code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
|---|---|---|
"""Remove dead ads table
Revision ID: <KEY>
Revises: 1307b62614a4
Create Date: 2020-02-27 23:14:41.314000
"""
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = '1307b62614a4'
from alembic import op # lgtm[py/unused-import]
import sqlalchemy as sa # lgtm[py/unused-import]
from sqlalchemy.dialects import postgresql
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index('ind_ads_end', table_name='ads')
op.drop_table('ads')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('ads',
sa.Column('id', sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column('owner', sa.VARCHAR(length=254), autoincrement=False, nullable=False),
sa.Column('link_target', sa.TEXT(), autoincrement=False, nullable=False),
sa.Column('file', sa.INTEGER(), autoincrement=False, nullable=False),
sa.Column('start', postgresql.TIMESTAMP(), autoincrement=False, nullable=True),
sa.Column('end', postgresql.TIMESTAMP(), autoincrement=False, nullable=True),
sa.ForeignKeyConstraint(['file'], [u'media.mediaid'], name=u'ads_file_fkey', onupdate=u'CASCADE', ondelete=u'CASCADE'),
sa.PrimaryKeyConstraint('id', name=u'ads_pkey')
)
op.create_index('ind_ads_end', 'ads', ['end'], unique=False)
# ### end Alembic commands ###
| [
"sqlalchemy.ForeignKeyConstraint",
"alembic.op.drop_table",
"sqlalchemy.VARCHAR",
"sqlalchemy.PrimaryKeyConstraint",
"sqlalchemy.TEXT",
"sqlalchemy.INTEGER",
"alembic.op.drop_index",
"sqlalchemy.dialects.postgresql.TIMESTAMP",
"alembic.op.create_index"
] | [((434, 480), 'alembic.op.drop_index', 'op.drop_index', (['"""ind_ads_end"""'], {'table_name': '"""ads"""'}), "('ind_ads_end', table_name='ads')\n", (447, 480), False, 'from alembic import op\n'), ((485, 505), 'alembic.op.drop_table', 'op.drop_table', (['"""ads"""'], {}), "('ads')\n", (498, 505), False, 'from alembic import op\n'), ((1313, 1373), 'alembic.op.create_index', 'op.create_index', (['"""ind_ads_end"""', '"""ads"""', "['end']"], {'unique': '(False)'}), "('ind_ads_end', 'ads', ['end'], unique=False)\n", (1328, 1373), False, 'from alembic import op\n'), ((1131, 1253), 'sqlalchemy.ForeignKeyConstraint', 'sa.ForeignKeyConstraint', (["['file']", "[u'media.mediaid']"], {'name': 'u"""ads_file_fkey"""', 'onupdate': 'u"""CASCADE"""', 'ondelete': 'u"""CASCADE"""'}), "(['file'], [u'media.mediaid'], name=u'ads_file_fkey',\n onupdate=u'CASCADE', ondelete=u'CASCADE')\n", (1154, 1253), True, 'import sqlalchemy as sa\n'), ((1255, 1302), 'sqlalchemy.PrimaryKeyConstraint', 'sa.PrimaryKeyConstraint', (['"""id"""'], {'name': 'u"""ads_pkey"""'}), "('id', name=u'ads_pkey')\n", (1278, 1302), True, 'import sqlalchemy as sa\n'), ((673, 685), 'sqlalchemy.INTEGER', 'sa.INTEGER', ([], {}), '()\n', (683, 685), True, 'import sqlalchemy as sa\n'), ((747, 769), 'sqlalchemy.VARCHAR', 'sa.VARCHAR', ([], {'length': '(254)'}), '(length=254)\n', (757, 769), True, 'import sqlalchemy as sa\n'), ((838, 847), 'sqlalchemy.TEXT', 'sa.TEXT', ([], {}), '()\n', (845, 847), True, 'import sqlalchemy as sa\n'), ((909, 921), 'sqlalchemy.INTEGER', 'sa.INTEGER', ([], {}), '()\n', (919, 921), True, 'import sqlalchemy as sa\n'), ((984, 1006), 'sqlalchemy.dialects.postgresql.TIMESTAMP', 'postgresql.TIMESTAMP', ([], {}), '()\n', (1004, 1006), False, 'from sqlalchemy.dialects import postgresql\n'), ((1066, 1088), 'sqlalchemy.dialects.postgresql.TIMESTAMP', 'postgresql.TIMESTAMP', ([], {}), '()\n', (1086, 1088), False, 'from sqlalchemy.dialects import postgresql\n')] |
"""
http://yutori-datascience.hatenablog.com/entry/2014/12/10/123157
"""
from numba import cuda
import numpy as np
from numba import double
from numba.decorators import jit
from numba import guvectorize
import time
import math
@jit
def pairwise_numba(X,D):
M,N=X.shape[0],X.shape[1]
for i in range(M):
for j in range(M):
d=0.0
for k in range(N):
tmp=X[i,k]-X[j,k]
d+=tmp *tmp
D[i,j]=np.sqrt(d)
@jit('void(f8[:,:],f8[:,:])')
def pairwise_numba_with_type(X,D):
M,N=X.shape[0],X.shape[1]
for i in range(M):
for j in range(M):
d=0.0
for k in range(N):
tmp=X[i,k]-X[j,k]
d+=tmp *tmp
D[i,j]=np.sqrt(d)
@guvectorize(['void(f8[:, :], f8[:, :])'], '(x, y)->(x, x)')
def pairwise_vectorize(X, D):
M = X.shape[0]
N = X.shape[1]
for i in range(M):
for j in range(M):
d = 0.0
for k in range(N):
tmp = X[i, k] - X[j, k]
d += tmp * tmp
D[i, j] = np.sqrt(d)
def pairwise_python(X,D):
M,N=X.shape[0],X.shape[1]
for i in range(M):
for j in range(M):
d=0.0
for k in range(N):
tmp=X[i,k]-X[j,k]
d+=tmp *tmp
D[i,j]=np.sqrt(d)
@cuda.jit('void(f8[:, :], f8[:, :])')
def pairwise_numba_cuda1(X, D):
M = X.shape[0]
N = X.shape[1]
i, j = cuda.grid(2)
if i < M and j < M:
d = 0.0
for k in range(N):
tmp = X[i, k] - X[j, k]
d += tmp * tmp
D[i, j] = math.sqrt(d)
def measure_time(func,X,D):
start=time.time()
func(X,D)
end=time.time()
print("elapsed time",end-start)
def main():
griddim = (100, 100)
blockdim =(16, 16)
SIZE=5000
X=np.random.random((SIZE,3))
D=np.empty((SIZE,SIZE))
measure_time(pairwise_python,X,D)
measure_time(pairwise_numba,X,D)
measure_time(pairwise_numba_with_type,X,D)
measure_time(pairwise_vectorize,X, D)
start=time.time()
pairwise_numba_cuda1[griddim, blockdim](X, D)
end=time.time()
print("elapsed gpu=",end-start)
if __name__ == '__main__':
main() | [
"numpy.sqrt",
"numpy.random.random",
"numba.decorators.jit",
"numba.cuda.grid",
"math.sqrt",
"numba.cuda.jit",
"numba.guvectorize",
"numpy.empty",
"time.time"
] | [((422, 450), 'numba.decorators.jit', 'jit', (['"""void(f8[:,:],f8[:,:])"""'], {}), "('void(f8[:,:],f8[:,:])')\n", (425, 450), False, 'from numba.decorators import jit\n'), ((647, 706), 'numba.guvectorize', 'guvectorize', (["['void(f8[:, :], f8[:, :])']", '"""(x, y)->(x, x)"""'], {}), "(['void(f8[:, :], f8[:, :])'], '(x, y)->(x, x)')\n", (658, 706), False, 'from numba import guvectorize\n'), ((1167, 1203), 'numba.cuda.jit', 'cuda.jit', (['"""void(f8[:, :], f8[:, :])"""'], {}), "('void(f8[:, :], f8[:, :])')\n", (1175, 1203), False, 'from numba import cuda\n'), ((1290, 1302), 'numba.cuda.grid', 'cuda.grid', (['(2)'], {}), '(2)\n', (1299, 1302), False, 'from numba import cuda\n'), ((1518, 1529), 'time.time', 'time.time', ([], {}), '()\n', (1527, 1529), False, 'import time\n'), ((1546, 1557), 'time.time', 'time.time', ([], {}), '()\n', (1555, 1557), False, 'import time\n'), ((1660, 1687), 'numpy.random.random', 'np.random.random', (['(SIZE, 3)'], {}), '((SIZE, 3))\n', (1676, 1687), True, 'import numpy as np\n'), ((1690, 1712), 'numpy.empty', 'np.empty', (['(SIZE, SIZE)'], {}), '((SIZE, SIZE))\n', (1698, 1712), True, 'import numpy as np\n'), ((1873, 1884), 'time.time', 'time.time', ([], {}), '()\n', (1882, 1884), False, 'import time\n'), ((1937, 1948), 'time.time', 'time.time', ([], {}), '()\n', (1946, 1948), False, 'import time\n'), ((1469, 1481), 'math.sqrt', 'math.sqrt', (['d'], {}), '(d)\n', (1478, 1481), False, 'import math\n'), ((409, 419), 'numpy.sqrt', 'np.sqrt', (['d'], {}), '(d)\n', (416, 419), True, 'import numpy as np\n'), ((634, 644), 'numpy.sqrt', 'np.sqrt', (['d'], {}), '(d)\n', (641, 644), True, 'import numpy as np\n'), ((969, 979), 'numpy.sqrt', 'np.sqrt', (['d'], {}), '(d)\n', (976, 979), True, 'import numpy as np\n'), ((1154, 1164), 'numpy.sqrt', 'np.sqrt', (['d'], {}), '(d)\n', (1161, 1164), True, 'import numpy as np\n')] |
#!/usr/bin/env python
import itertools
import torch
import mlflow
from selective_gp.utils import (
load_data, get_model, get_ELBO, get_experiment_id, eprint, bold, green,
remove_points, run_exists)
import click
def run_single(device, M, noise, epochs, adaptive, prior_weight, dataset_name,
fold, n_folds):
# Get data and model
test_size = 1 / n_folds if n_folds > 1 else 0
dataset = load_data(dataset_name, seed=fold, device=device,
test_size=test_size)
dataset.add_noise(noise)
collapsed = (dataset.task_type == "regression")
model = get_model(
dataset, n_inducing=M, device=device, collapsed=collapsed,
prior_weight=prior_weight)
# Initialize hyperparameters
gp, = model.gps
gp.kernel.outputscale = 1.0
gp.kernel.base_kernel.lengthscale = 1.0
if not adaptive:
# Fit model
model.fit(X=dataset.X_train, Y=dataset.Y_train, max_epochs=epochs)
else:
# Pre-fit
model.fit(X=dataset.X_train, Y=dataset.Y_train, max_epochs=epochs // 2)
# Prune
gp.prior_point_process.rate.fill_(prior_weight)
gp.variational_point_process.probabilities = 0.5
model.fit_score_function_estimator(
X=dataset.X_train, Y=dataset.Y_train, learning_rate=0.3,
max_epochs=300, n_mcmc_samples=64)
remove_points(gp)
eprint(f"Post pruning: {gp.n_inducing}\n")
# Post-fit
model.fit(X=dataset.X_train, Y=dataset.Y_train, max_epochs=epochs)
# Log metrics
scaled_log_lik, KL = get_ELBO(model, dataset, reps=1)
ELBO = scaled_log_lik - KL
mlflow.log_metrics({
"ELBO": ELBO,
"n_inducing": gp.n_inducing,
})
if adaptive:
vpp = gp.variational_point_process
mlflow.log_metrics({
"mean_M": vpp.expected_points.item(),
"var_M": vpp.expected_points_variance.item(),
})
@click.command()
@click.option("--epochs", default=1000)
@click.option("--device", default="cpu", type=click.Choice(["cpu", "cuda"]))
@click.option("--dataset-name", default="uci_energy")
@click.option("--inducing-range", nargs=3, default=(10, 101, 10))
@click.option("--noise", default=0.0)
@click.option("--n-folds", default=5)
@click.option("--adaptive/--no-adaptive", default=False)
@click.option("--prior-weight", default=0.5)
def run_all(dataset_name, epochs, device, n_folds, adaptive, prior_weight,
noise, inducing_range):
dataset = load_data(dataset_name)
eprint(f"{bold('Dataset: ')} {dataset_name}\n"
f"{bold('Task type: ')} {dataset.task_type}\n"
f"{bold('N: ')} {len(dataset)}\n"
f"{bold('D: ')} {dataset.input_dims}\n")
Ms = torch.arange(*inducing_range).tolist()
# ID of currently running experiment
exp_id = get_experiment_id("controlled_setting_real_data")
for M, fold in itertools.product(Ms, range(1, n_folds + 1)):
eprint(f"{bold('Noise: ')} {noise:.3f}\n"
f"{bold('M: ')} {M}\n"
f"Fold {fold}/{n_folds}")
# Set parameters defining this run
params = {"M": M, "noise": noise, "epochs": epochs,
"adaptive": adaptive, "prior_weight": prior_weight,
"dataset_name": dataset_name, "fold": fold}
if run_exists(params):
eprint(green("Already exists\n"))
continue
with mlflow.start_run(experiment_id=exp_id):
mlflow.log_params(params)
run_single(n_folds=n_folds, device=device, **params)
eprint()
if __name__ == "__main__":
run_all()
| [
"click.Choice",
"selective_gp.utils.eprint",
"selective_gp.utils.get_ELBO",
"mlflow.log_metrics",
"click.option",
"selective_gp.utils.run_exists",
"selective_gp.utils.get_model",
"selective_gp.utils.green",
"selective_gp.utils.remove_points",
"mlflow.log_params",
"selective_gp.utils.bold",
"ml... | [((1954, 1969), 'click.command', 'click.command', ([], {}), '()\n', (1967, 1969), False, 'import click\n'), ((1971, 2009), 'click.option', 'click.option', (['"""--epochs"""'], {'default': '(1000)'}), "('--epochs', default=1000)\n", (1983, 2009), False, 'import click\n'), ((2088, 2140), 'click.option', 'click.option', (['"""--dataset-name"""'], {'default': '"""uci_energy"""'}), "('--dataset-name', default='uci_energy')\n", (2100, 2140), False, 'import click\n'), ((2142, 2206), 'click.option', 'click.option', (['"""--inducing-range"""'], {'nargs': '(3)', 'default': '(10, 101, 10)'}), "('--inducing-range', nargs=3, default=(10, 101, 10))\n", (2154, 2206), False, 'import click\n'), ((2208, 2244), 'click.option', 'click.option', (['"""--noise"""'], {'default': '(0.0)'}), "('--noise', default=0.0)\n", (2220, 2244), False, 'import click\n'), ((2246, 2282), 'click.option', 'click.option', (['"""--n-folds"""'], {'default': '(5)'}), "('--n-folds', default=5)\n", (2258, 2282), False, 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import pytest
from d1lod.graph import Graph
from d1lod.interface import Interface
@pytest.fixture(scope="module")
def store():
return Graph('localhost', 8890, 'test')
@pytest.fixture(scope="module")
def graph(store):
namespaces = {
'owl': 'http://www.w3.org/2002/07/owl#',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'rdf': 'http://www.w3.org/1999/02/22-rdf-syntax-ns#',
'xsd': 'http://www.w3.org/2001/XMLSchema#',
'foaf': 'http://xmlns.com/foaf/0.1/',
'dcterms': 'http://purl.org/dc/terms/',
'datacite': 'http://purl.org/spar/datacite/',
'geolink': 'http://schema.geolink.org/1.0/base/main#',
'd1dataset': 'http://dataone.org/dataset/',
'd1person': 'http://dataone.org/person/',
'd1org': 'http://dataone.org/organization/',
'd1node': 'https://cn.dataone.org/cn/v1/node/',
'd1landing': 'https://search.dataone.org/#view/',
"prov": "http://www.w3.org/ns/prov#"
}
graphh = Graph('localhost', 8890, 'test', ns=namespaces)
return graphh
@pytest.fixture(scope="module")
def interface(graph):
return Interface(graph)
| [
"pytest.fixture",
"d1lod.interface.Interface",
"d1lod.graph.Graph"
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from setuptools import setup
ENVIRON_VERSION = "0.0.0"
with open("README.md", "r") as f:
README = f.read()
setup(
name="environ",
packages=["environ"],
version=ENVIRON_VERSION,
install_requires=[],
dependency_links=[],
description="easy but incompetent .env loader",
long_description=README,
license="Apache",
author="<NAME>",
author_email="<EMAIL>",
)
| [
"setuptools.setup"
] | [((114, 370), 'setuptools.setup', 'setup', ([], {'name': '"""environ"""', 'packages': "['environ']", 'version': 'ENVIRON_VERSION', 'install_requires': '[]', 'dependency_links': '[]', 'description': '"""easy but incompetent .env loader"""', 'long_description': 'README', 'license': '"""Apache"""', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""'}), "(name='environ', packages=['environ'], version=ENVIRON_VERSION,\n install_requires=[], dependency_links=[], description=\n 'easy but incompetent .env loader', long_description=README, license=\n 'Apache', author='<NAME>', author_email='<EMAIL>')\n", (119, 370), False, 'from setuptools import setup\n')] |
import sublime
import sublime_plugin
import os
from os import path
class LoadtemplateCommand(sublime_plugin.TextCommand):
def run(self, edit, **args):
template_path = os.path.join(
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'templates'),
args['module'],
'{}.tpl'.format(args['template'])
)
if path.exists(template_path):
with open(template_path, 'r') as template_file:
content = template_file.read()
template_file.close()
self.view.insert(
edit,
self.view.sel()[0].begin(),
content
)
else:
message = "Unable to find template:\n\n{}\n\nPlease check if template exists!".format(template_path)
sublime.error_message(message) | [
"os.path.abspath",
"os.path.exists",
"sublime.error_message"
] | [((326, 352), 'os.path.exists', 'path.exists', (['template_path'], {}), '(template_path)\n', (337, 352), False, 'from os import path\n'), ((667, 697), 'sublime.error_message', 'sublime.error_message', (['message'], {}), '(message)\n', (688, 697), False, 'import sublime\n'), ((218, 243), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (233, 243), False, 'import os\n')] |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Collections utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
def tf_namedtuple(name, fieldnames_and_docs):
"""A `namedtuple` class factory that supports field-docstrings.
```
cls = tf_namedtuple("MyNamedTuple",[("a", "Docs for a"),
("b", "Docs for b")])
cls.a.__doc__ # ==> "Docs for a"
```
Args:
name: The name of the new class.
fieldnames_and_docs: A sequence of `(fieldname, docstring)` pairs. The
fieldnames are passed to `collections.namedtuple`.
Returns:
A namedtuple class.
"""
fieldnames_and_docs = list(fieldnames_and_docs)
fieldnames = [fieldname for fieldname, doc in fieldnames_and_docs]
cls = collections.namedtuple(name, fieldnames)
for fieldname, doc in fieldnames_and_docs:
old_prop = getattr(cls, fieldname)
new_prop = property(fget=old_prop.fget, fset=old_prop.fset,
fdel=old_prop.fdel, doc=doc)
setattr(cls, fieldname, new_prop)
return cls
| [
"collections.namedtuple"
] | [((1477, 1517), 'collections.namedtuple', 'collections.namedtuple', (['name', 'fieldnames'], {}), '(name, fieldnames)\n', (1499, 1517), False, 'import collections\n')] |
from linz_logger import get_log
from topo_processor.util import time_in_ms
from .get_fs import get_fs
def transfer_file(source_file: str, checksum: str, content_type, target_file: str):
start_time = time_in_ms()
with get_fs(source_file).open(source_file, "rb") as f1:
data = f1.read()
with get_fs(target_file).open(target_file, "wb", ContentType=content_type, Metadata={"hash": checksum}) as f2:
f2.write(data)
get_log().debug(
"File transferred", source_file=source_file, target_file=target_file, duration=time_in_ms() - start_time
)
| [
"linz_logger.get_log",
"topo_processor.util.time_in_ms"
] | [((207, 219), 'topo_processor.util.time_in_ms', 'time_in_ms', ([], {}), '()\n', (217, 219), False, 'from topo_processor.util import time_in_ms\n'), ((463, 472), 'linz_logger.get_log', 'get_log', ([], {}), '()\n', (470, 472), False, 'from linz_logger import get_log\n'), ((575, 587), 'topo_processor.util.time_in_ms', 'time_in_ms', ([], {}), '()\n', (585, 587), False, 'from topo_processor.util import time_in_ms\n')] |
import os
import torch
import segmentation_models_pytorch as smp
import argparse
from refer.utils import *
from refer.datasets import *
from refer.model import build_model
# Argument Parsing
parser = argparse.ArgumentParser(description="Train the Model")
parser.add_argument('--base', '--b', help = 'the base address of the data')
parser.add_argument('--architecture', '--a', help = 'the Architecture of model')
parser.add_argument('--encoder', '--e', help = 'the Encoder of model')
parser.add_argument('--batch_size', '--bat', type=int, help = 'the batch_size of training')
parser.add_argument('--epochs', '--ep', type=int, help = 'the epochs of training')
args = parser.parse_args()
Basic = args.base
Basic = os.path.abspath(Basic)
archi = args.architecture
encoder = args.encoder
batch_size = args.batch_size
epochs = args.epochs
# Directory
x_train_dir = os.path.join(Basic, 'train')
x_train_dir = os.path.abspath(x_train_dir)
y_train_dir = os.path.join(Basic, 'M_train')
y_train_dir = os.path.abspath(y_train_dir)
x_valid_dir = os.path.join(Basic, 'valid')
x_valid_dir = os.path.abspath(x_valid_dir)
y_valid_dir = os.path.join(Basic, 'M_valid')
y_valid_dir = os.path.abspath(y_valid_dir)
x_test_dir = os.path.join(Basic, 'tests')
x_test_dir = os.path.abspath(x_test_dir)
y_test_dir = os.path.join(Basic, 'M_tests')
y_test_dir = os.path.abspath(y_test_dir)
model_dir = os.path.join(Basic, 'best_model.pth')
model_dir = os.path.abspath(model_dir)
# Basic Setting
CLASSES = ['gum']
ACTIVATION = 'sigmoid'
DEVICE = 'cuda'
ENCODER_WEIGHTS = 'imagenet'
loss = smp.utils.losses.DiceLoss()
metrics = [smp.utils.metrics.IoU(threshold=0.5),]
# Model Setting
model, preprocessing_fn = build_model (Architecture = archi, encoder = encoder, weights = ENCODER_WEIGHTS, CLASSES = CLASSES, activation = ACTIVATION)
optimizer = torch.optim.Adam([dict(params=model.parameters(), lr=0.0001),])
# Train
def train (x_train_dir, y_train_dir, x_valid_dir, y_valid_dir, model_dir, model, preprocessing_fn, CLASSES, DEVICE, loss, metrics, optimizer, batch_size, epochs) :
train_dataset = Dataset(
x_train_dir,
y_train_dir,
preprocessing=get_preprocessing(preprocessing_fn),
classes=CLASSES,
)
valid_dataset = Dataset(
x_valid_dir,
y_valid_dir,
preprocessing=get_preprocessing(preprocessing_fn),
classes=CLASSES,
)
train_loader = DataLoader(train_dataset, batch_size, shuffle=True, num_workers=4)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=1)
train_epoch = smp.utils.train.TrainEpoch(
model,
loss=loss,
metrics=metrics,
optimizer=optimizer,
device=DEVICE,
verbose=True,
)
valid_epoch = smp.utils.train.ValidEpoch(
model,
loss=loss,
metrics=metrics,
device=DEVICE,
verbose=True,
)
# train model for 40 epochs
max_score = 0
for i in range(0, epochs):
print('\nEpoch: {}'.format(i))
train_logs = train_epoch.run(train_loader)
valid_logs = valid_epoch.run(valid_loader)
# do something (save model, change lr, etc.)
if max_score < valid_logs['iou_score']:
max_score = valid_logs['iou_score']
torch.save(model, model_dir)
print('Model saved!')
if i == 25:
optimizer.param_groups[0]['lr'] = 1e-5
print('Decrease decoder learning rate to 1e-5!')
# Train
print ("Training the Model")
train (x_train_dir, y_train_dir, x_valid_dir, y_valid_dir, model_dir, model, preprocessing_fn, CLASSES, DEVICE, loss, metrics, optimizer, batch_size, epochs)
print ("Train is finished") | [
"segmentation_models_pytorch.utils.losses.DiceLoss",
"argparse.ArgumentParser",
"os.path.join",
"segmentation_models_pytorch.utils.metrics.IoU",
"segmentation_models_pytorch.utils.train.ValidEpoch",
"segmentation_models_pytorch.utils.train.TrainEpoch",
"torch.save",
"os.path.abspath",
"refer.model.b... | [((207, 261), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Train the Model"""'}), "(description='Train the Model')\n", (230, 261), False, 'import argparse\n'), ((720, 742), 'os.path.abspath', 'os.path.abspath', (['Basic'], {}), '(Basic)\n', (735, 742), False, 'import os\n'), ((870, 898), 'os.path.join', 'os.path.join', (['Basic', '"""train"""'], {}), "(Basic, 'train')\n", (882, 898), False, 'import os\n'), ((913, 941), 'os.path.abspath', 'os.path.abspath', (['x_train_dir'], {}), '(x_train_dir)\n', (928, 941), False, 'import os\n'), ((956, 986), 'os.path.join', 'os.path.join', (['Basic', '"""M_train"""'], {}), "(Basic, 'M_train')\n", (968, 986), False, 'import os\n'), ((1001, 1029), 'os.path.abspath', 'os.path.abspath', (['y_train_dir'], {}), '(y_train_dir)\n', (1016, 1029), False, 'import os\n'), ((1045, 1073), 'os.path.join', 'os.path.join', (['Basic', '"""valid"""'], {}), "(Basic, 'valid')\n", (1057, 1073), False, 'import os\n'), ((1088, 1116), 'os.path.abspath', 'os.path.abspath', (['x_valid_dir'], {}), '(x_valid_dir)\n', (1103, 1116), False, 'import os\n'), ((1131, 1161), 'os.path.join', 'os.path.join', (['Basic', '"""M_valid"""'], {}), "(Basic, 'M_valid')\n", (1143, 1161), False, 'import os\n'), ((1176, 1204), 'os.path.abspath', 'os.path.abspath', (['y_valid_dir'], {}), '(y_valid_dir)\n', (1191, 1204), False, 'import os\n'), ((1219, 1247), 'os.path.join', 'os.path.join', (['Basic', '"""tests"""'], {}), "(Basic, 'tests')\n", (1231, 1247), False, 'import os\n'), ((1261, 1288), 'os.path.abspath', 'os.path.abspath', (['x_test_dir'], {}), '(x_test_dir)\n', (1276, 1288), False, 'import os\n'), ((1302, 1332), 'os.path.join', 'os.path.join', (['Basic', '"""M_tests"""'], {}), "(Basic, 'M_tests')\n", (1314, 1332), False, 'import os\n'), ((1346, 1373), 'os.path.abspath', 'os.path.abspath', (['y_test_dir'], {}), '(y_test_dir)\n', (1361, 1373), False, 'import os\n'), ((1387, 1424), 'os.path.join', 'os.path.join', (['Basic', '"""best_model.pth"""'], {}), "(Basic, 'best_model.pth')\n", (1399, 1424), False, 'import os\n'), ((1437, 1463), 'os.path.abspath', 'os.path.abspath', (['model_dir'], {}), '(model_dir)\n', (1452, 1463), False, 'import os\n'), ((1578, 1605), 'segmentation_models_pytorch.utils.losses.DiceLoss', 'smp.utils.losses.DiceLoss', ([], {}), '()\n', (1603, 1605), True, 'import segmentation_models_pytorch as smp\n'), ((1702, 1819), 'refer.model.build_model', 'build_model', ([], {'Architecture': 'archi', 'encoder': 'encoder', 'weights': 'ENCODER_WEIGHTS', 'CLASSES': 'CLASSES', 'activation': 'ACTIVATION'}), '(Architecture=archi, encoder=encoder, weights=ENCODER_WEIGHTS,\n CLASSES=CLASSES, activation=ACTIVATION)\n', (1713, 1819), False, 'from refer.model import build_model\n'), ((1618, 1654), 'segmentation_models_pytorch.utils.metrics.IoU', 'smp.utils.metrics.IoU', ([], {'threshold': '(0.5)'}), '(threshold=0.5)\n', (1639, 1654), True, 'import segmentation_models_pytorch as smp\n'), ((2603, 2719), 'segmentation_models_pytorch.utils.train.TrainEpoch', 'smp.utils.train.TrainEpoch', (['model'], {'loss': 'loss', 'metrics': 'metrics', 'optimizer': 'optimizer', 'device': 'DEVICE', 'verbose': '(True)'}), '(model, loss=loss, metrics=metrics, optimizer=\n optimizer, device=DEVICE, verbose=True)\n', (2629, 2719), True, 'import segmentation_models_pytorch as smp\n'), ((2792, 2886), 'segmentation_models_pytorch.utils.train.ValidEpoch', 'smp.utils.train.ValidEpoch', (['model'], {'loss': 'loss', 'metrics': 'metrics', 'device': 'DEVICE', 'verbose': '(True)'}), '(model, loss=loss, metrics=metrics, device=DEVICE,\n verbose=True)\n', (2818, 2886), True, 'import segmentation_models_pytorch as smp\n'), ((3329, 3357), 'torch.save', 'torch.save', (['model', 'model_dir'], {}), '(model, model_dir)\n', (3339, 3357), False, 'import torch\n')] |
# Copyright 2020 (c) Aalto University - All Rights Reserved
# ELEC-E8125 - Reinforcement Learning Course
# AALTO UNIVERSITY
#
#############################################################
import numpy as np
from time import sleep
from sailing import SailingGridworld
epsilon = 10e-4 # TODO: Use this criteria for Task 3
# Set up the environment
env = SailingGridworld(rock_penalty=-2)
value_est = np.zeros((env.w, env.h))
env.draw_values(value_est)
if __name__ == "__main__":
# Reset the environment
state = env.reset()
# Compute state values and the policy
# TODO: Compute the value function and policy (Tasks 1, 2 and 3)
value_est, policy = np.zeros((env.w, env.h)), np.zeros((env.w, env.h)) # 15x10 grid
gamma = 0.9 # discount factor value
number_iterations = 100
actions = [0,1,2,3]
value_est_history = []
policy_history = []
for i in range(number_iterations):
print("iteration step: ", i)
env.clear_text() # remove previously drawn values
value_est_copy = value_est.copy() # copy to make sure that values are only taken from current state. In next iteration the upadted value_est will be retrieved
policy_copy = policy.copy()
value_est_history.append(value_est_copy)
policy_history.append(policy_copy)
for x in range(env.w): # loop through all states/cell in the environment
for y in range(env.h):
state_values_of_actions = [] # keep track of calculated state values of each action
# calculate new state value for every action and add to list above
#for transitions in env.transitions[x,y]: # the four different actions are 1) .LEFT 2) .DOWN 3) .RIGHT 4) .UP - each transition is a list with three tuples (state, reward, done, probability)
for action in actions:
transitions = env.transitions[x,y,action]
state_value_of_action = 0
for transition in transitions:
state_next = transition[0]
reward = transition[1]
done = transition[2]
probability = transition[3]
if (state_next == None):
state_value_of_action += 0
else:
state_value_of_action += probability * (reward + gamma * value_est_copy[state_next])
state_values_of_actions.append(state_value_of_action)
# update value_est and policy
value_est[x][y] = np.max(state_values_of_actions)
policy[x][y] = np.argmax(state_values_of_actions)
max_diff_val = np.max(abs(abs(value_est) - abs(value_est_copy)))
if (max_diff_val < epsilon):
print("Converged! Value state converged in iteration: ", i+1)
break
max_diff_policy = np.max(abs(policy_copy - policy))
if (max_diff_policy < epsilon):
print("Converged! Policy converged in iteration: ", i+1)
#env.draw_values(value_est) # draw the new calculated values after every iteration
#env.draw_actions(policy)
#env.render()
# Just for my understanding how the data is stored/provided
#print(env.transitions[3,3][0]) # gives us one of the four actions
#print(env.transitions[3,3][0][0]) # gives us one of the three tuples
#print(env.transitions[3, 3][0][0].state) # access entries of tuple
#print(env.transitions[3, 3][0][0][3])
#print(env.transitions[6,3])
# Show the values and the policy
env.draw_values(value_est)
env.draw_actions(policy)
env.render()
sleep(1)
# Save the state values and the policy
fnames = "values.npy", "policy.npy"
np.save(fnames[0], value_est)
np.save(fnames[1], policy)
print("Saved state values and policy to", *fnames)
# Run a single episode
# TODO: Run multiple episodes and compute the discounted returns (Task 4)
number_episodes = 1000
discounted_return_history = []
for i in range(number_episodes):
done = False
counter_discount = 0
discounted_return = 0
while not done:
# Select a random action
# TODO: Use the policy to take the optimal action (Task 2)
action = policy[state]
# Step the environment
state, reward, done, _ = env.step(action)
# calculate discounted reward
discounted_return += reward * gamma**counter_discount
counter_discount += 1
# Render and sleep
#env.render()
#sleep(0.5)
discounted_return_history.append(discounted_return)
state = env.reset()
print("discounted return (initial state) - mean: ", np.mean(discounted_return_history))
print("discounted return (initial state) - std: ", np.std(discounted_return_history)) | [
"numpy.mean",
"numpy.argmax",
"time.sleep",
"sailing.SailingGridworld",
"numpy.max",
"numpy.zeros",
"numpy.std",
"numpy.save"
] | [((356, 389), 'sailing.SailingGridworld', 'SailingGridworld', ([], {'rock_penalty': '(-2)'}), '(rock_penalty=-2)\n', (372, 389), False, 'from sailing import SailingGridworld\n'), ((402, 426), 'numpy.zeros', 'np.zeros', (['(env.w, env.h)'], {}), '((env.w, env.h))\n', (410, 426), True, 'import numpy as np\n'), ((3714, 3722), 'time.sleep', 'sleep', (['(1)'], {}), '(1)\n', (3719, 3722), False, 'from time import sleep\n'), ((3811, 3840), 'numpy.save', 'np.save', (['fnames[0]', 'value_est'], {}), '(fnames[0], value_est)\n', (3818, 3840), True, 'import numpy as np\n'), ((3845, 3871), 'numpy.save', 'np.save', (['fnames[1]', 'policy'], {}), '(fnames[1], policy)\n', (3852, 3871), True, 'import numpy as np\n'), ((671, 695), 'numpy.zeros', 'np.zeros', (['(env.w, env.h)'], {}), '((env.w, env.h))\n', (679, 695), True, 'import numpy as np\n'), ((697, 721), 'numpy.zeros', 'np.zeros', (['(env.w, env.h)'], {}), '((env.w, env.h))\n', (705, 721), True, 'import numpy as np\n'), ((4844, 4878), 'numpy.mean', 'np.mean', (['discounted_return_history'], {}), '(discounted_return_history)\n', (4851, 4878), True, 'import numpy as np\n'), ((4935, 4968), 'numpy.std', 'np.std', (['discounted_return_history'], {}), '(discounted_return_history)\n', (4941, 4968), True, 'import numpy as np\n'), ((2614, 2645), 'numpy.max', 'np.max', (['state_values_of_actions'], {}), '(state_values_of_actions)\n', (2620, 2645), True, 'import numpy as np\n'), ((2677, 2711), 'numpy.argmax', 'np.argmax', (['state_values_of_actions'], {}), '(state_values_of_actions)\n', (2686, 2711), True, 'import numpy as np\n')] |
""" Includes two functions which use shortest path policies
1) run_sss_curriculum - trains a PyMARL agent using experiences gathered
while following an epsilon greedy shortest path policy.
2) mean_sss_time - returns the mean time taken to complete a map while following
an epsilon greedy shortest path policy.
"""
import datetime
import os
from os.path import dirname, abspath
import time
#from sympy import EX
import yaml
import torch as th
from types import SimpleNamespace as SN
from utils.logging import Logger, log_mac_weights
import numpy as np
import random
from logging import getLogger, INFO
from rapport_topological.navigation import construct_shortest_path_policy
from rapport_models.markov.state import State
from learners import REGISTRY as le_REGISTRY
from runners import REGISTRY as r_REGISTRY
from controllers import REGISTRY as mac_REGISTRY
from components.episode_buffer import ReplayBuffer
from components.transforms import OneHot
from src.components.episode_buffer import EpisodeBatch
from runners import AsyncEpisodeRunner
from main import recursive_dict_update
from run import args_sanity_check
from torch.utils.tensorboard import SummaryWriter
def load_configs():
""" Load configuration dictionaries from default locations
"""
# Get the defaults from default.yaml
with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f:
try:
config_dict = yaml.load(f, Loader=yaml.FullLoader)
except yaml.YAMLError as exc:
assert False, "default.yaml error: {}".format(exc)
# Get qmix params from qmix.yaml
with open(os.path.join(os.path.dirname(__file__), "config", "algs", "qmix.yaml"), "r") as f:
try:
alg_dict = yaml.load(f, Loader=yaml.FullLoader)
except yaml.YAMLError as exc:
assert False, "default.yaml error: {}".format(exc)
# Get camas params from camas.yaml
with open(os.path.join(os.path.dirname(__file__), "config", "envs", "camas.yaml"), "r") as f:
try:
env_dict = yaml.load(f, Loader=yaml.FullLoader)
except yaml.YAMLError as exc:
assert False, "default.yaml error: {}".format(exc)
config_dict = recursive_dict_update(config_dict, alg_dict)
config_dict = recursive_dict_update(config_dict, env_dict)
return config_dict
class SSS_Runner(AsyncEpisodeRunner):
""" PyMARL Episode Runner for gathering shortest path based experience episodes
"""
debug = False
def __init__(self, args, logger, epsilon_mean=0.15, epsilon_var=0.1):
super().__init__(args, logger)
self.epsilon_mean = epsilon_mean
self.epsilon_var = epsilon_var
self.epsilon = self._draw_epsilon()
self.env.reset()
self.policies = {agent: construct_shortest_path_policy(self.env._tm, self.env._goal_states[agent])
for agent in self.env.agents}
def run(self) -> EpisodeBatch:
""" Returns an transistions for one episode for an agent acting in an
epsilon greedy fashion while following its shortest path.
"""
if self.debug: print('*** reset environment ***')
self.reset()
self.epsilon = self._draw_epsilon()
terminated = False
episode_return = 0
#self.mac.init_hidden(batch_size=self.batch_size) # NOTE not sure what this is
obs, reward, done, info = self.env.last()
k = 0
while not terminated:
k += 1
pre_transition_data = self.env.get_pretran_data()
if self.debug:
print(f'-- step {k} \nState: {self.env.state()}, Agent: {self.env.agent_selection}, Time: {self.env.sim_time()}')
print(f"Pre transition data: {pre_transition_data}")
self.batch.update(pre_transition_data, ts=self.t)
#actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, test_mode=False)
#print(f'actions {actions}, type {type(actions)}, size {actions.size()}')
#print('my selector: ', self.select_actions())
actions = self.select_actions()
action = actions[0][self.env.agent_idx()].item()
if action == 4:
self.env.step(None) # terminated action to update env correctly
else:
self.env.step(action)
obs, reward, done, env_info = self.env.last()
if done:
if self.debug: print(f'{self.env.agent_selection} done!')
if len(self.env.agents) == 1: terminated = True
if self.debug: print(f'Actions: {actions}\nReward {reward}, Time {self.env.sim_time()}')
episode_return += reward
post_transition_data = {
"actions": actions,
"reward": [(reward,)],
"terminated": [[(terminated),]], # NOTE used to be: [(terminated != env_info.get("episode_limit", False),)] # env info here is info from step()
}
self.batch.update(post_transition_data, ts=self.t)
self.t += 1
if self.t == self.episode_limit:
terminated = True
pre_transition_data = self.env.get_pretran_data()
self.batch.update(pre_transition_data, ts=self.t)
actions = self.select_actions()
self.batch.update({"actions": actions}, ts=self.t)
self.t_env += self.t
return self.batch
def select_actions(self) -> th.Tensor:
""" Choose the action to stay on the shorest path or a random action
depending on epsilon test.
"""
acts = th.ones(1, self.args.n_agents, dtype=int)*4
# choose action for agent acting
agent = self.env.agent_selection
agent_loc = self.env._agent_location[agent]
agent_idx = self.env.agent_name_mapping[self.env.agent_selection]
if self.debug: print(f'choosing action for {agent}, loc: {agent_loc}, idx: {agent_idx}')
if random.uniform(0, 1) > self.epsilon: # exploit
camas_act = self.policies[agent]._state_action_map[State({'loc': agent_loc})]
if self.debug: print(f'exploiting, camas act {camas_act}')
if camas_act is None:
action = 4
else:
action = self.env.to_gym_action(agent_loc, camas_act)
else: # explore
avail_actions = self.batch["avail_actions"][:, self.t]
action = random.choice([i for i, x in enumerate(avail_actions[0, agent_idx]) if x==1])
if self.debug: print(f'random, action {action}, avail agent acts {avail_actions[0, agent_idx]}')
acts[0, agent_idx] = action
if self.debug: print(f'acts {acts}')
return acts
def _draw_epsilon(self):
epsilon = np.random.normal(self.epsilon_mean, self.epsilon_var)
if epsilon < 0: epsilon = 0
return epsilon
def episode_makespan(self):
return self.env.sim_time()
def run_sss_curriculum(args,
logger,
num_episodes,
cycle_after,
max_train_steps,
test_makespan_cutoff,
test_episodes=20,
epsilon_mean=0.25,
epsilon_var=0.15,
log_freq=10000,
agent_weight_log_freq=20000):
"""Trains a PyMARL method using shortest path experiences and saves the result
to the results/model directory
Args:
num_episodes (int): number of experience episodes to gather
max_train_steps (int): number of steps to train the model for
test_episodes (int): number of episodes to evaluate the model on once training is complete
"""
def _gather_data(_num_episodes, _buffer, _sss_runner, _logger, _iteration=0):
_start_time = time.time()
_logger.console_logger.info(f'...gathering {_num_episodes} of data, iteration: {_iteration}...')
ep_rewards = np.zeros(_num_episodes)
ep_epsilons = np.zeros(_num_episodes)
ep_times = np.zeros(_num_episodes)
ep_step_count = np.zeros(_num_episodes)
for k in range(_num_episodes):
episode_batch = _sss_runner.run()
_buffer.insert_episode_batch(episode_batch)
ep_rewards[k] = th.sum(episode_batch["reward"])
ep_epsilons[k] = _sss_runner.epsilon
ep_times[k] = _sss_runner.episode_makespan()
ep_step_count[k] = _sss_runner.t
if k % log_freq == 0:
_logger.console_logger.info(f'...{k} episodes complete, mean time {np.mean(ep_times)} ({np.std(ep_times)}), mean step count {np.mean(ep_step_count)} ({np.std(ep_step_count)})...')
_logger.console_logger.info(f'...mean rewards {np.mean(ep_rewards)} ({np.std(ep_rewards)}), mean epsilon {np.mean(ep_epsilons)} ({np.std(ep_epsilons)})')
save_curriculum_data([ep_rewards, ep_epsilons, ep_times, ep_step_count], _iteration)
data_gathering_time = time.time() - _start_time
_logger.console_logger.info(f'...time to gather {_num_episodes} episodes: {datetime.timedelta(seconds=data_gathering_time)}, mean time {np.mean(ep_times)} ({np.std(ep_times)}), mean step count {np.mean(ep_step_count)} ({np.std(ep_step_count)})...')
_logger.console_logger.info(f'...mean rewards {np.mean(ep_rewards)} ({np.std(ep_rewards)}), mean epsilon {np.mean(ep_epsilons)} ({np.std(ep_epsilons)})')
def _test_env(_runner, _test_episdoes):
""" Test environment using `_runner`
Returns:
tt: test sim times
sc: test step counts
gc: test reached goal %'s
"""
tt, sc, gc = [], [], []
for _ in range(_test_episdoes):
_runner.run(test_mode=True)
tt.append(_runner.env.sim_time())
sc.append(_runner.env.step_count())
gc.append(_runner.env.agents_at_goal())
return tt, sc, gc
def _save_model(_args, _logger, _learner, label):
_logger.console_logger.info('...saving model...')
_save_path = os.path.join("curriculum", _args.unique_token, str(label))
os.makedirs(_save_path, exist_ok=True)
_logger.console_logger.info("Saving models to {}".format(_save_path))
_learner.save_models(_save_path)
print(' -- Env args', args.env_args)
start_time = time.time()
tb_logs_direc = os.path.join(dirname(dirname(abspath(__file__))), "results", "curriculum_tb")
tb_exp_direc = os.path.join(tb_logs_direc, "{}").format(args.unique_token)
logger.setup_tb(tb_exp_direc)
args.log_interval = log_freq
args.learner_log_interval = log_freq
main_runner = r_REGISTRY[args.runner](args=args, logger=logger)
sss_runner = SSS_Runner(args, logger, epsilon_mean=epsilon_mean, epsilon_var=epsilon_var)
# Set up schemes and groups
env_info = sss_runner.get_env_info()
args.n_agents = env_info["n_agents"]
args.n_actions = env_info["n_actions"]
args.state_shape = env_info["state_shape"]
scheme = {
"state": {"vshape": env_info["state_shape"]},
"obs": {"vshape": env_info["obs_shape"], "group": "agents"},
"actions": {"vshape": (1,), "group": "agents", "dtype": th.long},
"avail_actions": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.int},
"reward": {"vshape": (1,)},
"terminated": {"vshape": (1,), "dtype": th.uint8},
}
groups = {
"agents": args.n_agents
}
preprocess = {
"actions": ("actions_onehot", [OneHot(out_dim=args.n_actions)])
}
buffer = ReplayBuffer(scheme, groups, args.buffer_size, env_info["episode_limit"] + 1,
preprocess=preprocess,
device="cpu" if args.buffer_cpu_only else args.device)
# Setup multiagent controller here
mac = mac_REGISTRY[args.mac](buffer.scheme, groups, args)
# Give runners the scheme
main_runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)
sss_runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)
# Learner
learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)
if args.use_cuda:
learner.cuda()
## --- Save config ---
config_save_path = os.path.join("curriculum", args.unique_token)
os.makedirs(config_save_path, exist_ok=True)
with open(os.path.join(config_save_path, "config.yaml"), 'w') as outp: # NOTE this has not been tested
yaml.dump(args, outp)
## --- Gather Data ---
_gather_data(num_episodes, buffer, sss_runner, logger)
## --- Train Network ---
logger.console_logger.info(f'...training network...')
for i in range(max_train_steps):
episode_sample = buffer.sample(args.batch_size)
# Truncate batch to only filled timesteps
max_ep_t = episode_sample.max_t_filled()
episode_sample = episode_sample[:, :max_ep_t]
if episode_sample.device != args.device:
episode_sample.to(args.device)
learner.train(episode_sample, i, i)
if (i % cycle_after == 0) and (i > 0): # Gather new data with freq `cycle_after`
_gather_data(num_episodes, buffer, sss_runner, logger, i/cycle_after)
if i % log_freq == 0:
tt, sc, gc = _test_env(main_runner, test_episodes)
logger.log_stat("Test_mean_sim_time", np.mean(tt), i)
logger.log_stat("Test_mean_step_count", np.mean(sc), i)
logger.log_stat("Test_mean_goal_found", np.mean(gc), i)
logger.console_logger.info(f'...logging at step {i}, mean sim time {np.mean(tt)}...')
if np.mean(tt) < test_makespan_cutoff:
tt, _, _ = _test_env(main_runner, test_episodes)
if np.mean(tt) < test_makespan_cutoff:
logger.console_logger.info(f'Training passed evaluation at step {i}. Mean makespan: {np.mean(tt)}, cutoff: {test_makespan_cutoff}')
break
if i % agent_weight_log_freq == 0:
log_mac_weights(logger, mac, i)
_save_model(args, logger, learner, i)
_gather_data(num_episodes, buffer, sss_runner, logger, 1000)
# -- Train for 20e3 more steps --
for i in range(20000):
episode_sample = buffer.sample(args.batch_size)
# Truncate batch to only filled timesteps
max_ep_t = episode_sample.max_t_filled()
episode_sample = episode_sample[:, :max_ep_t]
if episode_sample.device != args.device:
episode_sample.to(args.device)
learner.train(episode_sample, i, i)
if i % log_freq == 0:
tt, sc, gc = _test_env(main_runner, test_episodes)
logger.log_stat("Test_mean_sim_time", np.mean(tt), i)
logger.log_stat("Test_mean_step_count", np.mean(sc), i)
logger.log_stat("Test_mean_goal_found", np.mean(gc), i)
logger.console_logger.info(f'...logging at step {i}, mean sim time {np.mean(tt)}...')
if i % agent_weight_log_freq == 0:
log_mac_weights(logger, mac, i)
tdelta = time.time()-start_time
logger.console_logger.info(f'...time taken for training: {datetime.timedelta(seconds=tdelta)}...')
## --- Evaluate final agent ---
logger.console_logger.info(f'...evaluating final agent...')
tt, sc, gc = _test_env(main_runner, test_episodes)
logger.log_stat("Test_mean_sim_time", np.mean(tt), i)
logger.log_stat("Test_mean_step_count", np.mean(sc), i)
logger.log_stat("Test_mean_goal_found", np.mean(gc), i)
logger.console_logger.info(f'-- evaluation av test time: {np.mean(tt)} ({np.var(tt)}), av step count {np.mean(sc)} ({np.var(sc)}), percentage at goal {np.mean(gc)} ({np.var(gc)}), {len(sc)} episodes')
_save_model(args, logger, learner, i+1)
def save_curriculum_data(array_to_save, iteration=0):
save_path = os.path.join(args.local_results_path, "curriculum", "ep_data", args.unique_token)
os.makedirs(save_path, exist_ok=True)
np.save('{}/ep_data_{}.npy'.format(save_path, iteration), array_to_save, allow_pickle=True)
def mean_sss_time(args, logger, num_episodes, epsilon_mean):
"""Runs a PyMARL-Camas map using an epsilon greedy shortest path policy
Args:
num_episodes (int): number of episodes to run for
epislon (int): epsilon to use in action selection
"""
print(' -- Env args', args.env_args)
start_time = time.time()
sss_runner = SSS_Runner(args, logger, epsilon_mean=epsilon_mean, epsilon_var=0.0)
# Set up schemes and groups
env_info = sss_runner.get_env_info()
args.n_agents = env_info["n_agents"]
args.n_actions = env_info["n_actions"]
args.state_shape = env_info["state_shape"]
scheme = {
"state": {"vshape": env_info["state_shape"]},
"obs": {"vshape": env_info["obs_shape"], "group": "agents"},
"actions": {"vshape": (1,), "group": "agents", "dtype": th.long},
"avail_actions": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.int},
"reward": {"vshape": (1,)},
"terminated": {"vshape": (1,), "dtype": th.uint8},
}
groups = {
"agents": args.n_agents
}
preprocess = {
"actions": ("actions_onehot", [OneHot(out_dim=args.n_actions)])
}
buffer = ReplayBuffer(scheme, groups, args.buffer_size, env_info["episode_limit"] + 1,
preprocess=preprocess,
device="cpu" if args.buffer_cpu_only else args.device)
# Setup multiagent controller here
mac = mac_REGISTRY[args.mac](buffer.scheme, groups, args)
# Give runners the scheme
#main_runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)
sss_runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)
# Learner
learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)
if args.use_cuda:
learner.cuda()
logger.console_logger.info(f'...running {num_episodes} episodes...')
episode_times = []
step_count = []
rewards = []
for i in range(num_episodes):
batch = sss_runner.run()
episode_times.append(sss_runner.env.sim_time())
step_count.append(sss_runner.t)
rewards.append(th.sum(batch["reward"]))
if i % 50 == 0:
logger.console_logger.info(f'...{i} episodes complete...')
print(f'Mean sim time for {num_episodes} on {args.env_args["map_name"]} and an epsilon of {epsilon_mean}: {np.mean(episode_times)} ({np.var(episode_times)})')
print(f'mean step count {np.mean(step_count)} ({np.var(step_count)}), mean reward: {np.mean(rewards)} ({np.var(rewards)})')
return np.mean(episode_times), np.mean(step_count)
def load_default_params(map_name="bruno"):
pass
#TODO
if __name__ == "__main__":
## *** Curriculum specific variables ***
num_episodes = int(5e3)
cycle_after = int(2.5e4)
train_steps_max = int(5e5)
test_episodes = 20
test_makespan_cutoff = 60
console_logger = getLogger()
logger = Logger(console_logger)
config_dict = load_configs() # NOTE should sanity check
args = SN(**config_dict) # gives attribute access to namespace
if args.use_cuda:
args.use_cuda = th.cuda.is_available() # Check that cuda is valid
args.device = "cuda" if args.use_cuda else "cpu"
if args.use_cuda: logger.console_logger.info('... Using CUDA...')
args.unique_token = "cur<PASSWORD>{}".format(args.name, datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
#args.batch_size = 64
logger.console_logger.setLevel(INFO)
if args.prioritised_replay:
logger.console_logger.warning('Turning PER off')
args.prioritised_replay = False # not implemented
#mean_sss_time(args, logger, 200, 0.0)
if num_episodes > args.buffer_size:
args.buffer_size = num_episodes
print(f'Buffer size now {args.buffer_size}')
run_sss_curriculum(args, logger, num_episodes, cycle_after, train_steps_max, test_makespan_cutoff,
test_episodes=test_episodes, log_freq=int(2e4), agent_weight_log_freq=int(4e4))
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import copy
import pickle
import unittest
from op_hierarchical_chainmap._ext import ChainMap
class TestChainMap(unittest.TestCase):
def test_basics(self):
c = ChainMap()
c['a'] = 1
c['b'] = 2
d = c.new_child()
d['b'] = 20
d['c'] = 30
self.assertEqual(d.maps, [{'b':20, 'c':30}, {'a':1, 'b':2}]) # check internal state
self.assertEqual(d.items(), dict(a=1, b=20, c=30).items()) # check items/iter/getitem
self.assertEqual(len(d), 3) # check len
for key in 'abc': # check contains
self.assertIn(key, d)
for k, v in dict(a=1, b=20, c=30, z=100).items(): # check get
self.assertEqual(d.get(k, 100), v)
del d['b'] # unmask a value
self.assertEqual(d.maps, [{'c':30}, {'a':1, 'b':2}]) # check internal state
self.assertEqual(d.items(), dict(a=1, b=2, c=30).items()) # check items/iter/getitem
self.assertEqual(len(d), 3) # check len
for key in 'abc': # check contains
self.assertIn(key, d)
for k, v in dict(a=1, b=2, c=30, z=100).items(): # check get
self.assertEqual(d.get(k, 100), v)
for e in d.copy(), copy.copy(d): # check shallow copies
self.assertEqual(d, e)
self.assertEqual(d.maps, e.maps)
self.assertIsNot(d, e)
self.assertIsNot(d.maps[0], e.maps[0])
for m1, m2 in zip(d.maps[1:], e.maps[1:]):
self.assertIs(m1, m2)
# check deep copies
# pybind11 doesn't seem to support pickle v0 & v1
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
e = pickle.loads(pickle.dumps(d, proto))
self.assertEqual(d, e)
self.assertEqual(d.maps, e.maps)
self.assertIsNot(d, e)
for m1, m2 in zip(d.maps, e.maps):
self.assertIsNot(m1, m2, e)
for e in [copy.deepcopy(d),
]:
self.assertEqual(d, e)
self.assertEqual(d.maps, e.maps)
self.assertIsNot(d, e)
for m1, m2 in zip(d.maps, e.maps):
self.assertIsNot(m1, m2, e)
f = d.new_child()
f['b'] = 5
self.assertEqual(f.maps, [{'b': 5}, {'c':30}, {'a':1, 'b':2}])
self.assertEqual(f.parents.maps, [{'c':30}, {'a':1, 'b':2}]) # check parents
self.assertEqual(f['b'], 5) # find first in chain
self.assertEqual(f.parents['b'], 2) # look beyond maps[0]
def test_ordering(self):
# Combined order matches a series of dict updates from last to first.
# This test relies on the ordering of the underlying dicts.
baseline = {'music': 'bach', 'art': 'rembrandt'}
adjustments = {'art': 'van gogh', 'opera': 'carmen'}
cm = ChainMap(adjustments, baseline)
combined = baseline.copy()
combined.update(adjustments)
self.assertEqual(list(combined.items()), list(cm.items()))
def test_constructor(self):
self.assertEqual(ChainMap().maps, [{}]) # no-args --> one new dict
self.assertEqual(ChainMap({1:2}).maps, [{1:2}]) # 1 arg --> list
def test_bool(self):
self.assertFalse(ChainMap())
self.assertFalse(ChainMap({}, {}))
self.assertTrue(ChainMap({1:2}, {}))
self.assertTrue(ChainMap({}, {1:2}))
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# Copyright 2018 Red Hat, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os.path
from six.moves import configparser
from sphinx.util import logging
import pbr.version
_project = None
logger = logging.getLogger(__name__)
def _find_setup_cfg(srcdir):
"""Find the 'setup.cfg' file, if it exists.
This assumes we're using 'doc/source' for documentation, but also allows
for single level 'doc' paths.
"""
# TODO(stephenfin): Are we sure that this will always exist, e.g. for
# an sdist or wheel? Perhaps we should check for 'PKG-INFO' or
# 'METADATA' files, a la 'pbr.packaging._get_version_from_pkg_metadata'
for path in [
os.path.join(srcdir, os.pardir, 'setup.cfg'),
os.path.join(srcdir, os.pardir, os.pardir, 'setup.cfg')]:
if os.path.exists(path):
return path
return None
def _get_project_name(srcdir):
"""Return string name of project name, or None.
This extracts metadata from 'setup.cfg'. We don't rely on
distutils/setuptools as we don't want to actually install the package
simply to build docs.
"""
global _project
if _project is None:
parser = configparser.ConfigParser()
path = _find_setup_cfg(srcdir)
if not path or not parser.read(path):
logger.info('Could not find a setup.cfg to extract project name '
'from')
return None
try:
# for project name we use the name in setup.cfg, but if the
# length is longer then 32 we use summary. Otherwise thAe
# menu rendering looks brolen
project = parser.get('metadata', 'name')
if len(project.split()) == 1 and len(project) > 32:
project = parser.get('metadata', 'summary')
except configparser.Error:
logger.info('Could not extract project metadata from setup.cfg')
return None
_project = project
return _project
def _builder_inited(app):
# TODO(stephenfin): Once Sphinx 1.8 is released, we should move the below
# to a 'config-inited' handler
project_name = _get_project_name(app.srcdir)
try:
version_info = pbr.version.VersionInfo(project_name)
except Exception:
version_info = None
if version_info and not app.config.version and not app.config.release:
app.config.version = version_info.canonical_version_string()
app.config.release = version_info.version_string_with_vcs()
def setup(app):
app.connect('builder-inited', _builder_inited)
return {
'parallel_read_safe': True,
'parallel_write_safe': True,
}
| [
"sphinx.util.logging.getLogger",
"six.moves.configparser.ConfigParser"
] | [((731, 758), 'sphinx.util.logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (748, 758), False, 'from sphinx.util import logging\n'), ((1714, 1741), 'six.moves.configparser.ConfigParser', 'configparser.ConfigParser', ([], {}), '()\n', (1739, 1741), False, 'from six.moves import configparser\n')] |
# -*- coding: utf-8 -*-
"""
Created on Sun May 21 14:31:32 2017
@author: <NAME>
"""
import random
inf = 0
prime = 2**17-1
order = 131307
# remember to check isSingular(a,b,p) == False
a = 1
b = 6
# Uses the idea of squareAndMultiply to compute c*p quickly in E.
# Based on algorithm on p. 266 in course literature.
# Using NAF representation (and adding subtract step) makes our implementation
# a bit faster.
def doubleAndAddOrSubtract(p, c):
q = inf
b = intToNAF(c)
for i in range(len(b)-1,-1, -1):
q = addPoints(q,q)
if b[i] == 1:
q = addPoints(p,q)
elif b[i] == -1:
q = addPoints(negPoint(p),q)
if q == inf:
return inf
else:
return (q[0], q[1])
# Adds two points P and Q in E.
# P and Q should be tuples of the form (x,y)
# This method returns a tuple representing the new coordinates.
# See p. 258 in course lit.
def addPoints(p,q):
if q == inf:
return p
if p == inf:
return q
if q[0] == p[0] and q[1] == -p[1] % prime:
return inf
l = 0
if p != q:
#maybe need add prime to q1-p1
l = ((q[1] - p[1] ) * modInverse(q[0]-p[0], prime)) % prime
else:
l = ((3 *p[0]**2 + a) * modInverse(2*p[1], prime)) % prime
x3 = (l**2 - p[0]-q[0]) % prime
return (x3, (l*(p[0] - x3) - p[1]) % prime);
# Computes the multiplicative inverse of the 'number' in modulo 'base'.
# If no inverse exists, '-1' will be returned. Inverse otherwise.
# Based on algorithm on p. 168 in course lit.
def modInverse(number, base):
number0 = number % base
base0 = base
t0 = 0
t = 1
q = base0 // number0
r = base0 - q * number0
while r > 0:
temp = (t0 - q*t) % base
t0 = t
t = temp
base0 = number0
number0 = r
q = base0 // number0
r = base0 - q* number0
if number0 != 1:
return 0 #-1
else:
return t
# Returns number of affine points on the elliptic curve (wihout point at inf).
def countResidues(a, b, p):
counter = 0
for x in range(0,p):
# the curve in
y2 = (x**3+a*x+b) % p
# if x^3+ax+b = 0 (mod p), then there exists one point only.
# i.e. the point is still on the curve even if there is no residue.
if y2 == 0:
counter += 1
continue
# if quadratic residues exists, two points are on the curve
# i.e. the solutions to y^2 = x^3+ax+b (mod p)
if isQuadRes(y2, p):
counter +=2
return counter
# Check if a curve is singular by checking that 4a^3+27b^2 = 0
# See p. 255 in course lit.
def isSingular(a, b, p):
return ( -(4*a*a*a % p)-(27*b*b % p)) % p == 0
# Determines if there exists an x such that x^2=a (mod p), in other words,
# it will return true if the quadratic residue exists.
# Based on Euler's criterion, see course lit p. 180.
def isQuadRes(a,p):
return squareAndMultiply(a,((p-1)//2), p) == 1
# Convert a number to Non-adjacent form
# Based on pseudo-code at https://en.wikipedia.org/wiki/Non-adjacent_form
def intToNAF(number):
i = 0
z = []
#z = [0]*(math.floor(math.log2(number) +1) +10)
while number > 0:
if number % 2 == 1:
z.append(2 - (number % 4))
number = number - z[i]
else:
z.append(0)
number = number // 2
i = i +1
return z
# Get the negative point, eg. -p, given p.
def negPoint(point):
return (point[0], (-point[1]) % prime)
# Return an array that contains the binary representation of the number.
def intToBin(number):
return '{:b}'.format(number)
def mod(number, base):
return number - base * (number//base)
# Calculate 'x^c mod n' using square-and-multiply algorithm.
def squareAndMultiply(x,c,n):
# get the binary representation of c, the exponent.
b = intToBin(c)
z = 1
# the main algorithm, adjusted from p. 177 from course lit.
for i in range(len(b)):
z = mod(z*z, n)
if b[i] == '1':
z = mod(z*x, n)
return z
def find_g(order):
g = random.randint(0,prime)
if mod(prime,4) != 3:
print("Cannot use this method to find the root.")
return
while True: # not(isQuadRes(g, prime)):
g = random.randint(0,prime)
root = squareAndMultiply(g,3,prime)
g1 = (g,root)
if doubleAndAddOrSubtract(g1, order) == inf:
return g1
return -1
def find_order(g):
r = order_range()
for i in range(int(r[0]), int(r[1])+1):
if doubleAndAddOrSubtract(g, i) == inf:
return i
return -1 #countResidues(a, b, prime)+1
def order_range():
import math
return ( prime +1 -2*math.sqrt(prime), prime +1 +2*math.sqrt(prime) )
def find_public_key(g, secret):
return doubleAndAddOrSubtract(g, secret)
def sign(g,secret, k_rnd, hash_val):
m = secret
k = k_rnd
q = order
u, v = doubleAndAddOrSubtract(g, k)
r = mod(u,q)
s = mod(modInverse(k, q) * mod(hash_val + m*r, q), q)
# NOTE: if either r==0 or s==0, a new random
# value k needs to be chosen
return (r,s)
def verify(g, signature, public_key, hash_value):
h = hash_value
q = order
r = signature[0]
s = signature[1]
w = modInverse(s, q)
i = mod(w * h, q)
j = mod(w*r, q)
t1 = doubleAndAddOrSubtract(g, i)
t2 = doubleAndAddOrSubtract(public_key, j)
print(addPoints(t1, t2))
r1, _ = addPoints(t1, t2)
#return addPoints(t1, t2)
return r1 == r
def find_gen_order(g):
i = 2
while doubleAndAddOrSubtract(g, i) != 0:
i+=1
return i
print(isSingular(a,b,prime) == False)
#order = countResidues(a, b, prime)
secret = 111
g = (89887, 3726)
#order = #find_order(g)
pub = find_public_key(g, secret)
res = sign(g, secret, 16, 44444)
print(res)
print(verify(g, res, pub, 44444))
# https://rosettacode.org/wiki/Miller%E2%80%93Rabin_primality_test#Python:_Probably_correct_answers
def is_Prime(n):
"""
Miller-Rabin primality test.
A return value of False means n is certainly not prime. A return value of
True means n is very likely a prime.
"""
if n!=int(n):
return False
n=int(n)
#Miller-Rabin test for prime
if n==0 or n==1 or n==4 or n==6 or n==8 or n==9:
return False
if n==2 or n==3 or n==5 or n==7:
return True
s = 0
d = n-1
while d%2==0:
d>>=1
s+=1
assert(2**s * d == n-1)
def trial_composite(a):
if pow(a, d, n) == 1:
return False
for i in range(s):
if pow(a, 2**i * d, n) == n-1:
return False
return True
for i in range(8):#number of trials
a = random.randrange(2, n)
if trial_composite(a):
return False
return True
| [
"math.sqrt",
"random.randint",
"random.randrange"
] | [((4258, 4282), 'random.randint', 'random.randint', (['(0)', 'prime'], {}), '(0, prime)\n', (4272, 4282), False, 'import random\n'), ((4447, 4471), 'random.randint', 'random.randint', (['(0)', 'prime'], {}), '(0, prime)\n', (4461, 4471), False, 'import random\n'), ((7013, 7035), 'random.randrange', 'random.randrange', (['(2)', 'n'], {}), '(2, n)\n', (7029, 7035), False, 'import random\n'), ((4926, 4942), 'math.sqrt', 'math.sqrt', (['prime'], {}), '(prime)\n', (4935, 4942), False, 'import math\n'), ((4956, 4972), 'math.sqrt', 'math.sqrt', (['prime'], {}), '(prime)\n', (4965, 4972), False, 'import math\n')] |
'''
Description: This script calculate the sentiment score for every headline in a dataset of over a million headlines taken from the Australian news source ABC. This is done using the spaCyTextBlob approach. It saves a plot of sentiment over time with a 1-week rolling average and a plot of sentiment over time with a 1-month rolling average.
'''
# Packages
# Path operations
import os
# Suppress warnings
import warnings
warnings.filterwarnings("ignore")
# Importing pandas to make changes in dataframe
import pandas as pd
# NLP tools
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
# Plotting
import matplotlib.pyplot as plt
# Commandline arguments
import argparse
class headline_polarity:
def __init__(self, args):
self.args = args # Assigns args to use it throughout the script
'''
Load data and change data formats
'''
print('[INFO] Loading and prepocessing data...')
# Defining path and csv
self.ds = pd.read_csv(args['path'])
# Sample data
if self.args['sample'] is not None:
self.ds = self.ds.sample(self.args['sample'])
# Change date format to year-month-day
self.ds["publish_date"]= pd.to_datetime(self.ds.publish_date, format="%Y%m%d")
# Achieving polarity
def polarity(self):
'''
Function that calculates text polarity with SpacyTextBlob
'''
print('[INFO] Calculating polarity...')
# Initialising spaCy
nlp = spacy.load('en_core_web_sm')
# Spacy text blob
spacy_text_blob = SpacyTextBlob()
nlp.add_pipe(spacy_text_blob)
# Initialising Vader
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sentiment = SentimentIntensityAnalyzer()
# Empty array
polarity_scores = []
polarity_scores_vader = []
# Looping through headline in dataset and appending polarities to the array defined above
for doc in nlp.pipe(self.ds["headline_text"]):
polarity_scores.append(doc._.sentiment.polarity)
# Assigning polarity to the dataset
self.ds["polarity_TextBlob"] = polarity_scores
# Vader polarity
for headline in self.ds["headline_text"]:
polarity_scores_vader.append(sentiment.polarity_scores(headline).get('compound'))
self.ds["polarity_Vader"] = polarity_scores_vader
# Plot function
def plot_polarity(self, save = True):
'''
Function that plots text polarity with a weekly and monthly rolling mean
'''
# Splitting argument up
rolling_means = self.args['rolling_means'].split(sep=" ")
print(f'[INFO] Creating plot of polarity with rolling means of {rolling_means[0]} and {rolling_means[1]}...')
# Changing the color map to Paired instead of Viridis
plt.rcParams["image.cmap"] = "Paired"
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Paired.colors)
# Grouping dataset by date and taking mean polarity within 7 and 30 days
pol_1 = self.ds[["publish_date", "polarity_TextBlob"]].groupby("publish_date", as_index = False).mean("polarity_TextBlob").rolling(int(rolling_means[0])).mean()
pol_2 = self.ds[["publish_date", "polarity_TextBlob"]].groupby("publish_date", as_index = False).mean("polarity_TextBlob").rolling(int(rolling_means[1])).mean()
pol_3 = self.ds[["publish_date", "polarity_Vader"]].groupby("publish_date", as_index = False).mean("polarity_Vader").rolling(int(rolling_means[0])).mean()
pol_4 = self.ds[["publish_date", "polarity_Vader"]].groupby("publish_date", as_index = False).mean("polarity_Vader").rolling(int(rolling_means[1])).mean()
# Plotting
linewidth=0.5
plt.figure(figsize=(15, 10))
plt.plot(pol_1, label = 'Weekly TextBlob', linewidth=linewidth)
plt.plot(pol_2, label = 'Monthly TextBlob',linewidth=linewidth)
plt.plot(pol_3, label = 'Weekly Vader',linewidth=linewidth)
plt.plot(pol_4, label = 'Monthly Vader',linewidth=linewidth)
plt.title('Headline polarity scores')
plt.xlabel('Date')
plt.ylabel('Polarity score')
plt.legend()
# Save figure
if save == True:
plt.savefig(os.path.join(self.args['outpath'], 'polarity_plot.png'))
plt.show()
def main():
ap = argparse.ArgumentParser(description = "[INFO] calculating sentiments") # Defining an argument parse
ap.add_argument("--path", "-p",
default = os.path.join("..","..", "data", "1","abcnews-date-text.csv"), help = "str, path to data")
ap.add_argument("--sample", "-s",
default = 10000, help = "sample of data to perform sentiment analysis on, if nothing is specified, if nothing is specified, the entire data set is used")
ap.add_argument("--rolling_means", "-r",
default = "7 30",
help = "str, timeframes to create mean over")
ap.add_argument("--outpath", "-o",
default = os.path.join("..","..", "out", "1"),
help = "str, output path")
args = vars(ap.parse_args()) # Adding them together
# Code to execute to calculate polarity and plot it
headline_polarity_calculator = headline_polarity(args)
headline_polarity_calculator.polarity()
headline_polarity_calculator.plot_polarity()
# Define behaviour when called from command line
if __name__=="__main__":
main()
| [
"nltk.sentiment.vader.SentimentIntensityAnalyzer",
"argparse.ArgumentParser",
"pandas.read_csv",
"spacy.load",
"nltk.download",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"spacytextblob.spacytextblob.SpacyTextBlob",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.legend",
"os.path.join... | [((426, 459), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (449, 459), False, 'import warnings\n'), ((4562, 4630), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""[INFO] calculating sentiments"""'}), "(description='[INFO] calculating sentiments')\n", (4585, 4630), False, 'import argparse\n'), ((1003, 1028), 'pandas.read_csv', 'pd.read_csv', (["args['path']"], {}), "(args['path'])\n", (1014, 1028), True, 'import pandas as pd\n'), ((1235, 1288), 'pandas.to_datetime', 'pd.to_datetime', (['self.ds.publish_date'], {'format': '"""%Y%m%d"""'}), "(self.ds.publish_date, format='%Y%m%d')\n", (1249, 1288), True, 'import pandas as pd\n'), ((1531, 1559), 'spacy.load', 'spacy.load', (['"""en_core_web_sm"""'], {}), "('en_core_web_sm')\n", (1541, 1559), False, 'import spacy\n'), ((1612, 1627), 'spacytextblob.spacytextblob.SpacyTextBlob', 'SpacyTextBlob', ([], {}), '()\n', (1625, 1627), False, 'from spacytextblob.spacytextblob import SpacyTextBlob\n'), ((1724, 1754), 'nltk.download', 'nltk.download', (['"""vader_lexicon"""'], {}), "('vader_lexicon')\n", (1737, 1754), False, 'import nltk\n'), ((1843, 1871), 'nltk.sentiment.vader.SentimentIntensityAnalyzer', 'SentimentIntensityAnalyzer', ([], {}), '()\n', (1869, 1871), False, 'from nltk.sentiment.vader import SentimentIntensityAnalyzer\n'), ((3073, 3111), 'matplotlib.pyplot.cycler', 'plt.cycler', ([], {'color': 'plt.cm.Paired.colors'}), '(color=plt.cm.Paired.colors)\n', (3083, 3111), True, 'import matplotlib.pyplot as plt\n'), ((3923, 3951), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(15, 10)'}), '(figsize=(15, 10))\n', (3933, 3951), True, 'import matplotlib.pyplot as plt\n'), ((3960, 4021), 'matplotlib.pyplot.plot', 'plt.plot', (['pol_1'], {'label': '"""Weekly TextBlob"""', 'linewidth': 'linewidth'}), "(pol_1, label='Weekly TextBlob', linewidth=linewidth)\n", (3968, 4021), True, 'import matplotlib.pyplot as plt\n'), ((4033, 4095), 'matplotlib.pyplot.plot', 'plt.plot', (['pol_2'], {'label': '"""Monthly TextBlob"""', 'linewidth': 'linewidth'}), "(pol_2, label='Monthly TextBlob', linewidth=linewidth)\n", (4041, 4095), True, 'import matplotlib.pyplot as plt\n'), ((4106, 4164), 'matplotlib.pyplot.plot', 'plt.plot', (['pol_3'], {'label': '"""Weekly Vader"""', 'linewidth': 'linewidth'}), "(pol_3, label='Weekly Vader', linewidth=linewidth)\n", (4114, 4164), True, 'import matplotlib.pyplot as plt\n'), ((4175, 4234), 'matplotlib.pyplot.plot', 'plt.plot', (['pol_4'], {'label': '"""Monthly Vader"""', 'linewidth': 'linewidth'}), "(pol_4, label='Monthly Vader', linewidth=linewidth)\n", (4183, 4234), True, 'import matplotlib.pyplot as plt\n'), ((4254, 4291), 'matplotlib.pyplot.title', 'plt.title', (['"""Headline polarity scores"""'], {}), "('Headline polarity scores')\n", (4263, 4291), True, 'import matplotlib.pyplot as plt\n'), ((4300, 4318), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Date"""'], {}), "('Date')\n", (4310, 4318), True, 'import matplotlib.pyplot as plt\n'), ((4327, 4355), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""Polarity score"""'], {}), "('Polarity score')\n", (4337, 4355), True, 'import matplotlib.pyplot as plt\n'), ((4364, 4376), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (4374, 4376), True, 'import matplotlib.pyplot as plt\n'), ((4515, 4525), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (4523, 4525), True, 'import matplotlib.pyplot as plt\n'), ((4732, 4794), 'os.path.join', 'os.path.join', (['""".."""', '""".."""', '"""data"""', '"""1"""', '"""abcnews-date-text.csv"""'], {}), "('..', '..', 'data', '1', 'abcnews-date-text.csv')\n", (4744, 4794), False, 'import os\n'), ((5264, 5300), 'os.path.join', 'os.path.join', (['""".."""', '""".."""', '"""out"""', '"""1"""'], {}), "('..', '..', 'out', '1')\n", (5276, 5300), False, 'import os\n'), ((4449, 4504), 'os.path.join', 'os.path.join', (["self.args['outpath']", '"""polarity_plot.png"""'], {}), "(self.args['outpath'], 'polarity_plot.png')\n", (4461, 4504), False, 'import os\n')] |
from app.api import apiRestful
from flask_restplus import Resource
class Admin:
@apiRestful.route('/admin/anotheradmin')
class AnotherAdminView(Resource):
def get(self):
return {"return" : "Hello World from AnotherMyAdmin!"}, 200
@apiRestful.route('/admin/myadminview')
class MyAdminView(Resource):
def get(self):
return {"return" : "Hello World from MyAdmin!"}, 200
| [
"app.api.apiRestful.route"
] | [((89, 128), 'app.api.apiRestful.route', 'apiRestful.route', (['"""/admin/anotheradmin"""'], {}), "('/admin/anotheradmin')\n", (105, 128), False, 'from app.api import apiRestful\n'), ((278, 316), 'app.api.apiRestful.route', 'apiRestful.route', (['"""/admin/myadminview"""'], {}), "('/admin/myadminview')\n", (294, 316), False, 'from app.api import apiRestful\n')] |
import json
import time
from typing import Dict
from scrapy.exceptions import NotConfigured
from scrapy.http.response.html import HtmlResponse
from scrapy_cdr import CDRItem
from dd_crawler.utils import get_domain
class RequestLogMiddleware:
def __init__(self, *, jl_logger, relevancy_threshold: float):
# This are per-worker values, while stats values updated in
# dd_crawler.queue are global.
self.domains = set()
self.relevant_domains = set()
self.total_score = 0.
self.n_crawled = 0
self.jl_logger = jl_logger
self.relevancy_threshold = relevancy_threshold
@classmethod
def from_crawler(cls, crawler) -> 'RequestLogMiddleware':
log_path = crawler.settings.get('RESPONSE_LOG_FILE')
if not log_path:
raise NotConfigured('RESPONSE_LOG_FILE not defined')
jl_logger = get_jl_logger(log_path)
threshold = crawler.settings.getfloat('PAGE_RELEVANCY_THRESHOLD', 0.5)
return cls(jl_logger=jl_logger, relevancy_threshold=threshold)
def process_spider_output(self, response, result, spider):
for item in result:
if isinstance(item, CDRItem):
self.log_item(item, response)
yield item
def log_item(self, item: CDRItem, response: HtmlResponse):
self.n_crawled += 1
domain = get_domain(item['url'])
self.domains.add(domain)
metadata = item.get('metadata', {})
score = metadata.get('page_score', 0.)
if score is not None:
self.total_score += score
if score > self.relevancy_threshold:
self.relevant_domains.add(domain)
log_entry = {
'time': time.time(),
'url': response.url,
'id': metadata.get('id'),
'parent': metadata.get('parent'),
'depth': response.meta.get('depth', ''),
'priority': response.request.priority,
'score': score,
'total_score': self.total_score,
'n_crawled': self.n_crawled,
'n_domains': len(self.domains),
'n_relevant_domains': len(self.relevant_domains),
}
if metadata.get('has_login_form'):
log_entry['has_login_form'] = True
if 'autologin_active' in response.meta:
log_entry['login_success'] = response.meta['autologin_active']
self.jl_logger.write_entry(log_entry)
class JsonLinesLogger:
def __init__(self, log_path):
self._log_file = open(log_path, 'at')
def write_entry(self, log_entry: Dict):
json.dump(log_entry, self._log_file)
self._log_file.write('\n')
self._log_file.flush()
_loggers = {}
def get_jl_logger(log_path):
if log_path not in _loggers:
_loggers[log_path] = JsonLinesLogger(log_path)
return _loggers[log_path]
| [
"dd_crawler.utils.get_domain",
"scrapy.exceptions.NotConfigured",
"time.time",
"json.dump"
] | [((1370, 1393), 'dd_crawler.utils.get_domain', 'get_domain', (["item['url']"], {}), "(item['url'])\n", (1380, 1393), False, 'from dd_crawler.utils import get_domain\n'), ((2608, 2644), 'json.dump', 'json.dump', (['log_entry', 'self._log_file'], {}), '(log_entry, self._log_file)\n', (2617, 2644), False, 'import json\n'), ((817, 863), 'scrapy.exceptions.NotConfigured', 'NotConfigured', (['"""RESPONSE_LOG_FILE not defined"""'], {}), "('RESPONSE_LOG_FILE not defined')\n", (830, 863), False, 'from scrapy.exceptions import NotConfigured\n'), ((1727, 1738), 'time.time', 'time.time', ([], {}), '()\n', (1736, 1738), False, 'import time\n')] |
import pandas as pd
import openpyxl
df = pd.read_excel("Arquivo.xlsx", engine="openpyxl")
print(df.loc[[True, False, False, True, True, True, False], [True, False, False]]) #Mostra apenas linhas e colunas que estão True na lista passada
print()
print(df.Nome == "Jonathan") #Retorna uma lista de True e False com True apenas nas linhas que correspondem ao que foi passado
print()
print(df[df.Idade == 19]) #Retorna as linhas em que o valor foi igual ao valor passado
print()
print(df[(df.Nome == "Lucas") | (df.Idade == 19)]) #Retorna as linhas em que Nome for igual a lucas ou idade for igual a 19
print()
| [
"pandas.read_excel"
] | [((42, 90), 'pandas.read_excel', 'pd.read_excel', (['"""Arquivo.xlsx"""'], {'engine': '"""openpyxl"""'}), "('Arquivo.xlsx', engine='openpyxl')\n", (55, 90), True, 'import pandas as pd\n')] |
import pygame, thorpy
from utilidades.texto import Texto, TextArea
from utilidades.button import Button
from utilidades.colores import *
import config as config
"""
CLASE PRINCIPAL: Aplicación
"""
ScreenSize = width, height = 1056, 672
Caption = "CMC v0.11"
if not config.pantalla_completa:
thorpy.Application(size=ScreenSize, caption=Caption, flags= pygame.DOUBLEBUF) # Declarar propiedades ventana
else:
thorpy.Application(size=ScreenSize, caption=Caption,flags= pygame.DOUBLEBUF | pygame.FULLSCREEN) # Declarar propiedades ventana
class App:
# Constructor
def __init__(self, resolucion = ScreenSize):
# self.screen = None
self.pantalla_completa = config.pantalla_completa
# Tamaño de la ventana
self.ScreenSize = resolucion
# Crear pantalla
self.screen = thorpy.get_screen()
pygame.init()
# self.screen.fill((0,0,0))
# pygame.display.set_caption('Proyecto v3', 'icontitle=None')
def getScreen(self):
return self.screen
def getScreenSize(self):
return self.ScreenSize
def getPantallaCompleta(self):
return self.pantalla_completa
def general_events(self, pantalla):
# Eventos generales - No usar eventos de tipo KEYDOWN
keys = pygame.key.get_pressed()
# Quitar con Alt + F4
if keys[pygame.K_LALT] and keys[pygame.K_F4]:
thorpy.functions.quit_func()
# Alternar entre pantalla completa/ventana con ALT + ENTER
if keys[pygame.K_LALT] and keys[pygame.K_RETURN]:
self.togglePantallaCompleta(pantalla)
# Alternar entre pantalla completa/ventana
def togglePantallaCompleta(self, pantalla):
if not self.pantalla_completa:
self.pantalla_completa = True
pygame.display.quit()
thorpy.Application(size=self.ScreenSize, caption=Caption,flags= pygame.FULLSCREEN | pygame.HWSURFACE) # Declarar propiedades ventana
# Crear pantalla
self.screen = thorpy.get_screen()
pygame.display.init()
pantalla.thorpy()
else:
self.pantalla_completa = False
pygame.display.quit()
thorpy.Application(size=self.ScreenSize, caption=Caption) # Declarar propiedades ventana
# Crear pantalla
self.screen = thorpy.get_screen()
pygame.display.init()
pantalla.thorpy()
# Escribir en el archivo config.py la configuración de pantalla completa
f = open("config.py","r+")
d = f.readline(0)
f.seek(0)
for i in d:
if i != "pantalla_completa=False" or i != "pantalla_completa=True":
f.write(f'pantalla_completa={self.pantalla_completa}')
# f.truncate()
f.close()
pass
| [
"thorpy.get_screen",
"pygame.init",
"pygame.display.init",
"thorpy.Application",
"thorpy.functions.quit_func",
"pygame.key.get_pressed",
"pygame.display.quit"
] | [((298, 374), 'thorpy.Application', 'thorpy.Application', ([], {'size': 'ScreenSize', 'caption': 'Caption', 'flags': 'pygame.DOUBLEBUF'}), '(size=ScreenSize, caption=Caption, flags=pygame.DOUBLEBUF)\n', (316, 374), False, 'import pygame, thorpy\n'), ((417, 517), 'thorpy.Application', 'thorpy.Application', ([], {'size': 'ScreenSize', 'caption': 'Caption', 'flags': '(pygame.DOUBLEBUF | pygame.FULLSCREEN)'}), '(size=ScreenSize, caption=Caption, flags=pygame.DOUBLEBUF |\n pygame.FULLSCREEN)\n', (435, 517), False, 'import pygame, thorpy\n'), ((826, 845), 'thorpy.get_screen', 'thorpy.get_screen', ([], {}), '()\n', (843, 845), False, 'import pygame, thorpy\n'), ((854, 867), 'pygame.init', 'pygame.init', ([], {}), '()\n', (865, 867), False, 'import pygame, thorpy\n'), ((1293, 1317), 'pygame.key.get_pressed', 'pygame.key.get_pressed', ([], {}), '()\n', (1315, 1317), False, 'import pygame, thorpy\n'), ((1414, 1442), 'thorpy.functions.quit_func', 'thorpy.functions.quit_func', ([], {}), '()\n', (1440, 1442), False, 'import pygame, thorpy\n'), ((1820, 1841), 'pygame.display.quit', 'pygame.display.quit', ([], {}), '()\n', (1839, 1841), False, 'import pygame, thorpy\n'), ((1854, 1960), 'thorpy.Application', 'thorpy.Application', ([], {'size': 'self.ScreenSize', 'caption': 'Caption', 'flags': '(pygame.FULLSCREEN | pygame.HWSURFACE)'}), '(size=self.ScreenSize, caption=Caption, flags=pygame.\n FULLSCREEN | pygame.HWSURFACE)\n', (1872, 1960), False, 'import pygame, thorpy\n'), ((2042, 2061), 'thorpy.get_screen', 'thorpy.get_screen', ([], {}), '()\n', (2059, 2061), False, 'import pygame, thorpy\n'), ((2074, 2095), 'pygame.display.init', 'pygame.display.init', ([], {}), '()\n', (2093, 2095), False, 'import pygame, thorpy\n'), ((2195, 2216), 'pygame.display.quit', 'pygame.display.quit', ([], {}), '()\n', (2214, 2216), False, 'import pygame, thorpy\n'), ((2229, 2286), 'thorpy.Application', 'thorpy.Application', ([], {'size': 'self.ScreenSize', 'caption': 'Caption'}), '(size=self.ScreenSize, caption=Caption)\n', (2247, 2286), False, 'import pygame, thorpy\n'), ((2373, 2392), 'thorpy.get_screen', 'thorpy.get_screen', ([], {}), '()\n', (2390, 2392), False, 'import pygame, thorpy\n'), ((2405, 2426), 'pygame.display.init', 'pygame.display.init', ([], {}), '()\n', (2424, 2426), False, 'import pygame, thorpy\n')] |
# Generated by Django 2.0.4 on 2018-04-13 07:47
from django.conf import settings
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('report_builder_scheduled', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='scheduledreport',
name='last_run_at',
field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now),
preserve_default=False,
),
migrations.AlterField(
model_name='scheduledreport',
name='users',
field=models.ManyToManyField(blank=True, help_text='Staff users to notify', limit_choices_to={'is_staff': True}, to=settings.AUTH_USER_MODEL),
),
]
| [
"django.db.models.DateTimeField",
"django.db.models.ManyToManyField"
] | [((418, 492), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)', 'default': 'django.utils.timezone.now'}), '(auto_now_add=True, default=django.utils.timezone.now)\n', (438, 492), False, 'from django.db import migrations, models\n'), ((658, 797), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'blank': '(True)', 'help_text': '"""Staff users to notify"""', 'limit_choices_to': "{'is_staff': True}", 'to': 'settings.AUTH_USER_MODEL'}), "(blank=True, help_text='Staff users to notify',\n limit_choices_to={'is_staff': True}, to=settings.AUTH_USER_MODEL)\n", (680, 797), False, 'from django.db import migrations, models\n')] |
import pandas as pd
def lookup_dates(s):
"""
This is an extremely fast approach to datetime parsing.
For large data, the same dates are often repeated. Rather than
re-parse these, we store all unique dates, parse them, and
use a lookup to convert all dates.
"""
dates_dict = {date:pd.to_datetime(date,errors='coerce') for date in s.unique()}
return s.map(dates_dict)
def end_quarter(series):
return (series - pd.tseries.offsets.DateOffset(days=1) + pd.tseries.offsets.QuarterEnd()) | [
"pandas.tseries.offsets.QuarterEnd",
"pandas.to_datetime",
"pandas.tseries.offsets.DateOffset"
] | [((310, 347), 'pandas.to_datetime', 'pd.to_datetime', (['date'], {'errors': '"""coerce"""'}), "(date, errors='coerce')\n", (324, 347), True, 'import pandas as pd\n'), ((487, 518), 'pandas.tseries.offsets.QuarterEnd', 'pd.tseries.offsets.QuarterEnd', ([], {}), '()\n', (516, 518), True, 'import pandas as pd\n'), ((447, 484), 'pandas.tseries.offsets.DateOffset', 'pd.tseries.offsets.DateOffset', ([], {'days': '(1)'}), '(days=1)\n', (476, 484), True, 'import pandas as pd\n')] |
# coding: UTF-8
from unittest import TestCase
from chipy8.chip8 import Chip8
class TestChip8Architecture(TestCase):
def setUp(self):
self.cpu = Chip8()
def test_memory_length(self):
'Chip8 has 4096 bytes of memory.'
self.assertEqual(4096, len(self.cpu.memory))
def test_register_count(self):
'Chip8 has 16 registers.'
self.assertEqual(16, len(self.cpu.registers))
def test_program_counter(self):
'Chip8 has a program counter starting at 0x200.'
self.assertEqual(0x200, self.cpu.program_counter)
def test_index_register(self):
'Chip8 has an index register.'
self.assertEqual(0, self.cpu.index_register)
def test_screen(self):
'Chip8 has a 64 * 32 screen (2048 pixels).'
self.assertEqual(2048, len(self.cpu.screen))
def test_delay_timer(self):
'Chip8 has a delay timer that counts to 0 at 60Hz.'
self.assertEqual(0, self.cpu.delay_timer)
def test_sound_timer(self):
'Chip8 has a count down sound timer that beeps on non-zero value.'
self.assertEqual(0, self.cpu.sound_timer)
def test_stack(self):
'Chip8 has a stack to return from jumps and calls.'
self.assertIsInstance(self.cpu.stack, list)
def test_keyboard(self):
'Chip8 has a hex keyboard with 16 keys.'
self.assertEqual(16, len(self.cpu.keyboard))
| [
"chipy8.chip8.Chip8"
] | [((158, 165), 'chipy8.chip8.Chip8', 'Chip8', ([], {}), '()\n', (163, 165), False, 'from chipy8.chip8 import Chip8\n')] |
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
def test_drawdown_and_returns_series():
index_range = pd.date_range(start=datetime(2000, 1, 1), periods=4, freq='AS-JAN')
wealth_index = pd.Series(data=[0.4, 0.3, 0.2, 0.5], index=index_range)
dd = wealth_index.drawdown
assert dd is not None
drawdown_df = dd.data
assert drawdown_df is not None
assert isinstance(drawdown_df, pd.Series)
assert drawdown_df.dtypes == 'float64'
assert drawdown_df.name == 'Drawdown'
np.testing.assert_almost_equal(drawdown_df['2000-01-01'], 0.0)
np.testing.assert_almost_equal(drawdown_df['2001-01-01'], -0.25)
np.testing.assert_almost_equal(drawdown_df['2002-01-01'], -0.5)
np.testing.assert_almost_equal(drawdown_df['2003-01-01'], 0.0)
def test_max_drawdown():
index_range = pd.date_range(start=datetime(2000, 1, 1), periods=4, freq='AS-JAN')
wealth_index = pd.Series(data=[0.4, 0.3, 0.2, 0.5], index=index_range)
assert wealth_index.drawdown.max_drawdown == -0.5
def test_durations():
index_range = pd.date_range(start=datetime(2000, 1, 1), periods=9, freq='AS-JAN')
wealth_index = pd.Series(data=[0.4, 0.3, 0.2, 0.5, 0.4, 0.4, 0.3, 0.3, 0.5], index=index_range)
durations = wealth_index.drawdown.durations
assert isinstance(durations, pd.Series)
assert durations.dtypes == 'timedelta64[ns]'
assert durations.name == 'Durations'
assert len(durations) == 2
assert durations['2003-01-01'] == timedelta(days=1096)
assert durations['2008-01-01'] == timedelta(days=1826)
| [
"pandas.Series",
"numpy.testing.assert_almost_equal",
"datetime.timedelta",
"datetime.datetime"
] | [((227, 282), 'pandas.Series', 'pd.Series', ([], {'data': '[0.4, 0.3, 0.2, 0.5]', 'index': 'index_range'}), '(data=[0.4, 0.3, 0.2, 0.5], index=index_range)\n', (236, 282), True, 'import pandas as pd\n'), ((536, 598), 'numpy.testing.assert_almost_equal', 'np.testing.assert_almost_equal', (["drawdown_df['2000-01-01']", '(0.0)'], {}), "(drawdown_df['2000-01-01'], 0.0)\n", (566, 598), True, 'import numpy as np\n'), ((603, 667), 'numpy.testing.assert_almost_equal', 'np.testing.assert_almost_equal', (["drawdown_df['2001-01-01']", '(-0.25)'], {}), "(drawdown_df['2001-01-01'], -0.25)\n", (633, 667), True, 'import numpy as np\n'), ((672, 735), 'numpy.testing.assert_almost_equal', 'np.testing.assert_almost_equal', (["drawdown_df['2002-01-01']", '(-0.5)'], {}), "(drawdown_df['2002-01-01'], -0.5)\n", (702, 735), True, 'import numpy as np\n'), ((740, 802), 'numpy.testing.assert_almost_equal', 'np.testing.assert_almost_equal', (["drawdown_df['2003-01-01']", '(0.0)'], {}), "(drawdown_df['2003-01-01'], 0.0)\n", (770, 802), True, 'import numpy as np\n'), ((935, 990), 'pandas.Series', 'pd.Series', ([], {'data': '[0.4, 0.3, 0.2, 0.5]', 'index': 'index_range'}), '(data=[0.4, 0.3, 0.2, 0.5], index=index_range)\n', (944, 990), True, 'import pandas as pd\n'), ((1174, 1259), 'pandas.Series', 'pd.Series', ([], {'data': '[0.4, 0.3, 0.2, 0.5, 0.4, 0.4, 0.3, 0.3, 0.5]', 'index': 'index_range'}), '(data=[0.4, 0.3, 0.2, 0.5, 0.4, 0.4, 0.3, 0.3, 0.5], index=index_range\n )\n', (1183, 1259), True, 'import pandas as pd\n'), ((1507, 1527), 'datetime.timedelta', 'timedelta', ([], {'days': '(1096)'}), '(days=1096)\n', (1516, 1527), False, 'from datetime import datetime, timedelta\n'), ((1566, 1586), 'datetime.timedelta', 'timedelta', ([], {'days': '(1826)'}), '(days=1826)\n', (1575, 1586), False, 'from datetime import datetime, timedelta\n'), ((160, 180), 'datetime.datetime', 'datetime', (['(2000)', '(1)', '(1)'], {}), '(2000, 1, 1)\n', (168, 180), False, 'from datetime import datetime, timedelta\n'), ((868, 888), 'datetime.datetime', 'datetime', (['(2000)', '(1)', '(1)'], {}), '(2000, 1, 1)\n', (876, 888), False, 'from datetime import datetime, timedelta\n'), ((1107, 1127), 'datetime.datetime', 'datetime', (['(2000)', '(1)', '(1)'], {}), '(2000, 1, 1)\n', (1115, 1127), False, 'from datetime import datetime, timedelta\n')] |
# -*- coding: utf-8 -*-
# MinIO Python Library for Amazon S3 Compatible Cloud Storage, (C)
# 2020 MinIO, Inc.
#
# 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.
"""Time formatter for S3 APIs."""
from __future__ import absolute_import
import locale
from contextlib import contextmanager
from datetime import datetime, timezone
from . import __LOCALE_LOCK__
_HTTP_HEADER_FORMAT = "%a, %d %b %Y %H:%M:%S GMT"
@contextmanager
def _set_locale(name):
"""Thread-safe wrapper to locale.setlocale()."""
with __LOCALE_LOCK__:
saved = locale.setlocale(locale.LC_ALL)
try:
yield locale.setlocale(locale.LC_ALL, name)
finally:
locale.setlocale(locale.LC_ALL, saved)
def _to_utc(value):
"""Convert to UTC time if value is not naive."""
return (
value.astimezone(timezone.utc).replace(tzinfo=None)
if value.tzinfo else value
)
def from_iso8601utc(value):
"""Parse UTC ISO-8601 formatted string to datetime."""
if value is None:
return None
try:
time = datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%fZ")
except ValueError:
time = datetime.strptime(value, "%Y-%m-%dT%H:%M:%SZ")
return time.replace(tzinfo=timezone.utc)
def to_iso8601utc(value):
"""Format datetime into UTC ISO-8601 formatted string."""
if value is None:
return None
value = _to_utc(value)
return (
value.strftime("%Y-%m-%dT%H:%M:%S.") + value.strftime("%f")[:3] + "Z"
)
def from_http_header(value):
"""Parse HTTP header date formatted string to datetime."""
with _set_locale("C"):
return datetime.strptime(
value, _HTTP_HEADER_FORMAT,
).replace(tzinfo=timezone.utc)
def to_http_header(value):
"""Format datatime into HTTP header date formatted string."""
with _set_locale("C"):
return _to_utc(value).strftime(_HTTP_HEADER_FORMAT)
def to_amz_date(value):
"""Format datetime into AMZ date formatted string."""
return _to_utc(value).strftime("%Y%m%dT%H%M%SZ")
def utcnow():
"""Timezone-aware wrapper to datetime.utcnow()."""
return datetime.utcnow().replace(tzinfo=timezone.utc)
def to_signer_date(value):
"""Format datetime into SignatureV4 date formatted string."""
return _to_utc(value).strftime("%Y%m%d")
| [
"datetime.datetime.strptime",
"locale.setlocale",
"datetime.datetime.utcnow"
] | [((1042, 1073), 'locale.setlocale', 'locale.setlocale', (['locale.LC_ALL'], {}), '(locale.LC_ALL)\n', (1058, 1073), False, 'import locale\n'), ((1556, 1605), 'datetime.datetime.strptime', 'datetime.strptime', (['value', '"""%Y-%m-%dT%H:%M:%S.%fZ"""'], {}), "(value, '%Y-%m-%dT%H:%M:%S.%fZ')\n", (1573, 1605), False, 'from datetime import datetime, timezone\n'), ((1172, 1210), 'locale.setlocale', 'locale.setlocale', (['locale.LC_ALL', 'saved'], {}), '(locale.LC_ALL, saved)\n', (1188, 1210), False, 'import locale\n'), ((1644, 1690), 'datetime.datetime.strptime', 'datetime.strptime', (['value', '"""%Y-%m-%dT%H:%M:%SZ"""'], {}), "(value, '%Y-%m-%dT%H:%M:%SZ')\n", (1661, 1690), False, 'from datetime import datetime, timezone\n'), ((2628, 2645), 'datetime.datetime.utcnow', 'datetime.utcnow', ([], {}), '()\n', (2643, 2645), False, 'from datetime import datetime, timezone\n'), ((1105, 1142), 'locale.setlocale', 'locale.setlocale', (['locale.LC_ALL', 'name'], {}), '(locale.LC_ALL, name)\n', (1121, 1142), False, 'import locale\n'), ((2129, 2174), 'datetime.datetime.strptime', 'datetime.strptime', (['value', '_HTTP_HEADER_FORMAT'], {}), '(value, _HTTP_HEADER_FORMAT)\n', (2146, 2174), False, 'from datetime import datetime, timezone\n')] |
from mongoengine import Document, StringField, BooleanField, IntField, ListField, ReferenceField, EmailField, LongField
from mongoengine import NULLIFY, PULL
class User(Document):
ID = LongField(unique=True, required=True)
Username = StringField(required=True)
Password = StringField()
IsLock = BooleanField(required=True)
| [
"mongoengine.BooleanField",
"mongoengine.StringField",
"mongoengine.LongField"
] | [((191, 228), 'mongoengine.LongField', 'LongField', ([], {'unique': '(True)', 'required': '(True)'}), '(unique=True, required=True)\n', (200, 228), False, 'from mongoengine import Document, StringField, BooleanField, IntField, ListField, ReferenceField, EmailField, LongField\n'), ((244, 270), 'mongoengine.StringField', 'StringField', ([], {'required': '(True)'}), '(required=True)\n', (255, 270), False, 'from mongoengine import Document, StringField, BooleanField, IntField, ListField, ReferenceField, EmailField, LongField\n'), ((286, 299), 'mongoengine.StringField', 'StringField', ([], {}), '()\n', (297, 299), False, 'from mongoengine import Document, StringField, BooleanField, IntField, ListField, ReferenceField, EmailField, LongField\n'), ((313, 340), 'mongoengine.BooleanField', 'BooleanField', ([], {'required': '(True)'}), '(required=True)\n', (325, 340), False, 'from mongoengine import Document, StringField, BooleanField, IntField, ListField, ReferenceField, EmailField, LongField\n')] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.13 on 2017-11-22 15:40
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('surveys', '0021_surveyresponserule'),
]
operations = [
migrations.AddField(
model_name='molosurveyformfield',
name='page_break',
field=models.BooleanField(default=False, help_text='Inserts a page break which puts the next question onto a new page'),
),
migrations.AddField(
model_name='personalisablesurveyformfield',
name='page_break',
field=models.BooleanField(default=False, help_text='Inserts a page break which puts the next question onto a new page'),
),
]
| [
"django.db.models.BooleanField"
] | [((416, 534), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""Inserts a page break which puts the next question onto a new page"""'}), "(default=False, help_text=\n 'Inserts a page break which puts the next question onto a new page')\n", (435, 534), False, 'from django.db import migrations, models\n'), ((676, 794), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""Inserts a page break which puts the next question onto a new page"""'}), "(default=False, help_text=\n 'Inserts a page break which puts the next question onto a new page')\n", (695, 794), False, 'from django.db import migrations, models\n')] |
# Simple script that uses dataPaser.py to get symbols on Binance
# By default it only shows symbols with the status of 'TRADING'
# You can get all symbols by setting "onlyTrading=False" and
# use "includes='LTC|DAI'" to pull the symbols that include
# LTC and/or DAI. We use getKlines to retrieve the kline data
# of each of those symbols.
from bapiw.dataParser import DataParser
import pandas as pd
dp = DataParser()
# Puts list of all symbols on Binance into symbols var that
# includes LTC and DAI in them.
# dataParser already put's them into a Dataframe
symbols = dp.getSymbols(includes='LTC|DAI')
# Print symbols list
print(symbols)
# Convert symbols dataframe column to a list
symbol_list = symbols['symbols'].tolist()
# Pull every symbols Kline data of 1min intervals and print them
# Using data='ohlcv' we get open, high, low, close and volume values
for symbol in symbol_list:
df = dp.getKlines(symbol=symbol, interval=dp.INTERVAL_1MIN, data='ohlcv')
print(symbol, "Kline data:")
print(df) | [
"bapiw.dataParser.DataParser"
] | [((407, 419), 'bapiw.dataParser.DataParser', 'DataParser', ([], {}), '()\n', (417, 419), False, 'from bapiw.dataParser import DataParser\n')] |
"""PFIM: Personal Finance Manager"""
import os
from queue import Queue
import sys
import sqlite3
import logging
import statistics
import functools
from datetime import date, timedelta
from enum import Enum, auto
from collections import namedtuple
from typing import List, Dict, Callable, Generator, Mapping, Union
## -- set up a logger for the application
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
_filehandler = logging.FileHandler(
os.path.join(os.environ["HOME"], ".pfim.log"))
_filehandler.setLevel(logging.DEBUG)
_consolehandler = logging.StreamHandler()
_consolehandler.setLevel(logging.ERROR)
_formatter = logging.Formatter(
"[%(asctime)s]::%(name)s::%(message)s")
_filehandler.setFormatter(_formatter)
_consolehandler.setFormatter(_formatter)
# Global Constants and Structures
PfimEntry = namedtuple("PfimEntry", "date tag description amount")
PfimOutput = namedtuple("PfimOutput", "report summary")
PfimQuery = namedtuple("PfimQuery", "query args")
_DBNAME = os.path.join(os.environ["HOME"], ".pfimdata.db")
_EARN_KIND = "E"
_SPENT_KIND = "S"
# ---- DATABASE OPERATION CONSTANT ----
RECORD = 1
FETCH = 2
UPDATE = 3
DELETE = 4
_PFIMCmdDoc = {}
def _register_cmd_doc(func):
functools.wraps(func)
def wrapper(*args, **kws):
fname = func.__name__
fname = fname[1:] if fname.startswith("_") else fname
# fname = fname[:len(fname)-4] if fname[-4:] == "_doc" else fname
_PFIMCmdDoc[fname] = func.__doc__
return
return wrapper
#-record-rcv, -record-xpx, -show, -show-rcv, -show-xpx, -update,
#-update-rcv, -update-xpx, -delete, -delete-rcv, -delete-xpx
@_register_cmd_doc
def _record_rcv():
"""record_rcv()
Record an income entry into PFIM backend database.
This command requires information supplied through its options.
The option are as follow.
Options:
--tag Tag given to this entry. The tag option can be ignored. If
ignored, the field will simply be filled with a default
symbol "N/A", meaning Not Available.
--date This is the date on which the supposed entry was actually
made. The date option can be ignored, in which the current
date is considered.
--descr A short description for the entry. It must not exceed 30
ASCII characters, otherwise it will be truncated.
--amount The value of the income being entered into the database.
Example
-------
In pfim default mode:
$ pfim --date=2021-12-26 --descr="Payment from <NAME>" --tag=PWM \
--amount=275.00
In interactive mode:
$ pfim --interactive
pfim>> record_rcv --date=2021-12 --descr="Payment from <NAME>" --tag=PWM \
--amount=275.00
"""
pass
_record_rcv()
@_register_cmd_doc
def _record_xpx():
"""record_xpx()
Record an expense entry into PFIM backend database.
This command requires information supplied through its options.
The option are as follow.
Options:
--tag Tag given to this entry. The tag option can be ignored. If
ignored, the field will simply be filled with a default
symbol "N/A", meaning Not Available.
--date This is the date on which the supposed entry was actually
made. The date option can be ignored, in which the current
date is considered.
--descr A short description for the entry. It must not exceed 30
ASCII characters, otherwise it will be truncated.
--amount The value of the expense being entered into the database.
Example
-------
In pfim default mode:
$ pfim --date=2021-12-26 --descr="Bought Cinema ticket" --tag=OUT \
--amount=75.00
In interactive mode:
$ pfim --interactive
pfim>> record_rcv --date=2021-12 --descr="Bout Ciema ticket" --tag=OUT \
--amount=275.00
"""
pass
@_register_cmd_doc
def _show():
"""show()
"""
pass
@_register_cmd_doc
def _show_xpx():
"""show_xpx()
"""
pass
@_register_cmd_doc
def _show_rcv():
"""show_rcv()
"""
pass
@_register_cmd_doc
def _update():
"""update()
"""
pass
@_register_cmd_doc
def _update_xpx():
"""update_xpx()
"""
pass
@_register_cmd_doc
def _update_rcv():
"""update_rcv()
"""
pass
@_register_cmd_doc
def _delete():
"""delete()
"""
pass
@_register_cmd_doc
def _delete_xpx():
"""delete_xpx()
"""
pass
@_register_cmd_doc
def _delete_rcv():
"""delete_rcv()
"""
pass
# --- PFIM utility class --
class OutputBeautify:
def __init__(self, textstr: str):
#self._logger = logging.getLogger("pfim.Pfim.OutPutBeautify")
self._textstr = textstr
def decorate(self, color="green", slant="normal", underline=False,
bold=False):
mapping = {
"black": 30, "red": 31, "green": 32, "yellow": 33,
"blue": 34, "magenta": 35, "cyan": 36, "white": 37,
}
stylemap = {"noraml": 0, "bold": 1, "italic": 3, "underline": 4}
base = 38
if color.lower() in mapping.keys():
cval = mapping[color.lower()]
else:
cval = mapping["black"]
txt = self._textstr
if slant.lower() in ("italic", "normal"):
sval = stylemap[slant.lower()]
else:
sval = stylemap["normal"]
uval = stylemap["underline"] if underline else None
bval = stylemap["bold"] if bold else None
deco = f"\x1b[{base};" #"\x1b[m"
if sval:
deco = deco + f"{sval};"
if uval:
deco = deco + f"{uval};"
if bval:
deco = deco + f"{bval};"
deco = deco + f"{cval}m;{txt}\x1b[m"
return deco
def _adapter(dateObj: date):
return dateObj.isoformat()
def _converter(datestr: str):
return date.fromisoformat(datestr)
sqlite3.register_adapter(date, _adapter)
sqlite3.register_converter("date", _converter)
class PfimQueryCmdEnum(Enum):
RECORD_RCV = auto()
RECORD_XPX = auto()
SHOW = auto()
SHOW_RCV = auto()
SHOW_XPX = auto()
UPDATE = auto()
# UPDATE_DESC = auto() # FIXME: maybe included as a new command
UPDATE_RCV = auto()
UPDATE_XPX = auto()
DELETE = auto()
DELETE_RCV = auto()
DELETE_XPX = auto()
def _validate_datestr(datestr: str) -> bool:
result = None
try:
result = date.fromisoformat(datestr)
except Exception:
result = None
if result is None:
return False
return True
class PfimData:
"""PFIM database interface."""
def __init__(self):
self._logger = logging.getLogger("pfim.PfimData")
# start the db: create a new if it doesn't exists
sql = """CREATE TABLE IF NOT EXISTS pfim(
id INTEGER PRIMARY KEY AUTOINCREMENT NON NULL,
opdate DATE,
tag TEXT,
description TEXT,
amount REAL
)"""
with sqlite3.connect(_DBNAME,
detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES
) as conn:
conn.cursor().execute(sql).commit()
def _run_query(self, query, *args, out=False) ->Union[sqlite3.Cursor, None]:
retval = None
with sqlite3.connect(_DBNAME) as conn:
if args:
retval = conn.cursor().execute(query, args)
else:
retval = conn.cursor().execute(query)
conn.commit()
if out:
return retval
def add_entry(self, query: str, *args) -> None:
try:
self._run_query(query, args)
self._logger.debug("New entry added")
except sqlite3.Error as err:
self._logger.error(f"Failed to add a new entry to database. {err}")
sys.exit(1)
def fetch(self, query: str, *args) -> Generator:
try:
retval = self._run_query(query, args)
self._logger.debug("Fetched data from database")
except sqlite3.Error as err:
self._logger.error(f"Failed to fetch data from database. {err}")
sys.exit(1)
for row in retval:
yield row
def update(self, query: str, *args) -> None:
try:
retval = self._run_query(query, args)
self._logger.debug("Updated database content")
except sqlite3.Error as err:
self._logger.error(f"Failed to update database content. {err}")
sys.exit(1)
for row in retval:
yield row
def delete(self, query: str, *args) -> None:
try:
retval = self._run_query(query, args)
self._logger.debug("Deleted data from database")
except sqlite3.Error as err:
self._logger.error(f"Failed to delete data from database. {err}")
sys.exit(1)
for row in retval:
yield row
class InteractivePfim:
# Use builtin module *Cmd* ?
PROMPT = "pfim>> "
def __init__(self):
self._logger = logging.getLogger("pfim.InteractivePfim")
pass
def record(self, kw: Dict) -> PfimQuery:
pass
def report(self, kw: Dict) -> PfimQuery:
pass
def delete(self, kw: Dict) -> PfimQuery:
pass
def update(self, kw: Dict) -> PfimQuery:
pass
def quit(self) -> None:
import sys
sys.exit(0)
def clear_console(self) -> None:
pass
class Report:
def __init__(self):
self._logger = logging.getLogger("pfim.Report")
self._fmt = None
self._head = None
self._line = None
def create_entry(self, *args, **kw):
pass
class ReportSummary:
def __init__(self, data: List[float]):
self._logger = logging.getLogger("pfim.ReportSummary")
self._data = data
self._compute()
def _compute(self):
self._count = len(self._data)
self._min = min(self._data)
self._max = max(self._data)
self._median = statistics.median(self._data)
self._mean = statistics.fmean(self._data)
self._stdev = statistics.stdev(self._data)
def __str__(self):
summary = f"""
SUMMARY
-------
Count: {self._count}
Minimum: {self._min}
Maximum: {self._max}
Average: {self._mean}
Median: {self._median}
Stdev: {self._stdev}
"""
return summary
class PfimCore:
"""PFIM Core class."""
prolog = ""
epilog = ""
def __init__(self):
self._logger = logging.getLogger("pfim.PfimCore")
self._mode = None
self._output = None
self._query_history = Queue(maxsize=128)
def record(self, kw: Dict) -> PfimQuery:
pass
def update(self, kw: Dict) -> PfimQuery:
pass
def delete(self, kw: Dict) -> PfimQuery:
pass
def report(self, kw: Dict) -> PfimQuery:
pass
def make_output(self) -> PfimOutput:
pass
def write_output(self, file=sys.stdout):
pass
def process_fetch_result(self, *args) -> None:
pass
""" def create_query(self, cmd: PfimQueryCmdEnum, kw: Dict) -> str:
# -- Create a new query--
query = None
# addgrp
# addcmds = (record-rcv|record-xpx)
# addOpts = [recDate|recTag]
if cmd == PfimQueryCmdEnum.RECORD_RCV:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.RECORD_XPX:
# TODO: make query then return it immediatly
return query
# showcmds=(show|show-rcv|show-spent)
# showOpts=[sort-(date|tag|amount)]|[after-date|before-date|on-date|
# --desc]
if cmd == PfimQueryCmdEnum.SHOW:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.SHOW_RCV:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.SHOW_XPX:
# TODO: make query then return it immediatly
return query
# upcmds = (update|update-rcv|update-spent)
# upOpts = [(old-tag, new-tag)|(old-date,new-date)|(old-amount,
# new-amount)]
if cmd == PfimQueryCmdEnum.UPDATE:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.UPDATE_RCV:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.UPDATE_XPX:
# TODO: make query then return it immediatly
return query
# rmcmds = (delete|delete-rcv|delete-spent)
# rmOpts = [target-tag|target-date|target-mount]
if cmd == PfimQueryCmdEnum.DELETE:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.DELETE_RCV:
# TODO: make query then return it immediatly
return query
if cmd == PfimQueryCmdEnum.DELETE_XPX:
# TODO: make query then return it immediatly
return query
"""
del _filehandler, _consolehandler, _formatter
| [
"logging.getLogger",
"sqlite3.register_converter",
"collections.namedtuple",
"logging.StreamHandler",
"sqlite3.register_adapter",
"enum.auto",
"statistics.stdev",
"sqlite3.connect",
"logging.Formatter",
"statistics.fmean",
"os.path.join",
"functools.wraps",
"statistics.median",
"sys.exit",... | [((368, 395), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (385, 395), False, 'import logging\n'), ((569, 592), 'logging.StreamHandler', 'logging.StreamHandler', ([], {}), '()\n', (590, 592), False, 'import logging\n'), ((646, 703), 'logging.Formatter', 'logging.Formatter', (['"""[%(asctime)s]::%(name)s::%(message)s"""'], {}), "('[%(asctime)s]::%(name)s::%(message)s')\n", (663, 703), False, 'import logging\n'), ((836, 890), 'collections.namedtuple', 'namedtuple', (['"""PfimEntry"""', '"""date tag description amount"""'], {}), "('PfimEntry', 'date tag description amount')\n", (846, 890), False, 'from collections import namedtuple\n'), ((904, 946), 'collections.namedtuple', 'namedtuple', (['"""PfimOutput"""', '"""report summary"""'], {}), "('PfimOutput', 'report summary')\n", (914, 946), False, 'from collections import namedtuple\n'), ((959, 996), 'collections.namedtuple', 'namedtuple', (['"""PfimQuery"""', '"""query args"""'], {}), "('PfimQuery', 'query args')\n", (969, 996), False, 'from collections import namedtuple\n'), ((1008, 1056), 'os.path.join', 'os.path.join', (["os.environ['HOME']", '""".pfimdata.db"""'], {}), "(os.environ['HOME'], '.pfimdata.db')\n", (1020, 1056), False, 'import os\n'), ((6134, 6174), 'sqlite3.register_adapter', 'sqlite3.register_adapter', (['date', '_adapter'], {}), '(date, _adapter)\n', (6158, 6174), False, 'import sqlite3\n'), ((6175, 6221), 'sqlite3.register_converter', 'sqlite3.register_converter', (['"""date"""', '_converter'], {}), "('date', _converter)\n", (6201, 6221), False, 'import sqlite3\n'), ((467, 512), 'os.path.join', 'os.path.join', (["os.environ['HOME']", '""".pfim.log"""'], {}), "(os.environ['HOME'], '.pfim.log')\n", (479, 512), False, 'import os\n'), ((1228, 1249), 'functools.wraps', 'functools.wraps', (['func'], {}), '(func)\n', (1243, 1249), False, 'import functools\n'), ((6105, 6132), 'datetime.date.fromisoformat', 'date.fromisoformat', (['datestr'], {}), '(datestr)\n', (6123, 6132), False, 'from datetime import date, timedelta\n'), ((6270, 6276), 'enum.auto', 'auto', ([], {}), '()\n', (6274, 6276), False, 'from enum import Enum, auto\n'), ((6294, 6300), 'enum.auto', 'auto', ([], {}), '()\n', (6298, 6300), False, 'from enum import Enum, auto\n'), ((6312, 6318), 'enum.auto', 'auto', ([], {}), '()\n', (6316, 6318), False, 'from enum import Enum, auto\n'), ((6334, 6340), 'enum.auto', 'auto', ([], {}), '()\n', (6338, 6340), False, 'from enum import Enum, auto\n'), ((6356, 6362), 'enum.auto', 'auto', ([], {}), '()\n', (6360, 6362), False, 'from enum import Enum, auto\n'), ((6376, 6382), 'enum.auto', 'auto', ([], {}), '()\n', (6380, 6382), False, 'from enum import Enum, auto\n'), ((6469, 6475), 'enum.auto', 'auto', ([], {}), '()\n', (6473, 6475), False, 'from enum import Enum, auto\n'), ((6493, 6499), 'enum.auto', 'auto', ([], {}), '()\n', (6497, 6499), False, 'from enum import Enum, auto\n'), ((6513, 6519), 'enum.auto', 'auto', ([], {}), '()\n', (6517, 6519), False, 'from enum import Enum, auto\n'), ((6537, 6543), 'enum.auto', 'auto', ([], {}), '()\n', (6541, 6543), False, 'from enum import Enum, auto\n'), ((6561, 6567), 'enum.auto', 'auto', ([], {}), '()\n', (6565, 6567), False, 'from enum import Enum, auto\n'), ((6659, 6686), 'datetime.date.fromisoformat', 'date.fromisoformat', (['datestr'], {}), '(datestr)\n', (6677, 6686), False, 'from datetime import date, timedelta\n'), ((6892, 6926), 'logging.getLogger', 'logging.getLogger', (['"""pfim.PfimData"""'], {}), "('pfim.PfimData')\n", (6909, 6926), False, 'import logging\n'), ((9277, 9318), 'logging.getLogger', 'logging.getLogger', (['"""pfim.InteractivePfim"""'], {}), "('pfim.InteractivePfim')\n", (9294, 9318), False, 'import logging\n'), ((9624, 9635), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (9632, 9635), False, 'import sys\n'), ((9750, 9782), 'logging.getLogger', 'logging.getLogger', (['"""pfim.Report"""'], {}), "('pfim.Report')\n", (9767, 9782), False, 'import logging\n'), ((10003, 10042), 'logging.getLogger', 'logging.getLogger', (['"""pfim.ReportSummary"""'], {}), "('pfim.ReportSummary')\n", (10020, 10042), False, 'import logging\n'), ((10251, 10280), 'statistics.median', 'statistics.median', (['self._data'], {}), '(self._data)\n', (10268, 10280), False, 'import statistics\n'), ((10302, 10330), 'statistics.fmean', 'statistics.fmean', (['self._data'], {}), '(self._data)\n', (10318, 10330), False, 'import statistics\n'), ((10353, 10381), 'statistics.stdev', 'statistics.stdev', (['self._data'], {}), '(self._data)\n', (10369, 10381), False, 'import statistics\n'), ((10827, 10861), 'logging.getLogger', 'logging.getLogger', (['"""pfim.PfimCore"""'], {}), "('pfim.PfimCore')\n", (10844, 10861), False, 'import logging\n'), ((10946, 10964), 'queue.Queue', 'Queue', ([], {'maxsize': '(128)'}), '(maxsize=128)\n', (10951, 10964), False, 'from queue import Queue\n'), ((7221, 7313), 'sqlite3.connect', 'sqlite3.connect', (['_DBNAME'], {'detect_types': '(sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES)'}), '(_DBNAME, detect_types=sqlite3.PARSE_DECLTYPES | sqlite3.\n PARSE_COLNAMES)\n', (7236, 7313), False, 'import sqlite3\n'), ((7506, 7530), 'sqlite3.connect', 'sqlite3.connect', (['_DBNAME'], {}), '(_DBNAME)\n', (7521, 7530), False, 'import sqlite3\n'), ((8048, 8059), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (8056, 8059), False, 'import sys\n'), ((8364, 8375), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (8372, 8375), False, 'import sys\n'), ((8723, 8734), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (8731, 8734), False, 'import sys\n'), ((9086, 9097), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (9094, 9097), False, 'import sys\n')] |
import torch
from torch import nn
from torch.nn import functional as F
from .resnet import resnet18, resnet34
from .segmentation import SegmentationHead
from .attention import Attention
from .erfnet import ERFNet
class Normalize(nn.Module):
""" ImageNet normalization """
def __init__(self, mean, std):
super().__init__()
self.mean = nn.Parameter(torch.tensor(mean), requires_grad=False)
self.std = nn.Parameter(torch.tensor(std), requires_grad=False)
def forward(self, x):
return (x - self.mean[None,:,None,None]) / self.std[None,:,None,None]
class RGBModel(nn.Module):
def __init__(self, seg_channels, pretrained=True):
super().__init__()
self.num_channels = len(seg_channels)
self.backbone = resnet18(pretrained=pretrained)
self.normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.head = None
def forward(self, rgb):
embd = self.backbone(self.normalize(rgb/255.))
return self.head(embd).squeeze(-1)
class RGBSegmentationModel(nn.Module):
def __init__(self, seg_channels):
super().__init__()
self.erfnet = ERFNet(len(seg_channels)+1)
self.normalize = lambda x: (x/255.-.5)*2
def forward(self, rgb):
return self.erfnet(self.normalize(rgb))
class RGBBrakePredictionModel(nn.Module):
def __init__(self, seg_channels, pretrained=True):
super().__init__()
self.conv_backbone = resnet18(pretrained=pretrained)
self.normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.seg_head = SegmentationHead(512, len(seg_channels)+1)
self.classifier = nn.Sequential(
nn.Linear(1024,1),
nn.Sigmoid()
)
def forward(self, rgb1, rgb2, mask=False):
x1 = self.conv_backbone(self.normalize(rgb1/255.))
x2 = self.conv_backbone(self.normalize(rgb2/255.))
h1 = x1.mean(dim=[2,3])
h2 = x2.mean(dim=[2,3])
pred_bra = self.classifier(torch.cat([h1,h2], dim=1))
if mask:
pred_sem1 = F.interpolate(self.seg_head(x1), scale_factor=4)
pred_sem2 = F.interpolate(self.seg_head(x2), scale_factor=4)
return pred_bra[:,0], pred_sem1, pred_sem2
else:
return pred_bra[:,0]
| [
"torch.tensor",
"torch.cat",
"torch.nn.Sigmoid",
"torch.nn.Linear"
] | [((373, 391), 'torch.tensor', 'torch.tensor', (['mean'], {}), '(mean)\n', (385, 391), False, 'import torch\n'), ((446, 463), 'torch.tensor', 'torch.tensor', (['std'], {}), '(std)\n', (458, 463), False, 'import torch\n'), ((1732, 1750), 'torch.nn.Linear', 'nn.Linear', (['(1024)', '(1)'], {}), '(1024, 1)\n', (1741, 1750), False, 'from torch import nn\n'), ((1763, 1775), 'torch.nn.Sigmoid', 'nn.Sigmoid', ([], {}), '()\n', (1773, 1775), False, 'from torch import nn\n'), ((2062, 2088), 'torch.cat', 'torch.cat', (['[h1, h2]'], {'dim': '(1)'}), '([h1, h2], dim=1)\n', (2071, 2088), False, 'import torch\n')] |
"""Implementation of predictive classifiers."""
from abc import ABC, abstractmethod
import pandas as pd
from google_drive_downloader import GoogleDriveDownloader as gdd
from joblib import load as jload
from geniepy.errors import ClassifierError
import geniepy.config as gc
ERROR_SCORE = float(-1)
PCPCLSFR_NAME = "pub_score"
CTCLSFR_NAME = "ct_score"
class BaseClassifier(ABC):
"""Base Classifier Abstract Class."""
__slots__ = [
"_is_trained", # True if classifier is trained
"_model", # The classifier model
"_name", # The name of the classifier (used as name of prediction col)
]
def __init__(self, name):
"""
Construct classifier obj.
Arguments:
name {[type]} -- The classifier's name
"""
self._name = name
self._is_trained = False
self._model = None
@property
def name(self):
"""Return name prediction column."""
return self._name
@property
def is_trained(self):
"""Return true is model is trained."""
return self._is_trained
@abstractmethod
def predict(self, features: pd.DataFrame) -> float:
"""
Calculate publication count label.
It is import for this method to always produce results and handle exceptions
internally, so execution isn't halted due to exceptions.
Arguments:
features {pd.DataFrame} -- Pandas series necessary prediction data
Returns:
float -- the classifier prediction
"""
def load(self):
"""
Load classifier model.
Raises:
ClassifierError -- If model doesn't load successfully
"""
# Download model from google drive
model_path = gc.TMP_DIR.joinpath("gene_disease_gbc.joblib")
model_id = gc.get_model()
try:
gdd.download_file_from_google_drive(
file_id=model_id, dest_path=model_path, unzip=True,
)
self._model = jload(model_path)
self._is_trained = True
model_path.unlink()
except Exception:
# If load fail
raise ClassifierError("Unable to load model")
class Classifier(BaseClassifier):
"""Implementation of Publication Count Predictive Classifier."""
def predict(self, features: pd.DataFrame):
"""
Calculate publication count label.
It is import for this method to always produce results and handle exceptions
internally, so execution isn't halted due to exceptions.
Arguments:
features {pd.DataFrame} -- Pandas series necessary prediction data
Returns:
float -- the classifier prediction
"""
if (not self._is_trained) or (features is None):
# TODO log event "Received None features to predict"
# TODO log event "Untrained classifier can't calculate predictions"
return ERROR_SCORE
# Only keep expected columns
features.dropna(inplace=True)
scores_df = pd.DataFrame(features["gene_disease_relationship"])
filtered_features = features[
[
"num_publications",
"citations_cum_sum",
"authors_cum_sum",
"chemicals_cum_sum",
"cum_sum_journals",
"num_languages",
"sjr",
"h_index",
"us_published",
"us_uk_published",
]
]
# Predictions and probabilities
predictions = self._model.predict(filtered_features)
probs = self._model.predict_proba(filtered_features)
prob_1 = [item[0] for item in probs] # Get positiive probs only
# Add new fields into original df
scores_df["classifier_prediction"] = predictions
scores_df["classifier_prob"] = prob_1
return scores_df
| [
"geniepy.errors.ClassifierError",
"geniepy.config.get_model",
"geniepy.config.TMP_DIR.joinpath",
"google_drive_downloader.GoogleDriveDownloader.download_file_from_google_drive",
"joblib.load",
"pandas.DataFrame"
] | [((1781, 1827), 'geniepy.config.TMP_DIR.joinpath', 'gc.TMP_DIR.joinpath', (['"""gene_disease_gbc.joblib"""'], {}), "('gene_disease_gbc.joblib')\n", (1800, 1827), True, 'import geniepy.config as gc\n'), ((1847, 1861), 'geniepy.config.get_model', 'gc.get_model', ([], {}), '()\n', (1859, 1861), True, 'import geniepy.config as gc\n'), ((3092, 3143), 'pandas.DataFrame', 'pd.DataFrame', (["features['gene_disease_relationship']"], {}), "(features['gene_disease_relationship'])\n", (3104, 3143), True, 'import pandas as pd\n'), ((1887, 1978), 'google_drive_downloader.GoogleDriveDownloader.download_file_from_google_drive', 'gdd.download_file_from_google_drive', ([], {'file_id': 'model_id', 'dest_path': 'model_path', 'unzip': '(True)'}), '(file_id=model_id, dest_path=model_path,\n unzip=True)\n', (1922, 1978), True, 'from google_drive_downloader import GoogleDriveDownloader as gdd\n'), ((2032, 2049), 'joblib.load', 'jload', (['model_path'], {}), '(model_path)\n', (2037, 2049), True, 'from joblib import load as jload\n'), ((2189, 2228), 'geniepy.errors.ClassifierError', 'ClassifierError', (['"""Unable to load model"""'], {}), "('Unable to load model')\n", (2204, 2228), False, 'from geniepy.errors import ClassifierError\n')] |
from keras_metrics import f1, f1b
import keras.backend as K
import tensorflow as tf
def f1_loss(y_true, y_pred):
return 1 - K.mean(f1(y_true, y_pred))
def f1b_loss(y_true, y_pred):
return 1-K.mean(f1b(y_true, y_pred))
def KerasFocalLoss(target, input):
"""
Should be applied without sigmoid activtion layer
from https://www.kaggle.com/rejpalcz/focalloss-for-keras
:param target:
:param input:
:return:
"""
gamma = 2.
input = tf.cast(input, tf.float32)
max_val = K.relu(-input)
loss = input - input * target + max_val + K.log(K.exp(-max_val) + K.exp(-input - max_val))
invprobs = tf.log_sigmoid(-input * (target * 2.0 - 1.0))
loss = K.exp(invprobs * gamma) * loss
return K.mean(K.sum(loss, axis=1))
| [
"keras.backend.sum",
"keras.backend.exp",
"keras_metrics.f1",
"keras_metrics.f1b",
"tensorflow.cast",
"tensorflow.log_sigmoid",
"keras.backend.relu"
] | [((475, 501), 'tensorflow.cast', 'tf.cast', (['input', 'tf.float32'], {}), '(input, tf.float32)\n', (482, 501), True, 'import tensorflow as tf\n'), ((517, 531), 'keras.backend.relu', 'K.relu', (['(-input)'], {}), '(-input)\n', (523, 531), True, 'import keras.backend as K\n'), ((642, 687), 'tensorflow.log_sigmoid', 'tf.log_sigmoid', (['(-input * (target * 2.0 - 1.0))'], {}), '(-input * (target * 2.0 - 1.0))\n', (656, 687), True, 'import tensorflow as tf\n'), ((699, 722), 'keras.backend.exp', 'K.exp', (['(invprobs * gamma)'], {}), '(invprobs * gamma)\n', (704, 722), True, 'import keras.backend as K\n'), ((749, 768), 'keras.backend.sum', 'K.sum', (['loss'], {'axis': '(1)'}), '(loss, axis=1)\n', (754, 768), True, 'import keras.backend as K\n'), ((136, 154), 'keras_metrics.f1', 'f1', (['y_true', 'y_pred'], {}), '(y_true, y_pred)\n', (138, 154), False, 'from keras_metrics import f1, f1b\n'), ((207, 226), 'keras_metrics.f1b', 'f1b', (['y_true', 'y_pred'], {}), '(y_true, y_pred)\n', (210, 226), False, 'from keras_metrics import f1, f1b\n'), ((584, 599), 'keras.backend.exp', 'K.exp', (['(-max_val)'], {}), '(-max_val)\n', (589, 599), True, 'import keras.backend as K\n'), ((602, 625), 'keras.backend.exp', 'K.exp', (['(-input - max_val)'], {}), '(-input - max_val)\n', (607, 625), True, 'import keras.backend as K\n')] |
#!/usr/bin/env python3
from setuptools import find_packages, setup
setup(
name="lean_proof_recording",
version="0.0.1",
packages=find_packages(),
package_data={},
install_requires=[
"mpmath",
"pandas",
"jsonlines",
"tqdm",
],
)
| [
"setuptools.find_packages"
] | [((142, 157), 'setuptools.find_packages', 'find_packages', ([], {}), '()\n', (155, 157), False, 'from setuptools import find_packages, setup\n')] |
from shminspector.api.context import Context
from shminspector.api.reactor import Reactor, ReactorCommand, UserInput
from shminspector.api.tags import macos, interactive, experimental, prerequisites
from shminspector.api.validator import ValidationResult, Status
@macos
@interactive
@experimental
@prerequisites("homebrew")
class GCloudInstallReactor(Reactor):
def react(self, data: ValidationResult, ctx: Context):
commands = []
if data.status == Status.NOT_FOUND:
commands.append(ReactorCommand(cmd=["brew", "cask", "install", "google-cloud-sdk"]))
else:
ctx.logger.info("Gcloud already installed")
return commands
@macos
@interactive
@experimental
@prerequisites("gcloud", "network-connectivity")
class GCloudConfigInstallReactor(Reactor):
def react(self, data: ValidationResult, ctx: Context):
commands = []
if data.status == Status.NOT_FOUND:
ctx.logger.info("Going to configure missing gcloud authentication...")
if data.input_data is None or not data.input_data.docker_ok:
commands.append(ReactorCommand(cmd=["gcloud", "auth", "configure-docker", "--quiet"]))
if data.input_data is None or not data.input_data.auth_ok:
email_input = UserInput(key="gcloud_email", prompt="\nPlease enter your GCloud email address: ")
commands.append(ReactorCommand(cmd=["gcloud", "auth", "login", email_input]))
commands.append(ReactorCommand(cmd=["gcloud", "auth", "application-default", "login"]))
else:
ctx.logger.info("Gcloud authentication already configured")
return commands
| [
"shminspector.api.tags.prerequisites",
"shminspector.api.reactor.ReactorCommand",
"shminspector.api.reactor.UserInput"
] | [((300, 325), 'shminspector.api.tags.prerequisites', 'prerequisites', (['"""homebrew"""'], {}), "('homebrew')\n", (313, 325), False, 'from shminspector.api.tags import macos, interactive, experimental, prerequisites\n'), ((719, 766), 'shminspector.api.tags.prerequisites', 'prerequisites', (['"""gcloud"""', '"""network-connectivity"""'], {}), "('gcloud', 'network-connectivity')\n", (732, 766), False, 'from shminspector.api.tags import macos, interactive, experimental, prerequisites\n'), ((518, 585), 'shminspector.api.reactor.ReactorCommand', 'ReactorCommand', ([], {'cmd': "['brew', 'cask', 'install', 'google-cloud-sdk']"}), "(cmd=['brew', 'cask', 'install', 'google-cloud-sdk'])\n", (532, 585), False, 'from shminspector.api.reactor import Reactor, ReactorCommand, UserInput\n'), ((1299, 1389), 'shminspector.api.reactor.UserInput', 'UserInput', ([], {'key': '"""gcloud_email"""', 'prompt': '"""\nPlease enter your GCloud email address: """'}), '(key=\'gcloud_email\', prompt=\n """\nPlease enter your GCloud email address: """)\n', (1308, 1389), False, 'from shminspector.api.reactor import Reactor, ReactorCommand, UserInput\n'), ((1126, 1195), 'shminspector.api.reactor.ReactorCommand', 'ReactorCommand', ([], {'cmd': "['gcloud', 'auth', 'configure-docker', '--quiet']"}), "(cmd=['gcloud', 'auth', 'configure-docker', '--quiet'])\n", (1140, 1195), False, 'from shminspector.api.reactor import Reactor, ReactorCommand, UserInput\n'), ((1414, 1474), 'shminspector.api.reactor.ReactorCommand', 'ReactorCommand', ([], {'cmd': "['gcloud', 'auth', 'login', email_input]"}), "(cmd=['gcloud', 'auth', 'login', email_input])\n", (1428, 1474), False, 'from shminspector.api.reactor import Reactor, ReactorCommand, UserInput\n'), ((1508, 1578), 'shminspector.api.reactor.ReactorCommand', 'ReactorCommand', ([], {'cmd': "['gcloud', 'auth', 'application-default', 'login']"}), "(cmd=['gcloud', 'auth', 'application-default', 'login'])\n", (1522, 1578), False, 'from shminspector.api.reactor import Reactor, ReactorCommand, UserInput\n')] |
import re
from .AssertionException import AssertionException
class _Assert(object):
def __init__(self, log):
if callable(log):
self.__log = log
else:
self.__log = log
#
def isIn(self, value, valueList, message = None):
Assert.l_isIn(self.__log, value, valueList, message)
#
def isNotIn(self, value, valueList, message = None):
Assert.l_isNotIn(self.__log, value, valueList, message)
#
def raisesException(self, function, arguments, message = None):
Assert.l_raisesException(self.__log, function, arguments, message)
#
def isCallable(self, value, message = None):
Assert.l_isCallable(self.__log, value, message)
#
def isInstance(self, value, typeOrTypes, message = None):
Assert.l_isInstance(self.__log, value, typeOrTypes, message)
#
def isRegExMatch(self, value, regexPattern, message = None):
Assert.l_isRegExMatch(self.__log, value, regexPattern, message)
#
def isEqual(self, value, otherValue, message = None):
Assert.l_isEqual(self.__log, value, otherValue, message)
#
def isGreater(self, value, otherValue, message = None):
Assert.l_isGreater(self.__log, value, otherValue, message)
#
def isGreaterOrEqual(self, value, otherValue, message = None):
Assert.l_isGreaterOrEqual(self.__log, value, otherValue, message)
#
def isSmaller(self, value, otherValue, message = None):
Assert.l_isSmaller(self.__log, value, otherValue, message)
#
def isSmallerOrEqual(self, value, otherValue, message = None):
Assert.l_isSmallerOrEqual(self.__log, value, otherValue, message)
#
def isNotEqual(self, value, otherValue, message = None):
Assert.l_isNotEqual(self.__log, value, otherValue, message)
#
def isNone(self, value, message = None):
Assert.l_isNone(self.__log, value, message)
#
def isNotNone(self, value, message = None):
Assert.l_isNotNone(self.__log, value, message)
#
def isNotNoneOrEmpty(self, value, message = None):
Assert.l_isNotNoneOrEmpty(self.__log, value, message)
#
def isTrue(self, value, message = None):
Assert.l_isTrue(self.__log, value, message)
#
def isFalse(self, value, message = None):
Assert.l_isFalse(self.__log, value, message)
#
#
class Assert(object):
@staticmethod
def createCustomAssert(log):
return _Assert(log)
#
"""
@staticmethod
def getAllBaseClasses(cls):
# TODO: convert this to an iteration
c = list(cls.__bases__)
for base in c:
c.extend(getAllBaseClasses(base))
return c
#
"""
@staticmethod
def isIn(value, valueList, message = None, log = None, identifier:str = None):
bSuccess = value in valueList
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is " + repr(value) + " so value is not an element of list " + repr(valueList) + "!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isIn(log, value, valueList, message = None, identifier:str = None):
bSuccess = value in valueList
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is " + repr(value) + " so value is not an element of list " + repr(valueList) + "!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isNotIn(value, valueList, message = None, log = None, identifier:str = None):
bSuccess = value not in valueList
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is " + repr(value) + " so value is not an element of list " + repr(valueList) + "!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isNotIn(log, value, valueList, message = None, identifier:str = None):
bSuccess = value not in valueList
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is " + repr(value) + " so value is not an element of list " + repr(valueList) + "!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def raisesException(function, arguments, message = None, log = None, identifier:str = None):
bSuccess = True
try:
function(*arguments)
bSuccess = False
except Exception as ee:
pass
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "No exception was raised!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_raisesException(log, function, arguments, message = None, identifier:str = None):
bSuccess = True
try:
function(*arguments)
bSuccess = False
except Exception as ee:
pass
if not bSuccess:
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "No exception was raised!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isCallable(value, message = None, log = None, identifier:str = None):
if callable(value):
return
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is not a callable but of type " + str(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isCallable(log, value, message = None, identifier:str = None):
if callable(value):
return
if message is None:
message = ""
else:
message += " :: "
message = "ASSERTION ERROR :: " + message + "Value is not a callable but of type " + str(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isInstance(value, typeOrTypes, message = None, log = None, identifier:str = None):
if isinstance(value, typeOrTypes):
return
if issubclass(type(value), typeOrTypes):
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is of type " + str(type(value)) + " and not of type " + str(typeOrTypes)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isInstance(log, value, typeOrTypes, message = None, identifier:str = None):
if isinstance(value, typeOrTypes):
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is of type " + str(type(value)) + " and not of type " + str(typeOrTypes)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isEqual(value, otherValue, message = None, log = None, identifier:str = None):
if value == otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " and not " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isEqual(log, value, otherValue, message = None, identifier:str = None):
if value == otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " and not " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isRegExMatch(value, regexPattern, message = None, log = None, identifier:str = None):
m = re.match(regexPattern, value)
if m:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which does not match " + repr(regexPattern) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isRegExMatch(log, value, regexPattern, message = None, identifier:str = None):
m = re.match(regexPattern, value)
if m:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which does not match " + repr(regexPattern) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isNotEqual(value, otherValue, message = None, log = None, identifier:str = None):
if value != otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which is not expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isNotEqual(log, value, otherValue, message = None, identifier:str = None):
if value != otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which is not expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isGreater(value, otherValue, message = None, log = None, identifier:str = None):
if value > otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not greater than " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isGreater(log, value, otherValue, message = None, identifier:str = None):
if value > otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not greater than " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isGreaterOrEqual(value, otherValue, message = None, log = None, identifier:str = None):
if value >= otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not greater or equal to " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isGreaterOrEqual(log, value, otherValue, message = None, identifier:str = None):
if value >= otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not greater or equal to " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isSmaller(value, otherValue, message = None, log = None, identifier:str = None):
if value < otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not smaller than " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isSmaller(log, value, otherValue, message = None, identifier:str = None):
if value < otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not smaller than " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isSmallerOrEqual(value, otherValue, message = None, log = None, identifier:str = None):
if value <= otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not smaller or equal to " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isSmallerOrEqual(log, value, otherValue, message = None, identifier:str = None):
if value <= otherValue:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is " + repr(value) + " which not smaller or equal to " + repr(otherValue) + " as expected!"
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isNone(value, message = None, log = None, identifier:str = None):
if value is None:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not None as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isNone(log, value, message = None, identifier:str = None):
if value is None:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not None as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isNotNone(value, message = None, log = None, identifier:str = None):
if value is not None:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is None which is not expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isNotNone(log, value, message = None, identifier:str = None):
if value is not None:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is None which is not expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isNotNoneOrEmpty(value, message = None, log = None, identifier:str = None):
if value:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is None or empty which is not expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isNotNoneOrEmpty(log, value, message = None, identifier:str = None):
if value:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is None or empty which is not expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isTrue(value, message = None, log = None, identifier:str = None):
if value is True:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not true as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isTrue(log, value, message = None, identifier:str = None):
if value is True:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not true as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def isFalse(value, message = None, log = None, identifier:str = None):
if value is False:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not false as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
@staticmethod
def l_isFalse(log, value, message = None, identifier:str = None):
if value is False:
return
if message is None:
message = "ASSERTION ERROR"
else:
message += " ::"
message = "ASSERTION ERROR :: " + message + " Value is not false as expected: " + repr(value)
if identifier:
message = "<" + identifier + "> " + message
if log != None:
if callable(log):
log(message)
else:
log.error(message)
else:
print(message)
raise AssertionException(message)
#
#
| [
"re.match"
] | [((9429, 9458), 're.match', 're.match', (['regexPattern', 'value'], {}), '(regexPattern, value)\n', (9437, 9458), False, 'import re\n'), ((10022, 10051), 're.match', 're.match', (['regexPattern', 'value'], {}), '(regexPattern, value)\n', (10030, 10051), False, 'import re\n')] |
import sqlite3
import argparse
from os.path import join
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from keras.models import model_from_json
import preprocessing.config as cfg
import preprocessing.file_utils as futils
from preprocessing.data import LightDataManager
def paper_plot():
cnn_stator_id = 'ebb3'
cnn_rotor_id = '2950'
rnn_stator_id = 'f334'
rnn_rotor_id = 'c329'
plt.figure(figsize=(2*5, 2*2))
hp_reports = futils.HyperparameterSearchReport()
hp_reports.read_search(cnn_stator_id)
hp_reports.read_search(cnn_rotor_id)
hp_reports.read_search(rnn_stator_id)
hp_reports.read_search(rnn_rotor_id)
# hack: cut off first 50 iterations in RNN Stator search
tab = hp_reports.hp_searches[rnn_stator_id]
hp_reports.hp_searches[rnn_stator_id] = \
tab[tab.n_iter >= 50].reset_index(drop=True)
plt.subplot(2, 2, 1)
hp_reports.plot_convergence(rnn_stator_id, 'Stator (RNN)')
plt.ylabel(r'MSE in $\mathrm{K^2}$ (RNN)')
plt.xlabel('')
plt.title('')
plt.subplot(2, 2, 2)
hp_reports.plot_convergence(rnn_rotor_id, 'Rotor (RNN)')
plt.xlabel('')
plt.ylabel('')
plt.legend().remove()
plt.title('')
plt.subplot(2, 2, 3)
hp_reports.plot_convergence(cnn_stator_id, 'Stator (CNN)')
plt.legend().remove()
plt.title('')
plt.xlabel('Stator search iteration')
plt.ylabel(r'MSE in $\mathrm{K^2}$ (CNN)')
plt.subplot(2, 2, 4)
hp_reports.plot_convergence(cnn_rotor_id, 'Rotor (CNN)')
plt.xlabel('Rotor search iteration')
plt.ylabel('')
plt.legend().remove()
plt.title('')
# find best performing models
hp_reports.plot_best_models_performance(rnn_rotor_id, rnn_stator_id)
hp_reports.plot_best_models_performance(cnn_rotor_id, cnn_stator_id)
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Visualize performance of the '
'given model uid.')
parser.add_argument('-s', '--stator_id', required=False,
help='The 4-digit id in hex of the experiment on '
'stator temperatures')
parser.add_argument('-r', '--rotor_id', required=False,
help='The 4-digit id in hex of the experiment on '
'rotor temperatures')
parser.add_argument('-p', '--paper_plot', required=False,
action='store_true',
help='Flag for ignoring given IDs and instead plot '
'four predefined searches for the IEMDC Paper')
args = parser.parse_args()
sns.set_context('paper')
sns.set_style('whitegrid')
if args.paper_plot:
paper_plot()
else:
assert args.rotor_id is not None and args.stator_id is not None
hp_reports = futils.HyperparameterSearchReport()
hp_reports.read_search(args.rotor_id)
hp_reports.read_search(args.stator_id)
try:
print('Plot stator temperature convergence ..')
plt.figure(figsize=(5, 2.4))
hp_reports.plot_convergence(args.stator_id)
print('Plot rotor temperature convergence ..')
plt.figure(figsize=(5, 2.4))
hp_reports.plot_convergence(args.rotor_id)
plt.show()
except Exception:
print('plot failed')
| [
"argparse.ArgumentParser",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"seaborn.set_context",
"seaborn.set_style",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"preprocessing.file_utils.HyperparameterSearchReport",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.legend",
"m... | [((447, 481), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(2 * 5, 2 * 2)'}), '(figsize=(2 * 5, 2 * 2))\n', (457, 481), True, 'import matplotlib.pyplot as plt\n'), ((495, 530), 'preprocessing.file_utils.HyperparameterSearchReport', 'futils.HyperparameterSearchReport', ([], {}), '()\n', (528, 530), True, 'import preprocessing.file_utils as futils\n'), ((911, 931), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(2)', '(2)', '(1)'], {}), '(2, 2, 1)\n', (922, 931), True, 'import matplotlib.pyplot as plt\n'), ((999, 1041), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""MSE in $\\\\mathrm{K^2}$ (RNN)"""'], {}), "('MSE in $\\\\mathrm{K^2}$ (RNN)')\n", (1009, 1041), True, 'import matplotlib.pyplot as plt\n'), ((1046, 1060), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['""""""'], {}), "('')\n", (1056, 1060), True, 'import matplotlib.pyplot as plt\n'), ((1065, 1078), 'matplotlib.pyplot.title', 'plt.title', (['""""""'], {}), "('')\n", (1074, 1078), True, 'import matplotlib.pyplot as plt\n'), ((1083, 1103), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(2)', '(2)', '(2)'], {}), '(2, 2, 2)\n', (1094, 1103), True, 'import matplotlib.pyplot as plt\n'), ((1169, 1183), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['""""""'], {}), "('')\n", (1179, 1183), True, 'import matplotlib.pyplot as plt\n'), ((1188, 1202), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['""""""'], {}), "('')\n", (1198, 1202), True, 'import matplotlib.pyplot as plt\n'), ((1233, 1246), 'matplotlib.pyplot.title', 'plt.title', (['""""""'], {}), "('')\n", (1242, 1246), True, 'import matplotlib.pyplot as plt\n'), ((1252, 1272), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(2)', '(2)', '(3)'], {}), '(2, 2, 3)\n', (1263, 1272), True, 'import matplotlib.pyplot as plt\n'), ((1367, 1380), 'matplotlib.pyplot.title', 'plt.title', (['""""""'], {}), "('')\n", (1376, 1380), True, 'import matplotlib.pyplot as plt\n'), ((1385, 1422), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Stator search iteration"""'], {}), "('Stator search iteration')\n", (1395, 1422), True, 'import matplotlib.pyplot as plt\n'), ((1427, 1469), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""MSE in $\\\\mathrm{K^2}$ (CNN)"""'], {}), "('MSE in $\\\\mathrm{K^2}$ (CNN)')\n", (1437, 1469), True, 'import matplotlib.pyplot as plt\n'), ((1474, 1494), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(2)', '(2)', '(4)'], {}), '(2, 2, 4)\n', (1485, 1494), True, 'import matplotlib.pyplot as plt\n'), ((1561, 1597), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Rotor search iteration"""'], {}), "('Rotor search iteration')\n", (1571, 1597), True, 'import matplotlib.pyplot as plt\n'), ((1602, 1616), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['""""""'], {}), "('')\n", (1612, 1616), True, 'import matplotlib.pyplot as plt\n'), ((1647, 1660), 'matplotlib.pyplot.title', 'plt.title', (['""""""'], {}), "('')\n", (1656, 1660), True, 'import matplotlib.pyplot as plt\n'), ((1847, 1857), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (1855, 1857), True, 'import matplotlib.pyplot as plt\n'), ((1900, 1989), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Visualize performance of the given model uid."""'}), "(description=\n 'Visualize performance of the given model uid.')\n", (1923, 1989), False, 'import argparse\n'), ((2708, 2732), 'seaborn.set_context', 'sns.set_context', (['"""paper"""'], {}), "('paper')\n", (2723, 2732), True, 'import seaborn as sns\n'), ((2737, 2763), 'seaborn.set_style', 'sns.set_style', (['"""whitegrid"""'], {}), "('whitegrid')\n", (2750, 2763), True, 'import seaborn as sns\n'), ((2913, 2948), 'preprocessing.file_utils.HyperparameterSearchReport', 'futils.HyperparameterSearchReport', ([], {}), '()\n', (2946, 2948), True, 'import preprocessing.file_utils as futils\n'), ((1207, 1219), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (1217, 1219), True, 'import matplotlib.pyplot as plt\n'), ((1341, 1353), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (1351, 1353), True, 'import matplotlib.pyplot as plt\n'), ((1621, 1633), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (1631, 1633), True, 'import matplotlib.pyplot as plt\n'), ((3128, 3156), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(5, 2.4)'}), '(figsize=(5, 2.4))\n', (3138, 3156), True, 'import matplotlib.pyplot as plt\n'), ((3284, 3312), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(5, 2.4)'}), '(figsize=(5, 2.4))\n', (3294, 3312), True, 'import matplotlib.pyplot as plt\n'), ((3380, 3390), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (3388, 3390), True, 'import matplotlib.pyplot as plt\n')] |
import json
def get_event():
return json.dumps({
'test': True
})
| [
"json.dumps"
] | [((41, 67), 'json.dumps', 'json.dumps', (["{'test': True}"], {}), "({'test': True})\n", (51, 67), False, 'import json\n')] |
from discord.ext import commands
import discord
import logging
class Log(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.Cog.listener()
async def on_connect(self):
logging.getLogger().addHandler(DiscordLogger(self.bot))
@commands.Cog.listener()
async def on_disconnect(self):
logging.getLogger().removeHandler(DiscordLogger)
def setup(bot):
bot.add_cog(Log(bot))
class DiscordLogger(logging.StreamHandler):
"""
A handler class which sends messages to a Discord channel
"""
LEVEL_COLORS = {
logging.NOTSET: discord.colour.Color.darker_grey(),
logging.DEBUG: discord.colour.Color.blue(),
logging.INFO: discord.colour.Color.green(),
logging.WARNING: discord.colour.Color.gold(),
logging.ERROR: discord.colour.Color.red(),
logging.CRITICAL: discord.colour.Color.red(),
}
def __init__(self, bot):
super().__init__()
self.bot = bot
self.setLevel(logging.WARN)
def emit(self, record):
self.bot.loop.create_task(self.send_discord_err(record))
async def send_discord_err(self, record):
channel = self.bot.get_channel(self.bot.bot_config['error_channel_id'])
embed = discord.Embed(title=f"{record.levelname} in {record.name}")
embed.description = f"```{record.getMessage()}```"
embed.colour = self.LEVEL_COLORS[record.levelno]
if record.exc_text is not None:
embed.add_field(name="Exception Info", value=record.exc_text)
embed.add_field(name=f"Error in {record.funcName} on line {record.lineno}", value=record.filename)
try:
await channel.send(embed=embed)
except (RuntimeError, discord.ClientException):
# RuntimeError: raised by aiohttp when the bot is closing; quiets log spam on exit
# ClientException: raised when we can't send the message; prevents infinite cycle of errors
pass
| [
"discord.ext.commands.Cog.listener",
"logging.getLogger",
"discord.colour.Color.darker_grey",
"discord.colour.Color.green",
"discord.colour.Color.red",
"discord.colour.Color.gold",
"discord.Embed",
"discord.colour.Color.blue"
] | [((148, 171), 'discord.ext.commands.Cog.listener', 'commands.Cog.listener', ([], {}), '()\n', (169, 171), False, 'from discord.ext import commands\n'), ((274, 297), 'discord.ext.commands.Cog.listener', 'commands.Cog.listener', ([], {}), '()\n', (295, 297), False, 'from discord.ext import commands\n'), ((603, 637), 'discord.colour.Color.darker_grey', 'discord.colour.Color.darker_grey', ([], {}), '()\n', (635, 637), False, 'import discord\n'), ((662, 689), 'discord.colour.Color.blue', 'discord.colour.Color.blue', ([], {}), '()\n', (687, 689), False, 'import discord\n'), ((713, 741), 'discord.colour.Color.green', 'discord.colour.Color.green', ([], {}), '()\n', (739, 741), False, 'import discord\n'), ((768, 795), 'discord.colour.Color.gold', 'discord.colour.Color.gold', ([], {}), '()\n', (793, 795), False, 'import discord\n'), ((820, 846), 'discord.colour.Color.red', 'discord.colour.Color.red', ([], {}), '()\n', (844, 846), False, 'import discord\n'), ((874, 900), 'discord.colour.Color.red', 'discord.colour.Color.red', ([], {}), '()\n', (898, 900), False, 'import discord\n'), ((1261, 1320), 'discord.Embed', 'discord.Embed', ([], {'title': 'f"""{record.levelname} in {record.name}"""'}), "(title=f'{record.levelname} in {record.name}')\n", (1274, 1320), False, 'import discord\n'), ((212, 231), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (229, 231), False, 'import logging\n'), ((341, 360), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (358, 360), False, 'import logging\n')] |
from classes.database import *
import shutil
import os
if (os.getcwd().endswith('functions')):
os.chdir('..')
def move_known(src, dest=None, rename=False):
src = src.replace('//', '/')
if not dest:
dest = src+'known/'
db = Database()
# db.loadCache()
for root, dirs, files in os.walk(src):
for file_name in files:
file_path = os.path.join(root, file_name).replace('\\', '/')
file = GameFile(file_path)
game = db.getGameByFileMD5(file.getMD5())
# print(src+file_name, dest+file_name)
if game:
print(file, 'known as', game.findFileByMD5(file.md5).getTOSECName())
dest_path = dest+file_path.replace(src, '')
# print(file_path, os.path.dirname(dest_path))
# print(dest_path)
# continue
os.makedirs(os.path.dirname(dest_path), exist_ok=True)
if os.path.exists(dest_path):
os.unlink(dest_path)
dest_dir = os.path.dirname(dest_path)
shutil.move(file_path, dest_dir)
if rename:
os.rename(dest_path, os.path.join(dest_dir, game.findFileByMD5(file.md5).getTOSECName()))
if __name__=='__main__':
move_known('tosec/unsorted files/spectrum4ever.org/dest/renamed/Unknown/', rename=True)
pass | [
"os.path.exists",
"shutil.move",
"os.path.join",
"os.getcwd",
"os.chdir",
"os.path.dirname",
"os.unlink",
"os.walk"
] | [((99, 113), 'os.chdir', 'os.chdir', (['""".."""'], {}), "('..')\n", (107, 113), False, 'import os\n'), ((309, 321), 'os.walk', 'os.walk', (['src'], {}), '(src)\n', (316, 321), False, 'import os\n'), ((59, 70), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (68, 70), False, 'import os\n'), ((953, 978), 'os.path.exists', 'os.path.exists', (['dest_path'], {}), '(dest_path)\n', (967, 978), False, 'import os\n'), ((1048, 1074), 'os.path.dirname', 'os.path.dirname', (['dest_path'], {}), '(dest_path)\n', (1063, 1074), False, 'import os\n'), ((1091, 1123), 'shutil.move', 'shutil.move', (['file_path', 'dest_dir'], {}), '(file_path, dest_dir)\n', (1102, 1123), False, 'import shutil\n'), ((379, 408), 'os.path.join', 'os.path.join', (['root', 'file_name'], {}), '(root, file_name)\n', (391, 408), False, 'import os\n'), ((891, 917), 'os.path.dirname', 'os.path.dirname', (['dest_path'], {}), '(dest_path)\n', (906, 917), False, 'import os\n'), ((1000, 1020), 'os.unlink', 'os.unlink', (['dest_path'], {}), '(dest_path)\n', (1009, 1020), False, 'import os\n')] |
import yaml
from makestack import appdir
from makestack.helpers import progress
DEFAULT_CONFIG = {
'BOARD': {
'type': 'str',
'value': 'esp8266'
}
}
def main(args):
appdir.chdir_to_app_dir(args.appdir)
application_yaml = yaml.load(open('application.yaml'))
app_config = {} if application_yaml['config'] is None else application_yaml['config']
config = DEFAULT_CONFIG
for k, c in app_config.items():
if 'default' in c:
c['value'] = c['default']
del c['default']
config[k] = c
progress('GEN', '.config.yaml')
yaml.dump(config, open('.config.yaml', 'w'), default_flow_style=False)
| [
"makestack.appdir.chdir_to_app_dir",
"makestack.helpers.progress"
] | [((195, 231), 'makestack.appdir.chdir_to_app_dir', 'appdir.chdir_to_app_dir', (['args.appdir'], {}), '(args.appdir)\n', (218, 231), False, 'from makestack import appdir\n'), ((569, 600), 'makestack.helpers.progress', 'progress', (['"""GEN"""', '""".config.yaml"""'], {}), "('GEN', '.config.yaml')\n", (577, 600), False, 'from makestack.helpers import progress\n')] |
import tkinter
import random
from tkinter import messagebox
colours = ['Red','Blue','Green','Pink','Black',
'Yellow','Orange','White','Purple','Brown']
score = 0
timeleft = 30
def startGame(event):
if timeleft == 30:
timer()
changeColor()
def changeColor():
global score
global timeleft
if timeleft > 0:
inputBox.focus_set()
if inputBox.get().lower() == colours[0].lower():
score += 1
inputBox.delete(0, tkinter.END)
random.shuffle(colours)
colorlabel.config(text=str(colours[1]), fg=str(colours[0]))
scorelabel.config(text="Score: "+str(score))
def timer():
global timeleft
if timeleft > 0:
timeleft -= 1
timelabel.config(text="Time left: "+str(timeleft))
timelabel.after(1000, timer)
else:
endgame()
def endgame():
global score
global timeleft
q = messagebox.askyesno("Play again", "Do you want to play again?")
if q:
score = 0
timeleft = 30
scorelabel.config(text="Press Enter to start\n")
timelabel.config(text="Time left: "+str(timeleft))
colorlabel.config(text="")
else:
root.quit()
root = tkinter.Tk()
root.title("COLOR GAME")
root.geometry("400x300")
instructions = tkinter.Label(root, text = "Type in the colour\nof the words, and not the word text!\n",
font = ('Helvetica', 14))
instructions.pack()
scorelabel = tkinter.Label(root, text="Press Enter to start\n", font=('Helvetica', 14))
scorelabel.pack()
timelabel = tkinter.Label(root, text="Time left: "+str(timeleft), font=('Helvetica', 14))
timelabel.pack()
colorlabel = tkinter.Label(root, font = ('Helvetica', 60))
colorlabel.pack()
inputBox = tkinter.Entry(root, font =('Helvetica',15))
root.bind('<Return>', startGame)
inputBox.pack()
inputBox.focus_set()
root.mainloop()
| [
"tkinter.Entry",
"random.shuffle",
"tkinter.messagebox.askyesno",
"tkinter.Tk",
"tkinter.Label"
] | [((1230, 1242), 'tkinter.Tk', 'tkinter.Tk', ([], {}), '()\n', (1240, 1242), False, 'import tkinter\n'), ((1310, 1432), 'tkinter.Label', 'tkinter.Label', (['root'], {'text': '"""Type in the colour\nof the words, and not the word text!\n"""', 'font': "('Helvetica', 14)"}), '(root, text=\n """Type in the colour\nof the words, and not the word text!\n""", font=(\n \'Helvetica\', 14))\n', (1323, 1432), False, 'import tkinter\n'), ((1498, 1572), 'tkinter.Label', 'tkinter.Label', (['root'], {'text': '"""Press Enter to start\n"""', 'font': "('Helvetica', 14)"}), "(root, text='Press Enter to start\\n', font=('Helvetica', 14))\n", (1511, 1572), False, 'import tkinter\n'), ((1713, 1756), 'tkinter.Label', 'tkinter.Label', (['root'], {'font': "('Helvetica', 60)"}), "(root, font=('Helvetica', 60))\n", (1726, 1756), False, 'import tkinter\n'), ((1789, 1832), 'tkinter.Entry', 'tkinter.Entry', (['root'], {'font': "('Helvetica', 15)"}), "(root, font=('Helvetica', 15))\n", (1802, 1832), False, 'import tkinter\n'), ((923, 986), 'tkinter.messagebox.askyesno', 'messagebox.askyesno', (['"""Play again"""', '"""Do you want to play again?"""'], {}), "('Play again', 'Do you want to play again?')\n", (942, 986), False, 'from tkinter import messagebox\n'), ((510, 533), 'random.shuffle', 'random.shuffle', (['colours'], {}), '(colours)\n', (524, 533), False, 'import random\n')] |
import os, json, base64, cv2, glob
import numpy as np
import matplotlib.pyplot as plt
from coco import CocoConfig
from Mask.config import Config
import Mask.utils as utils
import Mask.model as modellib
import Mask.visualize as visualize
from convert_file import load_image
def init():
np.set_printoptions(threshold=np.inf)
def run(input_df):
data = json.loads(input_df)
im = load_image(image64=data)
config = CocoConfig()
model = modellib.MaskRCNN(mode="inference", model_dir="./models/", config=config)
model.load_weights(filepath="./models/mask_rcnn_moles_0090.h5", by_name=True)
class_names = ["BG", "malignant", "benign"]
# predict the mask, bounding box and class of the image
r = model.detect([im])[0]
prediction = None
for idx, val in enumerate(class_names):
if idx == r["class_ids"]:
prediction = val
print(val)
else:
continue
return prediction | [
"json.loads",
"Mask.model.MaskRCNN",
"convert_file.load_image",
"coco.CocoConfig",
"numpy.set_printoptions"
] | [((290, 327), 'numpy.set_printoptions', 'np.set_printoptions', ([], {'threshold': 'np.inf'}), '(threshold=np.inf)\n', (309, 327), True, 'import numpy as np\n'), ((359, 379), 'json.loads', 'json.loads', (['input_df'], {}), '(input_df)\n', (369, 379), False, 'import os, json, base64, cv2, glob\n'), ((389, 413), 'convert_file.load_image', 'load_image', ([], {'image64': 'data'}), '(image64=data)\n', (399, 413), False, 'from convert_file import load_image\n'), ((428, 440), 'coco.CocoConfig', 'CocoConfig', ([], {}), '()\n', (438, 440), False, 'from coco import CocoConfig\n'), ((454, 527), 'Mask.model.MaskRCNN', 'modellib.MaskRCNN', ([], {'mode': '"""inference"""', 'model_dir': '"""./models/"""', 'config': 'config'}), "(mode='inference', model_dir='./models/', config=config)\n", (471, 527), True, 'import Mask.model as modellib\n')] |
import io
import logging
import re
from datetime import datetime
from typing import List, Optional
from pydantic import BaseModel
from ..types import ChineseOrthography
from omniglot.lexeme import Lexeme
from omniglot.sense import Sense
from omnilingual import LanguageCode, PartOfSpeech
class CcCedictCounter(BaseModel):
traditional: str
simplified: Optional[str]
pinyin: str
class CcCedictEntry(BaseModel):
traditional: str
simplified: str
pinyin: str
equivalents: List[str]
counters: List[str]
class CcCedictJSON(BaseModel):
created_date: str
entries: List[CcCedictEntry]
def extract_created_date(data: str) -> Optional[str]:
CC_CEDICT_created_regexp = r"^#! date=(.+)$"
for line in io.StringIO(data).readlines():
# Passed the header without finding the date. Abort.
if not line.startswith("#"):
return None
match = re.search(CC_CEDICT_created_regexp, line)
if match:
extracted_date = match.group(1)
if extracted_date[-1] == "Z":
extracted_date = extracted_date[:-1]
return datetime.fromisoformat(extracted_date).date().isoformat()
return None
def convert_CC_CEDICT_to_JSON(data: str) -> CcCedictJSON:
entry_regex = r"^(.+?)\s(.+?)\s\[([\w\s:,·]*?)\]\s\/(.*)\/$"
created_date = extract_created_date(data)
if created_date is None:
raise ValueError("[CC-CEDICT] Could not find the created date")
entries: List[CcCedictEntry] = []
buffer = io.StringIO(data)
for line in buffer.readlines():
entry = line.strip()
# Skip the header
if entry.startswith("#"):
continue
match = re.match(entry_regex, entry)
if match is None:
raise ValueError("Failed to parse entry\n%s" % (entry))
logging.debug(match.group(1), match.group(2), match.group(3), match.group(4))
traditional = match.group(1)
simplified = match.group(2)
pinyin = match.group(3).replace("u:", "ü")
equivalents = match.group(4).split("/")
entries.append(
CcCedictEntry(
traditional=traditional,
simplified=simplified,
pinyin=pinyin,
equivalents=equivalents,
counters=[],
)
)
return CcCedictJSON(created_date=created_date, entries=entries,)
def create_CC_CEDICT_database_entries(
cc_cedict_dictionary: CcCedictJSON,
) -> List[Lexeme]:
database_entries: List[Lexeme] = []
for entry in cc_cedict_dictionary.entries:
# TODO: /abbr. for 四清運動|四清运动[Si4 qing1 Yun4 dong4]/
# TODO: Taiwanese pronunciation /Taiwan pr. []/ or /*sense* (Taiwan pr. [])/
# TODO: Cross-reference classifiers: /CL:封[feng1]/
# TODO: Extract tags (coll., idiom, math., finance, etc)
# TODO: Parse traditional/simplified phrases with ， and pinyin with ,
# TODO: Parse triditional/simplified names with · and pinyin with ·
senses: List[Sense] = [
Sense(
definitions={LanguageCode.English: [equivalent]},
tags=[],
information=[],
references=[],
antonyms=[],
synonyms=[],
source_language_words=[],
)
for equivalent in entry.equivalents
]
database_entries.append(
Lexeme(
language=LanguageCode.Chinese,
lemma=entry.traditional,
pos=PartOfSpeech.Nil,
sources={"CC-CEDICT": cc_cedict_dictionary.created_date},
orthography=ChineseOrthography(
all=[entry.traditional, entry.simplified],
Hant=entry.traditional,
Hans=entry.simplified,
),
pronounce={"pinyin": entry.pinyin},
tags=[],
senses=senses,
features={},
declensions=None,
conjugations=None,
)
)
return database_entries
| [
"omniglot.sense.Sense",
"re.match",
"datetime.datetime.fromisoformat",
"io.StringIO",
"re.search"
] | [((1540, 1557), 'io.StringIO', 'io.StringIO', (['data'], {}), '(data)\n', (1551, 1557), False, 'import io\n'), ((918, 959), 're.search', 're.search', (['CC_CEDICT_created_regexp', 'line'], {}), '(CC_CEDICT_created_regexp, line)\n', (927, 959), False, 'import re\n'), ((1722, 1750), 're.match', 're.match', (['entry_regex', 'entry'], {}), '(entry_regex, entry)\n', (1730, 1750), False, 'import re\n'), ((748, 765), 'io.StringIO', 'io.StringIO', (['data'], {}), '(data)\n', (759, 765), False, 'import io\n'), ((3092, 3247), 'omniglot.sense.Sense', 'Sense', ([], {'definitions': '{LanguageCode.English: [equivalent]}', 'tags': '[]', 'information': '[]', 'references': '[]', 'antonyms': '[]', 'synonyms': '[]', 'source_language_words': '[]'}), '(definitions={LanguageCode.English: [equivalent]}, tags=[],\n information=[], references=[], antonyms=[], synonyms=[],\n source_language_words=[])\n', (3097, 3247), False, 'from omniglot.sense import Sense\n'), ((1138, 1176), 'datetime.datetime.fromisoformat', 'datetime.fromisoformat', (['extracted_date'], {}), '(extracted_date)\n', (1160, 1176), False, 'from datetime import datetime\n')] |
# -*- coding: utf-8 -*-
import os
import shutil
import uuid
from django.conf import settings
from django.contrib.auth.models import User
from django.test import TestCase, override_settings
from filebrowser.models import Directory
from filebrowser.utils import to_download_url
from loader.exceptions import FileNotFound, SemanticError, SyntaxErrorPL
from loader.parsers import pl
FAKE_FB_ROOT = os.path.join("/tmp", str(uuid.uuid4()))
FAKE_PL = os.path.join(settings.APPS_DIR, 'loader/tests/fake_pl')
@override_settings(FILEBROWSER_ROOT=FAKE_FB_ROOT)
class PlParserTestCase(TestCase):
""" Test functions of parsers/pl.py """
@classmethod
def setUpTestData(cls):
if os.path.isdir(FAKE_FB_ROOT):
shutil.rmtree(FAKE_FB_ROOT)
cls.user = User.objects.create_user(username='user', password='<PASSWORD>')
cls.dir = Directory.objects.create(name='dir1', owner=cls.user)
cls.dir2 = Directory.objects.create(name='dir2', owner=cls.user)
shutil.rmtree(cls.dir.root)
shutil.copytree(FAKE_PL, cls.dir.root)
with open(os.path.join(FAKE_FB_ROOT, 'dir2', 'fake.pl'), "w") as f:
f.write("a=a")
@classmethod
def tearDownClass(cls):
if os.path.isdir(FAKE_FB_ROOT):
shutil.rmtree(FAKE_FB_ROOT)
super().tearDownClass()
def test_init_parser(self):
pl.Parser(self.dir, "working.pl")
with self.assertRaises(FileNotFoundError):
pl.Parser(self.dir, "unknown.pl").parse()
def test_get_parser(self):
self.assertEqual({
'ext': ['.pl'],
'parser': pl.Parser,
'type': 'pl'
}, pl.get_parser())
def test_parse(self):
dic, war = pl.Parser(self.dir, "full.pl").parse()
# warning
self.assertIn("Key 'e.f.h' overwritten at line", str(war))
# = += +
self.assertEqual(dic['title'], 'testtesttest')
# = -= -
self.assertEqual(dic['title2'], 'AABBCC.')
# ==
self.assertEqual(dic['text'], 'tests')
# extends
self.assertIn({
'directory_name': 'dir1', 'lineno': 18, 'line': 'extends=working.pl\n',
'path': 'working.pl'
}, dic["__extends"])
# =@ +=@ -=@
with open(os.path.join(FAKE_FB_ROOT, "dir1/working.pl")) as f:
self.assertEqual(len(dic['test']), 3 * len(f.read()))
# sub keys
self.assertEqual('12', dic['e']['f']['g'])
# % %=
self.assertEqual({'a': 1, 'b': 2}, dic['e']['f']['i'])
self.assertEqual({'a': 1, 'b': 2}, dic['b'])
# Override % with a.a
self.assertEqual({'a': '3', 'b': 2}, dic['a'])
def test_parse_url(self):
dic, war = pl.Parser(self.dir, "image.pl").parse()
self.assertEqual(to_download_url('dir1/image.png'), dic['img'])
def test_parse_component(self):
pass # TODO
def test_parse_errors(self):
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "no_string_in_sub_key.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "syntax_extends.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "syntax_file.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "syntax_error.pl").parse()
with self.assertRaises(FileNotFound):
pl.Parser(self.dir, "extends_no_lib.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "open_multiline.pl").parse()
with self.assertRaises(SemanticError):
pl.Parser(self.dir, "append_no_key.pl").parse()
with self.assertRaises(FileNotFound):
pl.Parser(self.dir, "no_file_from.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "invalid_one_line_json.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "invalid_multiline_json.pl").parse()
with self.assertRaises(FileNotFound):
pl.Parser(self.dir, "no_file_sandbox.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "syntax_sandbox.pl").parse()
with self.assertRaises(SyntaxErrorPL):
pl.Parser(self.dir, "reference_binary.pl").parse()
with self.assertRaises(FileNotFound):
pl.Parser(self.dir, "no_image.pl").parse()
with self.assertRaises(SemanticError):
pl.Parser(self.dir, "prepend_no_key.pl").parse()
| [
"filebrowser.models.Directory.objects.create",
"loader.parsers.pl.get_parser",
"filebrowser.utils.to_download_url",
"os.path.join",
"loader.parsers.pl.Parser",
"uuid.uuid4",
"shutil.copytree",
"django.test.override_settings",
"os.path.isdir",
"shutil.rmtree",
"django.contrib.auth.models.User.obj... | [((449, 504), 'os.path.join', 'os.path.join', (['settings.APPS_DIR', '"""loader/tests/fake_pl"""'], {}), "(settings.APPS_DIR, 'loader/tests/fake_pl')\n", (461, 504), False, 'import os\n'), ((509, 557), 'django.test.override_settings', 'override_settings', ([], {'FILEBROWSER_ROOT': 'FAKE_FB_ROOT'}), '(FILEBROWSER_ROOT=FAKE_FB_ROOT)\n', (526, 557), False, 'from django.test import TestCase, override_settings\n'), ((424, 436), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (434, 436), False, 'import uuid\n'), ((702, 729), 'os.path.isdir', 'os.path.isdir', (['FAKE_FB_ROOT'], {}), '(FAKE_FB_ROOT)\n', (715, 729), False, 'import os\n'), ((799, 863), 'django.contrib.auth.models.User.objects.create_user', 'User.objects.create_user', ([], {'username': '"""user"""', 'password': '"""<PASSWORD>"""'}), "(username='user', password='<PASSWORD>')\n", (823, 863), False, 'from django.contrib.auth.models import User\n'), ((882, 935), 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'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""syntax_file.pl"""'], {}), "(self.dir, 'syntax_file.pl')\n", (3339, 3367), False, 'from loader.parsers import pl\n'), ((3435, 3473), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""syntax_error.pl"""'], {}), "(self.dir, 'syntax_error.pl')\n", (3444, 3473), False, 'from loader.parsers import pl\n'), ((3540, 3580), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""extends_no_lib.pl"""'], {}), "(self.dir, 'extends_no_lib.pl')\n", (3549, 3580), False, 'from loader.parsers import pl\n'), ((3648, 3688), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""open_multiline.pl"""'], {}), "(self.dir, 'open_multiline.pl')\n", (3657, 3688), False, 'from loader.parsers import pl\n'), ((3756, 3795), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""append_no_key.pl"""'], {}), "(self.dir, 'append_no_key.pl')\n", (3765, 3795), False, 'from loader.parsers import pl\n'), ((3862, 3900), 'loader.parsers.pl.Parser', 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'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""reference_binary.pl"""'], {}), "(self.dir, 'reference_binary.pl')\n", (4424, 4457), False, 'from loader.parsers import pl\n'), ((4524, 4558), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""no_image.pl"""'], {}), "(self.dir, 'no_image.pl')\n", (4533, 4558), False, 'from loader.parsers import pl\n'), ((4626, 4666), 'loader.parsers.pl.Parser', 'pl.Parser', (['self.dir', '"""prepend_no_key.pl"""'], {}), "(self.dir, 'prepend_no_key.pl')\n", (4635, 4666), False, 'from loader.parsers import pl\n')] |
"""
This module implements a Flask-based web application based on the functionality provided by the "flight_model"
package.
The site is not responsive but the Bootstrap customizer has been used to generate a cut-down version of bootstrap
to provide button and form element styling.
"""
import os
from flask import Flask, redirect
from booking_web.airports import airports_bp
from booking_web.airlines import airlines_bp
from booking_web.layouts import layouts_bp
from booking_web.passengers import passengers_bp
from booking_web.flights import flights_bp
from booking_web.boarding_cards import boarding_cards_bp
app = Flask("Flight Booking",
static_folder=os.path.join(os.path.dirname(__file__), "static"),
template_folder=os.path.join(os.path.dirname(__file__), "templates"))
app.secret_key = b'some secret key'
app.register_blueprint(airports_bp, url_prefix='/airports')
app.register_blueprint(airlines_bp, url_prefix='/airlines')
app.register_blueprint(layouts_bp, url_prefix='/layouts')
app.register_blueprint(passengers_bp, url_prefix='/passengers')
app.register_blueprint(flights_bp, url_prefix='/flights')
app.register_blueprint(boarding_cards_bp, url_prefix='/boarding_cards')
@app.route("/")
def home():
"""
Serve the home page for the site. This just redirects to the flights list
:return: Redirect to the flights list page
"""
return redirect("/flights/list")
| [
"flask.redirect",
"os.path.dirname"
] | [((1397, 1422), 'flask.redirect', 'redirect', (['"""/flights/list"""'], {}), "('/flights/list')\n", (1405, 1422), False, 'from flask import Flask, redirect\n'), ((685, 710), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (700, 710), False, 'import os\n'), ((764, 789), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (779, 789), False, 'import os\n')] |
import streamlit as st
import pandas as pd
import altair as alt
import pickle
import numpy as np
from map import create_map
from airdata import AirData
from utils import parse_time, parse_time_hms
from vega_datasets import data
#st.set_page_config(layout="wide")
# Getting data ready, Refresh every hour (same data when user refreshes within an hour)
@st.cache(ttl=60 * 60, suppress_st_warning=True)
def get_AD_data():
ad = AirData()
flight_df = ad.get_flights_df()
flight_df = ad.add_time_to_df(flight_df)
return ad, flight_df
# Cache to prevent computation on every rerun
@st.cache
def save_AD_data(df):
return df.to_csv().encode('utf-8')
ad, flight_df = get_AD_data()
# Definitions for flight delay
## Prepare data
# load in files
origin = pickle.load(open('flight-price/DestState.sav','rb'))
dest = pickle.load(open('flight-price/DestState.sav','rb'))
air = pickle.load(open('flight-price/AirlineCompany.sav','rb'))
miles_dic = pickle.load(open('flight-price/miles_dic.sav','rb'))
quarter_dic= {'Spring':'Q1','Summer':'Q2','Fall':'Q3','Winter':'Q4'}
df_viz = pd.read_csv('flight-price/df_viz.csv').iloc[:,:]
# fit the prediction model, get prediction and prediction interval
def get_pi(X):
all_models = pickle.load(open('flight-price/all_models.sav', 'rb'))
lb = all_models[0].predict(X)
pred = all_models[2].predict(X)
ub = all_models[1].predict(X)
return (round(np.exp(lb[0]),2), round(np.exp(pred[0]),2), round(np.exp(ub[0]),2))
# load data for non ML visual
def load_data_viz():
return pd.read_csv('flight-price/train_viz.csv').iloc[:,:]
# visual for price comparison
@st.cache
def get_slice_ogstate(df, ogstate=None):
labels = pd.Series([1] * len(df), index=df.index)
labels &= df['OriginState'] == ogstate
return labels
def get_slice_destate(df, destate=None):
labels = pd.Series([1] * len(df), index=df.index)
labels &= df['DestState'] == destate
return labels
def get_slice_membership(df, ogstate=None, destate=None, quarter=None,airline=None):
labels = pd.Series([1] * len(df), index=df.index)
if ogstate:
labels &= df['OriginState'] == ogstate
if destate is not None:
labels &= df['DestState'] == destate
if quarter:
labels &= df['Quarter'].isin(quarter)
if airline:
labels &= df['AirlineCompany'].isin(airline)
return labels
#-------------------- Price Heat Map-------------------------------------------
def load_data(url):
file = url
df = pd.read_csv(file)
return df
def get_season(df, quarter):
sub = df[df['Quarter']== quarter]
return sub
menu_selection = st.sidebar.radio("Menu", ["Introduction","Flight Map", "Flight Delay Analysis",
"Flight Price Analysis"])
if menu_selection == 'Introduction':
#col1, col2, col3,col4 = st.columns([0.5,1,2,1])
#col2.image("image/flight-logo.jpg", width=150)
#col3.markdown("<h1 style='text-align: left; color: #072F5F;'>Flight Traffic Brain</h1>",
# unsafe_allow_html=True)
col1, col2, col3 = st.columns([0.5,1,4])
col2.image("image/flight-logo.jpg", width=150)
col3.markdown("<h1 style='text-align: left; color: #072F5F;'>Flight Traffic Brain</h1>",
unsafe_allow_html=True)
text = "<p style='font-size:18px'>Nowadays, air traffic control has become a complicated task as there are\
more and more flights and airlines. There has also been rising cases of flight delays possibly due to poor\
management and massive volume of traffic. While air traffic is important to manage from the perspective of\
airports and airlines, flight prices are crucial for customers who usually make decisions of their travel\
plans based on them. In this project we hope to help airports better manage airlines and control airline\
traffic and passengers make wiser decisions about airline flights.</p>"
st.write(text, unsafe_allow_html=True)
text = "<p style='font-size:18px'>A <span style='color: #1167b1'> real-time map of flights </span> with interactive information such as speed and altitude can help the specialists\
to make better decisions. Meanwhile, an <span style='color: #1167b1'> interactive network graph </span> that shows the connections between airports and\
flights can also improve the handling of dependencies among the traffic. A <span style='color: #1167b1'> data visualization section of delay time </span>\
can also enable users to analyze different flights in real time and in more detail. By filtering the flight according to their\
departure airport, the users can not only view the delay time of different flights, but also have a high-level overview of\
the delay information of flights of different airlines. This information will help airport specialists to better communicate\
with the airports and passengers, and make better decisions in terms of resource distribution. In addition, a <span style='color: #1167b1'> \
machine learning model </span> using historical data to <span style='color: #1167b1'> predict flight price </span> can help passengers\
estimate the potential fare of flight of their interest. An <span style='color: #1167b1'> interactive platform with visualizations of airline comparisons </span> can also allow\
them to compare different flight prices by modifying parameters of interest, thus helping optimize their travel plan.</p>"
st.write(text, unsafe_allow_html=True)
text = "<br><br><br>This project was created by [<NAME>](<EMAIL>), [<NAME>](<EMAIL>), \
[<NAME>](<EMAIL>) and [<NAME>](<EMAIL>) for the [Interactive Data Science](https://dig.cmu.edu/ids2022) course at\
[Carnegie Mellon University](https://www.cmu.edu)"
st.write(text, unsafe_allow_html=True)
elif menu_selection == "Flight Map":
st.title("Real-time Flight Data Visualization")
# ------------ Map starts ---------------------
with st.sidebar.expander("Analysis for flights/airports"):
st.write("This is an analysis tool from the perspective of flights or airports")
to_show = st.selectbox("Data to look at", ["flight", "airport"])
if to_show == "flight":
field = st.selectbox("Variable of interest", ["heading", "altitude", "ground_speed"])
else:
field = st.selectbox("Variable of interest", ["origin_airport_iata", "destination_airport_iata"])
st.write("This is a map of real-time flights and airports. The blue circles are \
the airport, while the red squares are the flights. You can utilize \
the tool bar on the left tab to explore the data. You can also \
move your mouse over the map to see more information.")
map_air = create_map(flight_df, field, to_show)
st.altair_chart(map_air,use_container_width=True)
st.sidebar.title("Note")
st.sidebar.write("This visualization consists of three components.\
The first component is a map that shows real-time flights and airports\
in the U.S. The second component, linked to the first component, \
is an analysis tool for the real-time flight and airport data. \
The third component displays the time information of a flight.")
st.sidebar.download_button("Download real-time data", data=save_AD_data(flight_df),
file_name='airdata.csv', mime='text/csv')
# ------------ Map ends ---------------------
# ------------ Flight time starts ---------------------
st.write("Here we display the time information of a flight.")
option = st.selectbox("Which flight number are you looking into?",
flight_df['number'].sort_values())
# Get the corresponding flight row in the dataframe
option_row = flight_df[flight_df['number'] == option]
option_id = option_row.id.values[0]
option_detail = ad.get_flight_details(option_id)
option_time = option_detail['time']
# Display scheduled and actual time for departual and arrival using metric
col1, col2 = st.columns(2)
col1.metric("Scheduled departure time",
parse_time(option_time['scheduled']['departure']))
if option_time['real']['departure'] and option_time['scheduled']['departure']:
depart_delta = option_time['real']['departure'] - option_time['scheduled']['departure']
else:
depart_delta = None
col2.metric("Actual departure time",
parse_time(option_time['real']['departure']),
parse_time_hms(depart_delta),
delta_color='inverse')
col3, col4 = st.columns(2)
col3.metric("Scheduled arrival time", parse_time(option_time['scheduled']['arrival']))
arrival_time = option_time['real']['arrival']
if not arrival_time:
arrival_time = option_time['estimated']['arrival']
col4.metric("Estimated/Actual arrival time", parse_time(arrival_time))
# Note that some flights are not displayed due to... so the number of routes
# may appear larger than...
# ------------ Flight time ends ---------------------
elif menu_selection == "Flight Delay Analysis":
# ------------ Delay Analysis starts ---------------------
st.title("Flight Delay Analysis")
st.sidebar.title("Note")
st.sidebar.write("This flight delay analysis consists of four parts: \
The first part is a data slicing tool that allows the users to filter any flight data according to the different departure airport.\
The second part lists out all the flights flying from the selected departure airport, and displays the relevant delay time information of the flights. \
The third part displays a stripplot graph to allow the users to visually compare the different departure delay time of flights of different airlines.\
The last part compares the average delay time of different airlines. ")
ad = AirData()
flight_df = ad.get_flights_df()
st.header("Slice Data")
st.write("You can filter the airline data by choosing the different departure airport.")
with st.expander("Airports"):
origin_airport_list = flight_df['origin_airport_iata'].drop_duplicates()
option1 = st.selectbox("Departure Airport:",
(origin_airport_list))
flight_df_selected1 = flight_df[(flight_df['origin_airport_iata'] == option1)]
st.header("Data Visualization")
with st.expander("Flight delay from different departure airports"):
st.write("This data indicates all the current flights coming from the departure airport and their related delay times.")
index = 0
for row in flight_df_selected1.iterrows():
flight_number = flight_df_selected1['number'].values[index]
option_id = flight_df_selected1['id'].values[index]
option_detail = ad.get_flight_details(option_id)
option_time = option_detail['time']
if option_time['real']['departure'] is None:
continue
elif option_time['real']['arrival'] is None:
depart_delta = option_time['real']['departure'] - option_time['scheduled']['departure']
arrive_delta = None
col1, col2, col3 = st.columns(3)
col1.metric("Flight number",
flight_number)
col2.metric("Departure delay",
parse_time_hms(depart_delta))
col3.metric("Arrival delay",
arrive_delta)
else:
depart_delta = option_time['real']['departure'] - option_time['scheduled']['departure']
arrive_delta = option_time['real']['arrival'] - option_time['scheduled']['arrival']
col1, col2, col3 = st.columns(3)
col1.metric("Flight number",
flight_number)
col2.metric("Departure delay",
parse_time_hms(depart_delta))
col3.metric("Arrival delay",
parse_time_hms(arrive_delta))
index = index + 1
with st.expander("Flight delay of different airlines"):
st.write("This data compares the punctuality and departure delay times between different airlines.")
depart_delay = []
index = 0
for row in flight_df_selected1.iterrows():
option_id = flight_df_selected1['id'].values[index]
option_detail = ad.get_flight_details(option_id)
option_time = option_detail['time']
if option_time['real']['departure'] is None:
continue
else:
depart_delta = option_time['real']['departure'] - option_time['scheduled']['departure']
depart_delta = parse_time_hms(depart_delta)
depart_delay.append(depart_delta)
index = index + 1
flight_df_selected1['depart_delay'] = depart_delay
stripplot = alt.Chart(flight_df_selected1, width=640).mark_circle(size=30).encode(
x=alt.X(
'depart_delay',
title='Departure delay',
scale=alt.Scale()),
y=alt.Y(
'airline_iata',
title='Airline iata'),
color=alt.Color('airline_iata', legend=alt.Legend(orient="right")),
tooltip=['number', 'airline_iata', 'depart_delay']
).transform_calculate(
jitter='sqrt(-2*log(random()))*cos(2*PI*random())'
).configure_facet(
spacing=0
).configure_view(
stroke=None
)
stripplot
with st.expander("Compare average departure delay of different airlines"):
depart_delay = []
index = 0
for row in flight_df_selected1.iterrows():
option_id = flight_df_selected1['id'].values[index]
option_detail = ad.get_flight_details(option_id)
option_time = option_detail['time']
if option_time['real']['departure'] is None:
continue
else:
depart_delta = option_time['real']['departure'] - option_time['scheduled']['departure']
# depart_delta = parse_time_hms(depart_delta)
depart_delay.append(depart_delta)
index = index + 1
flight_df_selected1['depart_delay'] = depart_delay
average_delay = []
airline_average_delay_parsed = []
index = 0
for row in flight_df_selected1.iterrows():
ite_airline = flight_df_selected1['airline_iata'].values[index]
airline_data = flight_df_selected1[flight_df_selected1['airline_iata'] == ite_airline]
airline_average_delay = airline_data['depart_delay'].mean()
average_delay_parsed = parse_time_hms(airline_average_delay)
average_delay_parsed = str(average_delay_parsed).rstrip(':0')
airline_average_delay = round(airline_average_delay, 2)
# airline_average_delay = parse_time_hms(airline_average_delay)
average_delay.append(airline_average_delay)
airline_average_delay_parsed.append(average_delay_parsed)
index = index + 1
flight_df_selected1['airline_average_delay'] = average_delay
flight_df_selected1['average_delay_parsed'] = airline_average_delay_parsed
flight_df_selected2 = flight_df_selected1.drop_duplicates(subset=['airline_iata'], keep='first')
flight_df_selected2 = flight_df_selected2.sort_values(by=['airline_average_delay'], ascending=False)
barchart = alt.Chart(flight_df_selected2, width=640).mark_bar().encode(
x=alt.X('airline_average_delay', axis=alt.Axis(labels=False)),
y=alt.Y('airline_iata', sort=alt.EncodingSortField(field="airline_average_delay", op="count", order='ascending')),
tooltip=['airline_iata', 'average_delay_parsed']
)
text = barchart.mark_text(
align='left',
baseline='middle',
dx=3 # Nudges text to right so it doesn't appear on top of the bar
).encode(
text='average_delay_parsed'
)
(barchart + text).properties(height=900)
barchart + text
index = 0
for row in flight_df_selected2.iterrows():
ite_airline = flight_df_selected2['airline_iata'].values[index]
ite_delay = flight_df_selected2['average_delay_parsed'].values[index]
# ite_delay = parse_time_hms(ite_delay)
ite_delay = str(ite_delay).rstrip(':0')
col1, col2 = st.columns(2)
col1.metric("Airline",
ite_airline)
col2.metric("Average departure delay",
ite_delay)
index = index + 1
# ------------ Delay Analysis ends ---------------------
else:
# ------------------------ Flight price prediction starts ------------------------------
## Price Prediction
st.title("Flight Price Analysis")
# 1. ML prediction
st.header("Flight Price Prediction")
st.write("Tell us your intended flight information and get predicted flight price value and range.")
X_train=pd.read_csv('flight-price/X_train.csv')
features = list(X_train.columns)
del X_train
df_pred = pd.DataFrame(0, index=np.arange(1), columns=features)
col1, col2 = st.columns([3, 2])
with col2:
og = st.selectbox('Origin', np.array(origin),index=30)
de = st.selectbox('Destination', np.array(dest),index=4)
season = st.selectbox('Season', ['Spring','Summer','Fall','Winter'])
airline = st.selectbox('Airline Company', np.array(air))
numT = st.slider('Number of tickets', 1, 15, 1)
if og != "Virgin Islands":
df_pred[f'o{og}'] = 1
else:
df_pred['oU.S. Virgin Islands']=1
if de != "Virgin Islands":
df_pred[f'd{de}'] = 1
else:
df_pred['dU.S. Virgin Islands']=1
if season!='Spring':
df_pred[quarter_dic[season]] = 1
if airline[-3:-1]!='AA':
df_pred[airline[-3:-1]] = 1
df_pred['NumTicketsOrdered'] = numT
if og!=de:
try:
miles = miles_dic[(og,de)]
except:
miles = miles_dic[(de,og)]
df_pred['log_miles']=np.log(miles)
else:
st.markdown(" ")
if og!=de:
low, mean, high = get_pi(pd.DataFrame(df_pred))
with col1:
st.subheader("Predicted Price per Ticket")
st.metric("Low", f'${low}',"+$",delta_color="inverse")
st.metric("Mean", f'${mean}')
st.metric("High", f'${high}',"-$",delta_color="inverse")
df_interval = pd.DataFrame([[low,mean,high]],columns=['Low','Mean','High'])
st.write("See where your flight falls in the historical price distribution (2018)")
with st.expander("See price distribution"):
# plot price dist
bar = alt.Chart(df_viz).mark_bar(opacity=0.3,tooltip = True).encode(
alt.X('PricePerTicket:Q',title="Price per Ticket ($)"),#scale=alt.Scale(type='log')),
alt.Y('count()',title='Raw Frequency Count')
).properties(
title='Unit Price Distribution',
width=600,
height=400
#).transform_filter(
).interactive()
mean = alt.Chart(df_interval).mark_rule(color='purple',tooltip=True).encode(
x='Mean:Q',
size=alt.value(4),
)
low = alt.Chart(df_interval.sample(1)).mark_rule(color='darkblue',tooltip=True).encode(
x='Low:Q',
size=alt.value(2),
#strokeDash='Quarter'
)
high = alt.Chart(df_interval.sample(1)).mark_rule(color='darkblue',tooltip=True).encode(
x='High:Q',
size=alt.value(2),
#strokeDash='Quarter'
)
price_chart = bar + mean + low+ high
st.altair_chart(price_chart,use_container_width=True)
else:
with col1:
st.metric(" ", 'Not Available')
st.markdown("**Please choose a different origin or destination!**")
# ------------------------ Flight price prediction ends ------------------------------
# ------------------------ Flight price comparison starts ------------------------------
## Price comparison
st.header("Check the historical information of the flight you are interested in")
st.write('We will look at some historical data in 2018.')
df = load_data_viz()
cols = st.columns(4)
with cols[0]:
ogs = sorted(df['OriginState'].unique())
ogstate = st.selectbox('Origin State', ogs,index=ogs.index('New York'))
with cols[1]:
des = sorted(df['DestState'].unique())
destate = st.selectbox('Destination State', des,index=des.index('California'))
with cols[2]:
quarter = st.multiselect('Quarter',sorted(df['Quarter'].unique()))
with cols[3]:
airline = st.multiselect('Airline Company', sorted(df['AirlineCompany'].unique()))
slice_labels = get_slice_membership(df, ogstate, destate, quarter,airline)
slice_labels.name = "slice_membership"
df_show = df[slice_labels].iloc[:,:][['PricePerTicket','og','dest','Quarter','AirlineCompany']].sort_values(by='PricePerTicket')
df_show = df_show.rename(columns={'PricePerTicket':'Price per Ticket ($)','og':'Origin','dest':'Destination'}).reset_index(drop=True)
df_show['Price per Ticket ($)'] = df_show['Price per Ticket ($)'].apply(lambda x: "{:.2f}".format(x))
if df_show.empty:
st.metric(" ", "No Historical Data Available")
st.write("Please deselect some quarter/airline options or change origin/destination state.")
else:
st.dataframe(data=df_show)
# ------------------------ Flight price comparison starts ------------------------------ --------
df = load_data('flight-price/train_viz.csv')
st.header("Choose the season you want to travel, find the most economical route and airline")
quarter = st.selectbox('Season(Quarter)', sorted(df['Quarter'].unique()))
season_df = get_season(df,quarter)
# Take top 20 frequency states
statelist = ['California','Florida','Texas','New York','Georgia','Illinois','Nevada','Virginia','Massachusetts',
'Washington','Pennsylvania','Arizona','New Jersey','Minnesota','Michigan','Missouri','Maryland','Hawaii']
heat_price = season_df[season_df['OriginState'].isin(statelist) ]
heat_price = heat_price[heat_price['DestState'].isin(statelist) ]
# Take average price and miles per route
heat_price = heat_price.groupby(['OriginState','DestState'])[['PricePerTicket','Miles']].mean().reset_index()
# Drop the invalid value(origin = destination)
heat_price = heat_price[heat_price['OriginState'] != heat_price['DestState']]
pts = alt.selection(type="multi", encodings=['x','y'])
heat = alt.Chart(heat_price).mark_rect().encode(
x='OriginState:O',
y='DestState:O',
color=alt.condition(pts,'PricePerTicket:Q', alt.ColorValue("grey")),
tooltip=['OriginState', 'DestState', 'PricePerTicket','Miles']
).add_selection(pts)
box = alt.Chart(df).mark_boxplot(extent='min-max').encode(
x='AirlineCompany:O',
y='PricePerTicket:Q',
color=alt.Color('AirlineCompany')
).properties(
width=500,
height=300,
).transform_filter(
pts
)
st.altair_chart(alt.vconcat(heat,box),use_container_width=True)
st.header("Compare the price of different destination based on the origin you choose")
origin = st.selectbox('Origin', sorted(df['OriginState'].unique()))
def origin_data(origin,df):
subset = df[df['OriginState']==origin]
subset = subset.groupby(['OriginState','DestState'])[['PricePerTicket','Miles']].mean().reset_index()
merged = subset.merge(data.income().groupby('name').mean().reset_index(), how = 'inner', left_on ='DestState', right_on= 'name')
return merged
subset = origin_data(origin,df)
pts = alt.selection(type="multi", encodings=['x','y'])
heat_bar = alt.Chart(subset).mark_rect().encode(
x='DestState:O',
y='OriginState:O',
color=alt.condition(pts,'PricePerTicket:Q', alt.ColorValue("grey")),
tooltip=['DestState']
).add_selection(pts)
states = alt.topo_feature(data.us_10m.url, 'states')
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white'
).properties(
width=600,
height=400
).project('albersUsa')
foreground = alt.Chart(subset).mark_geoshape().encode(
shape='geo:G',
color=alt.condition(pts, 'name:N', alt.value('lightgray')),
tooltip=['OriginState', 'DestState', 'PricePerTicket','Miles']
).transform_lookup(
lookup='id',
from_=alt.LookupData(data=states, key='id'),
as_='geo'
).properties(
width=600,
height=400,
).project(
type='albersUsa'
)
map = background + foreground
st.altair_chart(alt.vconcat(heat_bar, map),use_container_width=True)
st.sidebar.title("Note")
st.sidebar.write("This flight price analysis consists of four parts.\
The first part is a flight customization section with predicted price range \
against historical price distribution.\
The second part presents a table of customized historical flights of interest. \
The third part displays the historical average flight price by route and airline company.\
The last part shows the available destination on the map and other information based on a chosen origin. ")
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import numpy as np
import pandas as pd
def Loader(events,args):
"""
Create a table with the pulses
"""
gb = events.groupby('Pulse',sort=False)
pulses = events.loc[gb.Sigma.idxmax()]
pulses.index = pulses.Pulse
pulses.index.name = None
pulses = pulses.drop('Pulse', axis='columns')
pulses.index.name = 'idx'
pulses['Rank'] = 0
pulses.Rank = pulses.Rank.astype(np.int8)
pulses['Candidate'] = -1
pulses.Candidate = pulses.Candidate.astype(np.int32)
pulses['N_events'] = gb.DM.count()
pulses.N_events = pulses.N_events.astype(np.int16)
pulses = pulses[pulses.N_events >= args.N_min]
if pulses.shape[0] == 0: return pulses
# Apply filters to discriminate interesting pulses
if not args.no_filter: classic_filters(events[events.Pulse.isin(pulses.index)], pulses, args)
#Store the pulses
pulses.sort_values(['DM','Time'], inplace=True)
if not args.no_store: pulses.to_hdf(args.store_name, 'pulses')
return pulses
def classic_filters(events, pulses, args):
"""
Apply RFI filters to the pulses
"""
RFI_code = 9
events = events[events.Pulse.isin(pulses.index)]
events.sort_values(by='DM',inplace=True)
gb = events.groupby('Pulse')
pulses.sort_index(inplace=True)
#Remove flat SNR pulses
pulses.Rank[pulses.Sigma / gb.Sigma.min() <= args.SNR_peak_min / args.SNR_min] = RFI_code
#Remove flat duration pulses (from Eq.6.21 of Pulsar Handbook)
pulses.Rank.loc[gb.Downfact.max() / pulses.Downfact < (args.SNR_peak_min / args.SNR_min)**2] = RFI_code
#Remove pulses peaking near the DM edges
if args.DM_range is not None:
DM_frac = (args.DM_range[1] - args.DM_range[0]) * 0.05 #Remove 5% of DM range from each edge
pulses.Rank[(pulses.DM < args.DM_range[0] + DM_frac) | (pulses.DM > args.DM_range[1] - DM_frac)] = RFI_code
#Remove pulses intersecting half the maximum SNR other than 2,4,6,8 times
def crosses(sig):
diff = sig - (sig.max() + sig.min()) / 2.
count = np.count_nonzero(np.diff(np.sign(diff)))
return (count != 2) & (count != 4) & (count != 6) & (count != 8)
pulses.Rank[gb.apply(lambda x: crosses(x.Sigma))] = RFI_code
#Remove weaker pulses within 20 ms of brighter ones
def simultaneous(p):
puls = pulses.Rank[np.abs(pulses.Time-p.Time) < 0.02]
if puls.shape[0] == 1: return False
if p.name == puls.index[0]: return False
else: return True
pulses.Rank[pulses.apply(lambda x: simultaneous(x), axis=1)] = RFI_code
return
| [
"numpy.abs",
"numpy.sign"
] | [((1990, 2003), 'numpy.sign', 'np.sign', (['diff'], {}), '(diff)\n', (1997, 2003), True, 'import numpy as np\n'), ((2239, 2267), 'numpy.abs', 'np.abs', (['(pulses.Time - p.Time)'], {}), '(pulses.Time - p.Time)\n', (2245, 2267), True, 'import numpy as np\n')] |
from scapy.all import *
import argparse
parser = argparse.ArgumentParser(description="Simple SYN Flood Script")
parser.add_argument("target_ip", help="Target IP address (e.g router's IP)")
parser.add_argument("-p", "--port", help="Destination port (the port of the target's machine service, \
e.g 80 for HTTP, 22 for SSH and so on).")
# parse arguments from the command line
args = parser.parse_args()
# target IP address (should be a testing router/firewall)
target_ip = args.target_ip
# the target port u want to flood
target_port = args.port
# forge IP packet with target ip as the destination IP address
ip = IP(dst=target_ip)
# or if you want to perform IP Spoofing (will work as well)
# ip = IP(src=RandIP("192.168.1.1/24"), dst=target_ip)
# forge a TCP SYN packet with a random source port
# and the target port as the destination port
tcp = TCP(sport=RandShort(), dport=target_port, flags="S")
# add some flooding data (1KB in this case, don't increase it too much,
# otherwise, it won't work.)
raw = Raw(b"X"*1024)
# stack up the layers
p = ip / tcp / raw
# send the constructed packet in a loop until CTRL+C is detected
send(p, loop=1, verbose=0) | [
"argparse.ArgumentParser"
] | [((50, 112), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Simple SYN Flood Script"""'}), "(description='Simple SYN Flood Script')\n", (73, 112), False, 'import argparse\n')] |
from datetime import datetime, timedelta
def ts_current_second_start(ts: int) -> int:
return ts - (ts % 1000)
def ts_current_minute_start(ts: int) -> int:
return ts - (ts % 60000)
def ts_current_hour_start(ts: int) -> int:
return ts - (ts % 3600000)
def ts_last_monday_start(ts: int) -> int:
dt = datetime.fromtimestamp(ts / 1000.0)
dt = dt - timedelta(days=dt.weekday() % 7)
dt = dt.replace(hour=0, minute=0, second=0, microsecond=0)
return round(dt.timestamp() * 1000)
def ts_current_day_start(ts: int) -> int:
dt = datetime.fromtimestamp(ts / 1000.0)
dt = dt.replace(hour=0, minute=0, second=0, microsecond=0)
return round(dt.timestamp() * 1000)
def ts_current_month_start(ts: int) -> int:
dt = datetime.fromtimestamp(ts / 1000.0)
dt = dt.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
return round(dt.timestamp() * 1000)
def ts_current_year_start(ts: int) -> int:
dt = datetime.fromtimestamp(ts / 1000.0)
dt = dt.replace(month=1, day=1, hour=0, minute=0, second=0, microsecond=0)
return round(dt.timestamp() * 1000)
| [
"datetime.datetime.fromtimestamp"
] | [((321, 356), 'datetime.datetime.fromtimestamp', 'datetime.fromtimestamp', (['(ts / 1000.0)'], {}), '(ts / 1000.0)\n', (343, 356), False, 'from datetime import datetime, timedelta\n'), ((560, 595), 'datetime.datetime.fromtimestamp', 'datetime.fromtimestamp', (['(ts / 1000.0)'], {}), '(ts / 1000.0)\n', (582, 595), False, 'from datetime import datetime, timedelta\n'), ((754, 789), 'datetime.datetime.fromtimestamp', 'datetime.fromtimestamp', (['(ts / 1000.0)'], {}), '(ts / 1000.0)\n', (776, 789), False, 'from datetime import datetime, timedelta\n'), ((954, 989), 'datetime.datetime.fromtimestamp', 'datetime.fromtimestamp', (['(ts / 1000.0)'], {}), '(ts / 1000.0)\n', (976, 989), False, 'from datetime import datetime, timedelta\n')] |
import sqlite3
from typing import Union, Any, List
import re
mydb = sqlite3.connect("Routing")
cursor = mydb.cursor()
cursor_2 = mydb.cursor()
route_tables = []
def get_db_tables_with_data() -> list:
"""Gets database tables. If table is empty pass"""
full_dbs = []
get_tables = cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
for table in get_tables:
check_table_rows = cursor_2.execute('SELECT count(*) FROM {}'.format(table[0]))
for row in check_table_rows:
if row[0] == 0:
pass
else:
full_dbs.append(table[0])
return full_dbs
def query_db_asa(**attributes: Union[str, List[int]]) -> None:
"""Find databse entries with arbitrary routing attributes. Checks for single hope metric or multi path
using \',\' between metrics"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_ASA')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
context_query = cursor.execute('SELECT * FROM Routing_ASA WHERE context=?', ("None",))
matched_query = 0
for row in context_query:
# Find single metric route
if attributes["query"] == row[attributes["index"]]:
print("Context: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\n".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
else:
pass
# Find routes with multihop. Sperator is ","
if attributes["query"] in row[attributes["index"]]:
if "," in row[attributes["index"]]:
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '', row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '',row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
else:
pass
print("Total Routes: %s" % matched_query)
def query_db_ios(**attributes: Union[str, List[int]]) -> None:
"""Find databse entries with arbitrary routing attributes"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_IOS_XE')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
vrf_query = cursor.execute('SELECT * FROM Routing_IOS_XE WHERE vrf=?', (attributes["vrf"],))
matched_query = 0
for row in vrf_query:
# Find single metric route
if attributes["query"] == row[attributes["index"]]:
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
else:
pass
# Find routes with multihop. Sperator is ","
if attributes["query"] in row[attributes["index"]]:
if "," in row[attributes["index"]]:
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '',
row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4],row[5], row[6], row[7]))
matched_query = matched_query + 1
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '',
row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4],row[5], row[6], row[7]))
matched_query = matched_query + 1
else:
pass
print("Total Routes: %s" % matched_query)
def query_db_ios_routes(**attributes: Union[str, List[int]]) -> None:
"""Find databse entries with arbitrary routing attributes"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_IOS_XE')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
vrf_query = cursor.execute('SELECT * FROM Routing_IOS_XE WHERE vrf=?', (attributes["vrf"],))
matched_query = 0
for row in vrf_query:
# Find single metric route
if attributes["query"] in row[attributes["index"]]:
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
else:
pass
print("Total Routes: %s" % matched_query)
def query_db_nexus_routes(**attributes: Union[str, List[int]]) -> None:
"""Find routes based off query, can be full route with prefix, no mask, or just octetes (10.173.)"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_Nexus')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
vdc_query = cursor.execute('SELECT * FROM Routing_Nexus WHERE vdc=?', (attributes["vdc"],))
matched_query = 0
for row in vdc_query:
# Find single metric route
if attributes["vrf"] == row[1] and attributes["query"] in row[attributes["index"]]:
print(
"\nVDC: {}\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4],row[5], row[6], row[7], row[8]))
matched_query = matched_query + 1
else:
pass
print("Total Routes: %s" % matched_query)
def query_db_asa_routes(**attributes: Union[str, List[int]]) -> None:
"""Find routes based off query, can be full route with prefix, no mask, or just octetes (10.173.)"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_ASA')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
context_query = cursor.execute('SELECT * FROM Routing_ASA WHERE context=?', ("None",))
matched_query = 0
for row in context_query:
# Find single metric route
if attributes["query"] in row[attributes["index"]]:
print("Context: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\n".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
matched_query = matched_query + 1
print("Total Routes: %s" % matched_query)
def query_db_nexus(**attributes: Union[str, List[int]]) -> None:
"""Find routes based off query, can be full route with prefix, no mask, or just octetes (10.173.)"""
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM Routing_Nexus')]
if get_table_rows[0][0] == 0:
print("No Routes In Table")
else:
vdc_query = cursor.execute('SELECT * FROM Routing_Nexus WHERE vdc=?', (attributes["vdc"],))
matched_query = 0
for row in vdc_query:
# Find single metric route
if attributes["vrf"] == row[1] and attributes["query"] == row[attributes["index"]]:
print(
"\nVDC: {}\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}".format(row[0], row[1], row[2], row[3], row[4],row[5], row[6], row[7], row[8]))
matched_query = matched_query + 1
else:
pass
# Find routes with multihop. Sperator is ","
if attributes["query"] in row[attributes["index"]]:
if "," in row[attributes["index"]]:
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '', row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\nCDP neighbor(s): {}".format(row[0], row[1], row[2], row[3],
row[4], row[5], row[6],
row[7], row[8]))
matched_query = matched_query + 1
if attributes["query"] in re.findall(r'^' + attributes["query"] + '(?=,)', row[attributes["index"]]) \
or \
attributes["query"] in re.findall(r'(?<=,)' + attributes["query"] + '',row[attributes["index"]]):
print("\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\nCDP neighbor(s): {}".format(row[0], row[1], row[2], row[3],
row[4], row[5], row[6],
row[7], row[8]))
matched_query = matched_query + 1
else:
pass
print("Total Routes: %s" % matched_query)
def get_routing_interfaces(table: str = None) -> dict:
"""Gets routing interfaces from table"""
interfaces = {}
get_interfaces = cursor.execute('SELECT interfaces FROM {}'.format(table))
for row in get_interfaces:
if ", " in row[0]:
split_interfaces = row[0].split(", ")
for i in split_interfaces:
interfaces[i[0]] = None
else:
interfaces[row] = None
return interfaces
def print_routing_interfaces(table: str = None) -> None:
"""Gets routing interfaces from table, prints"""
interfaces = get_routing_interfaces(table=table)
print("\nRouting Interfaces ---------\n")
if len(interfaces) == 0:
pass
else:
for k in interfaces.keys():
if len(k[0]) < 2 or "None" in k[0]:
pass
else:
print("+ " + k[0])
print("\n")
def get_protocols(table: str = None) -> dict:
"""Gets route protocol with types from the the routing table"""
protocol = {}
get_protocol = cursor.execute('SELECT protocol FROM {}'.format(table))
for i in get_protocol:
protocol[i[0][0]] = "None"
return protocol
def get_vrfs(table: str) -> None:
"""Gets VRFs from the device. Databse default is "global"""
vrf = {}
get_vrfs = cursor.execute('SELECT vrf FROM {}'.format(table))
for i in get_vrfs:
vrf[i] = None
print("\nVRFs ---------\n")
if len(vrf) == 0:
print("\nPress enter to use global")
else:
for k in vrf.keys():
print("+ " + k[0])
print("\n")
def get_vdcs():
"""Gets VDCs from the device table"""
vdc = {}
get_vdcs = cursor.execute('SELECT vdc FROM Routing_Nexus')
for i in get_vdcs:
vdc[i] = None
print("\nVDCs ---------\n")
if len(vdc) == 0:
pass
else:
for k in vdc.keys():
print("+ " + k[0])
print("\n")
def get_admin_disatnces(table: str = None) -> None:
"""Gets administrative distance from the routing table"""
ad = {}
get_ads = cursor.execute('SELECT admin_distance FROM {}'.format(table))
for i in get_ads:
ad[i] = None
print("\nAdmin Distances ---------\n")
if len(ad) == 0:
pass
else:
for k in ad.keys():
print("+ " + k[0])
print("\n")
def get_tags(table: str = None) -> None:
"""Gets tags from the the routing table"""
tag = {}
get_tags = cursor.execute('SELECT tag FROM {}'.format(table))
for i in get_tags:
tag[i] = None
print("\nRoute Tags ---------\n")
if len(tag) == 0:
pass
else:
for k in tag.keys():
print("+ " + k[0])
print("\n")
def print_protocols(table: str = None) -> None:
"""Gets route protocol with types from the the routing table"""
protocol = {}
get_protocol = cursor.execute('SELECT protocol FROM {}'.format(table))
for i in get_protocol:
protocol[i] = None
print("\nProtocols ---------\n")
if len(protocol) == 0:
pass
else:
for k in protocol.keys():
if "None" in k[0]:
pass
else:
print("+ " + k[0])
print("\n")
def search_db_ios(vrf: str = None, **attributes: Union[str, List[int]]) -> None:
"""Find databse entries by artbitrary attribute using **attributes (kwargs)
vrf, admin-distance, metric, prefix, next-hop, tag"""
# Create variables from **attributes (**kwargs). Pass on KeyErrors which is when the keyword argument
# wasn't passed
prefix = None
protocol = None
metric = None
ad = None
tag = None
interface = None
if vrf is None or vrf == "":
vrf = "global"
else:
pass
try:
prefix = attributes["prefix"]
except KeyError:
pass
try:
protocol = attributes["protocol"]
except KeyError:
pass
try:
metric = attributes["metric"]
except KeyError:
pass
try:
ad = attributes["ad"]
except KeyError:
pass
try:
tag = attributes["tag"]
except KeyError:
pass
try:
interface = attributes["interface"]
except KeyError:
pass
# Check to see if the argument has been passed, call db_query with args and db index of the query argument
try:
if attributes["protocol"]:
query_db_ios(vrf=vrf, query=protocol, index=2)
except KeyError:
pass
try:
if attributes["prefix"]:
query_db_ios_routes(vrf=vrf, query=prefix, index=1)
except KeyError:
pass
try:
if attributes["metric"]:
query_db_ios(vrf=vrf, query=metric, index=6)
except KeyError:
pass
try:
if attributes["ad"]:
query_db_ios(vrf=vrf, query=ad, index=3)
except KeyError:
pass
try:
if attributes["tag"]:
query_db_ios(vrf=vrf, query=tag, index=7)
except KeyError:
pass
try:
if attributes["interface"]:
query_db_ios(vrf=vrf, query=interface, index=5)
except KeyError:
pass
def search_db_nexus(vdc: str = None, vrf: str = None, **attributes: Union[str, List[int]]) -> None:
"""Find databse entries by artbitrary attribute using **attributes (kwargs)
vrf, admin-distance, metric, prefix, next-hop, tag"""
prefix = None
protocol = None
metric = None
ad = None
tag = None
interface = None
if vrf is None or vrf == "":
vrf = "default"
else:
pass
# Create variables from **attributes (**kwargs)
try:
prefix = attributes["prefix"]
except KeyError:
pass
try:
protocol = attributes["protocol"]
except KeyError:
pass
try:
metric = attributes["metric"]
except KeyError:
pass
try:
ad = attributes["ad"]
except KeyError:
pass
try:
tag = attributes["tag"]
except KeyError:
pass
try:
interface = attributes["interface"]
except KeyError:
pass
# Check to see if the argument has been passed, call db_query with args and db index of the query argument
try:
if attributes["protocol"]:
query_db_nexus(vdc=vdc, vrf=vrf, query=protocol, index=3)
except KeyError:
pass
try:
if attributes["prefix"]:
query_db_nexus_routes(vdc=vdc, vrf=vrf, query=prefix, index=2)
except KeyError:
pass
try:
if attributes["metric"]:
query_db_nexus(vdc=vdc, vrf=vrf, query=metric, index=7)
except KeyError:
pass
try:
if attributes["ad"]:
query_db_nexus(vdc=vdc, vrf=vrf, query=ad, index=4)
except KeyError:
pass
try:
if attributes["tag"]:
query_db_nexus(vdc=vdc, vrf=vrf, query=tag, index=8)
except KeyError:
pass
try:
if attributes["interface"]:
query_db_nexus(vdc=vdc, vrf=vrf, query=interface, index=6)
except KeyError:
pass
def search_db_asa(context: str = None, **attributes: Union[str, List[int]]) -> None:
"""Find databse entries by artbitrary attribute using **attributes (kwargs)
vrf, admin-distance, metric, prefix, next-hop, tag"""
# Create variables from **attributes (**kwargs). Pass on KeyErrors which is when the keyword argument
# wasn't passed
prefix = None
protocol = None
metric = None
ad = None
tag = None
interface = None
if context is None or "":
context = "None"
else:
pass
try:
prefix = attributes["prefix"]
except KeyError:
pass
try:
protocol = attributes["protocol"]
except KeyError:
pass
try:
metric = attributes["metric"]
except KeyError:
pass
try:
ad = attributes["ad"]
except KeyError:
pass
try:
tag = attributes["tag"]
except KeyError:
pass
try:
interface = attributes["interface"]
except KeyError:
pass
# Check to see if the argument has been passed, call db_query with args and db index of the query argument
try:
if attributes["protocol"]:
query_db_asa(context=context, query=protocol, index=2)
except KeyError:
pass
try:
if attributes["prefix"]:
query_db_asa_routes(context=context, query=prefix, index=1)
except KeyError:
pass
try:
if attributes["metric"]:
query_db_asa(context=context, query=metric, index=6)
except KeyError:
pass
try:
if attributes["ad"]:
query_db_asa(context=context, query=ad, index=3)
except KeyError:
pass
try:
if attributes["tag"]:
query_db_asa(context=context, query=tag, index=7)
except KeyError:
pass
try:
if attributes["interface"]:
query_db_asa(context=context, query=interface, index=5)
except KeyError:
pass
def get_tables_names() -> None:
"""Get database table which are the routing tables"""
try:
for row in cursor.execute('SELECT name FROM sqlite_master WHERE type=\'table\''):
route_tables.append(row[0])
except sqlite3.OperationalError:
pass
def view_routes_asa() -> None:
"""View all database entries"""
get_tables_names()
for table in route_tables:
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM {}'.format(table))]
if get_table_rows[0][0] == 0:
continue
else:
print("\nRouting Table: " + table + "\n")
print("__________________" + "\n")
query = cursor.execute('SELECT * FROM {}'.format(table))
for row in query:
print(
"Context: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\n"
.format(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7]))
print("Total Routes: %s" % get_table_rows[0][0])
def view_routes_ios() -> None:
"""View all database entries"""
get_tables_names()
for table in route_tables:
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM {}'.format(table))]
if get_table_rows[0][0] == 0:
continue
else:
print("\nRouting Table: " + table + "\n")
print("__________________" + "\n")
query = cursor.execute('SELECT * FROM {}'.format(table))
for row in query:
print("VRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\n".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6],
row[7]))
print("Total Routes: %s" % get_table_rows[0][0])
def view_routes_nexus() -> None:
"""View all database entries"""
get_tables_names()
for table in route_tables:
get_table_rows = [row for row in cursor.execute('SELECT count(*) FROM {}'.format(table))]
if get_table_rows[0][0] == 0:
continue
else:
print("\nRouting Table: " + table + "\n")
print("__________________" + "\n")
query = cursor.execute('SELECT * FROM {}'.format(table))
for row in query:
print(
"VDC: {}\nVRF: {}\nPrefix: {}\nProtocol: {}\nAdmin-Distance: {}\nHop(s): {}\nOut-Interface(s): {}\n"
"Metric(s): {}\nTag: {}\n".format(row[0], row[1], row[2], row[3], row[4], row[5], row[6],
row[7], row[8]))
print("Total Routes: %s" % get_table_rows[0][0])
| [
"re.findall",
"sqlite3.connect"
] | [((73, 99), 'sqlite3.connect', 'sqlite3.connect', (['"""Routing"""'], {}), "('Routing')\n", (88, 99), False, 'import sqlite3\n'), ((1892, 1965), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (1902, 1965), False, 'import re\n'), ((2055, 2128), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (2065, 2128), False, 'import re\n'), ((2496, 2569), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (2506, 2569), False, 'import re\n'), ((2659, 2732), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (2669, 2732), False, 'import re\n'), ((4297, 4370), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (4307, 4370), False, 'import re\n'), ((4460, 4533), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (4470, 4533), False, 'import re\n'), ((4961, 5034), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (4971, 5034), False, 'import re\n'), ((5124, 5197), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (5134, 5197), False, 'import re\n'), ((9965, 10038), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (9975, 10038), False, 'import re\n'), ((10128, 10201), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (10138, 10201), False, 'import re\n'), ((10766, 10839), 're.findall', 're.findall', (["('^' + attributes['query'] + '(?=,)')", "row[attributes['index']]"], {}), "('^' + attributes['query'] + '(?=,)', row[attributes['index']])\n", (10776, 10839), False, 'import re\n'), ((10929, 11002), 're.findall', 're.findall', (["('(?<=,)' + attributes['query'] + '')", "row[attributes['index']]"], {}), "('(?<=,)' + attributes['query'] + '', row[attributes['index']])\n", (10939, 11002), False, 'import re\n')] |
# -*- coding: utf-8 -*-
import pytest
from cards.api import Card
def assert_identical(c1: Card, c2: Card):
__tracebackhide__ = True
assert c1 == c2
if c1.id != c2.id:
pytest.fail(f"id's don't math. {c1.id} != {c2.id}")
def test_identical():
c1 = Card("foo", id=123)
c2 = Card("foo", id=123)
assert_identical(c1, c2)
def test_identical_fail():
c1 = Card("foo", id=123)
c2 = Card("foo", id=456)
assert_identical(c1, c2)
| [
"pytest.fail",
"cards.api.Card"
] | [((275, 294), 'cards.api.Card', 'Card', (['"""foo"""'], {'id': '(123)'}), "('foo', id=123)\n", (279, 294), False, 'from cards.api import Card\n'), ((304, 323), 'cards.api.Card', 'Card', (['"""foo"""'], {'id': '(123)'}), "('foo', id=123)\n", (308, 323), False, 'from cards.api import Card\n'), ((391, 410), 'cards.api.Card', 'Card', (['"""foo"""'], {'id': '(123)'}), "('foo', id=123)\n", (395, 410), False, 'from cards.api import Card\n'), ((420, 439), 'cards.api.Card', 'Card', (['"""foo"""'], {'id': '(456)'}), "('foo', id=456)\n", (424, 439), False, 'from cards.api import Card\n'), ((190, 241), 'pytest.fail', 'pytest.fail', (['f"""id\'s don\'t math. {c1.id} != {c2.id}"""'], {}), '(f"id\'s don\'t math. {c1.id} != {c2.id}")\n', (201, 241), False, 'import pytest\n')] |
"""flint format command
:copyright: Copyright 2021 <NAME>, see AUTHORS for details.
:license: Apache License, Version 2.0, see LICENSE for details.
"""
import flint
def format_statements(srcdirs, includes=None, excludes=None):
proj = flint.parse(*srcdirs, includes=includes, excludes=excludes)
for src in proj.sources:
for stmt in src.statements:
print(stmt.reformat())
| [
"flint.parse"
] | [((241, 300), 'flint.parse', 'flint.parse', (['*srcdirs'], {'includes': 'includes', 'excludes': 'excludes'}), '(*srcdirs, includes=includes, excludes=excludes)\n', (252, 300), False, 'import flint\n')] |
import yaml
from googletrans import Translator
class ScopusScienceTopicSearch(object):
"""Mapping russian courses names to scopus classes"""
def __init__(self, topics_map_path: str = 'sources/scopus_science_map.yml'):
"""
Class constructor
Args:
topics_map_path: path to scopus_science_map
"""
with open(topics_map_path, 'r') as f:
self.topics_map = yaml.safe_load(f)
self.preprocess_topics_map = {
main_topic: [
subtopic.lower()
for subtopic in self.topics_map[main_topic]
]
for main_topic in self.topics_map
}
self.translator = Translator()
def translate(self, russian_string: str) -> str:
"""
Translate russian sentence to english
Args:
russian_string: string with sentence in russian
Returns:
string with sentence in english
"""
return self.translator.translate(text=russian_string, src='ru').text
def __call__(self, input_topic: str, ru: bool = False) -> list:
"""
Call method
Args:
input_topic: input topic description in english
ru: topic in russian
Returns:
List of strings with probability science themes from scopus list
with follow format: `main topic,subtopic`
"""
if ru:
tags = self.translate(input_topic).lower().split(' ')
else:
tags = input_topic.lower().split(' ')
result_topics = []
for main_topic in self.topics_map.keys():
for k, subtopic in enumerate(self.preprocess_topics_map[main_topic]):
# found_in_subtopics = False
added_value = [0, '']
for tag in tags:
if tag in subtopic:
# found_in_subtopics = True
added_value[0] += 1
added_value[1] = main_topic + ',' + self.topics_map[main_topic][k]
if added_value[0] > 0:
result_topics.append(added_value)
# if not found_in_subtopics:
# if tag in str(main_topic).lower():
# for subtopic in self.topics_map[main_topic]:
# result_topics.append(
# main_topic + ',' + subtopic
# )
result_topics.sort(key=lambda x: x[0], reverse=True)
return [value[1] for value in result_topics]
| [
"yaml.safe_load",
"googletrans.Translator"
] | [((698, 710), 'googletrans.Translator', 'Translator', ([], {}), '()\n', (708, 710), False, 'from googletrans import Translator\n'), ((424, 441), 'yaml.safe_load', 'yaml.safe_load', (['f'], {}), '(f)\n', (438, 441), False, 'import yaml\n')] |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.11.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| [
"PyQt5.QtCore.qVersion",
"PyQt5.QtCore.qUnregisterResourceData",
"PyQt5.QtCore.qRegisterResourceData"
] | [((980271, 980372), 'PyQt5.QtCore.qRegisterResourceData', 'QtCore.qRegisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (980299, 980372), False, 'from PyQt5 import QtCore\n'), ((980402, 980505), 'PyQt5.QtCore.qUnregisterResourceData', 'QtCore.qUnregisterResourceData', (['rcc_version', 'qt_resource_struct', 'qt_resource_name', 'qt_resource_data'], {}), '(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n', (980432, 980505), False, 'from PyQt5 import QtCore\n'), ((980038, 980055), 'PyQt5.QtCore.qVersion', 'QtCore.qVersion', ([], {}), '()\n', (980053, 980055), False, 'from PyQt5 import QtCore\n')] |
# tests unix socket dothttp apis
from unittest import skipIf
import requests
from dothttp.request_base import RequestCompiler, CurlCompiler
from test import TestBase
from test.core.test_request import dir_path
try:
import requests_unixsocket
except:
requests_unixsocket = None
base_dir = f"{dir_path}/requests"
@skipIf(requests_unixsocket is None, "in wasm mode, it will not be available")
class TestUnixSocketRequests(TestBase):
def test_simple(self):
request = self.get_request(
file=f"{base_dir}/unix.http"
)
self.assertEqual(request.url, "http+unix://%2Fvar%2Frun%2Fdocker.sock/info")
def test_integration(self):
from requests_unixsocket.testutils import UnixSocketServerThread
with UnixSocketServerThread() as usock_thread:
urlencoded_usock = requests.compat.quote_plus(usock_thread.usock)
base_url = f"http+unix://{urlencoded_usock}"
req_comp: RequestCompiler = self.get_req_comp(
file=f"{base_dir}/unix.http"
, properties=["base_url=" + base_url]
, target=2
)
response = req_comp.get_response()
self.assertEqual(base_url + "/", response.url, "should be same request unix socket url")
self.assertEqual(200, response.status_code, "should be 200")
self.assertEqual('waitress', response.headers['server'], "server should be waitress")
self.assertEqual(usock_thread.usock, response.headers['X-Socket-Path'], "unix socket path should be same")
def test_url_extend_and_query(self):
from requests_unixsocket.testutils import UnixSocketServerThread
with UnixSocketServerThread() as usock_thread:
urlencoded_usock = requests.compat.quote_plus(usock_thread.usock)
base_url = f"http+unix://{urlencoded_usock}"
req_comp2: RequestCompiler = self.get_req_comp(
file=f"{base_dir}/unix.http"
, properties=["base_url=" + base_url, "urlpath=/"]
, target=3
)
response2 = req_comp2.get_response()
self.assertEqual(200, response2.status_code, "should be 200")
self.assertEqual('waitress', response2.headers['server'], "server should be waitress")
self.assertEqual(usock_thread.usock, response2.headers['X-Socket-Path'], "unix socket path should be same")
self.assertEqual('/containers/nginx/logs/', response2.headers['X-Requested-Path'],
"path should be same")
self.assertEqual('timestamp=true', response2.headers['X-Requested-Query-String'])
def test_curl(self):
request: CurlCompiler = self.get_req_comp(
file=f"{base_dir}/unix.http",
curl=True
)
self.assertEqual("""curl -X GET --url http://localhost/info \\
--unix-socket /var/run/docker.sock""", request.get_curl_output())
def test_curl_with_query_and_path(self):
request: CurlCompiler = self.get_req_comp(
file=f"{base_dir}/unix.http",
target=3
, properties=["base_url=" + "http+unix://%2Fvar%2Frun%2Fdocker.sock", "urlpath=/"],
curl=True
)
self.assertEqual("""curl -X GET --url http://localhost/containers/nginx/logs/?timestamp=true \\
--unix-socket /var/run/docker.sock""", request.get_curl_output())
| [
"requests.compat.quote_plus",
"unittest.skipIf",
"requests_unixsocket.testutils.UnixSocketServerThread"
] | [((326, 403), 'unittest.skipIf', 'skipIf', (['(requests_unixsocket is None)', '"""in wasm mode, it will not be available"""'], {}), "(requests_unixsocket is None, 'in wasm mode, it will not be available')\n", (332, 403), False, 'from unittest import skipIf\n'), ((762, 786), 'requests_unixsocket.testutils.UnixSocketServerThread', 'UnixSocketServerThread', ([], {}), '()\n', (784, 786), False, 'from requests_unixsocket.testutils import UnixSocketServerThread\n'), ((835, 881), 'requests.compat.quote_plus', 'requests.compat.quote_plus', (['usock_thread.usock'], {}), '(usock_thread.usock)\n', (861, 881), False, 'import requests\n'), ((1704, 1728), 'requests_unixsocket.testutils.UnixSocketServerThread', 'UnixSocketServerThread', ([], {}), '()\n', (1726, 1728), False, 'from requests_unixsocket.testutils import UnixSocketServerThread\n'), ((1777, 1823), 'requests.compat.quote_plus', 'requests.compat.quote_plus', (['usock_thread.usock'], {}), '(usock_thread.usock)\n', (1803, 1823), False, 'import requests\n')] |
"""add timer activity migration
Revision ID: <KEY>
Revises: <KEY>
Create Date: 2021-04-29 22:31:18.464080
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = '<KEY>'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('activity',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('user_id', sa.Integer(), nullable=True),
sa.Column('amount_time', sa.Integer(), nullable=True),
sa.Column('break_time', sa.Integer(), nullable=True),
sa.Column('break_activity', sa.String(length=300), nullable=True),
sa.Column('date_created', sa.DateTime(), nullable=True),
sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
sa.PrimaryKeyConstraint('id')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('activity')
# ### end Alembic commands ###
| [
"sqlalchemy.ForeignKeyConstraint",
"sqlalchemy.DateTime",
"alembic.op.drop_table",
"sqlalchemy.PrimaryKeyConstraint",
"sqlalchemy.Integer",
"sqlalchemy.String"
] | [((977, 1002), 'alembic.op.drop_table', 'op.drop_table', (['"""activity"""'], {}), "('activity')\n", (990, 1002), False, 'from alembic import op\n'), ((759, 809), 'sqlalchemy.ForeignKeyConstraint', 'sa.ForeignKeyConstraint', (["['user_id']", "['users.id']"], {}), "(['user_id'], ['users.id'])\n", (782, 809), True, 'import sqlalchemy as sa\n'), ((817, 846), 'sqlalchemy.PrimaryKeyConstraint', 'sa.PrimaryKeyConstraint', (['"""id"""'], {}), "('id')\n", (840, 846), True, 'import sqlalchemy as sa\n'), ((420, 432), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (430, 432), True, 'import sqlalchemy as sa\n'), ((476, 488), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (486, 488), True, 'import sqlalchemy as sa\n'), ((535, 547), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (545, 547), True, 'import sqlalchemy as sa\n'), ((593, 605), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (603, 605), True, 'import sqlalchemy as sa\n'), ((655, 676), 'sqlalchemy.String', 'sa.String', ([], {'length': '(300)'}), '(length=300)\n', (664, 676), True, 'import sqlalchemy as sa\n'), ((724, 737), 'sqlalchemy.DateTime', 'sa.DateTime', ([], {}), '()\n', (735, 737), True, 'import sqlalchemy as sa\n')] |
# -*- coding: utf-8 -*-
"""
Given two strings s and t, determine if they are isomorphic.
Two strings are isomorphic if the characters in s can be replaced to get t.
All occurrences of a character must be replaced with another character while
preserving the order of characters. No two characters may map to the same
character but a character may map to itself.
Example 1:
Input: s = "egg", t = "add"
Output: true
Example 2:
Input: s = "foo", t = "bar"
Output: false
Example 3:
Input: s = "paper", t = "title"
Output: true
Note:
You may assume both s and t have the same length
"""
from collections import defaultdict
class Solution:
def isIsomorphic(self, s, t):
"""
:type s: str
:type t: str
:rtype: bool
>>> s, t = "egg", "add"
>>> judge = Solution()
>>> judge.isIsomorphic(s, t)
True
>>> s, t = "foo", "bar"
>>> judge.isIsomorphic(s, t)
False
>>> s, t = "paper", "title"
>>> judge.isIsomorphic(s, t)
True
"""
s_c = defaultdict(list)
t_c = defaultdict(list)
for k, i in enumerate(s):
s_c[i].append(k)
for k, i in enumerate(t):
t_c[i].append(k)
s_c_values = list(s_c.values())
t_c_values = list(t_c.values())
return sorted(s_c_values) == sorted(t_c_values)
if __name__ == '__main__':
import doctest
doctest.testmod(verbose=True)
| [
"doctest.testmod",
"collections.defaultdict"
] | [((1426, 1455), 'doctest.testmod', 'doctest.testmod', ([], {'verbose': '(True)'}), '(verbose=True)\n', (1441, 1455), False, 'import doctest\n'), ((1059, 1076), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1070, 1076), False, 'from collections import defaultdict\n'), ((1091, 1108), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1102, 1108), False, 'from collections import defaultdict\n')] |
from django.shortcuts import render, redirect
from django.conf import settings
from editor.models import ContentModel
def index(request):
context = {}
template = "viewer/home.html"
try:
a = ContentModel.objects.get(ref_id='1')
except Exception as e:
return redirect('/welcome/')
return render(request, template, context)
def page_not_found(request):
url = request.get_full_path()
home_link = settings.SHARE_URL
template = "error.html"
context = {"url": home_link + url, "error_code": 404,
"error_message": "Oops, the page you're <br/> looking for does not exist.", "home_link": home_link}
return render(request, template, context)
def server_error(request):
url = request.get_full_path()
home_link = settings.SHARE_URL
template = "error.html"
context = {"url": home_link + url, "error_code": 500,
"error_message": "Sorry, but the requested page is unavailable <br/> due to a server hiccup.",
"home_link": home_link}
return render(request, template, context)
| [
"django.shortcuts.render",
"django.shortcuts.redirect",
"editor.models.ContentModel.objects.get"
] | [((336, 370), 'django.shortcuts.render', 'render', (['request', 'template', 'context'], {}), '(request, template, context)\n', (342, 370), False, 'from django.shortcuts import render, redirect\n'), ((692, 726), 'django.shortcuts.render', 'render', (['request', 'template', 'context'], {}), '(request, template, context)\n', (698, 726), False, 'from django.shortcuts import render, redirect\n'), ((1081, 1115), 'django.shortcuts.render', 'render', (['request', 'template', 'context'], {}), '(request, template, context)\n', (1087, 1115), False, 'from django.shortcuts import render, redirect\n'), ((221, 257), 'editor.models.ContentModel.objects.get', 'ContentModel.objects.get', ([], {'ref_id': '"""1"""'}), "(ref_id='1')\n", (245, 257), False, 'from editor.models import ContentModel\n'), ((302, 323), 'django.shortcuts.redirect', 'redirect', (['"""/welcome/"""'], {}), "('/welcome/')\n", (310, 323), False, 'from django.shortcuts import render, redirect\n')] |
"""
Class with nosetests for OptionGroup AbstractBlock in slack_view library
"""
from nose.tools import raises
from slackviews.view import OptionGroup, PlainText, Option
__author__ = '<NAME>'
__email__ = '<EMAIL>'
class TestOptiongroup:
def setup(self):
self.expected_label = 'any label'
self.expected_option0_text = 'option 1 text'
self.expected_option0_value = 'option 1 value'
self.expected_option1_text = 'option 2 text'
self.expected_option1_value = 'option 2 value'
self.expected_option0 = Option.Builder().text(self.expected_option0_text).value(self.expected_option0_value) \
.build()
self.expected_option1 = Option.Builder().text(self.expected_option1_text).value(self.expected_option1_value) \
.build()
self.expected_serialized_dict = {'options': [{'value': self.expected_option0_value,
'text': {'type': 'plain_text', 'text': self.expected_option0_text,
'emoji': False}},
{'value': self.expected_option1_value,
'text': {'type': 'plain_text', 'text': self.expected_option1_text,
'emoji': False}}],
'label': {'type': 'plain_text', 'text': self.expected_label, 'emoji': False}}
self.expected_serialized_json = f'{{"options": [{{"value": "{self.expected_option0_value}", ' \
f'"text": {{"type": "plain_text", "text": "{self.expected_option0_text}", ' \
f'"emoji": false}}}}, {{"value": "{self.expected_option1_value}", ' \
f'"text": {{"type": "plain_text", "text": "{self.expected_option1_text}", ' \
f'"emoji": false}}}}], "label": {{"type": "plain_text", ' \
f'"text": "{self.expected_label}", "emoji": false}}}}'
self.expected_option0 = Option.Builder().text(self.expected_option0_text).value(
self.expected_option0_value).build()
self.expected_option1 = Option.Builder().text(self.expected_option1_text).value(
self.expected_option1_value).build()
self.expected_options = [self.expected_option0, self.expected_option1]
_builder = OptionGroup.Builder().label(self.expected_label)
for opt_ in self.expected_options:
_builder.Option().text(getattr(getattr(opt_, '_text'), '_text')).value(getattr(opt_, '_value'))
self.optiongroup_instance = _builder.build()
def teardown(self):
OptionGroup.__all_slots__ = None
def test_should_optiongroup_builder_provide_a_valid_instance_with_required_values(self):
# GIVEN
expected_slots = ('_label', '_options')
expected_all_slots = expected_slots
# WHEN
instance = self.optiongroup_instance
# THEN
assert isinstance(instance, OptionGroup)
assert hasattr(instance, '__all_slots__')
assert hasattr(instance, '__slots__')
for att in expected_slots:
assert att in getattr(instance, '__slots__')
for att in expected_all_slots:
assert att in getattr(instance, '__all_slots__')
assert isinstance(getattr(instance, '_label'), PlainText)
assert getattr(getattr(instance, '_label'), '_text') == self.expected_label
_options = getattr(instance, '_options')
assert isinstance(_options, list)
for _opt in getattr(instance, '_options'):
assert isinstance(_opt, Option)
assert isinstance(getattr(_opt, '_text'), PlainText)
assert getattr(getattr(_options[0], '_text'), '_text') == self.expected_option0_text
assert getattr(_options[0], '_value') == self.expected_option0_value
assert getattr(getattr(_options[1], '_text'), '_text') == self.expected_option1_text
assert getattr(_options[1], '_value') == self.expected_option1_value
@raises(AttributeError)
def test_should_optiongroup_serialize_raise_attributeerror_if_trying_to_serialize_without_all_required_slots(self):
# WHEN
OptionGroup.Builder().build().serialize()
def test_should_optiongroup_serialize_provide_correct_dict_and_json_data(self):
# GIVEN
instance = self.optiongroup_instance
# WHEN
serialized_dict = instance.serialize()
serialized_json = instance.serialize(as_json=True)
# THEN
assert serialized_dict == self.expected_serialized_dict
assert serialized_json == self.expected_serialized_json
def test_should_optiongroup_deserialize_provide_correct_instances_from_dict_and_json(self):
# GIVEN
serialized_dict = self.expected_serialized_dict
serialized_json = self.expected_serialized_json
# WHEN
instance_from_dict = OptionGroup.deserialize(serialized_dict)
instance_from_json = OptionGroup.deserialize(serialized_json, from_json=True)
# THEN
assert isinstance(instance_from_dict, OptionGroup)
assert isinstance(instance_from_json, OptionGroup)
for instance in (instance_from_dict, instance_from_json):
assert isinstance(getattr(instance, '_label'), PlainText)
assert getattr(getattr(instance, '_label'), '_text') == self.expected_label
_options = getattr(instance, '_options')
assert isinstance(_options, list)
for _opt in getattr(instance, '_options'):
assert isinstance(_opt, Option)
assert isinstance(getattr(_opt, '_text'), PlainText)
assert getattr(getattr(_options[0], '_text'), '_text') == self.expected_option0_text
assert getattr(_options[0], '_value') == self.expected_option0_value
assert getattr(getattr(_options[1], '_text'), '_text') == self.expected_option1_text
assert getattr(_options[1], '_value') == self.expected_option1_value
| [
"slackviews.view.OptionGroup.deserialize",
"slackviews.view.OptionGroup.Builder",
"nose.tools.raises",
"slackviews.view.Option.Builder"
] | [((4221, 4243), 'nose.tools.raises', 'raises', (['AttributeError'], {}), '(AttributeError)\n', (4227, 4243), False, 'from nose.tools import raises\n'), ((5114, 5154), 'slackviews.view.OptionGroup.deserialize', 'OptionGroup.deserialize', (['serialized_dict'], {}), '(serialized_dict)\n', (5137, 5154), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((5184, 5240), 'slackviews.view.OptionGroup.deserialize', 'OptionGroup.deserialize', (['serialized_json'], {'from_json': '(True)'}), '(serialized_json, from_json=True)\n', (5207, 5240), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((2535, 2556), 'slackviews.view.OptionGroup.Builder', 'OptionGroup.Builder', ([], {}), '()\n', (2554, 2556), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((4388, 4409), 'slackviews.view.OptionGroup.Builder', 'OptionGroup.Builder', ([], {}), '()\n', (4407, 4409), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((555, 571), 'slackviews.view.Option.Builder', 'Option.Builder', ([], {}), '()\n', (569, 571), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((695, 711), 'slackviews.view.Option.Builder', 'Option.Builder', ([], {}), '()\n', (709, 711), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((2191, 2207), 'slackviews.view.Option.Builder', 'Option.Builder', ([], {}), '()\n', (2205, 2207), False, 'from slackviews.view import OptionGroup, PlainText, Option\n'), ((2329, 2345), 'slackviews.view.Option.Builder', 'Option.Builder', ([], {}), '()\n', (2343, 2345), False, 'from slackviews.view import OptionGroup, PlainText, Option\n')] |
# This script converts excel files to pdf files
# Import Module
from win32com import client
# Open Microsoft Excel
excel = client.Dispatch("Excel.Application")
excel_path = input("Enter the path of the excel file completely: ")
pdf_path = input(
"Enter the path of the folder where you want pdf file to be present: ")
pdf_name = input("Enter the name by which pdf file should be saved: ")
# Read Excel File
sheets = excel.Workbooks.Open(excel_path)
work_sheets = sheets.Worksheets[0]
# Convert into PDF File
work_sheets.ExportAsFixedFormat(0, pdf_path+'\ '+pdf_name+".pdf")
| [
"win32com.client.Dispatch"
] | [((125, 161), 'win32com.client.Dispatch', 'client.Dispatch', (['"""Excel.Application"""'], {}), "('Excel.Application')\n", (140, 161), False, 'from win32com import client\n')] |
from math import sin, cos, sqrt
class Vector():
def __init__(self, x, y, z):
if str in [type(x), type(y), type(z)]:
print('\n', x,y,z)
raise ValueError('Can only be numerical values')
self.x = x
self.y = y
self.z = z
self.texture = None
self.id = None
def __add__(self, other):
return Vector(self.x + other.x, self.y + other.y, self.z + other.z)
def __sub__(self, other):
return Vector(self.x - other.x, self.y - other.y, self.z - other.z)
def add(self, other):
self.x += other.x
self.y += other.y
self.z += other.z
def div(self, other):
self.x /= other
self.y /= other
self.z /= other
def mult(self, other):
self.x /= other
self.y /= other
self.z /= other
def get_xy(self):
return (int(self.x), int(self.y))
def get_xy_center(self, size):
return (int(self.x + size[0]/2), int(self.y + size[1]/2))
def rotate_x(self, a):
self.x = (1 * self.x) + (0 * self.y) + (0 * self.z)
self.y = (0 * self.x) + (cos(a) * self.y) + (-sin(a) * self.z)
self.z = (0 * self.x) + (sin(a) * self.y) + (cos(a) * self.z)
def rotate_y(self, a):
self.x = (cos(a) * self.x) + (0 * self.y) + (sin(a) * self.z)
self.y = (0 * self.x) + (1 * self.y) + (0 * self.z)
self.z = (-sin(a) * self.x) + (0 * self.y) + (cos(a) * self.z)
def rotate_z(self, a):
self.x = (cos(a) * self.x) + (-sin(a) * self.y) + (0 * self.z)
self.y = (sin(a) * self.x) + (cos(a) * self.y) + (0 * self.z)
self.z = (0 * self.x) + (0 * self.y) + (1 * self.z)
| [
"math.cos",
"math.sin"
] | [((1277, 1283), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1280, 1283), False, 'from math import sin, cos, sqrt\n'), ((1378, 1384), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1381, 1384), False, 'from math import sin, cos, sqrt\n'), ((1511, 1517), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1514, 1517), False, 'from math import sin, cos, sqrt\n'), ((1185, 1191), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1188, 1191), False, 'from math import sin, cos, sqrt\n'), ((1206, 1212), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1209, 1212), False, 'from math import sin, cos, sqrt\n'), ((1257, 1263), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1260, 1263), False, 'from math import sin, cos, sqrt\n'), ((1343, 1349), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1346, 1349), False, 'from math import sin, cos, sqrt\n'), ((1577, 1583), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1580, 1583), False, 'from math import sin, cos, sqrt\n'), ((1649, 1655), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1652, 1655), False, 'from math import sin, cos, sqrt\n'), ((1669, 1675), 'math.cos', 'cos', (['a'], {}), '(a)\n', (1672, 1675), False, 'from math import sin, cos, sqrt\n'), ((1476, 1482), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1479, 1482), False, 'from math import sin, cos, sqrt\n'), ((1598, 1604), 'math.sin', 'sin', (['a'], {}), '(a)\n', (1601, 1604), False, 'from math import sin, cos, sqrt\n')] |
import os
import pytest
from loguru import logger
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_deleted_before_write_without_delay(tmpdir):
file = tmpdir.join("test.log")
logger.add(str(file), format="{message}", watch=True, delay=False)
os.remove(str(file))
logger.info("Test")
assert file.read() == "Test\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_deleted_before_write_with_delay(tmpdir):
file = tmpdir.join("test.log")
logger.add(str(file), format="{message}", watch=True, delay=True)
logger.info("Test 1")
os.remove(str(file))
logger.info("Test 2")
assert file.read() == "Test 2\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_path_containing_placeholder(tmpdir):
logger.add(str(tmpdir.join("test_{time}.log")), format="{message}", watch=True)
assert len(tmpdir.listdir()) == 1
filepath = tmpdir.listdir()[0]
os.remove(str(filepath))
logger.info("Test")
assert len(tmpdir.listdir()) == 1
assert filepath.read() == "Test\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_reopened_with_arguments(tmpdir):
file = tmpdir.join("test.log")
logger.add(str(file), format="{message}", watch=True, encoding="ascii", errors="replace")
os.remove(str(file))
logger.info("é")
assert file.read() == "?\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_manually_changed(tmpdir):
file = tmpdir.join("test.log")
logger.add(str(file), format="{message}", watch=True, mode="w")
os.remove(str(file))
file.write("Placeholder")
logger.info("Test")
assert file.read() == "Test\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_folder_deleted(tmpdir):
file = tmpdir.join("foo/bar/test.log")
logger.add(str(file), format="{message}", watch=True)
os.remove(str(file))
os.rmdir(str(tmpdir.join("foo/bar")))
logger.info("Test")
assert file.read() == "Test\n"
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_deleted_before_rotation(tmpdir):
exists = None
file = tmpdir.join("test.log")
def rotate(_, __):
nonlocal exists
exists = file.exists()
return False
logger.add(str(file), format="{message}", watch=True, rotation=rotate)
os.remove(str(file))
logger.info("Test")
assert exists is True
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_deleted_before_compression(tmpdir):
exists = None
file = tmpdir.join("test.log")
def compress(_):
nonlocal exists
exists = file.exists()
return False
logger.add(str(file), format="{message}", watch=True, compression=compress)
os.remove(str(file))
logger.remove()
assert exists is True
@pytest.mark.skipif(os.name == "nt", reason="Windows can't delete file in use")
def test_file_deleted_before_retention(tmpdir):
exists = None
file = tmpdir.join("test.log")
def retain(_):
nonlocal exists
exists = file.exists()
return False
logger.add(str(file), format="{message}", watch=True, retention=retain)
os.remove(str(file))
logger.remove()
assert exists is True
def test_file_correctly_reused_after_rotation(tmpdir):
rotate = iter((False, True, False))
filepath = tmpdir.join("test.log")
logger.add(
str(filepath),
format="{message}",
mode="w",
watch=True,
rotation=lambda _, __: next(rotate),
)
logger.info("Test 1")
logger.info("Test 2")
logger.info("Test 3")
assert len(tmpdir.listdir()) == 2
rotated = next(f for f in tmpdir.listdir() if f != filepath)
assert rotated.read() == "Test 1\n"
assert filepath.read() == "Test 2\nTest 3\n"
@pytest.mark.parametrize("delay", [True, False])
@pytest.mark.parametrize("compression", [None, lambda _: None])
def test_file_closed_without_being_logged(tmpdir, delay, compression):
filepath = tmpdir.join("test.log")
logger.add(
str(filepath),
format="{message}",
watch=True,
delay=delay,
compression=compression,
)
logger.remove()
assert filepath.exists() is (False if delay else True)
| [
"pytest.mark.parametrize",
"loguru.logger.remove",
"loguru.logger.info",
"pytest.mark.skipif"
] | [((54, 132), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (72, 132), False, 'import pytest\n'), ((384, 462), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (402, 462), False, 'import pytest\n'), ((740, 818), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (758, 818), False, 'import pytest\n'), ((1160, 1238), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (1178, 1238), False, 'import pytest\n'), ((1496, 1574), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (1514, 1574), False, 'import pytest\n'), ((1835, 1913), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (1853, 1913), False, 'import pytest\n'), ((2182, 2260), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (2200, 2260), False, 'import pytest\n'), ((2615, 2693), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (2633, 2693), False, 'import pytest\n'), ((3050, 3128), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(os.name == 'nt')"], {'reason': '"""Windows can\'t delete file in use"""'}), '(os.name == \'nt\', reason="Windows can\'t delete file in use")\n', (3068, 3128), False, 'import pytest\n'), ((4039, 4086), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""delay"""', '[True, False]'], {}), "('delay', [True, False])\n", (4062, 4086), False, 'import pytest\n'), ((4088, 4150), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""compression"""', '[None, lambda _: None]'], {}), "('compression', [None, lambda _: None])\n", (4111, 4150), False, 'import pytest\n'), ((326, 345), 'loguru.logger.info', 'logger.info', (['"""Test"""'], {}), "('Test')\n", (337, 345), False, 'from loguru import logger\n'), ((627, 648), 'loguru.logger.info', 'logger.info', (['"""Test 1"""'], {}), "('Test 1')\n", (638, 648), False, 'from loguru import logger\n'), ((678, 699), 'loguru.logger.info', 'logger.info', (['"""Test 2"""'], {}), "('Test 2')\n", (689, 699), False, 'from loguru import logger\n'), ((1060, 1079), 'loguru.logger.info', 'logger.info', (['"""Test"""'], {}), "('Test')\n", (1071, 1079), False, 'from loguru import logger\n'), ((1444, 1460), 'loguru.logger.info', 'logger.info', (['"""é"""'], {}), "('é')\n", (1455, 1460), False, 'from loguru import logger\n'), ((1777, 1796), 'loguru.logger.info', 'logger.info', (['"""Test"""'], {}), "('Test')\n", (1788, 1796), False, 'from loguru import logger\n'), ((2124, 2143), 'loguru.logger.info', 'logger.info', (['"""Test"""'], {}), "('Test')\n", (2135, 2143), False, 'from loguru import logger\n'), ((2566, 2585), 'loguru.logger.info', 'logger.info', (['"""Test"""'], {}), "('Test')\n", (2577, 2585), False, 'from loguru import logger\n'), ((3005, 3020), 'loguru.logger.remove', 'logger.remove', ([], {}), '()\n', (3018, 3020), False, 'from loguru import logger\n'), ((3432, 3447), 'loguru.logger.remove', 'logger.remove', ([], {}), '()\n', (3445, 3447), False, 'from loguru import logger\n'), ((3770, 3791), 'loguru.logger.info', 'logger.info', (['"""Test 1"""'], {}), "('Test 1')\n", (3781, 3791), False, 'from loguru import logger\n'), ((3796, 3817), 'loguru.logger.info', 'logger.info', (['"""Test 2"""'], {}), "('Test 2')\n", (3807, 3817), False, 'from loguru import logger\n'), ((3822, 3843), 'loguru.logger.info', 'logger.info', (['"""Test 3"""'], {}), "('Test 3')\n", (3833, 3843), False, 'from loguru import logger\n'), ((4412, 4427), 'loguru.logger.remove', 'logger.remove', ([], {}), '()\n', (4425, 4427), False, 'from loguru import logger\n')] |
# Author: <NAME> at 16/08/2021 <<EMAIL>>
# Licence: MIT License
# Copyright: <NAME> (2018) <<EMAIL>>
from functools import partial
import numpy as np
from scipy import linalg
from .utils import (readout_forward, _initialize_readout,
_prepare_inputs_for_learning)
from ..base.node import Node
from ..base.types import global_dtype
def _solve_ridge(XXT, YXT, ridge):
return linalg.solve(XXT + ridge, YXT.T, assume_a="sym")
def partial_backward(readout: Node, X_batch, Y_batch=None):
transient = readout.transient
X, Y = _prepare_inputs_for_learning(X_batch, Y_batch,
transient=transient,
bias=readout.input_bias,
allow_reshape=True)
xxt = X.T.dot(X)
yxt = Y.T.dot(X)
XXT = readout.get_buffer("XXT")
YXT = readout.get_buffer("YXT")
# This is not thread-safe, apparently, using Numpy memmap as buffers
# ok for parallelization then with a lock (see ESN object)
XXT += xxt
YXT += yxt
def backward(readout: Node, X=None, Y=None):
ridge = readout.ridge
XXT = readout.get_buffer("XXT")
YXT = readout.get_buffer("YXT")
input_dim = readout.input_dim
if readout.input_bias:
input_dim += 1
ridgeid = (ridge * np.eye(input_dim, dtype=global_dtype))
Wout_raw = _solve_ridge(XXT, YXT, ridgeid)
if readout.input_bias:
Wout, bias = Wout_raw[1:, :], Wout_raw[0, :][np.newaxis, :]
readout.set_param("Wout", Wout)
readout.set_param("bias", bias)
else:
readout.set_param("Wout", Wout_raw)
def initialize(readout: Node,
x=None,
y=None,
Wout_init=None):
_initialize_readout(readout, x, y, bias=readout.input_bias,
init_func=Wout_init)
def initialize_buffers(readout):
# create memmaped buffers for matrices X.X^T and Y.X^T pre-computed
# in parallel for ridge regression
# ! only memmap can be used ! Impossible to share Numpy arrays with
# different processes in r/w mode otherwise (with proper locking)
input_dim = readout.input_dim
output_dim = readout.output_dim
if readout.input_bias:
input_dim += 1
readout.create_buffer("XXT", (input_dim,
input_dim))
readout.create_buffer("YXT", (output_dim,
input_dim))
class Ridge(Node):
def __init__(self, output_dim=None, ridge=0.0, transient=0, Wout=None,
input_bias=True, name=None):
super(Ridge, self).__init__(params={"Wout": None, "bias": None},
hypers={"ridge": ridge,
"transient": transient,
"input_bias": input_bias},
forward=readout_forward,
partial_backward=partial_backward,
backward=backward,
output_dim=output_dim,
initializer=partial(initialize,
Wout_init=Wout),
buffers_initializer=initialize_buffers,
name=name)
| [
"numpy.eye",
"functools.partial",
"scipy.linalg.solve"
] | [((401, 449), 'scipy.linalg.solve', 'linalg.solve', (['(XXT + ridge)', 'YXT.T'], {'assume_a': '"""sym"""'}), "(XXT + ridge, YXT.T, assume_a='sym')\n", (413, 449), False, 'from scipy import linalg\n'), ((1328, 1365), 'numpy.eye', 'np.eye', (['input_dim'], {'dtype': 'global_dtype'}), '(input_dim, dtype=global_dtype)\n', (1334, 1365), True, 'import numpy as np\n'), ((3167, 3202), 'functools.partial', 'partial', (['initialize'], {'Wout_init': 'Wout'}), '(initialize, Wout_init=Wout)\n', (3174, 3202), False, 'from functools import partial\n')] |
"""
Day 6: Universal Orbit Map
"""
from utils import get_lines
def distance(orbit_map, item):
if item == 'COM':
return 0
else:
return 1 + distance(orbit_map, orbit_map[item])
def orbit_path(orbit_map, item1):
if item1 == 'COM':
return []
else:
return orbit_path(orbit_map, orbit_map[item1]) + [item1]
def puzzle1():
orbits = get_lines('day6')
orbits_map = {}
objects = set()
for line in orbits:
orbitee, orbiter = line.split(')')
objects.add(orbiter)
orbits_map[orbiter] = orbitee
count = 0
for item in objects:
count += distance(orbits_map, item)
print(count)
def puzzle2():
orbits = get_lines('day6')
orbits_map = {}
objects = set()
for line in orbits:
orbitee, orbiter = line.split(')')
objects.add(orbiter)
orbits_map[orbiter] = orbitee
you_path = orbit_path(orbits_map, 'YOU')
san_path = orbit_path(orbits_map, 'SAN')
# determine length of common prefix
for idx, (i1, i2) in enumerate(zip(you_path, san_path)):
if i1 != i2:
common_path_len = idx
break
else:
raise RuntimeError("No common path")
# Don't care about the starting node
you_path.pop()
san_path.pop()
print(len(you_path) + len(san_path) - 2 * common_path_len)
if __name__ == '__main__':
puzzle1()
puzzle2()
| [
"utils.get_lines"
] | [((384, 401), 'utils.get_lines', 'get_lines', (['"""day6"""'], {}), "('day6')\n", (393, 401), False, 'from utils import get_lines\n'), ((708, 725), 'utils.get_lines', 'get_lines', (['"""day6"""'], {}), "('day6')\n", (717, 725), False, 'from utils import get_lines\n')] |
import ee
from zipfile import ZipFile
from io import BytesIO
import os
import requests
class S2indexes:
def __init__(self, area, dir, date_from, date_end, scope):
"""
given an area defined by a geoJSON, it returns rasters of
remote sensing indexes at the specified date at granularuity defined by the scope parameter
Args:
area: geoJSON, use squaretogeojson to generate
dir: directory where to save the easter
date_from, date_end: nightlights for what point in time?
scope (str): country or urban?
"""
self.area = area
self.dir = dir
self.date_from = date_from
self.date_end = date_end
self.scope = scope
self.files = None
def download(self):
print('INFO: downloading rms indexes for area of interest ...')
if os.path.exists(self.dir + str(self.area["coordinates"]) + "NDVI_max.tif") \
and os.path.exists(self.dir + str(self.area["coordinates"]) + "NDBI_max.tif") \
and os.path.exists(self.dir + str(self.area["coordinates"]) + "NDWI_max.tif"):
self.files = [str(self.area["coordinates"]) + "NDVI_max.tif", str(self.area["coordinates"]) + "NDBI_max.tif", str(self.area["coordinates"]) + "NDWI_max.tif"]
print('INFO: NDs data for {} already downloaded'.format(self.area["coordinates"]))
else:
ee.Initialize()
GREEN = 'B3'
RED = 'B4'
NIR = 'B8'
SWIR = 'B11'
sentinel = ee.ImageCollection('COPERNICUS/S2') \
.filterDate(ee.Date(self.date_from), ee.Date(self.date_end)) \
.filterBounds(self.area) \
.select([GREEN, RED, NIR, SWIR]) \
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 70)
def addIndices(image):
ndvi = image.normalizedDifference([NIR, RED])
ndbi = image.normalizedDifference([SWIR, NIR])
ndwi = image.normalizedDifference([GREEN, NIR])
return image.addBands(ndvi.rename('NDVI')) \
.addBands(ndbi.rename('NDBI')) \
.addBands(ndwi.rename('NDWI'))
img = sentinel.map(addIndices).select(['NDVI', 'NDBI', 'NDWI']).reduce("max").clip(self.area)
# download
if self.scope == 'urban':
print('INFO: NDs scope > urban')
scale = 100
else:
print('INFO: NDs scope -> country')
scale = 5000
for b in ['NDVI_max', 'NDBI_max', 'NDWI_max']:
url = img.select(b).getDownloadUrl({'crs': 'EPSG:4326', 'region': self.area, 'scale': scale})
r = requests.get(url)
z = ZipFile(BytesIO(r.content))
z.extract(z.namelist()[1], self.dir)
os.rename(self.dir + z.namelist()[1], self.dir + str(self.area["coordinates"]) + b+'.tif')
self.files = [str(self.area["coordinates"]) + "NDVI_max.tif", str(self.area["coordinates"]) + "NDBI_max.tif",
str(self.area["coordinates"]) + "NDWI_max.tif"]
def rms_values(self, longitudes, latitudes):
"""
Given a dataset with latitude and longitude columns, it returns the nightlight value at each point.
Args:
longitudes: list of longitudes
latitudes: list of latitudes
Returns:
Series
"""
import rasterio
try:
NDVI = rasterio.open(self.dir + self.files[0])
NDBI = rasterio.open(self.dir + self.files[1])
NDWI = rasterio.open(self.dir + self.files[2])
except MemoryError:
print('Remote sensing indexes rasters too big!')
raise
veg, build, wat = [], [], []
for lon, lat in zip(longitudes, latitudes):
i, j = NDVI.index(lon, lat)
veg.append(NDVI.read(1)[i, j])
build.append(NDBI.read(1)[i, j])
wat.append(NDWI.read(1)[i, j])
return veg, build, wat | [
"rasterio.open",
"io.BytesIO",
"ee.Date",
"requests.get",
"ee.ImageCollection",
"ee.Initialize"
] | [((1419, 1434), 'ee.Initialize', 'ee.Initialize', ([], {}), '()\n', (1432, 1434), False, 'import ee\n'), ((3548, 3587), 'rasterio.open', 'rasterio.open', (['(self.dir + self.files[0])'], {}), '(self.dir + self.files[0])\n', (3561, 3587), False, 'import rasterio\n'), ((3607, 3646), 'rasterio.open', 'rasterio.open', (['(self.dir + self.files[1])'], {}), '(self.dir + self.files[1])\n', (3620, 3646), False, 'import rasterio\n'), ((3666, 3705), 'rasterio.open', 'rasterio.open', (['(self.dir + self.files[2])'], {}), '(self.dir + self.files[2])\n', (3679, 3705), False, 'import rasterio\n'), ((2766, 2783), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (2778, 2783), False, 'import requests\n'), ((2812, 2830), 'io.BytesIO', 'BytesIO', (['r.content'], {}), '(r.content)\n', (2819, 2830), False, 'from io import BytesIO\n'), ((1620, 1643), 'ee.Date', 'ee.Date', (['self.date_from'], {}), '(self.date_from)\n', (1627, 1643), False, 'import ee\n'), ((1645, 1667), 'ee.Date', 'ee.Date', (['self.date_end'], {}), '(self.date_end)\n', (1652, 1667), False, 'import ee\n'), ((1554, 1589), 'ee.ImageCollection', 'ee.ImageCollection', (['"""COPERNICUS/S2"""'], {}), "('COPERNICUS/S2')\n", (1572, 1589), False, 'import ee\n')] |
import argparse
# This program takes as Input the gene reference folder
############ INPUT OF VARIABLES ###################
parser = argparse.ArgumentParser(description='Input program')
parser.add_argument('namefolder', type=str, help='GeneReference folder')
folder = parser.parse_args().namefolder
#################################################### 3UTR ############################################################
result_name,line0,line1,line2,line4,line5 = [],[],[],[],[],[]
data = open(folder+"/3UTR.bed", "r")
for line in data.readlines():
complete_name = (line.split('\t')[3])
partial_name = complete_name.split('_utr3')[0]
result_name.append(partial_name)
line0.append(line.split('\t')[0])
line1.append(line.split('\t')[1])
line2.append(line.split('\t')[2])
line4.append(line.split('\t')[4])
line5.append(line.split('\t')[5])
data.close()
data = open(folder+"/3UTR.bed", "w")
for x in range(len(line0)):
data.write(line0[x] + '\t' + line1[x] + '\t' +line2[x] + '\t' + result_name[x] + '\t' + line4[x] + '\t' + line5[x])
data.close()
##################################################### 5UTR ############################################################
result_name,line0,line1,line2,line4, line5 = [],[],[],[],[],[]
data = open(folder+"/5UTR.bed", "r")
for line in data.readlines():
complete_name = (line.split('\t')[3])
partial_name = complete_name.split('_utr5')[0]
result_name.append(partial_name)
line0.append(line.split('\t')[0])
line1.append(line.split('\t')[1])
line2.append(line.split('\t')[2])
line4.append(line.split('\t')[4])
line5.append(line.split('\t')[5])
data.close()
data = open(folder+"/5UTR.bed", "w")
for x in range(len(line0)):
data.write(line0[x] + '\t' + line1[x] + '\t' +line2[x] + '\t' + result_name[x] + '\t' + line4[x] + '\t' + line5[x])
data.close()
##################################################### CDE ############################################################
result_name,line0,line1,line2,line4, line5 = [],[],[],[],[],[]
data = open(folder+"/CDE.bed", "r")
for line in data.readlines():
complete_name = (line.split('\t')[3])
partial_name = complete_name.split('_cds')[0]
result_name.append(partial_name)
line0.append(line.split('\t')[0])
line1.append(line.split('\t')[1])
line2.append(line.split('\t')[2])
line4.append(line.split('\t')[4])
line5.append(line.split('\t')[5])
data.close()
data = open(folder+"/CDE.bed", "w")
for x in range(len(line0)):
data.write(line0[x] + '\t' + line1[x] + '\t' +line2[x] + '\t' + result_name[x] + '\t' + line4[x] + '\t' + line5[x])
data.close()
| [
"argparse.ArgumentParser"
] | [((136, 188), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Input program"""'}), "(description='Input program')\n", (159, 188), False, 'import argparse\n')] |
"""
Model architecture of Convolutional Autoencoder for converting grayscale images to RGB images.
"""
import torch
import torch.nn as nn
import torch.nn.functional as functional
class ConvAutoencoder(nn.Module):
def __init__(self, batch_size: int, ip_image_dims: int = 32, filter_count: tuple = (16, 32), kernel_dims: int = 3):
"""
The function requires batch size of the data for initializing the weights of the model.
:param batch_size: Batch_size of the train/val data.
:param ip_image_dims: Optional, image dims of the input image.
:param filter_count: The channel dims of the filters.
:param kernel_dims: The dims of the filters used to perform convolution operations.
"""
super(ConvAutoencoder, self).__init__()
# Parameters.
self.ip_image_dims = ip_image_dims
self.batch_size = batch_size
self.filter_count = filter_count
self.kernel_dims = kernel_dims
# Convolution transformations.
self.conv1 = nn.Conv2d(1, filter_count[0], kernel_dims, padding=1) # (32-3+2/1)+1=32>>max_pool>>16
self.conv2 = nn.Conv2d(filter_count[0], filter_count[1], kernel_dims) # (16-3/1)+1=14>>max_pool>>7
conv_output_dims = self.get_conv_output_dims(self.ip_image_dims, len(filter_count), 1, pool_dims=2)
# Linear transformations.
self.linear1 = nn.Linear(32*conv_output_dims**2, 512)
self.linear2 = nn.Linear(512, 3072)
def get_conv_output_dims(self, ip_dims, layer_count, padding, pool_dims):
"""
Computes the resulting dims of convolution and pool transformations
:param ip_dims: The dims of the input image
:param layer_count: Number of conv layers.
:param padding: If padding is used in the first layer.
:param pool_dims: The dims of the max_pool transformation.
:return: Integer value dims of the resulting square image.
"""
if not layer_count:
return (ip_dims-self.kernel_dims+2+1)/2
return self.get_conv_output_dims((ip_dims-self.kernel_dims+2*padding+1)/pool_dims, layer_count-1, 0, pool_dims)
def forward(self, x):
"""
The forward function for the the model.
:param x:
:return:
"""
# Encoder Phase
p1 = functional.max_pool2d(self.conv1(x), (2, 2))
p2 = functional.max_pool2d(self.conv2(p1), (2, 2))
# Decoder Phase
f1 = functional.relu(self.linear1(p2.view(self.batch_size, -1)))
f2 = torch.sigmoid(self.linear2(f1)).view(-1, 3, self.ip_image_dims, self.ip_image_dims)
return f2
| [
"torch.nn.Linear",
"torch.nn.Conv2d"
] | [((1032, 1085), 'torch.nn.Conv2d', 'nn.Conv2d', (['(1)', 'filter_count[0]', 'kernel_dims'], {'padding': '(1)'}), '(1, filter_count[0], kernel_dims, padding=1)\n', (1041, 1085), True, 'import torch.nn as nn\n'), ((1140, 1196), 'torch.nn.Conv2d', 'nn.Conv2d', (['filter_count[0]', 'filter_count[1]', 'kernel_dims'], {}), '(filter_count[0], filter_count[1], kernel_dims)\n', (1149, 1196), True, 'import torch.nn as nn\n'), ((1392, 1434), 'torch.nn.Linear', 'nn.Linear', (['(32 * conv_output_dims ** 2)', '(512)'], {}), '(32 * conv_output_dims ** 2, 512)\n', (1401, 1434), True, 'import torch.nn as nn\n'), ((1454, 1474), 'torch.nn.Linear', 'nn.Linear', (['(512)', '(3072)'], {}), '(512, 3072)\n', (1463, 1474), True, 'import torch.nn as nn\n')] |
import pandas as pd
import numpy as np
from numpy import corrcoef
import matplotlib.pyplot as plt
from sklearn.feature_selection import chi2
from sklearn.feature_selection import f_classif
from math import *
plt.style.use('ggplot')
fig = plt.figure()
COUNTER = 1
#Return the category dictionary,categorical variables list and continuous list for every column in dataframe.
#The categories are assigned as "target(type)_feature(type)"
def get_category(df,target_name,categorical_name,columns_name):
cat_dict = {}
fin_cat_dict = {}
catg_catg = []
cont_cont = []
catg_cont = []
cont_catg = []
for col in columns_name:
if len(df[col].unique())<=2:
cat_dict[col] = "categorical"
elif col in categorical_name:
cat_dict[col] = "categorical"
else:
cat_dict[col] = "continous"
for col in cat_dict:
if cat_dict[col]=="categorical" and cat_dict[target_name]=="categorical":
fin_cat_dict[col] = "catg_catg"
catg_catg.append(col)
elif cat_dict[col]=="continous" and cat_dict[target_name]=="continous":
fin_cat_dict[col] = "cont_cont"
cont_cont.append(col)
elif cat_dict[col]=="continous" and cat_dict[target_name]=="categorical":
fin_cat_dict[col] = "catg_cont"
catg_cont.append(col)
else:
fin_cat_dict[col] = "cont_catg"
cont_catg.append(col)
return fin_cat_dict,catg_catg,cont_cont,catg_cont,cont_catg
#Return True if the categorical_name are present in the orignal dataframe columns.
def is_present(columns_name,categorical_name):
ls = [i for i in categorical_name if i not in columns_name]
if len(ls)==0:
return True
else:
raise ValueError(str(ls)+" is not present as a column in the data,Please check the name")
#Function returns list of columns with non-numeric data.
def clean_str_list(df,lst):
rem=[]
for i in lst:
res = any(isinstance(n,str) for n in df[i])
if res == True:
rem.append(i)
for j in rem:
lst.remove(j)
return lst
#Returns the Pearson Correlation Coefficient for the continous data columns.
def pearson_correlation_cont_cont(x,y):
return corrcoef(x,y)
# This function is for the bivariate analysis between two continous varibale.Plots scatter plots and shows the coeff for the data.
def bivariate_analysis_cont_cont(cont_cont_list,df,target_name,sub_len,COUNTER,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE):
clean_cont_cont_list = clean_str_list(df,cont_cont_list)
if len(clean_str_list(df,[target_name])) == 0 and len(cont_cont_list)>0:
raise ValueError("You seem to have a target variable with string values.")
clean_df = df.dropna()
for col in clean_cont_cont_list:
summary = clean_df[col].describe()
count = summary[0]
mean = summary[1]
std = summary[2]
plt.subplot(PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,COUNTER)
plt.title("mean "+str(np.float32(mean))+" std "+str(np.float32(std)),fontsize=10)
x = clean_df[col]
y = np.float32(clean_df[target_name])
corr = pearson_correlation_cont_cont(x,y)
plt.xlabel(col+"\n count "+str(count)+"\n Corr: "+str(np.float32(corr[0][1])), fontsize=10)
plt.ylabel(target_name, fontsize=10)
plt.scatter(x,y)
print (col+" vs "+target_name+" plotted....")
COUNTER +=1
return plt,COUNTER
#Chi test is used to see association between catgorical vs categorical variables.
#Lower Pvalue are significant they should be < 0.05
#chi value = X^2 = summation [(observed-expected)^2/expected]
# The distribution of the statistic X2 is chi-square with (r-1)(c-1) degrees of freedom, where r represents the number of rows in the two-way table and c represents the number of columns. The distribution is denoted (df), where df is the number of degrees of freedom.
#pvalue = p(df>=x^2)
def evaluate_chi(x,y):
chi,p_val = chi2(x,y)
return chi,p_val
def bivariate_analysis_catg_catg(catg_catg_list,df,target_name,sub_len,COUNTER,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,bin_size="auto"):
clean_catg_catg_list = clean_str_list(df,catg_catg_list)
clean_df = df.dropna()
target_classes =df[target_name].unique()
label = [str(i) for i in target_classes]
c = 0
for col in clean_catg_catg_list:
summary = clean_df[col].describe()
binwidth = 0.7
if bin_size == 'auto':
bins_size =np.arange(min(clean_df[col].tolist()), max(clean_df[col].tolist()) + binwidth, binwidth)
else:
bins_size = bin_size
count = summary[0]
mean = summary[1]
std = summary[2]
plt.subplot(PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,COUNTER)
plt.title("mean "+str(np.float32(mean))+" std "+str(np.float32(std)),fontsize=10)
x = [np.array(clean_df[clean_df[target_name]==i][col]) for i in target_classes]
y = clean_df[target_name]
chi,p_val = evaluate_chi(np.array(clean_df[col]).reshape(-1,1),y)
plt.xlabel(col+"\n chi: "+str(np.float32(chi[0]))+" / p_val: "+str(p_val[0]), fontsize=10)
plt.ylabel("Frequency", fontsize=10)
plt.hist(x,bins=bins_size,stacked=True,label = label)
plt.legend(prop={'size': 10})
print (col+" vs "+target_name+" plotted....")
COUNTER +=1
c+=1
return plt,COUNTER
# Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups)
# In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups. ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance.
# A one-way ANOVA is used to compare the means of more than two independent groups. A one-way ANOVA comparing just two groups will give you the same results as the independent t test.
def evaluate_anova(x,y):
F_value,pvalue = f_classif(x,y)
return F_value,pvalue
# In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram.
# Quartile: In descriptive statistics, the quartiles of a ranked set of data values are the three points that divide the data set into four equal groups, each group comprising a quarter of the data
def bivariate_analysis_cont_catg(cont_catg_list,df,target_name,sub_len,COUNTER,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE):
clean_cont_catg_list = clean_str_list(df,cont_catg_list)
if len(clean_str_list(df,[target_name])) == 0 and len(cont_catg_list)>0:
raise ValueError("You seem to have a target variable with string values.")
clean_df = df.dropna()
for col in clean_cont_catg_list:
col_classes =clean_df[col].unique()
summary = clean_df[col].describe()
count = summary[0]
mean = summary[1]
std = summary[2]
plt.subplot(PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,COUNTER)
plt.title("mean "+str(np.float32(mean))+" std "+str(np.float32(std)),fontsize=10)
x = [np.array(clean_df[clean_df[col]==i][target_name]) for i in col_classes]
y = np.float32(clean_df[target_name])
f_value,p_val = evaluate_anova(np.array(clean_df[col]).reshape(-1,1),y)
plt.xlabel(col+"\n f_value: "+str(np.float32(f_value[0]))+" / p_val: "+str(p_val[0]), fontsize=10)
plt.ylabel(target_name, fontsize=10)
plt.boxplot(x)
print (col+" vs "+target_name+" plotted....")
COUNTER +=1
return plt,COUNTER
# This function is for the bivariate analysis between categorical vs continuous varibale.Plots box plots.
def bivariate_analysis_catg_cont(catg_cont_list,df,target_name,sub_len,COUNTER,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE):
# No need to remove string varible as they are handled by chi2 function of sklearn.
# clean_catg_cont_list = clean_str_list(df,catg_cont_list)
clean_catg_cont_list = catg_cont_list
clean_df = df.dropna()
for col in clean_catg_cont_list:
col_classes =df[target_name].unique()
summary = clean_df[col].describe()
count = summary[0]
mean = summary[1]
std = summary[2]
plt.subplot(PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,COUNTER)
plt.title("mean "+str(np.float32(mean))+" std "+str(np.float32(std)),fontsize=10)
x = [np.array(clean_df[clean_df[target_name]==i][col]) for i in col_classes]
y = clean_df[target_name]
f_value,p_val = evaluate_anova(np.array(clean_df[col]).reshape(-1,1),y)
plt.xlabel(target_name+"\n f_value: "+str(np.float32(f_value[0]))+" / p_val: "+str(p_val[0]), fontsize=10)
plt.ylabel(col, fontsize=10)
plt.boxplot(x)
print (col+" vs "+target_name+" plotted....")
COUNTER +=1
return plt,COUNTER
#returns the total number of subplots to be made.
def total_subplots(df,lst):
clean_df = df.dropna()
total = [len(clean_str_list(clean_df,i)) for i in lst]
return sum(total)
# This function returns new categotical list after removing drop values if in case they are written in both drop and categorical_name list.
def remove_drop_from_catglist(drop,categorical_name):
for col in drop:
if col in categorical_name:
categorical_name.remove(col)
return categorical_name
def plot(data_input,target_name="",categorical_name=[],drop=[],PLOT_COLUMNS_SIZE = 4,bin_size="auto",wspace=0.5,hspace=0.8):
"""
This is the main function to give Bivariate analysis between the target variable and the input features.
Parameters
-----------
data_input : Dataframe
This is the input Dataframe with all data.
target_name : String
The name of the target column.
categorical_name : list
Names of all categorical variable columns with more than 2 classes, to distinguish with the continuous variables.
drop : list
Names of columns to be dropped.
PLOT_COLUMNS_SIZE : int
Number of plots to display vertically in the display window.The row size is adjusted accordingly.
bin_size : int ;default="auto"
Number of bins for the histogram displayed in the categorical vs categorical category.
wspace : int ;default = 0.5
Horizontal padding between subplot on the display window.
hspace : int ;default = 0.5
Vertical padding between subplot on the display window.
-----------
"""
if type(data_input).__name__ == "DataFrame" :
# Column names
columns_name = data_input.columns.values
#To drop user specified columns.
if is_present(columns_name,drop):
data_input = data_input.drop(drop,axis=1)
columns_name = data_input.columns.values
categorical_name = remove_drop_from_catglist(drop,categorical_name)
else:
raise ValueError("Couldn't find it in the input Dataframe!")
if target_name == "":
raise ValueError("Please mention a target variable")
#Checks if the categorical_name are present in the orignal dataframe columns.
categorical_is_present = is_present(columns_name,categorical_name)
target_is_present = is_present(columns_name,[target_name])
if categorical_is_present:
fin_cat_dict,catg_catg_list,cont_cont_list,catg_cont_list,cont_catg_list = get_category(data_input,target_name,categorical_name,columns_name)
#Subplot(Total number of graphs)
total = total_subplots(data_input,[cont_cont_list,catg_catg_list,catg_cont_list,cont_catg_list])
if total < PLOT_COLUMNS_SIZE:
total = PLOT_COLUMNS_SIZE
PLOT_ROW_SIZE = ceil(float(total)/PLOT_COLUMNS_SIZE)
#Call various functions
plot,count = bivariate_analysis_cont_cont(cont_cont_list,data_input,target_name,total,COUNTER,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE)
plot,count = bivariate_analysis_catg_catg(catg_catg_list,data_input,target_name,total,count,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE,bin_size=bin_size)
plot,count = bivariate_analysis_cont_catg(cont_catg_list,data_input,target_name,total,count,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE)
plot,count = bivariate_analysis_catg_cont(catg_cont_list,data_input,target_name,total,count,PLOT_ROW_SIZE,PLOT_COLUMNS_SIZE)
fig.subplots_adjust(bottom=0.08,left = 0.05,right=0.97,top=0.93,wspace = wspace,hspace = hspace)
plot.show()
else:
raise ValueError("Make sure input data is a Dataframe.")
| [
"matplotlib.pyplot.boxplot",
"matplotlib.pyplot.hist",
"numpy.corrcoef",
"matplotlib.pyplot.ylabel",
"sklearn.feature_selection.f_classif",
"matplotlib.pyplot.style.use",
"numpy.array",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.scatter",
"sklearn.feature_selection.chi2",
"matplotlib.pyplot.... | [((208, 231), 'matplotlib.pyplot.style.use', 'plt.style.use', (['"""ggplot"""'], {}), "('ggplot')\n", (221, 231), True, 'import matplotlib.pyplot as plt\n'), ((239, 251), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (249, 251), True, 'import matplotlib.pyplot as plt\n'), ((2265, 2279), 'numpy.corrcoef', 'corrcoef', (['x', 'y'], {}), '(x, y)\n', (2273, 2279), False, 'from numpy import corrcoef\n'), ((4005, 4015), 'sklearn.feature_selection.chi2', 'chi2', (['x', 'y'], {}), '(x, y)\n', (4009, 4015), False, 'from sklearn.feature_selection import chi2\n'), ((6159, 6174), 'sklearn.feature_selection.f_classif', 'f_classif', (['x', 'y'], {}), '(x, y)\n', (6168, 6174), False, 'from sklearn.feature_selection import f_classif\n'), ((2942, 2996), 'matplotlib.pyplot.subplot', 'plt.subplot', (['PLOT_ROW_SIZE', 'PLOT_COLUMNS_SIZE', 'COUNTER'], {}), '(PLOT_ROW_SIZE, PLOT_COLUMNS_SIZE, COUNTER)\n', (2953, 2996), True, 'import matplotlib.pyplot as plt\n'), ((3124, 3157), 'numpy.float32', 'np.float32', (['clean_df[target_name]'], {}), '(clean_df[target_name])\n', (3134, 3157), True, 'import numpy as np\n'), ((3317, 3353), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['target_name'], {'fontsize': '(10)'}), '(target_name, fontsize=10)\n', (3327, 3353), True, 'import matplotlib.pyplot as plt\n'), ((3362, 3379), 'matplotlib.pyplot.scatter', 'plt.scatter', (['x', 'y'], {}), '(x, y)\n', (3373, 3379), True, 'import matplotlib.pyplot as plt\n'), ((4739, 4793), 'matplotlib.pyplot.subplot', 'plt.subplot', (['PLOT_ROW_SIZE', 'PLOT_COLUMNS_SIZE', 'COUNTER'], {}), '(PLOT_ROW_SIZE, PLOT_COLUMNS_SIZE, COUNTER)\n', (4750, 4793), True, 'import matplotlib.pyplot as plt\n'), ((5188, 5224), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""Frequency"""'], {'fontsize': '(10)'}), "('Frequency', fontsize=10)\n", (5198, 5224), True, 'import matplotlib.pyplot as plt\n'), ((5233, 5287), 'matplotlib.pyplot.hist', 'plt.hist', (['x'], {'bins': 'bins_size', 'stacked': '(True)', 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from django.conf.urls import patterns, include, url
from django.contrib import admin
from django.views.generic.base import TemplateView
urlpatterns = patterns('',
# Examples:
url(r'^$', TemplateView.as_view(template_name="home.html"), name='home'),
url(r'^auth-test', 'nepal.views.auth_test', name='auth-test'),
url(r'^twitter/', include('twython_auth.urls')),
url(r'^api/', include('api.urls', namespace='api')),
url(r'^admin/', include(admin.site.urls)),
)
urlpatterns += patterns('',
(r'^django-rq/', include('django_rq.urls')),
) | [
"django.conf.urls.include",
"django.conf.urls.url",
"django.views.generic.base.TemplateView.as_view"
] | [((262, 322), 'django.conf.urls.url', 'url', (['"""^auth-test"""', '"""nepal.views.auth_test"""'], {'name': '"""auth-test"""'}), "('^auth-test', 'nepal.views.auth_test', name='auth-test')\n", (265, 322), False, 'from django.conf.urls import patterns, include, url\n'), ((195, 242), 'django.views.generic.base.TemplateView.as_view', 'TemplateView.as_view', ([], {'template_name': '"""home.html"""'}), "(template_name='home.html')\n", (215, 242), False, 'from django.views.generic.base import TemplateView\n'), ((347, 375), 'django.conf.urls.include', 'include', (['"""twython_auth.urls"""'], {}), "('twython_auth.urls')\n", (354, 375), False, 'from django.conf.urls import patterns, include, url\n'), ((396, 432), 'django.conf.urls.include', 'include', (['"""api.urls"""'], {'namespace': '"""api"""'}), "('api.urls', namespace='api')\n", (403, 432), False, 'from django.conf.urls import patterns, include, url\n'), ((455, 479), 'django.conf.urls.include', 'include', (['admin.site.urls'], {}), '(admin.site.urls)\n', (462, 479), False, 'from django.conf.urls import patterns, include, url\n'), ((534, 559), 'django.conf.urls.include', 'include', (['"""django_rq.urls"""'], {}), "('django_rq.urls')\n", (541, 559), False, 'from django.conf.urls import patterns, include, url\n')] |
from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options
import scipy as sp
import os
try:
from qsrs import BFGS_Jac_SolverNative, LeastSquares_Jac_SolverNative
except ImportError:
BFGS_Jac_SolverNative = None
LeastSquares_Jac_SolverNative = None
import pytest
import tempfile
import os
import sys
qft3 = unitaries.qft(8)
def test_cobyla(project):
project.add_compilation('qft2', unitaries.qft(4))
project['solver'] = solvers.COBYLA_Solver()
project.run()
def test_bfgs_jac(project):
project.add_compilation('qft3', qft3)
project['solver'] = solvers.BFGS_Jac_Solver()
project.run()
def test_least_squares_jac(project):
project.add_compilation('qft3', qft3)
project['solver'] = solvers.LeastSquares_Jac_Solver()
project['error_residuals'] = utils.matrix_residuals
project['error_residuals_jac'] = utils.matrix_residuals_jac
project.run()
@pytest.mark.skipif(sys.platform == 'win32', reason="This test currently hangs due to the nested parallel executor")
def test_multistart_least_squares(project):
project.add_compilation('qft3', qft3)
project['solver'] = multistart_solvers.MultiStart_Solver(2)
project['inner_solver'] = solvers.LeastSquares_Jac_Solver()
project['parallelizer'] = parallelizers.ProcessPoolParallelizer
project['error_residuals'] = utils.matrix_residuals
project['error_residuals_jac'] = utils.matrix_residuals_jac
project.run()
@pytest.mark.skipif(sys.platform == 'win32', reason="This test currently hangs due to the nested parallel executor")
def test_multistart_bfgs(project):
project.add_compilation('qft3', qft3)
project['solver'] = multistart_solvers.MultiStart_Solver(2)
project['inner_solver'] = solvers.BFGS_Jac_Solver()
project['parallelizer'] = parallelizers.ProcessPoolParallelizer
project.run()
def compile(U, solver):
with tempfile.TemporaryDirectory() as dir:
opts = options.Options()
opts.target = U
opts.error_func = utils.matrix_distance_squared
opts.error_jac = utils.matrix_distance_squared_jac
opts.solver = solver
opts.log_file = os.path.join(dir, 'test.log')
comp = compiler.SearchCompiler()
res = comp.compile(opts)
return res
@pytest.mark.skipif(BFGS_Jac_SolverNative is None, reason="The rustopt feature has not been enabled")
def test_rust_solver_qft3():
U = unitaries.qft(8)
res = compile(U, BFGS_Jac_SolverNative())
circ = res['structure']
v = res['parameters']
assert utils.matrix_distance_squared(U, circ.matrix(v)) < 1e-10
@pytest.mark.skipif(LeastSquares_Jac_SolverNative is None, reason="The rustopt feature has not been enabled")
def test_rust_solver_qft3():
U = unitaries.qft(8)
res = compile(U, LeastSquares_Jac_SolverNative())
circ = res['structure']
v = res['parameters']
assert utils.matrix_distance_squared(U, circ.matrix(v)) < 1e-10
| [
"tempfile.TemporaryDirectory",
"qsrs.LeastSquares_Jac_SolverNative",
"qsearch.multistart_solvers.MultiStart_Solver",
"qsearch.solvers.BFGS_Jac_Solver",
"os.path.join",
"qsearch.compiler.SearchCompiler",
"qsearch.unitaries.qft",
"qsearch.options.Options",
"qsrs.BFGS_Jac_SolverNative",
"qsearch.solv... | [((371, 387), 'qsearch.unitaries.qft', 'unitaries.qft', (['(8)'], {}), '(8)\n', (384, 387), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((952, 1072), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(sys.platform == 'win32')"], {'reason': '"""This test currently hangs due to the nested parallel executor"""'}), "(sys.platform == 'win32', reason=\n 'This test currently hangs due to the nested parallel executor')\n", (970, 1072), False, 'import pytest\n'), ((1490, 1610), 'pytest.mark.skipif', 'pytest.mark.skipif', (["(sys.platform == 'win32')"], {'reason': '"""This test currently hangs due to the nested parallel executor"""'}), "(sys.platform == 'win32', reason=\n 'This test currently hangs due to the nested parallel executor')\n", (1508, 1610), False, 'import pytest\n'), ((2307, 2412), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(BFGS_Jac_SolverNative is None)'], {'reason': '"""The rustopt feature has not been enabled"""'}), "(BFGS_Jac_SolverNative is None, reason=\n 'The rustopt feature has not been enabled')\n", (2325, 2412), False, 'import pytest\n'), ((2632, 2745), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(LeastSquares_Jac_SolverNative is None)'], {'reason': '"""The rustopt feature has not been enabled"""'}), "(LeastSquares_Jac_SolverNative is None, reason=\n 'The rustopt feature has not been enabled')\n", (2650, 2745), False, 'import pytest\n'), ((493, 516), 'qsearch.solvers.COBYLA_Solver', 'solvers.COBYLA_Solver', ([], {}), '()\n', (514, 516), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((630, 655), 'qsearch.solvers.BFGS_Jac_Solver', 'solvers.BFGS_Jac_Solver', ([], {}), '()\n', (653, 655), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((778, 811), 'qsearch.solvers.LeastSquares_Jac_Solver', 'solvers.LeastSquares_Jac_Solver', ([], {}), '()\n', (809, 811), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((1178, 1217), 'qsearch.multistart_solvers.MultiStart_Solver', 'multistart_solvers.MultiStart_Solver', (['(2)'], {}), '(2)\n', (1214, 1217), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((1248, 1281), 'qsearch.solvers.LeastSquares_Jac_Solver', 'solvers.LeastSquares_Jac_Solver', ([], {}), '()\n', (1279, 1281), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((1707, 1746), 'qsearch.multistart_solvers.MultiStart_Solver', 'multistart_solvers.MultiStart_Solver', (['(2)'], {}), '(2)\n', (1743, 1746), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((1777, 1802), 'qsearch.solvers.BFGS_Jac_Solver', 'solvers.BFGS_Jac_Solver', ([], {}), '()\n', (1800, 1802), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((2445, 2461), 'qsearch.unitaries.qft', 'unitaries.qft', (['(8)'], {}), '(8)\n', (2458, 2461), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((2778, 2794), 'qsearch.unitaries.qft', 'unitaries.qft', (['(8)'], {}), '(8)\n', (2791, 2794), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((451, 467), 'qsearch.unitaries.qft', 'unitaries.qft', (['(4)'], {}), '(4)\n', (464, 467), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((1923, 1952), 'tempfile.TemporaryDirectory', 'tempfile.TemporaryDirectory', ([], {}), '()\n', (1950, 1952), False, 'import tempfile\n'), ((1976, 1993), 'qsearch.options.Options', 'options.Options', ([], {}), '()\n', (1991, 1993), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((2186, 2215), 'os.path.join', 'os.path.join', (['dir', '"""test.log"""'], {}), "(dir, 'test.log')\n", (2198, 2215), False, 'import os\n'), ((2231, 2256), 'qsearch.compiler.SearchCompiler', 'compiler.SearchCompiler', ([], {}), '()\n', (2254, 2256), False, 'from qsearch import Project, solvers, unitaries, utils, multistart_solvers, parallelizers, compiler, options\n'), ((2483, 2506), 'qsrs.BFGS_Jac_SolverNative', 'BFGS_Jac_SolverNative', ([], {}), '()\n', (2504, 2506), False, 'from qsrs import BFGS_Jac_SolverNative, LeastSquares_Jac_SolverNative\n'), ((2816, 2847), 'qsrs.LeastSquares_Jac_SolverNative', 'LeastSquares_Jac_SolverNative', ([], {}), '()\n', (2845, 2847), False, 'from qsrs import BFGS_Jac_SolverNative, LeastSquares_Jac_SolverNative\n')] |
import os
import numpy as np
def save_samples_truncted_prob(fname, points, prob):
'''
Save the visualization of sampling to a ply file.
Red points represent positive predictions.
Green points represent negative predictions.
Parameters
fname: File name to save
points: [N, 3] array of points
prob: [1, N] array of predictions in the range [0~1]
Return:
None
'''
prob = prob.transpose(0, 1).detach().numpy()
r = (prob > 0.5).reshape([-1, 1]) * 255
g = (prob < 0.5).reshape([-1, 1]) * 255
b = np.zeros(r.shape)
to_save = np.concatenate([points, r, g, b,prob], axis=-1)
return np.savetxt(fname,
to_save,
fmt='%.6f %.6f %.6f %d %d %d %.6f',
comments='',
header=(
'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nproperty float prob\nend_header').format(
points.shape[0])
)
def save_gallery(preds,samples,names,gallery_id,epoch):
pred = preds[0].cpu()
sample = samples[0].transpose(0, 1).cpu()
name = names[0]
save_gallery_path = os.path.join(gallery_id,name.split('/')[-2],"epoch_{:03d}".format(epoch))
os.makedirs(save_gallery_path,exist_ok=True)
path = os.path.join(save_gallery_path,'pred.ply')
save_samples_truncted_prob(path,sample,pred) | [
"os.makedirs",
"numpy.zeros",
"os.path.join",
"numpy.concatenate"
] | [((568, 585), 'numpy.zeros', 'np.zeros', (['r.shape'], {}), '(r.shape)\n', (576, 585), True, 'import numpy as np\n'), ((601, 649), 'numpy.concatenate', 'np.concatenate', (['[points, r, g, b, prob]'], {'axis': '(-1)'}), '([points, r, g, b, prob], axis=-1)\n', (615, 649), True, 'import numpy as np\n'), ((1380, 1425), 'os.makedirs', 'os.makedirs', (['save_gallery_path'], {'exist_ok': '(True)'}), '(save_gallery_path, exist_ok=True)\n', (1391, 1425), False, 'import os\n'), ((1436, 1479), 'os.path.join', 'os.path.join', (['save_gallery_path', '"""pred.ply"""'], {}), "(save_gallery_path, 'pred.ply')\n", (1448, 1479), False, 'import os\n')] |
import setuptools
name = "yamlval"
__version__ = "v1.0.2"
with open("README.md", "r") as f:
long_description = f.read()
setuptools.setup(
name=name,
version=__version__,
author="<NAME>",
author_email="<EMAIL>",
description="A YAML type validator",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/mycicle/yamlval/",
download_url="https://github.com/mycicle/yamlval/archive/v1.0.2.tar.gz",
packages=setuptools.find_packages(),
install_requires=[
'pyyaml >= 5.3.1',
'loguru >= 0.5.3'
],
classifiers=(
"Intended Audience :: Developers",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"License :: OSI Approved :: MIT License"
),
) | [
"setuptools.find_packages"
] | [((502, 528), 'setuptools.find_packages', 'setuptools.find_packages', ([], {}), '()\n', (526, 528), False, 'import setuptools\n')] |
import requests
import pprint
def format_open_search_results(results):
formatted_results = []
titles = results[1]
res_length = len(titles)
count = 0
while count < res_length:
formatted_results.append([titles[count], results[3][count]])
count += 1
return formatted_results
'''
Wikipedia
'''
def wikipedia_search(query):
results = requests.get('https://wikipedia.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wiktionary
'''
def wiktionary_search(query):
results = requests.get('https://wiktionary.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikibooks
'''
def wikibooks_search(query):
results = requests.get('https://wikibooks.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikiquote
'''
def wikiquote_search(query):
results = requests.get('https://wikiquote.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikivoyage
'''
def wikivoyage_search(query):
results = requests.get('https://wikivoyage.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikisource
'''
def wikisource_search(query):
results = requests.get('https://wikisource.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
https://commons.wikimedia.org/w/api.php?action=opensearch&search=code
'''
'''
Wikicommons
'''
def wikicommons_search(query):
results = requests.get('https://commons.wikimedia.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
# '''
# Media Wiki
# '''
# def mediawiki_search(query):
# results = requests.get('https://www.mediawiki.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
# return format_open_search_results(results)
#
'''
Wikispecies
'''
def wikispecies_search(query):
results = requests.get('https://wikispecies.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikinews
'''
def wikinews_search(query):
results = requests.get('https://wikinews.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikiversity
'''
def wikiversity_search(query):
results = requests.get('https://wikiversity.org/w/api.php?action=opensearch&search=' + query + '&format=json').json()
return format_open_search_results(results)
'''
Wikidata
'''
def wikidata_search(query):
# OpenSearch does not work for wikidata
non_open_search_endpoint = 'https://www.wikidata.org/w/api.php?action=query&list=search&srsearch='+ query +'&srlimit=10&srprop=size&formatversion=2&format=json'
results = requests.get(non_open_search_endpoint).json()
return results
'''
MetaWiki
'''
def meta_wiki_search(query):
# https://meta.wikimedia.org/w/api.php
results = requests.get('https://meta.wikimedia.org/w/api.php?action=opensearch&search=' + query).json()
return format_open_search_results(results)
# ---------------------
def wikimedia_search_v2(query):
# https://commons.wikimedia.org/w/api.php
results = requests.get('https://commons.wikimedia.org/w/api.php?action=opensearch&search=' + query).json()
print(results)
pprint.pprint(results)
def parse_wiktionary_search(url):
wikipage = requests.get(url).text
print(wikipage)
# TODO: Parse the page to find the type, definition, sentence, usage, and root
def wiktionary_search_direct_hit(query):
# https://commons.wikimedia.org/w/api.php
# https://en.wiktionary.org/w/api.php?action=query&titles=test&format=json
results = requests.get('https://en.wiktionary.org/w/api.php?action=query&format=json&titles=' + query).json()
print(results)
# pprint.pprint(results)
result_date = results['query']['pages']
page_number = list(result_date.keys())[0]
if page_number == '-1':
print('No results')
return []
# print(page_number)
wikipage_url = 'https://en.wiktionary.org/?curid=' + page_number
print(wikipage_url)
return [wikipage_url]
# -------------
def get_wikipedia_page_from_id(page_id):
# 'http://en.wikipedia.org/?curid=18630637'
results = requests.get('http://en.wikipedia.org/?curid=' + page_id).text
print(results)
# pprint.pprint(results)
def get_wikipedia_general_info(query):
# https://en.wikipedia.org/w/api.php?action=query&formatversion=2&prop=extracts%7Cpageimages%7Crevisions&titles=Albert+Einstein&redirects=&exintro=true&exsentences=2&explaintext=true&piprop=thumbnail&pithumbsize=300&rvprop=timestamp&format=json
results = requests.get('https://en.wikipedia.org/w/api.php?action=query&formatversion=2&prop=extracts%7Cpageimages%7Crevisions&titles=' + query + '&redirects=&exintro=true&exsentences=2&explaintext=true&piprop=thumbnail&pithumbsize=300&rvprop=timestamp&format=json').json()
print(results)
pprint.pprint(results)
def get_wikipedia_search_results(query):
query = query.replace(' ', '+')
endpoint = 'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=morelike:Marie_Curie%7Cradium&srlimit=10&srprop=size&formatversion=2'
endpoint = 'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=morelike:'+ query +'&srlimit=10&srprop=size&formatversion=2&format=json'
endpoint = 'https://wikipedia.org/w/api.php?action=query&list=search&srsearch='+ query +'&srlimit=10&srprop=size&formatversion=2&format=json'
results = requests.get(endpoint).json()
# print(results)
# pprint.pprint(results.json())
return results
def find_something_obscure_wikipedia_cirrus(query):
# https://www.mediawiki.org/wiki/API:Search_and_discovery
endpoint = 'https://en.wikipedia.org/w/index.php?title=Special:Search&cirrusDumpResult=&search=napolean+dog'
endpoint = 'https://en.wikipedia.org/w/index.php?title=Special:Search&cirrusDumpResult=&search=' + query
# TODO: Check if query is actually obscure enough and that the page does not redirect to one that already exists
results = requests.get(endpoint)
print(results)
pprint.pprint(results.json())
# ---------
def wikivoyage_search_v2(query):
endpoint = 'https://en.wikivoyage.org/w/api.php?action=query&list=search&srsearch=Panama&srlimit=10&srprop=size&formatversion=2&format=json'
results = requests.get(endpoint).json()
# print(results)
# pprint.pprint(results.json())
return results
def wikivoyage_similar_results(query):
endpoint = 'https://en.wikivoyage.org/w/api.php?action=query&list=search&srsearch=morelike:Panama|radium&srlimit=10&srprop=size&formatversion=2'
def get_all_results_v2(query):
results = {}
formatted_wikipedia = []
wikipedia_results = get_wikipedia_search_results(query)['query']['search']
print(wikipedia_results)
for result in wikipedia_results:
formatted_wikipedia.append([result['title'], 'https://en.wikipedia.org/wiki/?curid=' + str(result['pageid'])])
results['Wikipedia'] = formatted_wikipedia
formatted_wikivoyage = []
wikivoyage_results = wikivoyage_search(query)['query']['search']
print(wikivoyage_results)
for result in wikivoyage_results:
formatted_wikipedia.append([result['title'], 'https://en.wikivoyage.org/wiki/?curid=' + str(result['pageid'])])
results['Wikivoyage'] = formatted_wikipedia
print(results)
return results
def get_all_results_open_search(query):
results = {
'Wikipedia' : wikipedia_search(query),
'Wiktionary': wiktionary_search(query),
'Wikibooks': wikibooks_search(query),
'Wikiquote': wikiquote_search(query),
'Wikivoyage': wikivoyage_search(query),
'Wikisource': wikisource_search(query),
'Wikispecies': wikispecies_search(query),
'Wikinews': wikinews_search(query),
'Wikiversity': wikiversity_search(query),
# 'Wikidata': wikidata_search(query),
'Metawiki' : meta_wiki_search(query),
'Wikicommons' : wikicommons_search(query)
}
return results
| [
"pprint.pprint",
"requests.get"
] | [((3634, 3656), 'pprint.pprint', 'pprint.pprint', (['results'], {}), '(results)\n', (3647, 3656), False, 'import pprint\n'), ((5309, 5331), 'pprint.pprint', 'pprint.pprint', (['results'], {}), '(results)\n', (5322, 5331), False, 'import pprint\n'), ((6463, 6485), 'requests.get', 'requests.get', (['endpoint'], {}), '(endpoint)\n', (6475, 6485), False, 'import requests\n'), ((3708, 3725), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (3720, 3725), False, 'import requests\n'), ((4606, 4663), 'requests.get', 'requests.get', (["('http://en.wikipedia.org/?curid=' + page_id)"], {}), "('http://en.wikipedia.org/?curid=' + page_id)\n", (4618, 4663), False, 'import requests\n'), ((379, 481), 'requests.get', 'requests.get', (["('https://wikipedia.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikipedia.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (391, 481), False, 'import requests\n'), ((596, 699), 'requests.get', 'requests.get', (["('https://wiktionary.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wiktionary.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (608, 699), False, 'import requests\n'), ((812, 914), 'requests.get', 'requests.get', (["('https://wikibooks.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikibooks.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (824, 914), False, 'import requests\n'), ((1027, 1129), 'requests.get', 'requests.get', (["('https://wikiquote.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikiquote.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (1039, 1129), False, 'import requests\n'), ((1244, 1347), 'requests.get', 'requests.get', (["('https://wikivoyage.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikivoyage.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (1256, 1347), False, 'import requests\n'), ((1462, 1565), 'requests.get', 'requests.get', (["('https://wikisource.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikisource.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (1474, 1565), False, 'import requests\n'), ((1761, 1876), 'requests.get', 'requests.get', (["('https://commons.wikimedia.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')"], {}), "(\n 'https://commons.wikimedia.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (1773, 1876), False, 'import requests\n'), ((2223, 2327), 'requests.get', 'requests.get', (["('https://wikispecies.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikispecies.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (2235, 2327), False, 'import requests\n'), ((2438, 2539), 'requests.get', 'requests.get', (["('https://wikinews.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikinews.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (2450, 2539), False, 'import requests\n'), ((2656, 2760), 'requests.get', 'requests.get', (["('https://wikiversity.org/w/api.php?action=opensearch&search=' + query +\n '&format=json')"], {}), "('https://wikiversity.org/w/api.php?action=opensearch&search=' +\n query + '&format=json')\n", (2668, 2760), False, 'import requests\n'), ((3080, 3118), 'requests.get', 'requests.get', (['non_open_search_endpoint'], {}), '(non_open_search_endpoint)\n', (3092, 3118), False, 'import requests\n'), ((3251, 3342), 'requests.get', 'requests.get', (["('https://meta.wikimedia.org/w/api.php?action=opensearch&search=' + query)"], {}), "(\n 'https://meta.wikimedia.org/w/api.php?action=opensearch&search=' + query)\n", (3263, 3342), False, 'import requests\n'), 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import re
import pytest
from mimesis import config
from mimesis.enums import Gender
from mimesis.exceptions import NonEnumerableError
from mimesis.providers.base import BaseDataProvider, StrMixin
from . import patterns
def test_str_mixin():
mixin = StrMixin()
assert mixin
class TestBase(object):
@pytest.fixture
def base_data_provider(self):
return BaseDataProvider()
def test_str(self, base_data_provider):
assert re.match(patterns.STR_REGEX, str(base_data_provider))
@pytest.mark.parametrize(
'gender, excepted', [
(Gender.MALE, 'male'),
(Gender.FEMALE, 'female'),
(None, ['female', 'male']),
],
)
def test_validate_enum(self, base_data_provider, gender, excepted):
result = base_data_provider._validate_enum(gender, Gender)
assert (result == excepted) or (result in excepted)
assert result in [item.value for item in Gender]
with pytest.raises(NonEnumerableError):
base_data_provider._validate_enum('', '')
@pytest.mark.parametrize('locale', config.LIST_OF_LOCALES)
def test_get_current_locale(self, locale):
base = BaseDataProvider(locale=locale)
assert locale == base.get_current_locale()
class TestSeededBase(object):
@pytest.fixture
def _bases(self, seed):
return BaseDataProvider(seed=seed), BaseDataProvider(seed=seed)
def test_base_random(self, _bases):
b1, b2 = _bases
assert b1.random.randints() == b2.random.randints()
| [
"pytest.mark.parametrize",
"pytest.raises",
"mimesis.providers.base.BaseDataProvider",
"mimesis.providers.base.StrMixin"
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from collections import OrderedDict
from nnunet.paths import nnUNet_raw_data
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-dataset_path", type=str,
default='/home/lwt/data/synapse/RawData/')
args = parser.parse_args()
task_id = 17
task_name = "AbdominalOrganSegmentation"
prefix = 'ABD'
foldername = "Task%03.0d_%s" % (task_id, task_name)
out_base = join(nnUNet_raw_data, foldername)
imagestr = join(out_base, "imagesTr")
imagests = join(out_base, "imagesTs")
labelstr = join(out_base, "labelsTr")
labelsts = join(out_base, "labelsTs")
if isdir(imagestr):
shutil.rmtree(imagestr)
shutil.rmtree(imagests)
shutil.rmtree(labelstr)
shutil.rmtree(labelsts)
maybe_mkdir_p(imagestr)
maybe_mkdir_p(imagests)
maybe_mkdir_p(labelstr)
maybe_mkdir_p(labelsts)
val_id = test_id = [1, 2, 3, 4, 8, 22, 25, 29, 32, 35, 36, 38]
img_folder = join(args.dataset_path, "Training/img")
label_folder = join(args.dataset_path, "Training/label")
train_patient_names = []
test_patient_names = []
train_patients = subfiles(img_folder, join=False, suffix='nii.gz')
for p in train_patients:
serial_number = int(p[3:7])
train_patient_name = f'{prefix}_{serial_number:03d}.nii.gz'
label_file = join(label_folder, f'label{p[3:]}')
image_file = join(img_folder, p)
shutil.copy(image_file, join(
imagestr, f'{train_patient_name[:7]}_0000.nii.gz'))
shutil.copy(label_file, join(labelstr, train_patient_name))
train_patient_names.append(train_patient_name)
for p in test_id:
test_patient = f"img{p:04d}.nii.gz"
test_patient_name = f'{prefix}_{p:03d}.nii.gz'
label_file = join(label_folder, f'label{test_patient[3:]}')
image_file = join(img_folder, test_patient)
shutil.copy(image_file, join(
imagests, f'{test_patient_name[:7]}_0000.nii.gz'))
shutil.copy(label_file, join(labelsts, test_patient_name))
test_patient_names.append(test_patient_name)
json_dict = OrderedDict()
json_dict['name'] = "AbdominalOrganSegmentation"
json_dict['description'] = "Multi-Atlas Labeling Beyond the Cranial Vault Abdominal Organ Segmentation"
json_dict['tensorImageSize'] = "3D"
json_dict['reference'] = "https://www.synapse.org/#!Synapse:syn3193805/wiki/217789"
json_dict['licence'] = "see challenge website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = OrderedDict({
"00": "background",
"01": "spleen",
"02": "right kidney",
"03": "left kidney",
"04": "gallbladder",
"05": "esophagus",
"06": "liver",
"07": "stomach",
"08": "aorta",
"09": "inferior vena cava",
"10": "portal vein and splenic vein",
"11": "pancreas",
"12": "right adrenal gland",
"13": "left adrenal gland"}
)
json_dict['evaluationClass'] = [8, 4, 3, 2, 6, 11, 1, 7]
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['test'] = ["./imagesTs/%s" %
test_patient_name for test_patient_name in test_patient_names]
json_dict['training'] = [{'image': "./imagesTr/%s" % train_patient_name,
"label": "./labelsTr/%s" % train_patient_name} for train_patient_name in train_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
splits = []
splits.append(OrderedDict())
splits[-1]['train'] = [i[:7]
for i in train_patient_names if int(i[4:7]) not in val_id]
splits[-1]['val'] = [i[:7]
for i in train_patient_names if int(i[4:7]) in val_id]
save_pickle(splits, join(out_base, "splits_final.pkl"))
if __name__ == "__main__":
main()
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"shutil.rmtree"
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""" Network wiring """
import tensorflow as tf
import numpy as np
import glob, time, os
import functools
from utils import Utils
class Network(object):
@staticmethod
def _spectral_norm(w):
w_shape = w.shape.as_list()
w = tf.reshape(w, [-1, w_shape[-1]])
with tf.variable_scope("u", reuse=tf.AUTO_REUSE):
u = tf.get_variable(
"u", [1, w_shape[-1]],
dtype=w.dtype,
initializer=tf.truncated_normal_initializer(),
trainable=False)
v_hat = tf.nn.l2_normalize(tf.matmul(u, tf.transpose(w)))
u_hat = tf.nn.l2_normalize(tf.matmul(v_hat, w))
with tf.control_dependencies([tf.assign(u, u_hat, name="update_u")]):
u_hat = tf.identity(u_hat)
u_hat = tf.stop_gradient(u_hat)
v_hat = tf.stop_gradient(v_hat)
sigma_w = tf.squeeze(tf.matmul(v_hat, tf.matmul(w, tf.transpose(u_hat))))
assign_u = tf.assign(u, u_hat)
w_sn = tf.divide(w, sigma_w)
w_sn = tf.reshape(w_sn, w_shape)
return w_sn
@staticmethod
def dense_network(x, config, training, name='fully_connected', actv=tf.nn.relu, **kwargs):
# Toy dense network for binary classification
init = tf.contrib.layers.xavier_initializer()
shape = [512,512,512,512,512]
kwargs = {'center': True, 'scale': True, 'training': training, 'fused': True, 'renorm': True}
# x = tf.reshape(x, [-1, num_features])
# x = x[:,:-1]
print('Input X shape', x.get_shape())
with tf.variable_scope(name, initializer=init, reuse=tf.AUTO_REUSE) as scope:
h0 = tf.layers.dense(x, units=shape[0], activation=actv)
h0 = tf.layers.batch_normalization(h0, **kwargs)
h1 = tf.layers.dense(h0, units=shape[1], activation=actv)
h1 = tf.layers.batch_normalization(h1, **kwargs)
h2 = tf.layers.dense(h1, units=shape[2], activation=actv)
h2 = tf.layers.batch_normalization(h2, **kwargs)
h3 = tf.layers.dense(h2, units=shape[3], activation=actv)
h3 = tf.layers.batch_normalization(h3, **kwargs)
h4 = tf.layers.dense(h3, units=shape[3], activation=actv)
h4 = tf.layers.batch_normalization(h4, **kwargs)
out = tf.layers.dense(h4, units=1, kernel_initializer=init)
return out, h4
@staticmethod
def dense_network_ext(x, config, training, n_layers, n_classes, name='fcn', actv=tf.nn.relu, **kwargs):
# Toy dense network for binary classification
init = tf.contrib.layers.xavier_initializer()
shape = [64 for _ in range(int(n_layers))]
assert n_layers <= len(shape), 'Number of requested layers too high.'
kwargs = {'center': True, 'scale': True, 'training': training, 'fused': True, 'renorm': True}
print('Input X shape', x.get_shape())
with tf.variable_scope(name, initializer=init, reuse=tf.AUTO_REUSE) as scope:
h0 = tf.layers.dense(x, units=shape[0], activation=None)
# h0 = tf.layers.batch_normalization(h0, **kwargs)
h0 = tf.contrib.layers.layer_norm(h0, center=True, scale=True, activation_fn=actv)
h = h0
current_layer = 1
while current_layer < n_layers:
h = tf.layers.dense(h, units=shape[current_layer], activation=None)
# h = tf.layers.batch_normalization(h, **kwargs)
h = tf.contrib.layers.layer_norm(h, center=True, scale=True, activation_fn=actv)
current_layer += 1
out = tf.layers.dense(h, units=n_classes, kernel_initializer=init)
return out, h
@staticmethod
def MINE(x, y, y_prime, training, batch_size, name='MINE', actv=tf.nn.elu,
n_layers=2, dimension=2, labels=None, jensen_shannon=True,
standardize=False, apply_sn=False, **kwargs):
"""
Mutual Information Neural Estimator
(x,y): Drawn from joint p(x,y)
y_prime: Drawn from marginal p(y)
returns
MI: Lower bound on mutual information between x,y
"""
init = tf.contrib.layers.xavier_initializer()
drop_rate = 0.0
shape = [64 for _ in range(int(n_layers))]
assert n_layers <= len(shape), 'Number of requested layers too high.'
kwargs = {'center': True, 'scale': True, 'training': training, 'fused': True, 'renorm': False}
# y_prime = tf.random_shuffle(y)
# Standardize inputs
x_mu, x_sigma = tf.nn.moments(x, axes=0)
y_mu, y_sigma = tf.nn.moments(y, axes=0)
y_prime_mu, y_prime_sigma = tf.nn.moments(y_prime, axes=0)
if standardize:
x = (x - x_mu) / x_sigma
y = (y - y_mu) / y_sigma
y_prime = (y_prime - y_prime_mu) / y_prime_sigma
if dimension == 2:
y, y_prime = tf.expand_dims(y, axis=1), tf.expand_dims(y_prime, axis=1)
if len(x.get_shape().as_list()) < 2:
x = tf.expand_dims(x, axis=1)
z = tf.concat([x,y], axis=1)
z_prime = tf.concat([x,y_prime], axis=1)
z.set_shape([None, dimension])
z_prime.set_shape([None, dimension])
print('X SHAPE:', x.get_shape().as_list())
print('Z SHAPE:', z.get_shape().as_list())
print('Z PRIME SHAPE:', z_prime.get_shape().as_list())
def statistic_network(t, name='MINE', apply_spectral_norm=False, reuse=False):
if apply_spectral_norm is True:
kernel_constraint = Network._spectral_norm
print('Applying spectral norm')
else:
kernel_constraint = None
with tf.variable_scope(name, initializer=init, reuse=reuse) as scope:
h0 = tf.layers.dense(t, units=shape[0], activation=None,
kernel_constraint=kernel_constraint)
# h0 = tf.layers.batch_normalization(h0, **kwargs)
h0 = tf.contrib.layers.layer_norm(h0, center=True, scale=True, activation_fn=actv)
h = h0
current_layer = 1
while current_layer < n_layers:
h = tf.layers.dense(h, units=shape[current_layer], activation=None,
kernel_constraint=kernel_constraint)
h = tf.contrib.layers.layer_norm(h, center=True, scale=True, activation_fn=actv)
current_layer += 1
out = tf.layers.dense(h, units=1, kernel_initializer=init)
return out
def log_sum_exp_trick(x, batch_size, axis=1):
# Compute along batch dimension
x = tf.squeeze(x)
x_max = tf.reduce_max(x)
# lse = x_max + tf.log(tf.reduce_mean(tf.exp(x-x_max)))
lse = x_max + tf.log(tf.reduce_sum(tf.exp(x-x_max))) - tf.log(batch_size)
return lse
joint_f = statistic_network(z, apply_spectral_norm=apply_sn)
marginal_f = statistic_network(z_prime, reuse=True, apply_spectral_norm=apply_sn)
print('Joint shape', joint_f.shape)
print('marginal shape', marginal_f.shape)
# MI_lower_bound = tf.reduce_mean(joint_f) - tf.log(tf.reduce_mean(tf.exp(marginal_f)) + 1e-5)
MI_lower_bound = tf.squeeze(tf.reduce_mean(joint_f)) - tf.squeeze(log_sum_exp_trick(marginal_f,
tf.cast(batch_size, tf.float32)))
# H(p,q) = - E_p[log q]
joint_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=joint_f,
labels=tf.ones_like(joint_f)))
marginal_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=marginal_f,
labels=tf.zeros_like(marginal_f)))
JSD_lower_bound = -(marginal_loss + joint_loss) + tf.log(4.0)
# JSD_lower_bound = tf.squeeze(tf.reduce_mean(-tf.nn.softplus(-tf.squeeze(joint_f)))) - tf.squeeze(tf.reduce_mean(tf.nn.softplus(tf.squeeze(marginal_f))))
# GAN_lower_bound = tf.reduce_mean(tf.log(tf.nn.sigmoid(joint_f))) + tf.reduce_mean(tf.log(1.0-tf.nn.sigmoid(marginal_f)))
if jensen_shannon:
lower_bound = JSD_lower_bound
else:
lower_bound = MI_lower_bound
return (z, z_prime), (joint_f, marginal_f), lower_bound
@staticmethod
def kernel_MMD(x, y, y_prime, batch_size, name='kernel_MMD', actv=tf.nn.elu, dimension=2, labels=None, bkg_only=True):
"""
Kernel MMD
(x,y): Drawn from joint
y_prime: Drawn from marginal
returns
mmd2: MMD distance between two distributions
"""
def gaussian_kernel_mmd2(X, Y, gamma):
"""
Parameters
____
X: Matrix, shape: (n_samples, features)
Y: Matrix, shape: (m_samples, features)
Returns
____
mmd: MMD under Gaussian kernel
"""
XX = tf.matmul(X, X, transpose_b=True)
XY = tf.matmul(X, Y, transpose_b=True)
YY = tf.matmul(Y, Y, transpose_b=True)
M, N = tf.cast(XX.get_shape()[0], tf.float32), tf.cast(YY.get_shape()[0], tf.float32)
X_sqnorm = tf.reduce_sum(X**2, axis=-1)
Y_sqnorm = tf.reduce_sum(Y**2, axis=-1)
row_bc = lambda x: tf.expand_dims(x,0)
col_bc = lambda x: tf.expand_dims(x,1)
K_XX = tf.exp( -gamma * (col_bc(X_sqnorm) - 2 * XX + row_bc(X_sqnorm)))
K_XY = tf.exp( -gamma * (col_bc(X_sqnorm) - 2 * XY + row_bc(Y_sqnorm)))
K_YY = tf.exp( -gamma * (col_bc(Y_sqnorm) - 2 * YY + row_bc(Y_sqnorm)))
mmd2 = tf.reduce_sum(K_XX) / M**2 - 2 * tf.reduce_sum(K_XY) / (M*N) + tf.reduce_sum(K_YY) / N**2
return mmd2
def rbf_mixed_mmd2(X, Y, M, N, sigmas=[1.0, 2.0, 5.0, 10.0, 20.0, 40.0, 80.0]):
"""
Parameters
____
X: Matrix, shape: (n_samples, features)
Y: Matrix, shape: (m_samples, features)
sigmas: RBF parameter
Returns
____
mmd2: MMD under Gaussian mixed kernel
"""
XX = tf.matmul(X, X, transpose_b=True)
XY = tf.matmul(X, Y, transpose_b=True)
YY = tf.matmul(Y, Y, transpose_b=True)
X_sqnorm = tf.reduce_sum(X**2, axis=-1)
Y_sqnorm = tf.reduce_sum(Y**2, axis=-1)
row_bc = lambda x: tf.expand_dims(x,0)
col_bc = lambda x: tf.expand_dims(x,1)
K_XX, K_XY, K_YY = 0,0,0
for sigma in sigmas:
gamma = 1 / (2 * sigma**2)
K_XX += tf.exp( -gamma * (col_bc(X_sqnorm) - 2 * XX + row_bc(X_sqnorm)))
K_XY += tf.exp( -gamma * (col_bc(X_sqnorm) - 2 * XY + row_bc(Y_sqnorm)))
K_YY += tf.exp( -gamma * (col_bc(Y_sqnorm) - 2 * YY + row_bc(Y_sqnorm)))
mmd2 = tf.reduce_sum(K_XX) / M**2 - 2 * tf.reduce_sum(K_XY) / (M*N) + tf.reduce_sum(K_YY) / N**2
return mmd2
init = tf.contrib.layers.xavier_initializer()
if bkg_only:
batch_size_bkg_only = tf.cast(batch_size - tf.reduce_sum(labels), tf.float32)
if dimension == 2:
y, y_prime = tf.expand_dims(y, axis=1), tf.expand_dims(y_prime, axis=1)
if len(x.get_shape().as_list()) < 2:
x = tf.expand_dims(x, axis=1)
z = tf.concat([x,y], axis=1)
z_prime = tf.concat([x,y_prime], axis=1)
z.set_shape([None, dimension])
z_prime.set_shape([None, dimension])
print('X SHAPE:', x.get_shape().as_list())
print('Z SHAPE:', z.get_shape().as_list())
print('Z PRIME SHAPE:', z_prime.get_shape().as_list())
mmd2 = tf.nn.relu(rbf_mixed_mmd2(z, z_prime, M=batch_size_bkg_only, N=batch_size))
return z, z_prime, tf.sqrt(mmd2)
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'tensorflow.matmul', 'tf.matmul', (['X', 'Y'], {'transpose_b': '(True)'}), '(X, Y, transpose_b=True)\n', (10301, 10325), True, 'import tensorflow as tf\n'), ((10343, 10376), 'tensorflow.matmul', 'tf.matmul', (['Y', 'Y'], {'transpose_b': '(True)'}), '(Y, Y, transpose_b=True)\n', (10352, 10376), True, 'import tensorflow as tf\n'), ((10401, 10431), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['(X ** 2)'], {'axis': '(-1)'}), '(X ** 2, axis=-1)\n', (10414, 10431), True, 'import tensorflow as tf\n'), ((10453, 10483), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['(Y ** 2)'], {'axis': '(-1)'}), '(Y ** 2, axis=-1)\n', (10466, 10483), True, 'import tensorflow as tf\n'), ((11442, 11467), 'tensorflow.expand_dims', 'tf.expand_dims', (['x'], {'axis': '(1)'}), '(x, axis=1)\n', (11456, 11467), True, 'import tensorflow as tf\n'), ((11924, 11937), 'tensorflow.sqrt', 'tf.sqrt', (['mmd2'], {}), '(mmd2)\n', (11931, 11937), True, 'import tensorflow as tf\n'), ((573, 588), 'tensorflow.transpose', 'tf.transpose', (['w'], {}), '(w)\n', (585, 588), True, 'import tensorflow as tf\n'), ((3352, 3415), 'tensorflow.layers.dense', 'tf.layers.dense', (['h'], {'units': 'shape[current_layer]', 'activation': 'None'}), '(h, units=shape[current_layer], activation=None)\n', (3367, 3415), True, 'import tensorflow as tf\n'), ((3501, 3577), 'tensorflow.contrib.layers.layer_norm', 'tf.contrib.layers.layer_norm', (['h'], {'center': '(True)', 'scale': '(True)', 'activation_fn': 'actv'}), '(h, center=True, scale=True, activation_fn=actv)\n', (3529, 3577), True, 'import tensorflow as tf\n'), ((4945, 4970), 'tensorflow.expand_dims', 'tf.expand_dims', (['y'], {'axis': '(1)'}), '(y, axis=1)\n', (4959, 4970), True, 'import tensorflow as tf\n'), ((4972, 5003), 'tensorflow.expand_dims', 'tf.expand_dims', (['y_prime'], {'axis': '(1)'}), '(y_prime, axis=1)\n', (4986, 5003), True, 'import tensorflow as tf\n'), ((5744, 5798), 'tensorflow.variable_scope', 'tf.variable_scope', (['name'], {'initializer': 'init', 'reuse': 'reuse'}), '(name, initializer=init, reuse=reuse)\n', (5761, 5798), True, 'import tensorflow as tf\n'), ((5831, 5924), 'tensorflow.layers.dense', 'tf.layers.dense', (['t'], {'units': 'shape[0]', 'activation': 'None', 'kernel_constraint': 'kernel_constraint'}), '(t, units=shape[0], activation=None, kernel_constraint=\n kernel_constraint)\n', (5846, 5924), True, 'import tensorflow as tf\n'), ((6033, 6110), 'tensorflow.contrib.layers.layer_norm', 'tf.contrib.layers.layer_norm', (['h0'], {'center': '(True)', 'scale': '(True)', 'activation_fn': 'actv'}), '(h0, center=True, scale=True, activation_fn=actv)\n', (6061, 6110), True, 'import tensorflow as tf\n'), ((6534, 6586), 'tensorflow.layers.dense', 'tf.layers.dense', (['h'], {'units': '(1)', 'kernel_initializer': 'init'}), '(h, units=1, kernel_initializer=init)\n', (6549, 6586), True, 'import tensorflow as tf\n'), ((6912, 6930), 'tensorflow.log', 'tf.log', (['batch_size'], {}), '(batch_size)\n', (6918, 6930), True, 'import tensorflow as tf\n'), ((7348, 7371), 'tensorflow.reduce_mean', 'tf.reduce_mean', (['joint_f'], {}), '(joint_f)\n', (7362, 7371), True, 'import tensorflow as tf\n'), ((9365, 9385), 'tensorflow.expand_dims', 'tf.expand_dims', (['x', '(0)'], {}), '(x, 0)\n', (9379, 9385), True, 'import tensorflow as tf\n'), ((9416, 9436), 'tensorflow.expand_dims', 'tf.expand_dims', (['x', '(1)'], {}), '(x, 1)\n', (9430, 9436), True, 'import tensorflow as tf\n'), ((10514, 10534), 'tensorflow.expand_dims', 'tf.expand_dims', (['x', '(0)'], {}), '(x, 0)\n', (10528, 10534), True, 'import tensorflow as tf\n'), ((10565, 10585), 'tensorflow.expand_dims', 'tf.expand_dims', (['x', '(1)'], {}), '(x, 1)\n', (10579, 10585), True, 'import tensorflow as tf\n'), ((11322, 11347), 'tensorflow.expand_dims', 'tf.expand_dims', (['y'], {'axis': '(1)'}), '(y, axis=1)\n', (11336, 11347), True, 'import tensorflow as tf\n'), ((11349, 11380), 'tensorflow.expand_dims', 'tf.expand_dims', (['y_prime'], {'axis': '(1)'}), '(y_prime, axis=1)\n', (11363, 11380), True, 'import tensorflow as tf\n'), ((460, 493), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {}), '()\n', (491, 493), True, 'import tensorflow as tf\n'), ((686, 722), 'tensorflow.assign', 'tf.assign', (['u', 'u_hat'], {'name': '"""update_u"""'}), "(u, u_hat, name='update_u')\n", (695, 722), True, 'import tensorflow as tf\n'), ((906, 925), 'tensorflow.transpose', 'tf.transpose', (['u_hat'], {}), '(u_hat)\n', (918, 925), True, 'import tensorflow as tf\n'), ((6242, 6346), 'tensorflow.layers.dense', 'tf.layers.dense', (['h'], {'units': 'shape[current_layer]', 'activation': 'None', 'kernel_constraint': 'kernel_constraint'}), '(h, units=shape[current_layer], activation=None,\n kernel_constraint=kernel_constraint)\n', (6257, 6346), True, 'import tensorflow as tf\n'), ((6395, 6471), 'tensorflow.contrib.layers.layer_norm', 'tf.contrib.layers.layer_norm', (['h'], {'center': '(True)', 'scale': '(True)', 'activation_fn': 'actv'}), '(h, center=True, scale=True, activation_fn=actv)\n', (6423, 6471), True, 'import tensorflow as tf\n'), ((7428, 7459), 'tensorflow.cast', 'tf.cast', (['batch_size', 'tf.float32'], {}), '(batch_size, tf.float32)\n', (7435, 7459), True, 'import tensorflow as tf\n'), ((7606, 7627), 'tensorflow.ones_like', 'tf.ones_like', (['joint_f'], {}), '(joint_f)\n', (7618, 7627), True, 'import tensorflow as tf\n'), ((7747, 7772), 'tensorflow.zeros_like', 'tf.zeros_like', (['marginal_f'], {}), '(marginal_f)\n', (7760, 7772), True, 'import tensorflow as tf\n'), ((9772, 9791), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_YY'], {}), '(K_YY)\n', (9785, 9791), True, 'import tensorflow as tf\n'), ((11050, 11069), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_YY'], {}), '(K_YY)\n', (11063, 11069), True, 'import tensorflow as tf\n'), ((11234, 11255), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['labels'], {}), '(labels)\n', (11247, 11255), True, 'import tensorflow as tf\n'), ((9709, 9728), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_XX'], {}), '(K_XX)\n', (9722, 9728), True, 'import tensorflow as tf\n'), ((10987, 11006), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_XX'], {}), '(K_XX)\n', (11000, 11006), True, 'import tensorflow as tf\n'), ((6892, 6909), 'tensorflow.exp', 'tf.exp', (['(x - x_max)'], {}), '(x - x_max)\n', (6898, 6909), True, 'import tensorflow as tf\n'), ((9742, 9761), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_XY'], {}), '(K_XY)\n', (9755, 9761), True, 'import tensorflow as tf\n'), ((11020, 11039), 'tensorflow.reduce_sum', 'tf.reduce_sum', (['K_XY'], {}), '(K_XY)\n', (11033, 11039), True, 'import tensorflow as tf\n')] |
import os
from tkinter import *
from tkinter import messagebox
from pytube import YouTube, exceptions
app = Tk() # Inicia o programa
app.title("Youtube Downloader") # Título do programa.
app.geometry("600x400") # Dimensão do programa.
# app.configure(background="#334") # Configurações de cor.
# <------------------- Criando os componentes do programa.------------------->
# LOGO
lblLogo = Label(app, text="Youtube Downloader",
background="red", foreground="white", font="Arial")
lblLogo.pack(fill=X)
# Pega a URL
lblUrl = Label(app, text="Insira o link:", font="Arial")
lblUrl.place(x=10, y=80, width=150, height=30)
url = Entry(app)
url.place(x=170, y=80, width=400, height=30)
url.focus()
# Escolha do formato (Vídeo ou Áudio)
lblFormat = Label(app, text="Formato: ", font="Arial")
lblFormat.pack(fill=X, expand=True)
lblFormat.place(
x=10, y=120, width=150, height=50)
listaFormato = ["Vídeo", "Áudio"]
varFormato = StringVar()
varFormato.set(listaFormato[0])
opMenu = OptionMenu(app, varFormato, *listaFormato)
opMenu.pack(fill=X, expand=True)
opMenu.place(x=170, y=120, width=150, height=50)
# < ------------------ Função ao clicar em "BAIXAR" -------------------------->
def baixar():
varForm = varFormato.get() # Associando variável à escolha.
link = url.get() # Associando variável à URL
try:
yt = YouTube(link) # Associando variável à url
except exceptions.RegexMatchError:
# Tratamente de erro do link
messagebox.showerror(title="ERRO DE LINK!",
message="Verifique o link e tente novamente!").center()
except exceptions.VideoUnavailable:
messagebox.showerror(title="LINK INACESSÍVEL!",
message="Verifique o link e tente novamente!").center()
else:
if varForm == listaFormato[0]: # Vídeo
ys = yt.streams.get_highest_resolution()
ys.download()
else: # Áudio
ys = yt.streams.get_audio_only()
ys.download()
msg = f"O arquivo foi salvo em: {os.getcwd()}"
messagebox.showinfo(title="Download Concluído!", message=msg)
# < ------------------------------------------------------------------>
Button(app, text="Baixar Arquivo", font="Arial", activebackground="#c4302b", activeforeground="white", background="#333", foreground="white", borderwidth=8, command=baixar).place(
x=200, y=250, width=200, height=50)
app.mainloop()
| [
"tkinter.messagebox.showerror",
"tkinter.messagebox.showinfo",
"pytube.YouTube",
"os.getcwd"
] | [((1372, 1385), 'pytube.YouTube', 'YouTube', (['link'], {}), '(link)\n', (1379, 1385), False, 'from pytube import YouTube, exceptions\n'), ((2108, 2169), 'tkinter.messagebox.showinfo', 'messagebox.showinfo', ([], {'title': '"""Download Concluído!"""', 'message': 'msg'}), "(title='Download Concluído!', message=msg)\n", (2127, 2169), False, 'from tkinter import messagebox\n'), ((2085, 2096), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (2094, 2096), False, 'import os\n'), ((1500, 1595), 'tkinter.messagebox.showerror', 'messagebox.showerror', ([], {'title': '"""ERRO DE LINK!"""', 'message': '"""Verifique o link e tente novamente!"""'}), "(title='ERRO DE LINK!', message=\n 'Verifique o link e tente novamente!')\n", (1520, 1595), False, 'from tkinter import messagebox\n'), ((1677, 1776), 'tkinter.messagebox.showerror', 'messagebox.showerror', ([], {'title': '"""LINK INACESSÍVEL!"""', 'message': '"""Verifique o link e tente novamente!"""'}), "(title='LINK INACESSÍVEL!', message=\n 'Verifique o link e tente novamente!')\n", (1697, 1776), False, 'from tkinter import messagebox\n')] |
from model.group import Group
import random
__author__ = 'Pysarev'
def test_edit_first_group(app, db, check_ui):
if len(db.get_group_list()) == 0:
app.group.create(Group(name="Edit_test"))
old_groups = db.get_group_list()
old_group=random.choice(old_groups)
edit_group = Group(name="edited2", footer="edited2")
edit_group.id = old_group.id
app.group.edit_group_by_id(edit_group.id, edit_group)
new_groups = db.get_group_list()
assert len(old_groups) == len(new_groups)
old_groups_to_compare = [edit_group if group.id == edit_group.id else group for group in old_groups]
assert old_groups_to_compare == new_groups
if check_ui:
def clean(group):
return Group(id=group.id, name=group.name.strip())
ui_list = map(clean, new_groups)
assert sorted(ui_list, key=Group.id_or_max) == sorted(app.group.get_group_list(), key=Group.id_or_max) | [
"model.group.Group",
"random.choice"
] | [((254, 279), 'random.choice', 'random.choice', (['old_groups'], {}), '(old_groups)\n', (267, 279), False, 'import random\n'), ((297, 336), 'model.group.Group', 'Group', ([], {'name': '"""edited2"""', 'footer': '"""edited2"""'}), "(name='edited2', footer='edited2')\n", (302, 336), False, 'from model.group import Group\n'), ((178, 201), 'model.group.Group', 'Group', ([], {'name': '"""Edit_test"""'}), "(name='Edit_test')\n", (183, 201), False, 'from model.group import Group\n')] |
# encoding: utf-8
import pyxel
pyxel.init(160, 120, caption="Pong")
# **** Számold a pontokat! ****
# Legyen az app osztálynak egy-egy mezője, ami a jobb ill. bal
# játékos pontját tárolja.
# Legyen az app osztálynak egy-egy metódusa, ami a jobb ill. bal
# játékos pontszerzését kezeli le.
# Egy pont után induljon a labda újra középről, a nyert játékos
# irányába.
# Írd ki a pontokat!
class Labda:
def __init__(self, x, y):
self.x = x
self.y = y
self.sebx = 1
self.seby = 1
def update(self):
# ütközés
if self.x > 160 - 6:
if app.jobb_uto.rajta_van(self.y):
self.sebx = -self.sebx
if self.y > 120 - 6: self.seby = -self.seby
if self.x < 6:
if app.bal_uto.rajta_van(self.y):
self.sebx = -self.sebx
if self.y < 6: self.seby = -self.seby
# labda mozgatása
self.x = self.x + self.sebx
self.y = self.y + self.seby
def draw(self):
pyxel.circ(self.x, self.y, 4, 8)
class Uto:
def __init__(self, x, up, down):
self.y = 60
self.x = x
self.up = up
self.down = down
def update(self):
if pyxel.btn(self.up):
self.y = self.y - 1
if pyxel.btn(self.down):
self.y = self.y + 1
def draw(self):
pyxel.rect(self.x, self.y - 20/2, 3, 20, 9)
def rajta_van(self, y):
return self.y-20/2 < y < self.y+20/2
class App:
def __init__(self):
self.labda = Labda(80, 60)
self.bal_uto = Uto(0, pyxel.KEY_W, pyxel.KEY_S)
self.jobb_uto = Uto(160-3, pyxel.KEY_I, pyxel.KEY_K)
def update(self):
self.labda.update()
self.bal_uto.update()
self.jobb_uto.update()
def draw(self):
pyxel.cls(0)
self.labda.draw()
self.bal_uto.draw()
self.jobb_uto.draw()
app = App()
pyxel.run(app.update, app.draw)
| [
"pyxel.rect",
"pyxel.circ",
"pyxel.init",
"pyxel.run",
"pyxel.btn",
"pyxel.cls"
] | [((31, 67), 'pyxel.init', 'pyxel.init', (['(160)', '(120)'], {'caption': '"""Pong"""'}), "(160, 120, caption='Pong')\n", (41, 67), False, 'import pyxel\n'), ((1908, 1939), 'pyxel.run', 'pyxel.run', (['app.update', 'app.draw'], {}), '(app.update, app.draw)\n', (1917, 1939), False, 'import pyxel\n'), ((1004, 1036), 'pyxel.circ', 'pyxel.circ', (['self.x', 'self.y', '(4)', '(8)'], {}), '(self.x, self.y, 4, 8)\n', (1014, 1036), False, 'import pyxel\n'), ((1206, 1224), 'pyxel.btn', 'pyxel.btn', (['self.up'], {}), '(self.up)\n', (1215, 1224), False, 'import pyxel\n'), ((1269, 1289), 'pyxel.btn', 'pyxel.btn', (['self.down'], {}), '(self.down)\n', (1278, 1289), False, 'import pyxel\n'), ((1351, 1396), 'pyxel.rect', 'pyxel.rect', (['self.x', '(self.y - 20 / 2)', '(3)', '(20)', '(9)'], {}), '(self.x, self.y - 20 / 2, 3, 20, 9)\n', (1361, 1396), False, 'import pyxel\n'), ((1798, 1810), 'pyxel.cls', 'pyxel.cls', (['(0)'], {}), '(0)\n', (1807, 1810), False, 'import pyxel\n')] |
from vulkan import vk, helpers as hvk
class Renderer(object):
def __init__(self, engine):
self.engine = engine
self.image_ready = None
self.rendering_done = None
self.render_fences = ()
self.render_cache = {}
self.enabled = True
self._setup_sync()
self._setup_render_cache()
def free(self):
engine, api, device = self.ctx
hvk.destroy_semaphore(api, device, self.image_ready)
hvk.destroy_semaphore(api, device, self.rendering_done)
for f in self.render_fences:
hvk.destroy_fence(api, device, f)
del self.engine
@property
def ctx(self):
engine = self.engine
api, device = engine.api, engine.device
return engine, api, device
def render(self, scene_data):
if not self.enabled:
return
h = hvk
engine, api, device = self.ctx
render_queue = engine.render_queue.handle
rc = self.render_cache
image_index, result = h.acquire_next_image(api, device, engine.swapchain, semaphore = self.image_ready)
fence = self.render_fences[image_index]
h.wait_for_fences(api, device, (fence,))
h.reset_fences(api, device, (fence,))
scene_data.record(image_index)
submit = rc["submit_info"]
submit.command_buffers[0] = scene_data.render_commands[image_index]
h.queue_submit(api, render_queue, (submit,), fence = fence)
present = rc["present_info"]
present.image_indices[0] = image_index
h.queue_present(api, render_queue, present)
def enable(self):
self.enabled = True
def disable(self):
_, api, device = self.ctx
hvk.device_wait_idle(api, device)
self.enabled = False
def _setup_sync(self):
engine, api, device = self.ctx
info = hvk.semaphore_create_info()
self.image_ready = hvk.create_semaphore(api, device, info)
self.rendering_done = hvk.create_semaphore(api, device, info)
self.render_fences = []
info = hvk.fence_create_info(flags=vk.FENCE_CREATE_SIGNALED_BIT)
for _ in range(len(engine.render_target.swapchain_images)):
self.render_fences.append(hvk.create_fence(api, device, info))
def _setup_render_cache(self):
engine = self.engine
self.render_cache["submit_info"] = hvk.submit_info(
wait_dst_stage_mask = (vk.PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,),
wait_semaphores = (self.image_ready,),
signal_semaphores = (self.rendering_done,),
command_buffers = (0,)
)
self.render_cache["present_info"] = hvk.present_info(
swapchains = (engine.swapchain,),
image_indices = (0,),
wait_semaphores = (self.rendering_done,)
)
| [
"vulkan.helpers.create_fence",
"vulkan.helpers.device_wait_idle",
"vulkan.helpers.destroy_semaphore",
"vulkan.helpers.present_info",
"vulkan.helpers.create_semaphore",
"vulkan.helpers.destroy_fence",
"vulkan.helpers.semaphore_create_info",
"vulkan.helpers.fence_create_info",
"vulkan.helpers.submit_i... | [((417, 469), 'vulkan.helpers.destroy_semaphore', 'hvk.destroy_semaphore', (['api', 'device', 'self.image_ready'], {}), '(api, device, self.image_ready)\n', (438, 469), True, 'from vulkan import vk, helpers as hvk\n'), ((478, 533), 'vulkan.helpers.destroy_semaphore', 'hvk.destroy_semaphore', (['api', 'device', 'self.rendering_done'], {}), '(api, device, self.rendering_done)\n', (499, 533), True, 'from vulkan import vk, helpers as hvk\n'), ((1740, 1773), 'vulkan.helpers.device_wait_idle', 'hvk.device_wait_idle', (['api', 'device'], {}), '(api, device)\n', (1760, 1773), True, 'from vulkan import vk, helpers as hvk\n'), ((1885, 1912), 'vulkan.helpers.semaphore_create_info', 'hvk.semaphore_create_info', ([], {}), '()\n', (1910, 1912), True, 'from vulkan import vk, helpers as hvk\n'), ((1949, 1988), 'vulkan.helpers.create_semaphore', 'hvk.create_semaphore', (['api', 'device', 'info'], {}), '(api, device, info)\n', (1969, 1988), True, 'from vulkan import vk, helpers as hvk\n'), ((2019, 2058), 'vulkan.helpers.create_semaphore', 'hvk.create_semaphore', (['api', 'device', 'info'], {}), '(api, device, info)\n', (2039, 2058), True, 'from vulkan import vk, helpers as hvk\n'), ((2107, 2164), 'vulkan.helpers.fence_create_info', 'hvk.fence_create_info', ([], {'flags': 'vk.FENCE_CREATE_SIGNALED_BIT'}), '(flags=vk.FENCE_CREATE_SIGNALED_BIT)\n', (2128, 2164), True, 'from vulkan import vk, helpers as hvk\n'), ((2417, 2617), 'vulkan.helpers.submit_info', 'hvk.submit_info', ([], {'wait_dst_stage_mask': '(vk.PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,)', 'wait_semaphores': '(self.image_ready,)', 'signal_semaphores': '(self.rendering_done,)', 'command_buffers': '(0,)'}), '(wait_dst_stage_mask=(vk.\n PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,), wait_semaphores=(self.\n image_ready,), signal_semaphores=(self.rendering_done,),\n command_buffers=(0,))\n', (2432, 2617), True, 'from vulkan import vk, helpers as hvk\n'), ((2715, 2827), 'vulkan.helpers.present_info', 'hvk.present_info', ([], {'swapchains': '(engine.swapchain,)', 'image_indices': '(0,)', 'wait_semaphores': '(self.rendering_done,)'}), '(swapchains=(engine.swapchain,), image_indices=(0,),\n wait_semaphores=(self.rendering_done,))\n', (2731, 2827), True, 'from vulkan import vk, helpers as hvk\n'), ((584, 617), 'vulkan.helpers.destroy_fence', 'hvk.destroy_fence', (['api', 'device', 'f'], {}), '(api, device, f)\n', (601, 617), True, 'from vulkan import vk, helpers as hvk\n'), ((2271, 2306), 'vulkan.helpers.create_fence', 'hvk.create_fence', (['api', 'device', 'info'], {}), '(api, device, info)\n', (2287, 2306), True, 'from vulkan import vk, helpers as hvk\n')] |
# Display the ip's from all network interfaces on the display
import time
import Adafruit_Nokia_LCD as LCD
import Adafruit_GPIO.SPI as SPI
import netifaces
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
# Raspberry Pi hardware SPI config:
DC = 23
RST = 24
SPI_PORT = 0
SPI_DEVICE = 0
# Raspberry Pi software SPI config:
# SCLK = 4
# DIN = 17
# DC = 23
# RST = 24
# CS = 8
# Beaglebone Black hardware SPI config:
# DC = 'P9_15'
# RST = 'P9_12'
# SPI_PORT = 1
# SPI_DEVICE = 0
# Beaglebone Black software SPI config:
# DC = 'P9_15'
# RST = 'P9_12'
# SCLK = 'P8_7'
# DIN = 'P8_9'
# CS = 'P8_11'
# Hardware SPI usage:
disp = LCD.PCD8544(DC, RST, spi=SPI.SpiDev(SPI_PORT, SPI_DEVICE, max_speed_hz=4000000))
# Software SPI usage (defaults to bit-bang SPI interface):
#disp = LCD.PCD8544(DC, RST, SCLK, DIN, CS)
# Initialize library.
disp.begin(contrast=60)
# Clear display.
disp.clear()
disp.display()
# Create blank image for drawing.
# Make sure to create image with mode '1' for 1-bit color.
image = Image.new('1', (LCD.LCDWIDTH, LCD.LCDHEIGHT))
# Get drawing object to draw on image.
draw = ImageDraw.Draw(image)
# Draw a white filled box to clear the image.
draw.rectangle((0,0,LCD.LCDWIDTH,LCD.LCDHEIGHT), outline=255, fill=255)
# Load default font.
font = ImageFont.load_default()
# Refresh the ip's every second.
while True:
disp.clear();
# Draw a white filled box to clear the image.
draw.rectangle((0,0,LCD.LCDWIDTH,LCD.LCDHEIGHT), outline=255, fill=255)
cnt = 0
for i in netifaces.interfaces():
try:
ip = str(netifaces.ifaddresses(i)[2][0]['addr'])
# only display interfaces which aren't local
if ip != "127.0.0.1" :
draw.text((1,10 * cnt), i, font=font)
draw.text((6,10 * cnt + 10), ip, font=font)
cnt += 2
except:
continue
# Display image.
disp.image(image)
disp.display()
time.sleep(1.0)
| [
"PIL.ImageFont.load_default",
"PIL.Image.new",
"time.sleep",
"netifaces.ifaddresses",
"PIL.ImageDraw.Draw",
"netifaces.interfaces",
"Adafruit_GPIO.SPI.SpiDev"
] | [((1037, 1082), 'PIL.Image.new', 'Image.new', (['"""1"""', '(LCD.LCDWIDTH, LCD.LCDHEIGHT)'], {}), "('1', (LCD.LCDWIDTH, LCD.LCDHEIGHT))\n", (1046, 1082), False, 'from PIL import Image\n'), ((1130, 1151), 'PIL.ImageDraw.Draw', 'ImageDraw.Draw', (['image'], {}), '(image)\n', (1144, 1151), False, 'from PIL import ImageDraw\n'), ((1300, 1324), 'PIL.ImageFont.load_default', 'ImageFont.load_default', ([], {}), '()\n', (1322, 1324), False, 'from PIL import ImageFont\n'), ((1526, 1548), 'netifaces.interfaces', 'netifaces.interfaces', ([], {}), '()\n', (1546, 1548), False, 'import netifaces\n'), ((1879, 1894), 'time.sleep', 'time.sleep', (['(1.0)'], {}), '(1.0)\n', (1889, 1894), False, 'import time\n'), ((682, 736), 'Adafruit_GPIO.SPI.SpiDev', 'SPI.SpiDev', (['SPI_PORT', 'SPI_DEVICE'], {'max_speed_hz': '(4000000)'}), '(SPI_PORT, SPI_DEVICE, max_speed_hz=4000000)\n', (692, 736), True, 'import Adafruit_GPIO.SPI as SPI\n'), ((1569, 1593), 'netifaces.ifaddresses', 'netifaces.ifaddresses', (['i'], {}), '(i)\n', (1590, 1593), False, 'import netifaces\n')] |
import configparser
from whatcha_readin.paths import WhatchaReadinPaths
VERSION = "0.0.4"
def get_config():
config_path = WhatchaReadinPaths.get_config_path()
config = configparser.ConfigParser()
config.read(config_path)
return config
| [
"configparser.ConfigParser",
"whatcha_readin.paths.WhatchaReadinPaths.get_config_path"
] | [((130, 166), 'whatcha_readin.paths.WhatchaReadinPaths.get_config_path', 'WhatchaReadinPaths.get_config_path', ([], {}), '()\n', (164, 166), False, 'from whatcha_readin.paths import WhatchaReadinPaths\n'), ((180, 207), 'configparser.ConfigParser', 'configparser.ConfigParser', ([], {}), '()\n', (205, 207), False, 'import configparser\n')] |
from bokeh.application.handlers import FunctionHandler, DirectoryHandler
from bokeh.application import Application
import numpy as np
import holoviews as hv
import boto3
from PIL import Image
import holoviews.plotting.bokeh # important
from bokeh.io import show, curdoc
from bokeh.layouts import layout
import io
from holoviews.operation.datashader import datashade
from bokeh.models import Slider, Button
from marshmallow import Schema, fields, INCLUDE
renderer = hv.renderer('bokeh').instance(mode='server')
class BokehImageAppArgsSchema(Schema):
bucket = fields.List(fields.String())
key = fields.List(fields.String())
height = fields.List(fields.Integer())
width = fields.List(fields.Integer())
# Define valid function for FunctionHandler
# when deploying as script, simply attach to curdoc
def modify_doc(doc):
args = doc.session_context.request.arguments
args_schema = BokehImageAppArgsSchema()
loaded_args = args_schema.load(args, unknown=INCLUDE)
bucket = loaded_args['bucket'][0]
key = loaded_args['key'][0]
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucket)
object = bucket.Object(key)
file_stream = io.BytesIO()
object.download_fileobj(file_stream)
pil_image = Image.open(file_stream)
hv_img_plot = hv.Image(np.asarray(pil_image)).options(
height=loaded_args['height'][0], width=loaded_args['width'][0])
# Create HoloViews plot and attach the document
hvplot = renderer.get_plot(hv_img_plot, doc)
# Combine the holoviews plot and widgets in a layout
plot = layout([
[hvplot.state]], sizing_mode='fixed')
doc.add_root(plot)
return doc
bokeh_image_app = Application(FunctionHandler(modify_doc))
| [
"bokeh.layouts.layout",
"PIL.Image.open",
"holoviews.renderer",
"bokeh.application.handlers.FunctionHandler",
"io.BytesIO",
"numpy.asarray",
"boto3.resource",
"marshmallow.fields.String",
"marshmallow.fields.Integer"
] | [((1073, 1093), 'boto3.resource', 'boto3.resource', (['"""s3"""'], {}), "('s3')\n", (1087, 1093), False, 'import boto3\n'), ((1176, 1188), 'io.BytesIO', 'io.BytesIO', ([], {}), '()\n', (1186, 1188), False, 'import io\n'), ((1246, 1269), 'PIL.Image.open', 'Image.open', (['file_stream'], {}), '(file_stream)\n', (1256, 1269), False, 'from PIL import Image\n'), ((1572, 1617), 'bokeh.layouts.layout', 'layout', (['[[hvplot.state]]'], {'sizing_mode': '"""fixed"""'}), "([[hvplot.state]], sizing_mode='fixed')\n", (1578, 1617), False, 'from bokeh.layouts import layout\n'), ((1698, 1725), 'bokeh.application.handlers.FunctionHandler', 'FunctionHandler', (['modify_doc'], {}), '(modify_doc)\n', (1713, 1725), False, 'from bokeh.application.handlers import FunctionHandler, DirectoryHandler\n'), ((469, 489), 'holoviews.renderer', 'hv.renderer', (['"""bokeh"""'], {}), "('bokeh')\n", (480, 489), True, 'import holoviews as hv\n'), ((580, 595), 'marshmallow.fields.String', 'fields.String', ([], {}), '()\n', (593, 595), False, 'from marshmallow import Schema, fields, INCLUDE\n'), ((619, 634), 'marshmallow.fields.String', 'fields.String', ([], {}), '()\n', (632, 634), False, 'from marshmallow import Schema, fields, INCLUDE\n'), ((661, 677), 'marshmallow.fields.Integer', 'fields.Integer', ([], {}), '()\n', (675, 677), False, 'from marshmallow import Schema, fields, INCLUDE\n'), ((703, 719), 'marshmallow.fields.Integer', 'fields.Integer', ([], {}), '()\n', (717, 719), False, 'from marshmallow import Schema, fields, INCLUDE\n'), ((1298, 1319), 'numpy.asarray', 'np.asarray', (['pil_image'], {}), '(pil_image)\n', (1308, 1319), True, 'import numpy as np\n')] |
# coding=utf-8
u"""
User: xulin
Date: 13-6-6
Time: 上午11:08
"""
import datetime
from sqlalchemy import Column, DateTime, text
from sqlalchemy.ext.declarative import declarative_base
class TBase(object):
created_date = Column(DateTime, default=datetime.datetime.now)
modified_date = Column(DateTime, default=datetime.datetime.now, onupdate=text('current_timestamp'))
Base = declarative_base(cls=TBase)
if __name__ == '__main__':
pass | [
"sqlalchemy.text",
"sqlalchemy.Column",
"sqlalchemy.ext.declarative.declarative_base"
] | [((384, 411), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {'cls': 'TBase'}), '(cls=TBase)\n', (400, 411), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((223, 270), 'sqlalchemy.Column', 'Column', (['DateTime'], {'default': 'datetime.datetime.now'}), '(DateTime, default=datetime.datetime.now)\n', (229, 270), False, 'from sqlalchemy import Column, DateTime, text\n'), ((348, 373), 'sqlalchemy.text', 'text', (['"""current_timestamp"""'], {}), "('current_timestamp')\n", (352, 373), False, 'from sqlalchemy import Column, DateTime, text\n')] |
#Script to demonstrate overwriting a service.
import arcpy
from sddraft_modifiers import HostedFeatureServiceProperties
print("Imported ArcPy")
# Initialize the variables
mxd = r'E:\UC_demo\Publishing\advanced_publishing\Good_cartography.mxd'
server_con = r'MY_HOSTED_SERVICES'
service_name = "Fortune_500_companies"
# Set output path
sd_file = r'E:\UC_demo\Publishing\advanced_publishing\overwrite.sd'
sddraft_file = r'E:\UC_demo\Publishing\advanced_publishing\overwrite.sddraft'
# Create a service definition draft
analysis_result = arcpy.mapping.CreateMapSDDraft(mxd, sddraft_file, service_name, server_con)
print("Initial SDdraft file created")
# Convert service type to Feature Service
service_properties = HostedFeatureServiceProperties(sddraft_file)
service_properties.change_to_feature_service()
print("Service type changed to Hosted Feature Service")
# Enable editing capability
service_properties.modify_web_capabilities("Editing")
print("Edit capability enabled")
# Convert publishing operation as overwrite
service_properties.modify_for_overwriting()
print("Updated publishing operation as overwrite")
# Stage and create Service Definition file
arcpy.StageService_server(sddraft_file, sd_file)
print("Service Definition file created")
# Publish the SD file as a service
arcpy.UploadServiceDefinition_server(sd_file, "My Hosted Services")
print("Service published successfully") | [
"arcpy.mapping.CreateMapSDDraft",
"arcpy.StageService_server",
"arcpy.UploadServiceDefinition_server",
"sddraft_modifiers.HostedFeatureServiceProperties"
] | [((553, 628), 'arcpy.mapping.CreateMapSDDraft', 'arcpy.mapping.CreateMapSDDraft', (['mxd', 'sddraft_file', 'service_name', 'server_con'], {}), '(mxd, sddraft_file, service_name, server_con)\n', (583, 628), False, 'import arcpy\n'), ((735, 779), 'sddraft_modifiers.HostedFeatureServiceProperties', 'HostedFeatureServiceProperties', (['sddraft_file'], {}), '(sddraft_file)\n', (765, 779), False, 'from sddraft_modifiers import HostedFeatureServiceProperties\n'), ((1196, 1244), 'arcpy.StageService_server', 'arcpy.StageService_server', (['sddraft_file', 'sd_file'], {}), '(sddraft_file, sd_file)\n', (1221, 1244), False, 'import arcpy\n'), ((1327, 1394), 'arcpy.UploadServiceDefinition_server', 'arcpy.UploadServiceDefinition_server', (['sd_file', '"""My Hosted Services"""'], {}), "(sd_file, 'My Hosted Services')\n", (1363, 1394), False, 'import arcpy\n')] |
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from quantr.maindash import app
def make_layout():
return html.Div(
[
dcc.Input(id="my-id", value="initial value", type="text"),
html.Div(id="my-div"),
]
)
@app.callback(
Output(component_id="my-div", component_property="children"),
[Input(component_id="my-id", component_property="value")],
)
def update_output_div(input_value):
return f"You've entered {input_value}"
| [
"dash_core_components.Input",
"dash.dependencies.Output",
"dash_html_components.Div",
"dash.dependencies.Input"
] | [((343, 403), 'dash.dependencies.Output', 'Output', ([], {'component_id': '"""my-div"""', 'component_property': '"""children"""'}), "(component_id='my-div', component_property='children')\n", (349, 403), False, 'from dash.dependencies import Input, Output\n'), ((410, 465), 'dash.dependencies.Input', 'Input', ([], {'component_id': '"""my-id"""', 'component_property': '"""value"""'}), "(component_id='my-id', component_property='value')\n", (415, 465), False, 'from dash.dependencies import Input, Output\n'), ((212, 269), 'dash_core_components.Input', 'dcc.Input', ([], {'id': '"""my-id"""', 'value': '"""initial value"""', 'type': '"""text"""'}), "(id='my-id', value='initial value', type='text')\n", (221, 269), True, 'import dash_core_components as dcc\n'), ((283, 304), 'dash_html_components.Div', 'html.Div', ([], {'id': '"""my-div"""'}), "(id='my-div')\n", (291, 304), True, 'import dash_html_components as html\n')] |
#!/usr/bin/env python3
import RPi.GPIO as GPIO
import subprocess
import time
from gpiozero import OutputDevice
SLEEP_INTERVAL = 3 # (seconds) How often we check the core temperature.
GPIO_PIN = 13 # Which GPIO pin you're using to control the fan.
DEBUG_LOGGING = False
def setup():
if DEBUG_LOGGING: print('Setting up GPIO...')
global pwm
GPIO.setmode(GPIO.BCM)
GPIO.setup(GPIO_PIN, GPIO.OUT)
GPIO.output(GPIO_PIN, GPIO.LOW)
pwm = GPIO.PWM(GPIO_PIN, 25000)
pwm.start(0)
def get_temp():
output = subprocess.run(['vcgencmd', 'measure_temp'], capture_output=True)
temp_str = output.stdout.decode()
try:
return float(temp_str.split('=')[1].split('\'')[0])
except (IndexError, ValueError):
raise RuntimeError('Could not parse temperature output.')
def loop():
temp = 0
while True:
prevTemp = temp
temp = get_temp()
if DEBUG_LOGGING: print('Core temperature:', temp)
if temp >= 70:
if prevTemp < 70:
pwm.ChangeDutyCycle(100)
if DEBUG_LOGGING: print('Temp >= 70, setting fan speed to 100%. Previous temp: ', prevTemp)
elif temp >= 60:
if prevTemp < 60 or prevTemp >= 70:
pwm.ChangeDutyCycle(80)
if DEBUG_LOGGING: print('Temp >= 60, setting fan speed to 80%. Previous temp: ', prevTemp)
elif temp >= 50:
if prevTemp < 50 or prevTemp >= 60:
pwm.ChangeDutyCycle(60)
if DEBUG_LOGGING: print('Temp >= 50, setting fan speed to 60%, Previous temp: ', prevTemp)
elif temp >= 40:
if prevTemp < 40 or prevTemp >= 50:
pwm.ChangeDutyCycle(40)
if DEBUG_LOGGING: print('Temp >= 40, setting fan speed to 40%, Previous temp: ', prevTemp)
elif temp >= 30:
if prevTemp < 30 or prevTemp >= 40:
pwm.ChangeDutyCycle(20)
if DEBUG_LOGGING: print('Temp >= 30, setting fan speed to 20%, Previous temp: ', prevTemp)
elif temp < 30:
if prevTemp >= 30:
pwm.ChangeDutyCycle(0)
if DEBUG_LOGGING: print('Temp < 30, setting fan speed to 0%, Previous temp: ', prevTemp)
time.sleep(SLEEP_INTERVAL)
def destroy():
if DEBUG_LOGGING: print('Cleaning up GPIO...')
pwm.stop()
GPIO.output(GPIO_PIN, GPIO.LOW)
GPIO.cleanup()
if __name__ == '__main__':
setup()
if DEBUG_LOGGING: print('GPIO setup complete. Starting main loop...')
try:
loop()
except KeyboardInterrupt:
destroy() | [
"RPi.GPIO.cleanup",
"RPi.GPIO.setup",
"RPi.GPIO.output",
"subprocess.run",
"time.sleep",
"RPi.GPIO.PWM",
"RPi.GPIO.setmode"
] | [((357, 379), 'RPi.GPIO.setmode', 'GPIO.setmode', (['GPIO.BCM'], {}), '(GPIO.BCM)\n', (369, 379), True, 'import RPi.GPIO as GPIO\n'), ((384, 414), 'RPi.GPIO.setup', 'GPIO.setup', (['GPIO_PIN', 'GPIO.OUT'], {}), '(GPIO_PIN, GPIO.OUT)\n', (394, 414), True, 'import RPi.GPIO as GPIO\n'), ((419, 450), 'RPi.GPIO.output', 'GPIO.output', (['GPIO_PIN', 'GPIO.LOW'], {}), '(GPIO_PIN, GPIO.LOW)\n', (430, 450), True, 'import RPi.GPIO as GPIO\n'), ((461, 486), 'RPi.GPIO.PWM', 'GPIO.PWM', (['GPIO_PIN', '(25000)'], {}), '(GPIO_PIN, 25000)\n', (469, 486), True, 'import RPi.GPIO as GPIO\n'), ((534, 599), 'subprocess.run', 'subprocess.run', (["['vcgencmd', 'measure_temp']"], {'capture_output': '(True)'}), "(['vcgencmd', 'measure_temp'], capture_output=True)\n", (548, 599), False, 'import subprocess\n'), ((2365, 2396), 'RPi.GPIO.output', 'GPIO.output', (['GPIO_PIN', 'GPIO.LOW'], {}), '(GPIO_PIN, GPIO.LOW)\n', (2376, 2396), True, 'import RPi.GPIO as GPIO\n'), ((2401, 2415), 'RPi.GPIO.cleanup', 'GPIO.cleanup', ([], {}), '()\n', (2413, 2415), True, 'import RPi.GPIO as GPIO\n'), ((2252, 2278), 'time.sleep', 'time.sleep', (['SLEEP_INTERVAL'], {}), '(SLEEP_INTERVAL)\n', (2262, 2278), False, 'import time\n')] |
import pytest
import os
import core.utils.csv as csv
def test_csv():
test_headers = ['style','country']
test_dict = dict(style='Porter', country='United Kingdom')
test_file = "beer.csv"
try:
csv.dict_writer(test_file, test_headers, test_dict)
except:
pass
if os.path.exists(test_file):
os.remove(test_file) | [
"os.path.exists",
"core.utils.csv.dict_writer",
"os.remove"
] | [((300, 325), 'os.path.exists', 'os.path.exists', (['test_file'], {}), '(test_file)\n', (314, 325), False, 'import os\n'), ((216, 267), 'core.utils.csv.dict_writer', 'csv.dict_writer', (['test_file', 'test_headers', 'test_dict'], {}), '(test_file, test_headers, test_dict)\n', (231, 267), True, 'import core.utils.csv as csv\n'), ((335, 355), 'os.remove', 'os.remove', (['test_file'], {}), '(test_file)\n', (344, 355), False, 'import os\n')] |
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import argparse
import numpy as np
import quantities as pq
import nptdms
import axographio
def tdms2axg(filename, force=False, verbose=True):
"""
Convert a TDMS file to an AxoGraph (AXGX) file
"""
if not os.path.isfile(filename):
raise ValueError('error: file not found: ' + filename)
if filename.split('.')[-1] != 'tdms':
raise ValueError('error: file does not appear to be a TDMS file (does not end in ".tdms"): ' + filename)
output_filename = '.'.join(filename.split('.')[:-1]) + '.axgx'
if os.path.isfile(output_filename) and not force:
raise OSError('error: output file exists, use force flag to overwrite: ' + output_filename)
# read the TDMS file
tdms_file = nptdms.TdmsFile.read(filename)
group = tdms_file.groups()[0] # use only first group
channels = group.channels()
if verbose:
print()
print('Properties of "' + filename + '":')
print()
for name, value in tdms_file.properties.items():
print(str(name) + ': ' + str(value))
print()
# collect the data for writing to AxoGraph format
names = ['Time (s)']
columns = [axographio.aslinearsequence(channels[0].time_track())] # assume time is same for all columns
for c in channels:
# try to determine channel units
unit_string = None
if 'unit_string' in c.properties:
u = c.properties['unit_string']
try:
# test whether the unit is recognizable
q = pq.Quantity(1, u)
unit_string = u
except LookupError:
try:
# try assuming its a simple compound unit (e.g., Nm = N*m)
u = '*'.join(u)
q = pq.Quantity(1, u)
unit_string = u
except LookupError:
# unit string cannot be interpreted
pass
if unit_string:
name = c.name + ' (' + unit_string + ')'
else:
name = c.name
names += [name]
columns += [c[:]]
if verbose:
print('Channel names:', names[1:])
print()
# write the AxoGraph file
if verbose:
print('Writing contents to AxoGraph file "' + output_filename + '"...')
f = axographio.file_contents(names, columns)
f.write(output_filename)
if verbose:
print('Done!')
return output_filename
def parse_args(argv):
description = """
A simple script for converting LabVIEW TDMS files to AxoGraph files.
The AxoGraph (AXGX) file is saved with the same name and in the same
directory as the TDMS file. By default, an existing AxoGraph file will not
be overwritten; use --force to overwrite it.
"""
parser = argparse.ArgumentParser(description=description)
parser.add_argument('file',
help='the path to a TDMS file')
parser.add_argument('-f', '--force', action='store_true', dest='force',
help='overwrite the output file if it already exists')
parser.add_argument('-q', '--quiet', action='store_false', dest='verbose',
help='run silently')
args = parser.parse_args(argv[1:])
return args
def main():
args = parse_args(sys.argv)
try:
tdms2axg(args.file, args.force, args.verbose)
except Exception as e:
# skip the traceback when run from the command line
print(e)
| [
"argparse.ArgumentParser",
"nptdms.TdmsFile.read",
"quantities.Quantity",
"os.path.isfile",
"axographio.file_contents"
] | [((817, 847), 'nptdms.TdmsFile.read', 'nptdms.TdmsFile.read', (['filename'], {}), '(filename)\n', (837, 847), False, 'import nptdms\n'), ((2413, 2453), 'axographio.file_contents', 'axographio.file_contents', (['names', 'columns'], {}), '(names, columns)\n', (2437, 2453), False, 'import axographio\n'), ((2894, 2942), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': 'description'}), '(description=description)\n', (2917, 2942), False, 'import argparse\n'), ((309, 333), 'os.path.isfile', 'os.path.isfile', (['filename'], {}), '(filename)\n', (323, 333), False, 'import os\n'), ((628, 659), 'os.path.isfile', 'os.path.isfile', (['output_filename'], {}), '(output_filename)\n', (642, 659), False, 'import os\n'), ((1620, 1637), 'quantities.Quantity', 'pq.Quantity', (['(1)', 'u'], {}), '(1, u)\n', (1631, 1637), True, 'import quantities as pq\n'), ((1862, 1879), 'quantities.Quantity', 'pq.Quantity', (['(1)', 'u'], {}), '(1, u)\n', (1873, 1879), True, 'import quantities as pq\n')] |
"""
Defines the Repository dictionary, which maps names to Cards
"""
from collections import defaultdict
from mtg_mana_simulator.card import Card
from mtg_mana_simulator.sequence import Sequence
Repository = defaultdict(lambda: Card.filler)
Repository['Arcane Signet'] = Card.untapped_rock(2, 1)
Repository['Azorius Signet'] = Card.untapped_rock(2, 1)
Repository['Boros Signet'] = Card.untapped_rock(2, 1)
Repository['Golgari Signet'] = Card.untapped_rock(2, 1)
Repository['Gruul Signet'] = Card.untapped_rock(2, 1)
Repository['Izzet Signet'] = Card.untapped_rock(2, 1)
Repository['Dimir Signet'] = Card.untapped_rock(2, 1)
Repository['Orzhov Signet'] = Card.untapped_rock(2, 1)
Repository['Rakdos Signet'] = Card.untapped_rock(2, 1)
Repository['Selesnya Signet'] = Card.untapped_rock(2, 1)
Repository['Simic Signet'] = Card.untapped_rock(2, 1)
Repository['Azorius Locket'] = Card.untapped_rock(3, 1)
Repository['Boros Locket'] = Card.untapped_rock(3, 1)
Repository['Golgari Locket'] = Card.untapped_rock(3, 1)
Repository['Gruul Locket'] = Card.untapped_rock(3, 1)
Repository['Izzet Locket'] = Card.untapped_rock(3, 1)
Repository['Dimir Locket'] = Card.untapped_rock(3, 1)
Repository['Orzhov Locket'] = Card.untapped_rock(3, 1)
Repository['Rakdos Locket'] = Card.untapped_rock(3, 1)
Repository['Selesnya Locket'] = Card.untapped_rock(3, 1)
Repository['Simic Locket'] = Card.untapped_rock(3, 1)
Repository['Azusa, Lost but Seeking'] = Card(cost=3, land_sequence=Sequence.repeat(2))
Repository['Dark Ritual'] = Card(cost=1, mana_sequence=Sequence.once(3))
Repository['Gitaxian Probe'] = Card.draw_spell(0, 1)
Repository['Illusion of Choice'] = Card.cantrip
Repository['Peek'] = Card.cantrip
Repository['Phyrexian Arena'] = Card(cost=3, draw_sequence=Sequence.one.prefixed_by([0]))
Repository['Reach Through Mists'] = Card.cantrip
Repository['Plains'] = Card.untapped_land
Repository['Island'] = Card.untapped_land
Repository['Swamp'] = Card.untapped_land
Repository['Mountain'] = Card.untapped_land
Repository['Forest'] = Card.untapped_land
Repository['Azorius Guildgate'] = Card.tapped_land
Repository['Boros Guildgate'] = Card.tapped_land
Repository['Golgari Guildgate'] = Card.tapped_land
Repository['Gruul Guildgate'] = Card.tapped_land
Repository['Izzet Guildgate'] = Card.tapped_land
Repository['Dimir Guildgate'] = Card.tapped_land
Repository['Orzhov Guildgate'] = Card.tapped_land
Repository['Rakdos Guildgate'] = Card.tapped_land
Repository['Selesnya Guildgate'] = Card.tapped_land
Repository['Simic Guildgate'] = Card.tapped_land
Repository['Arcane Sanctum'] = Card.tapped_land
Repository['Crumbling Necropolis'] = Card.tapped_land
Repository['Frontier Bivouac'] = Card.tapped_land
Repository['Jungle Shrine'] = Card.tapped_land
Repository['Mystic Monastery'] = Card.tapped_land
Repository['Nomad Outpost'] = Card.tapped_land
Repository['Opulent Palace'] = Card.tapped_land
Repository['Sandsteppe Citadel'] = Card.tapped_land
Repository['Savage Lands'] = Card.tapped_land
Repository['Seaside Citadel'] = Card.tapped_land
Repository['Akoum Refuge'] = Card.tapped_land
Repository['Bloodfell Caves'] = Card.tapped_land
Repository['Blossoming Sands'] = Card.tapped_land
Repository['Dismal Backwater'] = Card.tapped_land
Repository['Graypelt Refuge'] = Card.tapped_land
Repository['Jungle Hollow'] = Card.tapped_land
Repository['Jwar Isle Refuge'] = Card.tapped_land
Repository['Kazandu Refuge'] = Card.tapped_land
Repository['Rugged Highlands'] = Card.tapped_land
Repository['Scoured Barrens'] = Card.tapped_land
Repository['Sejiri Refuge'] = Card.tapped_land
Repository['Swiftwater Cliffs'] = Card.tapped_land
Repository['Thornwood Falls'] = Card.tapped_land
Repository['Tranquil Cove'] = Card.tapped_land
Repository['Wind-Scarred Crag'] = Card.tapped_land
| [
"mtg_mana_simulator.sequence.Sequence.once",
"collections.defaultdict",
"mtg_mana_simulator.sequence.Sequence.one.prefixed_by",
"mtg_mana_simulator.sequence.Sequence.repeat",
"mtg_mana_simulator.card.Card.untapped_rock",
"mtg_mana_simulator.card.Card.draw_spell"
] | [((210, 243), 'collections.defaultdict', 'defaultdict', (['(lambda : Card.filler)'], {}), '(lambda : Card.filler)\n', (221, 243), False, 'from collections import defaultdict\n'), ((275, 299), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (293, 299), False, 'from mtg_mana_simulator.card import Card\n'), ((332, 356), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (350, 356), False, 'from mtg_mana_simulator.card import Card\n'), ((389, 413), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (407, 413), False, 'from mtg_mana_simulator.card import Card\n'), ((446, 470), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (464, 470), False, 'from mtg_mana_simulator.card import Card\n'), ((503, 527), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (521, 527), False, 'from mtg_mana_simulator.card import Card\n'), ((560, 584), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (578, 584), False, 'from mtg_mana_simulator.card import Card\n'), ((617, 641), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (635, 641), False, 'from mtg_mana_simulator.card import Card\n'), ((674, 698), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (692, 698), False, 'from mtg_mana_simulator.card import Card\n'), ((731, 755), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (749, 755), False, 'from mtg_mana_simulator.card import Card\n'), ((788, 812), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (806, 812), False, 'from mtg_mana_simulator.card import Card\n'), ((845, 869), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(2)', '(1)'], {}), '(2, 1)\n', (863, 869), False, 'from mtg_mana_simulator.card import Card\n'), ((902, 926), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (920, 926), False, 'from mtg_mana_simulator.card import Card\n'), ((959, 983), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (977, 983), False, 'from mtg_mana_simulator.card import Card\n'), ((1016, 1040), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1034, 1040), False, 'from mtg_mana_simulator.card import Card\n'), ((1073, 1097), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1091, 1097), False, 'from mtg_mana_simulator.card import Card\n'), ((1130, 1154), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1148, 1154), False, 'from mtg_mana_simulator.card import Card\n'), ((1187, 1211), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1205, 1211), False, 'from mtg_mana_simulator.card import Card\n'), ((1244, 1268), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1262, 1268), False, 'from mtg_mana_simulator.card import Card\n'), ((1301, 1325), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1319, 1325), False, 'from mtg_mana_simulator.card import Card\n'), ((1358, 1382), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1376, 1382), False, 'from mtg_mana_simulator.card import Card\n'), ((1415, 1439), 'mtg_mana_simulator.card.Card.untapped_rock', 'Card.untapped_rock', (['(3)', '(1)'], {}), '(3, 1)\n', (1433, 1439), False, 'from mtg_mana_simulator.card import Card\n'), ((1631, 1652), 'mtg_mana_simulator.card.Card.draw_spell', 'Card.draw_spell', (['(0)', '(1)'], {}), '(0, 1)\n', (1646, 1652), False, 'from mtg_mana_simulator.card import Card\n'), ((1507, 1525), 'mtg_mana_simulator.sequence.Sequence.repeat', 'Sequence.repeat', (['(2)'], {}), '(2)\n', (1522, 1525), False, 'from mtg_mana_simulator.sequence import Sequence\n'), ((1582, 1598), 'mtg_mana_simulator.sequence.Sequence.once', 'Sequence.once', (['(3)'], {}), '(3)\n', (1595, 1598), False, 'from mtg_mana_simulator.sequence import Sequence\n'), ((1794, 1823), 'mtg_mana_simulator.sequence.Sequence.one.prefixed_by', 'Sequence.one.prefixed_by', (['[0]'], {}), '([0])\n', (1818, 1823), False, 'from mtg_mana_simulator.sequence import Sequence\n')] |
import random
from enum import Enum
class OrderStatus(Enum):
INITIATED = "INITIATED"
PENDING = "PENDING"
COMPLETE = "COMPLETE"
class Order:
"""Order object class."""
def __init__(self, details):
self.id = random.randint(100000, 999999)
self.details = details
self.status = OrderStatus.INITIATED.value
def __str__(self):
return f"Order id: {self.id} with details: {self.details}"
| [
"random.randint"
] | [((238, 268), 'random.randint', 'random.randint', (['(100000)', '(999999)'], {}), '(100000, 999999)\n', (252, 268), False, 'import random\n')] |
test_input = r'''..#.#..#####.#.#.#.###.##.....###.##.#..###.####..#####..#....#..#..##..###..######.###...####..#..#####..##..#.#####...##.#.#..#.##..#.#......#.###.######.###.####...#.##.##..#..#..#####.....#.#....###..#.##......#.....#..#..#..##..#...##.######.####.####.#.#...#.......#..#.#.#...####.##.#......#..#...##.#.##..#...##.#.##..###.#......#.#.......#.#.#.####.###.##...#.....####.#..#..#.##.#....##..#.####....##...##..#...#......#.#.......#.......##..####..#...#.#.#...##..#.#..###..#####........#..####......#..#
#..#.
#....
##..#
..#..
..###
'''
puzzle_input = open(__file__.replace('.py', '_input.txt')).read()
import copy
from collections import defaultdict
from operator import itemgetter
import time
class Image:
def __init__(self, input_):
self.parse_input(input_)
self.step = 0
def parse_input(self, input_):
lines = input_.splitlines()
self.key = lines[0]
self.initialize_image(self.key[0], lines[2:])
def initialize_image(self, default, input_):
# This part is tricky, and the key to the whole thing... when accessing
# pixels outside the known extents, those pixels are turning on and
# off each step, and will influence the values along the border of the
# known pixels.
if default == '#':
pixels = defaultdict(lambda:self.step % 2)
else:
pixels = defaultdict(lambda:0)
# Convert the "#" and "." to 0s and 1s so we can count the pixels
# easier at the end.
for row, line in enumerate(input_):
for column, pixel in enumerate(line):
pixels[(column,row)] = 1 if pixel == '#' else 0
self.image = pixels
self.get_image_dimensions()
def get_image_dimensions(self):
# since this only gets called once, and we start at 0,0 - this could
# be simplified.
coords = list(self.image.keys())
self.min_x = min(coords, key=itemgetter(0))[0]
self.max_x = max(coords, key=itemgetter(0))[0]
self.min_y = min(coords, key=itemgetter(1))[1]
self.max_y = max(coords, key=itemgetter(1))[1]
def enhance_image(self):
# iterate through the image, but we also need to consider each
# pixel just outside the current image. expand the range of pixel
# coordinates by 1 in each direction to get the border pixels.
enhanced_image = copy.copy(self.image)
for x in range(self.min_x - 1, self.max_x + 2):
for y in range(self.min_y - 1, self.max_y + 2):
enhanced_image[(x, y)] = self.get_enhanced_pixel(x, y)
self.image = enhanced_image
# expand the range for the next step.
self.min_x -= 1
self.max_x += 1
self.min_y -= 1
self.max_y += 1
self.step += 1
def get_enhanced_pixel(self, x, y):
# get the 8 neighbors of the current pixel, convert the values into
# a nine-digit binary string and convert to an integer to look up the
# new value from the "key". Neighrbors are read left-to-right, top-to-
# bottom.
binary = ''
for y1 in range(-1,2):
for x1 in range(-1,2):
binary += str(self.image[(x + x1, y + y1)])
offset = int(binary, 2)
new_pixel = self.key[offset]
return 1 if new_pixel == '#' else 0
def __repr__(self):
# print the current image for debugging.
rows = []
for y in range(self.min_y, self.max_y + 1):
r = ''
for x in range(self.min_x, self.max_x + 1):
r += '#' if self.image[(x, y)] == 1 else '.'
rows.append(r)
print('\n'.join(rows).count('#'))
return '\n'.join(rows)
def part_one(input_):
t0 = time.time()
im = Image(input_)
for i in range(2):
im.enhance_image()
print(time.time() - t0)
return sum(im.image.values())
def part_two(input_):
t0 = time.time()
im = Image(input_)
for i in range(50):
im.enhance_image()
print(time.time() - t0)
return sum(im.image.values())
assert part_one(test_input) == 35
assert part_two(test_input) == 3351
print(f'Part One: {part_one(puzzle_input)}.')
print(f'Part Two: {part_two(puzzle_input)}.') | [
"operator.itemgetter",
"copy.copy",
"time.time",
"collections.defaultdict"
] | [((3819, 3830), 'time.time', 'time.time', ([], {}), '()\n', (3828, 3830), False, 'import time\n'), ((4000, 4011), 'time.time', 'time.time', ([], {}), '()\n', (4009, 4011), False, 'import time\n'), ((2437, 2458), 'copy.copy', 'copy.copy', (['self.image'], {}), '(self.image)\n', (2446, 2458), False, 'import copy\n'), ((1343, 1378), 'collections.defaultdict', 'defaultdict', (['(lambda : self.step % 2)'], {}), '(lambda : self.step % 2)\n', (1354, 1378), False, 'from collections import defaultdict\n'), ((1412, 1435), 'collections.defaultdict', 'defaultdict', (['(lambda : 0)'], {}), '(lambda : 0)\n', (1423, 1435), False, 'from collections import defaultdict\n'), ((3916, 3927), 'time.time', 'time.time', ([], {}), '()\n', (3925, 3927), False, 'import time\n'), ((4098, 4109), 'time.time', 'time.time', ([], {}), '()\n', (4107, 4109), False, 'import time\n'), ((1983, 1996), 'operator.itemgetter', 'itemgetter', (['(0)'], {}), '(0)\n', (1993, 1996), False, 'from operator import itemgetter\n'), ((2038, 2051), 'operator.itemgetter', 'itemgetter', (['(0)'], {}), '(0)\n', (2048, 2051), False, 'from operator import itemgetter\n'), ((2093, 2106), 'operator.itemgetter', 'itemgetter', (['(1)'], {}), '(1)\n', (2103, 2106), False, 'from operator import itemgetter\n'), ((2148, 2161), 'operator.itemgetter', 'itemgetter', (['(1)'], {}), '(1)\n', (2158, 2161), False, 'from operator import itemgetter\n')] |
#import hickle as hkl
import numpy as np
from keras import backend as K
from keras.preprocessing.image import Iterator
import matplotlib.pyplot as plt
# Defines one class: SequenceGenerator. a subclass of Iterator
# ====================================
# Called from kitti_train.py and kitti_evaluate.py.
# Class SequenceGenerator is a subclass of Iterator.
# Data generator that creates sequences for input into PredNet.
class SequenceGenerator(Iterator): # Iterator: can be iterated over in for-loop
def __init__(self, data_file, source_file, nt,
batch_size=8, shuffle=False, seed=None,
output_mode='error', sequence_start_mode='all', N_seq=None,
data_format=K.image_data_format()):
print("\ndata_utils_RPB.py: Instantiating sequence generator:\n")
# LOAD DATA FILE
print("data_utils_RBP.py: Data file: \n", data_file)
self.X = np.load(data_file) # X will be like (n_images, nb_cols, nb_rows, nb_channels)
#self.X =hkl.transpose(self.X, (0, 3, 2, 1))
# ===============================================================
# Added statements to print out two consecutive frames. ASM
print("data_utils.py: self.X.shape\n", self.X.shape) # e.g., (41396, 128, 160, 3)
# print("1st row:\n", self.X[0,:,:,:]) # will print the raw array
# Print 1st two consecutive frames
# NOTE: the video sequence seems to be stored in reverse order!!! Is this a bug?
# 1. When called from "kitti_train.py" the frames for X_train.py and X_val.py seem
# to be stored in reverse order.
# 2. When called from "kitti_evaluate.py" the frames for X_test.py seem to be
# in correct order.
# 3. Need to make sure that the source files properly match the data files.
my_temp = np.array(self.X[0,:,:,:], dtype = int, copy = True) # convert from float to int
plt.imshow(my_temp) # look at 1st image
plt.show()
my_temp = np.array(self.X[1,:,:,:], dtype = int, copy = True) # convert from float to int
plt.imshow(my_temp) # look at 2nd image
plt.show()
# LOAD SOURCE FILE
print("data_utils.py: Source file: \n", source_file)
self.sources = np.load(source_file) # Labels in b'string' format
# Above: source for each image so when creating sequences can assure that consecutive
# frames are from same video
print("data_utils.py: self.sources.shape\n", self.sources.shape) # e.g., (41396,)
print(self.sources[0]) # should print a byte literal representation of a string
# End of print statements
# ===============================================================
# SET OTHER PARAMS
self.nt = nt # 10
self.batch_size = batch_size # 4 if called from "kitti_train.py"
self.data_format = data_format # K.image_data_format()
assert sequence_start_mode in {'all', 'unique'}, 'sequence_start_mode must be in {all, unique}'
self.sequence_start_mode = sequence_start_mode # default is 'all'
assert output_mode in {'error', 'prediction'}, 'output_mode must be in {error, prediction}'
self.output_mode = output_mode # default is 'error'
if self.data_format == 'channels_first': # tensorflow data format is 'channels_last'
self.X = np.transpose(self.X, (0, 3, 1, 2))
self.im_shape = self.X[0].shape # (128, 160, 3) I think.
if self.sequence_start_mode == 'all': # allow for any possible sequence, starting from any frame
self.possible_starts = np.array([i for i in range(self.X.shape[0] - self.nt) if self.sources[i] == self.sources[i + self.nt - 1]])
print("data_utils.py: possible_starts all: ", self.possible_starts)
elif self.sequence_start_mode == 'unique': #create sequences where each unique frame is in at most one sequence
curr_location = 0
possible_starts = []
while curr_location < self.X.shape[0] - self.nt + 1:
if self.sources[curr_location] == self.sources[curr_location + self.nt - 1]:
possible_starts.append(curr_location)
curr_location += self.nt
else:
curr_location += 1
self.possible_starts = possible_starts
print("data_utils.py: possible_starts unique: ", self.possible_starts)
if shuffle:
self.possible_starts = np.random.permutation(self.possible_starts)
if N_seq is not None and len(self.possible_starts) > N_seq: # select a subset of sequences if want to
self.possible_starts = self.possible_starts[:N_seq]
self.N_sequences = len(self.possible_starts)
super(SequenceGenerator, self).__init__(len(self.possible_starts), batch_size, shuffle, seed)
# End of __init__()
def __getitem__(self, null):
return self.next()
def next(self): # Returns a batch of x and y data
with self.lock:
current_index = (self.batch_index * self.batch_size) % self.n
index_array, current_batch_size = next(self.index_generator), self.batch_size
batch_x = np.zeros((current_batch_size, self.nt) + self.im_shape, np.float32)
for i, idx in enumerate(index_array):
idx = self.possible_starts[idx]
batch_x[i] = self.preprocess(self.X[idx:idx+self.nt])
if self.output_mode == 'error': # model outputs errors, so y should be zeros
batch_y = np.zeros(current_batch_size, np.float32)
elif self.output_mode == 'prediction': # output actual pixels
batch_y = batch_x
return batch_x, batch_y # inputs, targets
def preprocess(self, X):
return X.astype(np.float32) / 255 # maps to [0, 1]
# Returns 10 frames
def create_all(self):
# Below: plus operator is concatentation. Initialize multidim array of float32 zeros w/ specified shape
X_all = np.zeros((self.N_sequences, self.nt) + self.im_shape, np.float32)
for i, idx in enumerate(self.possible_starts):
X_all[i] = self.preprocess(self.X[idx:idx+self.nt]) # map [0,255] to [0,1] for 10 frames
return X_all
| [
"matplotlib.pyplot.imshow",
"numpy.transpose",
"keras.backend.image_data_format",
"numpy.random.permutation",
"numpy.array",
"numpy.zeros",
"numpy.load",
"matplotlib.pyplot.show"
] | [((718, 739), 'keras.backend.image_data_format', 'K.image_data_format', ([], {}), '()\n', (737, 739), True, 'from keras import backend as K\n'), ((928, 946), 'numpy.load', 'np.load', (['data_file'], {}), '(data_file)\n', (935, 946), True, 'import numpy as np\n'), ((1876, 1926), 'numpy.array', 'np.array', (['self.X[0, :, :, :]'], {'dtype': 'int', 'copy': '(True)'}), '(self.X[0, :, :, :], dtype=int, copy=True)\n', (1884, 1926), True, 'import numpy as np\n'), ((1964, 1983), 'matplotlib.pyplot.imshow', 'plt.imshow', (['my_temp'], {}), '(my_temp)\n', (1974, 1983), True, 'import matplotlib.pyplot as plt\n'), ((2012, 2022), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (2020, 2022), True, 'import matplotlib.pyplot as plt\n'), ((2041, 2091), 'numpy.array', 'np.array', (['self.X[1, :, :, :]'], {'dtype': 'int', 'copy': '(True)'}), '(self.X[1, :, :, :], dtype=int, copy=True)\n', (2049, 2091), True, 'import numpy as np\n'), ((2129, 2148), 'matplotlib.pyplot.imshow', 'plt.imshow', (['my_temp'], {}), '(my_temp)\n', (2139, 2148), True, 'import matplotlib.pyplot as plt\n'), ((2177, 2187), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (2185, 2187), True, 'import matplotlib.pyplot as plt\n'), ((2308, 2328), 'numpy.load', 'np.load', (['source_file'], {}), '(source_file)\n', (2315, 2328), True, 'import numpy as np\n'), ((5287, 5354), 'numpy.zeros', 'np.zeros', (['((current_batch_size, self.nt) + self.im_shape)', 'np.float32'], {}), '((current_batch_size, self.nt) + self.im_shape, np.float32)\n', (5295, 5354), True, 'import numpy as np\n'), ((6080, 6145), 'numpy.zeros', 'np.zeros', (['((self.N_sequences, self.nt) + self.im_shape)', 'np.float32'], {}), '((self.N_sequences, self.nt) + self.im_shape, np.float32)\n', (6088, 6145), True, 'import numpy as np\n'), ((3439, 3473), 'numpy.transpose', 'np.transpose', (['self.X', '(0, 3, 1, 2)'], {}), '(self.X, (0, 3, 1, 2))\n', (3451, 3473), True, 'import numpy as np\n'), ((4567, 4610), 'numpy.random.permutation', 'np.random.permutation', (['self.possible_starts'], {}), '(self.possible_starts)\n', (4588, 4610), True, 'import numpy as np\n'), ((5619, 5659), 'numpy.zeros', 'np.zeros', (['current_batch_size', 'np.float32'], {}), '(current_batch_size, np.float32)\n', (5627, 5659), True, 'import numpy as np\n')] |
import psycopg2
from datetime import datetime
from psycopg2 import sql
from est.fltr import county_return
from est.db.cur import con_cur
import numpy as np
import pandas as pd
def comp_find(est, a, b):
temp1 = est
temp2 = np.array(temp1[0])
county = temp2[0].strip()
state = temp2[1].strip()
cur, con = con_cur()
cur.execute("""
SELECT comp_st, comp_cty, comp_lv, comp_perc FROM est_LandValue(%s, %s, %s, %s)
""", (a, b, state, county))
comp_states = cur.fetchall()
con.close()
return(comp_states)
def find_comps(state, county, radius, population):
cur, con = con_cur()
cur.execute("""
SELECT comp_st, comp_cty, comp_lv, comp_perc FROM est_LandValue(%s, %s, %s, %s)
""", (radius, population, state, county))
comp_states = pd.DataFrame(cur.fetchall(), columns = ['State', 'County', 'Land Value', 'Perc Land Value'])
con.close()
return(comp_states)
| [
"numpy.array",
"est.db.cur.con_cur"
] | [((231, 249), 'numpy.array', 'np.array', (['temp1[0]'], {}), '(temp1[0])\n', (239, 249), True, 'import numpy as np\n'), ((325, 334), 'est.db.cur.con_cur', 'con_cur', ([], {}), '()\n', (332, 334), False, 'from est.db.cur import con_cur\n'), ((632, 641), 'est.db.cur.con_cur', 'con_cur', ([], {}), '()\n', (639, 641), False, 'from est.db.cur import con_cur\n')] |
#!/usr/bin/python3
import sys
import math
def getDigits(x):
digits = []
while x > 0:
digits.append(x % 10)
x = x // 10
digits.reverse()
return digits
def getInt(digits):
num = 0
digits.reverse()
multiplier = 1
for digit in digits:
num += digit * multiplier
multiplier *= 10
return num
# doesn't work if next largest palindrome is fewer digits than original, or if x isn't a palindrome
def genNextLargestPalindrome(x):
digits = getDigits(x)
numDigits = len(digits)
isEvenLength = numDigits % 2 == 0
innermostIndex = numDigits // 2
rightIndex = innermostIndex + 1
leftIndex = innermostIndex - 1
if isEvenLength:
leftIndex -= 1
if digits[innermostIndex] != 0:
digits[innermostIndex] -= 1
if isEvenLength:
digits[innermostIndex - 1] -= 1
else:
digits[innermostIndex] = 9
if isEvenLength:
digits[innermostIndex - 1] = 9
while digits[leftIndex] == 0:
digits[leftIndex] = 9
leftIndex -= 1
digits[rightIndex] = 9
rightIndex += 1
digits[leftIndex] -= 1
digits[rightIndex] -= 1
return getInt(digits)
def main():
maxDivisor = divisor = 999
maxProduct = palindromeCandidate = (maxDivisor + 1) * (maxDivisor + 1) - 1
while True:
quotient, remainder = divmod(palindromeCandidate, divisor)
isViableCandidate = True
squareRootOfCandidate = math.sqrt(palindromeCandidate)
while isViableCandidate:
while quotient < maxDivisor:
if remainder == 0:
print(palindromeCandidate)
print(quotient)
print(divisor)
return palindromeCandidate
else:
divisor -= 1
quotient, remainder = divmod(palindromeCandidate, divisor)
if divisor < squareRootOfCandidate:
isViableCandidate = False
isViableCandidate = False
palindromeCandidate = genNextLargestPalindrome(palindromeCandidate)
divisor = maxDivisor
# for arg in sys.argv[1::]:
# print(genNextLargestPalindrome(int(arg)))
if __name__ == '__main__':
main() | [
"math.sqrt"
] | [((1539, 1569), 'math.sqrt', 'math.sqrt', (['palindromeCandidate'], {}), '(palindromeCandidate)\n', (1548, 1569), False, 'import math\n')] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.1 on 2016-01-31 10:55
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Noun',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('word', models.CharField(max_length=100)),
('gender', models.CharField(choices=[('m', 'Maskulin'), ('f', 'Feminin'), ('n', 'Neutrum'), ('p', 'Plural')], max_length=1)),
('details', models.TextField(blank=True)),
('latest_update', models.DateTimeField(auto_now=True, db_index=True)),
],
),
migrations.AlterUniqueTogether(
name='noun',
unique_together=set([('word', 'gender')]),
),
]
| [
"django.db.models.DateTimeField",
"django.db.models.TextField",
"django.db.models.AutoField",
"django.db.models.CharField"
] | [((364, 457), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (380, 457), False, 'from django.db import migrations, models\n'), ((481, 513), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)'}), '(max_length=100)\n', (497, 513), False, 'from django.db import migrations, models\n'), ((543, 659), 'django.db.models.CharField', 'models.CharField', ([], {'choices': "[('m', 'Maskulin'), ('f', 'Feminin'), ('n', 'Neutrum'), ('p', 'Plural')]", 'max_length': '(1)'}), "(choices=[('m', 'Maskulin'), ('f', 'Feminin'), ('n',\n 'Neutrum'), ('p', 'Plural')], max_length=1)\n", (559, 659), False, 'from django.db import migrations, models\n'), ((686, 714), 'django.db.models.TextField', 'models.TextField', ([], {'blank': '(True)'}), '(blank=True)\n', (702, 714), False, 'from django.db import migrations, models\n'), ((751, 801), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now': '(True)', 'db_index': '(True)'}), '(auto_now=True, db_index=True)\n', (771, 801), False, 'from django.db import migrations, models\n')] |
from opentsp import helpers
def diamond_prune(instance):
# def list_prune(ls):
# var = True # may not be needed
# while var is True: # may not be needed
# bad_count = 0 # may not be needed
# semi_pruned_ls = [i for i in ls if i.fitness == 'good'] # may not be needed
# last_good_index = 0
# for index, edge in enumerate(semi_pruned_ls):
# if index == 0 or index == len(semi_pruned_ls) - 1:
# pass
# elif edge.length_ > semi_pruned_ls[last_good_index].length_ \
# or edge.length_ > semi_pruned_ls[index + 1].length_:
# edge.fitness = 'bad'
# # for i in instance.edges.values():
# # if i == edge:
# # i.fitness = 'bad'
# bad_count += 1 # may not be needed
# else:
# last_good_index = index
# if bad_count == 0: # may not be needed
# var = False # may not be needed
# pruned_ls = semi_pruned_ls # may not be needed
# return pruned_ls
#
# def list_prune_v2(ls, n): # changed 'bad' count check to a while loop less than three check, added another test
# loop = 1 # may not be needed
# while loop < n + 1: # may not be needed
# semi_pruned_ls = [i for i in ls if i.fitness == 'good'] # may not be needed
# if len(semi_pruned_ls) <= 2:
# break
# last_good_index = 0
# for index, edge in enumerate(semi_pruned_ls):
# if index == 0 or index == len(semi_pruned_ls) - 1:
# pass
# elif edge.length_ > semi_pruned_ls[last_good_index].length_ or \
# edge.length_ > semi_pruned_ls[index + 1].length_:
# edge.fitness = 'bad'
# else:
# last_good_index = index
# loop += 1
# if len(semi_pruned_ls) == 3:
# if semi_pruned_ls[1].length_ < semi_pruned_ls[0].length_ and \
# semi_pruned_ls[1].length_ < semi_pruned_ls[2].length_:
# if abs(helpers.angle(semi_pruned_ls[1].node_one, semi_pruned_ls[1].node_two,
# semi_pruned_ls[0].node_two)) < \
# abs(helpers.angle(semi_pruned_ls[1].node_one, semi_pruned_ls[1].node_two,
# semi_pruned_ls[2].node_two)):
# del semi_pruned_ls[0]
# else:
# del semi_pruned_ls[2]
# # add an else here in version 3 for when the middle edge is the middle length
# pruned_ls = semi_pruned_ls # may not be needed
# return pruned_ls
#
# def list_prune_v3(ls):
# loop = 1
# while loop < 3:
# # sp_ls stands for semi-pruned-ls
# sp_ls = [i for i in ls if i.fitness == 'good']
# if len(sp_ls) <= 3:
# break
# last_good_index = 0
# for index, edge in enumerate(sp_ls):
# if index == 0 or index == len(sp_ls) - 1:
# pass
# elif edge.length_ > sp_ls[last_good_index].length_ or edge.length_ > sp_ls[index + 1].length_:
# edge.fitness = 'bad'
# # for e in inst.edges.values():
# # if e == edge:
# # e.fitness = 'bad'
# else:
# last_good_index = index
# loop += 1
# if len(sp_ls) == 3:
# ang_to_0 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[0].node_two)
# ang_to_2 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[2].node_two)
# # if middle edge of remaining 3 is the shortest:
# if sp_ls[1].length_ < sp_ls[0].length_ and sp_ls[1].length_ < sp_ls[2].length_:
# # if angle to edge_0 in sp_ls is less then angle to edge_2 in sp_ls:
# if abs(ang_to_0) < abs(ang_to_2):
# del sp_ls[0]
# else:
# del sp_ls[2]
# elif sp_ls[1].length_ > sp_ls[0].length_ or sp_ls[1].length_ > sp_ls[2].length_:
# if abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
# del sp_ls[1]
# elif abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
# del sp_ls[0]
# elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
# del sp_ls[1]
# elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
# del sp_ls[2]
# else:
# print('The middle was the shortest but no edge was removed.')
# if len(sp_ls) > 3:
# print(f'The semi-pruned list had more three edges remaining on this iteration.')
# pruned_ls = sp_ls # may not be needed
# return pruned_ls
#
# def list_prune_v4(ls):
# while True:
# # sp_ls stands for semi-pruned-ls
# sp_ls = [i for i in ls if i.fitness == 'good']
# if len(sp_ls) <= 3:
# break
# last_good_index = 0
# for index, edge in enumerate(sp_ls):
# if index == 0 or index == len(sp_ls) - 1:
# pass
# elif edge.length_ > sp_ls[last_good_index].length_ or edge.length_ > sp_ls[index + 1].length_:
# edge.fitness = 'bad'
# # for e in inst.edges.values():
# # if e == edge:
# # e.fitness = 'bad'
# else:
# last_good_index = index
# if len(sp_ls) == 3:
# ang_to_0 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[0].node_two)
# ang_to_2 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[2].node_two)
# # if middle edge is the shortest:
# if sp_ls[1].length_ < sp_ls[0].length_ and sp_ls[1].length_ < sp_ls[2].length_:
# # if angle to edge_0 in sp_ls is less then angle to edge_2 in sp_ls:
# if abs(ang_to_0) < abs(ang_to_2):
# del sp_ls[0]
# else:
# del sp_ls[2]
# # else if middle edge is the middle length:
# elif sp_ls[1].length_ > sp_ls[0].length_ or sp_ls[1].length_ > sp_ls[2].length_:
# # comment what these ifs represent
# if abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
# del sp_ls[1]
# elif abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
# del sp_ls[0]
# elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
# del sp_ls[1]
# elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
# del sp_ls[2]
# else:
# print('The middle was the shortest but no edge was removed.')
# if len(sp_ls) > 3:
# print('The semi-pruned list had more three edges remaining on this iteration.')
# elif len(sp_ls) == 2:
# print('The semi-pruned list had two edges remaining on this iteration.')
# pruned_ls = sp_ls # may not be needed
# return pruned_ls
def list_prune_v5(ls):
while True:
# sp_ls stands for semi-pruned-ls
sp_ls = [i for i in ls if i.fitness == 'good']
if len(sp_ls) <= 4:
break
last_good_index = 0
for index, edge in enumerate(sp_ls):
if index == 0 or index == len(sp_ls) - 1:
pass
# if the edge is the middle in length:
elif edge.length_ > sp_ls[last_good_index].length_ or edge.length_ > sp_ls[index + 1].length_:
# print('Calculating angles.')
# if max(abs(sp_ls[last_good_index].angle), abs(sp_ls[index + 1].length)) - \
# min(abs(sp_ls[last_good_index].angle), abs(sp_ls[index + 1].length)) > 120:
edge.fitness = 'bad'
else:
last_good_index = index
if len(sp_ls) == 3:
ang_to_0 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[0].node_two)
ang_to_2 = helpers.angle(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[2].node_two)
# if middle edge is the shortest:
if sp_ls[1].length_ < sp_ls[0].length_ and sp_ls[1].length_ < sp_ls[2].length_:
# if angle to edge_0 in sp_ls is less then angle to edge_2 in sp_ls:
if abs(ang_to_0) < abs(ang_to_2):
del sp_ls[0]
else:
del sp_ls[2]
# else if middle edge is the middle length:
elif sp_ls[1].length_ > sp_ls[0].length_ or sp_ls[1].length_ > sp_ls[2].length_:
# comment what these ifs represent
if abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
del sp_ls[1]
elif abs(ang_to_0) < abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
del sp_ls[0]
elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ > sp_ls[2].length_:
del sp_ls[1]
elif abs(ang_to_0) > abs(ang_to_2) and sp_ls[0].length_ < sp_ls[2].length_:
del sp_ls[2]
else:
print('The middle was the shortest but no edge was removed.')
if len(sp_ls) > 3:
print('The semi-pruned list had more three edges remaining on this iteration.')
elif len(sp_ls) == 2:
print('The semi-pruned list had two edges remaining on this iteration.')
pruned_ls = sp_ls # may not be needed
return pruned_ls
# populate edge angles
# eap_start = time.time()
# edg_count = 0
for edge in instance.edges.values():
# edg_count += 1
# print(f'Populating edge angle: {edg_count}')
edge.angle = helpers.angle(edge.node_one, instance.average_node, edge.node_two)
# eap_end = time.time()
# for each node, list the edges for which it is the origin, and which have good fitness
good_edges = []
# node_count = 0
# pn_start = time.time()
for node in instance.nodes.values():
# node_count += 1
# print(node_count)
ls = [i for i in instance.edges.values() if i.node_one == node]
ls.sort(key=lambda x: x.angle)
# n_start = time.time()
good = list_prune_v5(ls) # prune the list of edges
# n_end = time.time()
# print(f'Time taken for this node: {n_end - n_start}')
# print(instance.edges)
good_edges.extend(good) # this may not be needed...
# pn_end = time.time()
# print(f'Populating edge angles took: {eap_end - eap_start}')
# print(f'Processing the nodes took: {pn_end - pn_start}')
| [
"opentsp.helpers.angle"
] | [((10504, 10570), 'opentsp.helpers.angle', 'helpers.angle', (['edge.node_one', 'instance.average_node', 'edge.node_two'], {}), '(edge.node_one, instance.average_node, edge.node_two)\n', (10517, 10570), False, 'from opentsp import helpers\n'), ((8650, 8720), 'opentsp.helpers.angle', 'helpers.angle', (['sp_ls[1].node_one', 'sp_ls[1].node_two', 'sp_ls[0].node_two'], {}), '(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[0].node_two)\n', (8663, 8720), False, 'from opentsp import helpers\n'), ((8744, 8814), 'opentsp.helpers.angle', 'helpers.angle', (['sp_ls[1].node_one', 'sp_ls[1].node_two', 'sp_ls[2].node_two'], {}), '(sp_ls[1].node_one, sp_ls[1].node_two, sp_ls[2].node_two)\n', (8757, 8814), False, 'from opentsp import helpers\n')] |
from aiohttp import web
import socketio
import numpy as np
def load_eigenvector(k,d):
vec_path = "eigenvectors/eigen_k=" + str(k) + ",d=" + str(d) + ".npy"
eigenvector_np = np.load(vec_path)
eigenvector_str = ""
for x in np.nditer(eigenvector_np):
eigenvector_str += str(x) + " "
# print()
# print(eigenvector_str)
return eigenvector_str
# creates a new Async Socket IO Server
sio = socketio.AsyncServer(cors_allowed_origins="*")
# Creates a new Aiohttp Web Application
app = web.Application()
# Binds our Socket.IO server to our Web App
# instance
sio.attach(app)
# we can define aiohttp endpoints just as we normally
# would with no change
async def index(request):
with open('index.html') as f:
return web.Response(text=f.read(), content_type='text/html')
async def test(request):
with open('test.js') as f:
return web.Response(text=f.read(), content_type='text/js')
# If we wanted to create a new websocket endpoint,
# use this decorator, passing in the name of the
# event we wish to listen out for
@sio.on('hi')
async def print_message(sid, message, d_JS):
k = message
d = d_JS
# print(k)
# print(d)
messageToJS = load_eigenvector(k,d)
# print()
# print(messageToJS)
# print()
# print(messageToJS)
# When we receive a new event of type
# 'message' through a socket.io connection
# we print the socket ID and the message
# print("Socket ID: " , sid)
# print(message) #message is the value sent from the HTML
await sio.emit('message', messageToJS)
# notice it has to be of type 'message' and then pass the
# value to send to html doc
# @sio.on('d')
# async def get_d_val(sid, message):
# d = message
# We bind our aiohttp endpoint to our app
# router
app.router.add_get('/', index)
app.router.add_get('/test.js', test)
# We kick off our server
if __name__ == '__main__':
web.run_app(app)
| [
"aiohttp.web.run_app",
"numpy.nditer",
"aiohttp.web.Application",
"socketio.AsyncServer",
"numpy.load"
] | [((423, 469), 'socketio.AsyncServer', 'socketio.AsyncServer', ([], {'cors_allowed_origins': '"""*"""'}), "(cors_allowed_origins='*')\n", (443, 469), False, 'import socketio\n'), ((516, 533), 'aiohttp.web.Application', 'web.Application', ([], {}), '()\n', (531, 533), False, 'from aiohttp import web\n'), ((182, 199), 'numpy.load', 'np.load', (['vec_path'], {}), '(vec_path)\n', (189, 199), True, 'import numpy as np\n'), ((238, 263), 'numpy.nditer', 'np.nditer', (['eigenvector_np'], {}), '(eigenvector_np)\n', (247, 263), True, 'import numpy as np\n'), ((1924, 1940), 'aiohttp.web.run_app', 'web.run_app', (['app'], {}), '(app)\n', (1935, 1940), False, 'from aiohttp import web\n')] |
import demistomock as demisto
from CommonServerPython import *
from CommonServerUserPython import *
from typing import Union, Optional
''' IMPORTS '''
import requests
import base64
import os
import binascii
# Disable insecure warnings
requests.packages.urllib3.disable_warnings()
""" GLOBALS/PARAMS """
# Global annotation
CONTEXT = demisto.getIntegrationContext()
DEMISTOBOT = 'https://demistobot.demisto.com/msg-mail-token'
# Credentials
TOKEN = demisto.params().get('token')
TENANT_ID = demisto.params().get('tenant_id')
# Remove trailing slash to prevent wrong URL path to service
URL = demisto.params().get('url')
SERVER = URL[:-1] if (URL and URL.endswith('/')) else URL
# Should we use SSL
USE_SSL = not demisto.params().get('unsecure', False)
# Service base URL
BASE_URL = str(SERVER) + '/v1.0'
# Remove proxy if not set to true in params
if not demisto.params().get('proxy'):
os.environ.pop('HTTP_PROXY', '')
os.environ.pop('HTTPS_PROXY', '')
os.environ.pop('http_proxy', '')
os.environ.pop('https_proxy', '')
''' HELPER FUNCTIONS '''
def error_parser(resp_err: requests.Response) -> str:
"""
Args:
error (requests.Response): response with error
Returns:
str: string of error
"""
try:
response = resp_err.json()
error = response.get('error', {})
err_str = f"{error.get('code')}: {error.get('message')}"
if err_str:
return err_str
# If no error message
raise ValueError
except ValueError:
return resp_err.text
def http_request(method: str, url_suffix: str = '', params: dict = None, data: dict = None, odata: str = None,
url: str = None) -> dict:
"""
A wrapper for requests lib to send our requests and handle requests and responses better
Headers to be sent in requests
Args:
method (str): any restful method
url_suffix (str): suffix to add to BASE_URL
params (str): http params
data (dict): http body
odata (str): odata query format
url (str): url to replace if need a new api call
Returns:
dict: requests.json()
"""
token = get_token()
headers = {
'Authorization': f'Bearer {token}',
'Content-Type': 'application/json',
'Accept': 'application/json'
}
if odata:
url_suffix += odata
res = requests.request(
method,
url if url else BASE_URL + url_suffix,
verify=USE_SSL,
params=params,
data=data,
headers=headers
)
# Handle error responses gracefully
if not (199 < res.status_code < 299):
error = error_parser(res)
return_error(f'Error in API call to Microsoft Graph Mail Integration [{res.status_code}] - {error}')
try:
return res.json()
except ValueError:
return_error('Could not decode response from API')
return {} # return_error will exit
def epoch_seconds(d: datetime = None) -> int:
"""
Return the number of seconds for given date. If no date, return current.
Args:
d (datetime): timestamp
Returns:
int: timestamp in epoch
"""
if not d:
d = datetime.utcnow()
return int((d - datetime.utcfromtimestamp(0)).total_seconds())
def get_token() -> str:
"""
Check if we have a valid token and if not get one from demistobot
Returns:
str: token from demistobot
"""
product = 'MicrosoftGraphMail'
token = CONTEXT.get('token')
stored = CONTEXT.get('stored')
if token and stored:
if epoch_seconds() - stored < 60 * 60 - 30:
return token
headers = {
'Authorization': TOKEN,
'Accept': 'application/json'
}
r = requests.get(
DEMISTOBOT,
headers=headers,
params={
'tenant': TENANT_ID,
'product': product
},
verify=USE_SSL
)
if r.status_code != requests.codes.ok:
return_error(
f'Error when trying to get token from Demisto Bot: [{r.status_code}] - {r.text}')
data = r.json()
demisto.setIntegrationContext(
{
'token': data.get('token'),
'stored': epoch_seconds()
}
)
return data.get('token')
def assert_pages(pages: Union[str, int]) -> int:
"""
Args:
pages (str or int): pages need to pull in int or str
Returns:
int: default 1
"""
if isinstance(pages, str) and pages.isdigit():
return int(pages)
elif isinstance(pages, int):
return pages
return 1
def build_folders_path(folder_string: str) -> Optional[str]:
"""
Args:
folder_string (str): string with `,` delimiter. first one is mailFolders all other are child
Returns:
str or None: string with path to the folder and child folders
"""
if isinstance(folder_string, str):
path = 'mailFolders/'
folders_list = argToList(folder_string, ',')
first = True
for folder in folders_list:
if first:
path += folder
first = False
else:
path += f'/childFolders/{folder}'
return path
return None
def pages_puller(response: dict, page_count: int) -> list:
""" Gets first response from API and returns all pages
Args:
response (dict):
page_count (int):
Returns:
list: list of all pages
"""
responses = [response]
i = page_count
while i != 0:
next_link = response.get('@odata.nextLink')
if next_link:
responses.append(
http_request('GET', url=next_link)
)
else:
return responses
i -= 1
return responses
def build_mail_object(raw_response: Union[dict, list], user_id: str, get_body: bool = False) -> Union[dict, list]:
"""Building mail entry context
Getting a list from build_mail_object
Args:
user_id (str): user id of the mail
get_body (bool): should get body
raw_response (dict or list): list of pages
Returns:
dict or list: output context
"""
def build_mail(given_mail: dict) -> dict:
"""
Args:
given_mail (dict):
Returns:
dict:
"""
# Dicts
mail_properties = {
'ID': 'id',
'Created': 'createdDateTime',
'LastModifiedTime': 'lastModifiedDateTime',
'ReceivedTime': 'receivedDateTime',
'SendTime': 'sentDateTime',
'Categories': 'categories',
'HasAttachments': 'hasAttachments',
'Subject': 'subject',
'IsDraft': 'isDraft'
}
contact_properties = {
'Sender': 'sender',
'From': 'from',
'CCRecipients': 'ccRecipients',
'BCCRecipients': 'bccRecipients',
'ReplyTo': 'replyTo'
}
# Create entry properties
entry = {k: given_mail.get(v) for k, v in mail_properties.items()}
# Create contacts properties
entry.update(
{k: build_contact(given_mail.get(v)) for k, v in contact_properties.items()} # type: ignore
)
if get_body:
entry['Body'] = given_mail.get('body', {}).get('content')
entry['UserID'] = user_id
return entry
def build_contact(contacts: Union[dict, list, str]) -> object:
"""Building contact object
Args:
contacts (list or dict or str):
Returns:
dict or list[dict] or str or None: describing contact
"""
if contacts:
if isinstance(contacts, list):
return [build_contact(contact) for contact in contacts]
elif isinstance(contacts, dict):
email = contacts.get('emailAddress')
if email and isinstance(email, dict):
return {
'Name': email.get('name'),
'Address': email.get('address')
}
return None
mails_list = list()
if isinstance(raw_response, list):
for page in raw_response:
# raw_response can be a list containing multiple pages or one response
# if value in page, we got
value = page.get('value')
if value:
for mail in value:
mails_list.append(build_mail(mail))
else:
mails_list.append(build_mail(page))
elif isinstance(raw_response, dict):
return build_mail(raw_response)
return mails_list
def file_result_creator(raw_response: dict) -> dict:
"""
Args:
raw_response (dict):
Returns:
dict:
"""
name = raw_response.get('name')
data = raw_response.get('contentBytes')
try:
data = base64.b64decode(data) # type: ignore
return fileResult(name, data)
except binascii.Error:
return_error('Attachment could not be decoded')
return {} # return_error will exit
''' COMMANDS + REQUESTS FUNCTIONS '''
def test_module():
"""
Performs basic get request to get item samples
"""
url = BASE_URL + '/me'
token = get_token()
headers = {
'Authorization': f'Bearer {token}',
'Content-Type': 'application/json',
'Accept': 'application/json'
}
response = requests.get(url, headers=headers, verify=USE_SSL)
if response.status_code == 403:
return True
else:
return_error(error_parser(response))
def list_mails(user_id: str, folder_id: str = '', search: str = None, odata: str = None) -> Union[dict, list]:
"""Returning all mails from given user
Args:
user_id (str):
folder_id (str):
search (str):
odata (str):
Returns:
dict or list:
"""
no_folder = f'/users/{user_id}/messages/'
with_folder = f'users/{user_id}/{build_folders_path(folder_id)}/messages/'
pages_to_pull = demisto.args().get('pages_to_pull', 1)
if search:
odata = f'?{odata}$search={search}' if odata else f'?$search={search}'
suffix = with_folder if folder_id else no_folder
response = http_request('GET', suffix, odata=odata)
return pages_puller(response, assert_pages(pages_to_pull))
def list_mails_command():
search = demisto.args().get('search')
user_id = demisto.args().get('user_id')
folder_id = demisto.args().get('folder_id')
odata = demisto.args().get('odata')
raw_response = list_mails(user_id, folder_id=folder_id, search=search, odata=odata)
mail_context = build_mail_object(raw_response, user_id)
entry_context = {'MSGraphMail(var.ID === obj.ID)': mail_context}
# human_readable builder
human_readable = tableToMarkdown(
f'### Total of {len(mail_context)} of mails received',
mail_context,
headers=['Subject', 'From', 'SendTime']
)
return_outputs(human_readable, entry_context, raw_response)
def delete_mail(user_id: str, message_id: str, folder_id: str = None) -> bool:
"""
Args:
user_id (str):
message_id (str):
folder_id (str):
Returns:
bool
"""
with_folder = f'/users/{user_id}/{build_folders_path(folder_id)}/messages/{message_id}' # type: ignore
no_folder = f'/users/{user_id}/messages/{message_id}'
suffix = with_folder if folder_id else no_folder
http_request('DELETE', suffix)
return True
def delete_mail_command():
user_id = demisto.args().get('user_id')
folder_id = demisto.args().get('folder_id')
message_id = demisto.args().get('message_id')
delete_mail(user_id, message_id, folder_id)
human_readable = tableToMarkdown(
'Message has been deleted successfully',
{
'Message ID': message_id,
'User ID': user_id,
'Folder ID': folder_id
},
headers=['Message ID', 'User ID', 'Folder ID'],
removeNull=True
)
entry_context = {
f'MSGraphMail(val.ID === {message_id}': None
}
return_outputs(human_readable, entry_context)
def get_attachment(message_id: str, user_id: str, attachment_id: str, folder_id: str = None) -> dict:
"""
Args:
message_id (str):
user_id (str_:
attachment_id (str):
folder_id (str):
Returns:
dict:
"""
no_folder = f'/users/{user_id}/messages/{message_id}/attachments/{attachment_id}'
with_folder = (f'/users/{user_id}/{build_folders_path(folder_id)}/' # type: ignore
f'messages/{message_id}/attachments/{attachment_id}')
suffix = with_folder if folder_id else no_folder
response = http_request('GET', suffix)
return response
def get_attachment_command():
message_id = demisto.args().get('message_id')
user_id = demisto.args().get('user_id')
folder_id = demisto.args().get('folder_id')
attachment_id = demisto.args().get('attachment_id')
raw_response = get_attachment(message_id, user_id, folder_id=folder_id, attachment_id=attachment_id)
entry_context = file_result_creator(raw_response)
demisto.results(entry_context)
def get_message(user_id: str, message_id: str, folder_id: str = None, odata: str = None) -> dict:
"""
Args:
user_id (str): User ID to pull message from
message_id (str): Message ID to pull
folder_id: (str) Folder ID to pull from
odata (str): OData query
Returns
dict: request json
"""
no_folder = f'/users/{user_id}/messages/{message_id}/'
with_folder = (f'/users/{user_id}/{build_folders_path(folder_id)}' # type: ignore
f'/messages/{message_id}/')
suffix = with_folder if folder_id else no_folder
response = http_request('GET', suffix, odata=odata)
# Add user ID
response['userId'] = user_id
return response
def get_message_command():
user_id = demisto.args().get('user_id')
folder_id = demisto.args().get('folder_id')
message_id = demisto.args().get('message_id')
get_body = demisto.args().get('get_body') == 'true'
odata = demisto.args().get('odata')
raw_response = get_message(user_id, message_id, folder_id, odata=odata)
mail_context = build_mail_object(raw_response, user_id=user_id, get_body=get_body)
entry_context = {'MSGraphMail(val.ID === obj.ID)': mail_context}
human_readable = tableToMarkdown(
f'Results for message ID {message_id}',
mail_context,
headers=['ID', 'Subject', 'SendTime', 'Sender', 'From', 'HasAttachments', 'Body']
)
return_outputs(
human_readable,
entry_context,
raw_response=raw_response
)
def list_attachments(user_id: str, message_id: str, folder_id: str) -> dict:
"""Listing all the attachments
Args:
user_id (str):
message_id (str):
folder_id (str):
Returns:
dict:
"""
no_folder = f'/users/{user_id}/messages/{message_id}/attachments/'
with_folder = f'/users/{user_id}/{build_folders_path(folder_id)}/messages/{message_id}/attachments/'
suffix = with_folder if folder_id else no_folder
return http_request('GET', suffix)
def list_attachments_command():
user_id = demisto.args().get('user_id')
message_id = demisto.args().get('message_id')
folder_id = demisto.args().get('folder_id')
raw_response = list_attachments(user_id, message_id, folder_id)
attachments = raw_response.get('value')
if attachments:
attachment_list = [{
'ID': attachment.get('id'),
'Name': attachment.get('name'),
'Type': attachment.get('contentType')
} for attachment in attachments]
attachment_entry = {'ID': message_id, 'Attachment': attachment_list, 'UserID': user_id}
entry_context = {'MSGraphMailAttachment(val.ID === obj.ID)': attachment_entry}
# Build human readable
file_names = [attachment.get('Name') for attachment in attachment_list if isinstance(
attachment, dict) and attachment.get('Name')]
human_readable = tableToMarkdown(
f'Total of {len(attachment_list)} attachments found in message {message_id} from user {user_id}',
{'File names': file_names}
)
return_outputs(human_readable, entry_context, raw_response)
else:
human_readable = f'### No attachments found in message {message_id}'
return_outputs(human_readable, dict(), raw_response)
def main():
""" COMMANDS MANAGER / SWITCH PANEL """
command = demisto.command()
LOG(f'Command being called is {command}')
try:
if command == 'test-module':
# This is the call made when pressing the integration test button.
test_module()
demisto.results('ok')
elif command in ('msgraph-mail-list-emails', 'msgraph-mail-search-email'):
list_mails_command()
elif command == 'msgraph-mail-get-email':
get_message_command()
elif command == 'msgraph-mail-delete-email':
delete_mail_command()
elif command == 'msgraph-mail-list-attachments':
list_attachments_command()
elif command == 'msgraph-mail-get-attachment':
get_attachment_command()
# Log exceptions
except Exception as e:
LOG(e)
LOG.print_log()
raise
if __name__ == "builtins":
main()
| [
"demistomock.params",
"demistomock.command",
"requests.packages.urllib3.disable_warnings",
"demistomock.args",
"base64.b64decode",
"requests.request",
"os.environ.pop",
"demistomock.getIntegrationContext",
"requests.get",
"demistomock.results"
] | [((238, 282), 'requests.packages.urllib3.disable_warnings', 'requests.packages.urllib3.disable_warnings', ([], {}), '()\n', (280, 282), False, 'import requests\n'), ((337, 368), 'demistomock.getIntegrationContext', 'demisto.getIntegrationContext', ([], {}), '()\n', (366, 368), True, 'import demistomock as demisto\n'), ((894, 926), 'os.environ.pop', 'os.environ.pop', (['"""HTTP_PROXY"""', '""""""'], {}), "('HTTP_PROXY', '')\n", (908, 926), False, 'import os\n'), ((931, 964), 'os.environ.pop', 'os.environ.pop', (['"""HTTPS_PROXY"""', '""""""'], {}), "('HTTPS_PROXY', '')\n", (945, 964), False, 'import os\n'), ((969, 1001), 'os.environ.pop', 'os.environ.pop', (['"""http_proxy"""', '""""""'], {}), "('http_proxy', '')\n", (983, 1001), False, 'import os\n'), ((1006, 1039), 'os.environ.pop', 'os.environ.pop', (['"""https_proxy"""', '""""""'], {}), "('https_proxy', '')\n", (1020, 1039), False, 'import os\n'), ((2387, 2514), 'requests.request', 'requests.request', (['method', '(url if url 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from django.conf import settings
from django.template.defaulttags import register
from django.urls import reverse
from accounts.models import User
from supply_chains.models import SupplyChain, SupplyChainUmbrella
@register.simple_tag
def get_feedback_emails_as_string() -> str:
"""Formats emails as comma separated string to be passed to a mailto link"""
users = User.objects.filter(receive_feedback_emails=True)
emails = [user.email for user in users]
return ",".join(emails)
@register.simple_tag
def get_action_progress_route(user) -> str:
"""Return SAP route based on the logged in user"""
route = None
if user.is_admin:
route = reverse("action-progress")
else:
route = reverse(
"action-progress-department", kwargs={"dept": user.gov_department.name}
)
return route
@register.simple_tag(takes_context=True)
def get_active_menu(context):
menu = None
resolver = context["request"].resolver_match
view_name, route = resolver.view_name, resolver.route
if route.startswith("supply-chains/"):
menu = "updates"
if route.startswith("action-progress/"):
menu = "sap"
if route.startswith("chain-details/"):
menu = "scd"
if route == "" and view_name == "index":
menu = "home"
return menu
@register.simple_tag(takes_context=False)
def quicksight_countries_dashboard_url():
return settings.QUICKSIGHT_COUNTRIES_DASHBOARD_URL
@register.simple_tag(takes_context=True)
def visualisation_link(context):
request = context["request"]
user = request.user
gov_department = user.gov_department
visualisation_url = gov_department.visualisation_url
return visualisation_url
@register.simple_tag(takes_context=False)
def get_tasklist_link(supply_chain_slug):
"""Return link to tasklist page.
With SC Umbrella feature, this link vary depending on the supply chain.
If part of umbrella, it goes back to umbrella tasklist otherwise to supply chain.
"""
sc = SupplyChain.objects.get(slug=supply_chain_slug)
if sc.supply_chain_umbrella:
slug = sc.supply_chain_umbrella.slug
else:
slug = supply_chain_slug
return reverse("supply-chain-task-list", kwargs={"supply_chain_slug": slug})
| [
"supply_chains.models.SupplyChain.objects.get",
"accounts.models.User.objects.filter",
"django.template.defaulttags.register.simple_tag",
"django.urls.reverse"
] | [((850, 889), 'django.template.defaulttags.register.simple_tag', 'register.simple_tag', ([], {'takes_context': '(True)'}), '(takes_context=True)\n', (869, 889), False, 'from django.template.defaulttags import register\n'), ((1329, 1369), 'django.template.defaulttags.register.simple_tag', 'register.simple_tag', ([], {'takes_context': '(False)'}), '(takes_context=False)\n', (1348, 1369), False, 'from django.template.defaulttags import register\n'), ((1470, 1509), 'django.template.defaulttags.register.simple_tag', 'register.simple_tag', ([], {'takes_context': '(True)'}), '(takes_context=True)\n', (1489, 1509), False, 'from django.template.defaulttags import register\n'), ((1730, 1770), 'django.template.defaulttags.register.simple_tag', 'register.simple_tag', ([], {'takes_context': '(False)'}), '(takes_context=False)\n', (1749, 1770), False, 'from django.template.defaulttags import register\n'), ((374, 423), 'accounts.models.User.objects.filter', 'User.objects.filter', ([], {'receive_feedback_emails': '(True)'}), '(receive_feedback_emails=True)\n', (393, 423), False, 'from accounts.models import User\n'), ((2030, 2077), 'supply_chains.models.SupplyChain.objects.get', 'SupplyChain.objects.get', ([], {'slug': 'supply_chain_slug'}), '(slug=supply_chain_slug)\n', (2053, 2077), False, 'from supply_chains.models import SupplyChain, SupplyChainUmbrella\n'), ((2212, 2281), 'django.urls.reverse', 'reverse', (['"""supply-chain-task-list"""'], {'kwargs': "{'supply_chain_slug': slug}"}), "('supply-chain-task-list', kwargs={'supply_chain_slug': slug})\n", (2219, 2281), False, 'from django.urls import reverse\n'), ((673, 699), 'django.urls.reverse', 'reverse', (['"""action-progress"""'], {}), "('action-progress')\n", (680, 699), False, 'from django.urls import reverse\n'), ((726, 811), 'django.urls.reverse', 'reverse', (['"""action-progress-department"""'], {'kwargs': "{'dept': user.gov_department.name}"}), "('action-progress-department', kwargs={'dept': user.gov_department.name}\n )\n", (733, 811), False, 'from django.urls import reverse\n')] |
from decimal import Decimal,getcontext
getcontext().prec=10**6
a,b = map(Decimal,input().split())
print(format(a * b,'f'))
| [
"decimal.getcontext"
] | [((39, 51), 'decimal.getcontext', 'getcontext', ([], {}), '()\n', (49, 51), False, 'from decimal import Decimal, getcontext\n')] |