary_replace, update_prefix,
warn_failed_remove, duplicates_to_remove)
from .decorators import skip_if_no_mock
from .helpers import mock
patch = mock.patch if mock else None
def generate_all_false_mocks(self):
return self.generate_mocks(False, False, False)
class duplicates_to_remove_TestCase(unittest.TestCase):
if __name__ == '__main__':
unittest.main()
| [
6738,
4732,
8019,
1330,
4732,
37153,
198,
11748,
4738,
198,
11748,
4423,
346,
198,
11748,
1185,
198,
11748,
20218,
7753,
198,
11748,
555,
715,
395,
198,
6738,
28686,
13,
6978,
1330,
4654,
628,
198,
6738,
1779,
64,
1330,
2721,
198,
6738,... | 2.834862 | 218 |
"""A Queryset slicer for Django."""
| [
37811,
32,
2264,
263,
893,
316,
14369,
263,
329,
37770,
526,
15931,
628
] | 2.846154 | 13 |
from django.urls import path
from . import views
urlpatterns = [
path('', views.news_home, name='news_home'),
path('create', views.create, name='create'),
path('<int:pk>', views.NewsDetailView.as_view(), name='news-detail'),
path('<int:pk>/update', views.NewsUpdateView.as_view(), name='news-update'),
path('<int:pk>/delete', views.NewsDeleteView.as_view(), name='news-delete'),
] | [
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
198,
198,
6738,
764,
1330,
5009,
198,
198,
6371,
33279,
82,
796,
685,
198,
220,
220,
220,
3108,
10786,
3256,
5009,
13,
10827,
62,
11195,
11,
1438,
11639,
10827,
62,
11195,
33809,
198,
220,... | 2.68 | 150 |
import requests
import lib.common
MODULE_NAME = 'headers'
| [
11748,
7007,
198,
198,
11748,
9195,
13,
11321,
198,
198,
33365,
24212,
62,
20608,
796,
705,
50145,
6,
628
] | 3.210526 | 19 |
import os
import requests
from bs4 import BeautifulSoup
from ekorpkit import eKonf
from ekorpkit.io.download.web import web_download, web_download_unzip
| [
11748,
28686,
198,
11748,
7007,
198,
6738,
275,
82,
19,
1330,
23762,
50,
10486,
198,
6738,
304,
74,
16300,
15813,
1330,
304,
42,
261,
69,
198,
6738,
304,
74,
16300,
15813,
13,
952,
13,
15002,
13,
12384,
1330,
3992,
62,
15002,
11,
39... | 3.142857 | 49 |
from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType
import numpy as np
import unittest
| [
6738,
367,
14175,
13,
9444,
24098,
50,
2703,
13,
9444,
5497,
31646,
17633,
1330,
2448,
69,
37,
2850,
432,
49106,
6030,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
555,
715,
395,
198
] | 3.454545 | 33 |
# coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from openapi_server.models.base_model_ import Model
from openapi_server.models.component import Component
from openapi_server import util
from openapi_server.models.component import Component # noqa: E501
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
198,
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
6738,
4818,
8079,
1330,
3128,
11,
4818,
8079,
220,
1303,
645,
20402,
25,
376,
21844,
198,
198,
6738,
19720,
1330,
7343,
11,
360,
713,
220... | 3.386792 | 106 |
import os
from os import getcwd
#---------------------------------------------#
# classes
# model_datatxt
#---------------------------------------------#
classes = ["cat", "dog"]
sets = ["train", "test"]
wd = getcwd()
for se in sets:
list_file = open('cls_' + se + '.txt', 'w')
datasets_path = "datasets/" + se
types_name = os.listdir(datasets_path)
for type_name in types_name:
if type_name not in classes:
continue
cls_id = classes.index(type_name)
photos_path = os.path.join(datasets_path, type_name)
photos_name = os.listdir(photos_path)
for photo_name in photos_name:
_, postfix = os.path.splitext(photo_name)
if postfix not in ['.jpg', '.png', '.jpeg']:
continue
list_file.write(str(cls_id) + ";" + '%s/%s'%(wd, os.path.join(photos_path, photo_name)))
list_file.write('\n')
list_file.close()
| [
11748,
28686,
201,
198,
6738,
28686,
1330,
651,
66,
16993,
201,
198,
201,
198,
2,
3880,
32501,
2,
201,
198,
2,
220,
220,
6097,
201,
198,
2,
220,
220,
2746,
62,
19608,
265,
742,
201,
198,
2,
3880,
32501,
2,
201,
198,
37724,
796,
... | 2.158242 | 455 |
from protocolbuffers import SimObjectAttributes_pb2 as protocols
from careers.career_unemployment import CareerUnemployment
import services
import sims4
logger = sims4.log.Logger('Relationship', default_owner='jjacobson')
| [
6738,
8435,
36873,
364,
1330,
3184,
10267,
29021,
62,
40842,
17,
355,
19565,
198,
6738,
16179,
13,
6651,
263,
62,
403,
28812,
1330,
32619,
3118,
28812,
198,
11748,
2594,
198,
11748,
985,
82,
19,
198,
6404,
1362,
796,
985,
82,
19,
13,
... | 3.7 | 60 |
# Given an integer n, count the total number of digit 1 appearing
# in all non-negative integers less than or equal to n.
#
# For example:
# Given n = 13,
# Return 6, because digit 1 occurred in the following numbers:
# 1, 10, 11, 12, 13.
#
if __name__ == "__main__":
print Solution().countDigitOne(13)
| [
2,
11259,
281,
18253,
299,
11,
954,
262,
2472,
1271,
286,
16839,
352,
12655,
198,
2,
287,
477,
1729,
12,
31591,
37014,
1342,
621,
393,
4961,
284,
299,
13,
198,
2,
198,
2,
1114,
1672,
25,
198,
2,
11259,
299,
796,
1511,
11,
198,
2... | 3.131313 | 99 |
from PIL import Image
from math import sqrt
import numpy as np
import time
import matplotlib.backends.backend_tkagg
import matplotlib.pyplot as plt
def distance(point, x, y):
return sqrt((point.x - x)**2 + (point.y - y)**2)
def insert_in_heap(heap, top, point):
heap.append(point)
i = top
parent = (i - 1)/2
while i >= 1 and heap[int(i)].f < heap[int(parent)].f:
heap[int(i)], heap[int(parent)] = heap[int(parent)], heap[int(i)] # swap
i = parent
parent = (i - 1) / 2
return
def calculate_weight(x, y, liste, top, point, visited, index1, index2):
if visited[int(x)][int(y)] == 0:
r, g, b = image.getpixel((x, y))
if x == end.x and y == end.y:
print("Path found.")
if r is 0:
r = 1
new_point = Point(x, y, 0)
new_point.parent = point
new_point.h = distance(end, x, y) * (256 - r)
new_point.g = 0
if index1 == 1: # a_star
new_point.g = new_point.parent.g + 256 - r
new_point.f = new_point.h + new_point.g # bfs'de g = 0
if index2 == 0: # stack
liste.append(new_point)
else: # heap
insert_in_heap(liste, top, new_point)
top += 1
visited[int(x)][int(y)] = 1
return top
def add_neighbours(point, liste, top, visited, index1, index2):
# print(point.x, point.y)
if (point.x == width - 1 and point.y == height - 1) or (point.x == 0 and point.y == 0) or \
(point.x == 0 and point.y == height - 1) or (point.x == width - 1 and point.y == 0):
# print("first if")
if point.x == width - 1 and point.y == height - 1:
constx = -1
consty = -1
elif point.x == 0 and point.y == 0:
constx = 1
consty = 1
elif point.x == width - 1 and point.y == 0:
constx = 1
consty = -1
else:
constx = -1
consty = 1
top = calculate_weight(point.x + constx, point.y, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x, point.y + consty, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + constx, point.y + consty, liste, top, point, visited, index1, index2)
elif point.x == 0 or point.x == width - 1:
# print("nd if")
top = calculate_weight(point.x, point.y - 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x, point.y + 1, liste, top, point, visited, index1, index2)
if point.x == 0:
const = 1
else:
const = -1
top = calculate_weight(point.x + const, point.y - 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + const, point.y + 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + const, point.y, liste, top, point, visited, index1, index2)
elif point.y == 0 or point.y == height - 1:
# print("3rd if")
top = calculate_weight(point.x - 1, point.y, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + 1, point.y, liste, top, point, visited, index1, index2)
if point.y == 0:
const = 1
else:
const = -1
top = calculate_weight(point.x - 1, point.y + const, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + 1, point.y + const, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x, point.y + const, liste, top, point, visited, index1, index2)
else:
# print("4th if")
top = calculate_weight(point.x - 1, point.y, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x - 1, point.y - 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x - 1, point.y + 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + 1, point.y - 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + 1, point.y, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x + 1, point.y + 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x, point.y + 1, liste, top, point, visited, index1, index2)
top = calculate_weight(point.x, point.y - 1, liste, top, point, visited, index1, index2)
return top
def paint(point):
yol = []
while not point.equal(start):
yol.append(point)
image.putpixel((int(point.x), int(point.y)), (60, 255, 0))
point = point.parent
end_time = time.time()
# image.show()
'''print("--------------YOL------------------")
for i in range(len(yol)):
print("x: {}, y:{}, distance:{}".format(yol[i].x, yol[i].y, yol[i].f))
print("------------------------------------")'''
return image, (end_time - start_time)
def bfs_and_a_star_with_stack(index):
stack = []
top = 0
found = False
point = None
stack.append(start)
visited = np.zeros((width, height))
visited[int(start.x)][int(start.y)] = 1
j = 0
max_element = 0
while stack and not found:
point = stack.pop(top)
# print("x: {}, y:{}, f:{}".format(point.x, point.y, point.f))
top -= 1
if point.equal(end):
found = True
else:
top = add_neighbours(point, stack, top, visited, index, 0)
stack.sort(key=lambda point: point.f, reverse=True)
if len(stack) > max_element:
max_element = len(stack)
j += 1
if found:
result_image, total_time = paint(point)
# print("Stackten ekilen eleman says: ", j)
# print("Stackteki maksimum eleman says: ", max_element)
return result_image, total_time, j, max_element
def find_smallest_child(heap, i, top):
if 2 * i + 2 < top: # has two child
if heap[2*i + 1].f < heap[2*i + 2].f:
return 2*i + 1
else:
return 2*i + 2
elif 2*i + 1 < top: # has one child
return 2*i + 1
else: # has no child
return 0
def remove_min(heap, top):
if top == 0:
return None
min_point = heap[0]
top -= 1
heap[0] = heap[top]
del heap[top]
i = 0
index = find_smallest_child(heap, i, top)
while index != 0 and heap[i].f > heap[index].f:
heap[i], heap[index] = heap[index], heap[i]
i = index
index = find_smallest_child(heap, i, top)
return min_point, top
def bfs_and_a_star_with_heap(index):
heap = []
found = False
yol = []
point = None
heap.append(start)
visited = np.zeros((width, height))
visited[int(start.x)][int(start.y)] = 1
j = 0
top = 1
max_element = 0
while heap and not found:
point, top = remove_min(heap, top)
# print("x: {}, y:{}, f:{}".format(point.x, point.y, point.f))
if point.equal(end):
found = True
else:
top = add_neighbours(point, heap, top, visited, index, 1)
if len(heap) > max_element:
max_element = len(heap)
j += 1
if found:
result_image, total_time = paint(point)
else:
return
return result_image, total_time, j, max_element
if __name__ == "__main__":
print("UYARI: Seilecek grnt exe dosyas ile ayn klasrde olmaldr.")
image_name = input("Algoritmann zerinde alaca grntnn ismini giriniz (rnek input: image.png): ")
print(image_name)
print("-------------------Algoritmalar------------------")
print("1- Best First Search with Stack")
print("2- Best First Search with Heap")
print("3- A* with Stack")
print("4- A* with Heap")
print("5- Analiz (tm algoritmalarn almalarn ve kyaslamalarn gr)")
alg = input("Algoritmay ve veri yapsnn numarasn seiniz (rnek input: 1): ")
image = Image.open(image_name)
width, height = image.size
image = image.convert('RGB')
print("Grntnn genilii: {}, ykseklii: {}".format(width, height))
print("NOT: Balang ve biti noktasnn koordinatlar genilik ve uzunluktan kk olmaldr.")
sx, sy = input("Balang noktasnn x ve y piksel koordinatlarn srasyla giriniz (rnek input: 350 100): ").split()
ex, ey = input("Biti noktasnn x ve y piksel koordinatlarn srasyla giriniz (rnek input: 200 700): ").split()
start = Point(int(sx), int(sy), -1)
start.parent = -1
end = Point(int(ex), int(ey), -1)
start_time = time.time()
if int(alg) == 1:
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(0)
elif int(alg) == 2:
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(0)
elif int(alg) == 3:
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(1)
elif int(alg) == 4:
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(1)
elif int(alg) == 5:
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(0)
output1 = Output(result_image, total_time, n_elements, max_elements)
print(n_elements, total_time, max_elements)
output1.name = "BFS with Stack"
print("1/4")
image = Image.open(image_name)
width, height = image.size
image = image.convert('RGB')
start_time = time.time()
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(0)
output2 = Output(result_image, total_time, n_elements, max_elements)
print(n_elements, total_time, max_elements)
output2.name = "BFS with Heap"
print("2/4")
image = Image.open(image_name)
width, height = image.size
image = image.convert('RGB')
start_time = time.time()
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_stack(1)
output3 = Output(result_image, total_time, n_elements, max_elements)
output3.name = "A* with Stack"
print(n_elements, total_time, max_elements)
print("3/4")
image = Image.open(image_name)
width, height = image.size
image = image.convert('RGB')
start_time = time.time()
result_image, total_time, n_elements, max_elements = bfs_and_a_star_with_heap(1)
output4 = Output(result_image, total_time, n_elements, max_elements)
output4.name = "A* with Heap"
print("4/4")
output1.plot_times(output2, output3, output4)
output1.plot_max_elements(output2, output3, output4)
output1.plot_n_elements(output2, output3, output4)
print("Bastrlan grntler srasyla BFS stack, BFS heap, A* stack ve A* heap eklindedir.")
fname = image_name.split('.')
output1.result_image.show()
output1.result_image.save(fname[0] + "BFS_stack.png")
output2.result_image.show()
output2.result_image.save(fname[0] + "BFS_heap.png")
output3.result_image.show()
output3.result_image.save(fname[0] + "A_star_stack.png")
output4.result_image.show()
output4.result_image.save(fname[0] + "A_star_heap.png")
exit(0)
else:
print("Algoritma numaras hatal girildi, tekrar deneyin.")
exit(0)
print("Stackten ekilen eleman says: ", n_elements)
print("Stackteki maksimum eleman says: ", max_elements)
print("Toplam sre: ", total_time)
result_image.show()
| [
6738,
350,
4146,
1330,
7412,
201,
198,
6738,
10688,
1330,
19862,
17034,
201,
198,
11748,
299,
32152,
355,
45941,
201,
198,
11748,
640,
201,
198,
11748,
2603,
29487,
8019,
13,
1891,
2412,
13,
1891,
437,
62,
30488,
9460,
201,
198,
11748,
... | 2.08578 | 5,689 |
#Author Shoaib Omar
import time
import rnnoise
import numpy as np
time_rnnoise() | [
2,
13838,
911,
12162,
571,
24980,
198,
11748,
640,
198,
11748,
374,
77,
3919,
786,
198,
11748,
299,
32152,
355,
45941,
198,
2435,
62,
35906,
3919,
786,
3419
] | 2.857143 | 28 |
""" shell sort tests module """
import unittest
import random
from sort import shell
from tests import helper
| [
198,
37811,
7582,
3297,
5254,
8265,
37227,
198,
198,
11748,
555,
715,
395,
198,
11748,
4738,
198,
198,
6738,
3297,
1330,
7582,
198,
6738,
5254,
1330,
31904,
198
] | 4.035714 | 28 |
import h5pyd
from datetime import datetime
import tzlocal
BUCKET="firefly-hsds"
inventory_domain = "/FIREfly/inventory.h5"
f = h5pyd.File(inventory_domain, "r", bucket=BUCKET)
table = f["inventory"]
for row in table:
filename = row[0].decode('utf-8')
if row[1]:
start = formatTime(row[1])
else:
start = 0
if row[2]:
stop = formatTime(row[2])
else:
stop = 0
print(f"{filename}\t{start}\t{stop}")
print(f"{table.nrows} rows")
| [
11748,
289,
20,
79,
5173,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
11748,
256,
89,
12001,
198,
198,
33,
16696,
2767,
2625,
6495,
12254,
12,
11994,
9310,
1,
198,
24807,
62,
27830,
796,
12813,
11674,
2200,
12254,
14,
24807,
13,
71,... | 2.210046 | 219 |
#------------------------------------------------------------------------------
# Copyright (c) 2013-2017, Nucleic Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
#------------------------------------------------------------------------------
from atom.api import Typed
from enaml.widgets.timer import ProxyTimer
from .QtCore import QTimer
from .qt_toolkit_object import QtToolkitObject
| [
2,
10097,
26171,
198,
2,
15069,
357,
66,
8,
2211,
12,
5539,
11,
399,
14913,
291,
7712,
4816,
13,
198,
2,
198,
2,
4307,
6169,
739,
262,
2846,
286,
262,
40499,
347,
10305,
13789,
13,
198,
2,
198,
2,
383,
1336,
5964,
318,
287,
262,... | 4.752381 | 105 |
# -*- coding: utf-8 -*-
# Copyright (c) 2020, Frappe Technologies Pvt. Ltd. and Contributors
# See license.txt
from __future__ import unicode_literals
from json import dumps
import frappe
import unittest
from erpnext.erpnext_integrations.doctype.mpesa_settings.mpesa_settings import process_balance_info, verify_transaction
from erpnext.accounts.doctype.pos_invoice.test_pos_invoice import create_pos_invoice
def get_test_account_balance_response():
"""Response received after calling the account balance API."""
return {
"ResultType":0,
"ResultCode":0,
"ResultDesc":"The service request has been accepted successfully.",
"OriginatorConversationID":"10816-694520-2",
"ConversationID":"AG_20200927_00007cdb1f9fb6494315",
"TransactionID":"LGR0000000",
"ResultParameters":{
"ResultParameter":[
{
"Key":"ReceiptNo",
"Value":"LGR919G2AV"
},
{
"Key":"Conversation ID",
"Value":"AG_20170727_00004492b1b6d0078fbe"
},
{
"Key":"FinalisedTime",
"Value":20170727101415
},
{
"Key":"Amount",
"Value":10
},
{
"Key":"TransactionStatus",
"Value":"Completed"
},
{
"Key":"ReasonType",
"Value":"Salary Payment via API"
},
{
"Key":"TransactionReason"
},
{
"Key":"DebitPartyCharges",
"Value":"Fee For B2C Payment|KES|33.00"
},
{
"Key":"DebitAccountType",
"Value":"Utility Account"
},
{
"Key":"InitiatedTime",
"Value":20170727101415
},
{
"Key":"Originator Conversation ID",
"Value":"19455-773836-1"
},
{
"Key":"CreditPartyName",
"Value":"254708374149 - John Doe"
},
{
"Key":"DebitPartyName",
"Value":"600134 - Safaricom157"
}
]
},
"ReferenceData":{
"ReferenceItem":{
"Key":"Occasion",
"Value":"aaaa"
}
}
}
def get_payment_request_response_payload(Amount=500):
"""Response received after successfully calling the stk push process request API."""
CheckoutRequestID = frappe.utils.random_string(10)
return {
"MerchantRequestID": "8071-27184008-1",
"CheckoutRequestID": CheckoutRequestID,
"ResultCode": 0,
"ResultDesc": "The service request is processed successfully.",
"CallbackMetadata": {
"Item": [
{ "Name": "Amount", "Value": Amount },
{ "Name": "MpesaReceiptNumber", "Value": "LGR7OWQX0R" },
{ "Name": "TransactionDate", "Value": 20201006113336 },
{ "Name": "PhoneNumber", "Value": 254723575670 }
]
}
}
def get_payment_callback_payload(Amount=500, CheckoutRequestID="ws_CO_061020201133231972", MpesaReceiptNumber="LGR7OWQX0R"):
"""Response received from the server as callback after calling the stkpush process request API."""
return {
"Body":{
"stkCallback":{
"MerchantRequestID":"19465-780693-1",
"CheckoutRequestID":CheckoutRequestID,
"ResultCode":0,
"ResultDesc":"The service request is processed successfully.",
"CallbackMetadata":{
"Item":[
{ "Name":"Amount", "Value":Amount },
{ "Name":"MpesaReceiptNumber", "Value":MpesaReceiptNumber },
{ "Name":"Balance" },
{ "Name":"TransactionDate", "Value":20170727154800 },
{ "Name":"PhoneNumber", "Value":254721566839 }
]
}
}
}
}
def get_account_balance_callback_payload():
"""Response received from the server as callback after calling the account balance API."""
return {
"Result":{
"ResultType": 0,
"ResultCode": 0,
"ResultDesc": "The service request is processed successfully.",
"OriginatorConversationID": "16470-170099139-1",
"ConversationID": "AG_20200927_00007cdb1f9fb6494315",
"TransactionID": "OIR0000000",
"ResultParameters": {
"ResultParameter": [
{
"Key": "AccountBalance",
"Value": "Working Account|KES|481000.00|481000.00|0.00|0.00"
},
{ "Key": "BOCompletedTime", "Value": 20200927234123 }
]
},
"ReferenceData": {
"ReferenceItem": {
"Key": "QueueTimeoutURL",
"Value": "https://internalsandbox.safaricom.co.ke/mpesa/abresults/v1/submit"
}
}
}
} | [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
15069,
357,
66,
8,
12131,
11,
39313,
27768,
21852,
18367,
83,
13,
12052,
13,
290,
25767,
669,
198,
2,
4091,
5964,
13,
14116,
198,
6738,
11593,
37443,
834,
1330,
280... | 2.396147 | 1,661 |
from __future__ import absolute_import
import yaml
with open("config.yml", "r") as f:
config = yaml.load(f)
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
198,
11748,
331,
43695,
198,
198,
4480,
1280,
7203,
11250,
13,
88,
4029,
1600,
366,
81,
4943,
355,
277,
25,
198,
220,
220,
220,
4566,
796,
331,
43695,
13,
2220,
7,
69,
8,
198
] | 2.651163 | 43 |
""" XVM (c) www.modxvm.com 2013-2017 """
# PUBLIC
# PRIVATE
from logger import *
from gui.shared.utils.requesters import REQ_CRITERIA
from helpers import dependency
from skeletons.gui.shared import IItemsCache
_special = {
# Data from http://forum.worldoftanks.ru/index.php?/topic/41221-
# Last update: 23.05.2017
# level 2
'germany:G53_PzI': [ 2, 2 ],
'uk:GB76_Mk_VIC': [ 2, 2 ],
'usa:A19_T2_lt': [ 2, 4 ],
'usa:A93_T7_Combat_Car': [ 2, 2 ],
# level 3
'germany:G36_PzII_J': [ 3, 4 ],
'japan:J05_Ke_Ni_B': [ 3, 4 ],
'ussr:R34_BT-SV': [ 3, 4 ],
'ussr:R50_SU76I': [ 3, 4 ],
'ussr:R56_T-127': [ 3, 4 ],
'ussr:R67_M3_LL': [ 3, 4 ],
'ussr:R86_LTP': [ 3, 4 ],
# level 4
'france:F14_AMX40': [ 4, 6 ],
'germany:G35_B-1bis_captured': [ 4, 4 ],
'japan:J06_Ke_Ho': [ 4, 6 ],
'uk:GB04_Valentine': [ 4, 6 ],
'uk:GB60_Covenanter': [ 4, 6 ],
'ussr:R12_A-20': [ 4, 6 ],
'ussr:R31_Valentine_LL': [ 4, 4 ],
'ussr:R44_T80': [ 4, 6 ],
'ussr:R68_A-32': [ 4, 5 ],
# level 5
'germany:G104_Stug_IV': [ 5, 6 ],
'germany:G32_PzV_PzIV': [ 5, 6 ],
'germany:G32_PzV_PzIV_ausf_Alfa': [ 5, 6 ],
'germany:G70_PzIV_Hydro': [ 5, 6 ],
'uk:GB20_Crusader': [ 5, 7 ],
'uk:GB51_Excelsior': [ 5, 6 ],
'uk:GB68_Matilda_Black_Prince': [ 5, 6 ],
'usa:A21_T14': [ 5, 6 ],
'usa:A44_M4A2E4': [ 5, 6 ],
'ussr:R32_Matilda_II_LL': [ 5, 6 ],
'ussr:R33_Churchill_LL': [ 5, 6 ],
'ussr:R38_KV-220': [ 5, 6 ],
'ussr:R38_KV-220_beta': [ 5, 6 ],
'ussr:R78_SU_85I': [ 5, 6 ],
# level 6
'germany:G32_PzV_PzIV_CN': [ 6, 7 ],
'germany:G32_PzV_PzIV_ausf_Alfa_CN': [ 6, 7 ],
'uk:GB63_TOG_II': [ 6, 7 ],
# level 7
'germany:G48_E-25': [ 7, 8 ],
'germany:G78_Panther_M10': [ 7, 8 ],
'uk:GB71_AT_15A': [ 7, 8 ],
'usa:A86_T23E3': [ 7, 8 ],
'ussr:R98_T44_85': [ 7, 8 ],
'ussr:R99_T44_122': [ 7, 8 ],
# level 8
'china:Ch01_Type59': [ 8, 9 ],
'china:Ch03_WZ-111': [ 8, 9 ],
'china:Ch14_T34_3': [ 8, 9 ],
'china:Ch23_112': [ 8, 9 ],
'france:F65_FCM_50t': [ 8, 9 ],
'germany:G65_JagdTiger_SdKfz_185': [ 8, 9 ],
'usa:A45_M6A2E1': [ 8, 9 ],
'usa:A80_T26_E4_SuperPershing': [ 8, 9 ],
'ussr:R54_KV-5': [ 8, 9 ],
'ussr:R61_Object252': [ 8, 9 ],
'ussr:R61_Object252_BF': [ 8, 9 ],
}
| [
37811,
1395,
15996,
357,
66,
8,
7324,
13,
4666,
87,
14761,
13,
785,
2211,
12,
5539,
37227,
198,
198,
2,
44731,
628,
198,
2,
4810,
3824,
6158,
198,
198,
6738,
49706,
1330,
1635,
198,
6738,
11774,
13,
28710,
13,
26791,
13,
8897,
8586,... | 1.484171 | 2,148 |
import math
from pypy.module.cpyext import pystrtod
from pypy.module.cpyext.test.test_api import BaseApiTest, raises_w
from rpython.rtyper.lltypesystem import rffi
from rpython.rtyper.lltypesystem import lltype
from pypy.module.cpyext.pystrtod import PyOS_string_to_double
| [
11748,
10688,
198,
198,
6738,
279,
4464,
88,
13,
21412,
13,
66,
9078,
2302,
1330,
12972,
2536,
83,
375,
198,
6738,
279,
4464,
88,
13,
21412,
13,
66,
9078,
2302,
13,
9288,
13,
9288,
62,
15042,
1330,
7308,
32,
14415,
14402,
11,
12073,... | 2.679612 | 103 |
# -*- coding: utf-8 -*-
from mathics.core.symbols import Symbol
# Some other common Symbols. This list is sorted in alphabetic order.
SymbolAssumptions = Symbol("$Assumptions")
SymbolAborted = Symbol("$Aborted")
SymbolAll = Symbol("All")
SymbolAlternatives = Symbol("Alternatives")
SymbolAnd = Symbol("And")
SymbolAppend = Symbol("Append")
SymbolApply = Symbol("Apply")
SymbolAssociation = Symbol("Association")
SymbolAutomatic = Symbol("Automatic")
SymbolBlank = Symbol("Blank")
SymbolBlend = Symbol("Blend")
SymbolByteArray = Symbol("ByteArray")
SymbolCatalan = Symbol("Catalan")
SymbolColorData = Symbol("ColorData")
SymbolComplex = Symbol("Complex")
SymbolComplexInfinity = Symbol("ComplexInfinity")
SymbolCondition = Symbol("Condition")
SymbolConditionalExpression = Symbol("ConditionalExpression")
Symbol_Context = Symbol("$Context")
Symbol_ContextPath = Symbol("$ContextPath")
SymbolCos = Symbol("Cos")
SymbolD = Symbol("D")
SymbolDerivative = Symbol("Derivative")
SymbolDirectedInfinity = Symbol("DirectedInfinity")
SymbolDispatch = Symbol("Dispatch")
SymbolE = Symbol("E")
SymbolEdgeForm = Symbol("EdgeForm")
SymbolEqual = Symbol("Equal")
SymbolExpandAll = Symbol("ExpandAll")
SymbolEulerGamma = Symbol("EulerGamma")
SymbolFailed = Symbol("$Failed")
SymbolFunction = Symbol("Function")
SymbolGamma = Symbol("Gamma")
SymbolGet = Symbol("Get")
SymbolGoldenRatio = Symbol("GoldenRatio")
SymbolGraphics = Symbol("Graphics")
SymbolGreater = Symbol("Greater")
SymbolGreaterEqual = Symbol("GreaterEqual")
SymbolGrid = Symbol("Grid")
SymbolHoldForm = Symbol("HoldForm")
SymbolIndeterminate = Symbol("Indeterminate")
SymbolImplies = Symbol("Implies")
SymbolInfinity = Symbol("Infinity")
SymbolInfix = Symbol("Infix")
SymbolInteger = Symbol("Integer")
SymbolIntegrate = Symbol("Integrate")
SymbolLeft = Symbol("Left")
SymbolLess = Symbol("Less")
SymbolLessEqual = Symbol("LessEqual")
SymbolLog = Symbol("Log")
SymbolMachinePrecision = Symbol("MachinePrecision")
SymbolMakeBoxes = Symbol("MakeBoxes")
SymbolMessageName = Symbol("MessageName")
SymbolMinus = Symbol("Minus")
SymbolMap = Symbol("Map")
SymbolMatrixPower = Symbol("MatrixPower")
SymbolMaxPrecision = Symbol("$MaxPrecision")
SymbolMemberQ = Symbol("MemberQ")
SymbolMinus = Symbol("Minus")
SymbolN = Symbol("N")
SymbolNeeds = Symbol("Needs")
SymbolNIntegrate = Symbol("NIntegrate")
SymbolNone = Symbol("None")
SymbolNot = Symbol("Not")
SymbolNull = Symbol("Null")
SymbolNumberQ = Symbol("NumberQ")
SymbolNumericQ = Symbol("NumericQ")
SymbolOptionValue = Symbol("OptionValue")
SymbolOr = Symbol("Or")
SymbolOverflow = Symbol("Overflow")
SymbolPackages = Symbol("$Packages")
SymbolPattern = Symbol("Pattern")
SymbolPi = Symbol("Pi")
SymbolPiecewise = Symbol("Piecewise")
SymbolPoint = Symbol("Point")
SymbolPossibleZeroQ = Symbol("PossibleZeroQ")
SymbolQuiet = Symbol("Quiet")
SymbolRational = Symbol("Rational")
SymbolReal = Symbol("Real")
SymbolRow = Symbol("Row")
SymbolRowBox = Symbol("RowBox")
SymbolRGBColor = Symbol("RGBColor")
SymbolSuperscriptBox = Symbol("SuperscriptBox")
SymbolRule = Symbol("Rule")
SymbolRuleDelayed = Symbol("RuleDelayed")
SymbolSequence = Symbol("Sequence")
SymbolSeries = Symbol("Series")
SymbolSeriesData = Symbol("SeriesData")
SymbolSet = Symbol("Set")
SymbolSimplify = Symbol("Simplify")
SymbolSin = Symbol("Sin")
SymbolSlot = Symbol("Slot")
SymbolStringQ = Symbol("StringQ")
SymbolStyle = Symbol("Style")
SymbolTable = Symbol("Table")
SymbolToString = Symbol("ToString")
SymbolUndefined = Symbol("Undefined")
SymbolXor = Symbol("Xor")
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
6738,
10688,
873,
13,
7295,
13,
1837,
2022,
10220,
1330,
38357,
198,
198,
2,
2773,
584,
2219,
41327,
10220,
13,
770,
1351,
318,
23243,
287,
435,
746,
33312,
1502,
... | 3.148082 | 1,121 |
# from https://github.com/kvwoerden/mcts-cartpole
# ---------------------------------------------------------------------------- #
# Imports #
# ---------------------------------------------------------------------------- #
import os
import time
import random
import argparse
<<<<<<< HEAD
=======
from types import SimpleNamespace
>>>>>>> MCTS
import gym
from gym import logger
from gym.wrappers.monitoring.video_recorder import VideoRecorder
from Simple_mcts import MCTSAgent
<<<<<<< HEAD
# ---------------------------------------------------------------------------- #
# Constants #
# ---------------------------------------------------------------------------- #
SEED = 28
EPISODES = 1
ENVIRONMENT = 'CartPole-v0'
LOGGER_LEVEL = logger.WARN
ITERATION_BUDGET = 80
LOOKAHEAD_TARGET = 100
MAX_EPISODE_STEPS = 1500
VIDEO_BASEPATH = '.\\video' # './video'
START_CP = 20
=======
from Agent import dqn_agent
# ---------------------------------------------------------------------------- #
# Constants #
# ---------------------------------------------------------------------------- #
LOGGER_LEVEL = logger.WARN
args = dict()
args['env_name'] = 'CartPole-v0'
args['episodes'] = 10
args['seed'] = 28
args['iteration_budget'] = 8000 # The number of iterations for each search step. Increasing this should lead to better performance.')
args['lookahead_target'] = 10000 # The target number of steps the agent aims to look forward.'
args['max_episode_steps'] = 1500 # The maximum number of steps to play.
args['video_basepath'] = '.\\video' # './video'
args['start_cp'] = 20 # The start value of C_p, the value that the agent changes to try to achieve the lookahead target. Decreasing this makes the search tree deeper, increasing this makes the search tree wider.
args = SimpleNamespace(**args)
>>>>>>> MCTS
# ---------------------------------------------------------------------------- #
# Main loop #
# ---------------------------------------------------------------------------- #
if __name__ == '__main__':
<<<<<<< HEAD
random.seed(SEED)
parser = argparse.ArgumentParser(
description='Run a Monte Carlo Tree Search agent on the Cartpole environment', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--env_id', nargs='?', default=ENVIRONMENT,
help='The environment to run (only CartPole-v0 is supperted)')
parser.add_argument('--episodes', nargs='?', default=EPISODES, type=int,
help='The number of episodes to run.')
parser.add_argument('--iteration_budget', nargs='?', default=ITERATION_BUDGET, type=int,
help='The number of iterations for each search step. Increasing this should lead to better performance.')
parser.add_argument('--lookahead_target', nargs='?', default=LOOKAHEAD_TARGET, type=int,
help='The target number of steps the agent aims to look forward.')
parser.add_argument('--max_episode_steps', nargs='?', default=MAX_EPISODE_STEPS, type=int,
help='The maximum number of steps to play.')
parser.add_argument('--video_basepath', nargs='?', default=VIDEO_BASEPATH,
help='The basepath where the videos will be stored.')
parser.add_argument('--start_cp', nargs='?', default=START_CP, type=int,
help='The start value of C_p, the value that the agent changes to try to achieve the lookahead target. Decreasing this makes the search tree deeper, increasing this makes the search tree wider.')
parser.add_argument('--seed', nargs='?', default=SEED, type=int,
help='The random seed.')
args = parser.parse_args()
logger.set_level(LOGGER_LEVEL)
env = gym.make(args.env_id)
env.seed(args.seed)
agent = MCTSAgent(args.iteration_budget, args.env_id)
=======
logger.set_level(LOGGER_LEVEL)
random.seed(args.seed)
env = gym.make(args.env_name)
env.seed(args.seed)
Q_net = dqn_agent()
agent = MCTSAgent(args.iteration_budget, env, Q_net)
>>>>>>> MCTS
timestr = time.strftime("%Y%m%d-%H%M%S")
reward = 0
done = False
for i in range(args.episodes):
ob = env.reset()
env._max_episode_steps = args.max_episode_steps
video_path = os.path.join(
args.video_basepath, f"output_{timestr}_{i}.mp4")
<<<<<<< HEAD
rec = VideoRecorder(env, path=video_path)
=======
# rec = VideoRecorder(env, path=video_path)
>>>>>>> MCTS
try:
sum_reward = 0
node = None
all_nodes = []
C_p = args.start_cp
while True:
print("################")
env.render()
<<<<<<< HEAD
rec.capture_frame()
=======
# rec.capture_frame()
>>>>>>> MCTS
action, node, C_p = agent.act(env.state, n_actions=env.action_space.n, node=node, C_p=C_p, lookahead_target=args.lookahead_target)
ob, reward, done, _ = env.step(action)
print("### observed state: ", ob)
sum_reward += reward
print("### sum_reward: ", sum_reward)
if done:
<<<<<<< HEAD
rec.close()
break
except KeyboardInterrupt as e:
rec.close()
=======
# rec.close()
break
except KeyboardInterrupt as e:
# rec.close()
>>>>>>> MCTS
env.close()
raise e
env.close()
| [
2,
422,
3740,
1378,
12567,
13,
785,
14,
74,
85,
21638,
263,
6559,
14,
76,
310,
82,
12,
26674,
36869,
198,
2,
16529,
10541,
1303,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 2.416355 | 2,409 |
from django_cron import CronJobBase, Schedule
| [
6738,
42625,
14208,
62,
66,
1313,
1330,
31683,
33308,
14881,
11,
19281,
628
] | 3.615385 | 13 |
import ASV
from simpletal import simpleTAL, simpleTALES
try:
import logging
except:
import InfoLogging as logging
import codecs
| [
11748,
7054,
53,
198,
198,
6738,
2829,
39240,
1330,
2829,
51,
1847,
11,
2829,
51,
1847,
1546,
198,
198,
28311,
25,
198,
197,
11748,
18931,
198,
16341,
25,
198,
197,
11748,
14151,
11187,
2667,
355,
18931,
198,
198,
11748,
40481,
82,
19... | 3.022222 | 45 |
from django.forms import BooleanField, ValidationError
from django.utils.timezone import now
from django.utils.translation import gettext as _
from .models import WagtailAdminModelForm
| [
6738,
42625,
14208,
13,
23914,
1330,
41146,
15878,
11,
3254,
24765,
12331,
198,
6738,
42625,
14208,
13,
26791,
13,
2435,
11340,
1330,
783,
198,
6738,
42625,
14208,
13,
26791,
13,
41519,
1330,
651,
5239,
355,
4808,
198,
198,
6738,
764,
2... | 3.836735 | 49 |
import os
from sqlalchemy.orm import Session
from db.database import SessionLocal
| [
11748,
28686,
198,
198,
6738,
44161,
282,
26599,
13,
579,
1330,
23575,
198,
198,
6738,
20613,
13,
48806,
1330,
23575,
14565,
628,
628,
628
] | 3.708333 | 24 |
#!/usr/bin/python
# -*- coding:utf-8 -*-
print("hello world")
f = None
try:
f = open("./hello.txt","r",encoding="utf8")
print(f.read(5),end='')
print(f.read(5),end='')
print(f.read(5))
except IOError as e:
print(e)
finally:
if f:
f.close()
# with auto call the methods' close
with open("./hello.txt","r",encoding="utf8") as f:
print(f.read())
# readlines()
with open("./hello.txt","r",encoding="utf8") as f:
for line in f.readlines():
print(line.strip())
#
with open("./hello_1.txt","w",encoding="utf8") as f:
f.write("!")
with open("./hello.txt","a",encoding="utf8") as f:
f.write(" 70!")
# StringIO / BytesIO
from io import StringIO
#
str = StringIO('init')
#
while True:
s = str.readline()
if s == '':
break
print(s.strip())
#
str.write("!")
str.write(" ")
#
print(str.getvalue())
'''
while True:
s = str.readline()
if s == '':
break
print(s.strip())
'''
#
from io import BytesIO
bi = BytesIO()
bi.write("".encode("utf-8"))
print(bi.getvalue())
by = BytesIO(b'\xe4\xbd\xa0\xe5\xa5\xbd')
print(by.read())
# OS
import os
# nt
print(os.name)
# python <module 'ntpath' from 'G:\\python-3.7\\lib\\ntpath.py'>
print(os.path)
#
print(os.environ)
# 'bobol'
print(os.getlogin())
#
os.mkdir("./foo/")
#
os.rmdir("./foo/")
'''
os.path
'''
# 'G:\\pythonDemo\\python-demo\\five'
print(os.path.abspath("./"))
# False
print(os.path.exists("./foo"))
# 4096
print(os.path.getsize("../"))
# False
print(os.path.isabs("../"))
| [
2,
48443,
14629,
14,
8800,
14,
29412,
198,
2,
532,
9,
12,
19617,
25,
40477,
12,
23,
532,
9,
12,
198,
198,
4798,
7203,
31373,
995,
4943,
198,
198,
69,
796,
6045,
198,
28311,
25,
198,
220,
220,
220,
277,
796,
1280,
7,
1911,
14,
... | 2.030928 | 776 |
# Copyright (c) 2009-2020, quasardb SAS. All rights reserved.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of quasardb nor the names of its contributors may
# be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY QUASARDB AND CONTRIBUTORS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
from __future__ import print_function
from builtins import range as xrange, int
import os
from socket import gethostname
import sys
import inspect
import traceback
import random
import time
import datetime
import locale
import numpy as np
import quasardb
STOCK_COLUMN = "stock_id"
OPEN_COLUMN = "open"
CLOSE_COLUMN = "close"
HIGH_COLUMN = "high"
LOW_COLUMN = "low"
VOLUME_COLUMN = "volume"
if __name__ == "__main__":
try:
if len(sys.argv) != 3:
print("usage: ", sys.argv[0], " quasardb_uri points_count")
sys.exit(1)
main(sys.argv[1], int(sys.argv[2]))
except Exception as ex: # pylint: disable=W0703
print("An error ocurred:", str(ex))
traceback.print_exc()
| [
2,
15069,
357,
66,
8,
3717,
12,
42334,
11,
627,
292,
446,
65,
35516,
13,
1439,
2489,
10395,
13,
198,
2,
1439,
2489,
10395,
13,
198,
2,
198,
2,
2297,
396,
3890,
290,
779,
287,
2723,
290,
13934,
5107,
11,
351,
393,
1231,
198,
2,
... | 3.01581 | 759 |
from nerwhal.backends.flashtext_backend import FlashtextBackend
from nerwhal.recognizer_bases import FlashtextRecognizer
| [
6738,
17156,
1929,
282,
13,
1891,
2412,
13,
34167,
5239,
62,
1891,
437,
1330,
9973,
5239,
7282,
437,
198,
6738,
17156,
1929,
282,
13,
26243,
7509,
62,
65,
1386,
1330,
9973,
5239,
6690,
2360,
7509,
628,
628
] | 3.351351 | 37 |
import boto3
import sys
import time
import logging
import getpass
dbname = sys.argv[1]
instanceID = sys.argv[2]
storage = sys.argv[3]
dbInstancetype = sys.argv[4]
dbusername = sys.argv[5]
new_rdsmysql(dbname, instanceID, storage, dbInstancetype, dbusername) | [
11748,
275,
2069,
18,
198,
11748,
25064,
198,
11748,
640,
198,
11748,
18931,
198,
11748,
651,
6603,
198,
198,
9945,
3672,
796,
25064,
13,
853,
85,
58,
16,
60,
198,
39098,
2389,
796,
25064,
13,
853,
85,
58,
17,
60,
198,
35350,
796,
... | 2.539216 | 102 |
import random
import requests
from api.block_api import BlockApi
from exception.tzscan import TzScanException
from log_config import main_logger
logger = main_logger
HEAD_API = {'MAINNET': {'HEAD_API_URL': 'https://api%MIRROR%.tzscan.io/v2/head'},
'ALPHANET': {'HEAD_API_URL': 'http://api.alphanet.tzscan.io/v2/head'},
'ZERONET': {'HEAD_API_URL': 'http://api.zeronet.tzscan.io/v2/head'}
}
REVELATION_API = {'MAINNET': {'HEAD_API_URL': 'https://api%MIRROR%.tzscan.io/v1/operations/%PKH%?type=Reveal'},
'ALPHANET': {'HEAD_API_URL': 'https://api.alphanet.tzscan.io/v1/operations/%PKH%?type=Reveal'},
'ZERONET': {'HEAD_API_URL': 'https://api.zeronet.tzscan.io/v1/operations/%PKH%?type=Reveal'}
}
if __name__ == '__main__':
test_get_revelation() | [
11748,
4738,
198,
198,
11748,
7007,
198,
6738,
40391,
13,
9967,
62,
15042,
1330,
9726,
32,
14415,
198,
6738,
6631,
13,
22877,
35836,
1330,
309,
89,
33351,
16922,
198,
198,
6738,
2604,
62,
11250,
1330,
1388,
62,
6404,
1362,
198,
198,
6... | 2.021635 | 416 |
import pytest
import cudf
import mock
from cuxfilter.charts.core.non_aggregate.core_non_aggregate import (
BaseNonAggregate,
)
from cuxfilter.dashboard import DashBoard
from cuxfilter import DataFrame
from cuxfilter.layouts import chart_view
| [
11748,
12972,
9288,
198,
11748,
269,
463,
69,
198,
11748,
15290,
198,
198,
6738,
269,
2821,
24455,
13,
354,
5889,
13,
7295,
13,
13159,
62,
9460,
49373,
13,
7295,
62,
13159,
62,
9460,
49373,
1330,
357,
198,
220,
220,
220,
7308,
15419,
... | 3.139241 | 79 |
# -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
5366,
13,
26791,
1330,
4818,
8079,
62,
26791,
355,
4818,
8079,
198,
6738,
5366,
13,
9945,
1330,
20613,
198,
6738,
5366,
13,
85,
17,
1330,
10011,
2611,
44,
4254,
... | 3.113208 | 53 |
#!/usr/bin/env python
# Copyright (C) 2013-2016 by Yu-Jie Lin
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
============
b.py command
============
Commands
========
============= =======================
command supported services
============= =======================
``blogs`` ``b``
``post`` ``b``, ``wp``
``generate`` ``base``, ``b``, ``wp``
``checklink`` ``base``, ``b``, ``wp``
``search`` ``b``
============= =======================
Descriptions:
``blogs``
list blogs. This can be used for blog IDs lookup.
``post``
post or update a blog post.
``generate``
generate HTML file at ``<TEMP>/draft.html``, where ``<TEMP>`` is the system's
temporary directory.
The generation can output a preview html at ``<TEMP>/preview.html`` if there
is ``tmpl.html``. It will replace ``%%Title%%`` with post title and
``%%Content%%`` with generated HTML.
``checklink``
check links in generated HTML using lnkckr_.
``search``
search blog
.. _lnkckr: https://pypi.python.org/pypi/lnkckr
"""
from __future__ import print_function
import argparse as ap
import codecs
import imp
import logging
import os
import sys
import traceback
from bpy.handlers import handlers
from bpy.services import find_service, services
__program__ = 'b.py'
__description__ = 'Post to Blogger or WordPress in markup language seamlessly'
__copyright__ = 'Copyright 2013-2016, Yu Jie Lin'
__license__ = 'MIT License'
__version__ = '0.11.0'
__website__ = 'http://bitbucket.org/livibetter/b.py'
__author__ = 'Yu-Jie Lin'
__author_email__ = 'livibetter@gmail.com'
# b.py stuff
############
# filename of local configuration without '.py' suffix.
BRC = 'brc'
if __name__ == '__main__':
main()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
15069,
357,
34,
8,
2211,
12,
5304,
416,
10605,
12,
41,
494,
5164,
198,
2,
198,
2,
2448,
3411,
318,
29376,
7520,
11,
1479,
286,
3877,
11,
284,
597,
1048,
16727,
257,
4866,
198,
... | 3.288299 | 829 |
# -*- coding: utf-8 -*-
#
# Copyright Spyder Project Contributors
# Licensed under the terms of the MIT License
# (see spyder/__init__.py for details)
"""Outline explorer API.
You need to declare a OutlineExplorerProxy, and a function for handle the
edit_goto Signal.
class OutlineExplorerProxyCustom(OutlineExplorerProxy):
...
def handle_go_to(name, line, text):
...
outlineexplorer = OutlineExplorerWidget(None)
oe_proxy = OutlineExplorerProxyCustom(name)
outlineexplorer.set_current_editor(oe_proxy, update=True, clear=False)
outlineexplorer.edit_goto.connect(handle_go_to)
"""
import re
from qtpy.QtCore import Signal, QObject
from qtpy.QtGui import QTextBlock
from spyder.config.base import _
from spyder.config.base import running_under_pytest
def is_cell_header(block):
"""Check if the given block is a cell header."""
if not block.isValid():
return False
data = block.userData()
return (data
and data.oedata
and data.oedata.def_type == OutlineExplorerData.CELL)
def cell_index(block):
"""Get the cell index of the given block."""
index = len(list(document_cells(block, forward=False)))
if is_cell_header(block):
return index + 1
return index
def cell_name(block):
"""
Get the cell name the block is in.
If the cell is unnamed, return the cell index instead.
"""
if is_cell_header(block):
header = block.userData().oedata
else:
try:
header = next(document_cells(block, forward=False))
except StopIteration:
# This cell has no header, so it is the first cell.
return 0
if header.has_name():
return header.def_name
else:
# No name, return the index
return cell_index(block)
def is_valid(self):
"""Check if the oedata has a valid block attached."""
block = self.block
return (block
and block.isValid()
and block.userData()
and hasattr(block.userData(), 'oedata')
and block.userData().oedata == self
)
def has_name(self):
"""Check if cell has a name."""
if self._def_name:
return True
else:
return False
def get_block_number(self):
"""Get the block number."""
if not self.is_valid():
# Avoid calling blockNumber if not a valid block
return None
return self.block.blockNumber()
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
198,
2,
15069,
220,
23688,
1082,
4935,
25767,
669,
198,
2,
49962,
739,
262,
2846,
286,
262,
17168,
13789,
198,
2,
357,
3826,
13997,
1082,
14,
834,
15003,
834,
13,
... | 2.420444 | 1,037 |
import weakref
from .mount import mountmanager
from .macro_mode import get_macro_mode, macro_mode_on
from . import cell as cell_module
from .cell import Cell, cell
from . import context as context_module
from .context import Context, context
from .worker import Worker
from .transformer import Transformer, transformer
from .structured_cell import StructuredCell, Inchannel, Outchannel
from .macro import Macro, macro, path
from .reactor import Reactor, reactor
from .unilink import unilink | [
11748,
4939,
5420,
198,
198,
6738,
764,
14948,
1330,
3817,
37153,
198,
6738,
764,
20285,
305,
62,
14171,
1330,
651,
62,
20285,
305,
62,
14171,
11,
15021,
62,
14171,
62,
261,
198,
6738,
764,
1330,
2685,
355,
2685,
62,
21412,
198,
6738,... | 3.806202 | 129 |
import os
import re
import sys
from oguilem.configuration.fitness import OGUILEMFitnessFunctionConfiguration
from oguilem.configuration.ga import OGUILEMGlobOptConfig
from oguilem.configuration.geometry import OGUILEMGeometryConfig
from oguilem.configuration.utils import ConnectedValue, ConfigFileManager
from oguilem.resources import options
| [
11748,
28686,
198,
11748,
302,
198,
11748,
25064,
198,
198,
6738,
267,
5162,
576,
76,
13,
11250,
3924,
13,
69,
3659,
1330,
34498,
10080,
2538,
49800,
3659,
22203,
38149,
198,
6738,
267,
5162,
576,
76,
13,
11250,
3924,
13,
4908,
1330,
... | 3.465347 | 101 |
from __future__ import (division, print_function)
import matplotlib.cm as cmx
import matplotlib.colors as colors
from matplotlib import gridspec
from metadatastore.api import db_connect as mds_db_connect
from filestore.api import db_connect as fs_db_connect
fs_db_connect(
**{'database': 'data-processing-dev', 'host': 'localhost', 'port': 27017})
mds_db_connect(
**{'database': 'data-processing-dev', 'host': 'localhost', 'port': 27017})
from databroker import db, get_events
from datamuxer import DataMuxer
from sidewinder_spec.utils.handlers import *
import logging
from xpd_workflow.parsers import parse_xrd_standard
logger = logging.getLogger(__name__)
if __name__ == '__main__':
import os
import numpy as np
import matplotlib.pyplot as plt
save = True
lam = 1.54059
# Standard reflections for sample components
niox_hkl = ['111', '200', '220', '311', '222', '400', '331',
'420', '422', '511']
niox_tth = np.asarray(
[37.44, 43.47, 63.20, 75.37, 79.87, 95.58, 106.72, 111.84,
129.98, 148.68])
pr3_hkl = ['100', '001', '110', '101', '111', '200', '002', '210', '211',
'112', '202']
pr3_tth = np.asarray(
[22.96, 24.33, 32.70, 33.70, 41.18, 46.92, 49.86, 52.86, 59.00, 60.91,
70.87]
)
pr4_hkl = ['111', '113', '008', '117', '200', '119', '028', '0014', '220',
'131', '1115', '0214', '317', '31Na', '2214', '040', '400']
pr4_tth = np.asarray(
[23.43, 25.16, 25.86, 32.62, 33.36, 37.67, 42.19, 46.11, 47.44, 53.18,
55.55, 57.72, 59.10, 59.27, 68.25, 68.71, 70.00]
)
pr2_tth, pr2int, pr2_hkl = parse_xrd_standard(
'/mnt/bulk-data/research_data/Pr2NiO4orthorhombicPDF#97-008-1577.txt')
pr2_tth = pr2_tth[pr2int > 5.]
prox_hkl = ['111', '200', '220', '311', '222', '400', '331', '420', '422',
'511', '440', '531', '600']
prox_tth = np.asarray(
[28.25, 32.74, 46.99, 55.71, 58.43, 68.59, 75.73, 78.08, 87.27,
94.12, 105.63, 112.90, 115.42]
)
standard_names = [
# 'NiO',
'Pr3Ni2O7',
'Pr2NiO4',
# 'Pr4'
'Pr6O11'
]
master_hkl = [
# niox_hkl,
pr3_hkl,
pr2_hkl,
# pr4_hkl
prox_hkl
]
master_tth = [
# niox_tth,
pr3_tth,
pr2_tth,
# pr4_tth
prox_tth
]
color_map = [
# 'red',
'blue',
'black',
'red'
]
line_style = ['--', '-.', ':', ]
ns = [1, 2, 3, 4, 5,
# 18, 20, 22, 16, 28, 29, 27, 26
]
# ns = [26]
ns.sort()
#
for i in ns:
legended_hkl = []
print(i)
folder = '/mnt/bulk-data/research_data/USC_beamtime/APS_March_2016/S' + str(
i) + '/temp_exp'
hdr = db(run_folder=folder)[0]
dm = DataMuxer()
dm.append_events(get_events(hdr))
df = dm.to_sparse_dataframe()
print(df.keys())
binned = dm.bin_on('img', interpolation={'T': 'linear'})
# key_list = [f for f in os.listdir(folder) if
# f.endswith('.gr') and not f.startswith('d')]
key_list = [f for f in os.listdir(folder) if
f.endswith('.chi') and not f.startswith('d') and f.strip(
'0.chi') != '' and int(
f.lstrip('0').strip('.chi')) % 2 == 1]
key_list.sort()
key_list = key_list[:-1]
# key_list2.sort()
idxs = [int(os.path.splitext(f)[0]) for f in key_list]
Ts = binned['T'].values[idxs]
output = os.path.splitext(key_list[0])[-1][1:]
if key_list[0].endswith('.gr'):
offset = .1
skr = 0
else:
skr = 8
offset = .001
data_list = [(np.loadtxt(os.path.join(folder, f),
skiprows=skr
)[:, 0],
np.loadtxt(os.path.join(folder, f),
skiprows=skr
)[:, 1])
for f
in key_list]
ylim_min = None
for xmax, length in zip(
[len(data_list[0][0]) - 1, len(data_list[0][0]) - 1],
['short', 'full']):
fig = plt.figure(figsize=(26, 12))
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax1 = plt.subplot(gs[0])
if length == 'short':
ax1.set_xlim(1.5, 4.5)
ax2 = plt.subplot(gs[1], sharey=ax1)
plt.setp(ax2.get_yticklabels(), visible=False)
cm = plt.get_cmap('viridis')
cNorm = colors.Normalize(vmin=0, vmax=len(key_list))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
for idx in range(len(key_list)):
xnm, y = data_list[idx]
colorVal = scalarMap.to_rgba(idx)
if output == 'chi':
x = xnm / 10.
ax1.plot(x[:xmax], y[:xmax] + idx * offset,
color=colorVal)
ax2.plot(Ts[idx], y[-1] + idx * offset, marker='o',
color=colorVal)
if ylim_min is None or ylim_min > np.min(
y[:xmax + idx * offset]):
ylim_min = np.min(y[:xmax + idx * offset])
ax2.set_xticklabels([str(f) for f in ax2.get_xticks()],
rotation=90)
if output == 'gr':
bnds = ['O-Pr', 'O-Ni', 'Ni-Ni', 'Pr-Pr', 'Ni-Pr', 'O-Pr',
'O-Ni',
'Ni-Ni-Ni', 'Pr-Ni', 'Pr-Pr', 'Pr-Ni-O', 'Ni-Pr-Ni',
'Pr-Pr', 'Rs:Pr-Pr', 'Rs:Pr_Pr']
bnd_lens = [2.320, 1.955, 3.883, 3.765, 3.186, 2.771, 2.231,
7.767, 4.426, 6.649, 4.989, 5.404, 3.374, 3.910,
8.801]
# ax1.grid(True)
# ax2.grid(True)
for bnd, bnd_len in zip(bnds, bnd_lens):
ax1.axvline(bnd_len, color='grey', linestyle='--')
ax3 = ax1.twiny()
ax3.set_xticks(np.asarray(bnd_lens) / x[xmax])
ax3.set_xticklabels(bnds, rotation=90)
else:
std_axis = []
for n, hkls, tths, color, ls in zip(standard_names, master_hkl,
master_tth,
color_map, line_style):
std_axis.append(ax1.twiny())
ax3 = std_axis[-1]
hkl_q = np.pi * 4 * np.sin(np.deg2rad(tths / 2)) / lam
for k, (hkl, q) in enumerate(zip(hkls, hkl_q)):
if n not in legended_hkl:
ax1.axvline(q, color=color, linestyle=ls,
lw=2,
label=n
)
legended_hkl.append(n)
else:
ax1.axvline(q, color=color, linestyle=ls,
lw=2,
)
a = hkl_q > ax1.get_xlim()[0]
b = hkl_q < ax1.get_xlim()[1]
c = a & b
ax3.set_xticks(list((hkl_q[c] - ax1.get_xlim()[0]) / (
ax1.get_xlim()[1] - ax1.get_xlim()[0])
))
ax3.set_xticklabels(hkls, rotation=90, color=color)
ax2.set_xlabel('Temperature C')
if output == 'gr':
fig.suptitle('S{} PDF'.format(i))
ax1.set_xlabel(r"$r (\AA)$")
ax1.set_ylabel(r"$G (\AA^{-2})$")
elif output == 'chi':
fig.suptitle('S{} I(Q)'.format(i))
ax1.set_xlabel(r"$Q (\AA^{-1})$")
ax1.set_ylabel(r"$I (Q) $")
ax1.set_ylim(ylim_min)
ax1.legend()
gs.tight_layout(fig, rect=[0, 0, 1, .98], w_pad=1e-6)
if save:
fig.savefig(os.path.join('/mnt/bulk-data/Dropbox/',
'S{}_{}_output_{}.png'.format(
i, length, output)))
fig.savefig(os.path.join('/mnt/bulk-data/Dropbox/',
'S{}_{}_output_{}.eps'.format(
i, length, output)))
else:
plt.show()
| [
6738,
11593,
37443,
834,
1330,
357,
21426,
11,
3601,
62,
8818,
8,
198,
198,
11748,
2603,
29487,
8019,
13,
11215,
355,
12067,
87,
198,
11748,
2603,
29487,
8019,
13,
4033,
669,
355,
7577,
198,
6738,
2603,
29487,
8019,
1330,
50000,
43106,
... | 1.596955 | 5,451 |
from __future__ import unicode_literals
import copy
import json
from six import string_types
from . import default_operators
from . import sql_prepare
from . import values
from .error import WinnowError
from .templating import SqlFragment
from .templating import WinnowSql
| [
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
11748,
4866,
198,
11748,
33918,
198,
198,
6738,
2237,
1330,
4731,
62,
19199,
198,
198,
6738,
764,
1330,
4277,
62,
3575,
2024,
198,
6738,
764,
1330,
44161,
62,
46012,
... | 3.538462 | 78 |
import regex as re
import time
import collections
import datetime
import six
import pandas as pd
import google.cloud.bigquery as bq
from multipledispatch import Dispatcher
import ibis
import ibis.common as com
import ibis.expr.operations as ops
import ibis.expr.types as ir
import ibis.expr.schema as sch
import ibis.expr.datatypes as dt
import ibis.expr.lineage as lin
from ibis.compat import parse_version
from ibis.client import Database, Query, SQLClient
from ibis.bigquery import compiler as comp
from google.api.core.exceptions import BadRequest
NATIVE_PARTITION_COL = '_PARTITIONTIME'
_IBIS_TYPE_TO_DTYPE = {
'string': 'STRING',
'int64': 'INT64',
'double': 'FLOAT64',
'boolean': 'BOOL',
'timestamp': 'TIMESTAMP',
'date': 'DATE',
}
_DTYPE_TO_IBIS_TYPE = {
'INT64': dt.int64,
'FLOAT64': dt.double,
'BOOL': dt.boolean,
'STRING': dt.string,
'DATE': dt.date,
# FIXME: enforce no tz info
'DATETIME': dt.timestamp,
'TIME': dt.time,
'TIMESTAMP': dt.timestamp,
'BYTES': dt.binary,
}
_LEGACY_TO_STANDARD = {
'INTEGER': 'INT64',
'FLOAT': 'FLOAT64',
'BOOLEAN': 'BOOL',
}
class BigQueryCursor(object):
"""Cursor to allow the BigQuery client to reuse machinery in ibis/client.py
"""
def _find_scalar_parameter(expr):
""":func:`~ibis.expr.lineage.traverse` function to find all
:class:`~ibis.expr.types.ScalarParameter` instances and yield the operation
and the parent expresssion's resolved name.
Parameters
----------
expr : ibis.expr.types.Expr
Returns
-------
Tuple[bool, object]
"""
op = expr.op()
if isinstance(op, ops.ScalarParameter):
result = op, expr.get_name()
else:
result = None
return lin.proceed, result
class BigQueryQuery(Query):
class BigQueryAPIProxy(object):
def get_datasets(self):
return list(self.client.list_datasets())
def get_dataset(self, dataset_id):
return self.client.dataset(dataset_id)
def get_table(self, table_id, dataset_id, reload=True):
(table_id, dataset_id) = _ensure_split(table_id, dataset_id)
table = self.client.dataset(dataset_id).table(table_id)
if reload:
table.reload()
return table
def get_schema(self, table_id, dataset_id):
return self.get_table(table_id, dataset_id).schema
def run_sync_query(self, stmt):
query = self.client.run_sync_query(stmt)
query.use_legacy_sql = False
query.run()
# run_sync_query is not really synchronous: there's a timeout
while not query.job.done():
query.job.reload()
time.sleep(0.1)
return query
class BigQueryDatabase(Database):
pass
bigquery_param = Dispatcher('bigquery_param')
class BigQueryClient(SQLClient):
sync_query = BigQueryQuery
database_class = BigQueryDatabase
proxy_class = BigQueryAPIProxy
dialect = comp.BigQueryDialect
def table(self, *args, **kwargs):
t = super(BigQueryClient, self).table(*args, **kwargs)
if NATIVE_PARTITION_COL in t.columns:
col = ibis.options.bigquery.partition_col
assert col not in t
return (t
.mutate(**{col: t[NATIVE_PARTITION_COL]})
.drop([NATIVE_PARTITION_COL]))
return t
def _build_ast(self, expr, context):
result = comp.build_ast(expr, context)
return result
def _execute_query(self, dml, async=False):
klass = self.async_query if async else self.sync_query
inst = klass(self, dml, query_parameters=dml.context.params)
df = inst.execute()
return df
def _fully_qualified_name(self, name, database):
dataset_id = database or self.dataset_id
return dataset_id + '.' + name
def _get_table_schema(self, qualified_name):
return self.get_schema(qualified_name)
def _execute(self, stmt, results=True, query_parameters=None):
# TODO(phillipc): Allow **kwargs in calls to execute
query = self._proxy.client.run_sync_query(stmt)
query.use_legacy_sql = False
query.query_parameters = query_parameters or []
query.run()
# run_sync_query is not really synchronous: there's a timeout
while not query.job.done():
query.job.reload()
time.sleep(0.1)
return BigQueryCursor(query)
def database(self, name=None):
if name is None:
name = self.dataset_id
return self.database_class(name, self)
_DTYPE_TO_IBIS_TYPE = {
'INT64': dt.int64,
'FLOAT64': dt.double,
'BOOL': dt.boolean,
'STRING': dt.string,
'DATE': dt.date,
# FIXME: enforce no tz info
'DATETIME': dt.timestamp,
'TIME': dt.time,
'TIMESTAMP': dt.timestamp,
'BYTES': dt.binary,
}
_LEGACY_TO_STANDARD = {
'INTEGER': 'INT64',
'FLOAT': 'FLOAT64',
'BOOLEAN': 'BOOL',
}
| [
11748,
40364,
355,
302,
198,
11748,
640,
198,
11748,
17268,
198,
11748,
4818,
8079,
198,
198,
11748,
2237,
198,
198,
11748,
19798,
292,
355,
279,
67,
198,
11748,
23645,
13,
17721,
13,
14261,
22766,
355,
275,
80,
198,
198,
6738,
5021,
... | 2.283295 | 2,185 |
from math import log, ceil | [
6738,
10688,
1330,
2604,
11,
2906,
346
] | 3.714286 | 7 |
import numpy as np
import ROOT
from dummy_distributions import dummy_pt_eta
counts, test_in1, test_in2 = dummy_pt_eta()
f = ROOT.TFile.Open("samples/testSF2d.root")
sf = f.Get("scalefactors_Tight_Electron")
xmin, xmax = sf.GetXaxis().GetXmin(), sf.GetXaxis().GetXmax()
ymin, ymax = sf.GetYaxis().GetXmin(), sf.GetYaxis().GetXmax()
test_out = np.empty_like(test_in1)
for i, (eta, pt) in enumerate(zip(test_in1, test_in2)):
if xmax <= eta:
eta = xmax - 1.0e-5
elif eta < xmin:
eta = xmin
if ymax <= pt:
pt = ymax - 1.0e-5
elif pt < ymin:
pt = ymin
ib = sf.FindBin(eta, pt)
test_out[i] = sf.GetBinContent(ib)
print(repr(test_out))
| [
11748,
299,
32152,
355,
45941,
198,
11748,
15107,
2394,
198,
198,
6738,
31548,
62,
17080,
2455,
507,
1330,
31548,
62,
457,
62,
17167,
198,
198,
9127,
82,
11,
1332,
62,
259,
16,
11,
1332,
62,
259,
17,
796,
31548,
62,
457,
62,
17167,
... | 2 | 345 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 14 17:42:27 2018
@author: zgeorg03
"""
import re
import json # Used for converting json to dictionary
import datetime # Used for date conversions
import matplotlib.pyplot as plt
import numpy as np
from sentiment import Sentiment
import json
if __name__ == '__main__':
file_name = "./log"
#max_articles = 1000
p = Parser(file_name,file_out='data-26-04.json')
p.parse()
p.write()
print('Finished')
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
41972,
319,
3300,
1526,
1478,
1596,
25,
3682,
25,
1983,
2864,
198,
198,
31,
9800,
25,
1976,
469,
2398... | 2.442922 | 219 |
"""Recursive implementations."""
def find_max(A):
"""invoke recursive function to find maximum value in A."""
def rmax(lo, hi):
"""Use recursion to find maximum value in A[lo:hi+1]."""
if lo == hi: return A[lo]
mid = (lo+hi) // 2
L = rmax(lo, mid)
R = rmax(mid+1, hi)
return max(L, R)
return rmax(0, len(A)-1)
def find_max_with_count(A):
"""Count number of comparisons."""
def frmax(lo, hi):
"""Use recursion to find maximum value in A[lo:hi+1] incl. count"""
if lo == hi: return (0, A[lo])
mid = (lo+hi)//2
ctleft,left = frmax(lo, mid)
ctright,right = frmax(mid+1, hi)
return (1+ctleft+ctright, max(left, right))
return frmax(0, len(A)-1)
def count(A,target):
"""invoke recursive function to return number of times target appears in A."""
def rcount(lo, hi, target):
"""Use recursion to find maximum value in A[lo:hi+1]."""
if lo == hi:
return 1 if A[lo] == target else 0
mid = (lo+hi)//2
left = rcount(lo, mid, target)
right = rcount(mid+1, hi, target)
return left + right
return rcount(0, len(A)-1, target)
| [
37811,
6690,
30753,
25504,
526,
15931,
198,
198,
4299,
1064,
62,
9806,
7,
32,
2599,
198,
220,
220,
220,
37227,
37669,
45115,
2163,
284,
1064,
5415,
1988,
287,
317,
526,
15931,
628,
220,
220,
220,
825,
374,
9806,
7,
5439,
11,
23105,
... | 2.207273 | 550 |
#from distutils.core import setup
from setuptools import setup, find_packages
from distutils.extension import Extension
import re
import os
import codecs
here = os.path.abspath(os.path.dirname(__file__))
try:
from Cython.Distutils import build_ext
except ImportError:
use_cython = False
else:
use_cython = True
cmdclass = { }
ext_modules = [ ]
if use_cython:
ext_modules += [
Extension("deepgmap.data_preprocessing_tools.seq_to_binary2", [ "deepgmap/data_preprocessing_tools/seq_to_binary2.pyx" ]),
#Extension("data_preprocessing_tools.queue", [ "deepgmap/data_preprocessing_tools/queue.pyx" ],libraries=["calg"]),
Extension("deepgmap.post_train_tools.cython_util", [ "deepgmap/post_train_tools/cython_util.pyx" ]),
]
cmdclass.update({ 'build_ext': build_ext })
else:
ext_modules += [
Extension("deepgmap.data_preprocessing_tools.seq_to_binary2", [ "deepgmap/data_preprocessing_tools/seq_to_binary2.c" ]),
Extension("deepgmap.post_train_tools.cython_util", [ "deepgmap/post_train_tools/cython_util.c" ]),
]
#print(find_version("deepgmap", "__init__.py"))
setup(
name='DeepGMAP',
#version=VERSION,
version=find_version("deepgmap", "__init__.py"),
description='Learning and predicting gene regulatory sequences in genomes',
author='Koh Onimaru',
author_email='koh.onimaru@gmail.com',
url='',
packages=['deepgmap','deepgmap.train','deepgmap.network_constructors','deepgmap.post_train_tools','deepgmap.data_preprocessing_tools','deepgmap.misc'],
#packages=find_packages('deepgmap'),
#packages=['deepgmap.'],
package_dir={'DeepGMAP':'deepgmap'},
#package_data = {
# '': ['enhancer_prediction/*', '*.pyx', '*.pxd', '*.c', '*.h'],
#},
scripts=['bin/deepgmap',
],
#packages=find_packages(),
cmdclass = cmdclass,
ext_modules=ext_modules,
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Intended Audience :: Developers',
'Programming Language :: Python :: 3.6',
'License :: OSI Approved :: Apache Software License ',
'Operating System :: POSIX :: Linux',
'Topic :: Scientific/Engineering :: Bio-Informatics',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
],
install_requires=['tensorflow>=1.15', 'numpy', 'matplotlib', 'sklearn', 'tornado', 'natsort', 'psutil', 'pyBigWig'],
long_description=open('README.rst').read(),
)
| [
2,
6738,
1233,
26791,
13,
7295,
1330,
9058,
198,
6738,
900,
37623,
10141,
1330,
9058,
11,
1064,
62,
43789,
198,
6738,
1233,
26791,
13,
2302,
3004,
1330,
27995,
198,
11748,
302,
198,
11748,
28686,
198,
11748,
40481,
82,
198,
1456,
796,
... | 2.493596 | 1,015 |
import logging
from flask import Flask
from flask_sqlalchemy import SQLAlchemy as _BaseSQLAlchemy
from flask_migrate import Migrate
from flask_cors import CORS
from flask_talisman import Talisman
from flask_ipban import IpBan
from config import Config, get_logger_handler
# database
db = SQLAlchemy()
migrate = Migrate()
cors = CORS()
talisman = Talisman()
global_config = Config()
ip_ban = IpBan(ban_seconds=200, ban_count=global_config.IP_BAN_LIST_COUNT)
# logging
logger = logging.getLogger('frontend')
from api import models
| [
198,
11748,
18931,
198,
6738,
42903,
1330,
46947,
198,
6738,
42903,
62,
25410,
282,
26599,
1330,
16363,
2348,
26599,
355,
4808,
14881,
17861,
2348,
26599,
198,
6738,
42903,
62,
76,
42175,
1330,
337,
42175,
198,
6738,
42903,
62,
66,
669,
... | 3.159763 | 169 |
# -*- coding: utf-8 -*-
import os
import csv
import pymongo
from pymongo.errors import DuplicateKeyError
from settings import MONGO_HOST, MONGO_PORT, SAVE_ROOT
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
11748,
28686,
198,
11748,
269,
21370,
198,
11748,
279,
4948,
25162,
198,
6738,
279,
4948,
25162,
13,
48277,
1330,
49821,
5344,
9218,
12331,
198,
6738,
6460,
1330,
25000,
1... | 2.745763 | 59 |
from pathlib import Path
from requests.auth import _basic_auth_str
import pytest
from bravado_core.formatter import SwaggerFormat, NO_OP
from gc3_query.lib.gc3_config import GC3Config, IDMCredential
TEST_BASE_DIR: Path = Path(__file__).parent.joinpath("GC3Config")
config_dir = TEST_BASE_DIR.joinpath("config")
# @pytest.fixture()
# def get_bravado_config_setup():
# gc3_config = GC3Config()
# assert 'iaas_classic' in gc3_config
# yield (gc3_config)
#
# def test_bravado_client_config(get_bravado_config_setup):
# gc3_config = get_bravado_config_setup
# assert 'iaas_classic' in gc3_config
# bravado_client_config = gc3_config.bravado_client_config
# assert bravado_client_config
# assert 'formats' not in bravado_client_config
# assert not 'include_missing_properties' in bravado_client_config
# assert 'also_return_response' in bravado_client_config
# bravado_client_config_2 = gc3_config.bravado_client_config
# assert bravado_client_config==bravado_client_config_2
# assert bravado_client_config is not bravado_client_config_2
# assert isinstance(bravado_client_config, dict)
#
# def test_bravado_core_config(get_bravado_config_setup):
# gc3_config = get_bravado_config_setup
# assert 'iaas_classic' in gc3_config
# bravado_core_config = gc3_config.bravado_core_config
# assert bravado_core_config
# assert 'formats' in bravado_core_config
# assert 'include_missing_properties' in bravado_core_config
# assert not 'also_return_response' in bravado_core_config
# bravado_core_config_2 = gc3_config.bravado_core_config
# assert bravado_core_config==bravado_core_config_2
# assert bravado_core_config is not bravado_core_config_2
# assert isinstance(bravado_core_config, dict)
# assert isinstance(bravado_core_config['formats'], list)
#
#
#
# def test_bravado_config(get_bravado_config_setup):
# gc3_config = get_bravado_config_setup
# assert 'iaas_classic' in gc3_config
# bravado_config = gc3_config.bravado_config
# assert bravado_config
# assert 'formats' in bravado_config
# assert 'include_missing_properties' in bravado_config
# assert 'also_return_response' in bravado_config
# bravado_config_2 = gc3_config.bravado_config
# assert bravado_config==bravado_config_2
# assert bravado_config is not bravado_config_2
# assert isinstance(bravado_config, dict)
# assert isinstance(bravado_config['formats'], list)
#
# def test_BRAVADO_CONFIG(get_constants_setup):
# gc3_config = get_constants_setup
# bravado_config = gc3_config.BRAVADO_CONFIG
# assert bravado_config
# assert 'formats' in bravado_config
# assert 'include_missing_properties' in bravado_config
# assert 'also_return_response' in bravado_config
# assert isinstance(bravado_config, dict)
# assert isinstance(bravado_config['formats'], list)
# assert bravado_config['formats']
# formats = [f.format for f in bravado_config['formats']]
# assert 'json-bool' in formats
# assert all([isinstance(i , SwaggerFormat) for i in bravado_config['formats']])
| [
6738,
3108,
8019,
1330,
10644,
198,
6738,
7007,
13,
18439,
1330,
4808,
35487,
62,
18439,
62,
2536,
198,
198,
11748,
12972,
9288,
198,
6738,
49025,
4533,
62,
7295,
13,
687,
1436,
1330,
2451,
7928,
26227,
11,
8005,
62,
3185,
198,
6738,
... | 2.528698 | 1,237 |
import machine
from machine import *
import ssd1306
import time
import socket
import urequests as requests
import json
word = {'body':8}
labels = ['c', 'o', 'l', 'u', 'm', 'b', 'i', 'a','null']
HOST = '18.218.158.249'
PORT = 8080
flag = 0
stop = False
data = {}
xdata = []
ydata = []
n = 0
do_connect()
switchA = machine.Pin(0, machine.Pin.IN, machine.Pin.PULL_UP)
switchA.irq(trigger=machine.Pin.IRQ_RISING, handler=switchAcallback)
switchC = machine.Pin(2, machine.Pin.IN, machine.Pin.PULL_UP)
switchC.irq(trigger=machine.Pin.IRQ_RISING, handler=switchCcallback)
spi = machine.SPI(1, baudrate=2000000, polarity=1, phase=1)
cs = machine.Pin(15, machine.Pin.OUT)
cs.value(0)
spi.write(b'\x2d')
spi.write(b'\x2b')
cs.value(1)
cs.value(0)
spi.write(b'\x31')
spi.write(b'\x0f')
cs.value(1)
i2c = machine.I2C(-1, machine.Pin(5), machine.Pin(4))
oled = ssd1306.SSD1306_I2C(128, 32, i2c)
while True:
x = 0
y = 0
sendstatus = "null"
if (flag):
cs.value(0)
test1 = spi.read(5, 0xf2)
cs.value(1)
cs.value(0)
test2 = spi.read(5, 0xf3)
cs.value(1)
cs.value(0)
test3 = spi.read(5, 0xf4)
cs.value(1)
cs.value(0)
test4 = spi.read(5, 0xf5)
cs.value(1)
x = dp(test2[1])
y = dp(test4[1])
xdata.append(x)
ydata.append(y)
sendstatus = "collect" + str(len(xdata)) + ' '+ ' ' + str(x) + ' ' + str(y)
if send:
word = sendData()
sendstatus = "send success"
flag = 0
send = False
oled.fill(0)
oled.text(labels[word['body']], 0, 0)
oled.text(sendstatus, 0,10)
oled.show()
| [
11748,
4572,
198,
6738,
4572,
1330,
1635,
198,
11748,
264,
21282,
12952,
21,
198,
11748,
640,
198,
11748,
17802,
198,
11748,
334,
8897,
3558,
355,
7007,
198,
11748,
33918,
198,
198,
4775,
796,
1391,
6,
2618,
10354,
23,
92,
198,
23912,
... | 1.921412 | 878 |
from Geometry.VeryForwardGeometry.dd4hep.v5.geometryRPFromDD_2021_cfi import *
| [
6738,
2269,
15748,
13,
16371,
39746,
10082,
15748,
13,
1860,
19,
258,
79,
13,
85,
20,
13,
469,
15748,
20031,
4863,
16458,
62,
1238,
2481,
62,
66,
12463,
1330,
1635,
198
] | 2.548387 | 31 |
import argparse
import matplotlib.pyplot as plt
import torch
from pytorch_warmup import *
parser = argparse.ArgumentParser(description='Warmup schedule')
parser.add_argument('--output', type=str, default='none',
choices=['none', 'png', 'pdf'],
help='Output file type (default: none)')
args = parser.parse_args()
beta2 = 0.999
max_step = 3000
plt.plot(range(1, max_step+1), get_rates(RAdamWarmup, beta2, max_step), label='RAdam')
plt.plot(range(1, max_step+1), get_rates(UntunedExponentialWarmup, beta2, max_step), label='Untuned Exponential')
plt.plot(range(1, max_step+1), get_rates(UntunedLinearWarmup, beta2, max_step), label='Untuned Linear')
plt.legend()
plt.title('Warmup Schedule')
plt.xlabel('Iteration')
plt.ylabel(r'Warmup factor $(\omega_t)$')
if args.output == 'none':
plt.show()
else:
plt.savefig(f'warmup_schedule.{args.output}')
| [
11748,
1822,
29572,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
11748,
28034,
198,
6738,
12972,
13165,
354,
62,
31975,
929,
1330,
1635,
628,
198,
198,
48610,
796,
1822,
29572,
13,
28100,
1713,
46677,
7,
11213,
116... | 2.4375 | 368 |
"""\
This plugin merely enables other plugins to accept data over HTTP. If
a plugin defines a module level function named "httpev" it will be
invoked for POST requests to the url http://$hostname/event/$pluginname.
The function is invoked from the thread in the web.py request context
and as such has access to the full web.py API.
"""
import base64
import json
import web
web.config.debug = False
def load():
s = Server()
s.start()
return s
def unload(s):
s.stop()
| [
37811,
59,
198,
1212,
13877,
6974,
13536,
584,
20652,
284,
2453,
1366,
625,
14626,
13,
1002,
198,
64,
13877,
15738,
257,
8265,
1241,
2163,
3706,
366,
2804,
431,
85,
1,
340,
481,
307,
198,
16340,
6545,
329,
24582,
7007,
284,
262,
19016... | 3.286667 | 150 |
somaIdade = 0
maiorIdade = 0
nomeVelho = ''
totmulher20 = 0
for p in range(1, 3):
print('---- {} PESSOA ----'.format(p))
nome = str(input('Nome: ')).strip()
idade = int(input('Idade: '))
sexo = str(input('Sexo [M/F]: '))
somaIdade += idade
if p == 1 and sexo in 'Mm':
maiorIdade = idade
nomeVelho = nome
if sexo in 'Mm' and idade > maiorIdade:
maiorIdade = idade
nomeVelho = nome
if sexo in 'Ff' and idade < 20:
totmulher20 += 1
mediaIdade = int(somaIdade / 4)
print('A mdia de idade do grupo de pessoas de {} anos.'.format(mediaIdade))
print('O homem mais velho tem {} anos e se chama {}.'.format(maiorIdade, nomeVelho))
print('Ao todo so {} mulher com menos de 20 anos.'.format(totmulher20)) | [
82,
6086,
7390,
671,
796,
657,
198,
2611,
1504,
7390,
671,
796,
657,
198,
77,
462,
46261,
8873,
796,
10148,
198,
83,
313,
76,
377,
372,
1238,
796,
657,
198,
1640,
279,
287,
2837,
7,
16,
11,
513,
2599,
198,
220,
220,
220,
3601,
1... | 2.142061 | 359 |
"""\
PROMORT example.
"""
import argparse
import random
import sys
import pyecvl.ecvl as ecvl
import pyeddl.eddl as eddl
from pyeddl.tensor import Tensor
import models
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("in_ds", metavar="INPUT_DATASET")
parser.add_argument("--epochs", type=int, metavar="INT", default=50)
parser.add_argument("--batch-size", type=int, metavar="INT", default=32)
parser.add_argument("--gpu", action="store_true")
parser.add_argument("--out-dir", metavar="DIR",
help="if set, save images in this directory")
main(parser.parse_args())
| [
37811,
59,
198,
4805,
2662,
9863,
1672,
13,
198,
37811,
198,
198,
11748,
1822,
29572,
198,
11748,
4738,
198,
11748,
25064,
198,
198,
11748,
12972,
721,
19279,
13,
721,
19279,
355,
9940,
19279,
198,
11748,
12972,
6048,
75,
13,
6048,
75,
... | 2.55303 | 264 |
from src.view.services_page import ServicesPage
from src.view.services_add_page import ServicesAddPage
| [
6738,
12351,
13,
1177,
13,
30416,
62,
7700,
1330,
6168,
9876,
198,
6738,
12351,
13,
1177,
13,
30416,
62,
2860,
62,
7700,
1330,
6168,
4550,
9876,
628,
220,
220,
220,
220,
220,
220,
220,
220
] | 3.2 | 35 |
import os
import gettext
import bisect
from locale import getdefaultlocale
from collections.abc import MutableMapping
from copy import copy, deepcopy
import six
_trans = Trans()
if six.PY2:
else:
| [
11748,
28686,
198,
11748,
651,
5239,
198,
11748,
47457,
478,
198,
6738,
36693,
1330,
651,
12286,
17946,
1000,
198,
6738,
17268,
13,
39305,
1330,
13859,
540,
44,
5912,
198,
6738,
4866,
1330,
4866,
11,
2769,
30073,
198,
198,
11748,
2237,
... | 3.215385 | 65 |
import socket
s = socket.socket()
s.bind(("localhost", 9999))
s.listen(1)
sc, addr = s.accept()
while True:
recibido = sc.recv(1024)
if recibido == "quit":
break
print "Recibido:", recibido
sc.send(recibido)
print "adios"
sc.close()
s.close()
| [
11748,
17802,
198,
198,
82,
796,
17802,
13,
44971,
3419,
198,
82,
13,
21653,
7,
7203,
36750,
1600,
860,
17032,
4008,
198,
82,
13,
4868,
268,
7,
16,
8,
198,
198,
1416,
11,
37817,
796,
264,
13,
13635,
3419,
198,
198,
4514,
6407,
25,... | 2.176 | 125 |
import numpy
import matplotlib.pyplot as plt
import geojsoncontour
# Create lat and lon vectors and grid data
grid_size = 1.0
latrange = numpy.arange(-90.0, 90.0, grid_size)
lonrange = numpy.arange(-180.0, 180.0, grid_size)
X, Y = numpy.meshgrid(lonrange, latrange)
Z = numpy.sqrt(X * X + Y * Y)
n_contours = 10
levels = numpy.linspace(start=0, stop=100, num=n_contours)
# Create a contour plot plot from grid (lat, lon) data
figure = plt.figure()
ax = figure.add_subplot(111)
contour = ax.contour(lonrange, latrange, Z, levels=levels, cmap=plt.cm.jet)
# Convert matplotlib contour to geojson
geojsoncontour.contour_to_geojson(
contour=contour,
geojson_filepath='out.geojson',
min_angle_deg=10.0,
ndigits=3,
unit='m'
)
| [
11748,
299,
32152,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
11748,
4903,
13210,
1559,
3642,
454,
198,
198,
2,
13610,
3042,
290,
300,
261,
30104,
290,
10706,
1366,
198,
25928,
62,
7857,
796,
352,
13,
15,
198,
... | 2.38141 | 312 |